The Evolution of the Built Environment: Complexity, Human Agency and Thermal Performance 9781407305950, 9781407335476

This study investigates the relationship between the thermal performance of building assemblages (classes of buildings)

237 95 28MB

English Pages [370] Year 2009

Report DMCA / Copyright

DOWNLOAD FILE

Polecaj historie

The Evolution of the Built Environment: Complexity, Human Agency and Thermal Performance
 9781407305950, 9781407335476

Table of contents :
Front Cover
Title Page
Copyright
TABLE OF CONTENTS
PREFACE
LIST OF FIGURES
LIST OF TABLES
PART 1 - A NEW APPROACH TO UNDERSTANDING THE BUILT ENVIRONMENT: SYNTHESISING THREE DISCIPLINES
CHAPTER 1 – Complexity, Society and Buildings
CHAPTER 2 – Background Theories: Architectural Studies
CHAPTER 3 – Background Theories: Archaeological Studies
CHAPTER 4 – Complexity Theory and the Approach Taken by the Study
Summary of Part 1
PART 2 - TESTING THE HYPOTHESIS: FOUR CASE STUDIES AT DIVERSE SCALES
CHAPTER 5 – The Methodology for Testing for Microclimatic Selection
CHAPTER 6 – A Global Case Study of Generic Buildings: An Ethnographic Sample
CHAPTER 7 – A Regional Case Study of Buildings: Long-Term Trends in the Old World
CHAPTER 8 – A Case Study of Rooms in Two Regions: The ‘Pithouse’-to-‘Pueblo’ Transition
PART 3 – ISSUES AND IMPLICATIONS
CHAPTER 9 – An Urban Site Example: Mohenjo-daro, Pakistan
CHAPTER 10 – Microclimatic Selection in the Built Environment: Implications
EPILOGUE
CHAPTER 10 – Microclimatic Selection in the Built Environment: Implications (Glossary)
APPENDIX A - Graphs of Temperature Differentials for Field Experiments (Simple Huts)
APPENDIX B – Datasets for Case Study 1
APPENDIX C – Dataset for Case Study 2
APPENDIX D – Dataset for Case Study 3
APPENDIX E – Dataset for Case Study 4a
APPENDIX F – Dataset for Case Study 4b
APPENDIX G – Structure Coefficients for Case Studies 2 (Test 1), 3 (Test 1) and 4a (Test 1)
REFERENCES

Citation preview

BAR S2020 2009 WILKINS

The Evolution of the Built Environment: Complexity, Human Agency and Thermal Performance Helen Wilkins

THE EVOLUTION OF THE BUILT ENVIRONMENT

B A R

BAR International Series 2020 2009

The Evolution of the Built Environment: Complexity, Human Agency and Thermal Performance Helen Wilkins

BAR International Series 2020 2009

B B A

b ........ h s l gu

i

g

BAR

PUBLISHING

his b

is

il bl

h B i ish ib

TABLE OF CONTENTS PREFACE LIST OF FIGURES LIST OF TABLES

iv vi xi

PART 1 – A NEW APPROACH TO UNDERSTANDING THE BUILT ENVIRONMENT: SYNTHESISING THREE DISCIPLINES INTRODUCTION

1

CHAPTER 1 COMPLEXITY, SOCIETY AND THERMAL PERFORMANCE Complex Systems and Issues of Reality Thermal Performance and Emergent Properties Buildings and Issues of Scale The Context of the Study Background Theories: Three Separate Disciplines Synthesising the Background Theories The Research Design, Scope and Methodology The Study Structure

4 4 5 6 7 11 12 13

CHAPTER 2 BACKGROUND THEORIES: ARCHITECTURAL STUDIES Introduction Humans and Thermal Response The Basic Thermodynamic Principles of Heat Exchange Space Heating and Cooling Technologies Adaptive Comfort Theory: Some Historical Background Adaptive Comfort Theory: the Congruences Human Thermal Variability Adaptive Comfort Theory: Shortcomings The Heat Balance Model Failure of the Heat Balance Model Comfortable Vernacular Theory Failure of the Comfortable Vernacular Theory Summary

14 15 17 19 26 28 31 33 34 35 37 40 43

CHAPTER 3 BACKGROUND THEORIES: ARCHAEOLOGICAL STUDIES Introduction Neo-Darwinian Archaeology: Some Historical Background Neo-Darwinian Archaeological Theory: The Congruences Neo-Darwinian Archaeology: Shortcomings Archaeological Approaches to the Evolution of Classes of Buildings Summary

44 45 46 55 57 58

CHAPTER 4 COMPLEXITY THEORY AND THE STUDY APPROACH Introduction Characteristics of Complex Systems Complexity Theory Fitness Landscapes Contradiction as Mechanism for Self-Organisation Complexity Theory and Cultural Evolution The Thermal Machine as a Complex System The Units of Thermal Analysis i

59 59 61 62 65 67 70 71

MicroClimatic Selection: Thermal Choices and Thermal Control MicroClimatic Selection: What to Expect from a Null Hypothesis Summary

SUMMARY OF PART 1

71 75 75 77

PART 2 – TESTING THE HYPOTHESIS: FOUR CASE STUDIES AT DIVERSE SCALES INTRODUCTION TO PART 2

78

CHAPTER 5 THE METHODOLOGY FOR TESTING FOR MICROCLIMATIC SELECTION Introduction Engineering-Analysis in the Study The Field Experiments: Rudimentary Structures MCS and Thermal Contradiction in Rudimentary Structures The Field Experiments: Simple Huts MCS and Thermal Contradiction in Simple Huts Relating the Engineering-Analysis to Real Buildings Multivariate-Analysis Discriminant Analysis Correlation Analysis and Principal Components Analysis (PCA) Relating the Multivariate Analysis to Real Buildings Summary

79 79 80 88 88 94 95 96 98 101 102 103

CHAPTER 6 A GLOBAL CASE STUDY OF GENERIC BUILDINGS: AN ETHNOGRAPHIC SAMPLE Introduction Case Study 1: Generic Ethnographic Buildings WorldWide The Data: Cross-Cultural Generic Buildings The Tests: Case Study 1 Generic Building Features: The Variables Results of the Global Ethnographic Case Study Conclusions

105 105 105 111 112 115 117

CHAPTER 7 A REGIONAL CASE STUDY OF BUILDINGS: LONG-TERM TRENDS IN THE OLD WORLD Introduction Case Study 2: An Extended Time Period in the Old World The Data: Cross-Cultural Buildings The Tests: Case Study 2 Archaeological Building Features: The Variables Results of the Regional Case Study of Buildings Conclusions

118 118 118 121 121 129 132

CHAPTER 8 A CASE STUDY OF ROOMS IN TWO REGIONS: THE ‘PITHOUSE'-TO-'PUEBLO’ TRANSITION Introduction Case Study 3: An Evolutionary Phase Shift in Two Regions The Data: Cross-Cultural Rooms The Tests: Case Study 3 Archaeological Room Features: The Variables Results of the Transitional Case Study of Rooms Conclusions

SUMMARY OF PART 2

133 135 136 142 142 149 151

153

ii

PART 3 – ISSUES AND IMPLICATIONS CHAPTER 9 AN URBAN SITE EXAMPLE: MOHENJO-DARO, PAKISTAN Introduction Case Study 4: A Conservative Dense Urban Site Mohenjo-daro, Pakistan Urban Saturation Diminishing Wall Thicknesses Diminishing Likelihood of Upper Storeys Introduction: Case Study 4a: Rooms at Mohenjo-daro The Data for Case Study 4a: Site Specific Rooms The Tests: Case Study 4a Archaeological Room Features: The Variables Results of the Urban Site Case Study Introduction: Case Study 4b:Buildings at Mohenjo-daro in a Wider Context The Data for Case Study 4b: Cross-Cultural Rooms The Tests: Case Study 4b Archaeological Building Features: The Variables Results of the Urban Site Comparative Case Study Conclusions Discussion

154 154 154 157 158 160 161 161 164 165 166 168 168 170 171 171 173 174

CHAPTER 10 MICROCLIMATIC SELECTION IN THE BUILT ENVIRONMENT: IMPLICATIONS Enhanced MicroClimatic Selection The Evolution of Buildings: Human Agency and Thermal Performance MCS as Mechanism for Self-Organisation The Evolution of the Built Environment The Null Hypothesis Invalidated Implications for Archaeology: Material Adjustability

176 178 179 181 183 184

EPILOGUE

186

GLOSSARY

187

APPENDICES Appendix A. Graphs of temperature differentials for field experiments (simple huts) Appendix B. Datasets for Case study 1 Appendix C. Dataset for Case study 2 Appendix D. Dataset for Case study 3 Appendix E. Dataset for Case study 4a Appendix F. Dataset for Case study 4b Appendix K. Structure Coefficients for Case studies 2 (Test 1), 3 (Test 1) and 4a (Test 1)

REFERENCES

190 199 208 225 293 312 327

329

iii

PREFACE Without the use of additional means, the great outdoors is an uncontrollable environment. Animals and humans have no control over the microclimates that are created by the natural elements. But for those creatures that are free to move about there is some degree of choice over the particular microclimates they occupy. They can move into the shade of a tree on a hot day or into the lee of an embankment on a windy day. A cave can provide shade from the sun as well as shelter from the wind. However, their choices are constrained by the conditions of the natural elements and by the available topography. Some climates are more variable and so offer greater thermal choices. Some locations are more diverse and offer greater microclimatic choices. Regardless of climate and location, however, the choices can only be met if the conditions exist to satisfy them and the more complex the choices, the less likely it is that the natural environment will be able to satisfy them. For example, if the choice were for a very dry, cool environment with a light breeze, neither the tree nor the embankment nor the cave would be able to provide this alone. The more complex the choices, the more complex arrangements of thermal and physical environments are required to satisfy them.

chancy. Lithics can be made outside, but making a chronometer outside is difficult. Making gunpowder outside is foolhardy! The thermal environments created by buildings provide the context within which social life operates. And adjustable built environments generate diverse thermal conditions. That is, they possess the thermal capacity to produce enhanced levels of thermal choices and thermal control. Classes and assemblages of buildings that generate diverse thermal environments will increase the range of social options that the building milieu can accommodate, compared with less adjustable classes and assemblages, because they are more readily able to accommodate changing social options and circumstances. A relationship therefore exists between the thermal operational adjustability (combining thermal choices and thermal control) associated with classes of buildings and the capacity for operational adjustability possessed by communities. This means that a class of building or an assemblage of buildings, eg. a ‘pueblo’ form, that provides a highly adjustable milieu is more likely to be occupied for longer periods of time, because it can accommodate more internal social changes prior to undergoing a system-level alteration into a different class of building or settlement. Conversely, an inflexible building milieu is more likely to be occupied for shorter periods of time prior to a system-level alteration, in which change will be observed in the class of building or settlement.

The emergence of clothing enhanced the ‘freedom’ of hominids, allowing them to move about the landscape enclosed within a personalised microclimate. Likewise, the controlled use of fire, definitely apparent half a million years ago, extended their freedom even further, allowing them to move between and within microclimates of their choice. These microclimates are not, however, wholly controllable, to which anyone who sits around a campfire on a breezy day can attest. Not only does the fire dance to the circulating currents, but often these currents are dependent on where the campers sit. Sitting downwind of a fire can draw the smoke in that direction because an area of negative air pressure is created, causing back-draft.

This study is a published version of my Doctoral thesis, essentially unaltered. The questions it asks about the relationship between society and the built environment is as relevant today as it was when it was first encompassed. If we are to create sustainable environments it is imperative that we understand the complex relationship between social life, which alters at will, and the built environment, which possesses huge inertia to change. Too few studies have sought to deal with this in terms of the complexity that is inherent in the relationship, or they have taken a deterministic approach, erroneously assuming that humans alter their built environment as spontaneously as they do their words and their actions. Yet anyone who has had to bide their time, waiting for their home renovations to begin and to finalise, will understand first hand that there is inertia in our buildings and that this impacts and constrains what we can do socially.

The emergence of built structures, however, provided hominids with the freedom to move between and within microclimates that were adjustable, controllable and to which they were not physically bound. Built structures generate a variety of thermal micro-climates between which hominids can selectively move and thermal conditions that they can selectively alter. A simple windbreak makes a campfire vastly more thermally stable. The entrance to a simple hut can be either open or closed, or only partially closed, making the natural air movement inside more controllable. The capacity for even further control of the natural elements is available in structures with multiple openings. Buildings thus possess the capacity to provide enhanced behavioural freedom to their occupants, enabling them to move about and perform tasks that may require unique thermal conditions. Football can be played outside, but stamp-collecting is

I would like to thank my principal supervisor, Roland Fletcher. His interest in the topic and philosophical guidance, given over many inspirational hours, gave the study a clarity of purpose and his ability to perceive core problems and turn them into points of interest made the entire process stimulating and gratifying. My sincere iv

thanks go to my associate supervisor, Peter White, whose unfailingly high standards of epistemological reasoning gave the study a methodological structure that has made the study logically robust.

Urban History, Faculty of Architecture, RWTH Aachen University, assisted with the Sind Volumes and Mohenjodaro material. Cameron Petrie, Somerville College, Oxford, generously gave me his support and gave advice on the Harappan material. Jaimie Lovell, Council for British Research in the Levant, Amman, gave advice on the Teleilat Ghassul material. Ian Gilligan, Department of Prehistoric and Historic Archaeology, University of Sydney, and I have had some very interesting discussions on the paradox of the reduced clothing worn by Tasmanian Aborigines. Ken Stewart and Phil Granger, of the Architectural & Technical Services Centre, Faculty of Architecture, University of Sydney, gave generous assistance with some of the thermal monitoring equipment. Michael McDiarmid, of the New Mexico State Energy Office, generously supplied me with wind data for the Zuni and Reserve areas of New Mexico and Jørgen Højstrup, of the Risø National Laboratory, Denmark, generously supplied me with wind data for Jordan. Appreciation is also expressed to the Australian Government Australian Postgraduate Award program for funding the research, the Sydney University Postgraduate Research Scholarship Scheme for funding the experimental work and presentation of the Mohenjo-daro material at the SAA conferences in Bonn 2003 and London 2005, and Fisher, Architecture, Badham and other Libraries at the University of Sydney. Thanks also go to Rod Dyson, Tam Dao and Bruce Isaacs of Fisher Interlibrary Loans for their invaluable assistance with acquiring external publications.

Special thanks are also due to the following individuals, who gave advice and much of their time, which has been very greatly appreciated: Bruce Forwood, Department of Architectural and Design Science, Faculty of Architecture, University of Sydney, assisted with the thermal component of the study and his insightful interest in the study and meticulous advice on the methodological approach to the thermal analyses made performing the engineering-analysis a logical and illuminating exercise. Isaac Meir, Professor, Jacob Blaustein Institute For Desert Research, Ben-Gurion University of the Negev, most generously gave hours of his time to comment on various conceptual issues discussed in the study as well as provided me with thermal data for Israel, which could not have been obtained by any other available means. Richard Wright, Emeritus Professor of Anthropology, University of Sydney, generously gave advice and counsel on which multivariate methodology was most appropriate for testing thermal performance, how best to approach the quantification of the building data so as to model thermal behaviour, and discriminant plot analysis generally. Marcel Harmon, Department of Anthropology, University of New Mexico, gave valuable advice and comment on various Neo-Darwinian components of the study and for some very interesting discussions on how to best approach the study of cultural evolution. Dr Richard de Dear, Division of Environmental and Life Sciences, Macquarie University, offered up his time to discuss his theories on and work in the ‘adaptive model of thermal comfort’, which helped put the theory into an evolutionary context. Dr Judith Brophy generously proofread the introductory sections of the study and offered valuable comments and advice. Mike O’Brien, Professor, Department of Anthropology, University of MissouriColumbia, showed support for the study and offered valuable comments on the approach taken. Peter Magee, Assistant Professor, Department of Classical and Near Eastern Archaeology, Bryn Mawr College, gave guidance and advice on the study of Mohenjo-daro material. Michael Jansen, Professor, Department of

Finally, my loving thanks to Eric and Meredith for their encouragement and patience.

Helen Wilkins

PhD, DipArts(Hons1stClass), MDesSc(EnConsDes), BArch, BSc(Arch)

Sydney July 2009

[email protected]

v

LIST OF FIGURES Figure 1.1. Complex thermal interactions between buildings and the outside Figure 1.2. The feedback loop between people and their thermal environment Figure 2.1. Thermal sensations are registered in the spinal cord and the hypothalamus in the brain. Figure 2.2. Thermal sensors in the skin Figure 2.3. Percentage importance placed on achievement of thermal satisfaction at various times in various Australian cities Figure 2.4. Thermal exchange between humans and the environment Figure 2.5. Thermal exchange between buildings and the thermal environment Figure 2.6. Conduction Figure 2.7. Convection Figure 2.8. Radiation Figure 2.9. Passive solar principles for buildings in hot, arid regions: time lag Figure 2.10. Passive solar principles for buildings in hot, humid regions: ventilation Figure 2.11. Passive solar principles for settlements in hot, arid regions: mutual shading Figure 2.12. Passive solar principles for settlements in hot, humid regions: ventilation Figure 2.13. Annual cycle of the earth around the sun Figure 2.14. Solar angles at high and tropical latitudes Figure 2.15. The path of the sun in summer and winter in high latitudes Figure 2.16. Time lag Figure 2.17. Inside temperatures with respect to outside temperatures for various types of construction Figure 2.18. Inside temperatures with respect to outside temperature for lightweight and conventional structures during closed and open modes Figure 2.19. Plan and section of the conventional structure monitored Figure 2.20. Inside temperatures with respect to volumes Figure 2.21. Soil temperatures at various depths internal and times of year at 46oN Figure 2.22. Passive heating Figure 2.23. Thermal daytime gains and night time heat losses from courtyards Figure 2.24. Trombe-Michel wall Figure 2.25. Wind induced positive windward pressure and negative leeward pressure Figure 2.26. Wind shadows induced by positive windward pressure and negative leeward pressure Figure 2.27. Wind shadows induced by positive windward pressure and negative leeward pressure Figure 2.28. Wind pressure zones with respect to building height Figure 2.29. Wind shadow size with respect to building height Figure 2.30. Wind shadows and gradients with respect to a windbreak Figure 2.31. Wind shadows in plan with respect to windward and leeward openings Figure 2.32. Wind shadows in section with respect to windward and leeward openings Figure 2.33. Wind flow patterns through various internal spaces Figure 2.34. Air flow through a windcatcher Figure 2.35. Air flow induced by ‘stack effect’ Figure 2.36. Air flow induced by temperature differential Figure 2.37. The effect of shading on ground temperatures Figure 2.38. Exclusion of (a) high angle sun via horizontal sunshading and (b) low angle sun via vertical sunshading Figure 2.39. (a) Admission and (b) exclusion of convective air currents Figure 2.40. Cooling of an incoming breeze via moisture evaporation by passing over (a) or through (b & c) water Figure 2.41. Active solar heating using dry heat storage medium Figure 2.42. Active solar heating using water as heat storage medium Figure 2.43. Active solar cooling system Figure 2.44. Humphreys’ scatter diagram of neutral temperatures Figure 2.45. Locations of thermal studies comprising the ACT global database Figure 2.46. Thermal strain relative to capacity to make thermal adjustments Figure 2.47. Thermal strain in a static and climatically-controlled environment Figure 2.48. Sensitization to additional stimuli under static thermal conditions Figure 2.49. An approximation of the amount of heat produced by the human body for various activities vi

5 6 15 15 17 17 18 18 18 18 19 19 19 19 20 20 20 20 20 21 21 21 21 22 22 22 22 22 23 23 23 23 23 23 23 24 24 24 24 24 24 25 26 26 26 27 28 29 29 29 32

Figure 2.50. Heat Balance equation Figure 2.51. Acceptable operative temperatures for free-running buildings as per current ASHRAE Standard 55 Figure 2.52. Summer and winter sun angles at (a) Mesa Verde and (b) Chaco Canyon Figure 2.53. Inuit igloo elevated warm interior Figure 2.54. Inuit igloo aerodynamic exterior Figure 2.55. Traditional middle-eastern courtyard house Figure 2.56. ‘Idealised’ pueblo (based on Pueblo Bonito) Figure 2.57. Traditional subterranean Matmata houses of southern Tunisia Figure 2.58. Traditional Southeast Asian hut (Malaysia) Figure 2.59. Seminole house, Florida Figure 2.60. Traditional minimalist shelter, Colombia Figure 2.61. Traditional Normandy farmhouse Figure 2.62. Traditional Tibetan farmhouse Figure 2.63. Selk’nam hut of Tierra del Fuego c. 1907-8 Figure 2.64. Yámana canoe of Tierra del Fuego c. 1907-8 Figure 2.65. Traditional Japanese townhouse Figure 2.66. Traditional Rabari tent Figure 2.67. Traditional seasonal orientation of Rabari tent Figure 2.68. Nalya village, Liberia c. 1900 Figure 2.69. Grebo village, Liberia c. 1900 Figure 2.70. Traditional Japanese farmhouse

36 37 37 37 38 38 38 39 39 39 39 39 40 40 41 41 41 42 42 43

Figure 3.1. Kroeber’s (a) divergent biological evolution and (b) reticulate cultural evolution Figure 3.2. The process of Darwinian evolution Figure 3.3. Hypothetical divergent object typology Figure 3.4. Hypothetical reticulate object typology Figure 3.5. Example of a histogram Figure 3.6. Tree of anatomical diversity from a common ancestor over time Figure 3.7. Distribution of anatomical complexity over time Figure 3.8. Hypothetical change in frequency of objects relative to (a & c) selective pressure or (b) drift Figure 3.9. Graphical representations of genealogy using (a) cladograms, (b) trees and (c) scenarios Figure 3.10. Chronological sequence of Egyptian pottery by Petrie Figure 3.11. Egyptian pottery seriation by Petrie

45 48 48 48 49 49 50 51 52 52 52

Figure 4.1. Life span of marine fossil vertebrates and invertebrates that follows a power law Figure 4.2. Evolution observed at macro and micro scales Figure 4.3. Various ways of interpreting detailed change within macro-scale events Figure 4.4. A strange attractor at (a) one orbit, then (b) ten, then (c) one hundred, then (d) 1,000 and the Poincaré 61section through (d) Figure 4.5. Self-similarity at different scales within the Mandelbrot set Figure 4.6. The ‘drunkard’s walk’ Figure 4.7. Bifurcation diagram showing steady states through to chaotic states Figure 4.8. A fitness landscape showing the evolutionary paths of various subspecies Figure 4.9. A system fixed on a local peak Figure 4.10. Punctuated evolution in Paleozoic Foraminiferans Figure 4.11. Rapid early morphological change followed by stasis in lungfishes Figure 4.12. Capacity to improve fitness vs. fitness Figure 4.13. Evolutionary persistence ‘between chaos and order’ at (b) in the NK model Figure 4.14. Increase and then decrease in fitness levels with respect to increasing K values in the NK model Figure 4.15. (a) increased waiting time to locate a fitter neighbour and (b) decreased number of fitter neighbours relative to increasing fitness over time Figure 4.16. Coevolution among (a) four, (b) eight and (c) 16 species Figure 4.17. Convergence to a common region of K Figure 4.18. Increasing variation relative to increasing number of residences within Ghanaian settlements Figure 4.19. Operational duration of dense urban settlements Figure 4.20. Hypothetical model of stress levels relative to frequency of occurrence of number of settlements Figure 4.21. Strange attractor in the Late La Tene rural-urban interaction model Figure 4.22. Chaotic behaviour in the Late La Tene rural-urban interaction model

60 60 61

vii

34

61 61 62 62 63 64 64 64 64 65 66 66 66 67 68 68 68 69 69

Figure 4.23. Potential for thermal choices in heavyweight construction in temperate, hot-arid and cold climates Figure 4.24. Potential for thermal choices in lightweight construction in temperate, hot- arid and cold climates Figure 4.25. Potential for thermal choices in heavyweight construction in hot-humid tropical climates Figure 4.26. Potential for thermal choices in lightweight construction in hot-humid tropical climates Figure 4.27a. Summer temperatures in I-n-Salah, Algeria Figure 4.27b. Winter temperatures in I-n-Salah, Algeria Figure 4.28a. Summer temperatures in Belem, Brazil Figure 4.28b. Winter temperatures in Belem, Brazil Figure 4.29a. Summer temperatures in Reykjavik, Iceland Figure 4.29b. Winter temperatures in Reykjavik, Iceland Figure 5.1. Plan of full-circular and semi-circular windbreaks and fire location in very cool conditions Figure 5.2. Semi-circular windbreak after completion of scenario Figure 5.3. Full-circular windbreak during setup Figure 5.4. Straight-sided windbreak (abandoned) prior to scenario Figure 5.5. Plan and elevation/section of hut and fire location in very cool conditions Figure 5.6. Hut interior during setup Figure 5.7. Vertical arrangement of globes in the scenarios in the very cool experiments Figure 5.8. Temperature gradient for fire only under very cool conditions Figure 5.9. Temperature gradient for 1.0m h. semi-circular windbreak without fire under very cool conditions Figure 5.10. Temperature gradient for 0.5m h. full-circular windbreak without fire under very cool conditions Figure 5.11. Temperature gradient for 1.0m h. semi-circular windbreak with fire under very cool conditions Figure 5.12. Temperature gradient for 0.5m h. full-circular windbreak with fire under very cool conditions Figure 5.13. Temperature gradient for hut without fire under very cool conditions Figure 5.14. Temperature gradient for fire with fire inside under very cool conditions Figure 5.15. Temperature gradient for hut with fire outside doorway under very cool conditions Figure 5.16. Temperature gradient for hut with fire downwind of doorway under very cool conditions Figure 5.17. Plan and elevation/section of shade structures Figure 5.18. 2.0m high shade structure during setup Figure 5.19. Vertical arrangement of globes in the scenarios in the hot-arid experiments Figure 5.20. Temperature gradients for 1.2m h. shade structure with 50% porosity under hot-arid conditions Figure 5.21. Temperature gradients for 1.2m h. shade structure with 94% porosity under hot-arid conditions Figure 5.22. Temperature gradients for 2.0m h. shade structure with 50% porosity under hot-arid conditions Figure 5.23. Temperature gradients for 2.0m h. shade structure with 94% porosity under hot-arid conditions Figure 5.24. Plan and section of lightweight semi-subterranean domed hut Figure 5.25. Interior of lightweight semi-subterranean domed hut Figure 5.26. Plan and section of lightweight on-ground domed hut Figure 5.27. Interior of lightweight on-ground domed hut Figure 5.28. Plan and section of lightweight rectilinear hut Figure 5.29. Rectilinear hut under construction Figure 5.30. Plan and section of heavyweight semi-subterranean domed hut Figure 5.31. Semi-subterranean domed hut under construction Figure 5.32. Plan and section of heavyweight on-ground domed hut Figure 5.33. Heavyweight on-ground domed hut Figure 5.34. Plan and section of heavyweight rectilinear hut Figure 5.35. Heavyweight rectilinear hut Figure 5.36. Cluster Analysis results for Late Prehistoric and Romano-British settlements in Northwest Wales Figure 5.37. The 85 settlements grouped into the 7 classes and 2 outliers Figure 5.38. Random discriminant plot Figure 5.39. Discriminant plot with 6 groups Figure 5.40. Discriminant plot with 6 groups viii

72 72 72 72 73 73 73 73 74 74 81 81 82 82 82 82 83 83 84 84 84 84 84 85 85 85 85 86 86 87 87 87 87 90 90 90 90 90 90 91 91 91 91 91 91 97 97 99 99 99

Figure 5.41. Discriminant plot with 4 groups Figure 5.42. Random discriminant plot becoming grouped Figure 5.43. Discriminant plot becoming grouped Figure 5.44. Discriminant plot grouping becoming distinct group Figure 5.45. Discriminant plot grouping with 1 outlier becoming distinct group Figure 5.46. Discriminant plot of MCS of Egyptian buildings showing grouping Figure 5.47. Temperatures at various heights outside and inside a flat roofed structure (as per engineering-analysis)

99 100 100 100 100 101

Figure 6.1. Locations and climates of type-sites in Case Study 1 Figure 6.2. Koppen-Trewartha world climate classification Figure 6.3. Discriminant plot of MCS of buildings in Case Study 1 grouped by cultural affiliation Figure 6.4. Discriminant plot of MCS of buildings in Case Study 1 grouped by sedentism/mobility

106 110 116 116

Figure 7.1. Locations of sites Case Study 2 Figure 7.2. Discriminant plot of MCS of buildings in Case Study 2 Figure 7.3. Graphs of the average range of built features within buildings in Egypt, the Negev and Paletinian highlands Figure 7.4. Discriminant plot of MCS of buildings in Case Study 2 using only thermally contradictory variables Figure 7.5. Discriminant plot of MCS of buildings in Case Study 2 using only variables in thermal accord

119 130

Figure 8.1. Generic pithouses and pueblos Figure 8.2. Distribution of pithouses in the archaeological record Figure 8.3a. Locations of Old World sites in Case Study 3 Figure 8.3b. Locations of New World sites in Case Study 3 Figure 8.4. Discriminant plot of MCS of rooms in Case Study 3 Figure 8.5. Discriminant plot of MCS of rooms in Case Study 3 using only thermally contradictory variables Figure 8.6. Discriminant plot of MCS of rooms in Case Study 3 using only variables in thermal accord

133 134 136 137 150

Figure 9.1. Location of Mohenjo-daro Figure 9.2. UNESCO site plan of Mohenjo-daro Figure 9.3. The ‘cluster growth model’ of the HR area by Jansen Figure 9.4. Urban expansion schematic of section 2, HR-B area Figure 9.5. Urban expansion schematic of section5, HR-B area Figure 9.6. Walls with external batter in the DK-G South area in (a) Long Lane and (b) Loop Lane Figure 9.7. Wall thicknesses as percentage of total wall length in (a) DK-G South area, sections 1, 4 & 10, and (b) HR-A, section 3 Figure 9.8. Types of sequential wall construction at Mohenjo-daro Figure 9.9. Walls with internal setbacks in (a) Room 28, section 1, DK-G South area and (b) Room 14, section 1, HR-A area Figure 9.10. Rooms in Section 3, HR-A area in Case Study 4a Figure 9.11. Rooms in DK-G South areas 1, 4 & 10, Intermediate III period, in Case Study 4a Figure 9.12. Rooms in DK-G South areas 1, 4 & 10, Intermediate I period, in Case Study 4a Figure 9.13. Rooms in DK-G South areas 1, 4 & 10, Late I & II periods, in Case Study 4a Figure 9.14. Discriminant plot of MCS of rooms in Case Study 4a Figure 9.15. Discriminant plot of MCS of rooms in Case Study 4a using only thermally contradictory variables Figure 9.16. Discriminant plot of MCS of rooms in Case Study 4a using only variables in thermal accord Figure 9.17. Locations of sites in Case Study 4b Figure 9.18. Discriminant plot of MCS of buildings in Case Study 4b Figure 9.19. Discriminant plot of MCS of buildings in Case Study 4b using only thermally contradictory variables Figure 9.20. Discriminant plot of MCS of buildings in Case Study 4b using only variables in thermal accord Figure 9.21. The ‘plus ça change’ model of evolutionary change Figure 9.22. Flexibility of the lightweight, low-inertia traditional Southeast Asian hut

156 156 157 158 158 159

Figure 10.1. Shahjahanabad Haveli, Old Delhi, c. 1648-1960 Figure 10.2. Reconstruction of Çatal Hüyük, Anatolia, c. 6000-5000B.C. Figure 10.3. Reconstruction of Hovenweep Castle, Square Tower Canyon, Colorado, c. 1200-1300A.D. ix

103

131 131 132

151 151

159 159 160 162 162 163 163 167 167 168 169 172 173 173 175 175 176 176 177

Figure 10.4. Pithouse, La Plata district, Colorado, c. 500-700A.D. Figure 10.5. Site 820, Mesa Verde, Colorado, c. 1000-1100A.D. Figure 10.6. Block 5, DK-G South area, Mohenjo-daro Figure 10.7. Thermal response relative to discrepancy from thermal stasis Figure 10.8. The ‘Zone of Evolution’ for the built environment Figure 10.9. Discriminant plot of MCS of buildings in extended Case Study 4b with max choices/min control and max control/min choices Figure 10.10. Threads and randomly connected buttons Figure 10.11. Phase shift of threads and connected buttons Figure 10.12. Increased biological operational complexity over time Figure 10.13. Hypothetical percentage occurrence of modes of transportation in Ohio Figure 10.14. The evolutionary advantage of systems ‘between chaos and order’

x

177 178 178 179 179 180 181 181 182 183 183

LIST OF TABLES Table 2.1: Thermoregulatory adjustment Table 2.2: Behavioural adjustment

16 17

Table 5.1: Horizontal temperature variability for closed mode through to fully open (open mode) Table 5.2: Outside – inside temperature differences (oc) for all modes averaged for all heights Table 5.3: Outside – inside vertical temperature range (oc) for all modes Table 5.4: Closed – open temperature differences (oc) for all modes averaged for all three heights Table 5.5: Contradictions and accords in primary base-line thermal traits

92 93 94 94 95

Table 6.1: Entities (generic buildings) included in case study 1 Table 6.2: Sixteen principal climates by Koppen-Trewartha classification Table 6.3: The ‘universal’ thermal scale Table 6.4: Discriminant values for large or impressive structures Table 6.5: Discriminant values for ground plan of dwelling Table 6.6: Discriminant values for floor level Table 6.7: Discriminant values for wall material Table 6.8: Discriminant values for roof shape Table 6.9: Discriminant values for roofing material

106-109 110 110 113 113 114 114 115 115

Table 7.1: Entities (buildings) included in case study Table 7.2: Thermal features (variables) included in case study 2 Table 7.3: Discriminant values for building exposure (n, ne, nw, s, se, sw, e, w & vertical) Table 7.4: Discriminant values for roof flatness/peakiness Table 7.5: Discriminant values for floor level relative to ground level Table 7.6: Discriminant values for wall and roof material (thermal mass) Table 7.7: Discriminant values for presence of wall and roof insulation Table 7.8: Discriminant values for number of internal angles Table 7.9: Discriminant values for ratio length/width Table 7.10: Discriminant values for number of posts Table 7.11: Discriminant values for number of niches Table 7.12: Discriminant values for number of benches Table 7.13: Discriminant values for degree of compactness Table 7.14: Discriminant values for number of discrete Table 7.15: Discriminant values for internal wall and ceiling/roof conductance Table 7.16: Discriminant values for number of roofs at different levels Table 7.17: Discriminant values for number of exterior openings in different directions Table 7.18: Discriminant values for solar penetration (from s, se, sw, e, w) Table 7.19: Discriminant values for cross-ventilation and corner-ventilation Table 7.20: Discriminant values for heating Table 7.21: Discriminant values for transitional spaces

120 122 123 123 123 124 124 124 125 125 125 126 126 126 127 127 127 128 128 129 129

Table 8.1: Entities (rooms) included in case study 3 Table 8.2: Thermal features (variables) included in case study 3 Table 8.3: Discriminant values for building exposure (n, ne, nw, s, se, sw, e, w & vertical) Table 8.4: Discriminant values for roof flatness/peakiness Table 8.5: Discriminant values for floor level relative to ground level Table 8.6: Discriminant values for wall and roof material (thermal mass) Table 8.7: Discriminant values for presence of wall and roof insulation Table 8.8: Discriminant values for number of internal angles Table 8.9: Discriminant values for ratio length/width Table 8.10: Discriminant values for number of posts Table 8.11: Discriminant values for number of niches Table 8.12: Discriminant values for number of benches Table 8.13: Discriminant values for number of connected rooms Table 8.14: Discriminant values for number of connected upper storeys and lower storeys Table 8.15: Discriminant values for nearest neighbour distance

138 143 143 143 144 144 144 145 145 145 146 146 146 147 147

xi

Table 8.16: Discriminant values for plan area Table 8.17: Discriminant values for number of exterior openings Table 8.18: Discriminant values for solar penetration (from s, se, sw, s, w) Table 8.19: Discriminant values for cross-ventilation and corner-ventilation Table 8.20: Discriminant values for heating Table 8.21: Discriminant values for number of connected transitional spaces Table 9.1: The occurrence of lightweight partition walls inside structurally-massive external boundary walls in the dk-g south area in each of mackay’s occupational periods Table 9.2: Entities (rooms) included in case study 4a Table 9.3: Thermal features/variables included in case study 4a Table 9.4: Discriminant values for wall thickness (thermal mass) Table 9.5: Entities (buildings) included in case study 4b Table 9.6: Thermal features (variables) included in case study 4b Table 10.1: Entities (buildings) included in an extension of case study 4b: the urban site comparative case study of buildings with chaotic and ordered regimes/entities

xii

147 148 148 148 149 149 164 164 165 166 169-170 171 179

PART 1 - A NEW APPROACH TO UNDERSTANDING THE BUILT ENVIRONMENT: SYNTHESISING THREE DISCIPLINES INTRODUCTION

Both sociality and the thermal performance of buildings are open, complex systems. Societies are an aggregate of their operations and components, and thermal performance is an aggregate of the operation of the physical and spatial properties of a building (the environment, the climate, the building fabric, the contents and the occupants), although the whole is greater than the sum of the parts. Complexity studies have empirically isolated the relationship between the degree of system level operational adjustability and long-term system viability. This has led to the proposition that large-scale consistencies of operational adjustability have occurred because adjustable systems are more evolutionarily robust to changing circumstances than inflexible systems (Kauffman 1993, 1995; Brandon 1996: 69-84). Evolutionarily robust systems are defined within Complexity theory as those that are capable of absorbing perturbations or contextual changes without becoming unstable at the level of the whole system. Open, complex systems contain inherent operational contradictions. These arise due to the non-deterministic interactions between operations that take place at diverse scales, both internally and externally. As a result, producing optimal performance in one aspect of the system will inherently contradict the production of optimal performance in other aspects. This can only be alleviated through adjustability at the level of the whole system, whereby multiple operations can be performed adequately within a finite period of time.

for example, to write a manuscript or assemble pyrotechnics in a strong breeze or in semi-darkness. The relationship between the built environment and social life is consequential because non-correspondence inherently exists between the material and the social. The built environment generally possesses greater inertia to change relative to the faster changing actions of sociality, such as the verbal and active components of social life (Fletcher 1995, 1996, 2004). This means that it is inherently easier to accommodate changes of use in structures that possess low inertia or that are highly adjustable than it is in structures with high inertia or that are non-adjustable, such as massive and/or inflexible structures. Structures where the choices of the occupants cannot be selectively met or where the behaviour of the system cannot be controlled are therefore likely to satisfy a narrower range of social options than structures where choices and control are selectively possible. Social changes that require a change to be made to the structure itself, rather than to just its movable components, are likely to be constrained by inertia in the built system. There are, therefore, consequences for communities where the material and social do not correspond, or cannot correspond, because the social is constrained by the material. This is exemplified in the case of slums where, although social options exist, they exist within the constraints of the inertial built environment. That is, whilst the buildings themselves might possess a light footprint, the urban infrastructure itself generally possesses a high inertia to change (Davis 2006).

Altering a building will result in an indeterminate change in the building’s thermal performance. Thermal systems are the end result of the thermal interaction of a building’s physical and spatial features, both internal and external, including those that operate in thermal contradiction with other features and those that operate in thermal accordance with other features. That is, thermal contradiction exists inherently within the operation of buildings. Where resolution of the thermal contradictions relies on adjustments to be made to the building, contradiction will also exist between the thermal and the material systems of a building. For example, allowing a breeze to blow into a space from outside is potentially in contradiction with keeping the sun out at the same time. This means that, where thermal-material contradiction exists, contradiction between the social and the material is also likely to exist because the thermal requirements of the social will potentially be at odds with the thermal operation and limitations of the material. It is not easy,

Built systems can accommodate certain types of social and thermal systems, but built systems and thermal systems that are adjustable can accommodate a greater range of social changes. They are more likely to be able to accommodate a greater diversity of (inevitable) longterm social change. The communities that possess adjustable buildings, in which a wide range of microclimates can be selectively implemented, are likely to be less constrained by their built environments and the building milieu is therefore likely to persist for longer periods of time within its general current form. This means that building milieux that are highly adjustable are more likely to have been occupied for longer periods of time before undergoing a system-level alteration. Conversely, inflexible building milieux are more likely to have been occupied for shorter periods of time before undergoing a system-level alteration, in which change should be observable in the classes of buildings, the

1

The Evolution of the Built Environment urban system and/or the social system. The thermal environments created by buildings constitute the environments within which communities operate, making certain functions possible and others prohibitive. There are therefore likely to be consequences for communities relative to the degree of thermal adjustability of the built environments that they inhabit. Social adjustability and flexibility (long-term options and capacities) are likely to be constrained relative to the degree of thermal adjustability within its built environment. The more thermally adjustable the built environment, the greater the range of social options that the environment is likely to be able to accommodate. That is, adjustable thermal environments are likely to possess the capacity to accommodate a wider range of contextual social change within the existing building milieu compared with less adjustable environments, because the buildings should more easily and more rapidly accommodate changing circumstances without requiring system level alteration.

given that social and contextual change is inevitable in the long-term. For example, the ‘pithouse’ class of buildings (discrete, semi-subterranean rooms) has been widely superseded by the ‘pueblo’ class of buildings (above ground rectilinear room complexes), a transition which I will argue resulted in more thermally and materially adjustable structures. The key questions asked in the study are, first, “are there operational patterns within the Neo-Darwinian evolution of classes of buildings and their associated classes of thermal performance?” If this were observed then selection could be deemed to have been culling particular classes of buildings and their associated classes of thermal system. Secondly, if patterns are evident at the scale of diverse societies and varied levels of detail (thus indicating that selection has been operating on thermal performance), and which might be otherwise elusive at the scale of specific sites or building components, “what is the nature of the thermal systems that have persisted for relatively longer periods of time in terms of thermal capacity, thermal adjustability, associated material adjustability, and the degree of correlation between the thermal and the material systems? What, therefore, is the relationship between thermal capacity and social systems?” The presence or absence of operational patterns can be brought to light through an operational uniformitarian approach to study of the archaeological record. Such a uniformitarian approach focuses on operational consistencies that exist within complex systems that transcend place and time, rather than on unique events or on the specific material record of those events (Gould 1965; Fletcher 1995: 230-231). Gould cites the example of striations on glaciers being the record of events that can be observed in the present, but which are operationally representative of events that can be considered to have taken place in the past. The focus is on the operation, not the individual event or its unique material remains (Gould 1965: 226-227). In this way the material thus ‘speaks’ for itself, independent of the individual, unique event that created it (Murray 2002). A uniformitarian approach to the study of the past has located operational relationships at diverse scales of cultural operation, from human-environment interaction systems (van der Leeuw 1998a) to settlement systems (Allen 1997; Pumain 1997; van der Leeuw & McGlade 1997; Fletcher 1995, 2004).

Contradictions are likely to arise in thermal systems due to the variability of thermal experiences and expectations of humans, between individual, between communities, and over time. People operate locally, but their actions have consequences that become evident only over time scales that they cannot possibly foresee. Buildings can meet the thermal preferences of all of the occupants some of the time and some of the occupants all of the time, but never all of the occupants all of the time, unless they are thermally diverse and maximally thermally adjustable. However, this becomes exponentially more difficult, because finding resolutions to the contradictions that inherently exist within complex systems becomes exponentially more difficult as the system approaches optimal contradiction resolution. For example, as a manufacturing process becomes more streamlined it becomes exponentially more difficult to streamline the process further, without making radical and/or systemlevel alterations. This means that, not only are those systems that have found adjustable resolutions to the contradictions likely to have been favoured over time, but, by inference, so too are those systems that possess contradictions to begin with. In this sense it is the contradiction and the way in which it has been resolved that is likely to have influenced the evolutionary trajectory of the built environment. This study investigates the relationship between the thermal performance of building assemblages (classes of buildings) and the social life of human communities using a multi-scalar Neo-Darwinian approach to study the evolution of the built environment. The philosophical position taken is that of selection. I argue, neither for individual direction of process, nor environmental determinism. I argue a third logical path, that which is taken in biology, which is that actors initiate many things and, in the long term, external factors create circumstances that lead to only some of those things persisting, ie. the ones that work. This study investigates levels of thermal operational adjustability associated with building assemblages and long-term social viability,

Thermal performance is an aggregate of building features and traits, because it operates at diverse levels of detail in space and time and every building feature and trait is intrinsically inter-related to the building’s thermal performance. Alteration of a building generates a change in the way it performs thermally, albeit potentially only in a very minor way and with some buildings potentially capable of a wider range of thermal changes than others. Placing a draft-extractor at a threshold creates a thermal change, by reducing infiltration, just as opening the side of a building creates a thermal change. The difference is only a matter of degree. Rapid changes, such as the placement of an ephemeral draft-extractor, may be 2

Introduction readily apparent to the ethnographer, but the archaeological record generally does not have access to such rapid or ephemeral changes, although it is the product of both rapid change and change that occurs over long periods of time (O'Brien & Lyman 2003b: 13). Rapid thermal changes in buildings generally operate at the level of unique detail, such as closing a window or the positioning of objects (including occupants) within a room, and tend to generate transitional thermal changes that are not readily apparent in the archaeological record. Slow building changes, however, operate at the level of the whole structure and affect the ‘base-line’ characteristics, such as the replacing of an old building with a new one or altering the arrangement of rooms or adding an upper storey or wing. Slow changes generate thermal changes that are apparent in the archaeological record and which may be read as changes between classes of buildings and their associated thermal systems.

range of scales of time, space and detail, so as to be able to isolate the thermal behaviour of buildings (and rooms) from the possible influence of climate and culture, thus making it visible. The analysis steps progressively down in scale, in keeping with the action of selection operating at finer and finer scales. The individual case studies encompass a global ethnographic study at the scale of generic buildings, a very long time-depth extensive region study at the scale of whole buildings from Old World societies, local regional scale studies of a phenomenological transitional (‘pithouse’-to-’pueblo’) change encompassing geographically independent Old World and New World societies at the scale of individual rooms, and a site scale illustrative study of a single early urban centre that experienced reducing thermal capacity due to increasing density and conservative building practices at both the scale of individual rooms and at the scale of whole buildings within a wider geographical and chronological context.

It is the overall thermal performance of classes of buildings that will be investigated. This will encompass a

3

CHAPTER 1 – Complexity, Society and Buildings

COMPLEX SYSTEMS AND ISSUES OF REALITY

THERMAL PERFORMANCE AND EMERGENT PROPERTIES

This study is an empirical examination of patterns within the evolution of complex systems. Complex systems are systems that are composed of numerous interacting parts. They exist in nature (the environment, the weather, ecological systems, and thermal systems) and in culture (social systems, economic systems, political systems, and the built environment). They are systems that exhibit sensitive dependence on initial conditions such that their behaviour cannot be determined or predicted at a fine level of detail. However, Complexity theory has demonstrated empirically that the large and long-term behaviour of complex systems is often operationally predictable to within definable boundaries and that the finite detail within the system, whilst being indeterminate, is often operationally similar to that of other, broader scales of operation. That is, complex systems exhibit self-similar behaviour (behaviour that is operationally similar at diverse scales of detail) (Gleick 1988: 115-116) and which is definable to within boundaries (strange attractors) (Gleick 1988: 119-153). For example, climate is far more predictable than the weather and both exhibit self-similarity (Cohen & Stewart 2000: 211-212). Glacials and inter-glacials occur in cycles (although their occurrences vary in time and magnitude), seasons occur in cycles (although the temperatures vary in time and magnitude) and diurnal temperatures occur in cycles (although the daily maximums and minimums vary inexactly in time and magnitude). Weather and climate conditions can only ever be approximated, and the level of confidence in the approximation reduces exponentially with the span of future time that is encompassed. This is due to the sensitivity of initial conditions and the magnitude of the interactions involved that operate at diverse scales of detail. These same ‘behaviours’ have been observed in the fossil record (Erwin 2005), the stock market (Gleick 1988: 85-86) and the Internet (Willinger & Doyle 2005).

The focus of this study is on the emergent properties of buildings. Emergent properties are high-level behaviours that arise spontaneously in complex systems. They are the result of the vast number of interactions between lowlevel phenomena. The ability of a car to move is an emergent property and mind is an emergent property of the brain (Cohen & Stewart 2000: 169). The abilities of a car to move and of a brain to think are features of the car and of the brain, but they cannot be studied directly in terms of isolated components. They are the holistic result of the structural organisation and workings of the systems of which they are composed. Thermal performance is an emergent property of buildings. It is the spontaneous result of the thermal interactions between a buildings features and traits, of the holistic way the thermal properties of the built components perform together. Buildings are thermal machines. A building possesses emergent thermal properties, whether its builders or occupants are aware of this or not, that are integral with every physical, aerodynamic and spatial feature and trait. Physical changes will alter the way in which the thermal machine performs. This aspect of buildings, and of classes of buildings, has not been studied before within the context of the archaeological record. There have been a small number of studies that have examined the evolution of buildings in terms of single building features, but single features do not equate to the thermal performance of a building. Thermal systems are complex systems and cannot be precisely ascertained from knowledge of only single features. They are the product of a vast number of physical, thermodynamic and spatial features and traits (Szokolay 1987: 21; Clarke 2001: ix) and must be studied at the scale of whole assemblages of these features and traits, or, at least, of extensive assemblages that must include the building’s primary thermal features.

Whilst social explanations seek to explain why people do what they do, Complexity theory seeks to explain why operational similarities appear to exist within both nature and culture. It seeks to explain the nature of what people do and the large-scale operational consequences of what they have done. In terms of the built environment, social explanations seek to discern why people built certain forms, but Complexity theory has the potential to explain the long-term consequences of their having built those forms and the operational conditions and constraints that have been potentially present within large-scale uniformitarian patterns in the evolution of the built environment.

A study of the emergent thermal properties of buildings is necessary to gain a complete understanding of buildings. The speed of a car and the mental capacity of a brain have been studied extensively and intensively, not so much as the raison d’etre of cars and brains, but as a consequence of their structure and workings. Likewise, an understanding of economic and agricultural systems does not come from studying isolated components, but from studying the systems as a whole, because the whole is greater than the sum of the parts (Eldredge 1989; Conway Morris 1998: 9). Buildings are no different. Their thermal properties are a consequence of the interaction of their parts (both active and passive)

4

Complexity, Society and Buildings operating in conjunction with the natural elements (sun, water, wind etc.) (Fig. 1.1).

holographic building. The presence of the rope-line alters the thermal conditions of the natural environment that would have otherwise existed, however imperceptibly. It reduces airflow and possesses both thermal mass and insulative properties. On the other hand, the holographic building effects no reduction in airflow and possesses no thermal mass or insulative properties. A building’s thermal capacity defines its thermal class. It defines the nature of, or characteristics of, the range of thermal states and microclimates that the building is capable of producing. It does this in the same way that a class of car equates to the nature of what the car is capable of doing. Mazeratis, for example, constitute a separate class of car to Mini Minors or Bentleys. Each possess unique performance characteristics, not just in terms of speed, handling, etc. but also in terms of ease of parking, passenger capacity, etc. Likewise, windbreaks, for example, constitute a different thermal class to an enclosed hut. Whilst a windbreak can produce a particular range of thermal states and microclimates, most of which are free-running with the outside environment, an enclosed hut can produce a wider range of both. The hut can be either free-running when the doors and windows are open or it can be isolated from the outside conditions when they are closed. This is a study of changing thermal classes, of the changing capacities over time of built structures to alter the thermal conditions of the natural environment.

Figure 1.1. Complex thermal interactions in and around buildings (after Watson 1983: 29; Szokolay 1987: 21) Some building features and traits will have a greater influence on the building’s thermal capacity than others. For example, the characteristics of the material of which a wall or roof are built will have a greater influence on the overall thermal performance than the colour of the furnishings. Additionally, the thermal influence of some features may offset or contradict the influence of other features, whilst concurrently enhancing the performance of still other features. For example, the thermal properties of a brick wall, which conducts heat very slowly, will be contradicted if there is a large single-glazed window inset into it, which conducts heat rapidly. At the same time, its thermal properties will be enhanced if heavy timber shutters are fixed to the outside of the window. It is, in fact, theoretically possible to produce an identical thermal signature via an infinite number of ways, if the interplay between the thermal components produces the same end result.

BUILDINGS AND ISSUES OF SCALE The subject of this study is building assemblages or classes of buildings, not individual buildings, just as in biological evolution it is species and not individual organisms that are the subject of study. In biological evolution both species and individual organisms are subject to selective forces that operate over a hierarchy of scales (Stanley 1975: 648; Eldredge 1989: 14-15, 1995: 176-197). The scale at which one unit evolves cannot, however, be directly extrapolated to another (Gould 2002: 715-719). The operation of biological species cannot be reduced to processes that operate at the ‘lowest’ level, and evolution cannot be read from DNA (Conway Morris 1998: 8-9; Kauffman 1993, 1995; Cohen & Stewart 2000). Both an organism and its components (DNA and genotype) evolve through complex interactions with each other and within their contextual environment. “Evolution does not take place solely in DNA space, as is effectively the [reductionist] NeoDarwinian view. And it certainly doesn’t take place solely in creature space; that would be the Lamarckian view of the inheritance of acquired characteristics… Evolution happens in the combined space of DNA and organisms, and it is driven by their interaction. The crucial point is that this interaction works both ways. It cannot be reduced to events that happen in one space alone” (Cohen & Stewart 2000: 314, emphasis in original). Species evolve and the way in which they evolve is dependent on the way in which the contradictions are resolved, such that the system is

The thermal machine is a non-static system and it is therefore important to focus on thermal capacity. A car retains the capacity to move even when the engine is not running, because it still possesses the property ‘ability to move’. If, however, the engine is taken out or the wheels are removed, the potential ability is reduced to nil and it no longer possesses the capacity to move. Is it therefore still a car, given that it possesses different emergent properties, the capacity to move not being one of them? It is actually no more than an assemblage of components that only morphologically resembles a car. In the same way, for example, a metaphorical holographic projection of a building, which possesses no thermal properties, is less of a built structure than a rope-line stretched between four upright poles, even though the holographic building more closely visually resembles a building. This is because the rope-line possesses similar emergent properties to a building, only varying in degree, compared with the absence of thermal properties of the 5

The Evolution of the Built Environment orderly, chaotic, or self-organising and robust. The contradictions operate at diverse levels of detail, between genetic and phenotypic parts, between organisms and between species. The same applies to the thermal performance of buildings. Microclimates within a room, for example, interact to create the ambient thermal conditions within the room and understanding the characteristics of the ambient conditions involves an understanding of the operation of the microclimates. However, the ambient conditions cannot be directly extrapolated from the microclimatic conditions, nor mechanistically ‘read’ from their operations. At a larger scale of thermal operation, the ‘heat island’ effect generated by large industrial cities is a product of microclimatic heat retention by components of the urban landscape, but cannot be directly extrapolated from the micro-climatic thermal operations of the components.

THE CONTEXT OF THE STUDY Humans are homeotherms. Homeotherms are animals that have to maintain a stable core body temperature within relatively narrow limits regardless of activity and environmental temperature fluctuations. They have the ability to regulate their core body temperature, regardless of activity or environmental temperature, via involuntary responses involving an autonomic nervous system (Mount 1979: 1-4), although in maintaining a stable core body temperature the temperatures in the extremities vary as a factor of the body’s thermal control mechanism. Thermal response in humans differs from most other homeotherms due to their being bare-skinned, having relatively little subcutaneous fat and having the ability to sweat at a higher rate relative to other mammals (Mount 1979: 145). There are three ways in which humans adjust to their thermal environment. The first is physiologically (genetic and/or acclimatisation), the second is behaviourally (behavioural, environmental, technological and/or social adjustments), and the third is psychologically (habituation and/or expectation). Ultimately this means that there are a number of ways in which people react to their thermal environment, both consciously and subconsciously. This produces a complex feedback loop between the individual and their immediate thermal environment, both natural and built (Fig. 1.2).

Figure 1.2. The feedback loop between people and their thermal environment (Auliciems 1981: 115) windbreak structure at Olduvai Gorge, Tanzania, c. 1.861.75 m. B.P. (Leakey 1971: 260-262, Fig. 7; Phillipson 2005: 39-41). Some contemporary communities use open-weave brushwood huts, windbreaks and sunshades as their only form of built structures. These communities are, in consequence, living almost wholly within the thermal environment created by the natural elements. The degree to which they are either living wholly within the natural outside environment, or are independent of it, is a factor of the degree to which the structures can thermally isolate the occupants from the outside environment. In contrast, many communities today use thermal systems that wholly isolate a percentage of the population from the natural elements through the use of central heating and air-conditioning. These people are consequently living in almost complete thermal isolation from the outside environment. However, both extremes, those in which people live wholly with the natural environment and those in which people live wholly isolated from it, are relatively rare today. The vast majority of buildings of the past did, and those of today do, manipulate the elements to some degree to create particular classes of internal environment, composed of particular microclimates. The majority of buildings create internal environments that fall somewhere between the chaotic and uncontrollable outside environment (a chaotic regime) and a completely controlled and thermally-neutral artificial centrally heated and air-conditioned environment (an ordered regime). No building is an island. Buildings interface with the external environment and with other built structures, all to varying degrees. This makes the thermal systems within built structures operationally complex. For this reason, this study aims to establish whether or not long-term operational patterns exist within the complex behaviour

The vast majority of buildings today bear little resemblance to the brushwood structures and windbreaks that would have formed the world’s oldest and longest used classes of buildings and which possibly date to as early as the Lower Palaeolithic, as suggested by archaeological remains at Terra Amata, Nice, c. 450,000380,000 B.P. (Lumley & Boone 1976; Wymer 1982: 127128) through to the Upper Epipalaeolithic and the well preserved hut remains at Ohalo II, Israel, c. 17,400 B.P. (Nadel & Werker 1999). There is also some evidence that these brushwood huts were preceded by more rudimentary structures, such as wind breaks, as perhaps suggested by the archaeological remains of a possible

6

Complexity, Society and Buildings of the thermal systems of past built environments to within definable system-level boundaries, although within the boundaries the detailed thermal behaviour is likely to be non-deterministic.

Architectural Studies: Adaptive Comfort Theory The architectural component of the study centres on a discussion of Adaptive Comfort theory (ACT). This theory states that occupants of buildings generally prefer to have the capacity to make thermal choices and to selectively control the implementation of those choices, rather than to be held in an unvarying thermally neutral, albeit comfortable, state (an ordered state best represented by a centrally air-conditioned environment) or an unpredictable or uncontrollable thermal environment (a chaotic state best represented by the natural outside environment). This means, therefore, that a theoretically ideal environment would be one that lies somewhere between a homogenous centrally airconditioned environment and the uncontrollable outside environment. The numerous studies performed within the past few decades within the ACT philosophy have concluded that, first, occupant ‘satisfaction’ is higher when there is a perceived level of thermal control even if the occupant confesses to a level of thermal discomfort. Secondly, humans can experience thermal stress, where their physiology is thermally stressed, without showing signs of psychological strain in the form of mental dissatisfaction or distress. Thirdly, the average temperatures inside buildings at which habitual occupants are thermally ‘satisfied’ appear to track the average monthly temperatures outside, regardless of climatic and/or social differences. This thus indicates that an epiphenomenal relationship exists between humans and their selective thermal environments (Humphreys 1975; Humphreys 1978; Auliciems 1989; de Dear 1994; Nicol et al. 1994; Williamson et al. 1995; Baker & Standeven 1996; Nicol & Raja 1996; Humphreys 1997; Brager & de Dear 1998; de Dear & Brager 1998; McCartney et al. 1998; de Dear & Brager 2001; Nicol & Humphreys 2001). Most significantly, the international standards for thermal environmental conditions inside buildings, which are set by the American Society for Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) and which have to date been limited to only mechanically-conditioned buildings, have been recently amended to include buildings where the occupants can manually control their thermal environments. Past and continuing ASHRAE funded research (Plant Engineering 2005) has shown that where occupants can open and close windows their thermal responses will “depend in part on the outdoor climate and may differ from thermal responses in buildings with centralized HVAC systems primarily because of the different thermal experiences, changes in clothing, availability of control and shifts in occupational expectations” (ASHRAE 2004: 9-10; Olesen & Brager 2004).

To date, few studies have systematically investigated the possible presence or absence of general tendencies or trajectories within the built environment over the course of time. Such changes would be apparent as changes in the prevalence of classes of buildings. Focus has instead been mainly on seeking explanations for why a specific class of building came into existence at a specific moment in time, or why a new class of building replaced an older class. There has been a tendency to ‘explain’ classes of buildings in terms of a particular social system being the cause of a particular material form. Yet, archaeology is the discipline best suited to investigate long-term behavioural patterns and selective mechanisms. As Shennan has correctly stated (2002a: 10), “this does not mean archaeology is condemned to produce accounts of ‘progress’ leading up to the present. Rather, it means that archaeology should investigate the past in a way that plays to archaeologists’ strengths. These undoubtedly lie in the characterisation of long-term patterning in past societies and their material products” (see also Binford 1968; Hodder 1986: 1-28, 77-90, 1987; Fletcher 1995: xviii-xxii, 43-65). If the purpose of archaeology were to produce accounts of ‘progress leading up to the present’ it would be a simple matter of explaining why tower blocks have widely replaced brushwood huts, or why morphologically simple huts widely replaced windbreaks and shade structures. The study of the past is not, however, as neat as simple explanations for why one type of structure replaced another. The buildings of today do not represent the wholesale end result of a lineal progression from one class of structure to another. Morphologically simple huts, windbreaks and shade structures are still widely used today and often in conjunction with tower blocks, in the contemporary form of gazebos, wind-fences and verandahs. At no time has there ever been a wholly dominant type of building, as a lineal explanation for change in classes of buildings would claim and as would be defined by a simplistic progressivist stage-oriented explanatory position.

BACKGROUND THEORIES: THREE SEPARATE DISCIPLINES The study has drawn on, and synthesised, propositions from three separate disciplines and these are briefly introduced here. There is an architectural component, an archaeological multi-scalar Neo-Darwinian component and a component deriving from Complexity theory. The architectural and archaeological Neo-Darwinian components are reviewed in the next two chapters. The Complexity theory component is described in the subsequent chapter. In that chapter the approach taken within the study, a synthesis of theories from the three disciplines, is also outlined.

The scope of the study has been expanded beyond that of the studies that have investigated Adaptive Comfort theory, which are generally limited to only people from sedentary contemporary societies performing a very limited range of tasks, primarily that of office work. The expanded scope has included a much wider range of thermal environments and life-styles, incorporating both 7

The Evolution of the Built Environment local, transitional and outside environments and sedentary and mobile societies, and a much longer time frame that incorporates examples from the archaeological record. In so doing it is able to account for the phenomenon of humans occasionally selectively subjecting themselves to thermal stress without showing signs of thermal strain. The structures of mobile and traditional societies have been incorporated here because, whilst these types of societies have not to date been studied as a component of Adaptive Comfort theory, ACT states that humans develop preconceptions from their thermal experiences and that humans who spend more time outside will be accustomed to a wider range of temperatures than the habitual occupants of buildings, particularly if those buildings are centrally thermally controlled (Baker & Standeven 1996). Other types of spaces other than task specific spaces have been incorporated here because, whilst they have not been quantitatively incorporated into the ACT model, the theory states that humans utilise the full range of spaces available to them to create Adaptive Comfort. The extended range of spaces includes transitional spaces (courtyards, passageways, arcades, verandahs and atria) and the outdoors environment itself (Ong 1995: 75-76; Forwood et al. 2000; Potvin 2000). These types of spaces are generally more thermally variable and uncontrollable than local, task specific spaces. ACT is not contradicted by the inclusion of these types of spaces into the model. On the contrary, it is strengthened if they are included, because the outdoors and transitional spaces actually extend a person’s range of thermal choices and potential for thermal control. Finally, examples of thermal environments (built environments) from the archaeological record have been incorporated because to date no early structures have yet been examined quantitatively in terms of Adaptive Comfort, even though they represent an evolutionary link between the absence of built structures (the time prior to the building of structures by early hominids) and contemporary structures.

Adaptive Comfort theory also stands in contrast to the Comfortable Vernacular theory, which is currently central within architectural-history studies. The theory states that vernacular and traditional buildings have been developed over time to create thermally comfortable interiors by being attuned to the average ambient outside daily and seasonal cycle of temperatures. Comfortable Vernacular theory, however, fails to account for the numerous recorded instances of vernacular and traditional buildings within various regions and climates around the world that have not created thermally comfortable interiors, by the standards of the theory, but yet which have housed peoples who have shown no signs of thermal strain or discomfort.

Archaeological Studies: Multi-Scalar NeoDarwinian Theory The archaeological component of the study outlines the congruences of Neo-Darwinian archaeology, which, “like most such labels, … covers an enormous range of different, often mutually antagonistic views. [But] the unifying element is that all of these views draw on aspects of the modern Neo-Darwinian evolutionary synthesis in biology in attempting to explain patterns of cultural stability and change” (Shennan 2002a: 15). The approach taken in the study is a multi-scalar Neo-Darwinian view, upheld by various evolutionary biologists, that material assemblages have evolved because they have increased the overall fitness of human societies by increasing their capacity to be evolutionarily robust to ultimately unforeseeable changes in contextual circumstances. That is, therefore, that only those assemblages that have enhanced the relative adjustability and flexibility of social action are likely to have evolved (Brandon 1996: 69-84; Fletcher 2003: 289). The scope of the study has adopted a uniformitarian approach to the operational effects of the material components of human behaviour. A uniformitarian approach locates the boundary conditions within which self-organised complex systems operate and within which they are robust, though within these boundaries the actual processes are indeterministic and non-predictable. In this way the material is an “actor without intent” (Fletcher 2004: 111, see also Fletcher 1995: 230-231). The attributes of the material can be observed in relation to social action, rather than subsumed as mere physical manifestations of human cognition (as in memetics). A uniformitarian approach explores multi-dimensional processes to understand the holistic operation of the material, in relation to social phenomena and phenomenological change. Such an approach must include extensive assemblages and a range of functions over extensive periods of time and, preferably, at varying scales of analysis, as has been explored by Allen, Fletcher, van der Leeuw, Torrence, McGlade, Grattan and Smith (Allen 1983; Allen 1989; Torrence & van der Leeuw 1989; van der Leeuw 1989; Fletcher 1995: 43-65; Smith 1995; Fletcher 1996; Allen 1997; van der Leeuw &

Adaptive Comfort theory stands in contrast to the heat balance model, which states that people seek to be always thermally neutral and comfortable. The heat balance model formed the basis of the ASHRAE standards for acceptable thermal conditions inside buildings prior to 2004, when they were amended as a result of the recognition that humans can gain thermal ‘satisfaction’ (better overall thermal compromise solutions) inside naturally ventilated buildings where thermal neutrality is not necessarily experienced or sought. The heat balance model has failed to adequately address the condition known as Sick Building Syndrome, which is a range of physical and neurological symptoms that cannot be attributed to a specific illness or disease, but which is experienced by occupants of far more centrally airconditioned buildings than occupants of naturally ventilated buildings. The model has also failed to account for recorded instances of humans selectively subjecting themselves to thermally stressful (non-neutral) situations without showing signs of accompanying thermal strain.

8

Complexity, Society and Buildings McGlade 1997; van der Leeuw 1998d & e; Torrence & Grattan 2002; Fletcher 2003).

talk to. They often don’t understand them themselves. How then can we hope to do this with people whose only means of communication to the contemporary analyst is via their material remains?

Both a multi-scalar Neo-Darwinian archaeological theory and uniformitarianism stand in contrast to a Lamarckian or directed approach to explaining long-term material and social change, which generally holds the view that humans will make decisions in the present that give them future survival and reproductive success. The term Lamarckian refers specifically to deterministic responsebased explanations for morphological change. It was coined after Jean Baptiste de Lamarck, a French naturalist who devised a theory of evolution in the early 19th century, pre-dating Charles Darwin’s theory of evolution, in which he proposed that biological evolution was controlled by directed variation. Evolution was thought to occur as the result of the inheritance of acquired characteristics. That is, characteristics acquired by an organism during its lifetime in order to survive were thought to be preferentially passed on to their offspring. For example, an individual who gained a survival advantage from developing thick skin on the soles of their feet by walking barefoot for extended periods of time was thought to be able to then pass this characteristic on to their offspring, thus imparting a survival advantage to them (Dawkins 1986: 288-291). Contemporary evolutionary-biologists have widely rejected this theory, however, because no mechanism has yet been identified that can explain how acquired traits (as opposed to inherited traits) could be preferentially passed on to descendant organisms. No mechanism exists that genetically links the ‘needs’ of the offspring with the acquired properties of the parents. If such a mechanism did exist, there would be few instances of parents having failed to ensure the future success and survival of their offspring, yet examples abound within ecological systems.

The Lamarckian approach fails to explain long-term social and/or material change and does not possess the capacity to explain the presence of long-term social and/or material behavioural trends. This is primarily because of the role it has given to human agency, reducing explanations for long-term change to short-term decisions made by humans. Both Lamarckian and NeoDarwinian archaeologies acknowledge humans as agents of variation, but the Lamarckian approach also gives humans a directing role. They are regarded as consciously producing select (directed) variants that will better enable them and their descendants to encounter the future. However, humans do not possess the capacity both to make decisions within the present and also to know what the full consequences of those decisions will be when projected into the distant future (Rindos 1984). Nor can their cognitive abilities remove them completely from involuntary physiological actions/reactions or subconscious psychological processes (O'Brien & Lyman 2000a; O'Brien et al. 2001). “Contemporary processualists take agency very seriously because directed variation is an indispensable ingredient in their models” (Spencer 1997: 230). The processual archaeologists define adaptation as the ‘conscious or unconscious modification of human behaviour to better fit changing environmental and/or social circumstances’ (Binford 1972: 106; Binford 1989; Trigger 1989: 295297; Spencer 1997; Boyd & Richerson 2000). “Processualists tend to see culture not as a collection of traits but as a system populated by wilful human actors [and] entertain the possibility that directionality may sometimes be an integral – not merely accidental – part of the cultural evolutionary process” (Spencer 1997: 211). Culture is treated as people’s ‘extrasomatic means of adaptation’ (White 1959: 8; Binford 1962: 22) whereby humans are removed from the selection process. Various evolutionary scientists have also taken this approach (e.g. Gould 1991: 65; Gould 2002: 952-953). “The Lamarckian character of human cultural change – the inheritance by teaching of useful innovations acquired during the life of an inventor-provides an entirely plausible mechanism for [an] accumulative, progressive and gradual style of change in this realm” (Gould 2002: 953). That is, material and social behaviour are viewed as the mechanisms by which humans are removed from the selection process, with the material generally subsumed into the social. The spatially and temporally diverse and inter-related scales at which the material and the social operate are generally subsumed into “explanatory approaches oriented towards the short time scale of human action and expressed in terms of social life” (Fletcher 1995: 15). However, humans are not exempt from selective processes. They can have no control over the long-term action of selection (Teltser 1995: 6-7). This is in part because the material operates over time periods that can far exceed the human life span. It is thus an

A Lamarckian view also does not account for the numerous instances where humans have failed to ensure their future success and have suffered social upheaval and/or decline. Evolutionary patterns that have been made visible from the vantage of a long time depth view point would have played out over periods of time that generally far exceeded the average human life span. The people involved would have been only marginally aware of them at best and they would have been incapable of directing their course. “Social change proceeds through the conscious choices exerted by human actors at the household, community and urban levels of interaction, equally, the trajectory of history is strewn with discontinuities and abrupt transitions which are a consequence of the unintentional and the idiosyncratic, of the curious power of unanticipated or random events to alter and reshape the social trajectory” (McGlade & van der Leeuw 1997: 4) (see also Shennan 2002: 9-10). Getting inside the minds and understanding the thought processes of long deceased humans is therefore peripheral to understanding social and/or material evolutionary change. It is inherently difficult to understand the thought processes of people who are alive today and who we can 9

The Evolution of the Built Environment extrasomatic adaptation of humans and an actor in its own right, with both these aspects non-deterministically inter-related.

is, non-correspondence inherently exists between the material, social and verbal aspects of culture, which in turn indirectly correlates to the rate at which they replicate (Fletcher 2005, 1996). The high level of inertia (low rate of replication) of the built environment results in inherent non-correspondence between buildings and their capacity to accommodate the faster changing, and therefore inevitably changing, social aspects of culture, unless the built environment is itself highly adjustable. The social generates variability and change that an adjustable built environment should readily accommodate, but that a non-adjustable built environment cannot necessarily accommodate.

The presumed adaptational role of human intent has been used to ‘explain’ a hierarchical view of evolution and the rise of ‘higher’ levels of political organisation by both archaeologists and scientists (Gould 1989: 126; Spencer 1997; Gould 2002: 953). Rosenberg has argued that incremental upward shifts in political complexity were produced by “discontinuous infrastructural opportunism” and a hierarchy of evolutionary processes (Rosenberg 1994: 308). This view is partly the result of the traditional view that cultural change has occurred in successive hierarchical stages (e.g. Barton & Clark 1997: 14). It is also, however, partly the result of a misunderstanding of punctuated change in evolution and of the diverse scales at which evolution operates and is manifest. These are issues that have been successfully dealt with in multiscalar Neo-Darwinian evolution, which is, therefore, a more appropriate theoretical basis for studying long-term social and material change.

The Biological and Physical Sciences: Complexity Theory Complexity theory, or the theory of the Self-Organisation of Complex Systems, takes the approach that the behaviour of thermal systems can be understood in terms of the holistic operation of complex open systems. Real life is made up of complex open systems, such as the environment, the weather, evolution, human social systems and the material components of human behaviour. Complex systems are composed of numerous interacting components and, due to the complexity of the inter-relationships between the large number of components, exhibit sensitive dependence on initial conditions such that small changes can generate numerous different possible long-term outcomes. Minute changes in the initial conditions can wildly alter the longterm outcome, or they might not affect the outcome at all. This is known as the butterfly effect (Gleick 1988: 20-23; Kauffman 1995: 91). The overall behaviour of the system is thus non-deterministically related to the holistic operations of the system’s components, but it is not reducible to their behaviour. This means that complex systems must be studied at the level of the whole system and not at the level of individual components. The holistic behaviour cannot be studied in terms of Newtonian laws of mechanics and the detailed behaviour cannot therefore be reduced to a simple algorithm. This makes the behaviour of complex systems computationally intractable at a detailed scale of operation. The only way to ascertain the flow-on effect of making particular changes to the system is to actually run the system in real time, because the shortest algorithm that ‘captures’ its behaviour is one that runs in real time (Kauffman 1995: 153-154).

Lamarckian explanations have failed to account for longterm behavioural change in buildings, either in terms of specific morphological change in space or time, or in terms of ‘fit’ between climate, technology and culture. “Intent is a proximate cause of something, not the ultimate cause, and we find it lacking as an adequate explanation for why lineages of artefacts, including houses, take the form they do” (O'Brien & Lyman 2000b: 85). People make choices (social, ritual, economic, thermal), they “select the best, the most useful, the most desirable … Splendid adaptations to man’s desires occur, but they evolve over spans of time that preclude man’s ever knowing what the fruits of his selection will be” (Rindos 1984: 4). People make choices about their built environment, which are then translated into built form, but they cannot foresee where those choices will lead them or their built environment. “Large-scale, cumulative results are the end products of countless small-scale changes that took place over a very long time period” (O'Brien & Lyman 2000b: 100). Additionally, material objects, most especially buildings, cannot be presumed to have been a direct correlate with the social because inertia exists inherently in material objects. A time lag will have been present between the intent and the resultant object, and the greater the physical mass of the object, the longer the time lag will have been. Buildings, for example, can encompass very long time lags, such as the lag between the initial intent to create a particular built environment, the reality of implementing it and the time for which it is likely to endure in the material landscape. “Houses and settlements are not just a convenience to be altered at whim, merely reflecting social organisation. They must instead be viewed as potentially-inertial structures that obstruct and inconvenience active social life. This is not to doubt that they are initiated by human action, only to note that their inertia can lead to effects that the human occupants neither planned nor desired” (Fletcher 2003: 291). That

However, recent research at institutions such as the Santa Fe Institute, New Mexico (www.santafe.edu), the Centre for the Study of Complex Systems, University of Michigan (www.cscs.umich.edu), the Centre for Complex Systems Research, University of Illinois (www.ccsr.uiuc.edu), and the ARC Centre for Complex Systems, based at the University of Queensland (www.accs.edu.au), where the behaviour of complex systems is empirically and systematically investigated, shows that the underlying mechanisms and processes operating within complex systems can be holistically 10

Complexity, Society and Buildings

SYNTHESISING THE BACKGROUND THEORIES

modelled and are reducible to boundaries of certainty. These models, that represent core tools by which the behaviours of complex systems are studied, have shown that many complex systems from a wide range of fields, including various human social systems, display common behavioural tendencies and uniformitarian operational processes. Over time the holistic behaviour of these systems appears to tend towards a point ‘poised’ between a wholly chaotic state and a wholly ordered or static state. That is, when observed at a holistic scale of operation patterns of organised behaviour emerge spontaneously. Whilst the detailed behaviour is non-predictive and nondeterminate at the level of the detail, over the long-term systems that operate within the region ‘between order and chaos’ are likely to prevail over systems that tend either towards a more chaotic state or a more ordered state. Such systems tend to be characterised by high levels of system level adjustability.

The study is founded on the core propositions of Adaptive Comfort theory from architecture, the multiscalar approach within Neo-Darwinism from archaeology and Complexity theory from the biological and physical sciences, utilising this position to study the long-term behaviour of the thermal performance of classes of buildings. Adaptive Comfort theory has demonstrated that built environments that provide their occupants with greater thermal adjustability (or perceived thermal adjustability) are associated with higher overall levels of occupant satisfaction and that this is due to the occupants possessing a greater capacity to make satisfactory thermal compromises between conflicting parameters, in contrast to the provision of thermal neutrality. This implies that the thermal variability that exists at the level of individual humans (between humans) should have operated as a source of variability within the Neo-Darwinian evolution of the built environment. Adjustable built environments generate diverse thermal environments, which should more easily accommodate a wider range of social options and change. Complexity theory states that complex systems operate non-deterministically at the level of the detailed operation of the system. They can exhibit selfsimilar operations at diverse scales of operation, but at the macro-scale of operation of the system patterns appear in the form of boundary conditions. That is, the complex systems that are likely to have persisted for longer periods of time should have been those that were adjustable and robust to changing circumstances, within definable boundaries, such that they were capable of absorbing changes in contextual circumstances without becoming structurally unstable. Systems that operate outside of these boundaries are likely to have been unstable and unlikely to have persisted in the long-term. A uniformitarian approach to the study of the multi-scalar Neo-Darwinian evolution of classes of buildings should be able to isolate these boundaries if they have existed. In order to ascertain whether or not these boundaries have existed within the built environment, built systems needs to be examined holistically, at the level of the operation of the whole system and in relation to the material/social systems with which they are associated, and not just that of individual building components.

The study has utilised the logic of Complexity theory to study the thermal performance of classes of buildings, which have not been studied from this perspective before. This approach to the study of thermal performance, and of the material components of human behaviour generally, has great potential for understanding both the general tendencies and the idiosyncrasies exhibited within the long-term thermal behaviour of buildings, and its relationship to the social. Complexity theory stands in contrast to a mechanistic or reductionist approach to understanding the behaviour of complex systems. A reductionist approach holds that high-level phenomena can be understood in terms of lowlevel processes and that, if the low-level processes could only be sufficiently understood in every detail, then the high-level behaviour could be understood. Such an approach has, however, been successful to date in only gaining an understanding of closed or equilibrium systems, systems that do not exchange matter or energy with their environment. It has not managed to explain the complex behaviour of open systems, such as thermal systems, the built environment or social action. “Kauffman and his colleagues at the Santa Fe Institute for the study of complex systems are groping towards something important. If we have been unable, thus far, to achieve a rigorous formulation, we should at least recognize that science itself has been so tuned to other, largely reductionist, modes of thought, that the basic conceptual tools have never been developed. I welcome this exploration into terra largely incognita and would like to point out that the implications for evolutionary theory may extend even further than the major protagonists have recognized” (Gould 2002: 1213). This also applies to reductionist or contextual models and explanations in mainstream archaeology, which have overlooked the complex, holistically self-organised behaviour of social and material systems.

Buildings and thermal systems are complex systems. They possess numerous operationally interacting components. So do buildings and thermal systems therefore also display operational commonalities that are non-deterministically related to micro-scale operations? That is, do they operate within definable macro-scale boundary conditions, within which they are evolutionarily robust and outside of which they are unstable and ephemeral? If so, does Adaptive Comfort theory account for the micro-scale non-deterministic variability within the built environment and does Complexity theory account for the existence of the boundary conditions? If boundary conditions have existed within the operation of the built environment, a number 11

The Evolution of the Built Environment of implications arise. First, the thermal performance of buildings is subject to selective pressure and, therefore, certain classes of buildings and thermal capacity should have dominated others over the course of time. Secondly, processes that play out at the macro-scale are indirectly and non-deterministically related to processes that occur at the micro-scale in space and in time, and vice versa. Mapping the thermal capacity of buildings over extended periods of time, or of examples of fundamental transitional change, should, therefore, reveal either the existence or the absence of operational commonalities (boundary conditions) within classes of buildings and the built environment generally.

structures. A study of classes of thermal capacity, the focus of the study, is best performed using statistical analysis because a class exists within ‘statistical space’. It is a definitional categorisation based on a statistically significant likelihood that the majority of objects within the class possess the same morphological and compositional characteristics. The field simulation (engineering-analysis) was comprised of two parts. The first part was designed to investigate the thermal limits of various early and/or rudimentary generic built structures in climates that experience extreme seasonal temperatures (very hot and very cool). The range of structures included morphologically simple windbreaks, shade structures and domed huts. The second part carried the investigation forward in time to investigate the thermal performance of several morphologically simple generic hut structures. The range of structures included heavy and lightweight domes (semi-subterranean and on-ground) and heavy and lightweight on-ground rectilinear huts. The engineeringanalysis thus generated a set of thermal principles that applied to the thermal behaviour of generic structures under various conditions. That is, it produced a prescriptive logic for inputting real data pertaining to real structures into a database such that the associated thermal capacity could be derived. The structures were treated as composites of interrelated constituent built parts with definable thermal properties, which were then ordered according to the thermal principles. The result was that the thermal capacity of the real structures could then be analysed via statistics (multivariate analysis). The real structures included in the case studies included an ethnographic set of buildings plus various archaeological sets.

THE RESEARCH DESIGN, SCOPE AND METHODOLOGY In order to investigate the evolutionary path of the built environment for the possible existence of long-term patterns in the thermal performance of classes of buildings, the study proceeds by appraising a null hypothesis. This states that there has been no tendency for the thermal performance of classes of buildings in the archaeological record to display common or regular behaviour and that thermal performance has, therefore, been selectively neutral. That is, that selection has not and does not act upon it. Whilst a demonstration that the thermal signatures of classes of buildings have changed irregularly over time would not falsify the null hypothesis, a demonstration that thermal signatures have changed regularly over time would in a general way invalidate it. The research design was formulated to investigate longterm change in the thermal performance of classes of buildings and, because buildings are essentially thermal machines, the focus was on thermal capacity rather than thermal performance. Thermal capacity is a measure of the ability of a building to implement selective thermal environments, regardless of whether or not the occupants implement them. As such, thermal capacity is an emergent feature of buildings. It is a measure of the microclimates that can be produced within the spatial envelope of the building.

The first case study was an ethnographic set of generic buildings compiled from Murdock’s Ethnographic Atlas (Murdock 1967; Murdock & Wilson 1972), a compilation of building data from various regions and climates throughout the world. The Atlas was used in lieu of the collection of original ethnographic data as it was completely adequate for the purposes of the analysis. This study constituted a statistical analysis of the correlation between a global ethnographic set of generic buildings and their climatic and social contexts.

Thermal systems are complex systems and complex systems, possessing sensitive dependence on initial conditions, are computationally intractable at the level of the detail of the system. Thermal environments are highly sensitive to small changes in their physical surroundings, such as change to the buildings themselves, change to the fixtures and fittings, the occupants and the outside environment. The research design was therefore predicated on field simulation (engineering-analysis) as the best means by which to acquire a detailed picture of the thermal performance of various generic structures under a range of different circumstances and in such a way that the information could then be extrapolated to archaeological examples. This information then formed the basis for a statistical (multivariate) analysis of the thermal capacity of various case studies of archaeological

The second case study, the first archaeological study, was compiled from primary published archaeological data from regions within Egypt, the Negev and Palestine. This study constituted a statistical analysis of individual buildings over a very extended period of time. It encompassed a period of time from the earliest excavated structures through to examples of recent vernacular. Examples were selected from different, indirectly related societies and climates, so as to ascertain, first, whether or not long-term large scale patterns of thermal change are evident in the thermal performance of classes of buildings and, secondly, the likelihood that culture or climate had influenced the thermal performance of classes of buildings over a very extended period of time. 12

Complexity, Society and Buildings The third case study, also an archaeological study, was compiled from primary published archaeological data from four regions, two in the Old World and two in the New World, with each region encompassing a region that experiences hot-arid summers and a region that experiences very cool winters. The Old World regions consisted of the Lower Jordan Valley (hot-arid summers) and the Southern Jordanian Highlands (very cool winters). The New World regions consisted of the Phoenix Basin, Arizona (hot-arid summers) and the Pine Lawn Valley, New Mexico (very cool winters). These regions were selected on the basis that they were geographically and climatically distinct, but culturally related, albeit indirectly. This study therefore constituted a statistical analysis of the thermal behaviour of individual rooms within two diverse climates and two regions that are indirectly culturally related. It encompassed a period of time from the earliest recorded structures within the region (c. 10,500-8,300 B.C. in the Old World and 1,500 B.C.-A.D. 200 in the New World) through to the ‘pithouse’-to-’pueblo’ transition, which represented an almost universal phase transition from one class of building (‘pithouses’) to another (‘pueblos’) (Cordell & Gumerman 1989: 9). These examples were selected so as to ascertain, first, whether or not large scale patterns of thermal change are evident throughout the phase transition and, secondly, the likelihood that culture or climate had influenced the thermal performance of rooms throughout the transition.

THE STUDY STRUCTURE The study is presented in the ten following chapters and their contents are briefly described here. Chapter 2 presents an overview of Adaptive Comfort theory and the benefits of a non-deterministic approach to the study of thermal environments and built structures. Chapter 3 presents an overview of Neo-Darwinian archaeological theory and the benefits of a uniformitarian approach to the study of longterm material and social change. Chapter 4 presents an overview of Complexity theory and the approach taken in the study in which complex systems are viewed as nondeterministic at the scale of the holistic operation of the system. Chapter 5 presents an overview of engineering-analysis and a detailed outline of its application to analyse the emergent thermal properties of various generic structures. That is, their capacity to selectively produce a range of thermal environments. It also presents an overview of multivariate analysis (MVA) and the MVA methodology developed and utilised here to analyse the thermal performance of archaeological buildings, based on the results of the engineering-analysis. Chapter 6 presents a detailed analysis of the capacity of a global ethnographic set of generic buildings to selectively implement a range of thermal environments. Chapter 7 presents a detailed analysis of a cross-cultural long-term set of buildings from the Old World to selectively implement a range of thermal environments. Chapter 8 presents a detailed analysis of the capacity of a cross-cultural set of rooms drawn from the ‘pithouse’-to-’pueblo’ transition to selectively implement a range of thermal environments. Chapter 9 presents an illustrative example of a late 3rd Mill. B.C. site that experienced a declining capacity to implement a range of thermal environments, due to increasing urban density and conservative building practices. This is examined at both the scale of the site itself and within a broader scope of a cross-cultural long-term set of archaeological buildings from the Old World. Chapter 10 presents a discussion of the thermal and social implications of the capacity for buildings in the archaeological record to selectively implement a range of thermal environments and an overview of the implications for thermally and materially adjustable environments.

An overview and appraisal of the evidence gave rise to a fourth case study, which was performed as a single site that is an example of a settlement where the capacity to selectively implement a range of thermal environments gradually reduced over time, due to high inertia in the very massive load-bearing structures and very conservative building practices. This case study, also an archaeological study, was compiled from primary published archaeological data for the early 3rd Mill. B.C. city of Mohenjo-daro in Pakistan. This settlement has traditionally been regarded as having been architecturally successful throughout its several hundred year life, falling into a sudden and late decline for reasons as yet unexplained. This study constituted a statistical analysis of the thermal behaviour at both a detailed scale, that of individual rooms within the settlement itself, and at a broader scale, that of whole buildings within the crosscultural dataset of the second case study (for Egypt and Palestine). Examples were selected so as to ascertain, first, whether or not large scale patterns of thermal change are evident throughout the life of the settlement itself and, secondly, to ascertain how the nature of the thermal performance of the buildings at the settlement compared with a regionally and chronologically wider set of Old World buildings.

13

CHAPTER 2 – Background Theories: Architectural Studies

to make changes to their thermal environment. This factor is now being officially recognised by the engineering standards committees as a means by which Sick Building Syndrome (SBS) may be alleviated. As a result, emphasis within the architectural and mechanical-engineering professions has shifted away from thermal ‘comfort’ to thermal ‘satisfaction’.

INTRODUCTION “People are not passive receptors of their thermal environment. They take actions in order to improve their thermal comfort. These actions or adaptations include modifying the rate of internal body heat generation, modifying the rate of body heat loss, modifying the thermal environment, and selecting a different environment.” (Humphreys 1997: 129)

ACT states that thermal experiences result from a complex interaction between numerous physiological and psychological factors. A complex feedback loop is deemed to exist between a person’s physiological state and their past experiences and future expectations. A person’s thermal ‘satisfaction’, as opposed to thermal ‘comfort’, is dependent upon a person’s ability (or perceived ability) to make and act upon their thermal choices. The range of environmental conditions in which good thermal compromises are therefore possible includes states other than just thermal neutrality and may include temperatures at which the subject is actually thermally uncomfortable. It appears that there exists a general preference amongst humans for being thermally satisfied, rather than being thermally neutral, or even thermally comfortable. This is most evident from studies of habitual building occupants, but additional data indicates that it may be a universal phenomenon (Auliciems 1989; Humphreys 1997; de Dear & Brager 1998; McCartney et al. 1998; Brager & de Dear 2001; de Dear & Brager 2001; Nicol & Humphreys 2001). This means that humans, rather than seeking to be always thermally comfortable, will either seek to alleviate thermal discomfort (Forwood 1995) or to be selectively thermally uncomfortable.

The physiological effect that thermal conditions have on human bodies has been a topic of scientific inquiry since the 16th century (Foster 1901). In the 1770s Fordyce performed one of the earliest experiments on the effects of heat upon the human body (published in: Blagden 1775a & b). Since then studies have been performed both under laboratory conditions and in the field, in diverse climates and on subjects with diverse life-styles (including both sedentary and mobile societies) and ethnic backgrounds, investigating physiological and psychological factors. More recently the architectural and engineering professions have come to regard thermal performance as synonymous with the abstract concept of thermal ‘comfort’, although this has been shown to correlate with no specific physiological state (Clark & Edholm 1985: 176). The meaning given to the word ‘comfort’ has varied historically, but only in the 19th century did it come to refer to a physical state of well being within an environmental context that encompassed light, heat, coolness and ventilation (Rybczynski 1986; Heijs 1994: 43; Brager & de Dear 2002). The mechanical engineering profession, which emerged during the industrial revolution, has until very recently defined thermal comfort as synonymous with thermal neutrality, the physiological state in which a body’s metabolic rate is minimal and constant. Thermal ‘comfort’ was equated to the state produced when a person was in a steady-state heat balance with their immediate environment. This state was assumed to be constant and predictable, falling within a very tight temperature range regardless of social, climatic or seasonal differences (Fanger 1970). There has, however, been a recent paradigm shift within the profession, which has brought it more closely in line with the expanding field of inquiry within the architectural profession known as Adaptive Comfort theory (Brager & de Dear 2001; Fanger & Toftum 2001).

A study by Williamson, Coldicutt and Riordan, which was based on 48,000 thermal sensation votes from field studies carried out in various Australian towns in various climates, found that a considerable percentage of subjects experienced temperatures other than neutral without indicating a desire to change their thermal state (Williamson et al. 1989; Ballinger et al. 1991; Williamson et al. 1991). For example, in the summer 16.6% of subjects reported feeling ‘slightly warm’ or ‘warm’ with no desire to be cooler and in winter 5.9% reported feeling ‘slightly cool’ or ‘cool’ with no desire to be warmer (Williamson et al. 1995). This implies that thermal neutrality is not synonymous with thermal ‘comfort’ and that satisfactory thermal compromises can be produced under conditions where people may feel slight discomfort, having shown no preference for making a change. This implies that thermal neutrality is not an over-riding human ambition and that thermal ‘satisfaction’ is possible when people have the capacity (or perceived capacity) to make thermal choices and to selectively control the implementation of those choices,

Adaptive Comfort theory (ACT) has been studied within the architectural profession for several decades and has centred on the recognition that people can gain higher ‘satisfaction’ (a better overall thermal compromise between conflicting parameters) in states other than neutral if they possess the capacity, or perceived capacity, 14

Background Theories: Architectural Studies regardless of whether or not they do implement them. Thermal ‘satisfaction’ can thus be defined in terms of thermal choices and thermal control.

HUMANS AND THERMAL RESPONSE As homeotherms, humans (and other mammals and birds) have the capacity to maintain a stable core body temperature within relatively narrow limits regardless of activity and environmental temperature fluctuations, and which they must do if they are to function. Poikilotherms, on the other hand (reptiles, amphibians, fish and invertebrates) are animals whose body temperature follows changes in the environmental temperature. This can be extreme, as in the case of the Wood frog (rana sylvatica) of Alaska that survives the winter months by becoming frozen, whereupon it essentially becomes a different life form. 35-45% of the frog’s body turns to ice and its breathing, blood flow, and heart beat cease to function until the weather turns warmer and it is electrically ‘jump started’ back into motion (Kiehl 1995). Homeotherms, however, have the ability to regulate their core body temperature, regardless of activity or environmental temperature, by involuntary responses involving an autonomic nervous system (Mount 1979: 14). In maintaining a stable core body temperature, however, the temperatures in the extremities vary as a factor of the body’s thermal control mechanism. In addition, the body’s core temperature does not remain constant throughout the daily cycle. Diurnal animals will show a higher temperature during the daytime and a lower temperature during the night time and this pattern is reversed in nocturnal animals. In addition, some animals show varying core body temperatures throughout the changes of the season, the most extreme examples of this being animals that are dormant during the winter (hibernators) and animals that are dormant during the summer (aestivators).

Figure 2.1. Thermal sensations registered in the spinal cord and the hypothalamus (after Greenland & Szokolay 1985: 5). Humans perceive their immediate thermal environment via cutaneous temperature sensors, of which there are two types, those that initiate nerve impulses under cold conditions and those that initiate nerve impulses under hot conditions. Both types are anatomically and physiologically separate structures. Cold receptors are generally located closer to the skin surface than warm receptors, and thus humans are more sensitive to changes towards colder temperatures than changes towards warmer temperatures (Fig. 2.2). Under steady state thermal conditions, cold sensors generate a maximum signal at between 17oC and 36oC and warm sensors generate a maximum signal at between 41oC and 47oC (Hensel 1981).

Homeotherms receive thermal sensations via peripheral receptors in the skin, and central receptors in the spinal cord and the hypothalamus in the brain (Fig. 2.1). Sensations from both the peripheral and central receptors give rise to sensations in the sensory cortex. These in turn effect reactions in the body’s temperature control mechanisms, which are both physiological and behavioural, and are mediated in the hypothalamus. When an increase in environmental temperature is sensed, heat dissipation is increased by sweating, panting and dilation of the blood vessels in the skin (vasodilatation). When a decrease in environmental temperature is sensed, heat dissipation is reduced by fluffing up of the coat (pilo-erection) and closure of the blood vessels in the skin (vasoconstriction). Additional cold tolerance can be created via the production of additional heat by shivering, via non-shivering thermogenesis (heat production in the newborn and hibernating animals) (Mount 1979; Clark & Edholm 1985), or via possessing thick subcutaneous fat, a thick fur or hair coat, or pilo-erection (Clark & Edholm 1985: 134-136).

Figure 2.2. Thermal sensors in the skin. Thermal response in humans differs from most other homeotherms in a number of ways. First, humans are bare-skinned and have relatively little subcutaneous fat, making them relatively susceptible to cold. Secondly, humans can sweat at a higher rate than other mammals and are thus able to withstand relatively hot conditions (Mount 1979: 145). Thirdly, humans have the ability to utilise clothing and construct devices to alter their immediate environment and thus minimise their need to adapt physiologically. More specifically, there are three 15

The Evolution of the Built Environment modes of human thermal adjustment, both voluntary and involuntary (Brager & de Dear 1998). The first, physiological adjustment (genetic and/or acclimatisation), is generally involuntary. The second, behavioural adjustment (behavioural, environmental, technological and/or social), is generally voluntary and consciously performed. The third, psychological adjustment (habituation and/or expectation), can be either involuntary and unconsciously performed, or can be consciously influenced. Ultimately this means that there are a number of ways by which people react to their thermal environment, which produces a complex feedback loop (Fig. 1.2). To more fully illustrate the complex nature of this feedback loop, the three primary modes of thermal adjustment are outlined below.

thermoregulatory system acclimatises, when it then decreases.

Human Behavioural Thermal Adjustment Thermal behavioural adjustment refers to a conscious decision by a person to effect a change to either their person (personal and social adjustment) or to their environment (Brager & de Dear 1998: 85-87). There are three principal types of behavioural adjustment (Table 2.2). First is personal adjustment. This includes changes to the individual, such as their clothing type, activity level, posture, or location within the thermal environment. Second is physical or technological adjustment. This includes changes to the built environment such that the thermal environment is altered, by both active and passive means. Third is social adjustment. This includes changes in lifestyle, schedules, rituals and conventions. These factors operate at the scale of the individual and of the group.

Human Physiological Thermal Adjustment The human physiology adjusts to changes in the thermal environment in three principal ways, generally involuntary. The first is via the many involuntary changes in the thermoregulatory system outlined above, usually as an immediate response to sudden thermal stress (Table 2.1).

Human Psychological Thermal Adjustment A person’s expectations about their thermal environment have a significant influence over their thermal experience. Auliciems has linked psychological control processes with physiological and behavioural thermoregulation (Auliciems 1981, 1983). This complex link creates a feedback loop between past experiences and future expectations and can involve both conscious and subconscious processes. Conscious psychological adjustment includes mental processes that can potentially override physiological forms of thermal adjustment, such as those involved in ritual (Ali et al. 1998). Subconscious psychological adjustments include social factors that the person may or may not be aware of. This can include daily routines, such as a siesta in the heat of the day, or seasonal routines, such as differences between summer and winter clothing. However, conscious psychological adjustment, or attitude, appears to be most effective at levels of thermal stress that are moderate (Lee 1966). “As stress increases above moderate levels, interpersonal differences tend to decrease, until, with very severe stress, individual physical endurances come into play” (Lee 1966: 90).

THERMOREGULATORY ADJUSTMENT Thermoregulatory Adjustments to Cold

Thermoregulatory Adjustments to Heat

Vasoconstriction Increased muscle tension Shivering

Vasodilatation Increased muscle relaxation Sweating

Table 2.1. Thermoregulatory Adjustment The second way is via genetic adaptation and the third way is via acclimatisation, both of which are time dependent. Genetic adaptation refers to the gradual modification at the genetic level over subsequent generations as the result of natural selection. It is undirected, unconsciously performed and exceeds the lifespan of individual organisms. Acclimatisation refers to metabolic acclimatisation. That is, physiological change involving a number of interrelated responses that generally result in involuntary changes to the basal metabolic rate in individuals in reaction to some longterm thermal stress (Mount 1979: 116-117). The basal metabolic rate index (BMR) is the rate of energy produced by the body during absolute rest and is measured as a product of oxygen usage. It is the primary marker for measuring and comparing the energy expenditure of individuals and is expressed as energy expenditure per unit surface area per hour. Metabolic acclimatisation generally occurs over an extended period of time, which generally exceeds several days. The actual duration is dependent on extraneous physiological factors such as the age, sex, physical fitness, activity level and type, and state of health of the individual (Clark & Edholm 1985: 5-7). Generally, the BMR will increase relative to an increase in thermal stress until the

The survey by Williamson, Coldicutt, and Riordan (1995) of occupants living in different climates in Australia, mentioned above, has demonstrated that psychologically people place differing degrees of importance on preferred temperatures at different times of day and at different times of year (Fig. 2.3). For example, the majority of occupants in four areas, Perth, Melbourne, Sydney and Adelaide, placed maximum importance on comfortable temperatures during hot summer nights during sleep time and least importance on cold winter nights during sleep time. However, the occupants in the relatively hot climate of Perth placed a relatively greater degree of importance on the hot summer nights and lesser importance on the cold winter nights than did the occupants in the relatively mild climate of Adelaide. 16

Background Theories: Architectural Studies BEHAVIOURAL ADJUSTMENTS TO TEMPERATURE Personal Adjustments to Cold

Personal Adjustments to Heat

Eating hot food Drinking hot beverages Reducing body surface area Generating body heat (increasing activity level) Adding personal insulation (clothing etc.) Adding extraneous insulation (furnishings etc.)

Eating cold food Drinking cold beverages Increasing body surface area Minimising body heat (reducing activity level) Reducing personal insulation (clothing etc.) Reducing extraneous insulation (furnishings etc.)

Technological Adjustments to Cold

Technological Adjustments to Heat

Increasing air temperature Reducing air humidity Increasing surrounding surfaces temperature Reducing air movement Reducing infiltration of cold air Relocating to warmer microclimate

Reducing air temperature Increasing air humidity Reducing surrounding surfaces temperature Increasing air movement Increasing infiltration of cold air Relocating to cooler microclimate

Social Adjustments to Cold

Social Adjustments to Heat

Clothing customs Sleeping customs Culinary customs (meal times and diet) Seasonal migration (to warmer climate) Table 2.2. Behavioural Adjustments to Temperature.

Clothing customs Sleeping customs (siesta etc.) Culinary customs (meal times and diet) Seasonal migration (to cooler climate)

Figure 2.3. Percentage importance placed on achievement of thermal satisfaction at various times in various Australian cities (Williamson, et al. 1995)

Figure 2.4. Thermal exchange between humans and the environment (after Konya 1980: 26)

heat that occurs between a person or a building and their external environment is dependent on the microclimatic conditions. The heat exchange that occurs by conduction depends on the relative temperatures, convection depends on the rate of air movement, and radiation depends on solar intensity and relative temperatures. Evaporation rates, which are dependent on the relative humidity levels, also affect the way in which heat flows from a hotter to a cooler body.

THE BASIC THERMODYNAMIC PRINCIPLES OF HEAT EXCHANGE The first law of thermodynamics states that energy cannot be created or destroyed and the second law states that heat and energy travel from a hotter to a cooler body. There are three means by which heat flows from a hotter to a cooler body (Figs. 2.4-2.5). They are conduction, convection and radiation. The first two are relatively slow, but radiation occurs at the speed of light, although ultimately the outcome of the continuous exchange of 17

The Evolution of the Built Environment

Radiation Radiation is heat transfer via electromagnetic radiation across a space to a body. It occurs regardless of the temperature of the space and does not heat the space in the process (Fig. 2.8). Most radiant energy emanates from the sun in the form of shortwave radiation, although surfaces at normal terrestrial temperatures also emit radiant energy, in the form of long wave radiation. The amount of radiant energy that reaches an object on the earth from the sun is a factor of the amount reflected by clouds (cloud cover) and other atmospheric particles (dust, pollutants), the amount absorbed by molecules in the atmosphere (ozone, water, carbon dioxide), and the thickness of the atmosphere above the object (the altitude and latitude) (Konya 1980: 9-11). Generally, the higher the altitude and the closer to 0o latitude, the higher the amount of solar radiation, with the exception that the humidity in the humid tropics is generally very high and the radiation is therefore proportionately lower.

Figure 2.5. Thermal exchange between buildings and the thermal environment (after Watson 1983: 29; Szokolay 1987: 21)

Conduction Conduction is heat transfer by direct contact (Fig. 2.6). This includes heat transfer within a body itself, from a warmer to a cooler region. It takes place by the transfer of kinetic energy from a faster vibrating, warmer object to cooler molecules. The kinetic energy is converted to heat in the process (Szokolay 1987: 8; Givoni 1998: 110).

The amount of heat transferred by radiation is ultimately a factor of the temperature difference between the emitting and the receiving surfaces, the distance between them and the absorptance and reflectance qualities of the receiving surface. The temperature difference correlates directly with the wavelength of the radiation. Shortwave radiation is emitted from hotter surfaces and contains more energy than long wave radiation, which is emitted from cooler surfaces. The amount of energy drops off exponentially as the distance between the emitter and the receptor increases, by 1/r2, where r is the radial distance between them (note that the wavelength does not change, only the amount of energy being received). Finally, the surface qualities of the receptor affect how much energy is absorbed, and converted into heat energy, and how much is reflected away without affecting the surface temperature (Szokolay 1987: 9; Givoni 1998: 112-113). The absorptivity is mainly a property of the surface colour (darker colours absorb more energy than lighter colours) and the reflectivity is mainly a property of the surface sheen (shiny surfaces reflect more energy away than dull surfaces). For example, a dull, black surface will absorb large amounts of energy and reflect none away, whilst a white, shiny surface will reflect most of the energy away and absorb very little.

Convection Convection is heat transfer to or from a body to a gas or liquid in direct contact (Szokolay 1987: 9) (Fig. 2.7). There are two types of forces that cause convection. First is air movement induced by a temperature differential and second is forced air movement (Givoni 1998: 110-113). The first type of convection occurs where, for example, air is heated, expands, becomes lighter and rises. It may then come into contact with a cooler object, transfer heat to that object, become cooler and more dense and heavy, and finally fall, thus creating a convective current. The second type of convection occurs where, for example, natural air movement, such as a breeze, moves across a surface.

Figure 2.6. Conduction Clark 1978: 5)

(after

Figure 2.7. Convection (after Clark 1978: 6) 18

Figure 2.8. Radiation (after Clark 1978: 6)

Background Theories: Architectural Studies possible either via direct solar radiation and/or by indirect means, such as via the warming of the external building fabric. Cooling is possible via the ingress of cool air either by direct means, such as shading, and/or by indirect means, such as via moisture evaporation. Different combinations of these elements and principles have been applied, though not exclusively, in different climates and regions around the world to create different systems of heating and/or cooling. For example, in hot-arid countries the emphasis has generally been on the maximisation of the time lag between interior and exterior and on the mutual shading of the buildings. In hot-humid countries the emphasis has generally been on the maximisation of ventilation (Figs. 2.9-2.12).

SPACE HEATING AND COOLING TECHNOLOGIES There are several different methods of heating and/or cooling enclosed spaces, both internal and external. Below is a discussion of the four primary methods, of passive solar heating and cooling and active heating and cooling. As the focus of the study is on pre-industrial buildings, or buildings that use only the natural elements and pre-industrial technologies to create thermal environments, only those systems that are relevant to the study are discussed in detail below. That is, buildings that use post-industrial technologies, such as mechanical and electrical heating and cooling, are only briefly mentioned here.

Direct Gain Heating: Direct gain heating is the simplest and oldest form of solar heating. It refers to the heating of enclosed spaces and surfaces by direct solar gain through openings or through translucent materials, and/or by heat conduction from a sunlit exterior through to the interior surfaces. The amount of solar radiation that a building fabric is exposed to is a property of site location (altitude and latitude), climate (cloud cover), site topography (exposure vs. over shadowing) and building orientation. It is highest when the sun is at the zenith, assuming there is no cloud cover at the time, and it is proportionately lower when the sun is lower in the sky. At the equator the sun is directly overhead at the zenith, but in the southern hemisphere it is to the north and in the northern hemisphere it is to the south (Figs. 2.13-2.15). However, the outside air temperatures are generally not hottest at midday but, due to the time lag caused by the earth’s atmosphere, the hottest time of day is generally some time after this. Therefore a building will also be exposed to heat gain by convection through the middle of the day and into the afternoon.

Passive Heating and Cooling Passive heating and cooling systems are the simplest means by which structures can be thermally conditioned. They combine the principles of conduction, convection, radiation and evaporation as they are available in nature (sun, wind and moisture) with the spatial and material thermal properties of structures. A building’s spatial thermal properties constitute the volumes that comprise the building (the general space defined by the building envelope plus the constituent interior spaces) plus the way in which the spatial volumes interface with each other and the outside environment via fixed components (walls, floors, roofs etc.) and movable components (doors, windows, louvres etc.). A building’s material thermal properties constitute the materials of which the building is constructed. Below is a description of the various basic principles by which spaces are heated and/or cooled. Heating is

Figure 2.9. Passive solar principals for buildings in hot, arid regions: mutual shading.

Figure 2.10. Passive solar principals for buildings in hot, arid regions: time lag (after Konya 1980: 42)

Figure 2.12. Passive solar principals for buildings in hot, humid regions: ventilation (after Konya 1980: 43)

Figure 2.11. Passive solar principals for buildings in hot, humid regions: ventilation (after Konya 1980: 43) 19

The Evolution of the Built Environment

Figure 2.13. Annual cycle of the earth around the sun (after Rohr 1965: 21)

Figure 2.14. The path of the sun in high southern latitudes (after Parnell & Cole 1983: 3) The amount of solar energy that is transferred to the inside of a structure is a factor of, first, the orientation of the building and its openings relative to the position of the sun and, secondly, to the thermal properties of the building fabric, which affect the proportion of heat that is absorbed relative to the amount that is reflected away. The amount of solar energy that is reflected away is a factor of the surface properties of the fabric (reflectance, absorptance and emittance). Generally, lighter and shinier surfaces tend to reflect heat away, whilst darker surfaces tend to absorb and emit heat. Also smoother surfaces tend to reflect more heat, whilst rougher surfaces tend to absorb more.

time lag (the difference between the inside ambient temperature and the outside environment). Other factors that affect the time lag include the degree of direct interface between the interior space and the outside air and the volume of the interior. Generally, a long time lag is a property of a high thermal mass (Figs. 2.16-2.17), a low degree of interior-exterior interface (Figs. 2.18-2.19) and a large internal volume (Fig. 2.20), and vice versa for a short time lag. Materials that have high thermal mass include stone, concrete, clay, mud and water, and materials that have low thermal mass include thatch, skins, plastics, glass and air. Therefore, spaces below ground will have a long time lag and the further below ground a space is located, the longer the time lag will be. At 10m below ground the inside temperature will be as far removed in time as is possible (ie. 12 hours) from the outside temperatures, regardless of latitude, as a factor of the thermal mass of the surrounding earth (Fig. 2.21).

The amount of heat that is absorbed and stored within the building fabric itself, and eventually conducted through to the interior, is partly a factor of the thermal mass of the fabric because the thermal mass of a structure affects the

Figure 2.16. Time lag (after Konya 1980: 112)

Figure 2.15. Solar angles at high, and tropical latitudes (after Konya 1980: 44)

Figure 2.17. Time lag for various types of construction (after Konya 1980: 112)

20

Background Theories: Architectural Studies

Figure 2.19. Plan and section of the conventional structure (Pearlmutter & Meir 1995: 446)

Figure 2.18. Inside temperature with respect to outside temperatures for lightweight and conventional structures during closed and open modes (Pearlmutter & Meir 1995: 447)

Figure 2.20. Inside temperature with respect to internal volumes (after Parnell & Cole 1983: 15)

Figure 2.21. Soil temperature at various depths and times of year at 46oN (after Eckert 1979: 241)

Indirect Gain Heating: Indirect gain heating refers to the heating of an interior space and surfaces by indirect contact with spaces and surfaces that are directly warmed by solar gain (Fig. 2.22). The spaces that are directly warmed may or may not be sealed off from the surrounding spaces they are warming. If they are sealed off, they will warm the surrounding spaces by conduction and, if they are not, they will warm them by convection. The simplest and oldest examples of this type of heating are the use of courtyards. Courtyards work by holding air heated by direct radiation, which is trapped in the space due to a lack of ventilation, but only if the courtyards are proportionately wider than they are tall (Meir et al. 1995) and only as long as they are subject to direct solar gain.

They readily loose the heat to the outside air once the solar radiation no longer penetrates the courtyard interior (Fig. 2.23), unless they are first thermally sealed off from the outside air. More recent systems have utilised extensive glazing and examples include conservatories and other proprietary systems, such as the TrombeMichel wall (Fig. 2.24), Lawrence and Thermosyphon wall systems. These systems utilise the greenhouse effect, whereby shortwave, high-frequency radiation passes through the glass, is absorbed by the air and the internal surfaces, and re-radiated as long wave, low-frequency radiation, which cannot so easily pass back through the glass. The warm air is this trapped within the space, from where it can be selectively drawn.

21

The Evolution of the Built Environment

Figure 2.22. Passive heating (after Parnell & Cole 1983: 29)

Figure 2.24. Trombe-Michel wall (after Parnell & Cole 1983: 21

Figure 2.23. Thermal daytime heat gains and night time heat losses from courtyards (after Konya 1980: 39) Cooling by Ventilation: Natural ventilation inside buildings can result from air movement caused by differences in pressure or by air changes caused by differences in temperature. Pressure induced air movement is caused by wind blowing on an object, which generates a positive windward pressure and a negative leeward pressure (Figs. 2.25-2.30). If the pressure difference between the windward and the leeward sides is sufficient, air movement through the space will be generated, passing though openings on the high pressure side and exiting through openings on the low pressure side. Generally, larger openings generate larger internal airspeeds than smaller openings. However, if the outlet openings are larger than the inlet openings the air speed will be high but the coverage will be reduced, and vice versa (Figs. 2.31-2.32). The internal path of the air movement and the speed of the airflow are dependent on the layout of internal obstacles (such as walls) and the size of openings (both internal and external). Generally, an obstacle in the path of the air movement will reduce the speed, particularly if the obstacle forces a sharp

change in wind direction, or if it is located closer to the air inlet than to the outlet (Fig. 2.33). Obstacles can, however, create turbulence that can potentially force air to circulate into areas that would otherwise have been bypassed by the free flowing air. Windcatchers are devices that have, in part, been utilised to catch high level winds and redirect them to lower level spaces that would otherwise be bypassed, thus creating circulating currents of air (Fig. 2.34). Temperature induced air movement is called the ‘stack effect’ and is due to the displacement of warmer, lighter air by cooler, heavier air. The warm air rises and is replaced by cooler air from below (Fig. 2.35). If the temperature differential between air inlet and air outlet is sufficient, a movement of air will be generated and cooling by convection will occur. This process can occur either vertically, by locating the air intake at floor level and the outlet at ceiling level, or horizontally, by locating the inlet in the shade and the outlet in full sunshine (Fig. 2.36).

Figure 2.25. Positive and negative pressure zones (after Konya 1980: 53)

Figure 2.26. Wind shadows with respect to building arrangement (after Konya 1980: 55)

22

Background Theories: Architectural Studies

Figure 2.27. Wind shadows with respect to building height (after Konya 1980: 55)

Figure 2.29. Wind shadows with respect to courtyards (after Konya 1980: 72)

Figure 2.28.Wind turbulence with respect to building height (after Konya 1980: 37)

Figure 2.30. Wind shadows and gradients with respect to windbreaks (after Konya 1980: 36)

Figure 2.31. Air flow in plan with respect to windward and leeward openings (after Konya 1980: 53)

Figure 2.32. Air flow sections with respect to windward and leeward openings (after Konya 1980: 53-54)

Figure 2.33. Wind flow patterns through various internal spaces (after Givoni 1976: 303)

23

The Evolution of the Built Environment Figure 2.34. Air flow through a windcatcher (after Konya 1980: 56)

Figure 2.35. Air flow induced by ‘stack effect’ (after Konya 1980: 52)

Figure 2.36. Air flow induced by temperature differential.

is in winter), through the utilisation of movable shading devices and/or deciduous vegetative shading. Similarly, it is potentially possible to admit warm convective air currents during the winter months (Fig. 2.39a) whilst excluding them during the summer months if the building is detailed accordingly (Fig. 2.39b). It is also possible to cool a space by combining shading with ventilation. For example, the temperature of an incoming breeze that passes through a shaded space will be reduced prior to it entering the interior.

Cooling by Shading: Shading will cool a space by excluding or reducing the degree of exposure of the interior and/or the exterior of a structure to direct solar gain (Fig. 2.37). The high altitude midday sun can be excluded through the use of horizontal sunshading (Fig. 2.38a) and the lower altitude morning and afternoon sun can be excluded through the use of vertical sunshading (Fig. 2.38b). It is potentially possible to exclude the sun during the summer months whilst admitting it during the winter months, especially at times close to midday when the sun is at the zenith (which is higher in summer than it

Figure 2.37. The effect of shading on ground temperatures (after Konya 1980: 35)

Figure 2.38. Exclusion of (a) high angle sun via horizontal shading and (b) low angle sun via vertical shading (after Konya 1980: 103)

24

Figure 2.39. (a) Admission and (b) exclusion of convective air

Background Theories: Architectural Studies currents (after Konya 1980: 44)

Figure 2.40. Cooling of an incoming breeze via moisture evaporation by (a) passing over of (b & c) through water (after Konya 1980: 57) Cooling by Evaporation: Passive solar evaporative cooling works on the principle that the temperature of an incoming breeze will be reduced by first passing over (Fig. 2.40a) or through (Figs. 2.40b & c) a body of water prior to entering a space. As a wind blows over a body of cooler water, moisture is absorbed into the air, energy is absorbed in the process of turning the liquid water into a gas, and thus heat is removed from the system and the temperature of the air is reduced, unless the air is already saturated. The evaporative potential is proportionate with the ratio of air temperature, air movement and air dryness such that an increase in one will increase the amount of moisture that can be evaporated, and vice versa.

Active Solar Cooling: Active solar cooling refers to the absorption of hot air from an interior space using a system that is enhanced by solar energy (Fig. 2.43). The simplest system combines evaporative cooling (a passive cooling device) with solar powered fans that force the internal air across the water reservoir, enhancing the evaporative process that would otherwise rely on only natural air currents. The solar energy that runs the fans is converted to electricity via photovoltaic cells and stored in batteries. Other, vastly more complex systems exist but they will not be discussed here. No buildings that use active solar cooling are included in the study case studies.

Active heating and cooling systems fall into two categories. First is active solar heating and cooling and second is chemical or mechanical heating and cooling. Active solar heating and cooling systems use the forces of nature (sun, wind and moisture) in conjunction with mechanical and/or hydraulic engineering systems, which are generally complex, being composed of numerous parts that are spatially removed from the interior space that is to be heated or cooled. Chemical and mechanical systems use the energy that is released when the molecular composition of one substance is irreversibly converted to an alternate substance(s). Active systems generally utilise industrial technology and are thus mentioned only briefly here, with the exception of heating by fire (a chemical process).

Chemical and Mechanical Heating: Chemical heating, in the form of fire, is the simplest and oldest form of active heating. The controlled use of fire seems to have been present in the Middle Palaeolithic around 60,000 to 65,000 years ago, when hearths became a common feature of human occupation sites. More recent industrial means of chemical and mechanical heating currently exist that are vastly more complex and will not be discussed here, although they operate on the same principle, that the process is generally irreversible and that the heat generated drops away exponentially as the distance away from the source increases. When wood burns, for example, it combines with oxygen in the air, producing a range of breakdown products that include carbon and soot. In the process the kinetic energy within the molecules is released as heat and, because the wood's polymers are destroyed at high temperatures, flames are observed.

Active Solar Heating: Active Solar heating refers to the indirect heating of an interior space and surfaces via a mechanical (air-based) and/or hydraulic (water-based) connection to spaces and surfaces that are directly warmed by solar gain such that the heat is transferred by conduction from the collector to the interior via either a dry heat storage medium (Fig. 2.41) or water as a heat storage medium (Fig. 2.42). The basic components consist of a heat collection system (solar collectors), a storage medium (thermal mass) and a heat transfer system (air or water in connector pipes). The essential difference between active solar and passive solar heating systems is that passive systems heat the interior space directly, whereas active systems use separate heat collection points. No buildings that use active solar heating are included in the study case studies.

Chemical and Mechanical Cooling: Chemical and mechanical cooling refers to the absorption of hot air from an interior space using a system that is powered by electricity, that is produced via chemical or mechanical processes. The most common type of system is airconditioning (A/C). A/C operates on the principle that heat is absorbed from a space via the compression of a low-pressure liquid, which cools rapidly when it expands (evaporates) as the pressure drops to atmospheric. Air within the space blows across a container of refrigerant, removing heat from the air. The liquid is then collected, cooled and condensed to be reused, making this part of the system a closed system. The air and the refrigerant never mix, unlike in a passive solar water-based evaporative system. No buildings that use A/C are included in the study case studies.

Active Heating and Cooling

25

The Evolution of the Built Environment Figure 2.41. Active solar heating using a dry heat storage medium (Parnell & Cole 1983: 30) Figure 2.42. Active solar heating using water as a heat storage medium (Parnell & Cole 1983: 30)

Figure 2.43. Active solar cooling system (Parnell & Cole 1983: 41) refer to the principles of the theory itself, not to the ACT interpretations. Neo-Darwinian theory interprets ‘adaptedness’ to mean ‘the state a group is in relative to its competitors and peers as a result of its contingent adaptational history’, and ‘an adaptation’ is interpreted as ‘a randomly thrown up variant, behavioural or physical, within a group and on which natural selection has acted, resulting in increased fitness and ‘adaptedness’ of the group’ (O'Brien & Holland 1992; Van Pool 2002).

ADAPTIVE COMFORT THEORY: SOME HISTORICAL BACKGROUND The current interest in human thermal adjustment began in 1936 when focus shifted away from examination of physiological responses and moved towards examination of subjective responses. Bedford ran a series of investigations into the preferred thermal sensations of subjects based on a subjective and relative scale of responses: much too warm – too warm – comfortably warm – comfortable – comfortably cool – too cool – much too cool (Bedford 1936). However, it was not until the results of the early studies were compiled by Humphreys in the mid-1970s (Humphreys 1975; Humphreys 1978) that the theory was accepted within the mainstream architectural profession, where it became known as Adaptive Comfort theory (ACT). ACT adopted the Bedford scale of subjective thermal sensation in preference to the ASHRAE scale of absolute thermal sensation.

Humphreys and Auliciems Based on Auliciems’ earlier hypothesis that “man is not exclusively an indoor creature, relationships between comfort could be expected also with levels of outdoor warmth” (Auliciems 1983: 74), Humphreys ran an analysis of the relationship between mean monthly temperatures and the thermal neutralities of subjects in a series of field studies. He collated the results of thirty-six studies performed during the working day of occupants of buildings, primarily office buildings. That is, the studies did not include a wide range of types of spaces (omitting, for example, domestic and multi-purpose spaces), nor a wide range of life styles (omitting, for example, nomadic and rural). The mean outdoor temperatures within the studies ranged from -24oC to +33oC and the geographical range covered Northern Russia to Baghdad to Singapore to Melbourne (Humphreys 1975; Humphreys 1978; Humphreys 1995; de Dear 1998a). The studies generally used the Bedford scale of thermal sensation to ascertain the neutralities, rather than the ASHRAE scale. The two scales differ in that, whilst the ASHRAE scale records absolute sensation (hot – neutral – cold), the Bedford scale records relative sensation (much too hot – comfortable – much too cold). This is significant because it made it possible for the subjects to deal with situations in which they were either warmer or cooler than neutral, but still comfortable. The findings from the studies using

Note that the term ‘adaptive’, as it is applied in ACT, possesses an alternative meaning to that used in NeoDarwinian theory. ACT interprets ‘adaptive’ to mean ‘the ability to adjust or modify’. ACT also uses the term ‘adaptive opportunity’ to refer to ‘a person’s potential ability to alter their thermal environment by adjusting or modifying their physical surroundings and/or environment’ and the term ‘adaptive comfort’ to refer to ‘a persons’ preferred thermal state’, which they create through the process of acting upon their ‘adaptive opportunity’. ACT thus denotes subjective meanings to these terms. However, as the focus of the study is the non-subjective properties of thermal performance and thermal capacity, these terms and their ACT interpretations have been avoided in the study. The expression ‘Adaptive Comfort theory’ has, however, been utilised in the study, but it is used specifically and only to 26

Background Theories: Architectural Studies The second further thing that Humphreys found in relation to the average monthly outdoor temperatures was that the thermal neutralities varied by 13oC to 15oC, a range that could not be explained away by differences in the clothing or metabolic rates of the subjects (Auliciems 1989: 21). The correlation equation for free-running buildings accounted for 94% of the variance in thermal neutralities (correlation coefficient r = 0.97), but the correlation equation for actively heated and cooled buildings accounted for only 56%, which increased to 61% when the average daily maximum temperature for the hottest month of the year were incorporated (Humphreys 1997: 137).

the two different scales are still comparable, however, because they both use a seven point scale of thermal sensation and they both have mid-points that equate to thermal neutrality (Auliciems 1983). Humphreys’ found that, first, variables within and between individuals were statistically less significant than the statistical similarity between different groups, when correlated with the mean indoor and outdoor temperatures (Nicol & Humphreys 2001). Secondly, Humphrey’s found that the total range of preferred temperatures ranged from 17oC to 33oC. Of more significance, however, were the findings made when these figures were correlated with the average monthly outdoor temperatures. Using regression analysis Humphrey’s ascertained two further things. First, an empirical relationship appeared to exist between the thermal neutralities of the subjects and the external climate (Humphreys 1995). For free-running buildings (buildings without active heating or cooling) there appeared to be a strong linear relationship between the average monthly outdoor temperature and the indoor neutral temperatures. When heating and cooling systems were activated (climate controlled HVAC), however, a correlation still appeared to exist, but the relationship became more complex. The relationship to climate was no longer linear, but curved, and the ‘comfort’ temperatures were affected by the average daily maximum temperatures during the hottest month of the year (Humphreys 1995; Nicol & Humphreys 2001: 49-51) (Fig. 2.44). The relationship appeared to be sufficiently precise to allow empirical predictions of thermal neutrality to be made (Humphreys 1978; Humphreys 1994a & b). For freerunning buildings the residual standard deviation of the neutral temperatures about the regression line was estimated to be 1.0oC and for HVAC buildings it was estimated to be 1.4oC and, with the average daily maximum temperatures for the hottest month of the year incorporated, it was estimated to be 1.2oC (Humphreys 1975; Humphreys 1978; Auliciems 1983). For freerunning buildings Humphrey’s found the empirical relationship to be:

These results were later revised by Auliciems (1983), who removed the cases that were based on asymmetric rating scales, had used children as subjects and had mean monthly temperatures below –5oC. He then added the results of studies performed in the interim period, bringing the number up to 53 separate field studies, and he reworked the equations. For free-running buildings Auliciem’s found the empirical relationship to be: Tn = 17.6 + 0.31 Tm

(r = 0.88)

And for HVAC buildings: Tn = 9.22 + 0.14 Tm + 0.48 Ti (R = 0.95) (where Ti is the mean indoor temperature and R is the multiple correlation coefficient) (Auliciems 1989). These results are still held to be generally correct, although there is some debate over the detail (Nicol & Humphreys 2001: 49-50). Humphreys and Auliciems thus empirically demonstrated that a positive statistical correlation exists between the average monthly outdoor temperature and the neutral or ‘comfort temperatures’ of habitual occupants (of buildings in sedentary societies performing specific tasks) that varies by only +/- 4oC about the neutral temperature (Auliciems 1989: 23). That is, the thermally neutral temperatures for this class of people appear to be broadly predictive to within well defined boundaries and that within these boundaries the thermal neutralities are not predictive.

Tn = 11.9 + 0.53 Tm (r = 0.97) (where tn is the predicted neutral temperature and Tm is the mean monthly outdoor temperature) (Humphreys 1978).

Figure 2.44. Humphreys’ scatter diagram of neutral temperatures (Humphreys 1978; copyright BRE, reproduced with kind permission)

27

The Evolution of the Built Environment accompanying psychological strain. If, however, they do not possess this capacity they will experience psychological strain, even at thermally neutral temperatures. Thermal ‘satisfaction’ is thus seen as being synonymous with a lack of psychological strain, rather than a lack of thermal stress. Note, therefore, that stress and strain are terminologically distinct. Stress refers to forces exerted on an object, either towards it or away from it, and strain refers to the deformationary reaction of the object to that force. They are not synonymous terms, although this distinction has not always been clear (Auliciems 1983: 71). The result of people being able to make thermal choices and act upon them is that they will experience psychological satisfaction, whether they experience thermal strain or not. “Mental well-being needs varied perception, as many researchers have pointed out… The implication is that the change of stimulation, rather than the absolute level involved is the more important consideration” (Gerlach 1974: 15). Thermal environments that induce thermal strain in people should not, therefore, be automatically treated as deleterious but, rather, as environments in which people can make satisfactory thermal compromises between a wide range of contradictory thermal states.

ADAPTIVE COMFORT THEORY: THE CONGRUENCES In recent years the ACT model has been quantitatively augmented with additional studies carried out both in the field and under laboratory conditions. Many of these studies have been performed under the auspices of ASHRAE. The vast majority of these have again concentrated primarily on the thermal responses of habitual occupants of office buildings with the result that there now exists a large volume of data relating to the simultaneous responses of people performing a limited range of tasks, primarily indoor office work. The global database (Humphreys 1975; Humphreys 1978; de Dear 1994; de Dear & Brager 1998) now contains approximately 21,000 measurements from numerous regions around the world (Fig. 2.45) and, although the underlying causal mechanisms driving the common behaviours in human thermal adjustments indoors has been questioned (de Dear 1994: 111-113; Brager & de Dear 1998: 88-92), the central tenets of the theory have remained unchanged. These state that there appears to be a universal preference amongst humans for possessing the ability (or perceived ability) to make thermal choices and to be able to act on those choices, in preference to being always thermally neutral, regardless of social and climatic differences (ref. e.g. Auliciems 1989; Humphreys 1997; de Dear & Brager 1998; McCartney et al. 1998; Brager & de Dear 2001; de Dear & Brager 2001; Nicol & Humphreys 2001).

Recognition of this distinction implies that a person’s thermal environment is composed of not only indoor environments accommodating lightweight tasks, but is also made up of transient spaces and outdoor environments as well. There is to date only a small (but growing) number of studies that have investigated the thermal responses of people whilst out of doors. The incorporation of these studies into the ACT model, which is gradually occurring, does not contradict the model but, rather, supports it, because access to transient and outdoors environments extend a person’s capacity to make selective thermal choices. Below is an outline of some of these studies, plus a discussion of ACT in relation to indoors environments.

People are now regarded as active participants in personenvironment feedback loops, adjusting naturally to changing thermal conditions (Brager & de Dear 1998). If thermal discomfort is experienced people will react in ways to alleviate the discomfort, as long as they have the ability to do so, making adjustments to either their person or to their surroundings. If people do possess the capacity to make these thermal adjustments they will tolerate greater degrees of discomfort without experiencing

Figure 2.45. Locations of thermal studies comprising the ACT global database (after de Dear 1998a: 1144) 28

Background Theories: Architectural Studies buildings, and that this difference “could not be entirely accounted for by adjustments to clothing or activity. The most plausible explanation for these differences is the contextual influence of thermal history and its effect on expectations – past thermal experiences in a building create a benchmark for expectations of future thermal performance” (Brager & de Dear 1998: 92). That is, thermal experiences influence a person’s thermal preconceptions and expectations.

Indoor Environments Thermal neutrality is easier to produce inside enclosed spaces than it is to produce outside in the natural environment, although thermal satisfaction is possible both inside and outside if the subject is able to make thermal choices and to act upon them. For this reason Adaptive Comfort studies have generally used data gathered amongst building occupants carrying out sedentary daytime activities in their habitual setting, rather than in laboratory chambers, based on the reasoning that it is better to study physiological and psychological reactions under ‘real life’ thermal conditions in ‘real life’ settings under ‘artificial’ laboratory conditions. The level of experimental detail has varied slightly between studies, but the most common approach, including that of the early work of Humphreys and Auliciems, has generally been to record air temperatures (and sometimes humidity) and the simultaneous subjective responses of the subjects. Environmental conditions (air temperature and humidity) were recorded at one height above the floor and consisted of straight readings. Psychological responses were recorded simultaneously and were based on the answers by the subjects to questionnaires, which were most commonly based on the Bedford scale (Brager & de Dear 1998). This approach has encompassed cumulative data and multiple field studies conducted in a range of climates (Brager & de Dear 1998: 91).

Adaptive Comfort theory states that if people can make thermal adjustments to themselves and their surroundings they are more likely to be thermally satisfied, by possessing the capacity to make satisfactory thermal compromises. If, however, people are unable to make thermal adjustments they will experience psychological strain, possibly even under conditions of thermal neutrality (Fig. 2.46). Therefore, in conditions where people are unable to make thermal adjustments they are more likely to prefer to stay within thermally neutral temperatures, where in they will experience no thermal stress. However, if the thermal conditions remain static and neutral for an extended period of time, as in the case of wholly climatically-controlled (HVAC) interior environments, their thermal expectations will show a tendency to become heightened and small thermal changes that generate no thermal stress will generate a psychological strain (Auliciems 1983). This will effect a narrowing of the range of temperatures within which they are able to gain thermal ‘satisfaction’ and an accompanying increased likelihood that they will experience psychological strain, as they become ever more highly sensitised to smaller and smaller thermal changes (Fig. 2.47). Under wholly static thermal conditions a person appears to eventually become so highly sensitised that stimuli other than thermal stimuli can cause psychological strain (Fig. 2.48).

Recent studies funded by ASHRAE have, however, recorded a wider range of environmental and personal variables, making it possible to compare behavioural adjustments in the form of clothing, air movement etc. and psychological adjustments. This expanded methodology has further confirmed that there is a clear distinction between the thermal responses of occupants of climate-controlled buildings and those of free-running

Figure 2.46.Thermal strain relative to capacity to make thermal adjustments (Baker & Standeven 1996: 180)

Figure 2.48. Sensitization to additional stimuli under static thermal conditions (Baker & Standeven 1996: 181)

Figure 2.47. Thermal strain in a static and climatically controlled environment (Baker & Standeven 1996: 180) 29

The Evolution of the Built Environment personal thermal adjustment people appear to generally make (Humphreys 1977). Also, the more microclimatic variability that exists within a space, the more often it is likely to be used and the longer the periods of time that it is likely to be occupied, regardless of climate or season (Nikolopoulou et al. 1998).

Transitional Environments Spaces that are neither wholly outside, nor wholly inside, such as colonnades, loggias, gazebos, shade structures (including arbors etc.) and courtyards are thermally transitional between the thermally controllable indoors and the thermally uncontrollable outdoors (Ong 1995: 7576). They possess thermal characteristics intermediary between the extremes of inside-outside, hot-cold, shadedsunlit, calm-windy, although some types of transitional spaces will be more closely affiliated with the outside conditions than others, depending on their degree of exposure to the outside. For example, a large courtyard will generally be more closely synchronised with the outside conditions than a portico, where the conditions will generally be intermediate between inside and outside (Potvin 2000).

Finally, the degree of temperature change a person is subjected to as they move through a transitional space will affect their thermal response. A slight change will be registered subliminally, whilst an extreme change will be registered consciously.

The Outside Environment “Fifteen generations ago, a period of little consequence in evolutionary terms, most of our ancestors would spend the majority of their waking hours outdoors… Even when inside, the relatively poor performance of the building meant that the indoor conditions closely tracked the outdoor environment.” (Baker 2000: 553)

Studies have shown that the thermal responses of humans to transitional thermal environments are generally linked to four factors: their relative direction of movement, their thermal expectations, the amount of time they spend in the space and the degree of temperature change they are subjected to (Potvin 2000). First, the direction of movement affects a person’s thermal response because, for example, a person moving from a hot to a cold space will have a different thermal perception of the space that if they were moving from a cold space to a hot space. This means that a person is likely to perceive the same space differently at different times and that, also, different people will perceive the same space differently if they are travelling in opposite directions.

Early hominids and mobile societies generally, such as hunter gatherers, who would have spent/do spend much of their time out of doors, are likely to be accustomed to wider swings in temperature than the habitual occupants of buildings of today (Rybczynski 1986; Baker 2000). They are likely to feel no discomfort at temperatures that contemporary first-world societies would be likely to find uncomfortable (Auliciems 1989; Baker & Standeven 1996). Richard de Dear (2002: personal communication) holds that the range of temperatures within which they could potentially gain thermal satisfaction (vs. thermal neutrality) would likely have been, and is likely to be, more closely synchronised with the outside thermal conditions. Field studies that have focussed on the way in which people tend to use open spaces in urban environments today have shown that, even amongst people who spend much of their time indoors and who consequently would be likely to have a relatively narrow range of temperatures within which they could potentially gain thermal satisfaction, still use outdoors spaces as an extension of their indoor thermal environment, unless prevented from doing so (Nikolopoulou et al. 1998).

Secondly, thermal expectations of the type of space affect a person’s thermal response. In spaces primarily used as access routes, such as streets and laneways, people are likely to tolerate higher levels of perceived discomfort than they would in outside activity spaces, such as public colonnades and street markets (Humphreys 1977). In access ways people appear to use the natural thermal microclimates within the environment in preference to making other personal adjustments. For example, it appears that people are more likely to walk in the sunshine rather than put on extra clothing. Outdoor activity spaces, however, appear to generate a greater variety of thermal responses in people, beginning with variations in the choices to use outdoor spaces in the first place, but also variations in the types of microclimates occupied and the types of clothing worn amongst people who made an initial choice to occupy an outside space. The level of usage of outdoor activity spaces appears to relate to the level of microclimatic variability available within the space, with spaces with greater microclimatic variability being occupied generally more often than spaces with more restricted microclimatic variability (Nikolopoulou et al. 1998).

A purely physiological approach to human thermal response to outside environments, and usage of outdoors spaces, has been shown to be inadequate. The microclimatic range available outdoors appears to act as context to thermal responses, but psychological factors, such as thermal expectation, short-term and long-term experience, perceived thermal control and environmental stimulation, appear to influence individual patterns of usage of outdoors spaces (Nikolopoulou et al. 1998; Nikolopoulou & Steemers 2000).

Thirdly, the length of time a person spends in a particular space will affect their thermal expectations and the amount of thermal adjustment they appear to consciously make. The longer the time spent in a space, the more 30

Background Theories: Architectural Studies widely accepted that this is not the case and that differences between groups of people and within groups do exist. Individual differences of physiology, past thermal experience and future thermal expectations make the immediate thermal sensations of individuals impossible to accurately predict.

Overview The implications that arise from an overview of the issues discussed above are that the capacity for people to respond to their thermal environments is not limited to the immediate environment within which they perform a limited range of habitual tasks. Their capacity encompasses the full range of different thermal environments and microclimates available to them, those available out of doors plus those available indoors, for even office workers and school children frequently go outside to eat their lunch. The thermal responses people make to the environments they occupy and pass through are the result of a complex feedback loop between the thermal conditions within their immediate environment in the present time, their past experiences and their future expectations, both conscious and subliminal. Their present and past experiences can condition their future expectations, which in turn can condition their future experiences. Thus the implication also arises that the positive statistical correlation that appears to exist between the average monthly outdoor temperatures and the narrow range of preferred temperatures (+/- 4oC) of habitual occupants of buildings in sedentary societies performing specific tasks could be the result of long-term gradual conditioning to expect a narrow range of thermal conditions whilst performing these particular tasks. Thermally restricted environments cannot, however, be equated with an ‘ideal’ thermal environment, even for the habitual occupants of buildings in sedentary societies performing specific tasks, for even this class of people generally have access to diverse other types of environments at other times and for the performance of other types of tasks. Additionally, even the habitual occupants of buildings in sedentary societies performing specific tasks experience the same thermal environments differently and individually. Below is an outline of some of the ways in which the thermal responses of humans vary.

Individual Variability Field studies have demonstrated that the conditions under which people experience thermally neutrality are unlikely to be consistent from moment to moment, even when the conditions are held constant. It appears that sensation ‘drift’ occurs in individuals over extended periods. Chamber studies have demonstrated that an individual may register thermal variability over several hours equal in magnitude to that experienced between different individuals (McIntyre & Griffiths 1973; Griffiths & McIntyre 1974; McIntyre & Griffiths 1975). Sensation votes also appear to ‘drift’ to a relatively cooler setting over time, a phenomenon that cannot be explained by metabolic rate alone (McIntyre 1978). It is possible, however, that this is a factor of laboratory simulation, where subjects have minimal opportunity to make thermal adjustments to suit their circumstances. This is an area that has been recognised as needing further research (Auliciems 1989: 19). On the other hand, very slightly variable temperatures can be perceived as constant. Humphreys calculated that only when the temperature within a room varied by more than ±1oC would individuals consciously register a change in temperature. Additionally, whilst people are highly sensitive to sudden changes in temperature, particularly when moving into colder temperatures than into warmer temperatures, standard deviations of 2oC to 3oC could be perceived as being of equal warmth (Humphreys 1976). Humans adjust to rapid thermal changes both behaviourally and physiologically, but only if they are able to. New born babies, for example, display very few behavioural or physiological responses when subjected to cold stress, yet their degree of physiological strain cannot be considered to be minimal as they have inherent difficulty in maintaining a stable core-body temperature (Hey 1973). Physiological adjustments, such as sweating and shivering, are generally involuntary. Behavioural adjustments are generally voluntary, although may be subconsciously performed as in the case of a body posture change from extended to contracted as an adjustment to a cold change. Other behavioural changes include position, activity (Fig. 2.49), lifestyle and clothing adjustments (Humphreys 1977; Fishman & Pimbert 1979; Humphreys 1994b; Nicol et al. 1994). Clothing levels appear to relate more to preceding temperatures experienced outside than to current temperatures, but the degree of time lag appears to vary from person to person (Humphreys 1977; Humphreys 1979; Nicol & Raja 1996). There appears to be no universal clothing constant, within even a single society, except in that clothing choices are made and they are

HUMAN THERMAL VARIABILITY “A considerable variation has been observed in comfort studies between the subjective thermal sensations of different individuals. It follows that no particular single thermal level will satisfy all people and temperatures can at best be regulated to suit the “average person”. (Auliciems 1969: 562) ACT has found that, whilst humans have various physiological characteristics in common, thermal variability and individuality are inherent amongst humans. The feedback loop that exists between an individual’s thermal experiences and their thermal preconceptions and expectations effect individual and transient physiological, psychological, social, and seasonal thermal variability. The engineering profession’s assumption that thermal ‘comfort’ is synonymous with thermal neutrality was based on the assumption that thermal neutrality was universally predictable. It is now 31

The Evolution of the Built Environment influence, as will metabolic rates, but other intangible factors are also prevalent. Expectation and attitude affect a person’s response to a thermal change in that the change, whether anticipated or not, will be desirable or not depending on the individual’s prior expectations. “Past thermal experiences are functions of both the natural climatic and techno-cultural environments. Thus satisfaction is also logically related to both these environments, which in some cases may not be ones commonly identifiable with least thermoregulatory activity” (Auliciems 1983: 73-74). Studies of thermal responses outdoors have revealed that, although microclimatic factors strongly influence thermal sensation, psychological adaptation seems to become increasingly important with exposure time, encompassing such factors as expectations, past short-term and longterm thermal experiences, exposure time, perceived thermal control and degree of environmental stimulation (Nikolopoulou et al. 1998; Nikolopoulou & Steemers 2000).

acted upon more often by women than by men or children (both boys and girls) (ibid.). Finally, men, particularly older men, are more likely to suffer heat strain than women (Humphreys 1977).

Social Variability In the 1950s the engineering profession deemed it to be reasonable to assume that a group of people with similar ethnic and social backgrounds, of similar age, wearing similar clothes, engaged in similar activities and in close proximity, would experience similar thermal neutralities (Auliciems 1969). It has been demonstrated both before and since, however, that this is not the case and that variability within a group is the norm (Auliciems 1969; Rapoport & Watson 1972; Ong 1995; Williamson et al. 1995). Studies carried out in the 1950s concluded that there is no conclusive evidence to support an ethnic or racial basis for differences in climatic adaptation. Ethnic groups that cover a wide climatic range appeared to have basal metabolic rates negatively correlated with the average climatic temperature (Roberts 1952). This is countered, however, by studies that have demonstrated that the Australian Aborigine, for example, has a basal metabolic rate that is naturally lower than other racial groups, as well as a lower core-body temperature, pulse rate, perspiration rate and respiration rate (Wulsin 1949).

Figure 2.49. An approximation of the amount of heat produced by the human body during various activities (after Koenigsberger et al. 1974: 42). Both physiological and behavioural adjustments have an associated time lag from the start of the adjustment to the attainment of physiological thermal equilibrium. The degree of time lag will vary relative to the degree and type of adjustment and relative to the physiology of the individual subject. For example, behavioural adjustments that involve a change in activity generally relate to a person’s metabolic rate, which is related to their individual physiology and basal metabolic rate. Inherent differences in basal metabolic rates between individuals can be due to differences in age, sex, physical fitness, illness, pregnancy, body surface area relative to volume, and percentage of body fat relative to lean muscle (Ong 1995). Other activities that change the metabolic rate include activities such as eating, which can affect the basal metabolic rate for up to an hour afterwards. The metabolic rate can also be affected by a very localised thermal change if the change is large enough and/or extended over a long enough period of time (Mount 1979: 117). It has been observed that Gaspé fishermen in Quebec, who immerse bare hands into water of 9oC for extended periods of time while pulling in nets, experience no pain, a higher skin temperature and a smaller rise in blood pressure than ‘unacclimatised’ subjects who might perform the same task. This adaptation is highly specific to the left hand, however, with no such acclimatisation evident in the right hand (LeBlanc et al. 1960; LeBlanc 1975).

Variability between social groups has been explained as a process of acclimatisation, but it does in fact appear to be rather more complex. Adaptation to a change in climate over an extended period of time can involve both acclimatisation and/or fundamental lifestyle changes. Lifestyle changes are voluntary and may or may not effect changes in the basal metabolic rate. For example, Arctic Indians and Eskimos show a markedly less degree of metabolic acclimatisation to the cold than the Alacaluf Indians of Tierra del Fuego or the Aborigines of the southern latitudes of Australia (Mount 1979: 154-157). Eskimos show little difference in thermoregulation to people from temperate zones, shivering at similar levels of cold skin sensation. They actually shiver more and maintain higher skin and rectal temperatures than Kalahari Bushmen. This is probably due to their reduced exposure to cold. Eskimos wear clothing and inhabit

Individual thermal adjustments are thus multi-factorial and highly complex. People themselves are often unable to explain the reasons for their choice of thermal adjustment. Social and personal habits have a strong 32

Background Theories: Architectural Studies structures that maintain temperatures equivalent to tropical microclimates (Mount 1979: 55) and to which they have likely become accustomed and acclimatised.

of approximately 20 hours, and for night-time clothing of approximately twice as long (Humphreys 1979). This ‘predictive’ component to the choice of clothing thus indicates that temporal variation exists between individuals in their choices of clothing.

If a thermal change is of sufficiently long duration morphological changes will occur. This has been observed in experiments with animals where, for example, mice have been observed to grow thicker coats in colder climates and longer tails in warmer climates (Harrison 1963). Additionally, piglets born in the same litter have been observed to grow taller and leaner in warmer climates, and shorter and stockier in colder climates. The cold climate piglets also had fewer blood vessels in the skin, smaller ears and thicker hair than the warm climate piglets (Fuller 1965; Ingram & Weaver 1969). If a thermal change is multi-generational then changes at the genetic level will occur, although this has been a subject of debate. It has been claimed that members of the same taxon will grow larger in colder climates than their warmer climate relatives. Conversely, it has been claimed that in colder climates more compact body shapes with shorter appendages are more likely to ensue (Mount 1979: 125-129). The latter theory is the dominant theory (Eveleth 1966; Roberts 1972; Whittow 1973; Stinson & Frinsancho 1978). This issue is, however, made complicated by the recognition that, even though they have a significant environmental input, stature, body weight and limb length are phenotypically plastic and are affected by such factors as malnutrition and infectious diseases, which can limit potential growth (Hanna 1983).

ADAPTIVE COMFORT THEORY: SHORTCOMINGS ACT appears to have been successful in defining broad behavioural patterns amongst occupants of buildings in various climates and societies around the world, to within a statistically significantly narrow temperature range that is related to the prevailing climate. This model is, however, based on the findings of only habitual occupants of, primarily, office buildings from only contemporary sedentary societies. No mobile or traditional societies have yet been incorporated into the model, few buildings other than office buildings have been incorporated and, even though the importance of transitional and outside environments has been recognised, there have been few quantitative studies of thermal responses in these types of spaces incorporated. This omission is important because buildings do not operate in thermal isolation from other types of buildings, nor do building occupants generally inhabit only one type of building, but move between diverse thermal environments. Additionally, ACT has to date focussed on only contemporary structures, limiting study of the past to very general observations such as “traditional buildings provide a variety of thermal environments between rooms and within a single room. The bedroom of an English house was colder than the living room, but the bed is warm and snug. A coal-burning fire gives a focus of radiant heat, which provides a variety of environments depending on how close you are to it, and so on. In the Middle East, houses owned by the wealthy would have many rooms each with its own particular thermal characteristics from which the inhabitants could choose the most appropriate at different times of day and year” (Nicol & Raja 1993: 3). And elsewhere, “the traditional Japanese house is essentially a tropical house, being lightweight in construction with highly permeable external walls… At first inspection these houses seem inappropriate to this harsh [cold] climate. However, a more thoughtful analysis reveals several interesting insights. Firstly, the internal spatial volume can be modified seasonally to be subdivided into smaller spaces in winter to limit airflow and reduce draughts and heat losses. Secondly, thermal comfort in winter was achieved by employing small, portable braziers, hibachi or kotatsu, [that could be] placed under tables and carried from room to room and were quite efficient in directly heating the individual by radiation” (Forwood 1995: 176). With regard to very early and rudimentary structures, and traditional structures in cold climates, it has been recognised that the thermal choices offered by a fire play an integral role in the thermal performance and fires, by their nature, offer a range of thermal choices to the people

Temporal Variability The definition of ‘winter’ varies in different regions and climates. Additionally, field studies carried out over an extended period of time have shown that there is a quantitative (oC) difference between thermal neutrality in winter and that in summer (Osiba 1957; Humphreys 1975; Nicol 1995). One explanation for this is acclimatisation, involving both voluntary and involuntary processes. Field studies carried out to investigate basal metabolic rates have demonstrated that, whilst metabolic rates are related to thermal neutrality via acclimatisation, the relationship is not necessarily inversely proportional (Goldsmith 1973). Acclimatisation to cold in Europeans, for example, has been shown to involve increased metabolic rates. That is, as the basal metabolic rate increases, so does thermal neutrality. As the person acclimatises their metabolic rate increases, shivering decreases and heat production remains unaltered (Scholander et al. 1958; Davis 1961). Clothing is a principal means of adjusting to immediate temperature changes because, as field studies have suggested, people adjust easily to seasonal temperature changes and less easily to diurnal temperature changes (Humphreys 1979). It appears that change of clothing choice in response to a general change in temperature has an inherent time-lag. A recent study of British clothing choices indicated a ‘half-life’ for daytime clothing change 33

The Evolution of the Built Environment This occurs when the heat generated by the body balances the heat transferred away from it (Houghten & Yagloglou 1923; Hey 1973; Mount 1979; Folk Jr. 1981; Robertshaw 1981; Ong 1995). It is defined as the point at which an individual has no thermal sensation (Clark & Edholm 1985; ASHRAE 1992: 176). However, ACT has found that thermal ‘comfort’ and thermal neutrality are not synonymous. For example, an animal may experience a minimal metabolic rate maintained by increased sweating and/or panting. It is thus not thermally neutral, but it is also not thermally uncomfortable. It is, rather, thermally ‘satisfied’.

who sit or move around them, being warmer as they move closer and cooler as they move further away (Ong 1995: 75). Yet these types of buildings and contemporary buildings have a relatively long evolutionary history, that began with the construction of proto-structures by early hominids. These hominids may be dead and gone, but there are operational consequences arising from their material products that are visible and tangible, particularly from the vantage point of long-time depth hindsight. Much can be gleaned from a detailed and long time depth study of the thermal performance of buildings. First, because it can offer insights into what types of thermal systems have been, and are likely to be, more successful in the long-term relative to alternate systems. That is, that free-running environments are associated with a broad range of temperatures within which people can gain thermal ‘satisfaction’, whilst static thermally neutral environments are associated with a heightened expectation of thermal neutrality, in which thermal satisfaction is not ultimately possible.

The Heat Balance model is based on the work of Gagge et al. (1967; 1986) and Fanger (1970) who carried out climate chamber experiments in the 1960s under the auspices ASHRAE. Two early experiments were carried out, one at the Laboratory of Heating and Air Conditioning, Technical University of Denmark on 48 college-age subjects (either ‘tropical travellers’, winter swimmers or meat packers), and one at the Institute of Environmental Research, Kansas State University on 128 college-age Danes and 128 elderly Danes (half female and half male) (Fanger 1973). In the simulations the individuals nominated a thermal sensation based on the seven point scale of absolute sensation (ASHRAE 1992). In both sets of experiments the subjects recorded points of thermal neutrality at the same temperature, regardless of season, age, physical build, sex or degree of climatic acclimatisation (Fanger 1970). As a result, Fanger concluded that all people have the same environmental requirements for thermal neutrality and ‘comfort’. He also concluded that thermally controlled and standardised environments would meet these requirements, in spite of the limited data upon which they were based. “It is not often realised that the claims of [thermal constancy’s] universal applicability were based on remarkably limited and rather incompletely reported preference studies of only 16 travellers from Copenhagen and 32 Danes” (Auliciems 1989; Nicol & Raja 1993; Humphreys 1995; Brager & de Dear 1998; Darmawan 1999; de Dear 1999; Baker 2000; Brager & de Dear 2001; de Dear & Brager 2001). However, Fanger used the resultant Predicted Mean Votes (PMV) and Predicted Percentage Dissatisfied indices to derive the ‘heat balance equation’ (Fanger 1970: 37).

THE HEAT BALANCE MODEL The environmental-engineering profession has until recently equated thermal ‘comfort’ with thermal neutrality. Thermal neutrality has been defined using the Heat Balance model (Fig. 2.50), also referred to as the static or constancy model, based on the assumption that humans are passive recipients of thermal stimuli and that they experience their thermal environment via the physics of heat exchange between their body and the environment. It is presumed that the human body tries to always maintain a constant core body temperature and the magnitude of the physiological response required to do this is deemed to equate to feelings of comfort or discomfort. Thus the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), who set the international standards for environmental conditioning (ASHRAE Standard 55 and ISO 7730), have widely adopted a scale of absolute thermal sensation of hot – warm – slightly warm – neutral – slightly cool – cool – cold. Thermal ‘comfort’ is deemed to exist when a person is thermally neutral, or when a person’s metabolic rate is minimal and constant.

Fig. 2.50. Heat Balance Equation (Fanger 1970: 37) 34

Background Theories: Architectural Studies The equation is used to calculate for thermal neutrality, which he described as a ‘comfort zone’ of temperatures (Fanger 1970). Its implementation was presumed to create a thermal environment inside a space at which at least 80% of occupants would experience thermal neutrality and an absence of thermal sensation and would, therefore, be thermally comfortable. The equation assumes that thermal comfort is primarily the result of the heat exchange that occurs between a person’s body, plus their level of clothing and metabolic rate, and their thermal environment, which is composed of air temperature, radiant temperature, humidity and air speed. “These biophysical relationships have been assumed to be universally applicable across all building types, all climate zones, and all populations” (de Dear & Brager 2001: 100, emphasis in original).

The heat balance equation defines people by their level of clothing insulation and metabolic rate (Fanger 1970). Their thermal state is assumed to be a function of their body’s thermal balance and discrepancies in the results are put down to differences in clothing between individuals (ASHRAE 1992). This represents an oversimplification, however, as there are numerous other sources of variability. First, differences in clothing insulation values (clo) have been shown to have been underestimated, differing by as much as 20% (Brager et al. 1994), and in humid conditions this figure is probably higher (de Dear et al. 1989). Additionally, the operative temperature experienced by someone sitting in a chair can vary depending on the type of chair (Olesen et al. 1982; McCullough et al. 1994). Estimation of the metabolic rates of occupants is based on translating the activity levels to metabolic rates via standard tables. It has been shown, however, that psychological factors can affect the metabolic rate, as well as personal differences in the ways individuals might perform the same activity (Cena 1994).

Until very recently, the heat balance equation formed the core of the universal climatic design standards, ASHRAE 55 (ASHRAE 1992, 1995) and ISO 7730 (ISO 1984; Parsons 2001). Standard 55 specified the acceptable (thermally neutral) “temperatures to be applied uniformly across space and time” (de Dear 2001: 100), irrespective of building function, climate and whether the building was an HVAC or free-running building. For winter, the acceptable conditions were 20oC to 23.5oC effective temperature (ET), 18oC wet bulb and 2oC dew point. For summer, the acceptable conditions were 23oC to 26oC effective temperature (ET), 20oC wet bulb and 2oC dew point (ASHRAE 1995). In practice, however, engineers commonly aimed for a fixed set of temperatures that fell in the middle of the summer and winter acceptable conditions (de Dear & Brager 2001: 100).

Thermal neutrality is not synonymous with thermal ‘comfort’, and both concepts contrast with thermal ‘satisfaction’. Thermal neutrality is a purely physiological state in which the subject is in a steady-state heat balance with their environment. A subject may, however, be thermally comfortable yet still experience mild thermal sensation. They may be stressed, but they are not strained. Thermal ‘satisfaction’ is a physiological and psychological state in which the subject experiences an overall thermal compromise within their contradictory thermal environment, regardless of their physiological state, due to their state being the result of a conscious choice on their part. They may be physiologically stressed but they are not psychologically strained.

FAILURE OF THE HEAT BALANCE MODEL The end result of applying the heat balance equation is the creation of internal environments that are static, homogenous and thermally neutral. Until very recently the engineering profession made it its goal to create environments of this description, treating thermal comfort as a commercial product (Forwood 1995). The quality of this commercial product was judged on the basis of the degree of thermal homogeneity. Occupants were viewed as passive recipients of thermal stimuli whose presumed ideal temperature, for normally attired people performing light duties, was calculated at 24-25oC (ASHRAE 1992, 1995). “It may be said that the raison d’etre of traditional comfort research has been the prescription of temperatures for groups of people engaged in tasks requiring low metabolic rates” (Auliciems 1981: 113). Such a tight range of temperatures can only realistically be created, however, in an ‘ideal’ environment, such as that of an open-plan space with sealed openings, low level partitions and furniture, and no moveable heat sources (ie. people). People were regarded as a constraint upon the engineered system because they introduce a random and unpredictable element that is difficult to design for.

Sick Building Syndrome Sick building syndrome (SBS) is something that the airconditioning industry has been aware of since the 1970s. There is a multitude of slightly varying definitions of SBS, but that of the World Health Organisation (WHO 1983) is the most widely used. It describes a set of various physiological and neurological symptoms experienced by occupants of buildings for which there is no apparent or diagnosable cause and that cannot be attributed to a specific illness or disease (Raw 1992; de Dear 1998b). A building is deemed to be ‘sick’ if there is a “significant excess” of complaints from the occupants compared with the level amongst the general community, though no quantitative value is given. The buildings most closely associated with SBS have several factors in common. They are most likely to have centrally operated heating, ventilation and air-conditioning (HVAC) rather than be naturally ventilated (NV), airtight with nonopenable windows, and have a homogeneous thermal environment. They are most likely to possess an openplan layout and low levels of privacy (Rayner 1997; de Dear 1998b). Due to an associated drop in productivity, the mechanical engineering industry has tried to combat 35

The Evolution of the Built Environment (Heiselberg & Tjelfaat 1999; Wouters et al. 1999). Studies into the operational patterns for this type of system have shown that in dwellings manual operation is more commonly used and more popular than automatic operation (Kempton et al. 1992; Lutzenhiser 1992). In one such study over 75% of residents operated their airconditioners wholly manually and for only short periods of time. When given the choice of using a combination of manual and automatic operation, 58% of residents still chose to maintain full manual operation. Significantly, even though these studies were carried out on physically similar apartments, the energy consumption varied by two to three orders of magnitude, but the internal temperatures varied by only 2.4oC to 3.7oC. This finite temperature range occurred irrespective of differences in the residents’ physiology, daily schedules, personal preferences of ‘machine’ operation, understanding of possible alternative cooling methods, and beliefs concerning health and thermal comfort (Kempton et al. 1992: 177).

the problem by implementing ever tighter thermal conditions. This approach has not, however, solved the problem and SBS is still a factor of HVAC buildings. In contrast the incidence of SBS symptoms amongst occupants of free-running (non-air-conditioned) buildings is markedly reduced and the industry has now acknowledged that SBS appears to be a manifestation of static, homogeneous internal environments (ASHRAE 2004; Olesen and Brager 2004).

Conclusions on the Heat Balance Model It is now recognised that the occupants of HVAC buildings need to be “reconnected with their buildings – a connection that was severed by the Industrial Revolution and the rise of environmental technology” (Forwood 1995: 177). Building occupants do not seek or desire to be thermally neutral, but rather to maintain control over their immediate thermal environment. Whether or not they chose to implement that control appears to be of less importance that their actually possessing the control in the first place. The emphasis is on perceived control. Significantly, Ole Fanger, the ‘father’ of centrallycontrolled HVAC systems, now agrees that the only way of producing full occupant thermal satisfaction is by moving away from collective climate systems to systems that offer individually controlled local climates (Fanger & Toftum 2001). Within recent years various technological solutions have been developed to do just that. One such system, developed especially for open-plan offices, is called ‘task conditioning’ or ‘localised thermal distribution systems’. This is an air-conditioning system that is centrally powered but locally controlled at, and delivered to, individual workstations (Oseland 1993: 223). Users are able to individually and manually control their desired level of heating, cooling, ventilation, and lighting, which are supplied direct to their workstation (Kronor & Stark-Martin; de Dear 1998b; de Dear 2002: personal communication).

The most important development, however, towards implementing personal thermal control came from within the mechanical-engineering industry itself and as the result of ASHRAE funded research into Adaptive Comfort theory (Plant Engineering 2005). In 2004 the Standard 55 was revised to include environments that can be thermally manually adjusted by the occupants. The current Standard includes a separate method for ascertaining the acceptable thermal conditions for freerunning buildings. The method is based on the recognition that, where the thermal conditions can be regulated by the occupants, by opening and closing windows, making clothing changes, operating other controls such as fans etc., the range of acceptable operative temperatures becomes a function of the mean monthly outdoor temperature (ASHRAE 2004: 9-10; Olesen & Brager 2004) (Fig. 2.51). The revised Standard still permits a 20% ‘dissatisfaction’ level but, significantly, it has been acknowledged that “the best way to improve upon this [20%] level of acceptability is to provide occupants with personal control of their thermal environment, enabling them to compensate for inter- and intra-individual differences in preference” (Olesen & Brager 2004: 26).

Another type of system uses air-conditioning systems in conjunction with passive heating and cooling methods. The air-conditioning can be operated either manually or automatically, whereby the system turns on and off automatically relative to the temperature outside

Figure 2.51. Acceptable operative temperatures for free-running buildings as per ASHRAE Standard 55 (ASHRAE 2004 10)

36

Background Theories: Architectural Studies hesitates to use the terms comfort or ‘environmental control’ for this high performance shelter [the igloo] since our use of the word has a different meaning from the Inuit goals of environmental modification” (Cook 1996: 282).

COMFORTABLE VERNACULAR THEORY Vernacular refers to buildings that are ordinary, nonmonumental, and not architecturally designed. Vernacular has traditionally been approached in the architecturalhistorical literature from the point of view that ownerdesigned-and-built structures have been developed and fine-tuned over several millennia to create thermally comfortable interiors (Butti & Perlin 1980; Cofaigh et al. 1996). It is assumed that buildings and the methods by which they have been heated and/or cooled have been developed in synchronisation with the different climates in which they existed and that, over time, they were developed to be attuned to the average ambient outside daily and seasonal cycle of temperatures (Fig. 2.52). The ethnographic record is deemed to be the end product of this presumed deterministic process. “Perhaps they did not know technically what they were doing or why, but the results were effective, comfortable and practical” (Stead 1980: 41).

Prior to a discussion of examples of vernacular that are not climatically appropriate, it is necessary to introduce the main thermal principles that are presumed to have driven the development of vernacular form and usage in different regions and types of climates around the world.

Heating in Cold Climates The development of structures in cold climates is deemed to have been driven by the primary need for heating, accompanied by the need for minimising air movement. Of course, without heating humans would have been unable to occupy many of the regions of the world in which they have settled. Winter conditions in the arctic and sub-arctic are generally so cold that human life cannot be sustained by means of passive solar heating alone. Active heating, in the form of fires, is a fundamental component of thermal systems in cold climates. The symbiotic role that fire has played with the built structure in creating the thermal systems used in cold climates has, however, been generally overshadowed by studies having focussed on the thermal properties of the building envelopes themselves. Without fire as an active heat source, the thermal system must rely solely on energy from the sun and in some regions this is insufficient for human survival. For example, the Polar Inuit of northwest Greenland live in an arctic region where temperatures rise to over freezing for only one month of the year (Cook 1996: 279-280). Few thermal performance studies have looked at very cold climates and those that have have tended to focus almost exclusively on the Inuit igloo as the ‘ideal’ cold climate building, being elevated and relatively warm on the inside and aerodynamic to the cold winds on the outside (Rapoport 1969: 98-99; Coch 1988: 75-85; Cook 1996: 279-282; Crouch & Johnson 2001: 90-94) (Figs. 2.532.54), although the Mongolian yurt has also been mentioned (Rapoport 1969: 98). Igloos, with their oil burners, their low domed roofs, their subterranean and down-wind entranceways, and their insulating internal lining of skins, are presumed to be climatically ideal because they can create temperatures of up to 40oC higher than the outside air.

Figure 2.52. Summer and winter sun angles at (a) Mesa Verde and (b) Chaco Canyon (after Pillet 1980: 88) It is presumed that buildings in cold climates were predominantly geared towards heating and buildings in hot climates were predominantly geared towards cooling. “Indigenous architecture is often a clever series of construction strategies to amplify the effects of a comfort source. In cold climates the source is the open flame from burning fuel. In overheated climates the source of comfort is not always such a point source, although an overhead fan is a parallel device in many locations” (Cook 1996: 289). It has been noted that the focus on comfort as the driving force behind climatic determinism is possibly a First World-centric cultural derivative, as more traditional societies view their structures as modifiers of the outside environment, rather than as independent environments. For, as Cook states, “one

Figure 2.53. Inuit igloo relative temperatures (Cook 1996: 282) Figure 2.54. Inuit igloo aerodynamic exterior (Cook 1996: 282)

37

The Evolution of the Built Environment Gallo 1988; Hinrichs 1988; Cook 1991; Givoni 1991: 2631; Facey 1997; Sayigh & Marafia 1998; Crouch & Johnson 2001: 98-103) (Figs. 2.55-2.56), although the subterranean Matmata houses in southern Tunisia and courtyard houses of other bordering regions have also been mentioned (Rapoport 1969: 91; Butti & Perlin 1980: 2-27; Talib 1984: 45-47) (Fig. 2.57). These houses are presumed to be climatically ideal because they provide cool spaces for use in the summer and warm spaces for use in the winter. This key behavioural feature has been widely recognised, although the full significance of the inbuilt adjustability possessed by these types of structures has not been understood.

Cooling in Hot-Arid Climates The development of structures in hot-arid climates is deemed to have been driven by the primary need for cooling in summer, accompanied by a secondary need for maximising the cool breezes in summer and for heating in winter. More thermal performance studies have dealt with this type of climate than others and they have tended to focus almost exclusively on the middle eastern courtyard house and the Anasazi pueblo as ‘ideal’ hot-arid climate buildings, as both spatially adjustable and thermally appropriate (Pillet 1980; Stead 1980; Kriken 1983:99103; Talib 1984: 45-65; Alp 1988; Coch 1988: 77-81;

Figure 2.56. ‘Idealised’ pueblo (based on Pueblo Bonito) (Nabakov & Eastern 1989: 371; cf. Cook 1991: 149. Reproduced with kind permission of Oxford University Press, Inc)

Figure 2.55. Traditional middle-eastern courtyard house (Baghdad) (after Stead 1980: 36)

Figure 2.57 Traditional subterranean Matmata houses of southern Tunisia (Rapaport 1969: 91)

38

Background Theories: Architectural Studies

Figure 2.58. Traditional southeast Asian hut (Malaysia) (Crouch & Johnson 2001: 105)

Figure 2.59. Traditional Seminole house, Florida (Rapaport 1969: 94)

Figure 2.60. Traditional minimalist shelter, Colombia (Rapaport 1969: 95)

complex than other types of climate and the buildings in these types of climates have not been as tightly classified as the buildings in more severe climates. Very few thermal performance studies have looked at buildings in temperate climates and those that have have concentrated on, for example, Cape Cod cottages, Hungarian village houses and Normandy farmhouses (Rapoport 1969: 99; Cook 1996: 283-288) (Fig. 2.61). Likewise, very few studies have looked at high altitude climates and those that have have concentrated on, for example, Tibetan farmhouses and palaces (Crouch & Johnson 2001: 94-98) (Fig. 2.62). The development of structures in temperate and high altitude climates is deemed to have been driven by an equal need for cooling in summer and heating in winter.

Cooling in Hot-Humid Climates The development of structures in hot-humid climates is deemed to have been driven by the primary need for cooling throughout the year, which is produced by maximising air movement and minimising solar penetration. Few thermal performance studies have looked at buildings in the hot-humid tropics and the few that have have tended to focus almost exclusively on the light, raised, thatched hut that is widespread throughout Southeast Asia (Rapoport 1969: 93-95; Yuan 1987: 6879; Coch 1988: 81-82; Givoni 1991: 21-26; Crouch & Johnson 2001: 103-106) (Fig. 2.58), although the saddleroofed New Guinean ceremonial houses and various minimalist shade structures, such as the Seminole houses and minimalist shelters of tropical America, have also been mentioned (Rapoport 1969: 95; Coch 1988: 82-83; Crouch & Johnson 2001: 103-105) (Figs. 2.59-2.60). The raised thatched hut is presumed to be climatically ideal because the thick roof thatch minimises the amount of solar energy that can pass through the shaded interior and the raised, open sides allows for maximum ventilation.

Heating and Cooling in Temperate and High Altitude Climates

Figure 2.61. Traditional Normandy farmhouse (Rapaport 1969: 99)

Temperate and high altitude climates are far more

Figure 2.62. Traditional Tibetan farmhouse (Crouch & Johnson 2001: 96)

39

The Evolution of the Built Environment outside (Cook 1892; Keynes 1988; McEwan et al. 1997) (Fig. 2.63). No compensation existed in the way of clothing, which was minimal, consisting of loose animal furs thrown over the shoulders. The furs, however, were used and worn in a diverse number of ways, such as blankets to lie on and as covering to select areas of the body. Fires, too, which were heavily relied upon even though they had the capacity to raise a sweat on the natives (Keynes 1988: 135), were used in a diverse number of ways including accompanying the natives in their canoes (McEwan et al. 1997: 63) (Fig. 2.64).

FAILURE OF THE COMFORTABLE VERNACULAR THEORY The view that the development of vernacular has been driven by the desire to create thermally neutral and comfortable interiors is erroneous. It has been based largely on only select examples that have occurred in extreme and easily classifiable climates, such as hot-arid, hot-humid or very cold. Studies have tended to overlook or ignore other more complex types of climate, such as high altitude and temperate climates. Studies have also tended to ignore examples of structures that, by western standards of thermal comfort, are thermally marginal. Yet there are numerous examples within the ethnographic record to indicate that the people who inhabited such structures appear to have been thermally content. A theory that claims to be able to deterministically explain the development of vernacular or of whole classes of buildings must include a wide range of samples from diverse climates and cultures, for this is the only way to confidently assess the theory. “Details that could throw doubt on your interpretation must be given, if you know them. You must do the best you can – if you know anything at all wrong, or possibly wrong – to explain it. If you make a theory, for example, and advertise it, or put it out, then you must also put down all the facts that disagree with it, as well as those that agree with it” (Feynman 1985: 341). A brief introduction to some of the numerous ethnographic exceptions to the climatedeterminism theory is given below.

A less extreme example is that of the winter kata of the northern Scandinavian Lapps, who inhabit a subarctic climate, which consists of tee-pee shaped layers of logs, bark, peat and turf and a central fire pit. These have been described as “cold and drafty” by comparison with the Inuit igloo (Cook 1996). The Tasmanian aborigines also, like the Fuegans, inhabited only windbreaks and bark huts and wore only loose animal furs for clothing in a regularly cold and windy climate (Gilligan 2004). The above examples belong to mobile societies that, as a consequence of their mobile lifestyle, possess the capacity to move about the landscape as the seasons change. However, there are also examples of buildings that belong to sedentary societies that are climatically inappropriate (by Eurocentric standards). One well known example is that of the traditional houses of urban Japan where, in Hokkaido, for example, the average winter temperatures range from –1oC to –9oC. The traditional Japanese townhouse is constructed of lightweight plaster and timber walls with adjustable external timber shutters and internal rice-paper partitions (Engel 1964) (Fig. 2.65). The same clothes, thickly padded with woollen underwear, are worn both inside and outside the house because “there is little, if any, difference between interior and exterior temperatures” (Engel 1964: 360). Heat is provided by portable braziers, hibachi, which generate only local heat, although the heat is often confined to, for example, the area beneath a table around which several people can sit.

Cold Climates The most extreme example of structures that do not create comfortable interior environments (by Eurocentric standards) are the traditional structures of the Selk’nam and Yámana cultures of Tierra del Fuego, which contrast strikingly with the Inuit igloo even though their occupants inhabit a similar climate, a tundra climate where temperatures are commonly below zero (Rudloff 1981: 550). The Selk’nam structures, which were used until very recent times, consisted of no more than windbreaks and log or bark huts, permanently open to the

Figure 2.63. Selk’nam hut of Tierra del Fuego c. 1907-8 (McEwan 1997: 74)

Figure 2.64. Yámana canoe of Tierra del Fuego c. 19078 (McEwan 1997: 63) 40

Background Theories: Architectural Studies

Figure 2.65. Traditional Japanese townhouse (after Engel 1964: 92)

There are numerous examples of communities that inhabit climatically inappropriate very minimalist structures, such as simple shade structures, in very hotarid climates, most of them belonging to nomadic societies. One of the most extreme examples is that of the Rabaris, who inhabit the very hot semi-desert areas of

Kutch, in western India (Rudloff 1981: 257). The Rabaris are a semi-nomadic society whose structures consist of no more than cotton blankets or shoulder cloths raised on two sticks at one end and pegged to the ground at the other (Shah 1980) (Fig. 2.66). This simple structure, however, can be easily adjusted to face away from the hot northeasterly winds in summer and away from the cold westerly winds in winter (Fig. 2.67).

Figure 2.66. Traditional Rabari tent (after Shah 1980: 52)

Figure 2.67. Traditional seasonal orientation of Rabari tent (after Shah 1980: 52)

Hot-Arid Climates

41

The Evolution of the Built Environment

Figure 2.68. Nalya village, Liberia c.1900 (Denyer 1978: 60)

Figure 2.69. Grebo village, Liberia c.1900 (Denyer 1978: 152)

Hot-Humid Climates

Conclusions on the Comfortable Vernacular Theory

There are examples of communities who inhabit climatically inappropriate structures, structures that differ markedly from the light, well ventilated, elevated structures of Southeast Asia. One such example are the structures of the sedentary Ngelima and Nalya communities in tropical, equatorial Zaire, which are tall and narrow, wholly insulated and within which there is only a single low opening (Fig. 2.68) (Denyer 1978: 5960). The structures are also dispersed differently with regard to the breezes to those in Southeast Asia, being arranged close together in straight rows and without shade trees. This example contrasts with the neighbouring Panga community, who possess identical structures, but which differ in that they are arranged around open compounds, interspersed with saddleback roofed structures on raised platforms and shaded by trees (Denyer 1978: 60).

Humans have and do inhabit a wide range of classes of structures irrespective of the type of climate in which they live. Whilst some societies appear to be more focussed on the concept of thermal comfort than others, it seems that the societies that live in ‘thermally marginal’ structures are not oblivious to discomfort and are not immune to thermally-induced physiological strain. In Japan, for example, “the cold of the winter does not merely bother them. It actually affects the Japanese to such a degree that the proportional death rate of winter over summer is the highest in comparison with other countries” (Engel 1964: 358). Yet the traditional Japanese townhouse persisted as the dominant class of urban building even though alternative, climatically appropriate buildings were in existence in the form of the Japanese farmhouse, which descended from the pre-urban, climatically appropriate ‘pithouses’ (Engel 1964: 358359). The traditional Japanese farmhouse is thermally heavy by comparison with the urban house, with a compact floor plan, resting on the ground instead of elevated above it, with well insulated thatched roofs instead of wooden shingles, and with a large central fireplace instead of portable hibachi (Fig. 2.70).

There are, in fact, very few examples of elevated structures with numerous openings in tropical, equatorial Africa. The structures of the Grebo community on the border of Liberia and the Ivory Coast are characteristic of structures in this region and with this climate. The structures are located at ground level and generally have only one opening. They do, however, also have lightweight walls and high peaked heavily thatched roofs (Denyer 1978) (Fig. 2.69).

So why then have humans inhabited structures that were thermally inappropriate if there were alternatives in existence for them to draw on? Two factors need to be considered in order to answer this question. First, inertia exists inherently in the built environment. That is, it is far

42

Background Theories: Architectural Studies more difficult to make rapid changes to buildings, particularly massive load-bearing buildings, than it is to make rapid changes to more ephemeral entities such as attire or activity. Ergo, it is easier to alter one’s clothing and modus operandi than it is to alter one’s housing in order to generate a thermal change. Secondly, there is the degree of ease with which a building can be altered. The Japanese urban house is easily altered by the opening and closing of sliding panels to create larger or smaller compartments, giving the buildings a large capacity for thermal adjustment and the occupants a proportionately large potential for making thermal compromises. It is significant that contemporary buildings that possess mechanical HVAC systems are now slowly but gradually replacing, not only the Japanese urban house, but the traditional farmhouse as well.

and that falls within a 4oC band of the neutral temperature, which is dependant on the average outdoors monthly temperature. However, people have a dynamic relationship with their immediate thermal environment and humans make use of both ambient and transitional thermal environments. There appears to be an almost universal preference amongst humans for having control over their thermal environment, in preference to always experiencing thermal ‘comfort’. People gain thermal ‘satisfaction’ from being able to choose between different thermal states and/or to alter their thermal environment at will. Possessing the capacity to make a compromise between contradictory thermal factors appears to be of much greater importance than the possession of thermal neutrality/comfort in influencing the types of thermal environments people choose to inhabit.

SUMMARY

Consequently, a test of the long-term thermal behaviour of buildings should focus on the capacity for thermal compromise and not thermal neutrality. Thermal compromises are possible when the occupants have a range of thermal states available to them that they can selectively implement. That is, when they have the capacity to make thermal choices and to selectively control the implementation of those choices. A test of the long-term thermal behaviour of buildings should therefore investigate the thermal capacity of buildings, not the thermal states within them. Thermal capacity is a measure of the full range of thermal states that a building is potentially and selectively capable of producing. Thermal capacity exists irrespective of whether or not the full range of thermal states is implemented, because it equates to the range of thermal states that can potentially be implemented.

The ethnographic record contains numerous examples of classes of buildings in which the occupants would presumably not always have been thermally comfortable, although they would not always have been thermally uncomfortable. The same is quantifiably true of contemporary buildings and it appears that thermal environments in which the occupants are always thermally neutral show high levels of psychological strain amongst the occupants, who perceive that they possess insufficient thermal choices and control. Humphreys and other Adaptive Comfort theoreticians have demonstrated that contemporary occupants of buildings within sedentary societies appear to have a preferred ambient temperature range that is quantifiable

Figure 2.70. Traditional Japanese farmhouse (after Engel 1964: 95)

43

CHAPTER 3 – Background Theories: Archaeological Studies

At the scale of the built environment, a multi-scalar NeoDarwinian archaeological theory is appropriate for examining the evolution of classes of buildings. Classes of buildings have changed numerous times and significantly since rudimentary and proto-structures were erected. This has occurred faster in some parts of the world than in others, although no where has the rate of change been constant. Numerous theories have been put forward to explain this, some offering explanations at the global scale and some at the very local scale. What is needed is a large scale theory that is able to account for the behaviour of complex systems at both the micro and the macro scales. Tim Murray has stated that “the perspectives of evolutionary archaeology can provide ways in which [archaeological] phenomena might be saturated with meaning and value, but it is clear enough that the theories that will underwrite this are yet to be built in ways that will stand up to detailed examination” (Murray 2002: 57). The underwriting theories that Murray was referring to have been explored by Fletcher, who has examined the behaviour of settlements over very long periods of time at diverse scales. On the one hand he has examined proxemics and the spatial behaviour of individual rooms and, on the other hand, non-verbal communication and the dynamics of whole settlements (Fletcher 1995).

INTRODUCTION A cross-cultural and long-term viewpoint, a viewpoint that encompasses extended periods of time and large data sets, can contribute to a recognition of otherwise elusive processes that have underpinned long-term cultural change. A uniformitarian approach has been utilised to locate patterns within the macro-scale operation of the multi-scalar Neo-Darwinian evolution of culture (Allen 1987; Torrence & van der Leeuw 1989; van der Leeuw 1989; Fletcher 1995; Smith 1995; McGlade & van der Leeuw 1997b; van der Leeuw 1998a). Uniformitarianism establishes a logic that links diverse scales of operation and explains processes that are consistent over space and time. It establishes boundary conditions within which particular cultural phenomena have existed, without predefining their particular form or the likelihood of their persistence or demise (Gould 1965; Fletcher 1995: 230231) (see also van der Leeuw 1998b: 10-11). The process of evolution operates regardless of the specific forms present at moments in time. “An instance of operational uniformitarianism is the workings of the genetic system which operates in the same way over time but repeatedly generates different forms. The uniformity is in the process not in the specific products” (Fletcher 2001: 291). Uniformitarianism is, therefore, able to contribute to explanations for the persistence, as well as the demise, of particular social systems. It can offer insights into the way communities have been able, or unable, to deal with and cope with changing circumstances.

Buildings create thermal environments within which society operates, making certain functions possible and others prohibitive. There are essential thermal differences between having a limited range of thermal environments available to move about in and having a wide and/or adjustable range of thermal environments available. The capacity for societies to operate flexibly and adjustably will vary proportionately with the range of thermal environments available within which to operate. A multiscalar Neo-Darwinian study of cross-cultural, long-term change in the thermal performance of buildings can, therefore, offer insights into the evolutionary consequences for societies that possess either adjustable or inflexible thermal environments, as there will have been ramifications for the societies that possessed them. These ramifications will relate to their capacity to adjust to changing external and internal circumstances and, therefore, their long-term operable viability. Adjustable thermal systems will be able to accommodate greater levels of contradiction between the social and the material that will ultimately ensue, than inflexible thermal systems, which will experience increasing levels of contradiction between the social and the material as a result of the material being incapable of accommodating the changing thermal requirements that accompany a changing social context.

Human social activity operates at diverse scales from that of the individual to that of whole buildings, settlements and landscapes. This makes culture an open, dynamic system, as change occurs as the result of both internal and external processes operating from micro to macro scales of operation. Evolution appears to have favoured strategies that have incorporated adjustability at diverse scales (Kauffman 1995; Brandon 1996: 69-84), because operational adjustability enhances the fitness of the system by enabling it to accommodate the conflicting parameters that exist inherently within complex systems, as well as the changes in circumstances (internal and external) that inevitably occur over time. At the scale of the landscape, strategies that have maximised communication and exchange with other areas, land management techniques, and the incorporation of periodic disturbance (usually natural) seem to have possessed an evolutionary advantage (van der Leeuw 1998d: 382-385). At the scale of the settlement, strategies that have maximised communication and privacy in the face of increasing population and urban density seem to have possessed an evolutionary advantage (Fletcher 1995: 95-98).

44

Background Theories: Archaeological Studies whereas cultural evolution was seen as passing between generations, up or down, and laterally within the same generation (Kroeber 1923). This logic was picked up by, for example, Steward (1941, 1942, 1944) and Brew (1946) in the 1940s, who argued that “strict adherence to a method drawn from biology inevitably fails to take into account the distinctively cultural and unbiological fact of blends and crosses between essentially unlike types” (Steward 1941). This was based on the logic that, because ‘objects don’t breed’, phylogenetic relationships in culture cannot be studied using a genetic or biological paradigm (Brew 1946: 52-59). Cultural and biological evolution appeared to be too dissimilar to be comparable.

NEO-DARWINIAN ARCHAEOLOGY: SOME HISTORICAL BACKGROUND In the early 20th century a Darwinian biological evolutionary logic began to be applied to the study of human cultural development, viewing change in nonbiological phenomena as continuous and gradual both spatially and temporally. Opposition to the approach, however, resulted in it falling out of favour a few decades later, to be replaced by the ‘cultural-evolutionism’ that had previously been founded by Herbert Spencer in the mid-19th century and advocated by Leslie White in the 20th (Lyman & O’Brien 1997). Then, beginning with three papers published by Dunnell in 1978, a NeoDarwinian evolutionary logic began to be explored that differed to the way Darwinian evolution had been applied before the 1940s (Maschner & Mithen1996: 5-6; O'Brien 1996c: 26-27). It was this new approach that has established the theoretical basis for contemporary evolutionary approaches to the study of cultural evolution. Edward Burnett Tylor (1871) and Lewis Henry Morgan (1877) picked up Spencer’s theories of ‘culturalevolution’ in the late 19th and early 20th centuries. Their view of cultural change was ‘developmental’. It was definable, unilineal and progressive. They viewed human development as having occurred in straight lines from primitive through to advanced stages and generally driven by need (O'Brien & Lyman 2003a: 3). Boas subsequently refuted this version of cultural evolution stating that, if this were the case, similar features would have to have resulted from similar causes and this presumed that “there was one grand system according to which mankind has developed everywhere [and] that all the occurring variations are no more than minor details in this grand uniform evolution” (Boas 1896: 904). Boas viewed cultural change in terms of unique histories and felt that the Spencerian view of ‘developmental evolution’ had received acceptance before the mechanisms and processes underpinning it had been understood, or even observed (Lyman & O’Brien 1997: 28). “The grand system of the evolution of culture, that is valid for all humanity, is losing much of its plausibility. In place of a simple line of evolution there appear a multiplicity of converging and diverging lines” (Boas 1904: 522).

Figure 3.1. Kroeber’s (a) divergent biological evolution and (b) reticulate cultural evolution (after Kroeber 1948) This refutation of a biological analogy for archaeological change opened the way for the ‘cultural-evolutionists’ to take centre stage because “the only potential [Darwinian] competitor had been eliminated, and the winner was therefore chosen by default” (Lyman & O’Brien 1997: 35). Rather than examining the mechanisms underpinning biological evolution, to see if the processes were analogous to cultural change, they adopted a tangential approach. They adopted Willey’s axiom that “typological similarity is [an] indicator of cultural relatedness” (Willey 1953: 363) and White’s view that evolution was distinct from history (Lyman & O’Brien 1997: 35). By this White meant that “ new forms grow out of preceding forms” (White 1947: 175), that the evolutionary process was lawlike, and that societies progress through stages to become civilisations (White 1947, 1959). “In the history of culture, progress and evolution have gone hand in hand” (White 1943: 339). The law-like nature of evolution was based on the ‘historical reconstructions of the nineteenthcentury unilineal evolutionists’ who assumed “that all cultures pass through parallel and genetically unrelated sequences” (Steward 1953: 324, emphasis in original).

Following Boas’ lead, Alfred Kroeber conceptualised Boas’ ‘multiplicity of converging and diverging lines’ as a reticulate tree, with branches occasionally coming together to hybridize a new line: a ‘braided stream’. This contrasted with the simple evolutionary tree used in the biological sciences, where each branch remains discrete from other branches (Kroeber 1948) (Fig. 3.1). Although Kroeber regarded culture as heritable, being passed on via learning, he did not attempt to discover the mechanisms for cultural change. This was because he felt that the nature of biological and cultural evolution were too different to permit a borrowing of concepts by the latter from the former. Biological evolution was seen as genetically based and moving in only one direction,

This view is, however, based on a misunderstanding of Darwinian processes. As early as the 1930s Kroeber realised that the “fundamentally different evidential value of homologous and analogous similarities for determination of historical relationship, that is, genuine systematic or genetic relationship, has long been an axiom in biological science. The distinction has been much less clearly made in anthropology, and rarely 45

The Evolution of the Built Environment different kind of theory” (O'Brien & Lyman 2000c: 138).

explicitly, but holds with equal force” (Kroeber 1931). Analogous traits are those that have different evolutionary origins, but which perform the same function. Homologous traits are those that have the same evolutionary origin and which may or may not function the same (O'Brien & Lyman 2003a: 153-154). In 1968 Lewis Binford (1968: 8-12) reiterated the problem with there being a lack of distinction between homologous and analogous traits, primarily as a result of perceived ‘inherent unsolved problems of method and epistemology’, but he offered no method for solving it. He chose instead to focus on analogous similarity as a way of identifying cultural processes, rather than homologous similarity (O'Brien & Lyman 2000a: 261).

Neo-Darwinian theory is as relevant to understanding the evolution of human culture as Darwinian theory is relevant to understanding the evolutionary role of bird’s nests, spider’s webs and bee hives (Dawkins 1982: 199). This is because, despite the argument that ‘tools don’t breed’, “tool makers do breed, and they do transmit information to other tool makers, irrespective of whether those other tool makers are lineal descendants. Cultural transmission is a different kind of transmission than what is produced inter-generationally by genes, but this is irrelevant as far as [evolution] is concerned” (O'Brien et al. 2001: 1117). “Brains, books and computers exist [and] can propagate themselves from brain to brain, from brain to book, from book to brain, from brain to computer, from computer to computer. As they propagate they can change – mutate” (Dawkins 1986: 158). Much of NeoDarwinian archaeological theory is borrowed directly from Darwin’s theory of biological evolution, from his theory of explaining the nature of change and lack of change in species of organisms as a consequence of natural selection. Neo-Darwinian archaeologists have adopted the central tenets of this theory for studying change and stasis over time in cultural assemblages on the basis that culture evolves via the same mechanisms as biological species. The current schools of Neo-Darwinian thought vary in some essential aspects, however, differing in approaches to the material and evolutionary processes involved, although the majority of approaches hold that understanding cultural evolution is predicated on an understanding of the various mechanisms by which culture is transmitted.

David Clarke did, however, realise the importance of homologous traits when he stated that “it is the artefact maker who feeds back into the phenotypic constitution of the next generation of artefacts the modified characteristics of the preceding population of artefacts, and it is in this way that the artefact population has continuity in its trajectory and yet is continuously shifting its attribute format and dispersion” (Clarke 1968: 181). By trajectory, he was referring to ‘tradition’ or evolutionary lineage (O'Brien & Lyman 2000a: 262). For the first time the mechanisms by which cultural evolution occurred were being sought in order to explain the differential persistence of variation through time, by distinguishing between homologous and analogous traits, and by focusing on operational uniformitarianism rather than continuity of form and rates of change (Fletcher 2003). In so doing, Clarke was pre-empting the NeoDarwinian archaeologists of the 1980s, who have subsequently moved into the 1990s and 2000s with the aim of reconnecting the functionalist aspects of Darwinian theory with the post-processual role of individuals and historical contingency (Shennan 1996).

One school of thought, put forward by the Evolutionary Ecologists and the processualists, for example, is that “selection enters the explanation only indirectly, as the process that designed the behaving organism (or in fact its ancestors) to respond facultatively and adaptively to particular environmental conditions” (Boone & Smith 1998: S144). That is, artefacts are thought to be removed from the direct action of selection, being merely parts within the problem-solving tool-kit possessed by humans. The action of selection (which is regarded as synonymous with biological selection) upon humans is deemed to have ceased as a result of early hominid culture. Since that time, humans are presumed to have had the ability to make problem-solving decisions that operate on different time scales (immediate to lifelong) and at different cognitive levels (physiological, morphological and behavioural) that make them adaptively plastic (Schiffer 1996; Boone & Smith 1998).

NEO-DARWINIAN ARCHAEOLOGICAL THEORY: THE CONGRUENCES The adoption of Neo-Darwinian theory within archaeology has produced a range of different approaches. “[T]here is no single Darwinian approach that is sufficiently widely accepted to be awarded the title of the Darwinian archaeology” (Maschner & Mithen 1996: 5). It is not the intention of this study, however, to write a review of the various approaches, as this has been done well elsewhere (Maschner & Mithen 1996; Smith 2000; Murray 2002; Fletcher 2003). It is, however, pertinent to the study to outline the congruences, with the rider that it has been generally recognised that “there needs to be a coordinated search for common areas from which to build a useful theoretical and empirical framework” (O'Brien & Leonard 2001: 2) (see also Schiffer 1996). The various schools of Neo-Darwinian archaeology (evolutionary archaeology, behavioural archaeology, human behavioural ecology, selectionist archaeology) differ fundamentally from the more traditional schools of archaeological inquiry in that they represent “not only a different theory of change but a

A contrary school of thought to the adaptively plastic view of human culture, and currently the middle-ground in Neo-Darwinian archaeology, is held by the exponents of Dawkins’ meme concept (Dawkins 1976). Whilst individual approaches vary between the memeticists (Aunger 2000b; Salingaros & Mikiten 2002; Shennan 2002a), the cultural virus theoreticians (Cullen 1993, 1996a & b) and the cladists (O'Brien & Lyman 2003a; 46

Background Theories: Archaeological Studies Harmon et al. 2006), there is the common assumption that cultural information, whilst operating via a different and/or additional inheritance system to genetic information, is epitomised in the meme concept. Memes are defined as “a particle of information that is held in an individual’s memory and that is capable of being copied to another individual’s memory” (O'Brien & Lyman 2003a: 236). Teaching and learning are regarded as the means by which cultural information is transmitted, because artefacts do not reproduce on their own, and the paths of transmission can be inter- and intra-generational as compared with the means of biological transmission of genetic information, via sexual reproduction (Shennan 2002a & b; Terrell & Hart 2002). In this way the cultural virus theoreticians regard artefacts as akin to a virus because, as artefacts do not themselves contain genetic information, they rely on their human hosts as an extrasomatic means of replication (Cullen 1996a & b). The archaeological record is deemed to be the material record of the transmission of cultural information in the same way that evolutionary palaeontologists regard the fossil record as the material record of the transmission of genetic information. Both are seen as composed of the hard parts of their replicative agents, which show differential persistence of traits over time (O'Brien & Leonard 2001: 1). “Every meme is expressed as a cultural character, each of which has [a number of] possible character states that occur within the [particular] lineage in question during the time span under consideration” (O'Brien & Lyman 2003a: 108).

1996). “We need to treat the material as a class of behaviour with its own generative system, its own distinctive heritage constraints, and its own operational impact” (Fletcher 2004: 111). The adaptively plastic view of culture and the concept of memes cannot explain the existence of lineages and behavioural regularities in the archaeological record. Cultural change is composed of diverse scales of change and diverse rates of change. These operate in tandem, and often in conflict, with each other to produce the archaeological record, which is the material expression of these diverse processes. The central tenets of Darwinian evolution are held in common by most Neo-Darwinian archaeologies and these are outlined below.

Parallels with Biological Evolution Neo-Darwinian evolutionary theory is based on the notion of ‘descent with modification’. That is, for evolution to occur there must be four factors present. First, there must be a hereditary line of development. Secondly, there must be a source of new variants (via transmission, experimentation, accident etc.). Thirdly, there must be a selective mechanism (a process responsible for the differential sorting of variation over time, but separate from the processes that produce the variation) and, finally, there must be homologous similarities (similarities resulting from hereditary descent) (Jones et al. 1995; O'Brien & Lyman 2000b: 7780). The syllogistic core of Neo-Darwinian evolutionary theory is a set of three factual statements, followed by a fourth inferred statement. They are: Superfecundity: organisms produce more offspring than can survive. Variation: organisms vary in distinguishing features from other conspecifics. Heredity: some of the variation will be inherited by the offspring. Natural selection: “If only some offspring can survive (statement 1), then on average (as a statistical phenomenon, not a guarantee of any particular organism), survivors will be those individuals that, by their fortuity of varying in directions most suited for adaptation to changing local environments, will leave more surviving offspring than other members of the population (statement 2). Since these offspring will inherit those favourable traits (statement 3), the average composition of the population will change in the direction of phenotypes favoured in the altered environment” (Gould 2002: 125-126).

A contrary school of thought to the meme view of cultural transmission is the view that the material components of human behaviour are part of the human extended-phenotype, with artefacts subject to the same evolutionary processes as somatic features. Whilst it is acknowledged that artefacts can only reproduce via the actions of a human agent, cultural material is given precedence over human cognition on the basis that the archaeological record does not contain a one-to-one relationship with ‘social learning or the evolved decisionmaking’ processes of humans (Neff 2001: 33-34). The relationship is far more complex than that (Fletcher 1996, 2003, 2004). Some evolutionary archaeologists, for example, argue that it will not be until the concepts of memetics and cultural virus theory are put aside that a robust theory connecting cultural transmission with the hard part of culture, the archaeological record, will be developed (Neff 2001; Fletcher 2003). “Archaeology has no access to human minds or the memes they contain, except via the hard parts of the human phenotype that are preserved in the archaeological record. [We] caution against imagining that we can directly investigate social learning or the evolved decision-making capabilities of humans. We cannot do this” (Neff 2001: 34). An extension of this view is the view that a large proportion of cultural material actually operates on a different scale to social behaviour, with which it can be in conflict due to the different replicative time scales involved. This creates non-correspondence between the verbal, the social and the material components of culture (Fletcher 1995,

The process by which Darwinian evolution occurs is as follows (Fig. 3.2). First the system must have the capacity to produce offspring that vary from their parents, increasing the variation ‘pool’ with each subsequent generation. At the same time natural selection acts to reduce this range of variation and it does so in a manner that selects for characteristics that enhance the Darwinian fitness of the organisms (O'Brien & Holland 1990: 43). The characteristics of these individuals then ‘feed back’ 47

The Evolution of the Built Environment divide cultural from biological forms of evolution is spurious” (Lipo et al. 2006: 11-12). The archaeological phylogeneticists and cladists are forced into their position of drawing direct parallels from biology in order to fulfil their aims of ‘building maps that allow the tracking of information across space and through time’. Without the direct gene-information (meme) analogy the biologicalcladistic methodology would be epistemologically untenable.

into the population, enhancing the fitness of the population as a whole. Natural selection thus explains the ‘hierarchical character of nature’ via a single mechanism. This is because selection works upon the variation that occurs naturally within hereditary populations, changing the predominant traits of the population by conferring greater reproductive success upon individuals that possess advantageous variants (Gould 1980: 79). That is, characteristics that enhance the Darwinian fitness of a population, which in biological evolution refers to reproductive success, are more likely to disseminate through the population over time.

In contrast to the phylogeneticists and cladists is the view that, although a Neo-Darwinian logic is necessary for understanding cultural evolutionary processes, the operation of cultural change is multi-tempo and contains ‘more than one generative code system’. This means, therefore, that cultural evolution is not reducible to the biological model of evolution because information is not the only ‘thing’ transmitted, nor is it transmitted at the same replicative rate as other non-verbal and non-social aspects of culture (Fletcher 2003, 2004). “Simply on the physical grounds of differential replication rates a cultural assemblage is an incessant inconsistency of declaration, acts and material. This is a profound generator of change and the genetic system possesses nothing like it” (Fletcher 2003: 289-290). Fletcher argues that what is required is a coherent theory of uniformitarianism that refers only to ‘constancy of operations and boundary conditions’, similar to Gould’s view that operational uniformity occurs within the process, not the products, and should, therefore, not be equated to specific forms and associations and rates of change (Gould 1965).

Figure 3.2. The process of Darwinian evolution. Evolutionary fitness in culture is, however, more ambiguous than reproductive success in biology and there is current debate as to the degree to which biological evolutionary logic is applicable to archaeology. On the one hand are the archaeological phylogeneticists and cladists who claim that, whilst it is information rather than genes that are passed on, information is a ‘thing’ and the transmission of information will thus leave behind phylogenetic ‘effects’ that are traceable in just the same way that genetic transmission is traceable (Harmon et al. 2006; Lipo et al. 2006: 11-12; Lyman & O’Brien 2006; Pocklington 2006). “The distinction between genetic transmission and cultural transmission is artificial. Both genes and culture are transmission systems. They differ mechanistically and also in terms of their dynamics, but this is irrelevant to their information-theoretic structure. They differ as well in the degree of average fidelity of transmission, but this is a quantitative, not a qualitative, difference….Thus the ‘analogy’ argument that seeks to

Transmission: A direct connection is generally made between cultural transmission processes, in the form of the spread of cultural information, and artefactual heritability (O'Brien & Lyman 2003b: 29-31). There are three perceived modes of cultural transmission. They are ‘vertical’, ‘horizontal’, and ‘oblique’, which can be either one-to-many, concerted or many-to-one (Boyd & Richerson 1985; Shennan 2002a: 48-51). This is, in effect, the mechanism that produced Kroeber’s ‘braided stream’, the result being cross-related object typologies (Figs. 3.3-3.4).

Figure 3.3. Hypothetical divergent object typology (Dewar 1995: 302. Reproduced with kind permission of the publisher)

Figure 3.4. Hypothetical reticulate object typology (Dewar 1995: 303. Reproduced with kind permission of the publisher)

48

Background Theories: Archaeological Studies Variation: The variation upon which selection operates does not need to be visible and prominent. In fact, it will generally be small and copious, such that it will pass unnoticed (Bettinger & Eerkens 1999: 115; Gould 2002: 143-144). Small-scale cumulative changes, which come from small-scale variability, will, however, become visible over the long-term. “If selection consisted merely in separating some very distinct variety, and breeding from it, the principle would be so obvious as hardly to be worth notice; but its importance consists in the great effect produced by the accumulation in one direction, during successive generations, of differences absolutely inappreciable by an uneducated eye” (Darwin 1936: 30). It does not matter what the source of the variation is, whether invasive or experimental or accidental. What matters is only that once it appears it can then be selected for and fed back into the population. “The focus of explanation then is upon that variation which survives at a frequency and for sufficient time that it is evident archaeologically” (Jones et al. 1995: 24).

which occurs if the keys are pressed too fast (Gould 1991: 62-72). The QWERTY keyboard has persisted, however, into the post typewriter era even though potentially faster layouts exist. For example, most typing speed records are held by the Dvorak Simplified Keyboard, which has a different arrangement of letters and which has not supplanted the QWERTY arrangement (Cohen & Stewart 2000: 323). It is the inertia in the system that prevents faster arrangements from supplanting the established one, a situation that is characteristic of existing paradigms. Generally, only if something comes along ‘out of left field’ that performs the same type of task but in a better and different way will the established entity be unseated and replaced (Cohen & Stewart 2000: 323-324). This is why paradigm shifts can generally not be anticipated long in advance, occur rapidly and leave behind strong signatures. One of the most common methods for illustrating the amount of variation present in a system at a particular moment in time is a histogram, whereby usually a single variable (but occasionally a range of variables) that represents the frequency of occurrences within a group can be shown on a single two-dimensional graph (Fig. 3.5). Other methods exist for illustrating higher order variation, such as would be apparent over extended periods of time or between different groups (resultant variation, rather than the variation upon which selection initially operates). Of these, the most familiar method is the tree-of-life, which arranges organisms by line of descent. It shows the range of classes of species present at a particular moment in time. Over evolutionary time and under selection, anatomical diversity has reduced and the number of species based on the reduced number of basic anatomies has increased (Fig. 3.6). The tree-of-life, however, gives no indication of the relative number of organisms within each class and this must be shown graphically. Over evolutionary time, the number of simpler, older classes of organisms has numerically dominated the more complex classes. There are, and have been, many more bacteria in the world than humans (Fig. 3.7).

The variation should be random, in that it should be “unrelated to the direction of evolutionary change; or, more strongly, that nothing about the process of creating raw material biases the pathway of subsequent change in adaptive directions” (Gould 2002: 144). This does not mean, however, that all possible variations are equally likely to occur. “Organisms are tied to their phylogenetic histories; thus variation at any point in time is bounded by the nature of the organism itself” (O'Brien & Holland 1990: 42). This concept is referred to as the Panda’s Thumb principle. This is the principle that as long as something is performing the task required of it adequately it is unlikely to be replaced, even by something that performs the same task slightly better, due to the inertia inherent in the existing system. Gould discusses the example of the QWERTY typewriter keyboard, which has an arrangement of keys whereby right-handed typists (the majority) must type the most common letters of the English alphabet with their left hand, thus slowing them down. This was an intention of the design, so as to avoid jamming of the typewriter keys,

Figure 3.6. Tree of anatomical diversity from a common ancestor over time (after Gould 1994: 65)

Figure 3.5. Example of a histogram. 49

The Evolution of the Built Environment Figure 3.7. Distribution of anatomical complexity over time (after Gould 2002: 897)

Adaptations: ‘Adaptations’ are commonly defined in archaeology as the choices and actions taken by social groups acting under the direction of environmental pressures to give them an advantage over other groups. This is a Lamarckian definition and is based on the assumption that humans are exempt from selective forces due to their capacity for culture, learning and ‘innovation’ (Schiffer 1996; Boone & Smith 1998). That is, human activity and purpose is presumed to be the adaptive mechanism within human culture. Such a view stands in contradiction to the Neo-Darwinian definition.

over which they have no control. Numerous examples exist of this phenomenon, of developments that may have kept pace with their changing cultural landscape, but which ultimately and unpredictably became incongruent. One such example is that of the military tactic of massed advance towards an enemy, which prevailed from approximately the 14th to the 20th centuries A.D. (Kelly 2005). During this period gunpowder technology was developed in Europe for use in ballistics, making the massed advance ever more costly in human lives. Defensive tactics did alter in response to the introduction of the new weapons, but only marginally. The result was that discipline became more rigorous and the timing of rounds of fire became more precise. The development of ballistic technology, however, proceeded at a faster rate of change and, whilst numerous factors can account for the occurrence of events, the discrepancy between ballistic technology and massed infantry tactics is graphically illustrated by the high death rates experienced by both sides during the American Civil war and the European trench battles of the First World War. The massed bayonette charge represented a tangential technological development of the massed advances prior to Crécy and the Hundred Years War. Gunpowder technology altered the warfare ‘landscape’ and brought the preceding tactics and thrusting weapons into stark contrast. Other examples of this phenomenon have existed although they may not have been as apparent. One such example is that of early hominids’ cognitive capacity to plan and execute ballistic movements (Calvin 1994, 2004). Prior to this, the competition between early hominids and their prey would have been more evenly balanced. However, with the gradual increase in the cognitive capacity of hominids to ‘plan ballistic movements’, from striking two stones together to throwing a projectile along a preconceived trajectory, the balance was gradually altered in favour of the hominids and the competitive landscape was altered.

The Neo-Darwinian definition of ‘an adaptation’ is a trait or character state possessed by an organism that gives it greater Darwinian fitness within a current set of circumstances, making it more likely to survive and reproduce. It is the result of the operation of selective processes. That is, only traits or character states that are acted upon by natural selection can be considered adaptations (O'Brien & Lyman 2003a: 231). Selection operates to maintain levels of relative fitness between competing species or groups. Fitness is thus a relative term. As a result, organisms have to keep adapting in order to maintain their position relative to their competitors. If they stop adapting, the likelihood of their going extinct increases. This has been referred to as the Red Queen hypothesis, after the Red Queen in Lewis Carol’s Through the Looking Glass who had to keep running in order to stay in the same place (Van Valen 1973). Biologists commonly visualise the consistent interplay of relative fitness between competing species and groups as occurring on fitness landscapes, a concept developed by Sewell Wright (1932) and relative fitness can be visualised as the Red Queen and Alice running on the spot as the landscape beneath them consistently alters form. An alternative analogy might be a surfer who has to keep moving in order to stay in the same spot relative to the crest of a wave. Neo-Darwinian theory holds that adaptations cannot be directed by organisms. They cannot predict or control in which evolutionary direction their actions and choices will eventually take them, because they cannot know how the outcomes of their actions will correlate with the unpredictably ever-shifting landscape,

There are two ways in which adaptations are treated in Neo-Darwinian studies (Maxwell 2001). The first is from an aetiological viewpoint, which holds that, before a trait

50

Background Theories: Archaeological Studies can be labelled as an adaptation, the reason it came into being must first be found. It is presumed that only by knowing why the trait came into existence can the way in which it contributed to the survival of the species be known and, thus, correctly labelled as an adaptation. The trait must be located within a selective history based on the ‘triggering cause’. It is presumed that, without this, there is only description and no explanation (Dretske 1989; Griffiths 1993).

refers to as ‘just so stories’. The importance of discerning between functional and neutral traits is hotly debated in Neo-Darwinian archaeology (O’Brien & Holland 1995: 188-189; cf. Fletcher 2003). Complex systems operate at the level of correlated features and traits. Their operational outcome results from the workings and compositional properties of whole systems, not individual traits. Within the context of the whole system, however, some features will have been selected for at the relative expense of others, as the net result of the system’s level resolution of their contradictory operations. In this sense both functional traits (adaptations) and neutral traits (under drift) are components of evolutionary processes, but they are only discernible as such with the vantage of a thorough understanding of their contradictory operation and historical contingency (O'Brien & Lyman 2003a: 150154). In this sense only functional traits exhibit ordered behaviour, before eventually dying out and being replaced by an alternative functional trait with at least an equivalent selective value, and stylistic traits either drift randomly inter-generationally for a period of time and then either ‘die out’ or they become fixed in the population (O'Brien & Leonard 2001: 8-10; O'Brien & Lyman 2003b: 18) (Fig. 3.8).

The second way in which adaptations are treated is from the point of view that an aetiological viewpoint cannot explain the presence of adaptations that came about via a different route other than direct cause. Gould uses the example of feathers in birds, which purportedly originated for the purpose of insulation and only later took on the (dual) function of flight (Gould & Vrba 1982: 7). Such adaptations are known as exaptations. Exaptations contribute to the fitness of the species in just the same way as adaptations produced by direct cause. Therefore, a search for the moment in time when an adaptive trait came into being is irrelevant to Darwinian evolution because it is only with hindsight that a particular trait can be seen as either having contributed to the species’ fitness or not (Rindos 1984: 43). NeoDarwinian evolutionary processes operate on outcome and not cause. Evolution is predicated on the contingent consequences of an event, not the reason it occurred in the first place. As a result, Neo-Darwinian focus should be on the contingent operational behaviour of systems, not seeking to discern the finite moment in time an exaptation either came into being or became an adaptation. This type of approach places erroneous consequential importance on human decision making processes. Adaptations are frequently labelled ‘functional’ traits, to distinguish them from traits that have not contributed to evolutionary fitness and which are labelled ‘stylistic or neutral’ traits (Dunnell 1978; O'Brien & Holland 1992; O'Brien & Leonard 2001; Van Pool 2001). Because natural selection operates on functional traits and not on stylistic traits, it is argued that functional traits are more likely to exhibit ordered behaviour over time and stylistic traits to exhibit random behaviour over time (Dunnell 1978; Van Pool 2001). However, some biologists have criticised the search for functional traits (ref. e.g. Gould & Lewontin 1984) on the basis that most such studies have not examined the organisms at the scale of the integrated system, for traits rarely operate alone. They work in synchrony with numerous other traits, which together define the fitness of the whole organism. A study of functional traits is therefore, at best, a distraction from a multi-scalar Neo-Darwinian approach to the study of the evolution of past systems and, at worst, “pseudoexplanatory reductionist atomism and stultifying nonexplanatory holism” (Mayr 1988: 154). It is only by mapping the long-term behaviour of full assemblages of correlated traits that insights can be gained into which traits possibly contribute more than others to the overall fitness of the organism. The alternative is what Gould

Figure 3.8. Hypothetical change in frequency of objects relative to (a & c) selective pressure or (b) drift (O’Brien & Lyman 2000b: 89. Reproduced with kind permission of Springer Science and Business Media) Heritability: Both Darwinian biologists and archaeologists endeavour to systematise the variability in the material record according to lines of heritable descent. The aim is to sort classes of organisms or artefacts into heritably related groups, with those most closely related grouped closer together and those distantly related grouped further apart. The common image of the tree-oflife is actually a visual representation of the phylogenetic relationships between ancestral entities and their descendants. The ancestral entities possess primitive traits that are shared by their descendants, shared ancestral traits (symplesiomorphies), such as backbones in vertebrates. The descendants, on the other hand, will possess modified traits not present in their ancestors and not present in other branches of the family, shared derived characters (synapomorphies), such as warm blood in mammals and cold blood in reptiles. By this method 51

The Evolution of the Built Environment classes of organisms are sorted into phyla, classes, orders, families and genera.

result from a similar cultural heritage and paths of transmission (Shennan 2002a: 73-91; O'Brien & Lyman2003b: 22-29). More recently, cladistics has been used in Neo-Darwinian archaeology to investigate lines of cultural transmission (O'Brien & Lyman 2000a; O'Brien et al. 2001; O'Brien & Lyman 2003a; Harmon et al. 2006).

However, the process of ordering entities into phylogenetic relationships is not straightforward due to inherent gaps in the material record. Three main methodologies for deriving heritable relationships have been developed, each with a slightly different way of representing the relationships graphically. They are cladograms, trees and scenarios (Fig. 3.9). They represent hypotheses about evolutionary events and hereditary processes, requiring unverifiable assumptions to be made pertaining to the gaps in the record. They each answer different types of questions and are therefore equally valid. “A cladogram is the answer to the question, What is the nature of the diversity of these organisms? A tree is the answer to the question, How did this diversity come about historically? A scenario is the answer to the question, What caused it to come about?” (Kemp 1999: 19). However, they each operate at different scales of explanation. Cladograms require fewer ad hoc assumptions than do trees and trees require fewer ad hoc assumptions than do scenarios (Kemp 1999: 18-20). They are therefore associated with decreasing levels of confidence and increasing difficulty with judging between competing theories (Kemp 1999: 20). In archaeology life-histories of classes of artefacts have long been discerned using both stratigraphic observation and seriation. Stratigraphy places the artefact in a chronological sequence (Fig. 3.10) and seriation locates it in a class of artefacts that possess formal similarity (Fig. 3.11) (Willey 1953). A close phylogenetic relationship is presumed to manifest as formal stylistic similarity and

Figure 3.9. Graphical representations of genealogy using (a) cladograms, (b) trees and (c) scenarios (Kemp 1999: 20. Reproduced with kind permission of Oxford University Press)

Figure 3.10. Chronological sequence of Egyptian pottery by Petrie (Petrie 1901: II. Reproduced courtesy of the Egypt Exploration Society)

Figure 3.11. Egyptian pottery seriation by Petrie (Petrie 1899)

52

Background Theories: Archaeological Studies Cladistics purportedly “offers a means of reconstructing artifact lineages that reflect heritable continuity as opposed to simple historical continuity” (O'Brien et al. 2001: 1115). Cladograms are derived by using the techniques of occurrence and frequency seriation pioneered by Alfred Kidder (1917) and only homologous traits are used, specifically shared derived traits. Priority is placed on traits that show a higher degree of variation over time, as these are presumed to have the most easily detectable phylogenetic signals, and on traits that have not been retouched or modified from their original form, as this is presumed to introduce bias (O'Brien 2000b: 87; O'Brien & Lyman 2003a: 150). Cladistics relies on having a well established hereditary line of descent.

acquisition of culture, transcended the evolutionary process. Simply that there has been ‘a profound proportionate selectionist impact on the occurrence of culture’.

Human Agency Neo-Darwinian archaeology is almost universally agreed upon the view that humans are the agents of artefactual change that the archaeological record is the material record of. “In archaeological study, artefacts represent the empirically observable realm of study, and it is the differential representation of variation at all scales among artefacts for which [a Neo-Darwinian archaeology] seeks explanations” (Jones et al. 1995: 28). Neo-Darwinian archaeology simply does not regard humans as the conscious operators of evolutionary selection, because they cannot possibly influence or predict the long-term outcome of their actions and choices. Most humans presumably intend to be successful, but their choices and actions cannot inherently result in success in the longterm. The choices and actions of humans that resided in past societies that failed presumably intended to be successful, just as those residing in societies that succeeded intended to be successful, but neither could have foreseen the long-term outcome of their actions (Rindos 1984: 4; Jones et al. 1995: 19).

Differences to Biological Evolution Both palaeontologists and Neo-Darwinian archaeologists have noted differences between biological and cultural evolutionary processes. One of the most commonly recognised differences is the observation that biological species evolve along divergent paths, whilst culture evolves in a reticulate fashion (Fig. 3.1). Amongst biological species, once two ancestrally-related descendant species have become sufficiently genetically separated over time, they do not then cross-breed. Amongst cultural ‘species’, on the other hand, intrabreeding between ancestrally-related sub-species, as well as inter-breeding with ancestrally-unrelated species, is seen as one of the main ways in which culture is transmitted (Boas 1932: 609; Kroeber 1948; Dewar 1995). “The basic topologies of biological and cultural change are completely different. Biological evolution is a system of constant divergence without subsequent joining of branches. Lineages, once distinct, are separate forever. In human history, transmission across lineages is, perhaps, the major source of cultural change” (Gould 1991: 65).

The adaptively plastic view of cultural evolution opposes this view on the basis that humans are denigrated to mere cogs in a machine, lacking free-will and having no control over their future (Hodder 1982; Hodder 1986: 69; Tilley 1981; Shanks & Tilley 1987: 97-105). Hodder’s view that ‘individuals need to be part of theories of material and social change’ was a reaction against deterministic causal explanations for change on the basis that, whilst the material ‘acts back and affects the society and human actions that produced it’, it is not an actor in its own right. It exists only within the framework of social meaning (Hodder 1986: 1986: 7-8). This view is, however, based on a misinterpretation of the central role that variation plays in Darwinian theory, for ‘social learning and the evolved decision-making’ processes of humans do play a vital role in the evolution of culture. Humans are one of the main sources of cultural variation, upon which selection operates. Selection merely defines the boundary conditions for long-term (multigenerational) change (Fletcher 2003: 290). As one of the main generators of variation humans actually perform a key role in Neo-Darwinian cultural evolution because the wider the range of variants at the micro-scale, the more likely it is that selection will select for a variant that increases the overall macro-scale evolutionary fitness (Cohen & Stewart 2000). If the range of variants is narrow, selection will still select for the most fit variant, but there will be a reduced likelihood that it will enhance the overall fitness of the system to the same degree as if the selection were made from a wider range of variants, which is more likely to include more fit (and less fit) options. The macro-scale consequence of this is that a conservative or homogenous social group is less likely to

Some Neo-Darwinian archaeologists have gone further, however, stating that cultural evolution operates in an essentially different way to biological evolution by nature of its diverse rates of transmission and modes of replication. This approach views genetic replication as possessing only one generative code system and cultural replication as possessing more than one (Fletcher 1996). Fletcher, for example, argues that “in terms of NeoDarwinian theory, cultural evolution cannot operate in the same specific way as biological evolution. Nor can the former be reduced to the latter since otherwise culture could not have become more common and more substantial over time. Culture is a phenomenon that was vanishingly rare three million years ago and now remodels entire landscapes” (Fletcher 2003: 289). In other words, culture has itself come under selective pressure as a result of its inter-relationship with humans, which is complex because of the diverse range of replicative rates of different aspects of culture, some which replicate rapidly and some which persist for inter-generational spans of time. This view is not to be confused with the erroneous assumption that humans have, through the 53

The Evolution of the Built Environment be as evolutionarily robust as a more heterogenous social group. In other words, the effect of humans allowing diverse variants to propagate within a social system indirectly enhances the relative robustness of the system in the face of changing circumstances. Humans cannot anticipate the role that individual variants will play over the course of future generations and they cannot therefore determine what will or will not contribute to the success of the system, but the effect of allowing the propagation of widely diverse variants within the system will indirectly (and ultimately) increase the likelihood that the system will be more robust to changing circumstances than systems that are more conservative or within which the range of variants is more restricted.

et al. 1995: 22-23).

Contingency The ramifications for Neo-Darwinian evolutionary theory on the evolution of human culture are that contingency matters. “Anyone writing about history must be prepared to write about both change and continuity - and explain both the evolution of new kinds of things and situations and also the persistence of old ways and institutions” (Terrell & Hart 2002). Culture is comprised of component traits that may either stay the same or that may change over time relative to each other. “All traits, whether material or behavioural, have distribution in time and space, and all traits have what can be termed replicative success, or differential persistence through time” (Leonard 2001). A moment in the life of a community or a social or material assemblage is merely a single event fixed in space and time without contingency. Cultural change needs to be understood in context with other inter-related processes, the changes and continuities that have preceded it. Gould (1990) argued that the evolutionary success of species is ultimately a factor of contingency. That is, their exposure to potentially serious chance events that could potentially impact on their chances of survival, events that would not be repeated if were history re-run. This view places more importance on ‘good luck’ than on fitness in deciding ultimate evolutionary success. The creatures of the Burgess Shale may or may not have reached peak fitness within their niche, but contingency implies that ultimately it did them no good.

Scale It is critical to maintain a distinction between the rate at which change occurs and the rate at which it is manifest (Jones et al. 1995: 22). For example, the view that humans are adaptively plastic is erroneously based on temporally limited observations of micro-evolutionary change, rather than of actual evolutionary process (O'Brien & Lyman 2003b: 13). Also, cultural evolution has widely been thought of as occurring at a faster rate than biological evolution (ref. e.g. Dawkins 1986: 158). This view is, however, the result of assuming that different aspects of culture evolve at the same rate, but culture, like biology, encompasses varying rates of change. It is simply that the aspects of culture that evolve faster have given the appearance of overshadowing the existence of the slower aspects because they have evolved over shorter and more visible periods of time, as opposed to periods of time that are only observable in the palaeontological and/or archaeological records (Fletcher 1995: 13-20; O’Brien 2000b: 77-78). Methodologies that can relate short-term human behaviour to long-term evolutionary processes are necessary (Mithen 1989).

The over-riding evolutionary importance that Gould’s view places on contingency is, however, tempered by a shift in focus away from product or form and towards process and operational uniformitarianism. If the tape of life were played over again, the vast majority of today’s species would not now be here, but there would be species doing similar things and performing similar functions to today’s species. That is, the ‘space of the possible’ (the sea, the land and in the air) would still be filled, but with alternative creatures to those that did historically fill it (Cohen & Stewart 2000: 329). The creatures would simply not necessarily look the same as history’s creatures, nor necessarily occupy their niche by functioning in the same way. Cohen and Stewart cite the theoretical example of the possibility of the air having been occupied by helium-filled-balloon-creatures, rather than the winged creatures that did fill the air-niche and that retained it due to the inertia in the existing system: the Panda’s Thumb principle (Cohen & Stewart 2000: 375-377).

It is critical to maintain a distinction between the units that are used to observe things and the units that are used to measure evolution. That is, the way in which things are observed, measured and classified so that evolutionary mechanisms can be linked to the archaeological record (Jones et al. 1995: 22-23; Hunt et al. 2001: 10; O'Brien 2003a: 22-23; O'Brien 2003b: 15-16). Empirical units are analytical units that have a (real) physical existence, such as a set (group) of pots with the same characters or traits. Theoretical units are conceptual units that do not have an empirical existence, such as centimetres and degrees (characters and traits), and which are used to measure empirical units. A group is an empirical unit comprising things that have a location in space and time. A class is a theoretical unit which has been intentionally classified in terms of similar characters and traits and which has a distribution in space and time. “Classes do not evolve. [They] are counting units just as different variants of cows are counting units. We might say that end scrapers evolve over time, but what we are really saying is that, when arrayed chronologically, distinct classes of end scrapers are differentially represented over time” (Jones

Convergence undermines contingency and removes the consistently shifting emphases and priorities in Gould’s world (Conway Morris 1998: 9-14). Conway Morris has argued that, if the tape of life were re-run, convergence would probably have produced a similar array of creatures and that something similar to humans would have emerged (Conway Morris 1998: 199-202; 2003: 54

Background Theories: Archaeological Studies only those aspects of culture that do maximise fitness have persisted (Brandon 1996: 69-84).

271-282, 298-307). He holds that the human-equivalents would not necessarily be identical to today’s humans or possess an identical culture, but he does argue that a stone tool-wielding, large brained hominid would most likely emerge (Conway Morris 2003: 307). “Evolutionary convergence shows that we live in a constrained world, where all may not be possible… The many biological parallels to those features that define the emergence of humans suggest that something similar will emerge elsewhere” (Conway Morris 2003: 298-299). In his discussions on evolutionary trajectories, however, Conway Morris adopted a ‘starting point’ that commenced with early hominids. Cohen and Stewart discuss the ‘space of the possible’ with respect to a completely different, possibly much earlier, ‘starting point’ in which the world could have potentially been founded by very different organisms. Convergence would have ‘lead to’ the world being now populated with creatures that are functionally comparable to those that did exist, but which would be radically and wholly morphologically different (Cohen & Stewart 2000: 374376). This view is embodied in Kauffman’s models, in which he places importance on looking at uniformitarian principles, not phylogenetic specifics or contingent details (Kauffman 1995: 80).

The reliance on a one-to-one biological analogy to explain cultural evolution must logically ultimately lead to an acceptance of the meme concept as carrier of cultural information. In recent years there has been some attempt to define Dawkins’ memes (Cullen 1996a & b; Lipo & Madsen 2001; Shennan 2002a). Some studies have applied rigour in the attempt to define memes, but the concept remains still largely abstract, as illustrated by a recently published book devoted to the ‘science of memetics’ (Aunger 2000b) that did not include one quantitative study. Some archaeologists hold that memes are elusive by nature (Dennett 1995: 352-360; Aunger 2000a & c). Dennett has argued that this is because “what is preserved and transmitted in cultural evolution is information – in a media-neutral, language-neutral sense. Thus the meme is primarily a semantic classification, not a syntactic classification that might be directly observable” (Dennett 1995: 353-354, emphasis in original). Exponents of the meme concept, however, argue that this is not an insurmountable problem because Darwin defined natural selection long before DNA was ‘discovered’ (e.g. O’Brien & Lyman 2003b: 11) and that the main stumbling block is a shortage of quantitative cultural surveys amenable to cultural-phylogenetic studies of descent and diffusion (Jordan & Mace 2006: 150). Culture does, however, possess features and characteristics that are fundamentally different to biology and that makes the meme concept wholly untenable with regard to cultural evolution.

NEO-DARWINIAN ARCHAEOLOGY: SHORTCOMINGS There are currently a number of shortcomings within Neo-Darwinian archaeology that must be resolved if the theory is to become robust (Fletcher 2003). Current NeoDarwinian archaeology, which has the potential to explain the long-term behavioural change in buildings and that has been applied to a range of cultural phenomena from stone tools to buildings (with greater and lesser success), has rarely been applied to extensive assemblages of buildings or encompassed very long periods of time. Instead, Neo-Darwinian archaeology has to date tended to concentrate heavily on rhetoric and theory. Few studies have performed empirical analysis and those few have tended to concentrate on select cultural phenomena (Abbot 1996), or select functions (Graves & Sweeney 1993; Graves & Ladefoged 1995; Aranyosi 1999), or select moments in time or space (O'Brien & Lewarch 1984; Neiman 1995; Leonard 2001; Pfeffer 2001). However, the shortcoming that is most pertinent to this study is the largely unacknowledged difference between biological and cultural evolution. The specifics of biological evolutionary theory are insufficient for explaining the far more complex processes that operate in cultural evolution and that arise as a result of the diverse rates of replication that exist within culture, that do not exist in genetic replication. However, the logic that underpins Neo-Darwinian biological evolution is applicable to cultural evolution because the nature of change and continuity (of the operation of the system) in both biological and cultural evolution are the same (Fletcher 2003: 288). Human culture has evolved only because it increases the overall fitness of humans and

The view that it is only semantic information that is transmitted in cultural replication is symptomatic of the misconception common in archaeology that objects are merely physical manifestations of ideas, possessing no actuality in and of themselves. Fletcher, for example, holds that culture cannot be understood in terms of cognition alone and that material assemblages in and of themselves play an independent but interrelated role with cognition (Fletcher 1995, 1996, 2004). He has argued that, “currently, primary significance is allocated to whatever standard verbal meanings are ascribed to the material by the conventions of social analysis. Instead we might ask what the material does and how the collision of the material with verbal meaning and social action creates what actually happens” (Fletcher 2004: 111). Cultural evolution is the result of verbal, social and material behavioural processes that operate both separately and interactively, with highly complex results and profound long-term consequences. “Not only are there several levels of meaning (e.g. verbal and non-verbal), but also there are several operational levels, such as social action and the inertia of the material framework of social life. The implication is that each level of operation has its own coherence, its own suite of effects, and its own particular range of time spans over which impact is felt on the outcome of an activity” (Fletcher 2004: 130). The 55

main

objection

to

Fletcher’s

model

within

The Evolution of the Built Environment and modes of replication within the three different systems of verbal, social and material that are inherently contradictory. The verbal, the social and the material components of culture will not automatically always correspond and, where they do not, friction and dissonance will result. This is particularly important and cannot be ignored by studies that focus on material behaviour because “instead of just reinforcing, maintaining and complementing active social life, the material possesses patterns in its own right, has the effect of constraining options and creating friction, and is also potentially able to undermine viable social life” (Fletcher 2004: 111).

mainstream archaeology (e.g. Kent 1996) has been the apparent absence of human actors (Wright, R.P. 1997: 200). This view is, however, possibly only due to the macro-scale at which Fletcher has developed his model, which gives the appearance of human absence, as human agency is a fundamental component of the model. The quantitative component of Fletcher’s model incorporates comparative rates of settlement growth and demise based on the sizes of whole settlements and their population densities (Fletcher 1986, 1995), but this is tied back to an indirect relationship between settlement density and the culturally-derived characteristics of kinesics (body language) and proxemics (personal space) (Fletcher 1977). The model is a successful examination of multiscalar Neo-Darwinian process in, and boundary conditions to, settlement systems in the context of contradiction resolution and diverse scales and operative processes (Fletcher 1995: 230-231, 1996, 2003: 291), an approach that has otherwise been substantially overlooked in evolutionary archaeology.

These contradictions have the capacity to ‘drive’ the long-term behaviour of the social systems, within boundary conditions, especially if the level of contradiction is high (Fletcher 1995: 17; Kauffman 1995; Fletcher 2004: 133-135), because the resolutions found to resolve the contradictions in systems that encompass great inertia will generally only become evident over very long periods of time, under the long-term action of selection. Thus, the archaeological record is the material manifestation of long-term patterns of contradiction resolution plus some ‘white noise’ from social action (Kauffman 1995: 19; Fletcher 2004: 129). It is the diverse detail within the uniformity of process.

Contradiction One of the inherent features of complex systems (the environment, the weather, culture, technology and the economy) is contradiction. The central role that contradiction plays in complex systems has been recognised in the biological sciences (e.g. Gould & Lewontin 1984: Kauffman 1995), where it has been the focus of a growing field of quantitative research (Kauffman 1993, 1995). The focus of these studies has been on the relative performance of whole systems with respect to the way in which contradictory parameters have been resolved, on process, not product. This is discussed in great detail in the following chapter and so will not be discussed here except by way of comparison with Neo-Darwinian archaeological studies in which the vast majority have overlooked the importance of the role of contradiction. The model developed by Fletcher has, however, incorporated the role of contradiction resolution and the role it plays in the long-term behaviour of complex systems.

The role of contradiction in the evolution of complex biological systems has commonly been studied via a methodology known as engineering-analysis (Carlson & Doyle 1999; Jen 2005a & c). This methodology has been used in various recent Neo-Darwinian archaeological studies to study functional traits (Maxwell 1995: 118; Maxwell 2001: 46-47). It is regarded as a means by which the mechanical properties of artefacts can be discerned (Watson 1986: 446), or by which an individual trait that has come under selective pressure most strongly may be discerned from a suite of other functional traits (e.g. Maxwell 1995; O'Brien & Holland 1995: 188-189; Maxwell 2001). These views are based on the notion that a knowledge of functional traits can be used to predict the direction of change that the whole system is likely to follow (Neff & Larson 2003: 249-251). Application of this adaptational approach to the study of complex systems is based on inference, as the function of each adaptive trait is consecutively tested for until the ‘proper function’ is discerned (O'Brien & Holland 1995: 189190).

Fletcher’s model has defined two sources of contradiction operating in settlements. The first source of contradiction is that which exists between population density and ease of communication. As density increases, communication becomes more difficult. Levels of communication are only maintained in the face of increasing population via adjustable and diverse means. Additionally, levels of stress related to crowding are only alleviated in the face of increased population via maintaining a high level of privacy and communication. These macro-scale processes establish boundary conditions within which settlements are likely to operate long-term, but which relate back to the micro-scale processes of kinesics and proxemics, which are cultural and personal derivatives. For example, if cities become too large they become incapable of supporting the social structure they contain (Fletcher 1995: 71-82). The second source of contradiction is that which exists between the diverse rates of transmission

Gould and Lewontin have criticised this approach, not as O’Brien and Holland surmise, because the ‘try-until-youget-it-right’ approach is cumbersome and prolonged (O'Brien & Holland 1995: 189), but because “it proceeds by breaking an organism into unitary ‘traits’ and proposing an adaptive story for each considered separately” (Gould & Lewontin 1984: 252; see also Eldredge 1989: 14-15). This approach leads to what Gould refers to as ‘just-so-stories’. These are retrospective explanations for why the way things were was the best that was possible. It does not encompass the 56

Background Theories: Archaeological Studies possibility that things were the way they were simply because they were the best they could be under the circumstances, these consisting of the conflicting processes operating within and on an organism of their own anatomy, their competitors (their peers and their enemies) and their environment, plus the inertia in the system (Gould & Lewontin 1984). It misses the point that evolution does not operate on individual traits or properties, but on whole assemblages of interacting, often contradictory, traits (Gould & Lewontin 1984). “We cannot know the functions of the parts except in the context of the whole autonomous agent in its environment” (Kauffman 2000: 130). Attempting to locate individual adaptive traits can potentially divert attention away from the ‘best compromise’ solution that defines the whole system’s level of fitness that resulted from resolution of the contradictory operations. The focus should not be on the increase in frequency of darker colour in moths in industrial areas, or of soil water conservation in prehistoric Southwestern farming techniques. It should be on the camouflage-versusmobility compromise of the moths and the ecological sustainability-versus-yield compromise of the farming techniques, because these are the scenarios that most affect the long-term success or decline of the whole system.

importance of studying the feedback loop between a predator’s teeth, the camouflage of its prey and their environment in order to understand the dynamic processes in operation. This means that, just as in biological evolution where it is not individual organisms but species that evolve, it is classes of buildings that evolve and not individual rooms or buildings. So too, just as it is not the parts of the biological organisms or individual traits that evolve, it is not individual components or features of buildings that evolve. A multi-scalar Neo-Darwinian approach to examining change in classes of buildings contrasts to a Lamarckian or deterministic approach, which makes the general assumption that change in the material components of human behaviour is brought about by the perceived ability of humans to make conscious adaptations to changing circumstances. That is, humans are deemed to be able to make choices in the present, based on their immediate and anticipated future circumstances, and to generate variants that ‘fit’ future circumstances. Change is thus seen as a direct cause and effect process controlled by humans. It is approached from a reductionist point of view, as the specific effect of a specific cause, with which it has a direct and full relationship. Such an approach has been rejected by Neo-Darwinian archaeology (ref. e.g. Teltser 1995: 6; Dunnell 1996: 89; O’Brien 1996b: 22; Rindos 1996: 153; McGlade and van der Leeuw 1997a; van der Leeuw 1998c) because it cannot be assumed that the reason a thing came into being is synonymous with the reason it continued to exist long enough for it to make its way into the archaeological record (O'Brien & Lyman 2002: 64-65) and because the processes involved are vastly too complex to reduce to simple cause and effect explanations (van der Leeuw 1998c: 46-49).

ARCHAEOLOGICAL APPROACHES TO THE EVOLUTION OF CLASSES OF BUILDINGS Neo-Darwinian studies that have examined aspects of evolutionary change in buildings are currently small in number (O'Brien et al. 1980; O'Brien & Lewarch 1984; Graves & Ladefoged 1995; Harmon 2001) and few have taken a multi-scalar, uniformitarian approach to study of the built environment, to locate the boundary conditions within which built environments are likely to operate. There are currently none that have examined the thermal performance of buildings or boundary conditions within which thermal systems are likely to operate. As a result, no systems-level models for long-term change in classes of buildings have been thoroughly explored. The work of various Neo-Darwinian archaeologists, such as Fletcher, van der Leeuw and McGlade, and Allen, who have examined systems-level long-term change at the scale of settlements has, however, indicated in which direction such a model might go (discussed below in ‘Complexity Theory and Cultural Evolution’).

Lamarckian approaches to change in classes of buildings ask the questions ‘what do buildings reflect?’ and ‘to what do they correlate?’ They generally fall into three categories. First, there are those that track cultural change as a correlate of change in the shapes of buildings: culture can be read from the buildings (Glassie 1975; Brown 1990; Glassie 1990; Hingley 1990; Sanders 1990; Rapoport 1993; Kapches 1994; Rainsbury Dark 1994; Matson 1996; Warrick 1996; Gibbs 1998; Ainian 2001; Hellstrom 2001). Secondly, there are those that assume a direct correlation between the social aspects of culture and building shape: buildings are an extension of social cognition (Connah 1978; Hillier & Hanson 1984; Bailey 1990; Chapman 1990; Kent 1990; Rapoport 1990; Scott 1990; Alcock 1994; Schofield 1994; Barrett 1997; Knights 1997; Parker Pearson & Richards 1997a & b; Cameron 1998; Dietler 1998; Hanson 1998). Thirdly, there are those that focus on the mechanical functionalist aspects of buildings: buildings are an expression of deemed numbers of occupants, raw materials availability, of technology, of materials costs, of labour cost etc. (Hassan 1982; Abrams 1995; Hayden 1996).

Neo-Darwinian theory states that it is systems that evolve, not traits (Gould & Lewontin 1984). The corollary of this is that it is only when the operation of one building is viewed relative to another within an overall built environment and over an extended period of time that the evolutionary patterns within individual classes of buildings are made apparent. For example, the importance of single roomed or multi-roomed buildings or, rather, the rooms of which they are composed, only becomes apparent when their evolutionary patterns are viewed in context with each other and to their environment. A biological analogy to this would be the

None of the deterministic or reductionist approaches ask what does the material itself do? That is, does it possess 57

The Evolution of the Built Environment ‘pithouse’-to-’pueblo’ transition. A multi-scalar NeoDarwinian approach, however, whilst not necessarily being able to account for the cause of specific examples of change, can account for long-term outcomes. That is, the long-terms consequences of the change having occurred. This is because Neo-Darwinian theory is founded on discerning macro-scale and multi-scale evolutionary processes, of uniformity of process rather than product. A multi-scalar Neo-Darwinian model is thus able to define boundary conditions, prescribed by the long-term action of selection and within which microscale change has occurred.

properties and behaviours independent of humans and their thought processes, as would be expected in the case of slowly altering artefacts such as classes of buildings, for change within the high inertia built environment can far exceed the life span of individual humans and thus a direct reductionist cause and effect explanation cannot possibly encompass all the processes and factors involved. That is, if humans had been responsible for directing the course of change in the past from one class of building to another over thousands of years, they must therefore possess the ability to continue to do so into the future. If humans have truly been responsible for creating long series of buildings that were ‘ideal’ for their place and time, then it would be an easy matter to ascertain what it is about the ‘ideal’ building forms that made them ideal. The explanation would, however, have to account for all buildings, for a deterministic explanation is only as relevant as the number of entities it can account for. An ‘ideal’ building is only as ideal as the number of buildings that conform to its kind. The more variation within the group and the more buildings that deviate from the ‘ideal’, the less explanatory power the concept of the ‘ideal’ possesses. The archaeological record, however, abounds with anomalous buildings (non-‘ideal’ buildings that exist contemporaneously with ‘ideal’ buildings) and only a uniformitarian approach has the explanatory power to account for the co-existence of variant buildings, for variation is a fundamental component of evolving systems.

This does not mean, however, that humans have had no role to play in their own long-term evolutionary outcome, for selection must have raw material to work with and it is culture, the choices and actions of humans, that, in part, constitute the raw material. The wider the range of variation that selection has to work with, the greater will be the likelihood that traits will be selected for that enhance the fitness of the system. Neo-Darwinian archaeology cannot, however, rely on a straight one-to-one borrowing of biological evolutionary theory to account for cultural evolution. This is because, unlike biological replication, culture encompasses diverse rates of transmission and modes of replication, which are relative to the degree of inertia in the system (verbal, social and material) and that a direct biological model cannot account for. That is, cognitive processes by themselves cannot account for cultural transmission and, ergo, memes cannot be used as a cultural equivalent of genes. Cultural evolutionary models must therefore develop methods that can explain macro-scale process that is predicated on micro-scale phenomena and contradiction, for the way in which the contradiction inherent within complex systems is resolved will ultimately define the long-term evolutionary path of the system.

SUMMARY People perform actions, make choices and transmit information to others, but they cannot foresee what the long-term outcome of their actions, choices and teaching/learning will be. Deterministic Lamarckian explanations cannot account for instances of paradigmatic change from one class of building to another that are evident in the archaeological record, such as the

58

CHAPTER 4 – Complexity Theory and the Approach Taken by the Study

there have been definable boundary conditions delimiting the robust operation of the thermal performance of classes of buildings over time.

INTRODUCTION “Viewed on the most general level, living systems – cells, organisms, economies, societies – may all exhibit lawlike properties, yet be graced with a lacework of historical filigree, those wonderful details that could easily have been otherwise.” (Kauffman 1995: 19)

CHARACTERISTICS OF COMPLEX SYSTEMS The three scientific laws of motion and the law of gravitation were, until relatively recently, thought to be sufficient for explaining all that happens in nature. These laws were compiled by Newton in the 1660s and are still used today to predict the positions and properties of objects in motion. The second law was considered to be particularly powerful as it states that entropy in a closed system will inherently increase and that objects will ultimately disperse into the most improbable arrangement, although this ran counter to the general observation that regularities do appear in nature in such things as the seasons and the migratory habits of birds. The inability of these laws to explain all that happens in nature was assumed to be due to an inability to reduce what happens in nature to ever finer levels of knowledge, such that nature could be reduced to its basic operational rules. It is now recognised that these laws are applicable to only simple mechanical systems and closed systems, systems within which energy and matter are contained and isolated from their surroundings. Systems where nothing gets in and nothing gets out. They have no explanatory power when dealing with real life systems because nature is an open system. It is a system into which, and out of which, energy endlessly and consistently passes.

Whilst social theory seeks to explain why people do what they do, a uniformitarian approach to Neo-Darwinian cultural change seeks to account for the contingent outcomes of what people do, and a branch of science known as Complexity theory aims to explain why the contingent outcomes of what people do exhibit selforganised behaviour, for it has been observed that various cultural systems, such as economic systems, the stock market and the Internet, are robust to changing circumstances within definable boundary conditions. With regard to past change in classes of buildings, social theory seeks to explain why people changed their buildings as they did, but Neo-Darwinian theory places the change in a historical (evolutionary) context. When studied with a uniformitarian approach, Complexity theory can define the boundary conditions that de-limit the robust operation of the thermal environments associated with classes of buildings. The study has adopted the view that culture, and the thermal performance of buildings specifically, must be examined in terms of their being complex systems, for culture and thermal systems are complex systems in just the same way that the weather, ecological systems, economic systems and the Internet are complex systems. These systems cannot be reduced to deterministic explanations of their behaviour, but nor can selective forces alone account for behavioural consistencies that have been observed at diverse scales of examination. They each are the result of the interactions and feedback between their numerous individual components, a process that produces a potentially infinite range of outcomes that are highly sensitive to small changes in their initial conditions, such that an infinitesimally small change can result in widely varying outcomes over the long-term. This makes complex systems computationally intractable and indeterminable at a finite scale and over the longterm. Yet Complexity theory, using the tools of mathematical modelling and engineering-analysis, has empirically defined behavioural consistencies that can be accounted for by relatively straightforward and logically analysable processes. The study intends to examine the thermal performance of buildings to ascertain whether or not the same behavioural tendencies have existed in classes of buildings over time. That is, whether or not

Complex systems are not currently as well understood as simple mechanical systems and closed systems because of their preclusion to a reductionist approach. There are two reasons for this preclusion. The first is that there is a fundamental indeterminism at the subatomic level of complex systems and the second is that complex systems possess an ultra sensitivity to their initial conditions, such that an infinitesimally small change in the initial conditions can potentially completely alter the system’s trajectory of change (Kauffman 1995: 16-17). That is, it is impossible to have a sufficiently detailed understanding of the operations within complex systems such that the final outcome can be predicted, or the way in which change in the detailed structure of the system can be predicted. “Simplicity breeds complexity through sheer multiplication of possibilities” (Cohen & Stewart 2000: 219). This is known as the ‘butterfly effect’, for it is potentially possible for a butterfly to flap its wings in Peking and, a month later, generate storms in New York (Gleick 1988: 8-31).

59

The Evolution of the Built Environment For want of a nail, the shoe was lost; For want of the shoe, the horse was lost; For want of the horse, the rider was lost; For want of the rider, the battle was lost; For want of the battle, the kingdom was lost; And all for the want of a horseshoe nail. (‘Tremendous Trifles’ by Henry Van Dyke).

frequency of avalanches of a particular size will be proportionate with the size of the avalanche. This power law is an emergent property of sand piles and it is known as self-organised criticality. Other systems in nature also appear to follow power laws and the behaviour of selforganised criticality, such as the size distribution of earthquakes, the flooding of the Nile and species extinction events (Kauffman 1995: 235-241) (Fig. 4.1).

The only way to discern long-term or detailed change in a complex system is to actually run the system in real time. The pathway of change cannot be reduced to an algorithm, as mechanical systems can be, because the shortest algorithm will run at real time speed. In this way complex systems are computationally intractable at the scale of fine detail within the system (Kauffman 1995: 153-154).

Stasis and Punctuation Both stasis and punctuation appear to be normal behaviours of complex systems. “Gradual changes most of the time, with sudden occasional changes, is the norm for selforganizing [complex] systems. What we do not see in the mathematics is the existence of two separate kinds of system – one that never changes in any important manner, and another that, when it changes, changes dramatically. Nor do we see two different kinds of cause, little ones having small effects and big ones having dramatic effects. Instead we see one system with two different types of response. Behind the debate between the gradualists and the punctuationists in evolution is a shared assumption: that these two types of evolutionary change must arise through very different internal mechanisms and from very different underlying principles. The two schools think they can’t both be right. In fact, whatever the merits of their approaches, this shared assumption is wrong. The simplest mathematical systems demonstrate that exactly the same internal mechanism typically leads to both types of change.” (Cohen & Stewart 2000: 333-334)

Figure 4.1. Life span of marine fossil vertebrates and invertebrates that follows a power law (Kauffman 1995: 240) Self-organised criticality thus involves issues of scale of observation and analysis. If the sand pile were observed from a great height, the detailed behaviour would be lost in the over-riding observation that the sand pile is growing ever larger, but at the scale of an ant attempting to climb to the top of the pile, the detailed behaviour is omnipresent (Fig. 4.2). However, whilst the macro pattern of change provides the context within which the detail can be observed, no amount of understanding of the detail can be used to extrapolate the macro pattern. Both macro and micro scales are inter-related, but a finer and finer knowledge of the detail can only ever provide a finer and finer knowledge of the detail, not of the overall pattern of change. A change that might appear to be abrupt or punctuated in geological or evolutionary time can potentially have been produced by a number of different types of detailed changes, but the specific nature of the details do not alter the macro pattern (Fig. 4.3).

The issue of stasis versus punctuation involves issues of scale. Complexity theory uses the analogy of sand being poured onto a sand pile at a constant rate to illustrate the behaviour of stasis versus punctuation (Kauffman 1995: 28-29; Bak 1996). The sand pile will rapidly grow to a size where intermittent avalanches occur. Many avalanches will be small and a few will be large. There is no way to predict precisely when the avalanches will occur, or what size they will be. There are, however, two consistencies to note. The first is that an avalanche is as equally likely to be small as it is to be large and the second is that the overall behaviour appears to be regular. If the size of each avalanche were plotted against the number of avalanches of each size on an x-y axis, the result is a curve that follows a power law. That is, the

Figure 4.2. Evolution observed at macro and micro scales (Dennett 1995: 284) 60

Complexity Theory and the Approach Taken by the Study known as ‘antichaos’ or the ‘self-organisation of complex systems’. “The past three centuries of science have been predominantly reductionist, attempting to break complex systems into simple parts, and those parts into simpler parts. The reductionist program has been spectacularly successful, and will continue to be so. But it has often left a vacuum: How do we use the information gleaned about the parts to build up a theory of the whole? The deep difficulty here lies in the fact that the complex whole may exhibit properties that are not readily explained by understanding the parts. The complex whole, in a completely nonmystical sense, can often exhibit collective properties, ‘emergent’ features that are lawful in their own right” (Kauffman 1995: vii-viii) (see also Gould 2002: 714-719).

Figure 4.3. Various ways of interpreting detailed change within macro-scale events (Dennett 1995: 286)

Being fully aware of the limitations on making detailed predictions about the behaviour of complex systems, Complexity theory is predicated on explaining the operational uniformities that have been observed within complex systems and on empirically modelling the uniformitarian processes using a wide range of collaborative methodologies. “After all, what we are after here is not necessarily detailed prediction, but explanation. We can never hope to predict the exact branchings of the tree of life, but we can uncover powerful laws that predict and explain their general shape” (Kauffman 1995: 23). Complexity theory seeks to locate the strange attractors within complex systems. That is, the operational uniformities that, whilst not being discernible to within a finite scale of detail, are predictable to within set parameters or boundary conditions (Fig. 4.4) (Gleick 1988: 133-138). Such systems often exhibit self-similar behaviour, which is behaviour that is broadly similar through various scales of detail. The best known example of self-similarity is the Mandelbrot Set. This is a set of numbers produced by infinite iterations of a simple mathematical algorithm, the result of which is a complex and unpredictable mathematical pattern within which self-similarity occurs (Figs. 4.5 and front cover) (Gleick 1988: 115-116).

COMPLEXITY THEORY “The particular branchings of life, were the tape played again, might differ, but the patterns of the branching, dramatic at first, then dwindling to twiddling with details later, are likely to be lawful. Biological evolution may be a deeply historical process, as Darwin taught us, but lawlike at the same time.” (Kauffman 1995: 14) If complex systems, such as culture and thermal performance, cannot be reduced to predictive sets of rules, is it possible to study them scientifically, to set up hypotheses and test them empirically? And how can the hypotheses be tested except via first making predictions about specific outcomes? Recently a number of academic institutions have been set up specifically to investigate complex systems from a non-reductionist, uniformitarian perspective predicated on scientific methodology. These include the Centre for Complex Systems at the University of Illinois and the Santa Fe Institute in New Mexico, although Complexity theory is certainly not limited to these institutions. One of the key thinkers of the Santa Fe Institute is Stuart Kauffman, who has pioneered a concept

Figure 4.4. A strange attractor at (a) one orbit, then (b) ten, then (c) one hundred, then (d) 1,000 and the Poincaré section through (d) (Gleick 1988: 143)

Figure 4.5. Self-similarity at different scales within the Mandelbrot set (Gleick 1988: 195) 61

The Evolution of the Built Environment Complexity theory holds that selection cannot have worked alone to create the order that has been observed in the complex system of nature. Selection accounts for particular contingent histories, and contingency matters, but contingency cannot account for the operational regularities observed at diverse scales of observation. The biologist and Nobel Prize winner, Monod (1971), has referred to contingency as ‘chance caught on the wing’ and equated evolution to the perpetual tinkering of a bricoleur (a ‘satisficer’) who continually tinkers, producing “ad hoc solutions to historically mandated design problems” (Kauffman 1995: 98). According to Complexity theory, however, humans are not here only because of contingency. We are here because order and self-organisation are inevitable. If selection were the only means by which fitter species were produced, then complex species would be exponentially less likely to have evolved than simpler species. In complex systems the ‘butterfly effect’ has potentially disastrous ramifications if it goes wrong and it is exponentially more likely to go wrong as the system becomes more complex (Kauffman 1995: 154-157). Yet complex systems have evolved and have continued to increase in number and degree of complexity. Biological species, ecosystems, culture, economic systems, and buildings have become more complex over evolutionary time.

possessed by democratic Athens over oligarchic Sparta in the Archidamian War and that enabled the Athenians to defeat the Spartans on at least two occasions. Their advantages of military innovation and adjustability, which gave them an operational advantage during the war, were, however, only one component of a social system that appears to have been adjustable at all levels of organisation, principally due to the way in which competing factors were able to be resolved. “Democracy has evolved as perhaps the optimal mechanism to achieve the best attainable compromises among conflicting practical, political, and moral interests” (Kauffman 1995: 5). Self-organisation is thus related to the way in which numerous competing factors are resolved, contradictions that inherently exist in complex systems.

Figure 4.6. The ‘drunkard’s walk’ (after Gould 2002: 901)

Kauffman and Complexity theory argue that selection operates in conjunction with self-organisation (Kauffman 1995: 8, 185). “Natural selection is important, but it has not laboured alone to craft the fine architectures of the biosphere, from cell to organism to ecosystem. Another source – self-organization – is the root source of order. The order of the biological world is not merely tinkered, but arises naturally and spontaneously because of these principles of self-organization – laws of complexity that we are only just beginning to uncover and understand” (Kauffman 1995: vii). Self-organisation refers to the concept that complex systems that evolve towards the region ‘poised’ between a chaotic regime and an ordered regime are more likely to prevail, because they will be more robust to change. Gould has drawn the analogy between this principle and ‘the drunkard’s walk’ whereby a drunken person leaving a bar and walking along a pavement will continue their perambulation only as long as they don’t bump into either the buildings on one side (the ordered regime) or the gutter on the other side (the chaotic regime), in which case they will fall over (Fig. 4.6). The walls and the gutter both represent boundary constraints. The drunk can only continue to move forward within the boundary conditions of the pavement, although within this region of definable limits their exact behaviour and meanderings are unpredictable. Kauffman (1995: 258) has equated the Stalinist system of government with an ordered regime, or steady state, and the Leftist Italian system with a chaotic regime (Fig. 4.7). A democratic system operates within the boundary conditions set by these two extremes and inhabits this region as a result of the existence of, and resolution of, numerous competing factors. Josiah Ober (2006) has articulated this concept in relation to the advantages

Figure 4.7. Bifurcation diagram showing steady states through to chaotic states (Gleick 1988: 71)

FITNESS LANDSCAPES Before Complexity theory is discussed in detail, it is first necessary to describe the commonly used analogy of fitness landscapes, made up of peaks and valleys, which scientists use to visually describe the process of evolving populations striving to reach the peak of their potential fitness, which they do under the action of natural 62

Complexity Theory and the Approach Taken by the Study long periods of time have prevailed by keeping pace with the changing contextual circumstances in which they reside. That is, with their ecological, climatic, and/or social environments. Those that died out were ultimately unable to evolve fast enough to maintain their relative position. It can happen that the environment changes so fast that few species will be able to evolve sufficiently fast to survive. No species, for example, can evolve fast enough to keep pace with an environmental catastrophe such as a meteorite strike or tsunami, which are in essence no more than very, very fast environmental changes. A population’s chances of survival are inherently linked to scales of change.

selection (e.g. Wright, S. 1932; Futuyama 1986: 172; Kauffman 1993: 39-67, 1995: 149-271; Juarrero 1999: 151-162, 176-178; Gould 2002: 1176-1177). Fitness landscapes are a visual metaphor for a population’s contextual circumstances. A population at the height of its potential fitness is said to be at the top of a peak. However, the heights of the peaks are relative, with some peaks higher than others (Fig. 4.8). Therefore, the organisms on top of the highest peaks will be fitter than the organisms on top of lower peaks, even though both are at the peak of their potential fitness (Futuyama 1986: 172; Kauffman 1995: 154). Arriving at the top of the tallest peak does not, however, ensure indefinite survival because populations have no control over the landscape they reside on. Fitness landscapes alter as the status quo changes. An organism at the top of a peak at one moment may find itself reduced to a valley position the next, or vice versa, as the contextual circumstances change and for which populations cannot plan, being incapable of foretelling the future. They cannot ever rise above the landscape in order to view it from above so as to seek out the highest peak and the path they might take to scale it. They can only ever view the landscape and make actions based on their local position. This holds true for biological species as well as for humans for “despite the fact that human crafting of artifacts is guided by intent and intelligence, both processes confront problems of contradictory constraints… Much of technological evolution results from tinkering with little real understanding ahead of time of the consequences. We think; biological evolution does not. But when problems are very hard, thinking may not help very much. We may all be relatively blind watchmakers” (Kauffman 1995: 202).

A fast contextual change, either environmental or ecological, is generally associated with a shifting status quo. The arrival of new species into an existing system, whether from outside the system (as immigrants) or inside the system (as mutations), will disrupt the status quo. Each species, whether existing or new, must then evolve to keep pace with changed circumstances or they will die out. If such a change occurs, the populations that are most likely to survive and prevail will be those that can “melt off the local peaks they have become fixated on and flow along ridges toward distant regions of higher fitness” (Kauffman 1995: 27) (Fig. 4.9). Contextual changes that alter the status quo have been associated with bursts of new descendant species (the mechanism by which species ‘absorb’ contextual change) and the palaeontological record is rich in examples. Contemporary examples also exist. The species diversity within the Chernobyl nuclear disaster radiation fallout zone appears to be higher to date than it was prior to the explosion (Baker, et al. 1996; Hopkin 2005). At a broad operational scale this disaster equated to a sudden contextual change. The status quo was destabilised, thus allowing diverse species to populate niches that had been vacated by the previous resident species, creating new niches in the process. This phenomenon has been termed punctuated equilibrium and refers to the rapid ‘offshooting’ of new and varied descendant species from ancestral species. That is, with evolutionarily-short, concentrated bursts of rapid species transformation, represented by the propagation of new and varied descendant populations (Gould 1982; Gould 1989) (Figs. 4.10-4.11). Each burst of speciation has generally been followed by periods of dwindling alteration of the new species, until such a time as the process starts over again (Kauffman 1995). Evolution thus favours the most efficient ‘absorbers’ of change, the evolutionarily robust systems, because they are able to absorb change without undergoing major system-level alteration. They are structurally stable when subjected to many detailed alterations (Kauffman 1995: 187-189; Jen 2005b). In discussing how ‘optimization for the moment and adjustability for future change’ might offer an evolutionary advantage, Gould states that the advantage is not only “measurable in terms of capacity for success in the face of future environmental changes … [but that] such capacities can also evolve by direct selection, at a higher level, for species-individuals who win differential

Figure 4.8. Fitness landscape showing evolutionary paths of various subspecies (Dennett 1995: 258) The metaphor of ‘survival of the fittest’ or, rather, ‘survival of the strongest or fastest or smartest’ thus becomes ‘survival of the most in-sync with a consistently changing set of circumstances’ (Mayr 1982: 589). When the contextual circumstances change, as they inevitably will, only those populations most capable of adjusting to the new conditions within an appropriate timeframe will prevail. This implies that species that have survived over

63

The Evolution of the Built Environment reproductive success by their propensity for living through external crises that consign closely related species-individuals to extinction” (Gould 2002: 1214). In other words, species and systems that incorporate adjustability are more likely to be robust to contextual changes and this property is measurable in terms of those populations and/or systems that survive rapid or profound moments of change, as well as survive long-term, as the long-term inevitably encompasses change.

saturated with peaks that are each equally likely to be the highest. It can be visualised as a Himalayan landscape, where the local peak has an infinitely small chance of being the highest peak. A landscape that is halfway between ordered and random is one where the system cannot see the path to maximum fitness but, as Kauffman has postulated, the local peak is likely to be the highest peak because the largest peaks draw from the widest basins (Kauffman 1995: 171-178). On this type of landscape (rugged, but correlated) compromises are made, but not quickly and not easily, and it becomes exponentially hard to make better and better choices. Such systems have a high likelihood of possessing a high fitness level, although the closer they get to their fitness peak the exponentially hard it becomes for them to reach it, because the optimal search distances within the space of possible improvements exponentially reduce as fitness increases (Fig. 4.12).

Complexity theory views fitness landscapes as ranging in type from ordered to random. An ordered landscape is one where a population or system has only one path upward from which to choose. It is usually visualised as a Fujiama landscape, where the local peak is the highest peak because it is the only one within sight. This peak is, however, unlikely to be a tall peak. A random landscape is one where a population or system cannot ever find the path to maximum fitness because the landscape is

Figure 4.9. A system fixed on a local peak (Kauffman 1995: 257)

Figure 4.10. Punctuated evolution in Paleozoic foraminiferans (Hamada 1991: 22. Reproduced with kind permission of Springer Science and Business Media)

Figure 4.11. Rapid early morphological change followed by stasis in lungfishes (after Westoll 1949)

Figure 4.12. Capacity to improve fitness vs. fitness (Kauffman 1995: 198)

64

Complexity Theory and the Approach Taken by the Study as the connections between the entities increases, which is done by connecting entities to an ever increasing number of other randomly selected entities, the entities become intercorrelated. The state of each connected entity is thus affected by the states of the other connected entities plus its own, many of which will be contradictory. If the number of connections between the entities is very high (K=a number close to or equal to N), the landscape generated will be chaotic and random (a Himalayan landscape), because the overall number of contradictory constraints is infinitely high. The contradiction will be infinitely high if N is large because there will be an infinite number of factors influencing the state of each entity and a large percentage of these will be contradictory. Entities cannot settle into a robustly stable state because they are being consistently bombarded with contradictory input. “Increasing K increases the conflicting constraints. In turn, the increase in conflicting constraints makes the landscape more rugged and multipeaked. When K reaches its maximal value (K=N-1) in which every gene [entity] is dependent on every other), the landscape becomes random” (Kauffman 1995: 192).

CONTRADICTION AS MECHANISM FOR SELF-ORGANISATION Complexity theory has demonstrated empirically that natural selection appears to favour those systems that operate within a region bounded by chaotic states and ordered states. An ordered state results when compromise solutions to contradictory criteria are found too early and too easily, making it more likely that poor choices are made. A chaotic state results when compromise solutions to contradictory criteria are never found, or if they are, the result doesn’t last long enough to work back into the system and effect change. In both cases, a lower fitness state results and there is a consequentially high likelihood that the system or population will go extinct. Selforganised systems reside between these two states where compromises are made but not easily and not quickly. Consequentially, there is a high likelihood that good choices are made, resulting in a relatively high level of fitness and high likelihood that the system will prevail (Kauffman 1995: 28).

The NK Model One of the core mathematical models developed by Kauffman is the NK model (1995: 170-177). It was developed to investigate the factors that create different types of fitness landscapes and, hence, the factors that potentially affect the degree of evolutionary ease or difficulty that systems experience on different landscapes. The NK model is able to be tuned to different levels of landscape ruggedness by tuning (increasing or decreasing) the number of components and/or the number connections between the components. This thus tunes the level of complexity of the coupling effects, with the result that the level of contradictory constraints operating on each entity becomes a factor of the landscape that it inhabits. “Changing the level of conflicting constraints in the construction of an organism [or system] from low to high tunes how rugged a landscape such organisms can explore” (Kauffman 1995: 187). The model has empirically demonstrated that systems ‘poised’ between chaos and order are more likely to persist. Fig. 4.13 shows evolutionary persistence of (b) systems ‘poised’ between chaos and order, and evolutionary demise of (a) ordered systems and (c) chaotic systems. Systems ‘poised’ between chaos and order will be more likely to persist.

Figure 4.13. Evolutionary persistence between chaos and order at (b) in the NK model (Kauffman 1995: 230) If the level of contradiction is too low, the system must evolve on a smooth landscape (Kauffman 1995: 169180). This represents an ordered regime. Ordered regimes are conservative, restricted, inflexible, non-adjustable, cannot withstand change and cannot withstand competition. They exist where the number of connections between the different parts of the system begins to approach zero. These systems possess low potential fitness. If, on the other hand, the level of contradiction is too high, the system must evolve on a random landscape. Resolutions are rarely settled on and if they are then whether they are good or bad they do not exist within the system long enough to work back into the system to influence the ‘next generation’. This represents a chaotic regime. Chaotic regimes possess no heritage, fluctuate randomly, fluctuate at the slightest change in contextual circumstances, cannot withstand change for very long and will ultimately not last long. These systems too possess low potential fitness. They exist where the number of

The process works as follows. A number of entities (N) is set up with a certain number of correlated connections between them (K). The state of each entity is interdependent on the states of the entities with which they are connected in an interconnected loop (an ‘epistatic’ coupling). An entity that is connected to no other entities remains unchanged, even if other entities in the array should change. Even if the array is large, if the connections between them are few (K=a small number relative to N), the landscape generated will be ordered (a Fujiama landscape), because the overall number of contradictory constraints is small relative to N. However, 65

The Evolution of the Built Environment connections between the different parts of the system begins to equate to the number of parts in the system (Kauffman 1995: 169-180) (Fig. 4.14).

system, increasing the overall fitness. Such a regime is self-organised, possesses heritage, possesses variety and diversity, is flexible and adjustable, can withstand change and is ultimately robust in the face of changing circumstances and competition. Rugged correlated landscapes appear where the number of connections between the different parts of the system is poised between chaotic and ordered states (Kauffman 1995: 177178). “Both biological evolution and technological evolution are processes attempting to optimize systems riddled with contradictory constraints. Organisms, artifacts, and organizations evolve on correlated but rugged landscapes” (Kauffman 1995: 179). Conflicting constraints occur at diverse scales of detail, from internal (as engineering constraints) to external (as coevolution amongst species), as a consequence of their number of components and the level of interaction (interconnectedness) between them. The more components within the system and the higher the level of interaction, the higher the conflicting constraints and the more likely the system is to be chaotic. For example, the model has been used to simulate the coevolutionary behaviour amongst four, eight and sixteen species (Fig. 4.16). With four species the system eventually becomes stable, but with eight and sixteen species chaotic ‘red queen’ behaviour is still observed after 8,000 generations (Kauffman 1995: 227). However, if the value of K is allowed to alter between generations the coevolutionary behaviour appears to eventually converge on a common region of K that falls between chaotic and ordered states, the average fitness increases and the potential for the system to persist increases (Fig. 4.17).

Figure 4.14. Increase and then decrease in fitness levels with respect to increasing K values in the NK model (Kauffman 1995: 229) However, a system that lies at the border between the ordered and the chaotic regimes evolves on a rugged landscape where good resolutions are found, though not easily and not rapidly. In fact, the good resolutions become exponentially hard to find and the level of difficulty appears to follow a power-law (Kauffman 1995: 203-205). Whilst the relative speed at which change occurs relative to K varies, the waiting time to locate a fitter neighbour increases and the number of fitter neighbours decreases as fitness increases irrespective of the K values (Kauffman 1995: 179) (Fig. 4.15). When good resolutions are found they will work back into the

Figure 4.15. (a) increased waiting time to locate a fitter neighbour and (b) decreased number of fitter neighbours relative to increasing fitness over time (Kauffman 1995: 179)

Figure 4.16. Coevolution among (a) four, (b) eight and (c) 16 species (Kauffman 1995: 227)

66

Complexity Theory and the Approach Taken by the Study snowflakes, pinecones, the Mandelbrot set, the Great Red Spot on Jupiter, the Internet, the stock market and economic trends (Gleick 1988: 83-153; Erwin 2005; Willinger & Doyle 2005). Whilst the implications of Complexity theory and the dynamics of self-organising complex systems have been largely overlooked in archaeological studies, these represent an approach that has the potential to explain and quantitatively model large scale and long-term cultural behaviour. Various biologists have extensively articulated the concept that human culture might be subject to the same evolutionary uniformitarian behaviour as biological species, because they both evolve as complex systems (ref. e.g. Dennett 1995; Kauffman 1995). Both social and biological systems possess inherently contradictory constraints and Complexity theory has demonstrated empirically that the systems that have failed to resolve these contradictions, or that haven’t resolved them well, are more likely to have become extinct, whereas those that have persisted are more likely to have incorporated greater variation and system-level adjustability.

Figure 4.17. Convergence to a common region of K (Kauffman 1995: 232)

COMPLEXITY THEORY AND CULTURAL EVOLUTION “The parallels between branching evolution in the tree of life and branching evolution in the tree of technology bespeak a common theme: both the evolution of complex organisms and the evolution of complex artefacts confront conflicting ‘design criteria’… Conflicting design criteria, in organism or artifact, create extremely difficult ‘optimization’ problems – juggling acts in which the aim is to find the best array of compromises.” (Kauffman 1995: 14)

Given that optimum contradiction resolutions become exponentially hard to find, often the best solutions are those that embrace great change. Whilst great change can potentially lead to disaster, it can just as likely lead to great success. The systems that will ultimately be successful will be those that are robust to change such that they can absorb changing circumstances and will not be structurally destabilised by them. The mechanisms for this are greater levels of variation and system-level adjustability. “To engage in the Darwinian saga, a living system must first be able to strike an internal compromise between malleability and stability. To survive in a variable environment, it must be stable, to be sure, but not so stable that it remains forever static. Nor can it be so unstable that the slightest internal chemical fluctuation causes the whole teetering structure to collapse” (Kauffman 1995: 73, emphasis in original). Self-organised systems appear to evolve at the border between destructive chaos and paralysing order, where systems are robust to change.

The early attempts by Neo-Darwinian archaeology to derive ‘universal laws’ failed because the laws did not do what they purported to be able to do, which was to predict specific pathways of change and the detailed forms of objects. The current Neo-Darwinian approach, however, may have overshot the mark in rejecting the possibility that laws per se might exist, for a ‘polyocular pluralistic approach’ “recognizes process and event as complimentary aspects of human history and tries to steer a course between oversimplification which is inherent in so-called ‘laws of human behaviour’ and the limitations of extreme contextualism” (McGlade & van der Leeuw 1997: 2). The approach that “there are no laws, except those of historical contingency, that govern human behaviour” (O'Brien 1996a: 12, emphasis in original) has led to the current interest in cladistics and phylogenetic lineages as a means of interpreting past social behaviours. This is an approach that suffers from ‘the limitations of extreme contextualism’. It has led the exponents down the memetics pathway and diverted attention away from looking at the material as a player in its own right within the complex system that is culture. Cladistics may be a useful tool for representing and understanding past contingent pathways of change (ref. e.g. Graves & Ladefoged 1995), but the ‘laws of contingency’ (O'Brien & Holland 1992: 37) cannot explain the existence of spontaneous order within realms of chaos that are evident in the natural and the cultural worlds, in such things as

Therefore, the essential mechanism for self-organisation appears to be good contradiction resolution. Systems that are either not subject to contradiction or that cannot resolve them adequately do not evolve and will eventually go extinct. On the other hand, systems that are subject to too much contradiction, anarchical systems, likewise do not evolve. The systems that evolve are those in which the contradiction is resolved to the mutual ‘satisfaction’ of the greatest number of components. “Conflicting design criteria, in organism or artefact, create extremely difficult ‘optimization’ problems – juggling acts in which the aim is to find the best array of compromises. In such problems, crudely designed major innovations can be greatly improved by dramatic variations on the new theme” (Kauffman 1995: 14). Thus adjustable systems, in which variation can proliferate, have an evolutionary advantage over less adjustable or more restricted systems. Such systems are more likely to 67

The Evolution of the Built Environment find compromise solutions to the contradiction that will send the system off in a new direction and into a new ‘fitness landscape’. Upon entering the new landscape, the system is unlikely to be the ‘fittest’ or the most well adapted system. It will not occupy a peak position, which is reserved for the specialists and for those systems that are most perfectly synchronised with the current (though ever-changing) landscape. An adjustable and robust system will, however, be capable of moving easily through the landscape, absorbing the contextual changes it encounters. It will be able to ‘melt off’ its peak and flow over the landscape’, unlike its temporarily ‘fitter’ competitor that must first dismount its peak, a journey from which it is unlikely to survive if the distance to the top is very great (Kauffman 1995). If, then, contradiction is the mechanism for self-organisation, what is the nature of the contradiction that orders cultural evolution? Kauffman has touched on this but does not make a quantitative inquiry. Fletcher (1995, 2004), van der Leeuw and McGlade (1997, 1998b, d & e) and Allen (1983, 1997) have, however, examined self-organisation in culture at the level of the interaction between the social and settlement systems and have offered insights into the nature of robust self-organised social systems.

agrarian urban and industrial urban settlements appear to have been characterised by the enhancement of prerequisites for the transition that arose randomly in the preceding phase (Fletcher 1995: 187). These boundary conditions to the development of settlements appear to form the basis of a power law. Fletcher has found that there have been proportionately many more settlements with fewer residences than settlements with more residences and also that intra-site variation in the dimensions of the spatial units (such as room width, room length etc.) has increased relative to the increase in number of residences (Fletcher 1986) (Fig. 4.18). Likewise there have been proportionately many small, long-lived dense urban settlements and few larger, longlived dense urban settlements (Fletcher 2004) (Fig. 4.19). Fletcher has argued that this is a factor of the way in which contradictions between the material and the social have been resolved and that manifested as diverse means of communication and transmission (Fletcher 1995, 1996, 2004). This can therefore be deemed to have a direct influence on the evolutionary pattern of settlement systems, which appears to have occupied the space at the ‘border between chaos and order’ (Fletcher 2004: 128). That is, settlements appear to have evolved at the border between increasing stress levels and increasing frequency of occurrence of number of settlements (Fletcher 1995: 106) (Fig. 4.20).

Fletcher has specifically examined the contradictions that inherently exist within settlement systems as a result of the differential rates of social and material replication and found that the faster replicating social is potentially at odds with the slower replicating material of the built environment. His model examines the boundary conditions within which settlements are able to function, in terms of viable social interaction and communication, and encompasses diverse temporal and spatial scales (Fletcher 1977, 1995, 1996). The settlements in which there has been satisfactory compromise between social interaction and communication and the built environment are more likely to have persisted and those in which there has not are less likely to have persisted. Systems that have incorporated system-level adjustability are more likely to have been able to make satisfactory compromises (Fletcher 2004). The transitions from mobile to permanently sedentary settlements and to

Figure 4.18. Increasing variation relative to increasing number of residences within Ghanaian settlements (Fletcher 1995: 38)

Figure 4.20. Hypothetical model of stress levels relative to frequency of occurrence of number of settlements (Fletcher 1995: 106)

Figure 4.19. Operational duration of dense urban settlements (Fletcher 2004: 127) 68

Complexity Theory and the Approach Taken by the Study Van der Leeuw and McGlade, and Allen have devised quantitative models to simulate various contradictory components of the social-material interaction at the scale of settlements. Those devised by Van der Leeuw and McGlade (1997) examine various qualitative dynamic processes within the open dissipative rural-urban systems evident in the Late La Tene in northwestern Europe. The model examines the various processes from different temporal and spatial scales and applies the non-traditional application of ‘town’ as an open system in full interaction with its natural and social environment (including adjoining rural areas and other towns) (ibid.; 332-333). The ‘town’ is approached in terms of an ‘information processing landscape’, rather than as a ‘least cost’ scenario. The model has simulated phase shifts and evolutionary trends that are in evidenced in the archaeological data, including ‘strange attractors’ (Fig. 4.21) and chaotic states (Fig. 4.22). The models devised by Allen (1983, 1989, 1994, 1997) also examine various qualitative dynamic processes within open dissipative social-urban systems. Whilst Allen’s models lack the time-depth component of van der Leeuw and McGlade, and Fletcher, it examines the various processes from different spatial scales, particularly focussing on the social role of individuals within larger contemporary urban built networks. Allen concludes that, whilst it is impossible to predict the nature of future developmental urban trajectories due to their sensitive dependence on initial conditions, the planning policies that are more likely to facilitate successful urban futures are those that allow for diversity and redundancy in urban systems (both social and infrastructural), as these are unavoidable constituents of the space of possible futures (1997: 258259). “Maintaining diverse knowledge, multiple technologies and making plans which are seemingly suboptimal in a strict economic sense” (Allen 1997: 258) will facilitate urban systems that are evolutionarily robust.

Within the context of culture as an evolutionarily robust complex system, self-organisation and complexity have been occasionally misread. Charles Spencer has (1997: 231-239), for example, discussed self-organisation in social systems in his debate of the merits of processualism over selectionism and has argued that the self-organisation in social systems derives from the “human capacity to initiate strategies, build factions, organise work groups, launch war parties, and mount rebellions” (Spencer 1997: 231). He has argued that directed variation, in the form of “decision-making strategies [that] can be initiated spontaneously by human actors, prior to the operation of selection” (ibid. 1997: 231), is a more robust explanation for cultural evolution than the selectionists’ ‘inherently atomistic desire to find a cultural analog to the gene’. Spencer has, however, misinterpreted the theory of self-organisation in complex systems and has wrongly associated Kauffman’s ‘departure from the traditional Darwinian fixation with selection as the primary mechanism for evolution’ with a purported rejection of undirected variation, something Kauffman has not done. Kauffman simply regards the actions of individual humans as filigree within the largescale holistic operational consistencies that exist within social systems. They are the random detail within the strange attractors that have been empirically observed within culture.

Figure 4.21. Strange attractor in the Late La Tene ruralurban interaction model (van der Leeuw & McGlade 1997: 365)

Figure 4.22. Chaotic behaviour in the Late La Tene rural-urban interaction model (van der Leeuw & McGlade 1997: 358)

Spencer’s focus on the self-organisation of complex systems has an ancestry in past discussions of cultural complexity. Diverse definitions of complexity have been adopted within archaeology, commonly tending to define systems in terms of their complexity, so as to be able to quantitatively measure it. Fred Plog (1989), for example, whilst defining complexity in a way that is compatible with Complexity theory, as ‘inequivalent observations that have properties that are not mutually reducible to one another’ (Plog, F. 1989: 104-105), has argued that the development of societies from bands to tribes to

69

The Evolution of the Built Environment chiefdoms to states can be correlated with increasing levels of ‘scale, complexity and resiliency’ and that these three variables have a quantitative three-dimensional Cartesian relationship with each other. However, he, like Spencer, sees the human capacity to make cognitive decisions and transmit ideas inter-generationally as Lamarckian mechanisms for cultural self-organisation, which Plog has equated to ‘resiliency’. “Evolutionary studies of human behaviour are the study of the differential transmission of models of and behaviour from generation to generation. These models are not subject to strict genetic control and are therefore not subject to strict Darwinian control. Human behavioural evolution is Lamarckian in that acquired behaviours can be transmitted” (Plog, F. 1989: 116). He has contradictorily stated that study of complexity is irrelevant to an understanding of cultural evolution because it is a ‘judgement that an observer makes about an object(s)’, rather than a quantifiable variable. The empirical models derived both from within mainstream Complexity theory and as developed by Fletcher, van der Leeuw and McGlade, and Allen have, however, clearly demonstrated that complexity is a quantifiable, but holistic, variable and is vital to an understanding of cultural evolutionary processes.

to a fine level of detail. It is not possible to know what precise thermal states will result from effecting a change to the building or its components (its material parts, fittings and/or occupants). This is because thermal systems are highly sensitive to initial conditions such that small changes can have large-scale, long-term effects. Thermal systems are greater than the sum of the building parts. They possess emergent properties that cannot be reduced to reductionist explanations based on a knowledge of select parts. “If a system can be characterised by n parameters, each of which may assume 3 independent states, then the total number of combinations is 3n. A major problem encountered in the design [and assessment] of any energy system – from a component such as a boiler, to a system such as a building – is that n is large... Even a relatively low number of parameters will give rise to a large number of combinations: n = 10 equates to 59,000!” (Clarke 2001: ix). In other words, buildings must be studied at the scale of the whole system, at the scale of assemblages of interrelated parts and features, because only via this method can the emergent properties be studied. The emergent properties of buildings can be broadly classified in terms of: 1. characteristics and properties of the building envelope, 2. the way the building sits in the landscape (the characteristics and properties of its surroundings), 3. the arrangement, characteristics and properties of individual rooms, and 4. the presence or absence of active heating or cooling systems (fires etc.).

THE THERMAL MACHINE AS A COMPLEX SYSTEM Buildings are thermal machines. Each of their parts possesses thermal properties that interact to effect the thermal performance of the building, or what the building is capable of producing thermally. That is, the building’s thermal capacity. Thermal capacity equates to the range of thermal states that a building is selectively capable of producing. Thermal systems are dynamic, both spatially and temporally. Thermal environments vary both spatially, resulting in diverse microenvironments, and over time, resulting in diverse thermal states. This is due to the way the physical and spatial features and traits that comprise the space interact thermally with the outside environment. That is, the dynamic nature of thermal systems is due to the consistently changing relationship between heat inputs and heat outputs (Szokolay 1987: 21). Heat gains into the space are dependant on the area of each surface, the U-value (thermal transmittance) of each surface, the external surface resistance, the solar absorptance of each surface, the long wave radiation loss from each surface, internal heat gains (from occupants, fires, heat generating equipment), and the outside mean solar radiation intensity. Heat losses from the space are dependant on the area of each surface, the U-value of each surface, the spatial volume and the degree of infiltration and exfiltration of air from the space (which is dependant on the porosity of the spatial envelope and the external wind velocity). Most of these are properties are temporally variable.

Whilst these emergent properties (the thermal capacity) of a building are the result of the interplay between the building’s thermal features and traits, some will, however, have a greater influence over the thermal performance than others. These are defined here as the primary thermal features (Szokolay 1987: 21; Clarke 2001: ix). That is, the thermal capacity of buildings can be defined by the primary thermal features that include, but are not limited to: 1. the amount of thermal mass, 2. the degree of earth integration, 3. the amount of insulation, 4. the ventilation rate, 5. the infiltration rate, 6. the degree of solar penetration, 7. the external surface reflectance/absorptance, 8. the external and internal surface resistances, 9. the degree of active heating and/or cooling, 10. the properties of the above features within the adjoining built spaces, 11. the properties of the outside environment, with which each of the above features interact (Forwood 2001; Ballinger 1983; Szokolay 1987). These include, but are not limited to, air temperature, ground temperature, relative humidity, solar intensity, and wind speed and direction (Szokolay 1987).

Thermal machines are complex systems. The number of individual thermal states that can potentially be produced (the thermal capacity) is infinite and cannot be predicted 70

Complexity Theory and the Approach Taken by the Study

THE UNITS OF THERMAL ANALYSIS

MICROCLIMATIC SELECTION: THERMAL CHOICES AND THERMAL CONTROL

The amount of information that can be ascertained for each of the primary thermal features relates to the amount of extant building that remains in the archaeological record and the archaeological record does not generally provide a detailed amount of information. How then can the thermal capacity of ancient buildings be studied, when generally only partial remains are extant and much of the detail is long gone? Partial building remains are elusive to study of the temporal thermal changes (the sequential changes from thermal state to thermal state over time) that the original building would have experienced to a fine level of detail. Building remains are also elusive to study of the spatial thermal variability (the microclimatic range within a space) that the original building would have experienced to a fine level of detail. This situation is exacerbated by the tendency for archaeological building remains to consist overwhelmingly of components that have weathered the passage of time simply because they happened to have been made of a sufficiently durable material. There is, however, no direct relationship between material durability and thermal influence. Brick walls can have just as much influence on a building’s thermal performance as a thatched ceiling or a canvas awning over a courtyard. This problem is, however, resolved by selecting the correct units for studying thermal systems. The units selected should be those by which a building’s thermal capacity, or potential to produce a range of selective thermal states and microclimates, can be measured, rather than a historically account of the thermal states and microclimates that the building actually experienced.

People modify their thermal environment by either making adjustments to their person, or to their thermal surroundings, or by moving themselves to a microclimate that has different thermal properties (Humphreys 1995; Ong 1995). That is, a person’s thermal world, their mental ‘thermal map’, is thus made up of the range of thermal environments present at a range of spatial and temporal scales. These include scales at that of the landscape, the site, the building, the room, their own person, the present and the future. The means by which people can enhance this capacity is through their: 1. choice of geographic macro-location (high altitude, coastal plain, desert etc.), 2. choice of geographic micro-location (in the shade, in the sunshine, out of the wind etc.), 3. choice of building (shape, orientation, thermal capacity, thermal insulation, thermal infiltration etc.), 4. choice of heating and/or cooling mechanisms (fires, windscoops, shading etc.), 5. choice of clothing, 6. choice of physiological factors (posture, activity level, metabolic rate, fitness level etc.) A buildings thermal capacity is defined by its potential to produce a range of selective thermal states and microclimates. However, because the study is also a study of the role of human agency in the evolution of buildings, this should be redefined in terms relevant to the human occupants. Adaptive Comfort theory states that people have a preference for having a range of thermal environments from which to choose (thermal choices) and the ability, or perceived ability, to selectively alter them in line with their expectations (thermal control). Therefore, a study of the thermal capacity of archaeological buildings should focus on the building’s capacity to provide both thermal choices and thermal control. The term microclimatic selection (MCS) is used in the study to refer to both thermal choices and thermal control. This term was selected because ‘selection’ can mean both ‘a range of choices available from which to choose’ (a noun) and ‘the act of making a choice from a selection’ (a verb).

The primary thermal features, those that largely define the building’s thermal capacity, are primarily the product of specific gross-scale building components and characteristics or, rather, of arrays of specific components that interact thermally. These gross-scale building components are generally ascertainable or inferable from the archaeological record. They are defined here as baseline building features. Base-line building features include: 1. floor shape, 2. floor area, 3. floor level relative to external ground level, 4. number of floors, 5. spatial volume, 6. exterior wall thermal mass, 7. location of primary openings, and 8. location of adjoining structures.

A person can only gain enhanced MCS when they have the capacity to enhance both thermal choices and thermal control at the full range of scales of thermal environments. However, whilst thermal choices are potentially available both inside a building and in the outside environment, thermal control is available only via the use of devices that can modify the thermal environment, ranging in scale from the simplest device such as a windbreak through to whole building complexes.

A building’s base-line features can, therefore, be used to ascertain its thermal capacity, as long as they are studied as a comprehensive and interactive array and not as individual features. Therefore, the appropriate units for studying the thermal capacity (and thermal classes) of archaeological buildings derive from the thermal properties of base-line features, which should be studied concurrently and via a methodology that treats them as interacting variables. 71

The Evolution of the Built Environment

There are a number of factors relating to the outside environment that affect the capacity for thermal choices

because the outside environment is naturally variable, both diurnally and seasonally, and some climates (cold, temperate and hot-arid) are more seasonally variable than others (hot-humid) (Rudloff 1981; Szokolay 1987: 17; Martyn 1992). The first factor relates to the daily and seasonal temperature fluctuations outside. Where the outside temperature fluctuations are high, the potential exists to produce a high internal-external temperature difference by off-setting the internal temperature from the outside temperature. A structure with a long time lag will be further off-set than a structure with a short time lag and will, as a consequence, have a greater internalexternal temperature difference (Figs. 4.23-4.24). Therefore, classes of buildings in climates with highly variable daily and seasonal temperatures will have a high capacity for thermal choices by passive means alone. However, classes of buildings in climates with highly homogeneous daily and/or seasonal temperatures do not possess the same capacity to produce a significant internal-external temperature difference by passive means alone, even if the time lag is long. This is because the lack of outside temperature variability ‘caps’ the degree of internal-external variability that is possible, in both heavyweight and lightweight construction (Figs. 4.254.26). The second factor that affects the capacity to create thermal choices by passive means relates to the outside solar intensity. Solar intensity affects the temperature variability inside structures themselves. The higher the solar intensity, the greater the internal variability and vice versa. Therefore, the greatest potential for producing thermal choices exist where the outside environment is both daily and seasonally variable, where the solar intensity is high and where the buildings possess a long time lag. Note that where thermal choices are generated by active means, rather than passive means, these factors do not apply because the temperatures thus produced are essentially independent of the outside conditions.

Figure 4.23. Potential for thermal choices in heavyweight construction in temperate, hot-arid and cold climates.

Figure 4.24. Potential for thermal choices in lightweight construction in temperate, hot-arid and cold climates.

Figure 4.25. Potential for thermal choices in heavyweight construction in hot-humid tropical climates.

Figure 4.26. Potential for thermal choices in lightweight construction in hot-humid tropical climates.

Thermal Choices “Consider the traditional fireplace. When lit, it warms the room discriminately and a temperature gradient is generated across the room. We are able to sit closer to the fire if cold and then move away when warmed up. When moving from the fireplace to another part of the house, we experience the ambient cold again and are spurred to walk faster, thus increasing our metabolic rate. This environmental diversity is fundamental to our enjoyment of the built environment.” (Ong 1995: 75) Thermal choices represent the range of thermal states and microclimates available to people and within which they have the potential to selectively move. Built structures can extend this range beyond what is available naturally in the outside environment by offsetting the internal maximum and minimum temperatures from the external maximum and minimum temperatures over a 24-hour diurnal cycle and at each location within the space. Thermal choices within buildings are thus those thermal states and microclimates that are additional to those available naturally outside. They can represent, and can be measured, either as a temperature difference between the inside space and the outside environment and/or as a temperature variability present inside the structure itself that is not present outside (horizontally or vertically). The larger the temperature difference between inside and outside, or the greater the internal variability, the greater the occupants’ thermal choices provided by the structure, and vice versa.

72

Complexity Theory and the Approach Taken by the Study It can therefore be generally stated that classes of buildings in climates with high daily and/or seasonal temperature fluctuations and with high solar intensity will be most easily capable of producing enhanced thermal choices by passive means alone. On the other hand, classes of buildings in climates with minimal daily and/or seasonal temperature fluctuations and with low solar intensity would be able to only increase their thermal choices by active means. Note that it is easier to implement thermal choices by active means if it involves the addition of heat, rather than if it involves the removal of heat, which is what cooling is, because it is easier to add heat to a system than to remove it. This is best illustrated by reference to what is possible in various real environments, to the maximum possible differences between inside and outside temperatures in extreme climates. The greatest possible inside-outside difference will always exist between a subterranean space of at least 10m below ground and the outside environment. To demonstrate this, these differences have been graphed (Figs. 4.27-4.29) for the diurnal cycles in summer and winter for three extreme locations: I-n-Salah, Algeria (hot-arid), Belem, Brazil (hot-humid) and Reykjavik, Iceland (cold). The average summer differences are 10.2oC, 2.3 oC and 6.4 oC respectively and the average winter differences are 11.8 oC, 2.2 oC, and 5.5 oC respectively. The average yearly differences are, therefore, 11.0oC, 2.3 oC and 5.9 oC respectively. This

therefore illustrates that, by passive means alone, it is most easy to produce a high inside-outside temperature difference in a hot-arid climate, it is moderately easy in a cold climate and it is inherently difficult in a hot-humid climate.

Figure 4.27a. Summer temperatures in hot-arid I-nSalah, Algeria..

Figure 4.27b. Winter temperatures in hot-arid I-n-Salah, Algeria.

Figure 4.28a. Summer temperatures in hot-humid Belem, Brazil

Figure 4.28b. Winter temperatures in hot-humid Belem, Brazil

Thermal choices can be created either via active means or via passive means, or both. A passive system utilises features available in the external environment alone, that of sun, wind and humidity. An active system utilises additional sources of energy, that of chemical and/or mechanical. A fire is the simplest means by which thermal choices can be actively produced because it generates a wide range of microclimates within which people can selectively move, by either moving closer to the fire or further away. A thermal system can be either wholly passive or wholly active, or it can be potentially active if, for example, there is an unlit fire or an airconditioning unit that is not yet turned on. Thermal choices can also be produced either in the form of a single, thermally variable space, or in the form of a cluster of separate, but inter-connected, spaces that are each thermally different. For example, a single space within which there is a range of different microclimates would offer an equivalent level of thermal choices to that of a multi-room structure within which each room is thermally different.

73

The Evolution of the Built Environment

Figure 4.29a. Summer temperatures in cold Reykjavik, Iceland.

Figure 4.29b. Winter temperatures in cold Reykjavik, Iceland.

Thermal Control

able to capitalise on the inherent external thermal variability, but it will only ever be able to capitalise on the outside variability that is available.

Thermal control represents the degree to which an environment is able to be selectively and predictably altered or modified. It relates to a number of factors: 1. the occupant’s capacity to initiate a thermal change, 2. the speed and ease with which it can be implemented, 3. the degree to which it meets expectations (is operationally consistent), and 4. the ability to maintain it for a specific predefined period of time. There are two means by which thermal control is most easily produced in buildings. First, if the internal environment is homogenous. This is because it is most easy to implement and control change if the thing being changed is stable and homogenous, rather than if it is variable or chaotic. An interior space will be thermally homogenous if it is isolated from the inherent temperature variability present outside, or if the outside environment is itself homogenous. Maximum thermal isolation occurs when the interior space is fully closed (closed-mode) because the interior does not then interface directly with the outside, but interfaces with it only via the spatial envelope itself. That is, when the interior is as closed-off from the outside as is possible the inside will be as maximally thermally homogenous as possible. When the interior is fully open (open-mode), however, the interior will be only as thermally homogenous as the outside conditions, with which it will be free-running.

Thermal control can be produced by both active and passive means. Passive systems utilise built components that allow the interior to selectively interface with the external environment, such as external openings that can be selectively opened and closed (doors, windows, vents, windcatchers, chimneys with flue closers, and/or the walls and roof of the structure itself). Active systems are generally less dependent on the prevailing outside conditions. However, both passive and active systems produce enhanced levels of thermal control if the system is a graduated type of system rather than an on-off type of system. Additionally, thermal control is most easily produced in active systems if the system involves the addition of heat, rather than its removal. For example, in cold climates thermal control (and thermal choices) will be maximised by lighting a fire because a graduated range of microclimates will be generated that are easily modifiable. In hot climates opening a window to facilitate a cool breeze will generate a range of microclimates, but the range will be less than that produced by a fire in a cold climate. In both of these types of climates, however, thermal control will be only marginally increased by turning on a centrally controlled air-conditioning unit because it will have the effect of altering only the ambient temperature, rather than generating a range of microclimates. Thermal control equates to the number and ‘spread’ of the microclimates that can be created within a space. Ultimately, the more components within a space that can provide different modes of heating and cooling, and by different means, whether active or passive, the more thermal control the space will be potentially capable of producing. Historically the range of modes and means that have been utilised in buildings is vast, ranging from simple components that open and close (shutters, curtains, doors) to simple mechanisms that can generate complex results (fires) to complex systems that utilise several means at once.

Secondly, thermal control is most easily produced if the interior can be selectively opened-up to the outside environment. A minimal amount of thermal control will be produced if the interior can be slightly opened-up to the outside, and the more it can be opened-up the more thermally controllable it will be. However, a greater degree of thermal control will be possible if the means by which the space is opened and closed is graduated or incremental, rather than being only open or only closed. For example, a hinged door that can swing free offers more thermal control than does an awning window that can be either only open or only closed. The more an interior space can selectively interface with the outside environment, the more the internal environment will be

74

Complexity Theory and the Approach Taken by the Study seasonal temperature variability and solar radiation are high, but where the diurnal temperature variability is not as high, there is a reduced capacity for easily producing thermal choices by passive means alone, although the capacity for easily producing thermal control is very high through the use of fires.

MICROCLIMATIC SELECTION: WHAT TO EXPECT FROM A NULL HYPOTHESIS The study has proposed a null hypothesis, that the thermal capacity of classes of buildings will have been random over time and that, with Kauffman in mind, the way that the contradictions that inherently exist in thermal systems have been resolved in classes of buildings will have been random over time. A look at the range of building styles that have existed throughout the world over time might lead the observer to assume that the null hypothesis is correct. However, building style is not synonymous with thermal performance or thermal capacity. The thermal systems of archaeological buildings are complex systems and cannot be reduced to simplistic Lamarckian explanations or to the Neo-Darwinian behaviour of individual traits. At the same time, the thermal systems of archaeological buildings should not be studied in terms of thermal ‘comfort’, because Adaptive Comfort theory has demonstrated that ‘comfort’ is a subjective entity and is therefore not empirically quantifiable. What is required is a cognitive shift towards a study of the thermal capacity of archaeological buildings, a quantitative entity. Thermal capacity can be measured as thermal choices and thermal control, the two human-centric factors that effect the thermal capacity of buildings. In this way study of the thermal capacity of buildings becomes a study of microclimatic selection (MCS). Thus, the null hypothesis then shifts to a hypothesis that MCS within classes of buildings is selectively neutral and has not come under selective pressure over time. If MCS in different climates has not come under selective pressure the pattern of behaviour over time will have been random. If, however, MCS has come under selective pressure the pattern of behaviour over time will have been regular, regardless of the building form and style and regardless of the type of climate.

SUMMARY Complex systems cannot be understood via deterministic or reductionist methodologies, nor in terms of the behaviour of individual components within the system. Complex systems must be studied in terms of the interrelated behaviour of (at least) their primary features and traits, because complex systems possess emergent properties, properties that arise spontaneously as a result of the interaction between their parts. Emergent properties exceed the sum of the parts. They cannot be understood or predicted from a knowledge of the parts, however detailed the level of knowledge. Complexity theory, a branch of science that investigates the emergent properties of complex systems, has located various behavioural consistencies between (strange attractors) and within (self-similarity) complex systems. The emergent properties are the consequence of the way in which contradiction, which is inherent in complex systems, is resolved. Good resolution increases the fitness of a system and the likelihood that it will survive and prevail, because such a system is adjustable and robust to changing circumstances. Poor or hasty contradiction resolution decreases the fitness of the system and increases the likelihood that the system will ultimately die out. Buildings are thermally complex and possess emergent properties that are the result of the inherent thermal contradictions that exist within the system. Emergent properties equate to what the system is capable of producing, whether or not it actually does. The thermal capacity of buildings equates to the range of selective thermal states and microclimates that a building is capable of producing. Thermal capacity should be studied in terms of thermal choices and thermal controls, which together equate to microclimatic selection (MCS), because these are the human-centric equivalents of thermal capacity. Note that a human centric viewpoint of evolution does not imply directed variation by humans, for humans are incapable of acting with foreknowledge of the long-term consequences of their actions. Humans do, however, produce undirected variation, within which there is inherent contradiction. The contradictions arise spontaneously as a result of the non-correspondence between the built environment and the more ephemeral aspects of culture, such as the social, because different components of culture replicate at different rates and thus possess different degrees of inertia. Contradictions arise spontaneously in the thermal systems of buildings as a result of different individual thermal experiences and expectations both between people and over time. Complexity theory implies that the way in which the thermal contradictions are resolved in buildings will lead

If climate is a factor in the long-term behaviour of thermal capacity we would expect to see a direct correlation to what is potentially possible in different types of climate. That is, with the boundary conditions of what is possible in different types of climate. If, however, climate were not a factor we would expect to see random behaviour over time regardless of the boundary conditions of what is potentially possible. In hot-arid climates, where the outside diurnal and seasonal temperature variability and solar radiation are high, there is a large potential capacity to enhance MCS by passive means alone. The potential to easily produce thermal choices is high, although the potential for easily producing thermal control is not as high. In hot-humid climates, where the outside diurnal temperature variability is high, but where the seasonal temperature variability and the solar intensity are negligible and where the outside environment is very homogeneous, there is a negligible capacity for easily producing MCS by passive means alone. MCS can only be enhanced by active means, such as mechanical cooling (airconditioning). In cold climates, where the outside 75

The Evolution of the Built Environment to either the evolutionary longevity or demise of building classes because selection operates on the ways in which the contradictions are resolved. That is, Complexity theory implies that adjustable thermal solutions will have an evolutionary advantage over both ordered and chaotic systems because they are robust to inevitable changes in contextual circumstances. However, the null hypothesis of the study states that the way in which thermal

contradictions are resolved is not acted upon by selection and, therefore, that this has had no effect on the long-term pattern of change in thermal systems and classes of buildings. The null hypothesis states that the long-term pattern of behaviour of the thermal capacity of classes of buildings to selectively implement MCS will have been random over time.

76

Summary of Part 1

Adaptive Comfort theory states that, rather than being always thermally comfortable, people prefer to have thermal choices available to them and to have thermal control over their environment, such that they can implement a thermal change in a manner and at a time they select. This theory forms the basis of the decision by the air-conditioning industry and ASHRAE to amend the International Standards for the heating and cooling of buildings to include free-running buildings. It is based on the recognition that occupants of free-running buildings suffer no psychological strain at temperatures at which occupants of HVAC buildings suffer both psychological and physiological strain. Consequently, studies of the long-term thermal behaviour of buildings should focus on thermal capacity, the potential ability of a building to produce a selective range of thermal states and microclimates, rather than the contingent past thermal histories of buildings, which must encompass the spatially and temporally dynamic quality of thermal performance. Thermal capacity can be quantitatively measured in the human terms of thermal choices and thermal control. A human-centric mode of measuring thermal capacity is appropriate to evolutionary studies of change in buildings because humans are the reproductive agents of buildings and they generate the undirected variation that selection operates on.

properties that arise spontaneously as a result of low-level processes and that exceed the sum of the parts. Emergent properties equate to behavioural capacities, the capacity of the system to be able to do a particular thing, whether it does or not. Emergent properties are the result of the way in which the contradictions that are inherent in complex systems are resolved. Thermal systems contain inherent contradictions, just as culture itself contains inherent contradictions between the diverse replicative rates of the verbal, the social and the material. Complexity theory states that it is the way in which the inherent contradictions are resolved that produces the long-term behavioural consistencies that can be observed at diverse scales of detail in complex systems, consistencies that behave like strange attractors such that, whilst the fine detail is non-predictive and non-determinate, large scale patterns arise spontaneously. Systems where the level of contradiction is low (ordered regimes) will find resolutions too easily and too fast, resulting in low fitness. Systems where the level of contradiction is high (chaotic regimes) will not settle on resolutions or, if they do, will replace the resolutions too rapidly, resulting in low fitness. However, systems ‘poised’ between chaos and order will find good resolutions, although they will become exponentially hard to find, resulting in high fitness because they will be adjustable and robust to changing circumstances.

Variation in the built environment is undirected because humans are incapable of precisely predicting the thermal consequences of altering their physical and/or built environment. Humans build structures, but they cannot predict to a fine level of detail what the operational outcome of their actions will be. This is because buildings, as thermal machines, are complex systems, systems that are highly sensitive to initial conditions. They cannot be understood from a deterministic, Lamarckian viewpoint. Nor can they be understood from a reductionist, memetic, Neo-Darwinian viewpoint, or in terms of only individual components within the complex systems. Complex systems possess emergent properties,

The null hypothesis of the study states that, regardless of the theory outlined above, the thermal capacity of classes of buildings will have behaved randomly over time regardless of social context and climate and, therefore, the way in which thermal contradictions that inherently exist in thermal systems have been resolved will have been random over time. That is, there will have been no indication in the archaeological record that one class of thermal system (system of MCS) has become more common or dominant over time.

77

PART 2 - TESTING THE HYPOTHESIS: FOUR CASE STUDIES AT DIVERSE SCALES Introduction to Part 2

“Even in our incapacity to predict details, we can still have every hope of predicting kinds of things. The hope here is to characterize classes of properties of systems that are typical or generic and do not depend on the details.” (Kauffman 1995: 17)

(MVA) treats entities, in this case buildings, as though they are composed of vast arrays of operativelyinterrelated variables, in this case the thermal properties of base-line building features, some which will be operatively-contradictory and some which will not. That is, engineering-analysis defines how the array of variables that comprise individual buildings are arranged according to the degree to which they contribute to the building’s thermal choices and/or thermal control. When a sufficiently large number of individual buildings are statistically analysed via this prescriptive arrangement, the thermal signatures of classes of buildings are made apparent. It is thus necessary that the study first comprehensively describe the engineering-analysis and its results prior to describing the multivariate analysis and its results, because it is only with a full understanding of the results of the engineering-analysis that the way in which the data was set up in the multivariate analysis can be fully understood.

Part 2 of the study describes and explains the methodology devised and used to test the null hypothesis. The methodology had to do two things. First, it had to be able to quantitatively define the contradictory parameters operating within thermal systems. Secondly, it had to be able to quantitatively test for the thermal capacity of classes of buildings despite the fact that thermal systems are complex systems. This latter requirement is somewhat alleviated by the definition of thermal capacity as the ‘potential range of thermal states and microclimates’ or, in human terms, as thermal choices and thermal control, which makes it possible to define the dynamic nature of thermal systems in quantitative terms.

The results of the engineering-analysis revealed that buildings that utilise traditional thermal systems alone (that is, pre-industrial systems that do not utilise mechanical heating or cooling), in essence, operate differently in cold climates compared with those in hot climates. Buildings in hot climates embody a high level of contradiction between the (passive) thermal system and the material (built) system, whereas buildings in cold climates embody a low level of contradiction between the (active) thermal and the material systems because the two systems operate independently of each other. Historically, buildings in hot-arid climates developed the capacity to enhance MCS via the incorporation of diverse, interrelated and complex thermal and built systems. The buildings were able to resolve the high level of inherent thermal contradiction in a way that produced a thermally and materially adjustable system. Buildings in cold climates, however, were capable of producing an equivalently high level of MCS within a more materially restricted built environment, because the thermal system was generally independent of the built system, yet was capable of generating an equal diversity of microclimates through the use of fire.

The best method for meeting both methodological requirements was predicated on the consecutive application of engineering-analysis and statistical multivariate analysis. Engineering-analysis is real-time simulation aimed at highlighting the contradictory parameters operating within complex systems and is the best method for defining the thermal contradictions present in buildings. The methodology works on the principle that when testcase buildings are tested that incorporate base-line features of archaeological classes of buildings, a corpus of detailed thermal information pertaining to archaeological buildings is acquired that may then be extrapolated to archaeological classes of buildings. The extrapolation process itself is not performed via engineering-analysis because, by its nature, engineering-analysis is ahistoric (Maxwell 2001). It cannot say anything about the history of the contradictions that it highlights, or, in this case, the longterm pattern of change of MCS in the archaeological record. The best method by which the change in MCS over time can be made apparent is the statistical technique of multivariate analysis. Multivariate analysis

78

CHAPTER 5 – The Methodology for Testing for Microclimatic Selection

is recognised by biologists who have noted that contradictions exist in cases where particular traits possess more than one function. “The anatomy will be a compromise between the design requirements of the two or more respective functions, and only in the light of knowing for sure what the functions are can there be any hope of recognizing the complex, subtle nature of the compromise” (Kemp 1999: 77, emphasis added). Convergence is evidence of deeper, natural regularities only evident because “there are only so many good ways of building things, given the starting constraints, and evolution finds them again and again” (Dennett 1995: 225). To date, no engineering-analyses have studied the thermal performance of buildings but, as complex systems possessing emergent properties, buildings are ideally suited to this type of analysis. It is thus ideally suited for detecting the contradictory parameters inherent in thermal systems.

INTRODUCTION In this chapter the engineering-analysis is described, covering the general approach, methodology, experimentation and results. The general approach and methodology adopted for the statistical multivariate analysis are also described. The specific analyses and results for each of the four MVA case studies are described in the subsequent chapters. Engineering-analysis is a commonly used tool in evolutionary biology, although it has attracted much debate (ref. e.g. Dennett 1995: 207-228). Gould and Sober have discussed its merits as an independent means of measuring the relative fitness of organisms (Gould 1978: 42-45; Sober 1984: 81). It has recently been adopted into Neo-Darwinian archaeology as a tool for identifying the evolutionary-functional role of particular traits (Maxwell 1995; O'Brien & Holland 1995: 184; Maxwell 2001) and the mechanical properties of artefacts (Watson 1986: 446). The archaeological application has generally adopted a three-step process. The first step postulates possible functions for particular features or traits (Watson 1986: 81-82; Kemp 1999: 75). The second step then creates a model or paradigm for each function that, in principle, bears no reference to the original trait. This is based on the concept that “adaptation has been strongly implicated by the correspondence between the form of a structure and the design that an engineer might specify for a particular function” (Maxwell 1995: 116) (see also Gans 1974). The third step then compares the original trait with the paradigm structures and the one that corresponds most closely is taken as the best hypothesis. This is based on the concept that, where similar patterns of adaptation have evolved independently, such that transmission and diffusion can be ruled out as causal mechanisms, it may be more confidently hypothesised that the same evolutionary-functional traits were in existence (O'Brien & Holland 1992: 44; Maxwell 1995: 123; Kemp 1999: 76).

ENGINEERING-ANALYSIS IN THE STUDY The engineering-analysis in the study was set up to examine the emergent properties and contradictory parameters of various test buildings in such a way that the information could then be meaningfully transferred to archaeological buildings. The test structures were designed to accurately simulate the thermal behaviour of several classes of archaeological buildings, having incorporated their generic base-line building features. The capacity of the test structures to provide thermal choices and thermal control was measured, which established a corpus of detailed thermal information about the MCS of real classes of buildings. This experimental-based methodology produced a better result than could have been produced via computerised simulation. There are two reasons for this. First, engineering-analysis does not require the input of the same level of detailed information about the building that computer simulation requires. Computer simulation programs generally require data input concerning the building’s features and components, furnishings and furniture, and nature and practices of the occupants, information that is clearly not available within an archaeological context. Secondly, engineering-analysis produces a more detailed picture of the thermal performance of the structures than is possible via computer simulation, which generally calculates for only ambient temperature and not microclimatic range.

However, the way in which engineering-analysis has been applied in archaeology has overlooked its greatest strength. In assuming that there is a direct correlation between individual evolutionary-functional traits and the fitness of its possessor there has been a general failure to recognise the essential nature of complex objects and systems and the nature of emergent properties. Yet this is the main strength of engineering-analysis, to identify the ways in which contradictions have been resolved within complex systems and so highlight possible explanations for regularities that are apparent in the systems that cannot be explained away with deterministic arguments or contingent explanations, nor as just examples of convergence. The importance of the role of contradiction

The scope of the engineering-analysis had the potential to be vast, given the vast number of classes of buildings that exist in the archaeological record. The scope was therefore limited to those generic building forms that could establish the basic thermal principles applicable to archaeological, pre-industrial buildings. That is, the thermal behaviours were examined from first principles. 79

The Evolution of the Built Environment The generic building forms examined equate closely to those of the earliest, simplest and most persistent classes of buildings, that of various types of rudimentary structures and simple huts, for the thermal systems of the majority of ancient buildings are variations on the thermal principles embodied in these rudimentary and basic thermal systems.

degree to which the microclimates thus generated could be selectively altered (the thermal control). This was based on the notion that it is the range and controllability of the microclimates within a space, and not just the ambient temperature, that represents the structure’s thermal capacity. It was because the spatial boundary between the inside of the partially-enclosed test structures and the outside was very indistinct that the microclimatic range ‘inside’ and outside the structures was best investigated via simulation and field experiment.

The experiments were designed to test for the criteria by which thermal choices and thermal control are created and in such a way that the nature of the thermal contradictions could be made apparent. The thermal capacities were measured in terms of the difference between the outside conditions and the ‘artificial’ conditions inside the structures, rather than in terms of ‘raw’ temperatures. This made it possible, first, to rule out contextual differences that were bound to exist between separate test scenarios and, secondly, to directly measure the thermal environments created by the structures that were additional to that available naturally in the environment.

Due to the impossibility of testing for all types of rudimentary structures that have existed in all types of climates around the globe, the experiments were designed to highlight the adaptive opportunity of various generic structures under specific extreme ambient conditions, that of severely hot-arid and very cool conditions. These conditions are characteristic of conditions experienced in severely hot-arid (in excess of 35o C) and very cool (0-9o C) climates classified as such by the Koppen-Trewartha climate classification method (Rudloff 1981: 81-85).

The first set of experiments looked at rudimentary structures (windbreaks and shade structures), which interface extensively with the external environment and which cannot be isolated from it. The aim was to ascertain the thermal limits of the structures under extreme climatic conditions (very hot-arid and very cool). Under the very cool conditions various wind breaks and a lightweight hut were tested for, for both passive and active heating (with and without a fire). Under the very hot-arid conditions a series of sun shades were tested for, for passive cooling only, as active cooling only became available in the post-industrial era. The second set of experiments looked at a series of simple huts (round and rectilinear, semi-subterranean and on-ground, and heavy weight and lightweight huts). The aim was to ascertain the thermal choices and thermal control under different degrees of closure from the outside under temperate conditions and for only passive heating/cooling. The most significant findings were that the MCS of rudimentary structures (windbreaks and shade structures) and more substantial and complex structures (huts with closable openings) is only a matter of degree, not of substance. The implication thus arises that the archaeological transition from rudimentary structures to more complex buildings can be associated with a gradual increase in MCS, rather than a sudden increase.

Monitoring Equipment In both the hot and the very cool experiments the black globe temperature (Tg) was recorded, plus a separate measure for the wind-speed and the relative humidity. The black globe temperature was recorded as a means of calculating the combined effects of radiant temperature, ambient air (dry bulb) temperature and air velocity as a single measure. Black globe temperature was chosen in preference to wet bulb globe temperature, that incorporates the effect of humidity on the thermal sensations (thermal stress) of human subjects (Bernard 1999), because the focus of these experiments was on quantitative temperature and air movement differentials, not the thermal response in humans. The globes used in the scenarios consisted of a series of 40 mm dia. black-body globes (ping-pong balls covered in four layers of black stocking-nylon) with a 1.5 mm hole, into which a thermocouple could be inserted to record the still air temperature inside. This size of globe was found to have a response time of approximately ten minutes until equilibrium was reached. This elapse of time was sufficiently long as to be robust to minor thermal fluctuations (within the margin of error) and sufficiently short so as to allow the globes to be repositioned as necessary. The holes were painted white so as to be visible in the dark. The positioning of the globes varied slightly between each of the sets of scenarios and these are described in detail in each of the relevant sections below.

THE FIELD EXPERIMENTS: RUDIMENTARY STRUCTURES Aim

Recording Procedure

The aim of the first set of experiments was to record the range of temperatures in and around a series of rudimentary structures under defined ambient conditions. That is, the range of microclimates generated by a series of generic windbreaks, shade structures and simple huts that were additional to the ambient conditions of the natural environment (the thermal choices), plus the

The aim of the procedure was to record the temperature differences within and around the structures over as short a period of time as possible, so as to minimise changes in the ambient conditions. The average ambient temperature generally varied by 1-2o C and the average wind speed 80

The Methodology for Testing for Microclimatic Selection The first type of structure consisted of a 2.4m dia. semicircular black plastic windbreak supported on 1.0m tall wooden stakes and 50x50mm wire mesh, oriented with prow facing into the breeze (Figs. 5.1-5.2). The black plastic has negligible thermal mass, insulation, reflectivity and conductivity. The wire mesh, which was integral with the plastic, was of sufficiently open weave to provide the necessary structural support whilst minimising thermal conductivity. Two scenarios were run using this structure. The first was run with the structure only and no fire, and the second with an open fire placed centrally within the windbreak.

generally varied by 0-1 m/s over the recording period. This was regarded as an acceptable margin of variability given that the aim was to record temperature differences between ‘inside’ and outside of the structures, not raw temperatures. The wind speed and relative humidity were read both before and after each scenario. The temperatures inside the globes were read at each position using a thermocouple inserted into the globes. Approximately one minute was allowed to take each reading before the temperature was read off the multi-meter, so as to allow the wires to first adjust to the conditions inside the globe prior to the reading.

The Experiments in Very Cool Ambient Conditions The location and ambient conditions: The region of Adaminaby in the Snowy Mountains, south of Sydney, Australia, was selected as the location to perform the experiments under very cool conditions because the temperatures there in August (the time of the experimentation) range from –3 to 9o C (Australian Government Bureau of Meteorology 2006), which met the 0-9o C climatic temperature requirement, and because the climatic conditions at that time of year are relatively predictable and stable. The site itself was on a flat stretch of rural property that is fully exposed to the elements (sun and wind) in all directions. The outside ambient temperatures throughout the duration of each scenario were relatively consistent at 2.0-3.0o C at 0.5 m above ground level, a cold breeze blew at a relatively consistent speed of 1.0-2.0 m/s and relative humidity was 65-75 %.

Figure 5.1. Plan of full-circular and semi-circular windbreaks and fire location in very cool conditions.

The experiments were performed over a period of five consecutive nights from 12th to 16th August 2002, commencing one hour after sundown. This was done for two reasons. First, the ambient temperature following sundown is relatively stable, dropping only one to two degrees over the approx. one hour period it took to run each scenario. Secondly, this made it possible to exclude the effects of solar radiation and restrict the heat source to only those generated by the wood fires, which formed the focus of the experiment. Design and Construction of the Structures: There were ten different scenarios using four different types of generic structures, although the first scenario involved only an open fire and no built structures, and the experiments with the straight-sided windbreak had to be abandoned. Given that the range of materials that have been used in the construction of windbreaks by traditional societies has been immense, the decision was made to isolate the effects of the aerodynamic shape of the windbreak from the diverse and multiple effects of insulation, reflectivity and conductance present in natural materials by using black plastic sheeting supported on a wire-mesh frame. The hut experiments did, however, use a lightweight hessian covering. Each scenario faced the windbreak openings away from the wind direction

Figure 5.2. Semi-circular windbreak after completion of scenario. The second type of structure consisted of a 2.4m dia. fullcircular black plastic windbreak with a 0.4m wide opening and supported on 0.5 m high wooden stakes and 50x50mm wire mesh, oriented with prow facing into the breeze (Figs. 5.1, 5.3). Two scenarios were run using this structure. The first was run with the structure only and the second with a centrally placed open fire.

81

The Evolution of the Built Environment

Figure 5.3. Full-circular windbreak during setup. The third type of structure consisted of 1.8 m long a straight-sided black-plastic windbreak supported on 1.0m tall wooden stakes and 50x50 mm wire mesh (Fig. 5.4). The aim had been to face the windbreak directly into the wind and to record the temperatures behind it. However, this proved to be impossible to achieve due to variability in the directionality of the prevailing breeze. This slight variability, which did not constitute a problem with the curved windbreaks due to their aerodynamic shapes, proved to be an impediment with the straight windbreak because it was not possible to get a reliable temperature reading before the direction of the wind changed and altered the temperatures too much to be reliable. This was exacerbated by turbulence created by the stronger negative leeward pressure behind the straight windbreaks compared with that behind the curved windbreaks.

Figure 5.5. Plan and elevation/section of hut and fire location in very cool conditions.

Figure 5.4. Straight-sided windbreak (abandoned) prior to scenario. The fourth type of structure consisted of a 1.7m dia. circular, wooden framed domed hut with a single 0.4m wide by 0.8m high opening and a hessian covering (Figs. 5.5-5.6). Four scenarios were run using this structure. The first involved only the hut, the second involved the hut in conjunction with an open fire located centrally inside the hut, the third involved the hut in conjunction with an open fire located 1m downwind of the centre of the hut (which located it just outside of the doorway), and the fourth involved the hut in conjunction with an open fire located 2m downwind of the centre of the hut.

Figure 5.6. Hut during setup. In the scenarios where a fire was used it consisted of three or four hardwood logs approx. 350mm long arranged in a rough tee-pee fashion. This produced a steady flame that radiated an even heat when no wind was blowing on it and that burned evenly for much longer than the one hour required for each scenario. 82

The Methodology for Testing for Microclimatic Selection 9. Hut with fire at 1.0m leeward of the centre of the hut. 10. Hut with fire at 2.0m leeward of the centre of the hut.

Monitoring Equipment: In the experiments the globes were attached to strings that were tied between two rigid wire frames that projected 170mm out from 1.8m high vertical wooden stakes. The stakes were hammered rigidly into the ground, suspending the strings in a rigid vertical line (Fig. 5.7). The globes were tied on at 0.5m intervals apart, with the lowest ones 0.5m above the ground and the highest ones 1.5m above the ground. The highest globes were therefore higher than the structures. The string-lines were then arrayed as shown in the various respective plans and section drawings.

Results and Conclusions: Fire Only: The results of the cold climate experiments with only the fire showed that, as expected, the heat from the fire radiated outwards from the source but was blown horizontally by the wind (Fig. 5.8). That is, the hottest position was lower to the ground and leeward of the fire. At 0.5m distance from the centre of the fire on that side the heat was too intense (and it is too smoky) for a person to be able to sit there, as the flames would be directly in their face. Alternatively, at the same distance away on the windward side the heat was significantly reduced and, because the heat dropped away with height as well as with distance, the fire was warm only low to the ground. The heat generated by the fire was virtually negligible at a height of 1.5m above ground.

Figure 5.7. Vertical arrangement of globes in scenarios in the very cool experiments.

Figure 5.8. Temperature gradient for fire only under very cool conditions.

Equipment Setup: In each scenario the structures were positioned on an area of flat ground so as to be unobstructed from the wind. The main axials of both the windbreaks and the huts were then aligned with the wind direction and with the openings/entrances facing downwind. The fires for the hut scenarios were thus positioned in the lee of the hut. In the windbreak scenarios the fires were inside and at the centre of the structure. The windbreaks were fixed in place with the stakes positioned on the inside of the wind-sheet, so that the wind would blow the plastic onto the frame, rather than away from it, and the wire frame was positioned on the outside where it was thermally shielded from the fire.

Windbreaks Without Fire: The consequence of erecting a windbreak without also lighting a fire was that, whilst the temperature differential was negligible, a wind shadow was created that raised the temperature slightly. The wind shadow was created inside the windbreak. With the 1.0m high semi-circular windbreak the temperature differential was greater and the wind shadow continued further downwind (Fig. 5.9) than it did for the 0.5m high fullcircular windbreak (Fig. 5.10). Windbreaks With Fire: The consequence of erecting a windbreak in conjunction with lighting a fire at the central point was that the heat and the smoke from the fire became more ‘controllable’. They rose straight up until they reached a point above the top of the windbreak, where they were only then blown horizontally. Additionally, the hottest position was no longer necessarily lowest to the ground. For the 1.0m high windbreak, the heat at 1.0m above ground was consistently higher than at 0.5m (Fig. 5.11). For the 0.5m high windbreak, however, this situation was only repeated leeward of the fire (Fig. 5.12).

Recording Sequence: The sequence for recording the experiments was as follows: 1. Fire only. 2. 1.0m high semi-circular windbreak with no fire. 3. 1.0m high semi-circular windbreak with central fire. 4. 0.5m high circular windbreak with no fire. 5. 0.5m high circular windbreak with central fire. 6. 1.0m high straight windbreak with and without fire (experiment abandoned) 7. Hut with no fire. 8. Hut with fire at centre of the hut. 83

The Evolution of the Built Environment distribution, due to the wind blowing around the ends of the windbreak. Secondly, the fully-circular windbreak had a slight heat concentration on the windward side of the fire. The latter was presumably the result of areas of positive and negative pressure, which the open-sides of the semi-circular windbreak did not generate. Both structures, however, indicated that the higher the windbreak, the higher would be the even distribution of heat.

Figure 5.9. Temperature gradient for 1.0m h. semicircular windbreak without fire under very cool conditions.

Domed Hut With Fire: The cold climate hut experiments duplicated the thermal behaviour shown by the windbreaks. In the scenarios where the fire was not lit, the hut performed like a windbreak, creating a wind shadow both inside and leeward of the hut, which elevated the temperatures slightly (Fig. 5.13). Due to the sheltering effect of the roof, the hut behaved in a similar manner to that of a tall windbreak. The wind shadow inside the hut carried evenly straight up to roof level and was not blown horizontally. However, directly above and outside, it disappeared completely. With a central fire inside the hut the pattern of heat distribution was again similar to that of the windbreaks (Fig. 5.14). The sheltering effect of the roof created an elevated concentration of heat, which was evenly distributed up to the level of the ceiling, above which it was blown windward. The domed shape of the ceiling presumably also contributed to the elevated concentration at the peak of heat and smoke, of which only a small amount escaped through the loose weave of the hessian. The hut also behaved like a fully-circular windbreak, concentrating the heat at the windward side (the rear) of the hut, presumably an area of negative pressure. When the fire location was moved to outside of the hut doorway, however, the pattern changed significantly (Fig. 5.15). At the level lowest to the ground, the hottest position was windward of the fire, but at the level of the top of the hut the warmest position moved leeward of the fire. This was presumably the effect of the wind blowing around the outer edges of the hut and creating an area of negative pressure against the leeward outer surface of the hut, which followed the rounded contour of the hut wall. When the fire location was moved even further leeward of the hut, however, this effect disappeared (Fig. 5.16), whereupon the hut and the fire essentially act as two separate entities.

Figure 5.10. Temperature gradient for 0.5m h. fullcircular windbreak without fire under very cool conditions.

Figure 5.11. Temperature gradient for 1.0m h. semicircular windbreak with fire under very cool conditions.

Figure 5.12. Temperature gradient for 0.5m h. fullcircular windbreak with fire under very cool conditions. The horizontal heat distribution was similarly more ‘controlled’. The position lowest to the ground was no longer characterised by an uneven distribution of heat and smoke, but was markedly even around the whole perimeter of the fire, for both semi-circular and fullcircular windbreaks (the horizontal gradients are not reproduced here due to limitations of space). There were, however, two distinctions between the results for the two types of wind-break. First, the lowest position on the semi-circular windbreak had a rather uneven lateral heat

Figure 5.13. Temperature gradient for hut without fire under very cool conditions.

84

The Methodology for Testing for Microclimatic Selection daily maximum temperatures and predictability were accentuated by the continuance of an extended period of drought. This pushed up the daytime temperatures by several degrees (which met the 35o C minimum temperature requirement). The site itself was on a flat stretch of rural property that is fully exposed to the elements (sun and wind) in all directions and at the time the ground was barren of cover, exposing the mid-brown soil. The ambient outside temperatures throughout the duration of each scenario were relatively consistent at 48o C at 0.5 m above ground level, a light slightly cool breeze blew at a relatively consistent speed of 0-0.5 m/s and relative humidity was consistent at 8%. The experiments were performed over a period of four consecutive days from 13th to 16th January 2003. Only one scenario was run each day and was timed to centre on midday when the sun was at the zenith. At this time of day the ground area under the sunshade was in maximum shade throughout the duration of the experiment and the area shaded was most constant, increasing slightly and then decreasing slightly.

Figure 5.14. Temperature gradient for hut with fire inside under very cool conditions.

Figure 5.15. Temperature gradient for hut with fire outside doorway under very cool conditions.

Figure 5.16. Temperature gradient for hut with fire downwind of doorway under very cool conditions.

The Experiments in Hot Ambient Conditions The location and ambient conditions: The region of Forbes, central-western New South Wales, Australia, was selected as the location to perform the experiments under hot-arid conditions because the daily maximum temperatures there in January (the time of the experimentation) average 33o C (Australian Government Bureau of Meteorology 2006) and the climatic conditions at that time of year are relatively predictable and stable. At the time of performing the experiments, the average

Figure 5.17. Plan and elevation/section of shade structures. 85

The Evolution of the Built Environment Design and Construction of the Structures: There were four different scenarios using two different shade structures (Figs. 5.17-5.18). Both shade structures consisted of four white aluminium vertical corner poles supporting a white aluminium horizontal pole frame 2.4m square, but one structure was 1.2m height and the other was 2m height. Each structure was then sequentially covered in two different sunshade thicknesses (one layer and four layers of 50% porosity black plastic shade cloth, which equates to 50% and 94% porosity) suspended over the horizontal frame. The structures were oriented to face the prevailing northerly breeze, which advantageously corresponded to the northerly sun angle.

Figure 5.19. Vertical arrangement of globes in the scenarios in the hot-arid experiments. Results and Conclusions: The results of the experiments under hot ambient conditions were less dynamic than for those under very cool conditions, due to the absence of a fire (Figs. 5.205.23). They also relied on making adjustments to the structure itself, rather than on the addition of a thermal device that is physically independent of the structure, as is a fire.

Figure 5.18. 2.0m high shade structure during setup. Monitoring Equipment: The globes used in the scenarios were attached to strings that hung from the roof of the sunshade (Fig. 5.19). The globes were spaced at 0.5m intervals, with the lowest globe suspended 0.5m above the ground. That is, in the 1.2m high structure the spacing was 0.5m, 1.0m and 1.2m above ground and in the 2.0m structure the spacing was 0.5m, 1.0m, 1.5m and 2.0m above ground, placing the highest globes just below ceiling height in each case. The bottom of the string was then weighted to the ground to prevent it from blowing sideways in the breeze. The string-lines were then arrayed as shown in the various respective plans and section drawings. In addition to the under-sunshade globes a series of globes, suspended from a wooden stake similar to those used in the cold climate experiments, was located away from the structure to record the temperatures beyond the shaded area.

The results may be characterised as follows. First, at the lowest positions, the taller sunshades were characterised by a greater effect from the breeze, although the temperatures were less evenly distributed than they were under the lower sunshades, where temperatures were laterally evenly distributed. Secondly, in the higher positions, the taller sunshades were characterised by having the cooler sections towards the down-wind side of the structure, whereas the lower sunshades were characterised by having the cooler sections towards the up-wind side. Thirdly, the greater thickness of shadecloth was characterised by cooler temperatures below throughout the shaded space, with the exception of the area directly underneath the shadecloth. The temperatures there either equalled that of the outside air, under the taller structures, or exceeded it, under the lower structures. That is, the vertical temperature distribution was relatively small under the lower thinner shadecloth, slightly greater under the taller thinner shadecloth, marginally greater again under the lower thicker shadecloth, and very high under the taller thicker shadecloth.

Equipment Setup: The structures for each scenario were positioned on an area of flat ground and so as to be unobstructed from the sun and the breeze. The main axials of the shade structures were then aligned with one side facing due north and the wind direction. Recording Sequence: The sequence for recording the experiments was as follows: 1. 1 layer of shade-cloth on 1.2m high framework. 2. 4 layers of shade-cloth on 1.2m high framework. 3. 1 layer of shade-cloth on 2.0m high framework. 4. 4 layers of shade-cloth on 2.0m high framework.

86

The Methodology for Testing for Microclimatic Selection Figure 5.20. Temperature gradients for 1.2m h. shade structure with 50% porosity under hot-arid conditions.

Figure 5.21. Temperature gradients for 1.2m h. shade structure with 94% porosity under hot-arid conditions.

Figure 5.22. Temperature gradients for 2.0m h. shade structure with 50% porosity under hot-arid conditions.

Figure 5.23. Temperature gradients for 2.0m h. shade structure with 94% porosity under hot-arid conditions

. 87

The Evolution of the Built Environment systems in buildings that has created the impetus for evolutionary change in classes of buildings at the macroscale.

MCS AND THERMAL CONTRADICTION IN RUDIMENTARY STRUCTURES The first conclusion that can be drawn from the above experiments is that, as a consequence of humans having built structures, their thermal choices and thermal control increased markedly beyond what was available naturally in the environment, whether this was preconceived or not. However, these experiments also show that thermal choices and thermal control are more easily produced under cold conditions than hot-arid conditions. The implications of this are that MCS is more easily created in cold climates than hot-arid climates, due to the prevalence of the use of fire for spatial heating in cold climates compared with its more limited and utilitarian role in hot climates, and because fire, by its nature, creates a wide range of microclimates that are easily controlled by the simple construction of windbreaks etc. Producing the same degree of thermal choices and thermal control in hot-arid climates as that produced by a fire in cold climates requires the construction of vastly more complex structures, generally that of multi-roomed buildings composed of thermally diverse spaces, or single spaces that are easily adjusted to create a range of microclimatically diverse environments.

THE FIELD EXPERIMENTS: SIMPLE HUTS Aim The aim of the second set of experiments was to record the range of temperatures inside and outside of a series of six different generic huts in a temperate climate, so as to ascertain the degree of temperature variability generated by the structures (thermal choices), and the degree of behavioural consistency proportionate with progressively opening the interior up to the outside environment (thermal control). The six hut types multiplied by the four different open states produced twenty four different individual scenarios. The different huts included: 1. one lightweight on-ground dome, 2. one heavyweight on-ground dome, 3. one lightweight semi-subterranean dome, 4. one heavyweight semi-subterranean dome, 5. one lightweight on-ground rectilinear hut, and 6. one heavyweight on-ground rectilinear hut..

The second conclusion that can be drawn from the experiments is that even rudimentary structures possess emergent properties that are the result of the thermal interaction of their components, some of which operate in contradiction with other features and some of which do not. Whilst the level of thermal contradiction within simple and rudimentary structures is relatively low, it is, however, more easily resolved, and MCS is thus more easily enhanced in structures in cold climates than in hot climates. This is due to the closeness of the connection between the thermal system and the structures themselves. Under cold conditions, the warming of the space by fire physically separates the thermal system from the structure. The contradiction between them is therefore minimal. Under hot-arid conditions, however, the cooling of the space using features of the structure itself intimately connects the thermal system with the structure. The contradiction between the two systems is therefore potentially significant, because making alterations to one system can potentially contradict the operation of the other system, and vice versa. With this in mind, it is interesting to note that buildings in hot-arid regions of the world generally developed into far more complex forms long before structures in cold regions. Building systems in cold climates would have remained generally operationally separate from the thermal systems until the widespread introduction of glazing into building facades, which would have consequently replaced the active system provided by fire with the passive system provided by the green-house effect. The interconnectedness between the thermal and material systems would have thus generated a higher level of operational complexity and contradiction. The implication arises that there may be a connection between the level of contradiction between thermal systems and the material

There were four different states of openness: 1. hut fully closed, 2. eastern door only open, 3. door and roof only open, and 4. four wall sections and roof vent open.

The Location and Ambient Conditions These experiments were carried out on a rural property near Wilberforce, 40kms west of Sydney, Australia, where the spring months are characterised by stable and temperate weather. The site was selected because it was readily accessible, is flat and level and is exposed to the elements (sun and wind), but with the wind direction being confined to a reliably westerly/north-westerly direction due to the layout of buildings and natural features surrounding the site. The experiments were performed during SeptemberOctober (autumn) when the climate is consistent, reliable and temperate. They were also performed during a period of extended drought and consequently the sky was clear of cloud for the duration of the experimental period. Each scenario ran for twenty-four hours, commencing at 11pm and finishing at 11pm on the following evening, and so the environmental temperatures cycled through very cool in the early morning (low to mid 10s oC) to very warm during the day (high 20s oC). A light, westerly afternoon breeze blew consistently every day from approx. 11am to 3pm, peaking in intensity between 12noon and 1pm at approx. 1.5 to 2 m/sec.

88

The Methodology for Testing for Microclimatic Selection at 1.0m above floor at ceiling height. However, due to the arching over of the walls in the domed huts, the rectilinear hut could accommodate more globes than the domed huts. In each scenario a ‘control’ series of globes recorded the exposed environmental temperatures outside the huts, with globes arrayed on a vertical string-line at equivalent heights of 0m, 0.5m, 1.0m and 1.5m above ground.

Design of Structures and Scenarios The huts were designed and built as scaled-down models of various types of very early huts. They were sized to be small enough to be easily constructed, but large enough for the thermal performance to correctly simulate that of a full-scale structure and for the interiors to be easily accessible. Three different types of hut sub-structures were built to form the basis for six different types of hut, one each being lightweight and one heavyweight. The first type was a 1.7m dia. circular domed hut set 0.5m into the ground (Lightweight: Figs. 5.24- 5.25; Heavyweight: Figs. 5.26-5.27), the second was a 1.7m dia. circular domed hut set at ground level (Lightweight: Figs. 5.28- 5.29; Heavyweight: Figs. 5.30-5.31), and the third was a 1.5x1.5m rectilinear hut set at ground level (Lightweight: Figs. 5.32- 5.33; Heavyweight: Figs. 5.345.35).

Equipment Setup The huts for each scenario were positioned on an area of flat ground and so as to be unobstructed from the sun and the prevailing wind throughout the whole of the day. The main axials of the shade structures were then aligned with one of the four side openings facing due north and the midday sun. The first series of scenarios involved the lightweight huts (see below: Recording Procedure), for which the covering consisted of only the hessian bags. The second series involved the heavyweight huts, for which the bags were filled with the earth that had been excavated to accommodate the in-ground hut (and that had been set aside and kept dry for this purpose).

The lightweight structures consisted of an external covering of hessian sacks fitted evenly and tightly to the sub-structure and the surrounding ground. This was intended to simulate the thermal performance of reed or thatch and had a time lag of approximately 0.5hr. An open weave wire mesh of negligible thermal mass was attached to the roofs of the sub-structures to support the external covering and to prevent it from sagging. Four scenarios were run using these structures, with progressively more interior opened up to the exterior. The lightweight structures were then transformed into heavyweight structures by increasing the thermal mass of the external covering. This was achieved by filling the hessian bags that formed the covering with dry earth to an average of 80mm thick. The earth consisted of a sandclay-topsoil mixture that had been excavated from the pit into which the in-ground structure had been constructed. The sacks had been ribbed so as to produce a more even external coverage. This was intended to simulate the thermal performance of adobe or rammed earth and had a time lag of approximately 2.0hrs.

Recording Procedure The aim of the procedure was to record the temperature difference between the outside environment and the inside of the huts over a twenty-four hour period, a full diurnal cycle. The experiments were run back to back so as to keep the background environmental conditions as consistent as possible between scenarios, although this included a twenty four period in between each scenario to set up the following scenario and to allow the temperatures inside the huts to ‘normalise’. The temperatures, wind speed and humidity were recorded every two hours, commencing at 11pm and running through to 11pm the following evening. The temperatures inside the globes were read at each position using a thermocouple inserted into the globes. Approximately one minute was allowed for each reading so as to allow the wires to adjust to the conditions before the temperature was read off the multi-meter.

The six different types of huts had a floor to ceiling height of 1m, an internal floor area of 2.25m2, four openable side sections of 3.8% of the external wall surface area each and 15.6% of the interior surface area in total, and one openable roof vent of 2.0% of the interior surface area. The openings faced north, south, east and west respectively and were covered as per the walls, but in such a way that the panels could be progressively removed for each successive scenario.

The sequence for the scenarios was as follows: 1. Lightweight huts fully closed. 2. Lightweight huts with one east facing wall section (door) open. 3. Lightweight huts with one east facing wall section and roof vent open. 4. Lightweight huts with four wall sections (north, south, east and west facing) and roof vent open. 5. Heavyweight huts fully closed. 6. Heavyweight huts with one east facing wall section (door) open. 7. Heavyweight huts with one east facing wall section and roof vent open. 8. Heavyweight huts with four wall sections (north, south, east and west facing) and roof vent open.

Environmental Monitoring Equipment The environmental monitoring equipment was similar to that used in the hot and cold climate experiments outlined above. It consisted of a windspeed meter, a humidity meter, and a thermocouple and multi-meter to record the temperature inside the black globes tied to strings arrayed around the inside of the huts. The globes were hung on vertical string-lines inside the huts with the lowest globes at floor level, mid level globes at 0.5m and highest ones 89

The Evolution of the Built Environment

Figure 5.24. Plan and section of lightweight semisubterranean domed hut.

Figure 5.25. Interior of lightweight semi-subterranean domed hut.

Figure 5.26. Plan and section of lightweight on-ground domed hut.

Figure 5.27. Interior of on-ground domed hut.

Figure 5.28. Plan and section of lightweight rectilinear hut.

Figure 5.29. Rectilinear hut under construction. 90

The Methodology for Testing for Microclimatic Selection

Figure 5.31. Semisubterranean domed hut under construction. Figure 5.30. Plan and section of heavyweight semisubterranean domed hut.

Figure 5.32. Plan and section of heavyweight on-ground domed hut.

Figure 5.33.Heavyweight on-ground domed hut.

Figure 5.34. Plan and section of heavyweight rectilinear hut.

Figure 5.35. Heavyweight rectilinear hut. analysed according to the definition that thermal choices represent the maximum possible temperature range that is available in addition to that of the outside environment and thermal control represents the degree to which the internal environment can be selectively and predictably

Results and Conclusions The results were entered into a database in Microsoft Excel and analysed from the extensive set of temperature differential graphs that were generated. The graphs were

91

The Evolution of the Built Environment altered or modified. Thermal choices arise as the result of either a temperature difference between the inside of the structure and the outside environment, and/or to a temperature variability present inside the structure itself (horizontally or vertically). The larger the temperature difference between inside and outside, or the greater the internal variability, the greater the thermal choices. Thermal control arises as the result of a thermal change being made that is consistent with, and can be anticipated from, a physical change made to the structure. In a fully closed state, the inside environment will be as far removed as is possible from the outside, because there is no direct interface with the outside, but as a hut is progressively opened the inside environment interfaces progressively more and more with the outside environment. Thermal control is therefore represented by the degree of change proportionate with the degree of behavioural consistency, as the interior is incrementally opened up to the outside.

between the lightweight and heavyweight structures (including the in-ground structures), with the heavier thermal mass dampening out the external temperature extremes, most noticeably at ceiling level. The ceiling level temperatures in the heavyweight huts never exceeded the outside temperatures, but the ceiling level temperatures in the lightweight huts exceeded the outside temperatures both when the roof vent was open and when it was closed, by 3.5 oC to 4.5oC. This resulted even though the wind speed during the heavyweight scenarios was lower than that for the lightweight scenarios, thus presumably marginally increasing the heavyweight internal temperatures and lowering the lightweight internal temperatures. Category 2, which shows the horizontal temperature range at the various heights across the huts, illustrates that there is a behavioural difference between the heavyweight and the lightweight huts. This was compared with an outside horizontal temperature variability that was assumed to be approaching zero as the site is without significant microclimatic variability, being flat, level and fully exposed to the elements. When the huts were fully closed, the heavyweight huts showed zero horizontal temperature variability at equivalent levels, but the lightweight huts showed significant variability, particularly during the sunlit hours. However, as the huts were progressively opened to the outside the difference between the heavyweight and lightweight huts progressively decreased (Table 5.1). This was due to the increase in temperature variability inside the heavyweight huts, rather than an increase in homogeneity in the lightweight huts, that resulted from the increasing amount of direct sun ingress and the corresponding increasing temperature difference between the sunlit and shaded areas inside the huts. That is, as the huts were progressively opened the horizontal temperature variability increased more in the heavyweight huts (average increase of 3.6oc from fully closed to fully open) than in the lightweight huts (average increase of 1.3oc).

The results of the analysis of the graphs are summarised here in a series of tables, which give an overview of the thermal principles derived from the analysis. The full set of graphs is, however, shown in Appendix A (the graph numbers shown here refer to those that appear in the Appendix). The full set of graphs represent four different categories of temperature differentials – Category 1: The raw outside and inside temperatures for the various degrees of exposure (Graphs 1-6). Category 2: The internal temperature range (internal maximum minus internal minimum) at 3am, 9am, 3pm and 9pm for the various degrees of exposure. Category 3: The outside-inside temperature differences (outside temperature minus inside temperature) for the various degrees of exposure (Graphs 7-12). Category 4: The closed-open temperature differences (closed temperature minus open temperature) for the various degrees of exposure (Graphs 13-18). Category 1, which shows the raw temperatures, illustrates that, as expected, there is a behavioural difference

HORIZONTAL TEMPERATURE VARIABILITY Hut Type

Lightweight SemiSubterranean Domed Lightweight OnGround Domed Lightweight OnGround Rectilinear Heavyweight SemiSubterranean Domed Heavyweight OnGround Domed Heavyweight OnGround Rectilinear

Average Horizontal Range (oC) of heights and times in Closed Mode

Average Horizontal Range (oC) of heights and times with door open

Average Horizontal Range (oC) of heights and times with door and roof open

Average Horizontal Range (oC) of heights and times in Fully Open Mode

1.3

1.6

1.8

2.5

1.0

1.8

2.0

2.8

1.5

1.7

2.4

3.4

0.0

1.3

1.9

2.9

0.0

1.3

2.3

3.4

0.0

1.3

3.0

4.5

Table 5.1. Horizontal Temperature Variability for Closed Mode through to Fully Open (Open Mode). (nb. Bold underlined indicates maximums and minimums).

92

The Methodology for Testing for Microclimatic Selection heavyweight domed huts (average range of 2.2 oC). That is, the vertical temperature distribution in the lightweight domed hut and the heavyweight rectilinear hut were most similar to that experienced naturally outside, whilst in the heavyweight domed huts it was least similar. As the huts were progressively opened, however, this pattern of behaviour varied. In only one case did the conditions alter consistently and that was with the heavyweight rectilinear hut, where the temperature difference decreased consistently as the hut was progressively opened. In each other case the behaviour was variable. The difference in the two heavyweight domed huts decreased consistently until the hut was fully opened (four walls and roof vent open), when it increased, and the difference in the lightweight huts increased with the eastern panel only open, then decreased with eastern panel and roof vent open, and then increased again when fully opened. The conclusion may therefore be drawn from this that thermal choices available in the form of outside-inside temperature differences are higher in very heavyweight structures than lighter weight structures. This is true for both structures that can be fully sealed from the outside as well as structures that can’t, such as gazebo type structures.

The increase in horizontal variability relative to openness was greatest in the heavyweight rectilinear hut (4.5oc), followed by the heavyweight on-ground domed hut and heavyweight semi-subterranean domed hut (3.4oc and 2.9oc respectively), followed by the lightweight huts. Amongst the lightweight huts, the rectilinear hut again had the greatest increase (1.9oc compared with 0.8oc and 1.2oc). The conclusion may therefore be drawn from this that, for structures with few openings, the internal horizontal temperature range (thermal choices) is higher in lightweight structures than in heavyweight structures. For structures that cannot be fully closed to the outside, however, the internal horizontal temperature range is higher in rectilinear structures with flat ceilings than in domed structures, presumably because in the latter the incoming heat is more unevenly distributed. Thermal control however, which is proportionate with the temperature difference between closed and open states, is highest in the heavyweight flat-ceiling rectilinear hut. Category 3, which shows the temperature differences between the inside of the huts and the outside environment, illustrates that when the huts were fully closed there was a significant overall increase in thermal choices, in the form of a temperature difference between inside and outside, as the thermal mass of the huts increased. The temperature difference between the heavyweight huts and the lightweight huts varied by more than twice as much (Table 5.2). The lightweight huts averaged 1.5 oC whilst the heavyweight huts averaged 3.7 o C. The actual temperature differences increased by a similar amount in each of the hut types, although the inground domed huts increased by slightly more (2.5oC) and the on-ground domed ones increased by slightly less (1.7 oC). The thermal choices in the form of vertical temperature variability differed somewhat from hut type to hut type. It was least in the lightweight domed hut (average range of 1.2oC) and the heavyweight rectilinear hut (average range of 1.5 oC) and greatest in both the

Thermal choices available in the form of a vertical temperature range are highest in very heavyweight structures with domed ceilings, because the temperature gradient between floor and ceiling level is higher than it is in structures with flat ceilings (Table 5.3). However, thermal control is highest in heavyweight flat-ceilinged rectilinear structures, because the temperature change induced by opening up the interior to the exterior was most consistent and most predictable. It was consistent because the temperature change is linear and it is predictable because the change in the vertical temperature distribution is least. That is, it is most similar to the ‘background’ vertical temperature range present outside.

OUTSIDE – INSIDE TEMPERATURE DIFFERENCES (OC) Hut Type Lightweight Semi-subterranean Domed Lightweight On-Ground Domed Lightweight On-Ground Rectilinear Heavyweight Semi-subterranean Domed Heavyweight On-Ground Domed Heavyweight On-Ground Rectilinear

Closed Mode

Door Only Open

Door and Roof Open

Fully Open (Open Mode)

Average

1.9

2.8

2.3

2.5

2.1

1.4 1.3

2.4 1.4

1.6 1.4

2.4 1.6

2.0 1.4

4.4

3.9

2.9

3.0

3.6

3.1 3.5

3.0 2.9

2.2 2.6

2.6 2.3

2.7 2.8

Table 5.2. Outside – Inside Temperature Differences (oC) for All Modes Averaged for All Heights: ie. (o/s 1.0m h. – i/s 1.0m h.) + (o/s 0.5m h. – i/s 0.5m h.) + (o/s 0.0m h. – i/s 0.0m h.) / 3 (nb. Bold indicates maximum thermal choices and underlined indicates maximum thermal control).

93

The Evolution of the Built Environment

OUTSIDE – INSIDE VERTICAL RANGE (OC) Hut Type

Closed Mode

Door Only Open

Door and Roof Open

Fully Open (Open Mode)

Average

Lightweight Semi-subterranean Domed Lightweight On-Ground Domed Lightweight On-Ground Rectilinear Heavyweight Semi-subterranean Domed Heavyweight On-Ground Domed Heavyweight On-Ground Rectilinear

1.9

2.2

2.2

2.6

2.2

1.2 1.6

2.0 1.5

1.9 1.9

2.4 2.1

1.9 1.8

2.2

1.9

2.0

2.1

2.1

2.2 1.5

1.6 1.3

2.2 1.8

1.8 1.5

2.0 1.5

Table 5.3. Outside – Inside Vertical Temperature Range (oC) for All Modes (nb. Bold underlined indicates maximum thermal control). CLOSED-OPEN TEMPERATURE DIFFERENCES (OC) Hut Type Lightweight Semi-subterranean Domed Lightweight On-Ground Domed Lightweight On-Ground Rectilinear Heavyweight Semi-subterranean Domed Heavyweight On-Ground Domed Heavyweight On-Ground Rectilinear

Closed Mode Door Only Mode

Closed Mode - Door and Roof Open

Closed Mode Fully Open Mode

Average

2.1

3.1

3.1

2.8

2.2 2.3

3.5 3.0

2.5 2.6

2.7 2.6

2.3

3.0

3.6

3.0

1.9 2.1

2.6 2.6

2.7 3.1

2.4 2.6

Table 5.4. Closed – Open Temperature Differences (oC) for All Modes Averaged for All Three Heights: ie. (i/s 1.0m h.closed – i/s 1.0m h.open) + (i/s 0.5m h. closed – i/s 0.5m h. open) + (i/s 0.0m h. closed – i/s 0.0m h. open) / 3 (nb. Bold indicates maximum thermal choices and underlined indicates maximum thermal control). Category 4, which shows the temperature difference between the closed mode and progressively opened modes of the huts at equivalent heights on the central vertical axis of the huts, again shows that there is a difference between the heavyweight and the lightweight huts (Table 5.4). The difference between the closed mode and the opened modes of the heavyweight huts was consistent and linear, with the difference increasing incrementally. However, the difference for the lightweight huts was variable, first decreasing and then increasing. The behaviour of the vertical range of differences from floor to ceiling was variable in each case. However, the rectilinear huts, both heavyweight and lightweight, had the smallest degree of change in vertical range as they were progressively opened (average of 1.6 o C and 1.4 oC respectively, compared with an average of 1.7 oC to 2.0 oC for the domed huts). Conclusions may therefore be drawn that corroborate the previous findings, that thermal control in the form of incrementally and predictably alterable temperatures are highest in heavyweight flat-ceilinged rectilinear structures. The internal environment is incrementally adjustable because the thermal change is linear with respect to the structure being progressively opened to the outside and it is predictably alterable because a vertical temperature distribution is maintained throughout that is similar to that experienced naturally outside.

MCS AND THERMAL CONTRADICTION IN SIMPLE HUTS Conclusions can be drawn from the above experiments that thermal choices are highest in structures that have light thermal mass with few openings, high thermal mass with numerous openings, domed ceilings and are semisubterranean, and thermal control is highest in structures that have heavy thermal mass, flat ceilings, circular floor plan and numerous openings. This is due to the inherent vertical and horizontal thermal variability inside the domed structures and the inherent vertical homogeneity inside the rectilinear structures. Thermal choices and thermal control are, therefore, often in contradiction and producing maximum thermal choices and maximum thermal control within a single structure is inherently difficult. As one is enhanced by accentuating certain traits the other is degraded, and vice versa. That is, huts possess emergent properties that arise spontaneously from the way in which the thermal contradictions are resolved. Not all traits are thermally contradictory, however, and their accentuation enhances both thermal choices and thermal control concurrently. The thermal features and traits that constitute the MCS of a class of buildings can, therefore, be subdivided into two

94

The Methodology for Testing for Microclimatic Selection categories, those where thermal choices and thermal control operate in thermal contradiction with their converse feature (where thermal choices contradict thermal control, and vice versa) and those where they operate in accordance with other features (where thermal choices do not contradict thermal control). In the case of features that operate in thermal contradiction, as the value for thermal choices increases, the value for thermal control decreases. In the case of features that operate in thermal accordance, both values increase or decrease proportionately. The majority of primary building features and base-line traits fall into the former category. Below is a table (Table 5.5) that lists the primary baseline traits of simple structures according to whether they operate in thermal contradiction or in accordance.

archaeological buildings within a chronological context. Such a method would thus generate a ‘map’ of the pattern of change over time of the MCS of classes of buildings. The methodology would need to be capable of modelling the MCS of buildings in terms of the composite built features, features that are both variable (albeit graded) and interactive. It would also need to be capable of distinguishing between those features that operate in thermal contradiction (where thermal choices contradict thermal control, and vice versa) can be discerned from those that operate in accordance (where thermal choices do not contradict thermal control). The purpose of the engineering-analysis was to ascertain the qualitative relationship between the properties of the built features of structures and the structure’s capacity to produce thermal choices and thermal control. The relationship formulated a set of thermal principles derived from generic buildings and operationally applicable to real structures. The thermal principles constitute a prescriptive qualitative logic for ordering and grading the built features of archaeological structures such that their capacity to create MCS can be modelled using base-line features. The engineering-analysis showed that the built features will either contribute to thermal choices or to thermal control (if, as thermal choices are enhanced, thermal control is reduced, and vice versa) or both consecutively (if thermal choices and thermal control are both enhanced as the properties of the feature are accentuated). Whilst the ordering of the built features according to their capacity in this regard is qualitative, the properties of the features become amenable to quantitative numerical conversion based on their relative capacity. That is, it is based on their relative position within the scale of maximum thermal choices – minimal thermal control (e.g. 0 on a scale of 0-10) to minimum thermal choices – maximum thermal control (e.g. 10 on a scale of 0-10), if thermal choices and thermal control are contradictory. Alternatively, if thermal choices and thermal control are not contradictory, the scale will be minimal thermal choices – minimal thermal control (e.g. 0 on a scale of 0-10) to maximum thermal choices – maximum thermal control (e.g. 10 on a scale of 0-10).

PRIMARY BASE-LINE THERMAL TRAITS Contradictions

Accords

Building exposure Number of spaces/rooms Roof flatness/peakiness Number of openings Relative floor level Solar access Thermal mass Cross-ventilation Insulation Heating devices Floor plan shape Cooling devices Room obstacles Thermal conductivity Room fixtures and fittings Table 5.5. Contradictions and Accords in Primary BaseLine Thermal Traits. Note that thermal contradictions exist inherently within discrete spaces and in the form of thermal-social contradiction, because the built space is the (thermally contradictory) environment within which the social is accommodated. Multipurpose spaces will thus suffer greater thermal-social contradiction than functionally discrete spaces. The thermal-social contradiction can potentially be alleviated via the creation of spatial complexes comprised of numerous thermally discrete conjoined spaces, which may or may not be individually thermally adjustable. That is, the building as a whole can potentially create an equivalent level of MCS as that of a single thermally adjustable space within which there is increased thermal contradiction. However, in spatial complexes the level of contradiction can potentially increase, and is inevitably likely to if the individual spaces become more physically complex, due to the increased number of thermal interactions between the more numerous building features and traits.

A minimal amount of data about the archaeological buildings is required. This is because, although some features or traits will have had a greater inherent influence on the MCS than others, no one feature will have had a governing influence. Therefore, the minimal amount of data required would include the base-line primary thermal features, the base-line building features that have a significant influence on the overall thermal performance of the building. The features included need not, however, be limited to only this subset of building features, but could potentially include other thermal features pertaining to the built environment at other scales of detail. The more features that are included, and the more detailed the information, the more closely the model would approximate the thermal capacity of the original structure. A structure could, for example, be modelled as the string of features ‘brick wall – domed

RELATING THE ENGINEERING-ANALYSIS TO REAL BUILDINGS Engineering-analysis is ahistorical by nature (Maxwell 2001) and cannot of itself reveal anything about the longterm pattern of change in the MCS from one class of buildings to another. For this, a complimentary method is required, one that can add a historical component and thus place the data pertaining to the MCS of individual

95

The Evolution of the Built Environment ceiling – 2 openings – ground level floor’. Alternatively it could be modelled as the more comprehensive string of features ‘double cavity brick wall – domed thatch and mud plaster ceiling – 1 north facing opening – 1 south facing opening – full potential cross ventilation – ground level floor’. The more comprehensive string of variables would more closely model the building’s real-life thermal capacity. Note, however, that what ever level of detail is selected, in order to be able to compare the MCS of structures within a single dataset, it is necessary to apply the same level of detail throughout.

MULTIVARIATE-ANALYSIS The best method by which MCS can be empirically measured so that the thermal capacity of different buildings can be compared is multivariate-data analysis, or just multivariate-analysis (MVA). MVA refers to a number of a mathematically based statistical techniques devised to analyse corpuses of data that are composed of multiple (more than three) variables. It does this by reducing the data to a form that may be easily inspected for patterns and structure. “It is a way of disentangling complex patterns of variation which are not otherwise easily assimilated” (Shennan 1988: 243). There are three reasons why MVA is ideally suited to analysing thermal capacity. First, MVA is a statistical methodology specifically developed to analyse large numbers of complex entities (objects) that are composed of large strings of variables (features and traits). As such, it is a way of empirically analysing large arrays of complex data.

Classes of Buildings The focus of the analyses is on classes of buildings, rather than individual buildings at specific moments in time. A class of buildings is comprised of those characteristics that are statistically significantly representative of an assemblage or group of buildings that occupy a position in space and time. For example, if the majority of buildings in an assemblage (e.g. the buildings within a defined occupational period of time and in a defined area) possess a majority of characteristics, it is those characteristics that define the class of buildings, even though there may be no one individual building in the group that possesses all of the characteristics. A class exists in ‘statistical space’. Variation may exist between the individual real-life buildings within a group, in terms of their morphological and compositional detail, but at the scale of classes of buildings there is a statistically significant likelihood that the majority of buildings within the class will possess the same morphological and compositional characteristics. The definition of a class is thus related to issues of scale. At a very fine scale of detail no class can exist, because every individual building will constitute a class of its own. Conversely, at the macro-scale a class is likely to be based on characteristics that are too all encompassing to be useful as a basis for comparative classification. For example, a class based on round houses versus rectilinear houses subsumes those variants that are transitional between them, as well as other features and traits of which the houses are an aggregate.

Secondly, MVA can illuminate trends and patterns within large arrays of complex data that are impossible to discern by simple means. That is, patterns within data composed of only two variables are easily discernible by graphing them on a two-dimensional x-y axis, one variable represented by the x-axis and the other by the yaxis. Multivariate data, on the other hand, being composed of numerous variables, requires a graph of numerous dimensions upon which to plot them. This becomes visually and cognitively impossible to comprehend (Shennan 1988: 241). MVA, however, reduces the variables to a smaller number, two or three, which still retain the underlying structure of the original data. The smaller number of variables can then be easily interpreted, either numerically in table form, or graphically on a two-dimensional plot or scattergram. Thus the interaction of the whole assemblage of features and traits that a building is composed of can be encapsulated into a single expression that equates to the emergent properties of the building. That is, the thermal capacity and MCS of the building can thus be represented by a single expression that results from the interactions of features that operate both in contradiction and in accord.

Traditionally, classes of buildings have been defined using qualitative methods or in terms of only specific characteristics (e.g. Kniffen 1965). More recently, however, classes of buildings and settlements have been quantitatively defined using statistical analyses, such as Smith’s Cluster Analysis of 85 Late Prehistoric and Romano-British settlements in North West Wales based on morphological characteristics, which produced seven morphological classes and two outliers (Smith 1974) (Figs. 5.36-5.37). Statistical methods more precisely define the degree of relatedness between different buildings, and between the degree of morphological and compositional relatedness between classes of buildings, than culturally-based qualitative rule-of-thumb methods. This is because they can empirically define the statistically significant characteristics shared by a group of entities.

Thirdly, MVA is ideally suited to analysing thermal capacity specifically, because it equates the entities (the buildings) with their composite strings of conjoined and interrelated variables (built features and traits), including feedback between the operations (Plog, F. 1974: 150). MVA is thus capable of empirically modelling emergent (thermal) properties of buildings and of overcoming the problem of otherwise having to limit the analysis to individual or small numbers of features. The latter is a common fault of non-statistical analyses of complex systems and objects and is problematic because it introduces bias by placing importance and emphasis on the influence of individual features or traits before the level of their influence on the emergent properties is established.

96

The Methodology for Testing for Microclimatic Selection Figure 5.36. Cluster Analysis results for Late Prehistoric and Romano-British settlements in Northwest Wales (Smith, C.A. 1974: 163. Reproduced courtesy of The Prehistoric Society)

Figure 5.37. The 85 settlements grouped into the 7 classes and 2 outliers (Smith, C.A. 1974: 163. Reproduced courtesy of The Prehistoric Society) to ascertain what the long-term pattern of MCS change was and whether culture and/or climate have influenced the pattern. Case Study 3 looks at sets of buildings during a phase shift from one class of buildings (‘pithouses’) to another class (‘pueblos’) in regions that were culturally separate (Old World and New World), but which were climatically similar (two hot-arid sets and two sets with a very cool season). If MCS is selectively neutral then random behaviour will be most apparent during a phase shift, when the action of selective forces are most apparent. If MCS is selectively neutral then random behaviour will be apparent in spite of the diminishing capacity to produce thermal choices and thermal control that accompanies urban saturation. Case Study 4 is an illustrative example of a case where the MCS within a single settlement gradually reduced over time, due to high inertia in the very massive load-bearing structures and

Multivariate-Analysis in the Study The MVA was set up to test for the historical or longterm pattern of change in the MCS of classes of buildings within the archaeological record. The intention was to test the null hypothesis that the pattern of behaviour of MCS within classes of buildings will have been random over time. To test this hypothesis the MVA was performed on several large groups of buildings from numerous time periods and regions from around the world. These were divided into four separate case studies, each with a slightly different aim and set of buildings. Case Study 1 looks at a global ethnographic set of buildings to ascertain if a correlation exists between vernacular buildings and their climatic and/or social context. Case Study 2 looks at a regional set of buildings 97

The Evolution of the Built Environment very conservative building practices. The case study is subdivided into two parts, a and b. Both look at a set of buildings from one urban settlement, a settlement that altered from low-density to high-density and eventual collapse within a span of several centuries. Part a examines the MCS within the settlement itself and part b looks at the implications for this within a wider regional and temporal framework. The case studies were tested using the MVA techniques of Discriminant Analysis (also known as Cannonical Vector Analysis) and Multiple Correlation Analysis, but the ethnographic dataset also used Principal Components Analysis (PCA). These are each discussed below and their application is discussed in the chapters outlining each of the case studies.

analysis itself is not used to assign entities to a group. Discriminant Analysis is thus ideally suited to analysis of buildings, that tend not to move about the landscape and that can therefore be relatively confidently assigned to groups according to, for example, site location, stratigraphic level, time period, climate classification etc. Discriminant Analysis is performed by deriving a variate, which is a linear combination of the attributes of the variables, and weighting the variate such that the between-group differences are accentuated relative to the intra-group differences (Baxter 1994: 185-186; Hair et al. 1998: 244-246). The grouping of the entities can therefore be made graphically clear and outliers can be easily recognised. Discriminant Analysis can be performed only if the number of entities exceeds the number of variables. If the number of entities and the number of variables are too close the results will be unstable. A ratio ranging from 3:1 up to 5:1 (entities:variables) is generally recommended (Baxter 1994: 200). The datasets in the study comply with this recommendation.

The Software Program Used for the MVA The software program selected to do the Multivariateanalysis was StatistiXL. It was selected on the basis that it is very user friendly and operates as an add-in to Microsoft Excel, into which the primary data had been entered. The data could therefore be analysed straight from the Excel spreadsheets, using a wide range of statistical methods. Each method is easy to use and understand, using only the on-line Help.

What Various Discriminant Plots Mean Each entity on a discriminant plot is represented by a separate point. The different ways the points appear on the graph say something different about the similarities and dissimilarities between the entities in relation to the way the holistic sum of the features and traits describe the entities. That is, the nature of the different emergent properties.

However, the Structure Coefficients were best calculated using an alternate program (Emeritus Professor Richard Wright 2004: personal communication), as StatistiXL calculates them using only the Standardised Discriminant Function Coefficients, whilst four additional values should be included. The alternate program used was MVNUTSHELL. This process did, however, have drawbacks. Whilst StatistiXL presents the Function Coefficients graphically on the Discriminant plots in the form of a vector, where the length of the vector represents the strength of the influence of particular traits, MV-Nuts presents the data in the form of a numerical table, and this data is not transferable to the Discriminant plot. Therefore, whilst the principle behind presenting the Structure Coefficients in the form of a vector is discussed below, the study actually used the MV-Nuts numerical table to draw inferences about the relative influences of each of the variables over the final MVA results.

Figs. 5.38 to 5.45 show different arrangements of entities on different Discriminant plots. Each graph shows thirty individual entities that have been pre-classified into six different groups (1 to 6). Whether or not these six groups actually fall into six different classes of entities is ascertained by their relative positioning on the graph. If entities that belong to the same group form a tight cluster away from other groups, then they form a class. The graphs can also show the degree of influence that the various composite features and traits have had on the final positioning of points. These influential strengths are known as the structure coefficients. In each of the demonstration graphs there are three vectors that represent the structure coefficients, the three composite features that comprised the entities (A, B and C). The length of each vector represents the degree of influence of the feature relative to the strength of the influence of the other two features.

DISCRIMINANT ANALYSIS Discriminant Analysis is a technique for estimating the relationship between a single categorical variable (the entities: ie. the buildings) and a set of independent metric variables (the attributes of the entities: ie. the contribution of the built features to the MCS). It is a method used to distinguish between two or more categories or groups, using a weighted combination of continuous variables. It does much the same thing as multiple regression, but is used to discriminate between categories rather than predict a continuous variable. This technique is best applied to the analysis of entities where their grouping is independent of their variables, where the entities are grouped according to pre-existing criteria such that the

Fig. 5.38 shows a random patterning of entities and similarly sized vectors. No entities are sufficiently similar/dissimilar to other entities to form a class and no one feature has overriding responsibility for the final arrangement. Fig. 5.39 shows clear grouping of entities into six separate classes and similarly sized vectors. Fig. 5.40 shows clear grouping of entities into six separate classes and dissimilarly sized vectors. 98

The Methodology for Testing for Microclimatic Selection Features A and C bear greater responsibility for the final grouping than feature B. Fig. 5.41 shows clear grouping of entities into four separate classes and dissimilarly sized vectors. Entities in groups 3 and 4 form a single class and entities in groups 5 and 6 form another single class.

Fig. 5.42 shows an initial random patterning of entities that concentrates over time to form a definable class. Fig. 5.43 shows patterning that concentrates over time to form a definable class of entities. The behaviour moves in a particular direction over time under the influence of feature C. Fig. 5.44 shows clustering of data except for one whole class of entities (group 6), which is an outlier to the other groups, under the influence of feature C. Fig. 5.45 shows clustering of data except for one whole class of entities (group 6) and one entity within group 1, which is an outlier to the other entities, under the influence of features B and C.

The groups into which the entities have been placed might easily represent different chronological periods, in which case the final arrangement of the points on the graph would say something extra about the entities. It would say something about the way the holistic sum of the features and traits (the emergent properties) have changed over time. For example, the groups of the entities in the demonstration graphs might represent different chronological periods, with group 1 being the earliest and group 6 being the latest.

Figure 5.38. Random discriminant plot.

Figure 5.39. Disriminant plot with 6 groups.

Figure 5.40. Disriminant plot with 6 groups.

Figure 5.41. Disriminant plot with 4 groups.

99

The Evolution of the Built Environment

Figure 5.42. Random disriminant plot becoming grouped.

Figure 5.43. Disriminant plot becoming grouped.

Figure 5.44.Disriminant plot grouping becoming distinct group.

Figure 5.45. Disriminant plot grouping with 1 outlier becoming distinct group .

How Classes of Buildings Might Appear on a Discriminant Plot

subsequent chapters). For example, the Naqada 1 buildings operate very differently thermally to the recent vernacular buildings, because they are single-room, single-storey, lightweight thatch and slightly semisubterranean structures in low-density settlements, whereas the vernacular buildings are multi-room with courtyard/s, multi-storey, heavyweight brickwork with large lattice openings (mashrabiyya) in high-density settlements. However, where the properties of the MCS are relatively similar, resulting from the buildings possessing very similar thermal features and traits, there is minimal discrimination between the groups of buildings. The Old Kingdom and Middle Kingdom period buildings are both generally multi-room with no courtyards, one and two storey, heavyweight brickwork with insulated (thatch) plaster ceilings. The overall pattern of MCS change is one of regular movement away from the Naqada 1 class of buildings towards that of the Phaoronic, then the Byzantine and Arab and culminating

The application of Discriminant Analysis produces Discmininant plots in which the points represent the MCS or thermal capacities of individual buildings. Fig. 5.46 shows an example of a Discriminant plot of the MCS of several groups of buildings, of which each group represents a separate class of building. The plot was generated using archaeological examples from Egypt, grouped according to chronological period. Each point on the graph is positioned according to the building’s overall capacity to produce thermal choices and thermal control relative to that of the other buildings. The plot shows that certain classes of buildings can be clearly discriminated from other classes of buildings, based on the properties of the MCS (the methodology used to calculate the MCS is discussed in detail in the 100

The Methodology for Testing for Microclimatic Selection

Figure 5.46. Discriminant plot of MCS of Egyptian buildings showing grouping. with the recent vernacular class of buildings.

The Correlation Significance (p) is the value of how statistically significant the Correlation is or, rather, how likely the result is to be true. The value ranges from 0 (statistically significant and therefore likely to be true) and 1 (statistically insignificant and therefore likely to be not true). It is generally accepted that a value of 0.05 or less is required before it can be regarded as being true, but values of only slightly less are still likely to be true. A value of 0.06 equates to a 6% chance of being not true, compared with 0.01 or a 1% chance of being not true.

Therefore, in a case where a Discriminant plot showed a random scattering of points regardless of the grouping of buildings by region and period, the null hypothesis would not falsified and it could be stated that selection is unlikely to have operated on the MCS of the buildings. Conversely, in a case where a plot showed a regular or lineal patterning of points with regard to grouping by region and period, the null hypothesis would be invalidated and it could be stated that it is highly likely that selection has operated on the MCS of the buildings, selecting for buildings with relatively greater thermal choices and thermal control over others with lesser thermal capacity.

Principal Components Analysis (PCA) is a technique for reducing a large number of metric multivariate data to a smaller number of new uncorrelated variables. This is achieved by deriving factors from the total variance that are estimates of the variance that is shared or common to the variables. The factors contain small proportions of the variance that is unique to each variable (not predicated on or associated with other variables), but not in sufficient amounts that the overall factor structure is distorted (Baxter 1994: 48-50; Hair et al. 1998: 90-91). PCA derives the combinations of variables that explains the greatest amount of variance, starting with the combination that explains the greatest amount (the first principal component), and then the next greatest amount (the second principal component), and so on for smaller and smaller percentages of the variance. The first one or two principal components will generally account for the majority of the variance. These are then itemised as the casewise PCA scores. PCA was used in Case Study 1 to reduce the range of climatic variables to a single variable

CORRELATION ANALYSIS AND PRINCIPAL COMPONENTS ANALYSIS (PCA) Correlation, or Multiple Regression Analysis, is a measurement of the strength of the relationship between a single criterion variable and several independent metric variables, making no prior assumption of interrelationship. The Correlation Coefficient (r) is the value of the strength of the correlation (Hair et al. 1998: 148-149). Multiple Correlation was used in the study and is a measure of the relationship between a single variable and several independent variables. The values can range from 0 (not correlated) to +1 (perfectly positively correlated).

101

The Evolution of the Built Environment (PC 1) such that it could then be correlated with the building variables to generate a single Correlation Coefficient.

feature, it was given a numerical value of, for example, 0 if it created maximum thermal choices and minimum thermal control, and 10 if it created minimum thermal choices and maximum thermal control. However, where the feature operated in thermal accord with other features, it was given a numerical value of, for example, 0 if it created minimum thermal choices and thermal control and 10 if it created maximum.

RELATING THE MULTIVARIATE ANALYSIS TO REAL BUILDINGS The application of MVA to model the MCS of buildings is based on treating the archaeological buildings and rooms (the entities) as strings of thermal features and traits (their capacity to contribute to either thermal choices or thermal control, or both), using the building’s base-line traits and primary thermal features. Only a general introduction to the way in which the data was compiled in the datasets is given here. This is because, due to the different scales at which the MCS was calculated in each of the case studies (generic buildings, room-weighted buildings, or individual rooms), the way in which the numeric values of the variables were measured differed slightly in each of the datasets. Detailed descriptions of this are presented in each relevant chapter. Only buildings that were sufficiently well excavated and comprehensively documented were included in the analyses. Consequently, the data within the individual datasets was quantitatively equivalent and, where the scale of analysis was similar (eg. roomweighted buildings), could also be cross-compared with buildings from other datasets.

The qualitative variables listed in Table 5.5 can thus be magnified to encompass a number of graded qualitative categories (graded in terms of their capacity to produce thermal choices or thermal control, or both), which are then amenable to quantitative numerical equivalents. The features that operate in thermal contradiction then become: 1. Degree of exposure to the outside in eight compass directions plus vertically, ranging from either exposed (high thermal choices) to unexposed (high thermal control). The more exposed a building is to the outside environment, the more closely synchronised it will be with it and ergo the more capacity it will have to produce thermal choices but, therefore, the less capacity it will have to produce thermal control; 2. Degree of roof flatness, ranging from pyramidal or domed (high thermal choices) through to unroofed or flat (high thermal control). The greater the vertical temperature stratification, the greater the vertical temperature variability. Note that the natural temperature stratification outside is actually less than it is inside a flat roof structure (Fig. 5.47); 3. Degree of floor depth relative to ground level, ranging from below ground (high thermal choices) through to elevated above ground (high thermal control); 4. Degree of wall and roof thermal mass, ranging from none (high thermal choices) through to very heavy (high thermal control). Very heavy thermal mass was defined as stone, adobe, brick etc. in excess of 0.6m thickness or as earth-integration of more than 1.5m (ie. the floor is more than 1.5m below ground); 5. Degree of wall and roof insulation, ranging from absent (high thermal choices) to present (high thermal control). Insulation was deemed to exist if a high percentage of vegetable fibre existed (ie. wood, reeds or thatch); 6. Degree of interior roundedness, ranging from square (high thermal choices) through to circular (high thermal control). Circular was defined as consisting of 12 internal angles and square was defined as 4; 7. Degree of elongation, ranging from a ratio of 8:1 (high thermal choices) through to more than 1:1 (high thermal control); 8. Number of free-standing internal posts, niches and benches, ranging from many (high thermal choices) through to none (high thermal control); 9. Number of discrete spaces (for whole structures), or number of adjoining rooms, upper storeys and lower storeys and nearest neighbour distance (for individual rooms), ranging from many (high thermal

The numerous interactive thermal operations of which the thermal performance of buildings is comprised were categorised into different variables. This process was based on the principle that buildings and rooms are subdivisible into two theoretical sets of thermal operation, those that are purely internal to the building or room and those that involve interactions with the surrounding spaces, either built or external. The set of internal operations consisted of those that involved built features that existed within the spatial envelope, plus the spatial envelope itself (the walls, floor and roof). The set of external traits consisted of those that involved built features that existed beyond the spatial envelope. These were, however, influenced by features that existed inside the space, as they are the result of thermal interaction between the spatial interior and the spatial exterior. The conversion of the qualitative variables of which the rooms and buildings were comprised (e.g. 400mm below ground, or 7 internal posts) were converted to metric values that equated to their thermal contribution to either thermal choices or thermal control, if they were thermally contradictory, or both if they operated in thermal accord. For example, the shape of the floor plan (a qualitative trait) was converted to two separate quantitative values that together encapsulated the thermal properties of the original floor plan. First was the degree of roundness/angularity and, secondly, was the degree of elongation. The metric variables were thus ranked or graded in descending or ascending order based on the potential to contribute to the MCS. Where the particular feature operated in thermal contradiction to its converse 102

The Methodology for Testing for Microclimatic Selection choices) through to none (high thermal control); 10. Degree of thermal conductivity between floors (for whole structures only), ranging from non-conductive (high thermal choices and thermal control) through to fast conducting; 11. Size of floor plan area (for individual rooms only), ranging from large (high thermal choices) through to small (high thermal control), and the features that operate in thermal accordance become: 1. Number of openings, ranging from many (high thermal choices and thermal control) through to one; 2. Degree of solar access and cross-ventilation, ranging from yes if the potential exists (high thermal choices and thermal control) to no if it does not; 3. Degree of heating and cooling control, ranging from very high (high thermal choices and thermal control) through to low. Thermal control is deemed to be low if there is no apparent source of heating and/or cooling and very high if a graduated active heating or cooling source is present (e.g. mobile or numerous fires); 4. Number of transitional spaces as proportion of total number of spaces (for whole structures) or number of adjoining transitional spaces (for individual rooms), ranging from none (high thermal choices and thermal control) through to many.

Figure 5.47. Temperatures at various heights outside and inside a flat roofed structure (as per engineeringanalysis)

SUMMARY Engineering-analysis has previously been used in both archaeology and the sciences to discern the functional traits of objects. Its additional ability to discern the nature of the emergent properties of complex systems has, however, been largely overlooked. The emergent properties of complex systems cannot be understood in terms of reductionist reasoning because they arise spontaneously as the consequence of interactions between numerous, and often contradictory, operations. The emergent thermal properties of buildings are qualitatively and quantitatively discernible via engineering-analysis. They are comprised of operations that are either contradictory (where thermal choices contradict thermal control, and vice versa) or that are in accord (where thermal choices do not contradict thermal control). Generally, thermal choices within buildings equate to thermal variability. Conversely, thermal control equates to thermal homogeneity. Engineering-analysis is, however, ahistoric and tells us nothing about the way in which MCS in archaeological buildings has changed over time. Multivariate analysis can be used to calculate the MCS of archaeological buildings, based on thermal principles discerned from engineering-analysis. It can thus be used to map the pattern of change in the MCS of classes of buildings over time. It achieves this by treating complex objects (buildings) as synonymous with strings of inter-related variables, the built features and traits of which they are composed.

In the ethnographic Case Study 1, the regional Case Study 2 and the urban site comparative Case Study 4b the MVA was performed at the scale of whole buildings (generic buildings in Case Study 1 and room-weighted buildings in 2 and 4b). In the regional Case Study 3 and the urban site Case Study 4a the MVA was performed at the scale of individual rooms. An individual room was defined as a space that had walls on at least three sides, or at least two sides plus a roof. That is, courtyards and shade structures that were enclosed on at least two sides were treated as rooms. Courtyards bounded by only two walls were treated as exterior space and not as rooms. A building was defined as a series of conjoined rooms that shared a common exit or exits that lead to a publicly accessible outside area, such as a road or public open space. For the room-weighted buildings the numeric values of the variables were first calculated for each room separately and then these values were ‘weighted’ according to the number of rooms with similar numeric values as a proportion of the whole building. That is, the room-weighted numerical value for each room was multiplied by the percentage its ‘type’ occupied within the overall number of rooms. For example, a building composed of two rooms, both with high-domed roofs (numeric value of 1), one below ground and one at ground level (numeric values of 1 and 2 respectively), one with thatched walls and the other with timber walls (numeric values of 2 and 3 respectively), and both with thatch roof (numeric value of 2), would be calculated as 100% x 1 : 50% x 1 + 50% x 2 : 50% x 2 + 50% x 3 : 100% x 2 and represented by the string of room-weighted values 1 : 1.5 : 2.5 : 2.

Contradiction exists inherently in thermal systems, even in the most simple and rudimentary structures, although the contradictions can be resolved in a variety of ways. For example, in cold climates the contradiction is potentially relatively easily resolved, and the MCS is thus relatively easily enhanced, through the use of fire for warming. This is because fire readily generates a range of micro-climates without being directly connected to the material system of the building. Fire physically removes the structural system from the thermal system. By contrast, in hot-arid climates the level of contradiction is relatively difficult to resolve by passive means alone because the system for cooling the spaces is physically integral with the structure itself. Thermal contradictions also exist inherently between the thermal and the social,

103

The Evolution of the Built Environment because the built space is the (thermally contradictory) environment within which the social is accommodated. The thermal-social contradiction can potentially be alleviated via the creation of spatial complexes comprised of numerous thermally discrete conjoined spaces. However, in spatial complexes the level of contradiction

can potentially increase if the individual spaces become more physically complex. This is because the increased number of thermal interactions that would exist between the more numerous building features and traits would generate exponentially more thermal contradictions.

104

CHAPTER 6 – A Global Case Study of Generic Buildings: An Ethnographic Sample

of social, economic and technological information is listed. It was not necessary that the dataset be drawn from a wholly random set of societies, and the set used does not represent a wholly random sample. It was simply necessary that the set examined encompass a sufficiently wide range of regions and climates to be able to establish whether or not a correlation existed between the generic buildings, the social context and/or the prevailing climate. The data set used, whilst being somewhat lacking in specific types of climate (notably cold climates), was sufficient to adequately test for a possible correlation. That is, regardless of bias that might exist within the data, sufficient data existed to reveal whether a correlation existed or not.

INTRODUCTION This chapter outlines a global ethnographic case study examining MCS in generic buildings in different climates around the world (Case Study 1). It thus constitutes a global examination of MCS at the scale of generic buildings pertaining to whole ethnographic societies. Case Studies 2 (Chapter 7), 3 (Chapter 8) and 4 (Chapter 9) examine MCS at more detailed and ‘real-life’ scales of analysis.

CASE STUDY 1: GENERIC ETHNOGRAPHIC BUILDINGS WORLDWIDE This case study constituted an examination of generic buildings that pertain to only ethnographic societies. Its purpose was to establish an epistemological context for testing the null hypothesis in the following case studies, that each possess long time-depth. By limiting the time span in this case study to only ethnographic buildings it was possible to test for various factors that potentially influence MCS without the additional dynamic of a time dimension. The case study encompassed a wide range of climates and societies that post-date 1750. A uniformitarian approach was used so as to be able to ascertain whether or not a correlation exists between generic buildings and their social and/or climatic contexts.

Murdock’s 412 societies are not evenly distributed around the world, but tend to cluster in certain regions. There are, for example, very few examples from Europe and Northern Asia. The problems associated with the uneven distribution inherent in the Ethnographic Atlas have been recognised elsewhere (e.g. Cullen 1993: 226228). “An ‘autocorrelation’ problem exists to the extent that neighbouring societies tend to be similar. In a recent unpublished paper on “Design Effects in Standard Samples”, Malcolm Dow shows that use of the Ethnographic Atlas as a sample may lead to underestimates of variance by orders of magnitude” (White 1985: 5). To overcome these problems Murdock and Wilson reduced the original 412 societies to a smaller subset of 197 (Murdock & Wilson 1972). This smaller subset consists of a selection of one society from each ‘cultural group’, some of which originally contained several individual societies. The reduced subset has the additional advantage of having included the date to which the information pertains, as well as the bibliographical sources (Murdock & Wilson 1972: 254-255). Cullen, however, used an even further reduced dataset called the Standard Cross Cultural Sample, which contained 157 societies (Cullen 1993: 215-259). It is this Standard Cross Cultural Sample that forms the basis of the dataset used here (Fig. 6.1) although, for the purposes of this study, several further societies were removed for a number of reasons. The assemblage of societies used in the dataset is outlined below in conjunction with the climatic classification of each of the type-site locations.

Three separate MVA tests were performed. Test 1 analysed the degree of correlation between generic ethnographic buildings and their associated social context, Test 2 analysed the degree of correlation between the generic ethnographic buildings of only permanently based settlements and their associated climate, and Test 3 analysed the degree of correlation between generic ethnographic buildings and their associated degree of sedentism/mobility.

THE DATA: CROSS-CULTURAL GENERIC BUILDINGS A broad cross-section of generic buildings from around the world and from different climates and societies was examined. Given the vast array of different classes of buildings that could potentially be used, it was necessary to make a selection of buildings from diverse climates, regions and societies. For these reasons it was decided to base the dataset on the one used by Cullen, which was originally derived from Murdock’s Ethnographic Atlas (Murdock 1967). Murdock’s Atlas consists of 412 societies from around the world for which a wide range

The reasons for removing additional societies were, first, due to an absence of data about the buildings. For example, the Armenians lacked information pertaining to the building features. Secondly, examples that dated to A.D. 1750 or earlier were removed due to their nonethnographic origins. That is, earlier examples represented an early stage in the development of the buildings within the region. For example, the Inca in the New World, c. A.D. 1530, and the Babylonians in the 105

The Evolution of the Built Environment Old World, c. 1750 B.C. both represent an early phase in the development of buildings within the regions and are non-ethnographic. Where an alternative example from the same ‘cultural group’ existed this was used to replace the eliminated example. For example, the Neopolitans c. A.D. 1960 replaced the Romans c. A.D. 110. If there was no suitable replacement, however, the society was removed and not replaced. The final number of societies in the database was 145.

Most of the data used in the study was available from Murdock’s two sample sets (the Atlas and the reduced set). These included information about the time period (from the reduced set), the geographical bearing (from the reduced set), the degree of settlement sedentism/mobility (from the reduced set) and the building features (from the Atlas). The ‘cultural affiliations’, time periods, bearing, climate classifications and sedentism/mobility categories are listed in Table 6.1 below. This is followed by a detailed outline of the climatic classifications and the building features (the variables).

Figure 6.1. Locations and climates of type-sites in Case Study 1

ENTITIES (GENERIC BUILDINGS) IN THE GLOBAL ETHNOGRAPHIC CASE STUDY ‘Cultural Affiliation’

‘Cultural Approx. Bearing KoppenAffiliation’ Time Trewartha Grouping Period Climate in Database Khoisan and Mbuti Societies Nama 1 1860 27s,17e GBwal Kung 1 1950 20s,21e GBsab Hadza 1 1930 3s,34e GAwab Mbuti 1 1950 1n,28e Araa Niger-Kordofanian and Micellaneous African Societies Lozi 2 1900 16s,23e GAwal Mbundu 2 1890 12s,16e GAwbb Bemba 2 1897 11s,31e GAwbl Nyakyusa 2 1934 9s,34e GAwbl Thonga 2 1865 26s,32e GAwbl Luguru 2 1925 7s,38e GAwab Kikuyu 2 1920 1s,37e GAwbl Ganda 2 1875 0,32e GArbb Tiv 2 1920 7n,9e Awha Nkundo 2 1930 0,18e Araa

106

Simplified Climate

Murdock’s Sedentism/ Mobility

Sedentism/ Mobility Grouping in Database

Temperate Temperate Tropical Tropical

B B B B

1 1 1 1

Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical

R P I I I P P P P P

3 6 5 5 5 6 6 6 6 6

A Global Case Study of Generic Buildings Banen Ashanti Ibo Fon Azande Mende Wolof Otoro Songhai Masai Shilluk Afroasiatic Societies Fur Teda Kaffa Konso Somali Tuareg Riffians Amhara Egyptians Rwala

2 2 2 2 2 2 2 2 2 2 2

1935 1895 1935 1890 1905 1945 1950 1930 1940 1900 1910

5n,10e 7n,2w 5n,7e 7n,2e 5n,28e 8n,12w 14n,15e 11n,31e 17n,1w 3s,36e 10n,32e

Arab Awaa Araa Awha Awha Amha Bwha Bwha Bwia GAwab Bsha

Tropical Tropical Tropical Tropical Tropical Tropical Hot-arid Hot-arid Hot-arid Tropical Hot-arid

P P P P P P P P P B P

6 6 6 6 6 6 6 6 6 1 6

3 3 3 3 3 3 3 3 3 3

1880 1950 1905 1935 1900 1900 1926 1953 1950 1913

13n,25e 21n,17e 7n,36e 5n,37e 9n,47e 23n,6e 35n,3w 13n,37e 25n,33e 33n,38e

GBwhb GBwhl GAwbl GAwha GBwhb GBwhl Csak GAwab Bwhl GBwhk

Hot-arid Hot-arid Tropical Tropical Hot-arid Hot-arid Temperate Tropical Hot-arid Hot-arid

P Bs P P B B P P P B

6 1 6 6 1 1 6 6 6 1

Crak Csak Dolk Dcbc Csik GCshk Arha Amaa

Temperate Temperate Temperate Cold Temperate Temperate Tropical Tropical

T P P P P B S P

4 6 6 6 6 6 1 2

FTkc FTkc

Cold Cold

B B

6 1

GCsao GDcbc GBSld

Temperate Cold Cold

P S S

1 6 2

DCao Crak

Temperate Temperate

P P

2 6

Dcbo Dclc FTkd ECld

Temperate Cold Cold Cold

T S S B

6 4 2 2

GAwhb Amha Cwhl Awhb Cwhl Arha Arha Awha

Tropical Tropical Temperate Tropical Temperate Tropical Tropical Tropical

I R P T P B P S

1 5 3 6 4 6 1 6

Cwak

Temperate

P

2

Indo-European Societies Gheg 4 42n,20e 1910 Neapolitans 4 1960 41n,13e Irish 4 1932 53n,10w Russians 4 1955 53n,41e Kurd 4 1951 36n,44e Basseri 4 1958 29n,54e Vedda 4 1860 8n,81e Saramacca 4 1928 3n,56w Uralic Societies Lapps 5 1950 68n,22e Yurak 5 1894 68n,51e Altaic Societies Turks 6 1950 39n,34e Kazak 6 1885 48n,70e 47n,96e Khalka 6 1920 Japanese/Korean Societies Koreans 7 1947 38n,126e Japanese 7 1950 34n,134e Far North Asian Societies Ainu 8 1880 43n,143e Gilyak 8 1890 54n,142e Chukchee 8 1900 66n,177e Yukaghir 8 1900 70n,145e Dravidian and Mon-Khmer/Austroasiatic Societies Gond 9 1940 19n,82e Toda 9 1900 11n,76e Santal 9 1940 23n,87e Lamet 9 1940 20n,101e Vietnamese 9 20n,105e 1930 Semang 9 1925 4n,101e Nicobarese 9 1870 7n,94e Andamanese 9 1860 12n,93e Tibetan Societies Lolo 10 1910 29n,103e 107

The Evolution of the Built Environment Lepcha 10 1937 Garo 10 1955 Lakher 10 1930 Burmese 10 1965 Thai-Kadai Societies Rhade 11 1962 Tanala 11 1925 Javanese 11 1954 Balinese 11 1958 Iban 11 1950 Badjau 11 1963 Alorese 11 1938 Manus 11 1937 New 11 1930 Irelanders Trobrianders 11 1914 Tikopia 11 1930 Ajie 11 1845 Maori 11 1820 Marquesans 11 1800 Samoans 11 1829 Gilbertese 11 1890 Marshallese 11 1900 Trukese 11 1947 Yapese 11 1910 Palauans 11 1947 Ifugao 11 1910 Atayal 11 1930 Malayo-Polynesian Societies Orokaiva 12 1925 Kimam 12 1960 Kapauku 12 1955 Kwoma 12 1960 Siuai 12 1939 Tiwi 12 1929 Aranda 12 1896 Nadene and Eskimo Societies Ingalik 13 1885 Slave 13 1940 Kaska 13 1900 Eyak 13 1890 Haida 13 1875 Chiricahua 13 1875 Aleut 13 1800 Copper 13 1915 Eskimo Macro-Algonkian Societies Montagnais 14 1910 Saulteaux 14 1930 Gros Ventre 14 1880 Yurok 14 1850 Salishan, Hokan & Siouan Societies Bellacoola 15 1880 Twana 15 1860 Havasupai 15 1910 Eastern Pomo 15 1850 Omaha 15 1860 Hidatsa 15 1836

27n,89e 26n,91e 22n,93e 22n,96e

HBSlo Cwhl GAmhl Awhb

Temperate Temperate Tropical Tropical

P P P P

6 6 6 6

13n,108e 22s,48e 8s,112e 8s,115e 2n,112e 5n,120e 8s,125e 2s,147e 2s,151e

Awha Arab Araa Araa Araa Araa Awaa Araa Arha

Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical

I Ri P P I B P P P

6 5 3 6 6 5 1 6 6

9s,151e 12s,168e 21s,166e 35s,174e 9s,140w 14s,172w 3n,172e 6n,169e 7n,152e 9n,138e 7n,134e 17n,121e 24n,121e

Araa Araa Arab Crbl Araa Araa Arhh Araa Araa Arha Araa Arha Crhl

Tropical Tropical Tropical Temperate Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Temperate

P P P P P P P P P P P P I

6 6 6 6 6 6 6 6 6 6 6 6 6

8s,148e 7s,138e 4s,136e 4s,143e 7s,155e 12s,131e 24s,134e

Awha Araa GArab Arha Arha Awha GBWha

Tropical Tropical Tropical Tropical Tropical Tropical Hot-arid

P P P P P B B

5 6 6 6 6 6 1

62n,159w 62n,122w 60n,131w 60n,145w 54n,132w 32n,109w 54n,167w 69n,110w

EClc ECld GEClc EOlo DOlk GBWak EOlo FTkd

Cold Cold Cold Temperate Temperate Temperate Temperate Cold

R Rt S T T B T S

1 3 3 2 4 4 1 4

48n,72w 51n,95w 49n,109w 41n,124w

Dcbc Ecbc GDCbc DObk

Cold Cold Cold Temperate

S S B P

2 2 2 1

52n,126w 47n,123w 36n,112w 39n,123w 41n,96w 47n,101w

GDolc Dobk GBWhk Csbk Dcao GDcbc

Cold Temperate Hot-arid Temperate Temperate Cold

P S S T T T

6 6 2 2 4 4

108

A Global Case Study of Generic Buildings Uto-Aztecan & Zuni Societies Wadadika 16 1870 43n,119w GDcao Temperate S Comanche 16 1870 33n,100w GCwhk Temperate B Papago 16 1910 32n,112w BWhl Temperate R Huichol 16 1890 23n,105w GAwhl Tropical P Zuni 16 1880 36n,109w GBWak Temperate P Macro-Penutian, Kutenai, Caddoan, Iroquoian & Natchez-Muskogean Societies Klamath 17 1860 43n,122w Dobk Temperate S Lake Yokuts 17 1850 35n,119w Csll Temperate T Kutenai 17 1890 49n,117w GDobo Temperate S Pawnee 17 1867 42n,100w GBsao Temperate T Creek 17 1800 33n,85w Crak Temperate T Mayan, Cariban and Macro-Chibchan Societies Quiche 18 1930 15n,91w GAwbl Tropical P Black Carib 18 1940 16n,89w Arha Tropical P Carib 18 1932 7n,60w Araa Tropical I Miskito 18 1921 15n,91w GAwbl Tropical P Cuna 18 1927 9n,78w Araa Tropical P Warrau 18 1935 9n,62w Awaa Tropical S 1908 1n,79w Araa Tropical P Cayapa 18 Equatorial Societies Mundurucu 19 1850 7s,57w Amaa Tropical P Cubeo 19 1939 1n,71w Araa Tropical P Jivaro 19 1920 3s,78w HAwll Tropical I Siriono 19 1942 14s,63w Awab Tropical S Trumai 19 1938 12s,54w Awab Tropical P Ge-Panoan and Andean Societies Amahuaca 20 1960 10s,72w Araa Tropical I Nambicuara 20 1940 12s,59w Awab Tropical S Timbira 20 1915 6s,45w Awha Tropical I Aweikoma 20 1932 28s,50w Cral Temperate B Lengua 20 1889 23s,59w Awhb Tropical B Aymara 20 1940 16s,66w Arab Tropical P Mapuche 20 1950 38s,73w Dolk Temperate P Tehuelche 20 1870 46s,70w BSlk Temperate B Yahgan 20 1865 55s,69w FTkk Temperate B Table 6.1. Entities (Generic Buildings) Included in Case Study 1: The Global Ethnographic Case Study.

4 2 1 3 6 6 2 4 2 4 4 6 6 5 6 6 2 6 6 5 2 6 5 2 5 1 1 6 6 1 1

classification at that bearing, using either the KoppenTrewartha or the traditional method.

The Social and Sedentary/Mobility Data The social data was available from Cullen (1993: 228232), but a slightly expanded set of groups was used than was used in Cullen’s PCA (twenty instead of Cullen’s twelve). The sedentism/mobility data was available from Murdock (Murdock & Wilson 1972: 256-257).

The Koppen-Trewartha Classification: The KoppenTrewartha system categorises the climate in general terms according to temperature and precipitation throughout the year. There are six generally accepted principal classes of climate (tropical, subtropical, temperate, subarctic, polar and dry). These were developed by Koppen, but Trewartha further classified them into 16 principal climates by temperature for the warmest and coldest months and precipitation for the wettest and driest months of the year (Rudloff 1981: 81-85) (Fig. 6.2, Table 6.2). The climates are also prefixed with a G (for mountain climate) if the altitude is at least 500m or an H (for high mountain climate) if it is at least 2500m. The climate is also further described by the average air temperature during the warmest and the coldest months, according to the Universal Thermal Scale (Table 6.3). Because the number and distribution of weather stations is high, small differences that may exist between the

The Climatic Data Two different methodologies were used to classify the climate in which the societies resided. The first methodology used the Koppen-Trewartha classification method, which produced a relatively very detailed order of classification. The second methodology used the one traditionally adopted in ‘comfortable vernacular’ studies, which produced a very broad order of classification. Both methodologies used a two-step process. The first step established the geographical bearing of the type-site, available from Murdock (Murdock & Wilson 1972: 278295). The second step then established the climatic 109

The Evolution of the Built Environment type-site climates and that of the nearest weather stations were easily accommodated within the granularity of the classification system. However, in very high mountainous regions the difference between the climate at the nearest weather station and the type-site could potentially be

high. This problem was overcome by adopting the climate classification for the weather station that equated most closely to the type-site latitude, longitude and altitude above sea level.

Figure 6.2. Koppen-Trewartha world climate classification (Rudloff 1981) Table 6.2. Sixteen Principal Climates by KoppenTrewartha Classification

16 PRINCIPAL CLIMATES BY KOPPENTREWARTHA CLASSIFICATION Abbreviation Ar Am Aw As BS BW BM Cr Cw Cs DO DC EO EC FT FI

Climate tropical rain climate tropical monsoonal rain climate tropical summer rain climate tropical winter rain climate steppe climate desert climate marine desert climate subtropical rain climate subtropical summer rain climate subtropical winter rain climate temperate oceanic climate temperate continental climate subartic oceanic climate subartic continental climate tundra climate ice climate

‘UNIVERSAL’ THERMAL SCALE Temperature Term Range severely hot 35oC to ….. very hot 28oC to 34oC hot 23oC to 27oC warm 18oC to 22oC mild 10oC to 17oC cool 0oC to 9oC cold -9oC to -1oC very cold -24oC to –10oC severely cold -39oC to -25oC excessively cold …. to -40oC Table 6.3. The ‘Universal’ Thermal Scale

110

Code i h a b l k o c d e

A Global Case Study of Generic Buildings would appear to place fundamental constraints either on the nature of innovations, and/or on the uptake of those innovations” (Cullen 1993: 258). This is an unreasonable conclusion to draw, however, given that heritage constraint, which assumes a time component, cannot be tested via only examples drawn from a relatively limited period of time. This study addresses this issue by having eliminated all but the ethnographic data from the sample, by analysing the long time-scale archaeological cases separately (in the following case studies), by asking different types of questions of both the ethnographic and the archaeological data.

The Traditional Classification: The traditional method for classifying climates is simple and broad and uses only four different categories (cold, hot-arid, temperate and tropical). This method is used in the vast majority of literature that discusses thermal performance principles in buildings (e.g. Givoni 1998). Such literature tends to discuss thermal principles in detail and at a microclimatic scale, yet does so within this extremely broad climate classification. The logic of doing this is questionable given that as many different types of climates exist in the world as types of microclimates exist within buildings.

THE TESTS: CASE STUDY 1

This test used, first, Discrimant Analysis to provide a visual test to see if grouping was present relative to ‘cultural affiliation’ and, secondly, Correlation Analysis to provide a numerical test of the visual grouping (or absence of grouping) and record the strength of the statistical correlation.

As each of the three tests were looking for correlations between different factors and the MCS of the generic buildings, the entities (the generic buildings) were grouped differently in each test. In Test 1 they were grouped according to their ‘cultural affiliations’, in Test 2 according to the climate of their type-site, and in Test 3 according to the degree of seasonal sedentism/mobility of the communities. Likewise, the data in the different tests was analysed using different statistical MVA techniques, discussed below. However, because the scale at which the analysis was performed remained constant, the building features and traits (the variables) used in the analysis remained constant between the three tests. That is, the variables apply to the entities and not to the entity grouping. Six generic building variables were used in the analysis and these are discussed below.

Test 2. Ethnographic Buildings and Climate The purpose of this test was to ascertain the statistical correlation between the MCS of generic ethnographic buildings and their climatic context, for both mobile and sedentary societies. The concept of a correlation between the types of buildings used by both mobile and sedentary societies and their climatic context has been widely discussed in the architectural literature, where a high correlation is deemed to exist. Climate is assumed to be the determinant of the vernacular forms, responsible for having directed the development of successive classes of buildings in different climates around the globe. The assumption is that the passive heating and cooling components of the buildings have been gradually finetuned over time with the purpose of creating thermally comfortable interiors. However, this assumption has never been empirically tested. It is based on the conclusions of studies that have used only selective examples that appear to support the concept. Contradictory examples have been widely overlooked, even though they are common and widespread. However, “the idea is to try to give all of the information to help others to judge the value of your contribution; not just the information that leads to judgement in one particular direction or another” (Feynman 1985: 341).

Test 1. Ethnographic Buildings and Culture The purpose of this test was to ascertain the statistical correlation between the MCS of generic ethnographic buildings and their associated ‘cultural affiliations’. Ben Cullen (1993) used MVA to investigate the degree to which social diversity displayed distinctive patterns within evolutionary processes. In reality, however, Cullen’s study is a study of possible correlations between generic ethnographic buildings and their social context because his examples are overwhelmingly ethnographic. Of his 157 examples, only 10 predate 1750 A.D. and, of these, only 3 predate 1520 A.D. Cullen used PCA to test for his correlations and, at a very broad scale of observation, some correlation does appear to be present. The building assemblages pertaining to certain societies do appear to behave in a manner distinct from the overall pattern of behaviour (Cullen 1993: Figs. 8.03 to 8.12), but the question is, is this apparent behaviour statistically meaningful? Certain classes of buildings do appear to be absent from some of the assemblages, as Cullen (1993: 251) states, but is this visually apparent absence statistically indicative of a strong correlation?

Two slightly different methods were used to correlate the building variables with their climate as a result of the two different methods for classifying the climate. The correlation with the climate as classified by the traditional method, which consisted of a single factor, could be directly correlated with the building variables using Multiple Correlation Analysis to see if grouping was present and to record the strength of the statistical correlation. However, the correlation with the climate as classified by the Koppen-Trewartha method first had to convert the three climatic variables (the average summer and winter temperatures and average relative humidity) to a single factor before the correlation with the building variables could be performed. Consequently, the climatic

Based on his observations, Cullen concluded that patterns did exist within cultural phenomena and that these were due to heritage constraint, beyond which social processes are unable and/or unlikely to venture. “Human innovation tends not to transcend a cultural tradition, or, at least, not on a grand scale. In other words, a cultural tradition 111

The Evolution of the Built Environment variables were reduced to first principal components (PC1) using Principal Components Analysis. The PC1, which still retained the essential climatic variability, could then likewise be correlated directly with the building variables using Multiple Correlation Analysis to see if grouping was present.

presence/absence of communal buildings, which was added to the list of building variables used by Cullen so as to be able to examine the full MCS available within the settlement. Buildings do not operate in thermal isolation from their surroundings and the presence of other buildings, such as communal buildings, have the capacity to extend a person’s MCS.

Test 3. Ethnographic Buildings and Sedentism/Mobility

The variables included in the MVA dataset were: the degree of ‘Fixity of Settlement’ (Column 1 from Murdock & Wilson (1972); presence of ‘Large or Impressive Structures’ (Column 6 from Murdock & Wilson (1972); ‘Ground Plan of Dwelling’ (Columns 80 from Murdock (1967); ‘Floor Level’ (Column 81 from Murdock (1967); ‘Wall Material’ (Columns 82 from Murdock (1967); ‘Shape of Roof’ (Columns 83 from Murdock (1967); and ‘Roofing Material’ (Columns 84 from Murdock (1967). With the exception of ‘Fixity of Settlement’, Murdock’s data were in a form that was inappropriate for MVA, which requires metric variables that are numerically graded or ranked. Murdock’s variables were qualitative and not quantitative, and they were not graded. They therefore had to be converted to graded metric variables, but in such a way that the numerical equivalents modelled MCS. The metric equivalents (values) were therefore graded in terms of their capacity to produce thermal choices and thermal control.

The purpose of this test was to ascertain the statistical correlation between the MCS of generic ethnographic buildings and the degree to which their population was sedentary or mobile. The concept of a correlation between the types of buildings used by both mobile and sedentary societies and the degree to which the community is either sedentary or mobile has been widely discussed in the ethnographic and archaeological literature, where a correlation is deemed to exist. The degree of seasonal mobility/permanent sedentism is assumed to be the determinant of the types of buildings used at various times of the year, or throughout the year for sedentary communities. Studies have, however, looked at only such parameters as the materials available, the construction time, building durability, material portability. Thermal performance has been regarded as a factor, but only in as much as winter residences need to be warm and summer residences need to be cool. MCS has not been examined.

The tables below list Murdock’s original variables and the graded, metric values they were converted to, based on the results of the engineering-analysis (Table 5.5 reproduced below). Note that only in the case of Large or Impressive Structures do thermal choices and thermal control not contradict other features. They operate in accordance, with both thermal choices and thermal control increasing as a result of their presence. Each other variable operates in contradiction with its converse feature, because as thermal choices increase thermal control decreases, and vice versa.

This test used, first, Discriminant Analysis to provide a visual test to see if grouping was present relative to seasonal mobility and, secondly, Correlation Analysis to provide a numerical test of the visual grouping (or absence of grouping) and record the strength of the statistical correlation.

GENERIC BUILDING FEATURES: THE VARIABLES The building variables used in the study were mostly those also used by Cullen in his PCA. The data pertaining to the building features was taken from Murdock’s original Ethnographic Atlas, which lists various characteristics of the main dwellings plus the

Note that only the numeric equivalents of the variables used in the MVA are shown here. For the calculated values for each variable for each generic building in the dataset (the MVA spreadsheet) refer to Appendix B.

PRIMARY BASE-LINE THERMAL TRAITS Contradictions

Accords

Building exposure Number of spaces/rooms Roof flatness/peakiness Number of openings Relative floor level Solar access Thermal mass Cross-ventilation Insulation Heating devices Floor plan shape Cooling devices Room obstacles Thermal conductivity Room fixtures and fittings Table 5.5. Contradictions and Accords in Primary Base-Line Thermal Traits.

112

A Global Case Study of Generic Buildings Variable 1: Large or Impressive Structures: This variable is a measure of the MCS beyond the boundaries of individual buildings. The presence of Large or Impressive Structures in a settlement increases both the thermal choices and the thermal control for the occupants in proportion to the degree of public

accessibility. A structure that is fully publicly accessible extends the thermal environment of all occupants, but a structure that is only partially or selectively accessible extends the thermal environment of only those occupants who have access to it.

LARGE OR IMPRESSIVE STRUCTURES Murdock Code A

Converted Variable and Value Used in the Study Value Description 1 Public building fully accessible

Description Presence of public building (assembly hall etc.) T Presence of religious or ceremonial building (church etc.) F Presence of military installation 2 Public building selectively (fort etc.) accessible E Presence of economic or industrial building (storehouse, factory etc.) R Presence of impressive 3 Private buildings only residence (owned by headman etc.) O No structures larger than usual residential dwellings Table 6.4. Discriminant Values for Large or Impressive Structures Variable 2: Ground Plan of Dwelling: This variable is a measure of the building’s internal thermal homogeneity and variability. A structure that is only semi-enclosed to the outside or quadrangular in plan offers enhanced thermal choices, because it offers choice via the variety inherent in the external environment, but it offers minimal thermal control, for the same reason.

Conversely, a circular structure offers very few thermal choices, due to the homogeneousness of the spatial layout and the more homogeneous the environment the more easily controlled it is, but it does offer enhanced thermal control, for the same reason: a homogeneous environment is more easily altered than an already variable one.

GROUND PLAN OF DWELLING Murdock Code

Description

Q

Converted Variable and Value Used in the Study Value Description 1 Semi-enclosed 2 Quadrangular

Quadrangular or partially quadrangular R Rectangular or square 3 Rectilinear E Elliptical Elliptical S Semicircular Semicircular P Polygonal 4 Polygonal C Circular 5 Circular Table 6.5. Discriminant Values for Ground Plan of Dwelling Variable 3: Floor Level: This variable is a measure of the building’s vertical and horizontal temperature distributions. A structure that is raised high above the ground, such that it can interface

with the external environment in all directions (below as well as outwards and above) offers enhanced thermal choices, but minimal thermal control, because it

113

The Evolution of the Built Environment interfaces more with the uncontrolled natural elements. Conversely, a subterranean structure offers very few thermal choices, but it does offer enhanced thermal

control: the further below ground the space, the more homogeneous is the environment and therefore the more easy it is to alter.

FLOOR LEVEL Murdock

Converted Variable and Value Used in the Study Code Description Value Description P Raised substantially on piles 1 Raised substantially on piles etc. etc. E Slightly elevated on a raised 2 Slightly elevated on a raised platform etc. platform etc. G Floor at ground level 3 Floor at ground level S Subterranean or semi4 Subterranean or semisubterranean subterranean Table 6.6. Discriminant Values for Floor Level Variable 4: Wall Material: This variable is a measure of the building’s horizontal temperature distribution. A structure that has light thermal mass walls, such that it interfaces closely with the external environment, offers enhanced thermal choices, but minimal thermal control. Conversely, a structure that

has a very heavy thermal mass offers very few thermal choices, but it does offer enhanced thermal control, because the interior environment will be more homogeneous.

WALL MATERIAL Murdock Code M H G B F W P A S

Description Mats, latticework or wattle Hides or skins Grass, leaves or other thatch Bark Felt, cloth or other fabric Wood, logs, planks, poles, bamboo or shingles Plaster, mud and dung, or wattle and daub Adobe, clay, or dried brick Stone, stucco, concrete, or fired brick

Converted Variable and Value Used in the Study Value Description 1 No walls present 2 Permeable light thermal mass

3

Non-permeable medium thermal mass

4

Non-permeable heavy thermal mass

5 Table 6.7. Discriminant Values for Wall Material

Subterranean

Variable 5: Roof Shape: This variable is a measure of the building’s vertical temperature distribution. A tall, pointed, domed roof offers enhanced thermal choices, because accentuates the vertical temperature distribution generating hotter air at the peak relative to the air at the bottom, but it will have minimal thermal control for the same reason: the vertical

temperature distribution has an inherent variability. Conversely, a flat roof offers very few thermal choices, but it does offer enhanced thermal control, because the flat roof minimises the vertical temperature distribution, creating an homogeneous internal environment (Fig. 5.47).

114

A Global Case Study of Generic Buildings ROOF SHAPE Murdock

Converted Variable and Value Used in the Study Value Description 1 Beehive or pyramidal

Code B

Description Beehive shaped with pointed peak H Hipped or pyramidal D Dome shaped or hemispherical S Shed, ie. with one slope G Gabled, ie. with two slopes R Rounded or semi-cylindrical E Semi-hemispherical C Conical F Flat or horizontal Table 6.8. Discriminant Values for Roof Shape

2 3 4

Dome Skillion, ie. with one slope Gabled, rounded and semihemispherical

5

Flat

Variable 6: Roofing Material: This variable is also a measure of the building’s horizontal temperature distribution. A structure that has a light thermal mass roof operates in a similar manner to its having light thermal mass walls. A structure with a light roof will interface closely with the external environment

and offers enhanced thermal choices, but minimal thermal control. Conversely, a structure with a light roof will offer very few thermal choices, but it will offer enhanced thermal control.

ROOFING MATERIAL Murdock Code M F H B W T S G P I E

Description Mats Felt, cloth or other fabric Hides or skin Bark Wood, logs, planks, poles, bamboo or shingles Tile or fired brick Stone or slate Grass, leaves, brush or other thatch Plaster, clay, mud and dung, or wattle and daub Ice or snow Earth or turf

Converted Variable and Value Used in the Study Value Description 1 Permeable light thermal mass

2

Non-permeable medium thermal mass

3

Non-permeable heavy thermal mass Non-permeable very heavy thermal mass Subterranean

4

5 Table 6.9. Discriminant Values for Roofing Material

RESULTS OF THE GLOBAL ETHNOGRAPHIC CASE STUDY

Test 1. Ethnographic Buildings and Culture The results of the Discriminant Analysis are shown visually on the scatterplot (Fig. 6.3), where the entities (the MCS of generic buildings) are grouped according to their ‘cultural affiliations’. The scatterplot shows a wholly random pattern. A small number of the social groups do show an absence of certain classes of MCS, as Cullen concluded, in that some of the entities (points) within that group are less widely scattered than others and do not appear in all quadrants of the graph. For example, the Afroasiatic Societies tend towards the upper margins

It is important to note that the results outlined below relate to generic buildings. That is, the results are only relevant to very broad classes of buildings, those that operate at the scale of whole societies. The results cannot, therefore, infer anything about individual archaeological buildings or about classes of buildings observable at the scale of individual sites.

115

The Evolution of the Built Environment according to the degree to which their populations are sedentary or mobile. The scatterplot shows a mostly random pattern (Fig. 6.4). A small number of the groups do appear less widely scattered than others, such as the impermanent settlements (settlements that are periodically wholly relocated for ecological or social reasons). However, the Correlation Analysis shows that no statistical correlation exists (r = 0.336), a result that is statistically significant (p = 0.009). It can, therefore, be stated that no strong statistical correlation exists between the MCS of ethnographic buildings and the degree to which their population is sedentary or mobile.

of the scatterplot. However, the Correlation Analysis shows that no statistical correlation exists (r = 0.534) and that this result is statistically significant (p = 0.000). It can, therefore, be stated that no strong statistical correlation exists between the MCS of ethnographic buildings and their social context.

Test 2. Ethnographic Buildings and Climate The results of the Correlation Analysis show that no statistical correlation exists between the MCS of ethnographic buildings and their climate when classified either by the Koppen-Trewartha method (r = 0.348) or the traditional/simple method (r = 0.247). However, the Koppen-Trewartha classification, which uses an extended set of climatic data, gives a result that is statistically significant (p = 0.005), whereas the traditional method is not reliable (p = 0.177). It can, therefore, be stated that studies examining the thermal performance of classes of buildings in relation to climate should utilise as much climatic data as possible so as to get reliable results. However, the extended set of climatic data still shows no strong statistical correlation between the MCS of ethnographic buildings and their climate. This result thus challenges the ‘comfortable vernacular’ theory.

CONCLUSIONS When buildings are statistically analysed at the scale of generic buildings, with features and traits belonging to whole classes of ethnographic buildings, no correlation appears to exist between the MCS of the buildings and their social context. Likewise, although statistically significant results can only be achieved with greater, rather than lesser climatic information, no correlation appears to exist between the MCS of the buildings and their climatic context. This therefore challenges the ‘comfortable vernacular’ theory. Likewise no strong statistical correlation appears to exist between the MCS of the buildings and the degree to which the occupant population was sedentary or mobile. The results of this case study established an epistemological context within which the null hypothesis could be examined in the following long time-depth case studies.

Test 3. Ethnographic Buildings and Sedentism/Mobility The results of the Discriminant Analysis are shown visually on the scatterplot, where the entities are grouped

Figure 6.3. Discriminant plot of MCS of buildings in Case Study 1 grouped by cultural affiliation.

116

A Global Case Study of Generic Buildings Figure 6.4. Disciminant plot of MCS of buildings in Case Study 1 grouped by sedentism/mobility.

117

CHAPTER 7 – A Regional Case Study of Buildings: Long-Term Trends in the Old World

century). The cultural periods examined do not cover the same time periods, but they do cover the period from the earliest recorded permanent structures within each region up to the time of pre-industrial, vernacular structures. Specifically, the Negev covers the time period from the prehistoric (early Chalcolithic) up to the eleventh century (early Arab period), when permanent settlements were abandoned until modern times. Egypt covers the time period from the prehistoric (Naqada 1) up to the twentieth century (vernacular). The Palestinian highlands covers the time period from late fourth Mill. B.C. (Early Bronze Age) up to the nineteenth century (Ottoman period).

INTRODUCTION This chapter outlines a case study (Case Study 2) examining MCS in archaeological buildings in three Old World regions that encompass an extended period of time, three different geographical regions and two different climates. It thus examines MCS at a more detailed, ‘real-life’ scale than that of Case Study 1, which examined the generic buildings of whole societies. Whilst the MCS of the buildings was analysed at the scale of whole buildings, it was done in such a way that the MCS of the individual rooms, of which the buildings were composed, could be factored in. It was also done in such a way that the features that operate in contradiction to their converse feature (where thermal choices contradict thermal control, and vice versa) could be distinguished from the features that operate in accord, by differentiating between these two sets of variables. They could thus be analysed both together and separately.

Three separate MVA tests were performed. Test 1 analysed the MCS of room-weighted buildings, and the influence of culture and/or climate on the MCS, using the full set of variables, Test 2 analysed the MCS according to only those features that operate in thermal contradiction with other features, and Test 3 analysed the MCS according to only those features that operate in thermal accordance with other features.

CASE STUDY 2: AN EXTENDED TIME PERIOD IN THE OLD WORLD

THE DATA: CROSS-CULTURAL BUILDINGS

This case study represents a test of the null hypothesis with regard to a dataset that encompasses a very extended period of time and various indirectly culturally related Old World regions. A uniformitarian approach has been used so as to examine the processes that were operating over an evolutionary period of time, irrespective of culture and climate. The large-scale action of selection upon MCS will be most apparent over a very extended period of time (or over an evolutionary phase shift). Therefore, if MCS is selectively neutral, the thermal capacity of classes of buildings in the dataset will be random and if MCS was coming under selective pressure the thermal capacity will be ordered and consistent, to within definable boundaries. This case study was set up to extend the findings of the global ethnographic case study to an archaeological Neo-Darwinian context. That is, the analysis was designed to, first, test the findings of Case Study 1 that no statistical correlation is evident between the MCS of buildings and their associated culture and climate, and at the scale of individual buildings, not generic buildings. Secondly, the analysis was designed to test the long-term pattern of MCS within the multi-scalar Neo-Darwinian evolution of the built environment.

The three regions were selected because they are historically indirectly culturally related, but experience different climates (Fig. 7.1). The climate in these regions appears to have been sufficiently unchanged since the early Holocene, c. 10,000 B.P., to be able to perform this analysis of thermal capacity based on contemporary climate classifications, which are sufficiently broad as to encompass any differences that may have existed. Whilst it appears that relative humidity was slightly higher until approximately 5,000 years ago, the air temperatures appear to have been approximately the same as today’s (Goldberg & Bar Yosef 1982; Roberts 1982; Van Zeist 1985: 201; Horowitz 1989; Dominik & Stanley 1993). Today the Negev and Egypt are noted for their hot summers and are classified as hot-arid climates (Bmhb and Bwhl respectively by the Koppen-Trewartha climate classification method), whilst the Palestinian highlands experience cool winters and is classified as a cool climate (GCsak by the Koppen-Trewartha climate classification method). This ratio of 2:1 hot:cool was selected in order to be able to distinguish between the influence of climate from that of culture on the long-term behaviour of MCS in buildings. If the examples had been drawn from three different regions with three different climates, it would not have been possible to distinguish between an overriding social influence versus an over-riding climatic influence. The same would be true if the three examples had been all drawn from the same region, with the same climate. With a 2:1 ratio, however, if a correlation exists

The dataset encompassed a large set of archaeological buildings from the Negev, Egypt and the Palestinian highlands. The regions encompassed various climates (the Negev and Egypt are noted for hot-arid summers and the Palestinian highlands experience cool winters) and a very extended period of time (prehistoric to twentieth 118

A Regional Case Study of Buildings: Long-Term Trends in the Old World with the climate but not with the culture, or vice versa, the influence of one over the other will become apparent in the results of the MVA.

where the variables (the building features) listed in the array were ascertainable. For buildings where even one variable could not be ascertained it was not included in the set. The regions, time periods and entity (building) numbers within each group (each region and period) are listed in Table 7.1 below. Forty variables (the building features) were used in the analysis and these are discussed below. The numbers of the buildings used in the analysis are listed in Appendix C.

The tests were performed on an extensive dataset of buildings that was compiled from archaeological reports. Only buildings that were sufficiently comprehensively excavated and documented were included in the dataset. That is, the dataset was limited to only those buildings

Figure 7.1. Locations of sites in Case Study 2. 119

The Evolution of the Built Environment ENTITIES USED IN THE REGIONAL CASE STUDY OF BUILDINGS Region Negev

Egypt

Time Period Early Chalcolithic (4600- 4300 B.C.)

Time Period Group in Database 1

Palestinian Highlands

Safadi Abou Matar Neve Noy Safadi Abou Matar Shiqmim Safadi Abou Matar Shiqmim Tel Beersheba Tel Beersheba Horvat Haluqim Horvat Haluqim Tel Beersheba Nessana Rehovot Sobata Rehovot Rehovot Tel Beersheba Hemamiya Hierakonopolis Elephantine

12

79-82 83-86 87-92 93-95, 103 96-100 101-102 104-106

El Lahun Elephantine Deir el-Medina Medinet Habu Amarna Ramesseum Ismant el-Gharab

13 14

107-110 111-115

Medinet Habu Fustat

15

116 117 118-121 122-137

Cairo Marg New Gourna Ai

2

Late Chalcolithic (4000-3800 B.C.)

3

Iron Age (1200-586 B.C.)

4

Late Roman (A.D. 132-324)

5

Byzantine (A.D. 324-638)

6

Early Arab (A.D. 638-1099)

7

Naqada I (4000-3500 B.C.)

8

Old Kingdom (2686-2181 B.C.) Middle Kingdom (2025-1700 B.C.) New Kingdom and 3rd Intermediate (1550-664 B.C.)

9

Early Bronze (3300-2200 B.C.) Iron (1200-586 B.C.)

Sites

1 2-3 4-5 6-8 9-10 11 12 13 14-15 16-17 18-41 42-46 47 48 49-50 51 52-62 63-69 70 71 72 73-75 76-78

Middle Chalcolithic (4300- 4000 B.C.)

Late Roman (A.D. c. 211- 395) Byzantine (A.D. 395-640) Mediaeval Arab (A.D. 640-1517) Vernacular (A.D. 1805-1919)

Entity (Building) No. in Database

10 11

16 17

138-139 Khirbet ed-Dawwara 140-144 Ai 145-146 Shiloh 147-152 Tell en-Nasbeh Babylonian (586-539 B.C.) 18 153-155 Tell en-Nasbeh Ottoman (A.D.1516-1917) 19 156-157 Khan al-Lubban Table 7.1. Entities (Buildings) Included in Case Study 2: The Old World Regional Case Study. documented sites within this area are those of Safadi, Abou Matar, Neve Noy, Shiqmim, Tel Beersheba, Horvat Haluqim, Nessana, Rehovot-in-the-Negev and Sobata (Shivta) (Perrot 1957; Dunscombe Colt 1962; Cohen 1976; Herzog et al. 1977; Aharoni 1973; Baumgarten & Eldar 1983; Segal 1983; Herzog 1984;Perrot 1984; Eldar

The Negev: Hot-Arid Summers The Negev sites were limited to those bounded by the Gilat in the north, the Har Haluqim in the east, Nessana in the south and Rehovot-in-the-Negev in the west and only sites below 500m asl. The best excavated and 120

A Regional Case Study of Buildings: Long-Term Trends in the Old World & Baumgarten 1985; Levy 1987a & b; Tsafrir & Holum 1988; Tsafrir et al. 1988; Levy et al. 1991; Herzog 1993). These sites span a continuous period of time from the early Chalcolithic period through to the early Arab era and comprise 71 entities (buildings).

Test 1. Buildings, Culture and Climate The purpose of this test was to statistically ascertain the long-term pattern of MCS change in individual archaeological buildings and the degree to which culture and climate have influenced long-term change. The global ethnographic study showed that at the scale of generic buildings (classes of buildings apparent at the scale of whole cultures) no statistical correlation exists. It is possible, however, that at the scale of generic buildings the granularity is simply too all-encompassing to fully reveal the behaviour of MCS. Thermal environments are after all perceived by individuals at the scale of microclimates, not macro-climates.

Egypt: Hot-Arid Summers The Egyptian sites were limited to those within the hotarid region, thus excluding the whole of the Delta area north of Cairo. The best excavated and documented sites within this area are those of Hemamiya, Hierakonopolis, Elephantine, El Lahun, Deir el-Medina, Madinet Habu, Amarna, the Ramesseum, Medinet Habu, Ismant elGharab, Fustat, Cairo, Marg and New Gourna (Peet & Woolley 1923; Nelson 1929; Bruyere 1939; Badawy 1968; Fathy 1973Badawy 1978; Creswell 1978; Hoffman 1980; Toulan 1980; Schoenauer 1981; Hoffman 1982; Habachi 1985; Hope 1987; Uphill 1988; Hope et al. 1989;Bierbrier 1997; Lacovara 1997; German Institute of Cairo 1998). These sites span a continuous period of time from the Naqada I period through to the pre-industrial vernacular era and comprise 50 entities (buildings).

Test 2. Buildings and Features in Thermal Contradiction The purpose of this test was to statistically ascertain the degree to which features that operate in thermal contradiction to their converse feature (where thermal choices contradict thermal control, and vice versa) have influenced the overall pattern of MCS change in roomweighted buildings. This was achieved by comparing the overall pattern of MCS change with that of only the features that operate in contradiction to their converse feature. If the patterns in the two scatterplots appeared to be equivalent, it could be stated that the overall pattern of MCS change has been strongly influenced by the contradictions within the thermal system, but if the patterns appeared contrasting, it could be stated that the overall MCS has not been influenced by the contradictions.

The Palestinian Highlands: Cool Winters The Palestinian Highlands sites were limited to those bounded by the Wadi Far’ah in the north, the rim of the Jordan valley in the east, Jerusalem in the south and Khirbet Raddana in the west and only sites above 500m asl. The best excavated and documented sites within this area are those of Ai, Khirbet ed-Dawwara, Shiloh, Tell en-Nasbeh and Khan al-Lubban (Garstang 1931; McCown 1947; Marquet-Krause 1949; Callaway 1980; Finkelstein 1990; Finkelstein et al. 1993; Zorn 1993; Hawari 2001). These sites span a continuous period of time from the Early Bronze period through to the Ottoman era and comprise 36 entities (buildings).

Test 3. Buildings and Features in Thermal Accord The purpose of this test was to statistically ascertain the degree to which features that operate in thermal accordance with other features (where thermal choices do not contradict thermal control) have influenced the overall pattern of MCS change in room-weighted buildings. This was achieved by comparing the overall pattern of MCS change with that of the features that only operate in accordance. If the patterns in the two scatterplots appeared to be equivalent, it could be stated that the overall pattern of MCS change has been strongly influenced by the accords within the thermal system, but if the patterns appeared contrasting, it could be stated that the overall MCS has not been influenced by the accords.

THE TESTS: CASE STUDY 2 The three tests used the same set of entities (buildings). The entities were grouped according to region and phase and this grouping was, therefore, constant between tests. Likewise, the data in the different tests was analysed using the same MVA techniques, of Discriminant Analysis and Correlation Analysis. The Discriminant Analysis provided a visual test to see if grouping was present and the Correlation Analysis provided a numerical test of the visual grouping (or absence of grouping) and recorded the strength of the statistical correlation. The aims of the individual tests are discussed in greater detail below. The MCS of the individual buildings was again treated as equivalent to the string of their variables (the sum of the interactions between the thermal features).

ARCHAEOLOGICAL BUILDING FEATURES: THE VARIABLES The variables in the database consist of room-weighted building features. That is, the value of each variable was calculated for each individual room and then this value was weighted according to the relative proportion of rooms within the building that possessed that same value. Some variables were not required to be weighted, however, as they pertained to the whole building. For

121

The Evolution of the Built Environment example, the building’s potential to interface with the outside (represented by ‘building exposure’) is proportionate with the degree to which its exposure to the outside is blocked by adjoining structures or obstacles. This potential is the same regardless of the number of rooms within the building. However, in those cases where it was necessary to model the range of different types of spaces within the building, the room-weighted value was used, which is really a room-weighted averaged value. To supplement this an additional variable was also included, that of the range of that variable that existed within the building. That is, the range between the two extreme values for that variable within the building. For example, a building that consisted of two rooms (one with a flat ceiling and one with a domed ceiling) plus a courtyard would have a room-weighted value of 1/3 x 1 + 1/3 x 3 + 1/3 x 4 = 1.58 (where a flat ceiling has a value of 1, a domed ceiling has a value of 3 and a courtyard has a value of 4), but it would have a range of 4 – 1 = 3.

results of the engineering-analysis (Table 5.5). Table 7.2 below lists the variables according to this division. The division allowed the MCS to be calculated according to the full set of variables, or according to either of the two subsets. Note that more of a building’s primary thermal features operate in contradiction than operate in accordance. The table of the variables used is followed by a series of tables that list the graded, numeric values for each of the variables that were used in the MVA. Note that only the numeric values of the variables used in the MVA are shown here. For the calculated values for each variable for each building in the dataset (the MVA spreadsheet) refer to Appendix C. Features where thermal choices and thermal control are contradictory The variables where thermal choices and thermal control are contradictory, such that an increase in the value of one creates a proportionate reduction in the other, have been allocated values such that a lower value equates to high thermal choices and low thermal control, whilst a higher value equates to low thermal choice and high thermal control.

The variables in the dataset were divided into those that operate in contradiction with their converse feature (where thermal choices contradict thermal control, and vice versa) and those that operate in accordance (thermal choices do not contradict thermal control) based on the

VARIABLES USED IN THE REGIONAL CASE STUDY OF BUILDINGS Variable No. Variable 1-9 Building exposure (n, ne, nw, s, se, sw, e, w & vertical) 10-11 Roof flatness/peakiness and range 12-13 Floor level relative to ground level and range 14-16 Wall and roof material (thermal mass) and roof material range 17-18 Presence of wall & roof insulation 19-20 No. internal angles and range 21-22 Ratio length/width and range 23-26 No. posts and range, niches & benches 27 Compactness/longevity 28 No. rooms 29-30 Internal floor/ceiling thermal conductivity 31 No. roofs at different levels Features where thermal choices and thermal 32 Degree of opening in each direction control are in 33-37 Solar penetration (from s, se, sw, e, w) accordance 38 Cross ventilation 39 Heating 40 Degree of transitional space Table 7.2. Thermal Features (Variables) Included in Case Study 2: The Regional Case Study of Buildings. Features where thermal choices and thermal control are contradictory

Variables 1-9: Building Exposure (n, ne, nw, s, se, sw, e, w & vertical): This variable is a direct measure of the potential to interface with the outside environment. It is calculated for each compass direction plus vertical. A building with a low exposure value would have low thermal choices but high thermal control because it would be thermally homogenous and, ergo, thermally controllable. A building with a high exposure value would have high thermal choices and low thermal control because it

would ‘track’ the outside environment more closely and thus be thermally more variable and less controllable. For example, an isolated building built out in the open would have a value of 1 for each direction, and a subterranean building or a building that is wholly surrounded by other structures or obstacles would have a value of 2 for each direction.

122

A Regional Case Study of Buildings: Long-Term Trends in the Old World BUILDING EXPOSURE (N, NE, NW, S, SE, SW, E, W & VERTICAL) Feature Exposed Not exposed

Value 1 2

Table 7.3. Discriminant Values for Building Exposure (n, ne, nw, s, se, sw, e, w & vertical) Variable 10-11: Roof Flatness/Peakiness and Range: This variable is a room-weighted measure of the vertical temperature distribution. A room with a pyramidal roof would have a high temperature difference between the floor and the ceiling, whilst a room with a flat ceiling or no ceiling would have a minimal temperature difference over the same height (Cook 1996: 281). A room with high vertical temperature variability would have high thermal choices and low thermal control because it would be

thermally more variable and less controllable. Conversely, a room with low vertical temperature variability would be thermally homogenous and would therefore have high thermal control and low thermal choices. For example, a room within a building with a pyramidal roof would have a value of 1, a room with a flat roof would have a value of 3 and an unroofed room would have a value of 4.

ROOF FLATNESS/PEAKINESS Feature Pyramidal or Domed Skillion or Rounded Flat Unroofed Table 7.4. Discriminant Values for Roof Flatness/Peakiness

Value 1 2 3 4

Variable 12-13: Floor Level Relative to Ground Level and Range: This variable is a room-weighted measure of the vertical and horizontal temperature distributions. A room within a subterranean building would have a very homogenous internal temperature distribution, and high thermal control and low thermal choices. This is because a subterranean room is as isolated from the outside conditions as is possible. It will be as far removed in time as is possible and thus thermally homogenous.

Conversely, a room elevated to a level above ground level would have a comparatively high internal temperature variability due to its having a higher degree of exposure to the outside. It would have high thermal choices and low thermal control as its exposure to the outside is high. This means the interior would track the outside conditions more closely in time and be thermally variable.

FLOOR LEVEL RELATIVE TO GROUND LEVEL Feature Subterranean Ground Level Elevated to Upper Level

Value 1 2 3

Table 7.5. Discriminant Values for Floor Level Relative to Ground Level Variable 14-16: Wall and Roof Material (Thermal Mass) and Roof Material Range: control because it would be thermally variable and less controllable. Conversely, a room with walls and roof that have a heavy thermal mass will be isolated from the outside conditions and the internal temperatures will be offset from those outside. It would have high thermal control and low thermal choices .

This variable is also a room-weighted measure of the horizontal temperature distribution. A room within a building that is open on one wall, or which is unroofed, will interface closely with the outside conditions and the internal temperatures will consequently closely track those outside. It would thus have high thermal choices and low thermal

123

The Evolution of the Built Environment WALL AND ROOF MATERIAL (THERMAL MASS) Feature Value Nil 1 Light (thatch / reed / light timber / skins) 2 Medium (wattle and daub / jacal / heavy 3 timber) Heavy (stone / mud / brick / 0.5-1.5m 4 subterranean) Very Heavy (≥0.6m stone / ≥0.6m mud / 5 ≥0.6m brick / ≥1.5m subterranean) Table 7.6. Discriminant Values for Wall and Roof Material (Thermal Mass) Variable 17-18: Presence of Wall and Roof Insulation: This variable is a room-weighted measure of how closely the temperatures inside track those outside and, therefore, of the room’s horizontal thermal variability. The presence of insulation prohibits the flow of heat by conduction and, therefore, insulation in a roof or wall will offset the interior temperatures from those outside, creating a

thermally homogenous interior (Givoni 1976: 131). Consequently, a room with insulation would have high thermal control and low thermal choices. Conversely, a room without insulation would have high thermal choices and low thermal control.

PRESENCE OF WALL AND ROOF INSULATION Feature Absent Present

Value 1 2

Table 7.7. Discriminant Values for Presence of Wall and Roof Insulation Variable 19-20: Number of Internal Angles and Range: This variable is a room-weighted measure of horizontal thermal variability. A room within a building that is more angular will be more horizontally thermally variable than a room that is more circular, which will be more

thermally homogenous. A circular room will have high thermal control and low thermal choices, and an angular room will have high thermal choices and low thermal control.

NUMBER OF INTERNAL ANGLES Feature Value 3 – 4 (Rectilinear) 1 5–6 2 7–8 3 9 – 10 4 11 – 12 (Circular) 5 Table 7.8. Discriminant Values for Number of Internal Angles Variable 21-22: Ratio Length/Width and Range: This variable is also a room-weighted measure of horizontal thermal variability. A room within a building that has a ratio of 1:1 (length:width) will be more horizontally homogenous than a room that has a high

ratio of length:width. It will have high thermal control and low thermal choices, whilst a very elongated room will have high thermal choices and low thermal control.

124

A Regional Case Study of Buildings: Long-Term Trends in the Old World

RATIO LENGTH/WIDTH Feature ≥8 5–7 3–4 2 – 2.5 1.5 1 Table 7.9. Discriminant Values for Ratio Length/Width

Value 1 2 3 4 5 6

Variable 23-24: Number of Posts and Range: This variable is also a room-weighted measure of horizontal thermal variability. The number of internal obstacles within the volume of a room, particularly tall free-standing obstacles such as columns, will affect its thermal performance. A high number of columns will

reduce the thermal homogeneity. Therefore, a room with no internal columns will have high thermal control and low thermal choices, whilst a room with numerous columns will have high thermal choices and low thermal control.

NUMBER OF POSTS Feature ≥40 39 – 20 19 – 10 9–5 4–2 1 0 Table 7.10. Discriminant Values for Number of Posts

Value 1 2 3 4 5 6 7

Variable 25: Number of Niches: This variable is also a room-weighted measure of horizontal thermal variability. A niche is defined here as a space that is attached to, and not isolated from, a larger space that has the capacity to accommodate a person and which has a micro-climate that is different to that of the main space, by virtue of its being spatially discrete. The presence and number of niches within a room therefore

affects its horizontal thermal variability. A room with no niches will be more thermally homogenous than a room with numerous niches, which would be thermally variable. Therefore, a room with no niches will have high thermal control and low thermal choices, whilst a room with numerous niches will have high thermal choices and low thermal control.

NUMBER OF NICHES Feature ≥8 6–7 4–5 2–3 1 0 Table 7.11. Discriminant Values for Number of Niches

Value 1 2 3 4 5 6

Variable 26: Number of Benches: This variable is a room-weighted measure of vertical thermal variability. A bench is defined here as a horizontal surface within a room that has the capacity to accommodate a person and which has a micro-climate that is different to that of the main floor space, by virtue

of its being at a higher level: it creates vertical thermal variability. Therefore, a room with no benches will have high thermal control and low thermal choices, whilst a room with numerous benches at different heights will have high thermal choices and low thermal control. 125

The Evolution of the Built Environment NUMBER OF BENCHES Feature ≥7 5–6 3–4 2 1 0 Table 7.12. Discriminant Values for Number of Benches

Value 1 2 3 4 5 6

Variable 27: Degree of Compactness: This variable is a direct measure of the compactness of the spaces. That is, of the compactness or elongation (horizontal or vertical) of the room arrangement within the building. Where rooms are arranged in a tight cluster, individual rooms will have minimal capacity to interface with the outside. The building will consequently experience temperatures that are further offset from those outside and it will, therefore, be more thermally

homogenous. It will have high thermal control and low thermal choices. Conversely, where rooms are arranged in an elongated string, individual rooms will have a high capacity to interface with the outside, their temperatures will closely track those outside and they will, therefore, be thermally variable. The building will thus have high thermal choices and low thermal control.

DEGREE OF COMPACTNESS Feature Value 1 room minimum width 1 2 rooms minimum width 2 3 rooms minimum width 3 etc. etc. Table 7.13. Discriminant Values for Degree of Compactness Variable 28: Number of Rooms: This variable is a direct measure of the number of internal spaces. That is, the number of discrete thermal environments of which the building is composed. The fewer internal subdivisions within a building, the more thermally homogenous it will be. It will have high

thermal control and low thermal choices. Conversely, the more internal subdivisions within a building, the more thermally variable it will be. It will have high thermal choices and low thermal control.

NUMBER OF DISCRETE SPACES Feature Value ≥30 1 29 – 25 2 24 – 20 3 19 – 15 4 14 – 10 5 9–7 6 6–5 7 4 8 3 9 2 10 1 11 Table 7.14. Discriminant Values for Number of Discrete Spaces Variable 29-30: Internal floor/ceiling thermal conductivity: This variable is a direct measure of a building’s capacity for the internal partitions (walls and/or ceilings) to

conduct heat from one room to another. That is, the level of thermal homogeneity within the building as a whole

126

A Regional Case Study of Buildings: Long-Term Trends in the Old World versus the degree of thermal isolation between the rooms, their degree of thermal discreteness. A building where the rooms are thermally discrete from each other will have high thermal choices and low thermal control.

Conversely, a building where the rooms are thermally connected will have high thermal control and low thermal choices, because there is rapid and uncontrollable heat transfer between the rooms.

INTERNAL WALL AND CEILING/ROOF CONDUCTANCE Feature Non-conducting Slow conductance Fast conductance

Value 1 2 3

Table 7.15. Discriminant Values for Internal Wall and Ceiling/Roof Conductance

Features where thermal choices and thermal control are in accordance Variable 31: Number of Roofs at Different Levels: This variable is also a direct measure of a building’s overall thermal variability. Accessible roof spaces have the capacity to operate as an extension of a building’s internal space and must, therefore, be incorporated within its functional capacity. Roofs are not, however, treated here as rooms as they are predominantly outside spaces: a roof that is partially enclosed or walled-in is treated as a transitional space (see below). Outside space will, of course, behave very differently thermally to interior

spaces as it will equate to the outside conditions, rather than those inside. Roofs at different levels will, however, behave thermally differently to each other depending on the prevailing outside conditions: wind velocities are generally greater at higher levels and the temperature gradient alters with height above ground. A building with roofs at different levels will have high thermal choices and thermal control, whilst a building with no accessible roofs will have low thermal choices and thermal control.

NUMBER OF ROOFS AT DIFFERENT LEVELS Feature Value 0 0 1 1 2 2 3 3 etc. etc. Table 7.16. Discriminant Values for Number of Roofs at Different Levels Variable 32: Degree of Opening in Each Direction: This variable is a room-weighted measure of the building’s capacity to interface directly and selectively with the outside. A building with numerous openings in different directions in all of its rooms will have high thermal choices and thermal control, whilst a building with few openings with respect to its number of rooms will have low thermal choices and thermal control. The value for the degree of opening is measured as the number of openings in each room, in each different

direction, divided by the number of spaces within the building. For example, a building with four rooms in which each room has two openings in different directions would have a value of 8 / 4 = 2, but a building with four rooms in which each room has two openings in the same direction would have a value of 4 / 4 = 1. This is because the capacity of the first building to interface with the outside is twice as high. It has the capacity to utilise a wider range of outside conditions.

NUMBER OF EXTERIOR OPENINGS IN DIFFERENT DIRECTIONS Feature Value 1 1 2 2 3 3 etc. etc. Table 7.17. Discriminant Values for Number of Exterior Openings in Different Directions

127

The Evolution of the Built Environment Variable 33-37: Solar Penetration (from s, se, sw, e, w): This variable is a room-weighted measure of the building’s potential for solar radiation to selectively enter the rooms. A building with solar penetration into numerous rooms and from different directions will have high thermal choices and thermal control, whilst a building with minimal solar penetration will have low thermal choices and thermal control The presence of solar penetration into a room is dependent on the size and location of openings and the angle of the sun, which varies according to the time of day, the season, the site latitude and the site terrain. It is, therefore, multi-factorial and requires a high level of knowledge of the topography of the site and of openings in the building. The building’s potential or capacity for solar penetration to enter the rooms is, however, definable according to two factors:

subterranean structure within 7.5m, or on-ground structure within 10.0m of a single storey structure. A room faced east or west and was elevated to a higher, unobstructed level.

c.

2) a structural capacity for openings to be present, oriented in different directions (s, se, sw, e and w). Potential solar penetration was therefore deemed to exist where: a. Archaeological evidence for an opening was present in the extant remains. b. A facade was of sufficient length and structural stability to potentially accommodate a load-bearing lintel above an opening and if the building post-dated the time of their earliest archaeological evidence: the late Neolithic in the Levant, c. 6500 B.C. (Nissen et. al 1987: 93-94), and the Predynastic Gerzean Period in Egypt, c. 3600 B.C. (Randall-MacIver & Mace 1902: 42, pl. 10; Badawy 1966: 13; Uphill 1988). Whilst the potential exists for buildings to have had openings which had sills higher than floor level, evidence for which may not have survived, where no archaeological evidence for their presence existed openings of this type were not assumed to have been present in the buildings prior to these dates.

1) the angle of the sun. This can be deemed to be similar both diurnally and seasonally for each of the study regions due to their being located within relatively similar latitudes. The latitudes varied by only approximately 6o. Potential solar penetration was therefore deemed to exist where: a. A room faced south, b. A room faced southeast or southwest and had no semi-subterranean structure within 5.0m, or slightly-

SOLAR PENETRATION (FROM S, SE, SW, E, W) Feature No Penetration Penetration

Value 1 2

Table 7.18. Discriminant Values for Solar Penetration (from s, se, sw, e, w) Variable 38: Cross-Ventilation: This variable is a room-weighted measure of the building’s capacity for cross-ventilation in the rooms. Cross-ventilation is air flow across a room. A building with cross-ventilation in numerous rooms will have high thermal choices and thermal control, whilst a building with no cross-ventilation will have low thermal choices and thermal control Whether or not air does actually flow across a room is dependent on the direction of the prevailing breezes, the zones of positive and negative pressure on the upwind and downwind sides of the room,

the relative sizes of the ingress and egress openings, and the arrangement of internal obstacles. It is, therefore, multi-factorial and requires a high level of knowledge of the climate, the weather, the topography of the site, and of the openings and internal layout of the building. The building’s capacity for cross-ventilation, however, is equivalent to the potential for air to flow across the rooms. This is defined according to the principles in Figs. 2.25-2.36.

CROSS-VENTILATION AND CORNER-VENTILATION Feature No cross-ventilation

Value 1

Cross-ventilation

2

Table 7.19. Discriminant Values for Cross-Ventilation and Corner-Ventilation

128

A Regional Case Study of Buildings: Long-Term Trends in the Old World Variable 39: Heating: This variable is a room-weighted measure of a building’s capacity to add additional heat to its interior. Additional heat can be either from direct solar penetration or from an active heating source (mechanical or chemical). Note that the values are accumulative for multiple sources of heat.

A building with numerous and diverse ways of heating its rooms will have high thermal choices and thermal control, whilst a building with no active heating sources at all will have low thermal choices and thermal control.

HEATING Feature None Solar penetration Fire in fixed central location Fire in fixed peripheral location Mobile fires (braziers) or numerous fixed fires Table 7.20. Discriminant Values for Heating

Value 1 2 3 4 5

Variable 40: Degree of Transitional Space: This variable is a room-weighted measure of a building’s overall thermal variability. Transitional spaces have the capacity to operate as an extension of a building’s internal space and should, like accessible roof spaces, be incorporated within its functional capacity. Transitional spaces are defined here as rooms that are wholly open to the outside on at least one side or roof. This includes courtyards, colonnades, shade structures and loggias. Other types of transitional spaces not sufficiently enclosed to be classed as rooms were not included in the analysis, as they are too closely synchronised with the outside to be thermally distinct from it. Transitional spaces behave thermally neither like fully-enclosable

spaces, nor like the outside environment and, therefore, a building with numerous and diverse transitional spaces will have high thermal choices and thermal control, whilst a building with no transitional spaces will have low thermal choices and thermal control. The value for the degree of transitional space is measured as the number of diverse transitional spaces divided by the number of spaces within the building. For example, a building with four rooms in which one is a courtyard and one is a portico would have a value of 2 / 4 = 0.5, but a building with four rooms in which none are transitional would have a value of 0 / 4 = 0.

TRANSITIONAL SPACES Feature None of Total 1 of Total 2 of Total etc. Table 7.21. Discriminant Values for Transitional Spaces

Value 0/Total 1/Total 2/Total etc.

RESULTS OF THE REGIONAL CASE STUDY OF BUILDINGS

their region and period (Fig. 7.2). Consequently, conclusions can be made about the long-term pattern of change in the MCS of classes of buildings with respect to their culture and climate. Generally, the scatterplot shows that there is a discernible and gradual change over time in the character of the MCS of the buildings in all regions. Apart from the very early Naqada 1 buildings in Egypt, that appear as outliers, there is a gradual shift in the nature of the MCS away from that of the early buildings in each region towards a common region. This region is shared by the mediaeval (Late Roman, Byzantine and Arab) buildings of Egypt and the Negev, with the Ottoman buildings of Palestine and the vernacular buildings of Egypt closely associated. This change is reflected in the results of the Correlation Analysis, which shows that there is a high statistical correlation between

It is important to note that the results outlined below relate to whole archaeological buildings, which are treated as assemblages of individual rooms. As such, the thermal performance of individual rooms is subsumed into that of the whole building and is not distinguishable as a separate entity. The results cannot, therefore, directly infer anything about individual rooms.

Test 1. Buildings, Culture and Climate The results of the Discriminant Analysis are shown visually on the scatterplot, where the entities (the MCS of room-weighted buildings) were grouped according to 129

The Evolution of the Built Environment

Figure 7.2. Discriminant plot of MCS of buildings in Case Study 2. the MCS of the buildings and the regions and periods to which they belong (r = 0.787), a result that is statistically significant (p = 0.000).

buildings appears to be a factor of the very light thermal mass and insulation content of the buildings rather than of climate, as the thermal signatures of the early buildings in the other regions forms a distinct group composed of buildings with heavy thermal mass and negligible insulation.

The change in MCS appears to be characterised by a gradual increase in intra-building thermal complexity and by an ultimate increase in inter-building thermal diversity. The increasing inter-building thermal diversity is illustrated by the ultimate increase in the size of the clusters in the scattergram. The sizes of the scattergram clusters appear relatively uniform, irrespective of region and time period, up until the pre-industrial era. The vernacular Egyptian buildings present a relatively wide cluster of points. This indicates that a greater degree of inter-building thermal variability existed between these buildings than in the preceding periods in each region. The increasing intra-building thermal complexity is illustrated by the gradual increase in the average range of built features within the buildings. This is shown in Fig. 7.3 in which the average range of features in the buildings were plotted over time using the values used in the MVA analysis of the building variables that were calculated as a range, in addition to the room-weighted value.

An analysis of the Structure Coefficients shows that there are two variables that have influenced the relative positioning of the thermal states more than others (ref. Appendix G). They are, first, the vertical building exposure (ie. whether or not the building is subterranean) and, secondly, the presence/absence of insulation in the external walls. Both these features operate in contradiction to their converse feature. For example, thermal choices, but not thermal control, can be easily produced in above ground buildings, and vice versa for below ground buildings. Likewise, thermal choices, but not thermal control, can be easily produced in buildings with no insulation in the walls, and vice versa for buildings with insulation. The variable that has the next most influence is the thermal mass of the external wall material, also a feature that operates in contradiction to its converse feature (as the mass increases, thermal control increases, but thermal choices decrease). The other variables that have some significant influence are the northwestern building exposure, the shape and variability of the roofs within the building, the thermal mass of the external roof material, and the number of rooms within

There is no discernible relationship between the MCS of the buildings and the change over time with respect to either climate or culture. The nature of the change in MCS appears to behave similarly in each of the regions. The anomalous behaviour of the Naqada 1 Egyptian

130

A Regional Case Study of Buildings: Long-Term Trends in the Old World

Figure 7.3. Graphs of the average range of built features within buildings in Egypt (top), the Negev (below left) and the Palestinian highlands (below right). ( the building. These are also features that operate in contradiction to their converse feature.

Test 2. Buildings and Features in Thermal Contradiction

It can, therefore, be stated that a statistically meaningful trend has occurred in the long-term change in the MCS of classes of buildings over time. This trend is not related to either climatic or culture, but it is characterised by a gradual increase in intra-building thermal complexity and inter-building thermal diversity.

The discriminant plot generated from only those variables that operate in contradiction to their converse feature shows a very similar arrangement of points to that of the plot that uses the full array of variables (Fig. 7.4). This visual similarity is supported by similar Correlation Analysis results (r = 0.720), which is equally statistically significant (p = 0.000). It can, therefore, be stated that the long-term pattern of change in the MCS of classes of buildings appears to have been strongly influenced by the operation of those features in which thermal choices and thermal control are contradictory.

Figure 7.4. Discriminant plot of MCS of buildings in Case Study 2 using only thermally contradictory variables. 131

The Evolution of the Built Environment

Test 3. Buildings and Features in Thermal Accord

CONCLUSIONS

The discriminant plot generated from only those variables that operate in accordance with other features shows a random arrangement of points (Fig. 7.5). This visually random spread of points is confirmed by the Correlation Analysis results, which show no statistical correlation (r = 0.583), a result that is statistically significant (p = 0.000). It can, therefore, be stated that the long-term pattern of change in the MCS of classes of buildings appears to have been uninfluenced by the operation of those features in which thermal choices and thermal control are mutually enhanced.

When buildings are statistically analysed at the scale of whole room-weighted archaeological buildings, compared with that of generic ethnographic buildings (Case Study 1), it is apparent that a statistically meaningful trend has occurred in the long-term change in the MCS of classes of buildings over time. This trend is not related to either climate or culture, but it is characterised by a gradual increase in intra-building MCS complexity and inter-building diversity. Whilst the operation of some features appear to have influenced the long-term pattern of change more than others, the nature of the long-term pattern of change appears to be primarily due to the operation of those features in which thermal choices and thermal control are contradictory, with the features in which thermal choices and thermal control are mutually enhanced having had minimal influence.

Figure 7.5. Discriminant plot of MCS of buildings in Case Study 2 using only variables in thermal accord.

132

CHAPTER 8 – A Case Study of Rooms in Two Regions: The ‘Pithouse’-to-‘Pueblo’ Transition

above ground structures was wholesale. In the New World, whilst pithouses continued to be used alongside pueblos for many years, during which time the relative depth of the pithouses actually increased (Stuart & Farwell 1983), they have ultimately fallen out of use altogether.

INTRODUCTION “The appearance of pithouse architecture in the Southwest is but one example of a worldwide Neolithic phenomenon.” (Cordell & Gumerman 1989: 9) “Sedentary life in many parts of the ancient world began with settlements of circular huts like those of the preceramic Near East. Although the timing was different from region to region, many of those early settlements were eventually replaced by villages of rectangular, nuclear family houses.” (Flannery 2002: 431) This chapter outlines a case study (Case Study 3) examining MCS in individual archaeological rooms in two Old World and two New World regions that encompass an evolutionary phase shift, four different geographical regions and two different climates. It thus examines MCS at a more detailed scale than that of both Case Studies 1 and 2. The term ‘pithouse’ is most commonly used to refer to the circular, semisubterranean structures of the American Southwest, but in Southwest Asia most Epipalaeolithic and numerous Neolithic structures were of broadly similar construction, ranging from round pits to floors cut into the sides of a slope (earth integrated) and so the term can be equally applied to similar types of construction (Rocek 1998: 211). Likewise, the term pueblo is most commonly used to refer to the rectilinear, above-ground structures of the American Southwest, but can be equally applied to similar types of construction. “Rounded, usually semisubterranean structures are succeeded by multi-room rectilinear surface dwellings (the pithouse-topueblo transition) in the Southwest and the Natufian and Pre-pottery Neolithic A (PPNA) versus later Neolithic architecture in the Levant” (Rocek 1998: 200). The transition from ‘pithouses’ to ‘pueblos’ represented a transitional phenomenon, an evolutionary phase shift, when the predominant position of one class of buildings (in this case circular ‘pithouses’) was superseded by an alternate predominant class of buildings (rectilinear ‘pueblos’) (Fig. 8.1). The transition from ‘pithouses’ to ‘pueblos’ occurred in numerous regions around the world (Fig. 8.2) at different relative moments in time and the way in which the transition occurred varied from region to region. In areas where the transition did take place, however, ‘pueblos’ ultimately became the dominant form of building. For example, in the Old World the transition from circular, semi-subterranean structures to rectilinear,

Figure 8.1. Generic a) brushwood hut, b) semisubterranean pithouse and c) above-ground rectilinear hut.

133

The Evolution of the Built Environment

Figure 8.2. Distribution of pithouses in the archaeological record (after Gilman 1983: 84 & other references in the text). The traditional explanations for the shift from ‘pithouses’ to ‘pueblos’ encompass diverse and often contradictory causal ‘explanations’ for the shift. This means that, whilst some arguments might be correct, their Lamarckian approach can only ever at best hope to account for why the transition may have occurred in specific places or at specific moments in time. A multi-scale Neo-Darwinian approach, on the other hand, has the capacity to account for other types of phenomena and broad patterns of behaviour. A search for the reasons why humans have done what they have done can only ever at best explain why a particular (ultimately) successful variant came into being, not why it persisted through time. It cannot, for example, explain why ‘pithouses’ replaced woven brushwood huts or why ‘pueblos’ replaced ‘pithouses’, nor why ‘pithouses’ continued to be used contemporaneously with ‘pueblos’ or why brushwood huts, in the form of ramadas, came back into use with the shift to ‘pueblos’.

return to less-energy-efficient above-ground structures (Stea & Turan 1993: 91-92). It has been argued that the building of above-ground contiguous rooms, with their increased food storage space and ease of access, was necessary to support the increased population that accompanied a shift to sedentism and agriculturalism (Robbins 1966; Whalen 1981; Stea & Turan 1993; Gilman 1983, 1986, 1987; Schiffer & McGuire 1992). Contrary arguments to this have stated that no direct correlation can be made between agriculture and sedentism (or above ground buildings) because sedentism is considered to have preceded agriculture in Southwest Asia, whereas agriculture preceded sedentism in the American Southwest (Rocek 1998). Contrary arguments to this view have also stated that diverse other factors must be considered (Cordell 1984: 233; Lekson 1988), including the continued construction of deeper pithouses with more pits excavated into their floors during the pueblo period (Stuart & Farwell 1983; Wilshusen 1989a). Contrary arguments have also stated that the transition is reflective of economic intensification of food processing, but that there were diminished agricultural returns, rather than an increase, and this resulted in a shift away from corporate and communal organisation to a system of autonomous households (Wills 2001; Wills and Windes 1989). It has been argued, as an extension of the above debate, that circular structures are reflective of fully nomadic and seminomadic societies who invest less time in preparation of the site, whilst rectilinear structures are reflective of fully sedentary and semi-sedentary societies (Binford 1990). Contrary arguments to this have stated that the correlation between agriculture and the types of structures used is imperfect and cannot be used to explain the transition (Rocek 1995). Contrary arguments to this view have also stated that the period of increased sedentism actually occurred during the pithouse period when more

The traditional approaches to the study of the ‘pithouse’to-’pueblo’ transition regards humans as possessing the capacity to alter their built environment in immediate reaction to changing social and ecological circumstances. That is, humans are seen as making changes in the present that fit social and ecological needs that will arise in the future. There has, however, been debate over the various arguments and each factor that has been put forward as a causal explanation has to date been countered with contrary arguments and conflicting factors. For example, it has been argued that energyefficient below-ground structures were built in response to cold climate (Farwell 1981; Stuart & Farwell 1983; Gilman 1987). Contrary arguments to this have stated that other factors such as settlement size and permanence must be incorporated (Cordell 1984: 221), that the model does not account for variability between pithouses (Wills 2001) and that the argument conflicts with the subsequent 134

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition existing variants), the characteristics of the variants that are most strongly coming under selective pressure become apparent. That is, test of the null hypothesis will be most apparent over an evolutionary phase shift (or over a very extended period of time). Therefore, if MCS is selectively neutral the thermal capacity of classes of buildings in the dataset will be random and if MCS is coming under selective pressure the thermal capacity will be ordered.

effort went into site preparation (Diehl 1997). It has also been argued that the shift from small, circular buildings to larger, rectilinear buildings accompanied a shift from housing single-persons in functionally diverse buildings to housing nuclear families in functionally delineated buildings (Flannery 1972b; Saidel 1993; Belfer-Cohen 1993; Byrd 2000; Flannery 2002). It has been argued that this is reflective of an increase in personal wealth (Saidel 1993) and that larger, nucleated villages possessed an ‘adaptive value’ due to their differentiation of tasks, ease of structural growth, defence and control over the sharing of food (Flannery 1972b: 4749, 1993). Stephen Plog has proposed a slightly different multi-factorial model to that of Flannery, in which feedback between the environment, increased sedentism, reduced mobility, dependence on agriculture, and increased risk (both economic and social) amplified small initial changes in the system, which led to increased flexibility within the system (Plog, S. 1990, 1995). An extension of this argument is the view that house form is the material product of biological ‘living’ functions and social ‘role’ functions and that, the less the degree of similarity between them and the relative volume of materials and facilities associated with them, the more likely the house form is to be circular, and that the higher the similarity and volumes, the more likely the house is to be rectilinear (Hunter-Anderson 1977). Contrary arguments have stated that this view offers no explanation for why the mechanisms for change should be segregation and centralisation in preference to alternative mechanisms (O'Brien 1996b: 19; Spencer 1997: 214-215). Contrary arguments have also stated that spaces do not define the functions they contain and that functions within a room cannot be mapped onto room morphology due to the dynamic nature of space-function relationships (Fletcher 2004: 115).

This case study was set up to examine the findings of the regional case study of buildings, but at the scale of individual rooms. That is, the analysis was designed to, first, test the findings of Case Study 2 that there is no apparent statistical correlation between the MCS of buildings and their associated climate or culture, at the scale of individual rooms. Secondly, the analysis was designed to test the pattern of MCS across a phase shift within the multi-scalar Neo-Darwinian evolution of the built environment. The MCS of individual rooms was analysed in such a way that the features that operate in contradiction to their converse feature (where thermal choices contradict thermal control, and vice versa) could be distinguished from the features that operate in accord, by differentiating between these two sets of variables and analysing them together and separately. This dataset encompassed a large set of archaeological rooms from four regions (the Pine Lawn Valley, New Mexico, the Southern Jordanian highlands, the Phoenix Basin, Arizona, and the Lower Jordan Valley). The two New World regions, the Pine Lawn Valley occupied by the Mogollon culture and the Phoenix Basin occupied by the Hohokam culture, were both indirectly culturallyrelated. Likewise the two Old World regions, the Southern Jordanian highlands and the Lower Jordan Valley, were indirectly culturally-related. These cultures do not cover the same periods of time, but they cover the period from the earliest recorded structures within each region up to the common use of ‘pueblo’-style buildings. The Pine Lawn Valley covers the time period from 1500 B.C. (Cochise) to A.D. 1200 (Tularosa), the Southern Jordanian highlands covers the time period from 10,500 (Natufian) to 600 B.C. (LPPNB), the Phoenix Basin covers the time period from 300 B.C. (Vahki) to A.D. 1450 (Polvoron) and the Lower Jordan Valley covers the time period from 8300 B.C. (Proto-Neolithic) to 3800 B.C. (Late Chalcolithic).

Finally, it has been argued that the transition from pithouses to kivas during the pithouse-to-pueblo transition is reflective of a transition from domestic use of subterranean structures in the American Southwest to ritual use (Wilshusen 1989b; Mobley-Tanaka 1997). A contrary argument could be stated that, due to a lack of impartiality and a prevalence of supposition and suggestion from which qualitative conclusions are drawn (e.g. Mobley-Tanaka 1997: 446), other explanations cannot be ruled out as either partly or wholly contributory.

Three separate MVA tests were performed. Test 1 analysed the MCS of individual rooms, and the influence of culture and/or climate on the MCS, over an evolutionary phase shift from one class of building to another, using the full set of variables, Test 2 analysed the MCS according to only those features that that operate in contradiction to their converse feature, over an evolutionary phase shift, and Test 3 analysed the MCS according to only those features that operate in accordance with other features over an evolutionary phase shift.

CASE STUDY 3: AN EVOLUTIONARY PHASE SHIFT IN TWO REGIONS This case study represents a test of the null hypothesis with regard to the ‘pithouse’-to-’pueblo’ transition, an evolutionary phase shift. A uniformitarian approach has been used so as to examine the processes that were operating throughout the transition. By examining the processes throughout an evolutionary phase shift, when the action of selection is operating most overtly (selection of one ultimately successful system from a range of 135

The Evolution of the Built Environment capacity based on contemporary climate classifications (Hansen 1947; Martin et al. 1949: 56-57; Irwin-Williams 1979: 31-32; Goldberg & Bar Yosef 1982; Roberts 1982; Van Zeist 1985: 201; Horowitz 1989). Today the Pine Lawn Valley and the Southern Jordanian highlands experience similarly cool winters and are classified as cool climates (GBsbk and Gbsak by the KoppenTrewartha climate classification method) and the Phoenix Basin and the Lower Jordan Valley are similarly noted for their hot summers and are classified as hot-arid climates (Bwhl by the Koppen-Trewartha climate classification method).

THE DATA: CROSS-CULTURAL ROOMS The four regions chosen for the analysis were selected on the basis that, whilst the societies within each continent were indirectly culturally-related historically, the Old World societies and the New World societies were wholly culturally unrelated (Figs. 8.3a and b). Therefore, the ‘pithouse’-to-’pueblo’ transition can be regarded as an example of evolutionary convergence on two continents. The climate in these regions appears to have been sufficiently unchanged since the early Holocene, c. 10,000 B.P., thus allowing this analysis of thermal

Figure 8.3a. Locations of Old World sites in Case Study 3. 136

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition

Figure 8.3b. Locations of New World sites in Case Study 3. The tests were performed on an extensive dataset of rooms that was compiled from archaeological reports. Only rooms that were sufficiently comprehensively excavated and documented were included in the dataset, those where the full array of variables (the room features) were ascertainable. For rooms where even one variable could not be ascertained it was not included in the set. The rooms were treated as discrete entities whether they

comprised a whole, single-roomed building or merely one room within a multi-roomed structure. The regions, time periods and entity (room) numbers within each group (each region and period) are listed in Table 8.1 below. Thirty-five variables (the room features) were used in the analysis and these are discussed below. The room numbers of those used in the analysis are listed in Appendix D. 137

The Evolution of the Built Environment ENTITIES USED IN THE ‘PITHOUSE’-TO-‘PUEBLO’ CASE STUDY OF TWO REGIONS Region Pine Lawn Valley, New Mexico

Southern Jordanian Highlands

The Phoenix Basin, Arizona

The Lower Jordan Valley

Time Period Cochise (1500 B.C.-A.D. 200) Pine Lawn - Georgetown (A.D. 200-650)

Time Period Group in Database 1

Entity (Building) No. in Database

2

1

Sites Wet Leggett Pueblo

San Francisco – Three Circle (A.D. 650-1000)

3

Reserve - Tularosa (A.D. 1000-1250)

4

Natufian (10500-8300 B.C.) PPNB A1 – B (7600- 7000 B.C.) PPNB C (7000-6500 B.C.) LPPNB (6500-6000 B.C.) Vahki - Sweetwater (300 B.C.- A.D. 350) Snaketown - Santan (A.D. 350-1100) Early Soho – Late Soho (A.D. 1100-1300) Civano - Polvoron (A.D. 1300-1450)

5 6

2-29 30-34 35 36-39 40 41-48, 60-65, 67 49-59, 68-78 66 79-82 83 84-87 88-89, 106-108 90-92 93-99 100-105 109-137 138 139-160

7 8 9

161-188 189-321 322-326

10

327-332, 340-361 333-339, 362-372 373-418

Snaketown Pueblo Grande Pueblo Grande

419-480, 497-524 481-485 486-496 525

Pueblo Grande U:13:21 U:13:22 Jericho

526-527 528, 534-539, 542-553, 558-566 529-533, 540-541, 554-557 567-622

Gilgal 1 Jericho

Proto-Neolithic (c. 8300 B.C.) PPNA (8300-7600 B.C.)

11 12

13 14

SU Promontory Three Pines Starkweather Ruin Turkey Foot Ridge Starkweather Ruin Turkey Foot Ridge SU Twin Bridges South Leggett Pueblo Three Pines South Leggett Pueblo Sawmill / Fox farm Oak Springs Pueblo Wet Leggett Pueblo Starkweather Ruin Beidha Beidha Beidha Basta Snaketown

Netiv Hagdud

PPNB - PNB 15 Jericho (7600-4600 B.C.) Middle Chalcolithic – 16 623-626 En-gedi Late Chalcolithic 627-632 Fasael (4300-3800 B.C.) 633-673 Ghassul Table 8.1. Entities (Rooms) Included in Case Study 3: The ‘Pithouse’-to-‘Pueblo’ Case Study of Two Regions. built structures in the area date to those of the Late Archaic period, c. 1000 B.C. (Sayles & Antevs 1941; Martin & Rinaldo 1950b: 430). The dataset spans the pithouse-to-pueblo transition, which can be considered to be complete by the end of the Tularosa phase, c. A.D. 1250, after which the use of permanent settlements was generally abandoned in the area until modern times. The Pine Lawn Valley sites used in the analysis were those

Sites from the Pine Lawn Valley, New Mexico: Seasonally Cool, New World There has been human habitation within the vicinity of the Pine Lawn Valley in New Mexico possibly since the Chiricahua phase of the Cochise Tradition, c. 3500 B.C. (Irwin-Williams 1967: 446-447; Irwin-Williams 1979; Woodbury & Zubrow 1979: 47). The earliest published 138

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition on top of a mesa and is exposed in all directions. It was occupied c. A.D. 550-1000 during the Georgetown, San Francisco and Three Circle phases and then, following a gap in the occupation of the site, c. A.D. 1130-1250 during the Tularosa phase. At its greatest extent it covered an area of about 5,000 m2 (Nesbitt 1938). One area was excavated and all 30 room/spaces are sufficiently well excavated and documented for use in this analysis.

that were best excavated and most comprehensively published. These included Wet Leggett, SU, Promontory, Starkweather, Turkey Foot Ridge, Twin Bridges, South Leggett, Three Pines, Sawmill/Fox Farm and Oak Springs, which are located in and bordering the Pine lawn Valley, west of Reserve, New Mexico (Nesbitt 1938; Martin et al. 1940; Martin 1943; Martin & Rinaldo 1947; Martin & Rinaldo 1950a & b; Wills 1996; Martin et al. 1949; Martin, P.S., Rinaldo, J. & Antevs, E. 1949; Bluhm 1957) These sites, though excavated and published over fifty years ago, are acceptable for the purposes of this analysis in that the excavation of the structures was sufficiently detailed and documented. In the case of only one settlement has the original dating been revised, placing four structures in a different phase (Wills 1996). Otherwise, whilst there might be some debate as to exact dating, the structures themselves have remained within their original phase and, therefore, in their original relative sequence. The sites are classified as GBsbk under the Koppen-Trewartha climatic classification system and comprise 139 entities (spaces/rooms).

Turkey Foot Ridge: The Turkey Foot Ridge site is situated 1950m above sea level and about 11kms west of Reserve. It sits on top of a narrow ridge and is exposed in all directions. It was occupied c. A.D. 650-750 during the San Francisco phase and covered an area of about 2,450 m2. The site appears to have been separated into two areas divided by a 70m long land bridge (Martin et al. 1949; Martin & Rinaldo 1950a). Of the two areas, fifteen rooms/spaces were sufficiently well excavated and documented for use in this analysis. Twin Bridges: The Twin Bridges site is situated 1940m above sea level, adjacent to the Oak Springs Pueblo site. It sits on top of a gently sloping, eastern facing narrow ridge and is exposed in all direction. It was occupied c. A.D. 750-1000 during the Three Circle phase and covered an area of about 400 m2 (Martin et al. 1949). One area was excavated and all 4 rooms/spaces are sufficiently well excavated and documented for use in this analysis.

Wet Leggett: Wet Leggett is situated approximately 1890m above sea level and approximately 12km southwest of Reserve, bordering Wet Leggett Canyon. It sits on a gentle southeastern facing slope and is generally exposed in all directions. It was occupied briefly c. 1000 B.C. during the Late Archaic period (Cochise-Chiricahua culture) and then c. A.D 1000-1100 during the Reserve phase, when it covered an area of about 100 m2 (Martin et al. 1949: 7-79; Martin & Rinaldo 1950b). The single building complex has 6 spaces/rooms, all of which are sufficiently well excavated and documented for use in this analysis.

South Leggett: The South Leggett site is situated approximately 1880m above sea level and about 12kms southwest of Reserve. It sits on the valley floor and is only partially exposed in all directions. It was occupied c. A.D. 750-1000 during the Three Circle phase and then, following a gap in the occupation of the site, c. A.D. 1000-1130 during the Reserve phase when its covered an area of 360 m2 (Martin & Rinaldo 1950b). Two areas were excavated, the earlier Three Circle occupation, the later Reserve phase occupation and all 6 rooms/spaces were sufficiently well excavated and documented for use in this analysis.

SU: The SU site is situated 1960m above sea level and approximately 10kms southwest of Reserve. It sits at the top of an eastern facing slope and is exposed in all directions. It was occupied c. A.D. 200-550 during the Pine Lawn phase, when it covered an area of about 27,000 m2, and later during the Three Circle Phase, to which only a single building belongs (Martin et al. 1940; Martin 1943; Martin & Rinaldo 1947). One area was excavated belonging to the Pine Lawn phase and all 28 room/spaces are sufficiently well excavated and documented for use in this analysis. Note that tree-ring analysis performed in the 1960s lead to a revision of the dating of structures D, W, X and Z, placing them in the Pine Lawn phase rather than Martin, Rinaldo and Antev’s original Three Circle phase (Wills 1996).

Three Pines: The Three Pines site is situated approximately 1870m above sea level and about 13.5kms southwest of Reserve. It was occupied c. A.D. 200-550 during the Pine Lawn phase and then, following a gap in the occupation of the site, c. A.D. 1000-1130 during the Reserve phase when its covered an area of at least 290 m2 (Martin & Rinaldo 1950b). One area was excavated, although the Reserve phase structures were only partially excavated, and 5 rooms/spaces were sufficiently well excavated and documented for use in this analysis.

Promontory: The Promontory site is situated 1940m above sea level in the vicinity of the SU site. It sits on top of a mesa and is exposed in all directions. It was occupied c. 200-550 during the Pine Lawn phase and covered an area of about 3,000 m2 (Martin et al. 1949). One area was excavated and all 5 room/spaces are sufficiently well excavated and documented for use in this analysis.

Sawmill/Fox Farm: The Sawmill site is situated approximately 1890m above sea level and about 11kms west of Reserve. It sits on the edge of a mesa which slopes steeply on the western side and more gently on the eastern side and is exposed in all directions. It was occupied c. A.D. 1000-1130 during the Reserve phase

Starkweather: The Starkweather site is situated 1850m above sea level and about 6kms west of Reserve. It sits 139

The Evolution of the Built Environment and covered an area of 1300 m2 (Bluhm 1957). Four areas were excavated and, of these, 2 rooms/spaces were sufficiently well excavated and documented for use in this analysis.

covered an area of about 140,000m2 (Nissen et al. 1987; Gebel et al. 1988; Nissen et al. 1991; Stern 1993: 149152; Kuijt 2000). Two separate areas were excavated and, of these, 30 spaces/rooms in Area A and 38 in Area B were sufficiently well excavated and documented for use in this analysis. Many of these spaces/rooms, however, underwent various phases of alteration and each altered state has been treated as a separate entity.

Oak Springs: The Oak Springs site is situated approximately 1880m above sea level and in the vicinity of South Leggett Pueblo. It sits on the valley floor and is only partially exposed in all directions. It was occupied c. A.D. 1000-1130 during the Reserve phase when it covered an area of at least 170 m2 (Martin et al. 1949). One area was excavated, although only partially, and 7 rooms/spaces were sufficiently well excavated and documented for use in this analysis.

Sites from the Phoenix Basin, Arizona: Seasonally Hot, New World There has been human habitation within the vicinity of the Phoenix Basin in Arizona since possibly 9300 B.C. (Irwin-Williams 1979). The earliest published built structures in the area date to those of the Vahki phase at Snaketown, c. 300 B.C. (Gladwin 1948: 127-140; Wilcox et al. 1981). The dataset spans the pithouse-to-pueblo transition, which can be considered to be complete by the end of the Civano and Polvoron phases, c. A.D. 1450., after which the area appears to have been generally abandoned by permanent communities until the historic period. The Civano and Polvoron phases have been treated together here due to general disagreement over whether they represent separate phases or variations within a single phase (Doyel & Fish 2000: 10). There are numerous sites in the Phoenix Basin area that were appropriate to the analysis, of which an appropriate selection was made of four sites that were well excavated, comprehensively published and characteristic of the sites in the area during this period. They are Snaketown, Pueblo Grande, Arizona U:13:21 and Arizona U:13:22 (Gladwin 1948; Gladwin et al. 1965; Haury 1976; Wilcox et al. 1981; Jewett & Lightfoot 1986; Bostwick 1994; Bostwick & Downum 1994; Downum et al. 1994; Mitchell 1994a & b) These sites are classified as Bwhl under the Koppen-Trewartha climatic classification system and comprise 182 entities (spaces/rooms).

Sites from the Southern Jordan Highlands: Seasonally Cool, Old World There has been human habitation within the Southern Jordan Highlands since at least the Late EpipalaeolithicEarly Natufian, c. 19,500 B.C. (Gebel 1988). The earliest published built structures in the area date to those of the Late Natufian, c. 10,000 B.C., and appear to belong to seasonally mobile communities. The dataset used here spans the ‘pithouse’-to-’pueblo’ transition that can be considered to be complete by the end of the Late PPNB period, c. 6000 B.C., after which the large settlements were abandoned by permanently settled communities until modern times. The southern Jordanian highlands sites used in the analysis were those that were best excavated and most comprehensively published. These included Beidha and Basta (Kirkbride 1966a & b; Kirkbride 1967; Kirkbride 1968a & b; Nissen et al. 1987; Gebel et al. 1988; Byrd 1989; Nissen et al. 1991; Byrd 1994; Kuijt 2000; Byrd 2005). Both of these sites lie above 1000 m above sea level and are situated in the mountainous region northeast of Petra. Beidha is classified as GBsak and Basta is classified as GBsbk under the Koppen-Trewartha climatic classification system and the two sites comprise 184 entities (spaces/rooms).

Snaketown: Snaketown is situated approximately 360m above sea level and approximately 36km southeast of Phoenix. It sits on a flat, almost level river valley and is generally exposed in all directions. It was occupied c. 300 B.C.-A.D. 1100, during the Vahki through to Sacaton phases. It was at its greatest extent in the Sacaton phase when it covered an area of at least 650,000 m2, although this was very sparsely settled (Gladwin 1948; Gladwin et al. 1965; Haury 1976; Wilcox et al. 1981; Jewett & Lightfoot 1986). One irregularly shaped area was excavated and, within this, 33 rooms/spaces were sufficiently well excavated and documented for use in this analysis.

Beidha: Beidha is situated 1,020m above sea level in Wadi Beidha and approximately 4.5km north of Petra. It sits on top of a spur that extends eastward from taller and steeper mountains and so the site is exposed in all directions except to the west and the northwest. It was occupied from c. 7000. to 6500 B.C during the PPNB period and covered an area of about 1,050m2 (Kirkbride 1960; Kirkbride 1966a & b; Kirkbride 1967; Kirkbride 1968a & b; Byrd 1988; Byrd 1989; Stern 1993: 173-175; Byrd 1994). Three separate areas were excavated and, of these, 47 spaces/rooms in the main Tell area were sufficiently well excavated and documented for use in this analysis.

Pueblo Grande: Pueblo Grande is situated approximately 340m above sea level and on the south-eastern outskirts of Phoenix. It sits on a flat and almost level plain and is generally exposed in all directions. It was occupied c. A.D. 350-1450, during the Snaketown through to Civano/Polvoron phases. It was at its greatest extent in the Civano phase when it covered an area of at least 315,000 m2, although this was irregularly occupied

Basta: Basta is situated 1,420-1,460m above sea level southeast of Wadi Musa and approximately 19km southeast of Beidha. It sits on a southeastern facing slope and is generally exposed in all directions. It was occupied from c. 6,700 to 6000 B.C. in the Late PPNB period and 140

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition (Downum & Bostwick 1993; Bostwick & Downum1994; Mitchell 1994). One area was excavated and, within this, 115 rooms/spaces were sufficiently well excavated and documented for use in this analysis.

10,000 m2 (Noy et al 1980; Noy 1986; Noy 1989a & b; Stern 1993: 517-518). 13 spaces/rooms were excavated and, of these, 2 were sufficiently well excavated and documented for use in this analysis.

Arizona U:13:21 and Arizona U:13:22: Both U:13:21 and U:13:22 are situated approximately 340m above sea level and approximately 1.5 to 2 kms west of Snaketown. They sit on a flat, almost level river valley and are generally exposed in all directions. They were occupied chiefly c. A.D. 1300-1450, during the Civano phase, when they covered areas of approximately 650 m2 and 950 m2 respectively (Haury 1976). Several discontinuous trenches were excavated at U:13:21 and one area was excavated at U:13:22 and, within these, 5 and 11 rooms/spaces respectively were sufficiently well excavated and documented for use in this analysis.

Jericho: Jericho is situated at 250m below sea level and approximately 10km north of the Dead Sea. It was occupied, with occupation gaps, from the Mesolithic period, c. 9000 BC, through to the early Arab period. The early settlement sat on top of a gentle rise and was exposed in all direction but, with the gradual accumulation of deposits, the later settlement sat on the slope that faced away from the centre of the Tell, leaving the peripheral areas generally exposed in all directions. The periods covered by the study are from the ProtoNeolithic, c. 8700 B.C., through to the end of the PNB period, c. 4500 B.C., following which there appears to have been a gap in the occupation of the site until the Proto-Urban phase, c. 3400 B.C. At its height Jericho covered an area of about 40,000 m2 (Garstang & Garstang 1948; Kenyon 1952; Kenyon 1953; Kenyon 1956; Kenyon 1957; Kenyon 1959; Kenyon 1960; Kenyon 1981a & b; Bar Yosef 1986; Holland & Netzer 1992; Stern 1993: 674-678). Twelve separate areas were excavated and, of these, 26 spaces/rooms in Squares FI/DI/DII, 3 in Squares EI/EII/EV, 14 in Square MI, 1 in trench I and 3 in Trench III were sufficiently well excavated and documented for use in this analysis. The spaces encompass the Proto-Neolithic (Square MI), PPNA (Squares FI/DI/DII, EI/EII/EV and MI, and Trench I), PPNB (Squares FI/DI/DII and MI, and Trench III), PNA (Squares FI/DI/DII) and PNB periods (Square MI).

Sites from the Lower Jordan Valley: Seasonally Hot, Old World The Lower Jordan Valley has some of the oldest urban settlements in the world and the indications are that the area has been continuously occupied from at least 1.4 million years ago, as attested by the Acheulian hand axes found at ‘Ubeidiya approximately 90 kms north of Jericho. The Jordan Rift Valley had a continuous occupation sequence throughout the Quaternary, when prehistoric sites were common (Horowitz 1989: 5), and the earliest recorded structures in the area are the remains of a Palaeolithic woven brushwood hut at Ohalo II, c. 17,000 B.C. (Nadel & Werker 1999). The dataset spans the ‘pithouse’-to-’pueblo’ transition, which can be considered to be complete by the end of the PNB period, c. 4500 B.C. However, the analysis also includes the following Chalcolithic period, which terminates c. 3800 B.C., so as to offset the Jericho data that would otherwise have dominated the dataset. The Chalcolithic period still predates the development of the first fully urban settlements of the Early Bronze Age. The lower Jordan Valley sites used in the analysis were those that were best excavated and most comprehensively published. These included Gilgal 1, Jericho, Netiv Hagdud, En-gedi, Fasa’el and Teleilat Ghassul (Mallon et al. 1934; BarYosef et al. 1980; Ussishkin 1980; Kenyon 1981a & b; Bar-Yosef & Gopher 1984; Porath 1985; Bar-Yosef 1986; Noy 1989b; Bar-Yosef & Gopher 1997; Bourke 2001). These sites lie below sea level and they are situated in the Great Rift Valley on the northern shore of the Dead Sea, with the exception of En-gedi, which lies on the western shore of the Dead Sea. They are classified as Bwhl under the Koppen-Trewartha climatic classification system and comprise 149 entities (spaces/rooms.

Netiv Hagdud: Netiv Hagdud is situated at 170m below sea level and approximately 12 km north of Jericho and 1 km west of Gilgal I. It sits on an eastward facing slope and is generally exposed in all directions. It was occupied from c. 9900 to 9500-9400 B.C. during the PPNA period and covers an area of about 15,000 m2 (Bar Yosef et al. 1980; Bar Yosef & Gopher 1983; Bar Yosef & Gopher1984; Tsafrir & Holum 1987/1988; Bar Yosef et al. 1991; Stern 1993: 1150-1152; Bar Yosef & Gopher 1997). Four separate areas were excavated and, of these, 1 space/room in the Deep Sounding and 10 in the Upper Area were sufficiently well excavated and documented for use in this analysis. En-gedi: En-gedi is situated 170m below sea level on the western shore of the Dead Sea midway between the northern and southern shores. It sits on a spur that extends eastward from taller and steeper mountains and so the site is exposed in all directions except to the west. It was occupied and functioned as a shrine during the Chalcolithic period, c. 4500 to 3300 B.C., when it covered an area of 540 m2 (Ussishkin 1980: 399-405; Stern 1993). The single building complex had 4 spaces/rooms, all of which were sufficiently well excavated and documented for use in this analysis.

Gilgal 1: Gilgal 1 is situated at 225m below sea level and approximately 12 km north of Jericho on the eastern edge of the Salibiya basin, which drains into the Jordan River 7km away. It sits on top of a low ridge and is exposed in all directions. It was occupied from c. 9900 to 9600 B.C. during the PPNA period, when it covered an area of about

Fasa’el: Fasa’el is situated approximately 170m below sea level and approximately 15km north of Jericho. It sits 141

The Evolution of the Built Environment on a gentle east facing slope and is exposed in all directions. It was occupied during the Ghassulian phase of the Chalcolithic period, c. 4200 to 3800 B.C., when it covered an area of about 350,000 m2 (Porath 1985). Within the larger area a single building complex was excavated and all 4 spaces/rooms were sufficiently well excavated and documented for use in this analysis.

Test 2. Rooms and Features in Thermal Contradiction The purpose of this test was to statistically ascertain the degree to which features that operate in thermal contradiction to their converse feature (where thermal choices contradict thermal control, and vice versa) have influenced the overall pattern of MCS change in individual rooms throughout an evolutionary phase shift. This was achieved by comparing the overall pattern of MCS change with that of only the features that operate only in contradiction, for the same reasons as those outlined above in Test 2 of Case Study 2.

Teleilat Ghassul: Teleilat Ghassul is situated 295m below sea level and approximately 5km northeast of the Dead Sea. It was occupied from c. 4700 B.C., contemporary with the late PNA phase at Jericho, through to 3800 B.C. The site sits on top of a gentle southwest facing slope but the gradual accumulation of Tell deposits left the area of the site under investigation here exposed in all directions. It covered an area of about 200,000 m2, of which 12,000 m2 has been exposed (Mallon et al. 1934; Koeppel 1940; North 1961; Hennessy 1969; Hennessy 1982; Hennessy 1992; Stern 1993: 506-511; Bourke et al. 1995; Bourke 1997a & b 1997; Bourke et al. 2000; Lovell 2001). Twelve areas were excavated, with some of the more recent excavations overlapping areas that were previously excavated but not to any great depth. Of these areas, 41 spaces/rooms in Tell 1 were sufficiently well excavated and documented for use in this analysis and all date to the Late Chalcolithic period, c. 4200 to 3800 B.C.

Test 3. Rooms and Features in Thermal Accord The purpose of this test was to statistically ascertain the degree to which features that operate in thermal accordance with other features (where thermal choices do not contradict thermal control) influence the overall pattern of MCS change at the scale of individual rooms. This was achieved by comparing the overall pattern of MCS change with that of only the features that operate only in accordance, for the same reasons as those outlined above in Test 3 of Case Study 2.

ARCHAEOLOGICAL ROOM FEATURES: THE VARIABLES

THE TESTS: CASE STUDY 3 The three tests used the same set of entities (the rooms). The entities were again grouped according to region and phase and this grouping was, therefore, constant between the tests. Likewise, the data in the different tests was analysed using the MVA techniques of Discriminant Analysis and Correlation Analysis. The Discriminant Analysis provided a visual test to see if grouping was present and the Correlation Analysis provided a numerical test of the visual grouping (or absence of grouping) and recorded the strength of the statistical correlation. The aims of the individual tests are discussed in greater detail below. The MCS of the individual buildings was again treated as equivalent to the string of their variables (the sum of the interactions between the thermal features).

The variables used in this case study are built features and traits that operate at the scale of individual rooms. At this scale thermal variability within the buildings is factored in, but only in terms of the nature of the spaces that the individual rooms adjoin. That is, at this scale the thermal performance at a finer scale of detail (rooms within a building) is accentuated in preference to that of the thermal performance at a broader scale of detail (room-weighted buildings). The variables in the dataset were divided into those that operate in contradiction with other features (where thermal choices contradict thermal control, and vice versa) and those operating in accordance (thermal choices do not contradict thermal control) based on the results of the engineering-analysis (Table 5.5). Table 8.2 below lists the variables according to this division. The division allowed the MCS to be calculated according to the full set of variables, or according to either of the two subsets. The table of the variables used is followed by a series of tables that list the graded, numeric values for each of the variables that were used in the MVA.

Test 1. Rooms, Culture and Climate The purpose of this test was to statistically ascertain the pattern of MCS change in individual archaeological rooms throughout an evolutionary phase shift from one class of buildings to another and the degree to which culture and climate may have influenced the pattern of change, given that the regional study of buildings (Case Study 2) indicated that at the scale of whole roomweighted buildings no such statistical correlation existed.

Note that only the numeric values of the variables used in the MVA are shown here. For the calculated values for each variable for each room in the dataset (the MVA spreadsheet) refer to Appendix D.

142

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition value of one creates a proportionate reduction in the other, the values have been set up such that a lower value equates to high thermal choices and low thermal control, whilst a higher value equates to low thermal choices and high thermal control

Features where thermal choices and thermal control are contradictory In the set of variables where thermal choices and thermal control are contradictory, such that an increase in the

VARIABLES USED IN THE ‘PITHOUSE’-TO-‘PUEBLO’ CASE STUDY OF ROOMS IN TWO REGIONS Variable No. Variable 1-9 Building exposure (n, ne, nw, s, se, sw, e, w & vertical) 10 Roof flatness/peakiness 11 Floor level relative to ground level 12-13 Wall and roof material (thermal mass) 14-15 Presence of wall & roof insulation 16 No. internal angles 17 Ratio length/width 18-20 No. posts, niches & benches 21-23 No. connected rooms, upper storeys & lower storeys 24 Nearest neighbour 25 Plan area 26 No. exterior openings in each direction Features where thermal choices and thermal 27-31 Solar penetration (from s, se, sw, e, w) control are in 32-33 Cross ventilation & corner ventilation accordance 34 Heating (solar and/or active) 35 No. connected transitional spaces Table 8.2. Thermal Features (Variables) Included in Case Study 3: The ‘Pithouse’-to-‘Pueblo’ Case Study. Features where thermal choices and thermal control are contradictory

Variables 1-9: Building Exposure (n, ne, nw, s, se, sw, e, w & vertical): This variable is a measure of the room’s potential to interface with the outside environment. A fully exposed room will have high thermal choices and low thermal

control, whilst a room with no exposure will have high thermal control and low thermal choices.

BUILDING EXPOSURE (N, NE, NW, S, SE, SW, E, W & VERTICAL) Feature Exposed Not exposed

Value 1 2

Table 8.3. Discriminant Values for Building Exposure (n, ne, nw, s, se, sw, e, w & vertical) Variable 10: Roof Flatness/Peakiness: This variable is a measure of the room’s vertical temperature distribution. A room with a pyramidal roof will have high thermal choices and low thermal control,

whilst a room with no roof or with a flat roof will have high thermal control and low thermal choices (Cook 1996: 281).

ROOF FLATNESS/PEAKINESS Feature Pyramidal Domed Skillion Rounded Flat Unroofed Table 8.4. Discriminant Values for Roof Flatness/Peakiness

143

Value 1 2 3 4 5 6

The Evolution of the Built Environment Variable 11: Floor Level Relative to Ground Level: This variable is a measure of the room’s vertical and horizontal temperature distribution. A room in excess of 2m below ground will have high thermal control and low

thermal choices, whilst a room in excess of 6m above ground will have high thermal choices and low thermal control.

FLOOR LEVEL RELATIVE TO GROUND LEVEL Feature Value ≥-2000mm 1 -1990 – -1200mm 2 -1190 – -650mm 3 -640 – -300mm 4 -290 – -100mm 5 -90 – +90mm 6 100 – 440mm 7 450 – 990mm 8 1000 – 1790mm 9 1800 – 2990mm 10 3000 – 5990mm 11 ≥6000mm 12 Table 8.5. Discriminant Values for Floor Level Relative to Ground Level Variable 12-13: Wall and Roof Material (Thermal Mass): This variable is measure of the room’s horizontal temperature distribution. A room that is open on one wall, or which is unroofed, will closely track the outside conditions and will have high thermal choices and low

thermal control, whilst a room with walls and roof that have a heavy thermal mass will be isolated from the outside and will have high thermal control and low thermal choices.

WALL AND ROOF MATERIAL (THERMAL MASS) Feature Value Nil 1 Light (thatch / reed / light timber / skins) 2 Medium (wattle and daub / jacal / heavy 3 timber) Heavy (stone / mud / brick / 0.5-1.5m 4 subterranean) Very Heavy (≥0.6m stone / ≥0.6m mud / 5 ≥0.6m brick / ≥1.5m subterranean) Table 8.6. Discriminant Values for Wall and Roof Material (Thermal Mass) Variable 14-15: Presence of Wall and Roof Insulation: This variable is a measure of how closely the temperatures inside a room track those outside and, therefore, of the room’s horizontal thermal variability. An insulated room will have high thermal control and low

thermal choices, whilst an uninsulated room will more closely track the outside conditions and will have high thermal choices and low thermal control (Givoni 1976: 131).

PRESENCE OF WALL AND ROOF INSULATION Feature Present Absent

Value 1 2

Table 8.7. Discriminant Values for Presence of Wall and Roof Insulation

144

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition Variable 16: Number of Internal Angles: This variable is a measure of a room’s horizontal thermal variability. A room that is more angular will have high thermal choices and low thermal control, whilst a room

that is more circular will have high thermal control and low thermal choices.

NUMBER OF INTERNAL ANGLES Feature Value 3 – 4 (Rectilinear) 1 5–6 2 7–8 3 9 – 10 4 11 – 12 (Circular) 5 Table 8.8. Discriminant Values for Number of Internal Angles Variable 17: Ratio Length/Width: This variable is a measure of a room’s horizontal thermal variability. A room that has a ratio of 1:1 (length:width) will have high thermal control and low thermal choices,

whilst a room that has a high ratio of length:width will have high thermal choices and low thermal control.

RATIO LENGTH/WIDTH Feature ≥8 5–7 3–4 2 – 2.5 1.5 1 Table 8.9. Discriminant Values for Ratio Length/Width

Value 1 2 3 4 5 6

Variable 18: Number of Posts: This variable is a measure of a room’s horizontal thermal variability. A room with no internal columns will have high thermal control and low thermal choices, whilst a

room with numerous columns will have high thermal choices and low thermal control.

NUMBER OF POSTS Feature ≥40 39 – 20 19 – 10 9–5 4–2 1 0 Table 8.10. Discriminant Values for Number of Posts

Value 1 2 3 4 5 6 7

Variable 19: Number of Niches: This variable is also a measure of a room’s horizontal thermal variability. A room with no niches will have high thermal control and low thermal choices, whilst a room

145

with numerous niches will have high thermal choices and low thermal control.

The Evolution of the Built Environment

NUMBER OF NICHES Feature ≥8 6–7 4–5 2–3 1 0 Table 8.11. Discriminant Values for Number of Niches

Value 1 2 3 4 5 6

Variable 20: Number of Benches: This variable is also a measure of a room’s horizontal thermal variability. A room with no benches will have high thermal control and low thermal choices, whilst a

room with numerous benches will have high thermal choices and low thermal control.

NUMBER OF BENCHES Feature ≥7 5–6 3–4 2 1 0 Table 8.12. Discriminant Values for Number of Benches

Value 1 2 3 4 5 6

Variable 21: Number of Connected Rooms: This variable is a measure of a room’s thermal variability beyond the immediate horizontal confines of the room itself. A room with numerous adjacent other rooms will have a horizontally extended thermal environment. It will have high thermal choices and low thermal control because the adjacent rooms will isolate it from the outside

environment and make it thermally homogenous. Conversely, a room with no extended thermal environment will have high thermal control and low thermal choices because it will more closely interface with the outside environment.

NUMBER OF CONNECTED ROOMS Feature Value ≥8 1 7–4 2 2–3 3 1 4 0 5 Table 8.13. Discriminant Values for Number of Connected Rooms Variable 22-23: Number of Connected Upper Storeys and Lower Storeys: This variable is also a measure of a room’s thermal diversity beyond the immediate vertical confines of the room itself. As per a horizontally extended environment, a room with a connected upper or lower storey will have a vertically extended thermal environment. It will have high

thermal choices and low thermal control because it will be thermally isolated from the outside environment. Conversely, a room with no extended thermal environment will have high thermal control and low thermal choices.

146

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition

NUMBER OF CONNECTED UPPER STOREYS AND LOWER STOREYS Feature 2 1 0

Value 1 2 3

Table 8.14. Discriminant Values for Number of Connected Upper Storeys and Lower Storeys Variable 24: Nearest Neighbour Distance: This variable is also a measure of a room’s thermal diversity beyond the immediate confines of the room itself. A room with a distant nearest neighbour (nearest other room) will high thermal choices and low thermal control because it will be thermally maximally exposed to

the outside environment. Conversely, a room with an immediately adjacent nearest neighbour will have high thermal control and low thermal choices because it will be thermally less exposed to the outside.

NEAREST NEIGHBOUR DISTANCE Feature Value 0m 1 0.1 – 1.9m 2 2 – 3.9m 3 4 – 7.9m 4 8 – 15.9m 5 16 – 31.9m 6 32 – 63.9m 7 ≥64m 8 Table 8.15. Discriminant Values for Nearest Neighbour Distance Variable 25: Plan Area: This variable is a measure of a room’s sensitivity to thermal change. Rooms with large floor areas, and ergo large spatial volumes, will be less sensitive to immediate thermal changes than rooms with smaller floor areas and spatial volumes. A large floor area will be less sensitive

to immediate thermal changes and will have high thermal control and low thermal choices, whilst a room with a small floor area will have high thermal choices and low thermal control.

PLAN AREA Feature ≥200m2 150 – 199.9m2 100 – 149.9m2 70 – 99.9m2 50 – 69.9m2 40 – 49.9m2 30 – 39.9m2 20 – 29.9m2 15 – 19.9m2 10 – 14.9m2 5 – 9.9m2 0 – 4.9m2 Table 8.16. Discriminant Values for Plan Area

Value 1 2 3 4 5 6 7 8 9 10 11 12

147

The Evolution of the Built Environment

Features where thermal choices and thermal control are in accordance Variable 26: Number of Exterior Openings in Each Direction: This variable is a measure of the room’s capacity to interface directly and selectively with the outside. A room with numerous openings in different directions will have

high thermal choices and thermal control, whilst a room with few openings or openings in few directions will have low thermal choices and thermal control.

NUMBER OF EXTERIOR OPENINGS Feature Value 1 1 2 2 3 3 etc. etc. Table 8.17. Discriminant Values for Number of Exterior Openings Variable 27-31: Solar Penetration (from s, se, sw, e, w): This variable is a measure of the room’s capacity for solar radiation to selectively enter. A room with solar penetration into numerous rooms and from different directions will have high thermal choices and thermal control, whilst a room with minimal solar penetration will have low thermal choices and thermal control. For details as to the presence or absence of solar penetration, refer to

the ‘solar penetration’ for the regional case study of buildings above (Table 7.18). Note, however, that this applies only in cases where threshold level openings are present in the extant remains and that there is no archaeological evidence for the presence of ‘windows’ during this time period in these regions.

SOLAR PENETRATION (FROM S, SE, S W, E, W) Feature Penetration No Penetration

Value 1 2

Table 8.18. Discriminant Values for Solar Penetration (from s, se, sw, s, w) Variable 32-33: Cross-Ventilation and Corner-Ventilation: This variable is a measure of the room’s capacity for cross-ventilation and/or corner-ventilation. A room with cross-ventilation or corner-ventilation will have high

thermal choices and thermal control, whilst a room with neither will have low thermal choices and thermal control.

CROSS-VENTILATION AND CORNER-VENTILATION Feature Value 1 Path 1 2 Paths 2 3 Paths 3 etc. etc. Table 8.19. Discriminant Values for Cross-Ventilation and Corner-Ventilation Variable 34: Heating: This variable is a measure of a room’s capacity to add additional heat to its interior through the use of fires and/or solar penetration. The value is based purely on archaeological evidence for hearths or ash deposits that indicate the presence or absence of regular fires inside the

148

rooms. The associated degree of thermal homogeneity and control relates to the number of fire/s within the room and their relative positions. A room with more fires will have high thermal choices and thermal control, a room with more peripherally located fires will have higher

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition thermal choices and thermal control, and a room that has potential solar penetration in addition to an active fire will have even higher thermal choices and thermal control.

The value is calculated pertaining to the number and location of the fires, to which an additional value of 1 is added if the room has potential solar penetration.

HEATING Feature No Hearth 1 Central Hearth 1 Hearth Midway Between Centre & Perimeter 1 Perimeter Hearth 2 Hearths in Different Locations 3 Hearths in Different Locations Solar Penetration Present Table 8.20. Discriminant Values for Heating

Value 1 2 3 4 5 6 + 1 to value

Variable 35: Number of Connected Transitional Spaces: This variable is a measure of a room’s thermal diversity beyond the immediate confines of the room itself, to include transitional spaces, such as courtyards, colonnades, shade structures and loggias. A room with adjacent transitional spaces will have high thermal

choices and thermal control because it extends the overall thermal choices and thermal control of the occupants in line with the number of different types (orientations) of the transitional spaces, and without reducing a room’s overall capacity to interface with the outside environment.

NUMBER OF CONNECTED TRANSITIONAL SPACES Feature Value 0 1 1 2 2–3 3 4–7 4 ≥8 5 Table 8.21. Discriminant Values for Number of Connected Transitional Spaces of the MCS of the buildings in all regions over time. Apart from the Southern Jordanian highlands PPNB C buildings, which appears as an outlier grouping, there is a gradual shift in thermal signatures away from that of the early buildings in each region towards a commonality. This commonality is shared by the final phase rooms of each region, when the ‘pueblo’ building form was well established. The intra-building thermal diversity remained relatively consistent throughout the transition irrespective of region and time period. The scattergram clusters are relatively similar in size and each are accompanied by a similarly small number of outliers. That is, whilst the general nature of the change in MCS was relatively consistent, exceptions to the rule were common and usual.

RESULTS OF THE TRANSITIONAL CASE STUDY OF ROOMS It is important to note that the results outlined below relate to individual archaeological rooms. The results cannot, therefore, directly infer anything about other rooms that may exist within a multi-room building. The results can, however, be used to make indirect inferences about the MCS of a multi-roomed building to which it belongs, because the variables pertaining to the rooms incorporate thermal features of their adjoining rooms. No room within a multi-roomed building operates in thermal isolation from the other rooms around it and its thermal performance is a factor of that relationship.

Test 1. Rooms, Culture and Climate

This change is reflected in the results of the Correlation Analysis, which shows that there is a moderate statistical correlation between the MCS of the buildings and the regions and periods to which they belong (r = 0.680), a correlation that is statistically significant (p = 0.000). The correlation between the MCS of the rooms and the regions and periods to which they belong is, however, slightly weaker compared with that of the analysis performed at the scale of room-weighted buildings in

The results of the Discriminant Analysis are shown visually on the scatterplot, where the entities (the MCS of rooms) are grouped according to their region and period (Fig. 8.4). Consequently, conclusions can be made about the pattern of change in the MCS of classes of buildings over an evolutionary phase shift with respect to their culture and climate. Generally, the scatterplot shows that there is a gradual and directional change in the character 149

The Evolution of the Built Environment Case Study 2 above (r = 0.787). It can therefore be stated that MCS appears to be best studied at the scale of whole room-weighted buildings, rather than at the scale of individual rooms, although this indication would be strengthened with further testing.

appears to have been the result of numerous, but small and consistent changes in the holistic way that the features and traits operated thermally between successive buildings. The thermal sum of these small changes in the assemblages of features and traits would have had consequences for the operational capacities of the buildings.

The scatterplot shows that, with respect to culture, there is no distinction between the Old World pattern (the Southern Jordanian highlands and the Lower Jordan Valley) and the New World (the Pine Lawn Valley, New Mexico, and the Phoenix Basin, Arizona). The MCS in each of the regions changes in a similar fashion over time, with the points on the plot moving from a general position on the far left of the plot to a position on the far right. The late PPNB in the Southern Jordanian Highlands appears as an outlier, but the following period again conforms to the operational commonality. Interestingly the overall pattern of change shows no sudden transition in thermal signatures as the predominant class of buildings shifted from ‘pithouses’ to ‘pueblos’. This thus confirms that the aggregate thermal performance of buildings is a factor of more than just relative floor level and roof/wall shape. Irrespective of whether the floor level relative to the ground level changed abruptly, giving the appearance that the transition was sudden, this cannot be correlated with a sudden change in MCS. That is, at the scale of classes of buildings the MCS of a generic ‘pithouse’ may vary markedly from that of a generic ‘pueblo’, but the actual transition from the earlier class of MCS to the latter

With respect to climate, there is a distinction between the two regions that experience hot-arid summers (the Phoenix Basin and the Lower Jordan Valley) and the two regions that experience cool winters (the Pine Lawn Valley and the Southern Jordanian highlands). The points on the plot belonging to the two seasonally hot-arid regions are generally confined to the upper half of the plot, whilst those belonging to the two seasonally cool regions are generally confined to the lower half of the plot. That is, whilst there is no difference in the long-term pattern of MCS change in relation to culture, there does appear to be a shift relative to differences in climate at this scale of analysis of individual rooms. It can, therefore, be stated that a statistically meaningful change in the MCS of classes of buildings occurred throughout the transition from ‘pithouses’ to ‘pueblos’. This trend is not related to culture, but at this scale of analysis of individual rooms there is a slight correlation with climatic context. This correlation does, however, would be strengthened with further testing..

Figure 8.4. Discriminant plot of MCS of rooms in Case Study 3.

150

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition that, whilst the evolution of the built environment occurs at the scale of whole room-weighted buildings, it is intrinsically linked to finer levels of thermal operation.

Test 2. Rooms and Features in Thermal Contradiction The discriminant plot generated from only those variables that operate in contradiction to their converse feature (where thermal choices contradict thermal control, and vice versa) shows a very similar arrangement of points to that of the plot that uses the full array of the variables (Fig. 8.5). This visual similarity is supported by similar Correlation Analysis results (r = 0.648), which are equally statistically significant (p = 0.000). It can, therefore, be stated that the pattern of change in the MCS of classes of buildings over an evolutionary phase shift appears to have been strongly influenced by the operation of those features in which thermal choices and thermal control are contradictory.

Figure 8.6. Discriminant plot of MCS of rooms in Case Study 3 using variables in thermal accord.

CONCLUSIONS The results of the case study of the MCS of rooms in two regions during the phase shift from one predominant class of buildings (‘pithouses’) to another predominant (‘pueblos’) class show that there was a gradual shift in the general nature of the MCS. In each area the thermal signatures gradually shifted from one common type to another, which was shared by the final phase rooms of each region, when the ‘pueblos’ were well established. Outliers to the general trend were, however, common throughout the transition. That is, whilst the general nature of the MCS altered over the course of the transition, albeit with exceptions to the general behaviour, the level of inter-room thermal diversity remained relatively consistent.

Figure 8.5. Discriminant plot of MCS of rooms in Case Study 3 using thermally contradictory variables.

MCS appears to be better studied at the scale of whole room-weighted buildings than at the scale of individual rooms, although this indication would be strengthened with further testing. However, at both scales of analysis it is evident that, both during an evolutionary phase shift and over the long-term, selection appears to have been acting on the contradictions in the thermal system or, rather, on the way in which the thermal contradictions have been resolved, so as to enhance both thermal choices and thermal control. The various built features that operate in accordance with other features appear to have had negligible influence on the pattern of MCS change when observed at the scale of whole roomweighted buildings, although at the scale of individual rooms the degree of influence is slightly higher.

Test 3. Rooms and Features in Thermal Accord The discriminant plot generated from only those variables that operate in accordance with other features (where thermal choices do not contradict thermal control) shows a random arrangement of points (Fig. 8.6). This visually random spread of points is confirmed by the Correlation Analysis results, which show no statistical correlation (r = 0.394), a result that is statistically significant (p = 0.000). This correlation observed at the scale of individual rooms is, however, slightly stronger than the analysis performed at the scale of whole room-weighted buildings (cf. r = 0.583 for Case Study 2 above). It can, therefore, be stated that, whilst the pattern of change in the MCS of classes of buildings over an evolutionary phase shift appears to have been uninfluenced by the operation of those features in which thermal choices and thermal control are mutually enhanced, when observed at the scale of individual rooms these features have a slightly greater level of influence. This thus indicates

This implies that building features that have become generally more prevalent over time have done so because their incorporation within the built environment has introduced thermal contradiction. The contradiction has, in turn, been resolved in such a way that the solution has 151

The Evolution of the Built Environment given the class of building an evolutionary edge over classes that have either not experienced an equivalent level of contradiction, or that have failed to resolve the contradictions in ways that have enhanced the MCS. External windows and doors, for example, have become more prevalent over time (Brand 1997: 144-145). Brushwood huts generally don’t have windows and the presence of windows in ‘pueblos’ has become more

common over time. This is, presumably, not because they function well individually, but because they introduce thermal contradictions that, in turn, have been resolved most apparently at the scale of individual rooms. That is, whilst the evolution of the built environment occurs at the scale of whole room-weighted buildings, it is intrinsically linked to finer levels of thermal operation.

152

A Case Study of Rooms in Two Regions: The ‘Pithouse-to-Pueblo’ Transition

Summary of Part 2

Buildings have emergent properties, one of which is their capacity to provide thermal choices and thermal control (MCS) to their occupants. The way in which a building operates thermally is the result of the numerous thermal interactions between its features and traits, many of which operate in contradiction because producing thermal choices often contradicts the production of thermal control, and vice versa. Some classes of buildings have had a low capacity to produce thermal choices but a high capacity to produce thermal control, whilst other classes of buildings have had a high capacity to produce thermal choices but a low capacity to produce thermal control, and some classes of buildings have had a high overall capacity to produce both. These buildings have thus had a high capacity to create MCS. The ease with which MCS is created is influenced by the density of the surrounding built environment, but it is not inherently limited by it. A higher level of MCS appears to correlate with a higher level of variation in the built environment, as the system is more likely to be more adjustable and diverse overall.

adjustability and inter-building diversity appear to have persisted for relatively much longer periods of time. The pre-industrial vernacular buildings appear to have commonly produced enhanced levels of both thermal choices and thermal control as a result of the buildings having possessed numerous and diverse types of spaces that were thermally variable and selectively thermally alterable. This was in spite of the declining capacity to create MCS that accompanied increasing urban density. These classes of buildings and their associated thermal systems have only relatively recently begun to be replaced, at a time when MCS can be further enhanced by post-industrial mechanical heating and cooling systems, with which traditional passive systems cannot compete in terms of producing both thermal choices and thermal control. No correlation appears to exist between MCS and culture, either when observed at the scale of room-weighted buildings or individual rooms. That is, MCS appears to have operated independently of specific cultural assemblages. However, whilst no correlation appears to exist between MCS and climatic context when observed at the scale of whole room-weighted buildings, when observed at the scale of individual rooms a correlation does appear to exist. Likewise, when observed at the scale of whole room-weighted buildings, the evolution of the built environment appears to have been ‘driven’ by the thermal operation of those features in which thermal choices contradicted thermal control. However, when observed at the scale of individual rooms, the thermal operation of those features in which thermal choices and thermal control are in mutual accord (non-contradictory) is slightly more significant, though not significant enough to have a ‘driving’ influence on the evolution of the built environment. That is, whilst the evolution of the built environment occurs at the scale of whole room-weighted buildings, it is intrinsically linked to finer levels of thermal operation.

Thermal choices and thermal control have gradually increased overall within the built environment, though not in every class of building or every settlement and not always in concert with each other. Proto-structures increased the thermal choices and thermal control that were otherwise only marginally available within the landscape. ‘Pithouses’ enhanced thermal choices and the proceeding ‘pueblos’ enhanced thermal control, both of which were occupied alongside the earlier class of rudimentary structures. That is, the overall level of thermal choices and thermal control increased gradually and consistently throughout the ‘pithouse’-to-’pueblo’ transition. This increase indicates that the difference between the MCS of rudimentary structures and vastly more substantial and complex structures is only a matter of degree, not of substance. Overall, built environments within which enhanced levels of MCS were produced as the result of intra-building

153

PART 3 – ISSUES AND IMPLICATIONS CHAPTER 9 – An Urban Site Example: Mohenjo-daro, Pakistan

INTRODUCTION

The increasing density that occurred at Mohenjo-daro throughout the occupational duration resulted in a reduced capacity for the buildings to increase MCS, or to even maintain their initial level of MCS, as the potential for the buildings to interface with the outside environment gradually diminished. A reduced capacity can potentially be offset through the implementation of new thermal techniques and systems that do not rely on the direct and simple means discussed above. Such systems would have to contend with increasing inertia in the built environment and with existing cultural traditions. The buildings at Mohenjo-daro were very heavy, load-bearing buildings with a large degree of inertia and the building practices were very conservative. The question then arises, what are the consequences for a settlement that experiences great structural inertia that is not offset by material adjustability or thermal diversity? Mohenjo-daro was abandoned, c. 1900 B.C., and current theories to account for the movement of the population to a rural and/or mobile setting are currently diverse, although none to date have adopted a multi-scalar NeoDarwinian approach.

With the development of dense urban settlements, producing both thermal choices and thermal control would have become increasingly difficult, although not impossible. Reduced structural adjustability, brought about by increasing material inertia and urban density, can be potentially offset if the urban system generates a consistently high level of variable forms. The consistent presence of a wide range of variables within an inertial system increases the likelihood that more adjustable solutions to the reducing MCS will result. If, however, the system is inherently conservative, such that variable alternate forms are not generated, the result will be to reduce MCS. In order to illustrate this a further case study is examined here, focussing on Mohenjo-daro, Pakistan, c. 2600-1900 B.C., as an example of a dense urban settlement that experienced great structural inertia and conservative building practices throughout its occupation (Kenoyer 1998: 52-53; Lal 1997: 95-97; Wilkins 2005). An examination of the thermal capacity of the spaces shows that the settlement experienced an accompanying gradual decline in structural and thermal adjustability.

This case study was divided into two separate case studies so as to be able to, first, examine the MCS at Mohenjo-daro itself (Case Study 4a) and, secondly, the MCS at Mohenjo-daro within a cross-cultural and longterm context (Case Study 4b). Both illustrate the pattern of MCS change that accompanies a gradual decline in thermal capacity. Because Case Study 4a focused on the behaviour of MCS at Mohenjo-daro itself, the thermal analysis was performed at the scale of individual rooms. This detailed scale of analysis was deemed most appropriate for an intra-site thermal analysis. However, the results of Case Studies 2 and 3 indicated that a scale of whole room-weighted buildings was a more appropriate scale for cross-cultural thermal performance analyses. Therefore, because Case Study 4b focused on the behaviour of MCS at Mohenjo-daro over a more extended period of time and in a cross-cultural context, and the thermal analysis was performed at the scale of whole room-weighted buildings. The cross-cultural and long-term comparative sample encompassed three different indirectly culturally-related regions, that of Pakistan, Egypt and the Palestinian highlands.

The site was ultimately abandoned and the communities in the southern Indus region appear to have shifted to a mobile and/or rural lifestyle (Kenoyer 1998; Franke-Vogt 2001; Deshpande & Shinde 2005; Chakrabarti 2006). These later seasonally permanent settlements were characterised by low density, relatively lightweight rectilinear wattle and daub structures (Allchin & Allchin 1982). Whatever other socio-economic factors were involved, the consequences of such a move would have increased the structural and thermal adjustability available to the communities, although the overall potential of the settlements to create MCS would have been restricted compared with the enhanced MCS capacity of dense, thermally diverse and highly adjustable built environments.

CASE STUDY 4: A CONSERVATIVE DENSE URBAN SITE This case study illustrates possible implications for urban sites where the MCS declines, using the case of the early 3rd Mill. B.C. settlement of Mohenjo-daro in Pakistan as an example. A uniformitarian approach can reveal the processes that were operating, first, over the occupational duration of the settlement and, secondly, over a very extended period of time that encompassed various other cultures and climates.

MOHENJO-DARO, PAKISTAN Mohenjo-daro is situated on the Indus River floodplain which is 150km wide in places. The settlement is divided

154

An Urban Site Example: Mohenjo-daro, Pakistan levels new buildings were conservatively and rigorously rebuilt along the lines of older structures, up until the late occupation phase. The earliest recordable buildings were massive brick structures that would have offered thermal choices in the form of being cool inside compared to the heat outside, but they would have offered minimal interior thermal choices having minimal interior microclimatic variability. They would also have offered minimal thermal control, even though the early urban density was low, because the openings were small and few and the interior highly homogenous. This would, however, have increased significantly during the winter months when fires in mobile braziers appear to have been used to warm the otherwise homogenously cold interior spaces. As the urban density gradually increased the thermal choices would have markedly diminished, even though this was somewhat offset by a gradually decreasing average wall thickness that would have slightly increased the interior thermal variability and thermal choices. First, the thermal variability gradually diminished as the structural capacity to support upper storeys declined along with the decreasing wall thickness. Secondly, the increased shading by adjoining buildings and the increasing thermal mass of the settlement generally would have gradually reduced the temperature difference between the inside of the buildings and the outside. The thermal control would have also remained minimal as the openings remained consistently small and few (Urban 1987: 25, 32), although the wintertime thermal control would have been significantly higher.

into two parts, a ‘citadel’ and a ‘lower town’, both originally encircled by walls. The lower town was larger in area than the citadel, in a 2:1 proportion, and separated by the main north-south depression (Jansen 1993a: 46). It is the largest of the Harappan cities, with a minimum size of 95 ha. of visible urban space but a probable total extent of approximately 200 ha. Additionally, depending on how ‘outer suburbs’ and artefact scatters are interpreted, it could have extended for almost two kilometres eastward (Chakrabarti 1995: 61; Jansen 1987: 14-15, 1993a: 46). For example, in 1987 an area 200 metres long of Mature Harappan structures was exposed close to the Indus River. It consisted of rooms, streets, and wells (Jansen 1987: 14). These remains have been interpreted as a possible industrial supply area to the ‘lower city’ or an outer suburb (Jansen 1993a: 44-45). It has been traditionally held that the settlement had a well-defined, pre-planned axis-system embodied in the locations and orientations of the main streets running through the settlement. This view was based on the assumption that the ‘long topographical depressions’ were the locations of streets, ergo axes (Piggott 1950: 165; Wheeler 1953: 36; Wanzke 1987). From this a gridiron street plan was devised. This is not, however, supported by the archaeological evidence and the eastwest axis remains unproven (Jansen 1991; 77). Wanzke himself states that “even where topographical features would appear to indicate the continuation of certain axes, there is no factual justification for linking up street sections to form long straight thoroughfares” (Wanzke 1987: 33). The only apparent axis at Mohenjo-daro is the north-south one, defined by First Street. Two more are only vaguely indicated equidistant at 180m.

The settlement ultimately collapsed as the population appears to have abandoned the site, apparently and atypically replacing their permanent urban environment with either a low-density, rural environment or a wholly adjustable, mobile way of life (Kenoyer 1998; FrankeVogt 2001; Deshpande & Shinde 2005; Chakrabarti 2006). The way for this atypical shift from a permanent to a mobile and/or rural lifestyle had been inadvertently prepared by the preceding trend towards lighter structures, which increased the internal temperature variability in face of the general declining capacity to create MCS (Wilkins 2005).

The current theories of the Harappan axis-system, based primarily on the work of Jansen and the ARPM, holds that there were two independent axis systems that were pre-planned and astronomically aligned. This system set out the ‘precise stellar and/or solar orientation’ of houses, streets and large public buildings. ‘Changes’ over time in the orientation of the buildings has been deemed to be due to the relative change over time of the positions of the celestial bodies (Jansen 1977: 414; Kenoyer 1998: 52; Wanzke 1987: 34). It has been thought that the system was established on planning principles that were either developed at an earlier and smaller separate settlement (Jansen 1987: 15-16), or at Mohenjo-daro itself which was abandoned for a time to facilitate the building of the main settlement (Jansen 1993a: 47-48). Jansen has stated that the orientation of the buildings, which came first, established the orientation of the main streets, particularly in the peripheral residential areas (Jansen 1984b: 46) (Figs. 9.1-9.2)

The climate There is some debate over the nature of the climate at Mohenjo-daro during the late Holocene, ranging from wetter than the present and changing to drier at approx. 1800 B.C. (e.g. Staubwasser et al. 2003), to no difference from the present temperatures or rainfall (e.g. VishnuMittre 1974). It might, therefore, be concluded that, even if the climate had become slightly wetter towards the end of the urban period, temperatures were relatively the same as the present, with very hot summers (46oC mean) and very cool winters (12oC mean) (Siddiqi 1985). This thus makes it possible to perform the thermal analysis discussed here.

The settlement experienced increasing urban density typical of 2nd and 3rd Mill. B.C. South Asian settlements, except that the Harappan building practices were atypically conservative. From the earliest excavatable

155

The Evolution of the Built Environment

Figure 9.1. Location of Mohenjo-daro.

Figure 9.2.UNESCO site plan of Mohenjo-daro.(after Jansen 1981: 139) 156

An Urban Site Example: Mohenjo-daro, Pakistan Combining this model with the technique of using vertical wall joints to ascertain the relative constructional sequence produces schematics of the pattern of urban expansion and infill (Figs. 9.4-9.5). In this respect, the urban pattern in the HR and Moneer areas appears to represent an earlier phase of the same pattern undergone in the DK-G area at an earlier time (Jansen 1984a; Urban 1987). This indicates, therefore, that there was a trend towards the peripheral areas becoming as structurally saturated as the core area.

URBAN SATURATION The earliest dates for the structural and urban fabric at Mohenjo-daro are circa. 2400 B.C., when various mudbrick platforms were built to elevate certain select buildings above the level of the flood waters (Mackay 1938: 171; Curcarzi 1987: 80-81; Jansen 1987: 15; Jansen 1993b: 43). This event was then followed by gradual urban development, which was initially irregular and contiguous with the various early freestanding buildings, but which continued until the site was wholly saturated. Urban saturation meant that the open spaces had become demarcated in some physical way, either as building mass, private courtyards, thoroughfares or public squares, and the range of options available to the occupants of the settlement had been severely curtailed both structurally and socially.

This pattern of increasingly densely occupied settlement core, followed by settlement periphery, and combined with a general trend towards lighter structures has been observed at other contemporary sites such as Tell Taya and Ur in Mesopotamia, and Shahr-i Sokhta (Reade 1973: 157, 161; Mariani 1992: 186; Postgate 1992: 89; Van de Mieroop 1992: 23; Van de Mieroop 1997: 69). That is, urban development in this region and at this time experienced a common shift towards buildings that had a declining capacity to interface with the outside environment. Later buildings, however, are of a very different form and appear to have developed diverse and more adjustable ways to compensate for the loss of the reduced capacity to interface with the outside environment.

Urban infill and saturation is most evident in the residential core area, the DK-G area, but the peripheral areas, such as the HR and Moneer areas, show evidence of the early pattern of horizontal expansion that preceded saturation. Jansen and the Aachen University Research Project Mohenjo-daro (ARPM) have developed a ‘cluster’ growth model for the HR area that indicates urban expansion and infill (Jansen 1993a: 42) (Fig. 9.3).

Figure 9.3. The ‘cluster growth’model of the HR area by Jansen (1993a: 42. Reproduced with kind permission) 157

The Evolution of the Built Environment

Figure 9.4. Urban expansion schematic of Section 5, HRB area.

Figure 9.5. Urban expansion schematic of Section 2, HRB area.

Courtyards and Wells

(Mackay 1938: 164, 171) (Fig. 9.6). This is a structurally efficient method for building tall buildings, as the greatest lateral strength is provided by the least amount of material (Cowan 1976: 76). That is, the earliest buildings were probably of more than one story. However, when this practice of battering the outer walls was combined with the very conservative practice of building new buildings directly on top of the main structural walls of the older buildings, so as to use them as ready-made footings (Mackay 1938: 163), there were unforseen, longterm consequences. The massive walls became increasingly thin over time due to the reduced structural width available on which to build the walls of the later buildings (Figs. 9.7-9.8). No compensation appears to have been made for the diminishing wall thicknesses, such as cantilevering the later walls out past the line of the earlier walls, and in fact the situation was exacerbated by the occasional setting back of the inner face of the exterior walls from the inner face of the footing, producing a ledge (Mackay 1938: xv). This is best seen in the DK-G South area, which encompasses some of the deepest (and oldest) excavated sections (Jansen 1993a: 55) (Fig. 9.9). Concurrent with this, new buildings were built, against and around the earlier buildings, which were also of lighter and thinner construction than the earliest buildings. This is best seen in the HR Area where contiguous growth is easily discernible. Additionally, ephemeral structures would have presumably existed, both inside and outside of the brick buildings, which have not survived in the archaeological record.

Two features that clearly illustrate the transition towards urban saturation are the wells and the courtyards, which may be observed throughout the occupied areas. The gradual infill of courtyards often makes it difficult to locate them, most especially where the final room size is small. This uncertainly is illustrated in the variety of courtyard locations that appear in the literature (refer e.g. Haigh et al. 1981: III.19; cf. Schoenauer 1981: 72). Many of the wells appear to have been initially publicly accessible and open, but in time were subsumed into the buildings. This phenomenon is most easily observed in the DK-G area, because in the HR and Moneer areas there is a difficulty in ascertaining when the wells were first sunk relative to when their associated buildings were built. In the DK-G area, however, most of the wells that were initially external were subsequently enclosed by building. Block 7-House VI-Room 75, for example, within which there is a well, is initially an open-sided, colonnaded courtyard in the Intermediate III phase, but it is subsequently superseded by a closed, walled structure in the Intermediate II Phase. Mackay’s analysis is that the wells gradually had their public access subsumed into private ownership (Mackay 1938: 55, 73).

DIMINISHING WALL THICKNESSES The earliest excavated buildings were of massive brick construction, with relatively small openings and with a battered outer face of an average 4o 4’ from the vertical

158

An Urban Site Example: Mohenjo-daro, Pakistan Figure 9.6. Walls with external batter in the DK-G South area in (a) Long Lane and (b) Loop Lane (Jansen 1997: G2R03238, G2R03031. Reproduced with kind permission)

Figure 9.7. Wall thicknesses as percentage of total wall length in (a) DK-G South area, sections 1, 4 & 10 and (b) HRA area, section 3.

Figure 9.8. Types of sequential wall construction at Mohenjo-daro.

159

The Evolution of the Built Environment

Figure 9.9.Walls with internal setbacks in (a) Room 28, section 1, DK-G South area and (b) Room 14, section 1, HR-A area (Jansen 1997: G2R03138, H9R00117. Reproduced with kind permission) roof structure (Marshall 1931: 277) (e.g. Room 49, House V, Section 2, HR-B area). There are several examples of vertical clay pipes built into the walls and of a size that suggests that they were used for ablution purposes only (e.g. Room 16, House III, Section 2, HR-A area).

DIMINISHING LIKELIHOOD OF UPPER STOREYS This tendency towards an overall decreasing structural wall thickness would have produced an associated decreasing structural capacity to support upper storeys as a factor of the diminishing wall thickness. It is generally assumed that the buildings of the large Harappan settlements had at least one upper storey and, at Mohenjo-daro, sometimes two (Jarrige 1990: 199; Kenoyer 1998: 58; Possehl 2002: 108), with the roofs being used as an extension of the living space (Marshall 1931: 277; Possehl 2002: 108), although some archaeologists have stated that multi-storeyed buildings cannot be assumed (Jansen 1977: 406-407, 420). A compilation of an extended set of evidence, however, strongly suggests that at least some of the buildings had upper storeys, at least earlier on.

The later levels at Mojenjo-daro are, therefore, overwhelmingly represented by walls of insufficient thickness to reliably have been able to support an upper storey. Dr. George Gibbons, a structural-engineer and brick/mortar specialist, has estimated that a minimum wall thickness of 550mm is required for brick construction of this type to reliably support an upper storey, based on his knowledge of comparative examples, the closest being the early colonial buildings of New South Wales, most especially the First Government House (Pearson 1988; Gibbons 2001: personal communication). If upper storeys did exist in those earlier buildings that were capable of supporting them then their form suggests a pattern of domestic usage similar to that found in traditional buildings of south-west Asia today, where the occupants move up and down inside the buildings throughout the day and throughout the year, as the outside temperatures change. The warm upstairs areas are occupied during the winter, the cool downstairs areas are occupied during the summer daytime and the cool rooftops are occupied during the summer night times (Stead 1980: 36). Thermal choices would thus have been available because of the separation of the upstairs from the downstairs, each floor having different thermal properties. That is, an upper storey extends the thermal choices available within the building. However, the capacity to support upper floors diminished over time at

The earliest buildings had main structural walls that were battered on their exterior faces, compared with the nonstructural walls, or the main structural walls of the later buildings which had two vertical faces (e.g. House III, DK-G area, Section 10). The extant buildings were built extensively of load-bearing burnt brickwork, which is strong in compression, and had substantial and durable footings. Numerous buildings incorporated rooms that were built without doors, and were therefore presumably accessed from above (Marshall 1931: 274). There were numerous substantial stairways throughout the site, some of which connected only to the street and that were not a factor of site-terracing (e.g. stairway leading south off Central Street, DK-G South area, Late III Period). Many of the extant beam holes are of substantial dimensions, greater than would have been necessary to support only a 160

An Urban Site Example: Mohenjo-daro, Pakistan this method it possible to calculate both the relative horizontal and vertical structural sequence, which was also cross-checked against published photographs and the location of various specific constructional features such as doorways. Doorways are potentially important because vertical wall joints are unlikely to have occurred at a wall opening, this being a structurally week point. The point has been made that Mackay’s plans have no stratigraphical significance, having been based on his assumption of regular horizontal growth, and they therefore “represent nothing but horizontal cross-sections at the respective depths” rather than “actual stages in the construction of the city” (Jansen 1993a: 43; Jansen 1993b: 82-86). However, buildings that are stratigraphically higher than buildings immediately beneath them will be of later construction, and vice versa, and it is therefore possible to use them to sequence contiguous building units by the location of vertical joints. By this method a model of expansion was developed that, whilst not being detailed or precise, is robust and appropriate to an analysis of relative change over time to rooms and buildings that are contiguously joined.

Mohenjo-daro, a situation that would not have been wholly off-set by the declining wall thickness and the slightly increased associated internal thermal variability.

INTRODUCTION: CASE STUDY 4A: ROOMS AT MOHENJO-DARO This case study was set up to illustrate the possible implications for individual settlements that experience reducing thermal capacity, which can result from increasing urban saturation and conservative building practices. The MCS of individual rooms was analysed in such a way that the features that operate in contradiction to their converse feature could be distinguished from the features that operate in accord, by differentiating between these two sets of variables and analysing them together and separately. This dataset encompassed a large set of archaeological rooms from two sections of the settlement in which the buildings are contiguously joined at some point in time, thus making it possible to ascertain the relative sequence of structural growth. One section resided in the older settlement core area and one in the later peripheral area.

Another issue pertinent to the structural sequencing of the rooms in the DK-G South area is the debate over re-use and erosion of bricks in ancient times (Fentress 1984: 102; Mackay 1938: 42). However, this appears to have occurred relatively uniformly throughout each of the occupation levels (Marshall 1931: 262; Mackay 1938: 163) and thus does not alter the pattern of relative urban growth. A third issue pertinent to the structural sequencing of the rooms in the DK-G South area was the presumed late period subdivision of intermediate period houses with thin, internal, unbonded walls (Fentress 1984). However, whilst the subdivision of internal spaces with thin, unbonded walls certainly did occur in the late period, it appears to have also been a feature of the intermediate period houses (see Table 9.1 below). The presumption possibly arose as a result of the high number of unbonded partition walls that may be observed in the areas that were not excavated down below the late period levels (Blocks 6, 8A, 9 and 9A) (Mackay 1938: 33, 72). The issue was resolved here, however, by limiting the scope of the analysis to only Blocks 1, 4 and 10, which possess a full excavational sequence.

Three separate MVA tests were performed. Test 1 analysed the MCS of individual rooms using the full set of variables, Test 2 analysed the MCS according to only those features that that operate in contradiction to their converse feature, and Test 3 analysed the MCS according to only those features that operate in accordance with other features.

THE DATA FOR CASE STUDY 4A: SITE SPECIFIC ROOMS The two areas of contiguous building chosen for the analysis were selected on the basis that they are well documented, such that the sequence of contiguous growth either has been ascertained (HR-A area, Section 3) or was ascertainable (DK-G South area, Blocks 1, 4 and 10). The structural sequence for the HR-A area, Section 3 has been developed by Jansen and the ARPM (Jansen 1984b) (Fig. 9.10), as a more detailed component of his HR area ‘cluster’ growth model. It is estimated that Section A3 originally consisted of a single building, which then expanded southward, and then experienced other buildings being built hard against its external walls on the west and north and on the east, eventually forming First Street (Jansen 1984b: 46, 58). The structural sequence, wall thickness and locations of setbacks for the DK-G South area, Blocks 1, 4 and 10 are ascertainable from Mackays’s five ‘stratigraphical’ plans of the general DKG South area (Mackay 1938) (Figs. 9.11-9.13).

The areas, time periods and entity (room) numbers within each group (each area and period) are listed in Table 9.2 below. Note that the Time Periods listed for the DK-G South area are used only to refer to Mackay’s five sequential plans, which are designated accordingly. They do not refer to an established absolute chronology. Likewise the Time Periods listed for the HR-A area are used only to refer to Jansen’s six relative phases and do not refer to an established absolute chronology. Thirtyfive variables (the room features) were used in the analysis and these are discussed below. The room numbers of those used in the analysis are listed in Appendix E.

Estimating the growth sequence of the DK-G South area, Blocks 1, 4 and 10, utilised the common methodology of locating vertical joints that separate abutting walls. By

161

The Evolution of the Built Environment

Figure 9.10. Rooms in Section 3, HR-A area in Case Study 4a (after Jansen 1984: 47)

Figure 9.11. Rooms in DK-G South areas 1,4 & 10, Intermediate III period, in Case Study 4a (after Mackay 1938: pl. XV

162

An Urban Site Example: Mohenjo-daro, Pakistan

Figure 9.12. Rooms in DK-G South areas 1,4 & 10, Intermediate I period, in Case Study 4a (after Mackay 1938: pl. XVIII)

Figure 9.13. Rooms in DK-G South areas 1,4 & 10, Late I & II periods, in Case Study 4a (after Mackay 1938: pl. XX

163

The Evolution of the Built Environment

OCCURRENCE OF LIGHT-WEIGHT PARTITION WALLS INSIDE STRUCTURALLY-MASSIVE EXTERNAL BOUNDARY WALLS INT. III

INT. II House I-Block 1-Rms 73, 74, 80, 81 House IV-Block 1-Rms 26, 64 House III-Block 1

INT. I House I-Block 1-Rms 14 House IV-Block 1-Rms 26, 64 House VI-Block 1-Rms 36, 38, 55, 66

LATE III/INT. I House I-Block 1-Rms 14 House IV-Block 1Rms 26, 64

LATE I + II House I-Block 1-Rms 14

House IV-Block 3Rms 28, 48

House II-Block 3-Rms 28, 29, 48 House V-Block 3-Rms 10, 11, 12 House III-Block 5-Rms 8, 9, 16 House III-Block 7 House V-Block 7-Rms 52, 64, 65 House VI-Block 7-Rms 73, 74, 75, 76 House IX-Block 7-Rms 32, 33

House IV-Block 1-Rms 24, 25, 26, 64 House V-Block 1-Rms 46 House VI-Block 1-Rms 35, 66

House VIII-Block 1 House IV-Block 2-Rms 21 House I-Block 3-Rms16

House III-Block 3 House IV-Block 3-Rms 45

House III-Block 5Rms 8, 9 House IX-Block 7

House V-Block 7-Rms 80

House III-Block 10

House VII-Block 9 House III-Block 10

House IV-Block 10Rms 88

House VII-Block 9 House III-Block 10 House IV-Block 10-Rms 85

House III-Block 10Rm 64

House III-Block 10 House IV-Block 10-Rms 74, 82

Table 9.1. The Occurrence of Light-Weight Partition Walls Inside Structurally-Massive External Boundary Walls in the DK-G South Area in Each of Mackay’s Occupational Periods. ENTITIES USED IN THE URBAN SITE CASE STUDY OF ROOMS Area

Time Period

Time Period Group in Entity (Building) No. in Database Database Intermediate III 1 1-14 DK-G South, Blocks 1, 4 & 10 Intermediate II 2 15-43 Intermediate I (ph 1) 3 44-57 Intermediate (ph 2) 4 58-73 Late III 5 74-86 Late I & II 6 87-158 Phase 1 7 159-162 HR-A, Section 3 Phase 2 8 163-182 Phase 3 9 183-214 Phase 4 10 215-256 Phase 5 11 257-308 Phase 6 12 309-362 Table 9.2. Entities (Rooms) Included in Case Study 4a: The Urban Site Mohenjo-daro Case Study. grouping was present and the Correlation Analysis provided a numerical test of the visual grouping (or absence of grouping) and recorded the strength of the statistical correlation. The aims of the individual tests are discussed in greater detail below. The MCS of the individual buildings was again treated as equivalent to the string of their variables (the sum of the interactions between the thermal features).

THE TESTS: CASE STUDY 4A The three tests used the same set of entities (the rooms). The entities were again grouped according to area and phase and this grouping was, therefore, constant between the tests. Likewise, the data in the different tests was again analysed using the same MVA techniques of Discriminant Analysis and Correlation Analysis. The Discriminant Analysis provided a visual test to see if 164

An Urban Site Example: Mohenjo-daro, Pakistan

Test 1. Rooms at Mohenjo-daro

ARCHAEOLOGICAL ROOM FEATURES: THE VARIABLES

The purpose of this test was to statistically ascertain the pattern of MCS change in individual archaeological rooms throughout the course of occupation at Mohenjodaro, as a site that experienced increasing urban density and conservative building practices.

The array of variables (room features) used in the Mohenjo-daro case study are the same as the ones used in the ‘pithouse’-to-’pueblo’ transition case study (Case Study 3) outlined above. There is one exception, however. Rather than using the broad-brush classification for wall material (thermal mass) used in Case Study 3, a precise classification for wall thickness was used here. The reason for this was that the walls at Mohenjo-daro did not encompass the degree of variation in the thermal mass that was present in the wider regional case studies, having walls that were of the same class of construction, that of baked brick with a lime-free, mud mortar. The thermal mass could therefore be directly related to just the wall thickness as this was the only property that varied from wall to wall.

Test 2. Rooms at Mohenjo-daro and Features in Thermal Contradiction The purpose of this test was to statistically ascertain the degree to which features that operate in thermal contradiction to their converse feature (where thermal choices contradict thermal control, and vice versa) have influenced the overall pattern of MCS change in individual rooms at Mohenjo-daro. This was achieved by comparing the overall pattern of MCS change with that of only the features that operate only in contradiction with their converse feature, for the same reasons as those outlined above in Test 2 of Case Study 2.

The full set of variables used here are shown in Table 9.3 below, which lists the graded, numeric values for the variables and their subdivision into the two subsets of those that operate in thermal contradiction and those that operate in accordance. The full set comprises Variables 111 and 13-35 from Case Study 3 (Tables 8.3 to 8.21) and Variable 12 outlined below (Table 9.4). Variable 12 (Wall thickness (thermal mass) replaces the Variable 12 (Wall material (thermal mass) used in Case Study 3.

Test 3. Rooms at Mohenjo-daro and Features in Thermal Accord The purpose of this test was to statistically ascertain the degree to which features that operate in thermal accordance with other features (where thermal choices do not contradict thermal control) have influenced the overall pattern of MCS change in individual rooms at Mohenjo-daro. This was achieved by comparing the overall pattern of MCS change with that of only the features that operate only in accordance, for the same reasons as those outlined above in Test 3 of Case Study 2.

Note that Variables 14 (Presence of wall insulation), 24 (Nearest neighbour distance) and 30 (Eastern solar access) have been omitted as the degree of numerical variability did not reach the minimum required by the MVA program. For the calculated values for each Variable for each room in the dataset (the MVA spreadsheet) refer to Appendix E.

VARIABLES USED IN THE ‘PITHOUSE’-TO-‘PUEBLO’ CASE STUDY OF ROOMS IN TWO REGIONS Features where thermal choices and thermal control are contradictory

Variable No. 1-9 10 11 12

Variable Building exposure (n, ne, nw, s, se, sw, e, w & vertical) Roof flatness/peakiness Floor level relative to ground level Wall thickness (thermal mass)

14-15 Presence of wall & roof insulation 16 No. internal angles 17 Ratio length/width 18-20 No. posts, niches & benches 21-23 No. connected rooms, upper storeys & lower storeys 24 Nearest neighbour 25 Plan area 26 No. exterior openings in each direction Features where thermal choices and thermal 27-31 Solar penetration (from s, se, sw, e, w) control are in 32-33 Cross ventilation & corner ventilation accordance 34 Heating (solar and/or active) 35 No. connected transitional spaces Table 9.3. Thermal Features (Variables) Included in Case Study 4a: The Urban Site Mohenjo-daro Case Study of Rooms.

165

The Evolution of the Built Environment Variable 12: Wall Thickness (Thermal Mass): This variable is measure of the room’s horizontal temperature distribution. A room that is open on one wall or which is unroofed will closely track the outside conditions and will have high thermal choices and low

thermal control, whilst a room with walls and roof that have a heavy thermal mass will be isolated from the outside and will have high thermal control and low thermal choices.

WALL THICKNESS (THERMAL MASS) Feature

Value

0-200mm

1

210-400mm

2

410-600mm

3

610-800mm

4

810-1000mm

5

1110-1200mm

6

1210-1400mm

7

1410-1600mm

8

1610-1800mm

9

1810-2000mm

10

Table 9.4. Discriminant Values for Wall Thickness (Thermal Mass) the rooms and the area and period to which they belong (r = 0.796), a result that is statistically significant (p = 0.000).

RESULTS OF THE URBAN SITE CASE STUDY The scale at which the analysis in this case study was performed is equivalent to that of the regional case study (Case Study 3 above), that of individual archaeological rooms. The results cannot, therefore, infer anything about other rooms within the building if the room belongs to a multi-room building, although indirect inferences can be made because the variables pertaining to the rooms incorporate thermal features of their adjoining rooms.

That is, the consequence of the great inertia present in the older DK-G area, accompanied by the very conservative building practices, was a general trend in that sector towards an ultimately reduced thermal diversity. In view of this reducing inter-building thermal range in the older core area, it is significant that the DK-G area was abandoned prior to the peripheral areas and that it reverted to industrial use at a time when the HR area continued to be occupied as a residential area (Mackay 1938: 6). Had the occupation of the settlement continued, the pattern of ultimately reduced MCS range observed in the DK-G area would probably also have been experienced in the HR-A and other peripheral areas. There is indication of this in the position of outliers to the HR-A final phase group of buildings, which occupy a position on the plot common to the final phase buildings of the DK-G area buildings.

Test 1. Rooms at Mohenjo-daro The results of the Discriminant Analysis are shown visually on the scatterplot, where the entities are grouped according to their area and period (Fig. 9.14). In both the older settlement core and the later peripheral areas the pattern of MCS change over time is gradual and directional, though each area differs in the direction of MCS change. That is, the early buildings in each area are of a common type of MCS, as indicated by the occupation of a common region of the points on the plot, but the MCS of the buildings of the subsequent phases gradually diverge from this commonality. The final phase of the older settlement core is characterised by minimal inter-building thermal diversity and that of the later peripheral area is characterised by somewhat more thermal diversity, as indicated by the less concentrated spread of points on the plot. This thermal divergence is reflected in the results of the Correlation Analysis, which shows a strong statistical correlation between the MCS of

An analysis of the Structure Coefficients shows that there are two variables that have influenced the characters of the thermal states more than others (ref. Appendix G). They are the wall thickness (the thermal mass of the walls) and the presence/absence of built-in benches in the rooms, both of which are features that operate in contradiction to their converse feature. There are several other variables that have a lesser influence and they are building exposure in each direction except north and vertical, which are also features that operate in contradiction. 166

An Urban Site Example: Mohenjo-daro, Pakistan

Figure 9.14. Discriminant plot of MCS of rooms in Case Study 4a. 0.010). It can, therefore, be stated that the pattern of change in the MCS of classes of buildings at Mohenjodaro appears to have been uninfluenced by the operation of those features in which thermal choices and thermal control are mutually enhanced.

Test 2. Rooms at Mohenjo-daro and Features in Thermal Contradiction The discriminant plot generated from only those variables that operate in contradiction to their converse feature (where thermal choices contradict thermal control, and vice versa) shows a very similar arrangement of points to that of the plot that uses the full array of the variables (Fig. 9.15). This visual similarity is supported by similar Correlation Analysis results (r = 0.766), which are equally statistically significant (p = 0.000). It can, therefore, be stated that the pattern of change in the MCS of classes of buildings during the process of urban saturation at Mohenjo-daro appears to have been strongly influenced by the operation of those features in which thermal choices and thermal control are contradictory.

Test 3. Rooms at Mohenjo-daro and Features in Thermal Accord The discriminant plot generated from only those variables that operate in accordance with other features (where thermal choices do not contradict thermal control) shows a random arrangement of points (Fig. 9.16). This visually random spread of points is confirmed by the Correlation Analysis results, which show no statistical correlation (r = 0.365), a result that is statistically significant (p =

Figure 9.15. Discriminant plot of MCS of rooms in Case Study 4a using only thermally contradictory variables. 167

The Evolution of the Built Environment the twentieth century (vernacular), the Palestinian highlands covers the time period from late fourth millenium B.C. (Early Bronze Age) up to the nineteenth century (Ottoman period), and Egypt covers the time period from the prehistoric (Naqada 1) up to the twentieth century (vernacular). Three separate MVA tests were performed. Test 1 analysed the MCS of room-weighted buildings, and the influence of culture and/or climate on the MCS, using the full set of variables, Test 2 analysed the MCS according to only those features that operate in contradiction to their converse feature, and Test 3 analysed the MCS according to only those features that operate in accordance with other features.

THE DATA FOR CASE STUDY 4B: CROSSCULTURAL ROOMS The three regions were selected because they are culturally related historically (albeit indirectly), but experience different climates. Today Pakistan and Egypt experience hot-arid summers (Bmhb and Bwhl respectively by the Koppen-Trewartha climate classification method), whilst the Palestinian highlands experiences cool winters (GCsak by the KoppenTrewartha climate classification method). This ratio of 2:1 hot:cool was selected for the same reason that it was selected in the regional case study of buildings, so as to be able to distinguish between the influence of climate from that of culture on the long-term behaviour of MCS in buildings.

Figure 9.16. Discriminant plot of MCS of rooms in Case Study 4a using only variables in thermal accord.

INTRODUCTION: CASE STUDY 4B: BUILDINGS AT MOHENJO-DARO IN A WIDER CONTEXT This case study was set up to illustrate the wider implications for settlements that experience reducing MCS by illustrating the pattern of MCS change at Mohenjo-daro in a wider social and climatic context. This analysis was performed at the scale of room-weighted buildings, which was more appropriate for examining MCS at a wider chronological and geographical scale that that appropriate for within an individual site. The MCS of the buildings was again analysed in such a way that the features that operate in contradiction to their converse feature (where thermal choices contradict thermal control, and vice versa) could be distinguished from the features that operate in accord, by differentiating between these two sets of variables and analysing them together and separately.

The set of rooms for Mohenjo-daro was reduced to only those in the DK-G South area, Blocks 1, 4 and 10, and omitted those of HR-A area, Section 3. This omission was due to the difficulty of correlating the absolute age of the DK-G buildings with those of the HR-A buildings. Whilst this lack of chronological correlation was immaterial in Case Study 4a above, as no chronological correlation was drawn between the two areas, only a comparison of relative patterns of change of MCS, in this case study the focus shifted to comparing the MCS at Mohenjo-daro with other settlements. That is, the pattern of change of MCS in the DK-G South area was taken as representative of the settlement for the purposes of comparing relative patterns of change of MCS. The pattern of change in the DK-G area was chosen as representative of the settlement in preference to that of the HR-A area because it encompassed a longer period of time and the pattern of change at Mohenjo-daro itself would therefore be made more visible when viewed in context with that of other settlements. The set of buildings for Egypt and the Palestinian highlands was duplicated from that used in Case Study 2. Forty variables (the building features) were used in the analysis, duplicated from those used in Case Study 2. The building numbers of those used in the analysis are listed in Appendix F. The regions, time periods and entity (building) numbers within each group (each region and period) are listed in Table 9.5 below.

The dataset encompassed a large set of archaeological room-weighted buildings from the Old World regions of Pakistan (including a set of the buildings at Mohenjodaro that featured in Case Study 4a), the Palestinian highlands and Egypt (buildings for the latter two regions being the same as were used in the regional Case Study 2) (Fig. 9.17). Both Pakistan and Egypt are noted for their hot-arid summers and the Palestinian highlands experience cool winters. In essence, this case study is the same as the regional case study of buildings except that the Pakistani data has replaced the Negev data. The cultures examined do not cover the same periods of time, but they do cover the period from the earliest recorded permanent structures within each region up to the latest pre-industrial, vernacular structures. Pakistan covers the time period from Aceramic Neolithic, c. 6500 B.C. up to

168

An Urban Site Example: Mohenjo-daro, Pakistan

Figure 9.17. Locations of sites in Case Study 4b. ENTITIES USED IN THE URBAN SITE COMPARATIVE CASE STUDY OF BUILDINGS Region

Time Period

Time Period Group in Database

Entity (Building) No. in Database

Pakistan

Aceramic Neolithic (c. 6500 B.C.) Harappan “Intermediate III” (2600-1900 B.C.) Harappan “Intermediate II-Late III” (2600-1900 B.C.)

1

1

2

2-13

Mohenjo-daro DK-G South area, Sections 1, 4 & 10

3

14-25

Mohenjo-daro DK-G South area, Sections 1, 4 & 10 “Intermediate II” Mohenjo-daro DK-G South area, Sections 1, 4 & 10 “Intermediate I” Mohenjo-daro DK-G South area, Sections 1, 4 & 10 “Late III” Mohenjo-daro DK-G South area, Sections 1, 4 & 10 “Late I & II” Harappa Banawali

26-32 33-35 Harappan “Late I & II” (2600-1900 B.C.) Pakistani Harappan (2600-1900 B.C.)

4

36-44

5

45-46 47 169

Sites Nausharo

The Evolution of the Built Environment

Egypt

Palestinian Highlands

Post-Urban (1200-900 B.C.)

6

Pakistani vernacular (A.D. 1648- 1960)

7

Naqada I (4000-3500 B.C.)

8

Old Kingdom (2686-2181 B.C.) – Middle Kingdom (2025-1700 B.C.) New Kingdom and 3rd Intermediate (1550-664 B.C.)

9

48-51 52-64 65-69 70 71-72 73 74-76 77-79 80-83 84-87

10

Late Roman - Byzantine (A.D. c. 211-640 )

11

Mediaeval Arab (A.D. 640-1517) Vernacular (A.D. 1805-1919)

12

Early Bronze (3300-2200 B.C.) Iron (1200-586 B.C.)

14

13

88-93 94-96, 104 97-101 102-103 105-107 108-111 112-116 117 118 119-122 123-138

Pirak 1200-1100 B.C. Pirak 1100-900 B.C. Jaisalmer Bahawalpur Lahore Hemamiya Hierakonopolis Elephantine El Lahun Elephantine Deir el-Medina Medinet Habu Amarna Ramesseum Ismant el-Gharab Medinet Habu Fustat Cairo Marg New Gourna Ai

Khirbet ed-Dawwara 139-140 Ai 141-145 Shiloh 146-147 Tell en-Nasbeh 148-153 Babylonian (586-539 B.C.) Tell en-Nasbeh 16 154-156 Ottoman (A.D.1516-1917) Khan al-Lubban 17 157-158 Table 9.5. Entities (Buildings) Included in Case Study 4b: The Urban Site Comparative Case Study.

15

ascertain the degree to which culture and climate have influenced long-term change given that the regional study of buildings (Case Study 2) showed that, at the scale of room-weighted buildings, there was no statistical correlation with climate or with culture.

THE TESTS: CASE STUDY 4B The three tests used the same set of entities (the buildings). The entities were again grouped according to area and phase and this grouping was, therefore, constant between the tests. Likewise, the data in the different tests was again analysed using the same MVA techniques of Discriminant Analysis and Correlation Analysis. The Discriminant Analysis provided a visual test to see if grouping was present and the Correlation Analysis provided a numerical test of the visual grouping (or absence of grouping) and recorded the strength of the statistical correlation. The aims of the individual tests are discussed in greater detail below. The MCS of the individual buildings was again treated as equivalent to the string of their variables (the sum of the interactions between the thermal features).

Test 2. Buildings at Mohenjo-daro and Features in Thermal Contradiction in a Wider Context The purpose of this test was to statistically ascertain the degree to which features that operate in thermal contradiction to their converse feature have influenced the overall pattern of MCS in archaeological roomweighted buildings at Mohenjo-daro within a wider cultural and climatic context. This was achieved by comparing the overall pattern of MCS change with that of only the features that operate only in contradiction, for the same reasons as those outlined above in Test 2 of Case Study 2.

Test 1. Buildings at Mohenjo-daro in a Wider Context

Test 3. Buildings at Mohenjo-daro and Features in Thermal Accord in a Wider Context

The purpose of this test was to statistically ascertain the pattern of MCS change in individual archaeological room-weighted buildings at Mohenjo-daro within a wider cultural and climatic context, given that both the large and small scale regional studies (Case Studies 2 and 3) indicated that MCS is best measured at the scale of roomweighted buildings. The purpose of this test was also to

The purpose of this test was to statistically ascertain the degree to which features that operate in thermal accordance with other features have influenced the overall pattern of MCS in archaeological room-weighted buildings at Mohenjo-daro within a wider cultural and climatic context. This was achieved by comparing the 170

An Urban Site Example: Mohenjo-daro, Pakistan VARIABLES USED IN THE URBAN SITE COMPARATIVE CASE STUDY OF BUILDINGS Variable No.

Variable

Building exposure (n, ne, nw, s, se, sw, e, w & vertical) 1-9 Roof flatness/peakiness and range 10-11 Floor level relative to ground level and range 12-13 Wall and roof material (thermal mass) and roof material range 14-16 Presence of wall & roof insulation 17-18 No. internal angles and range 19-20 Ratio length/width and range 21-22 No. posts and range, niches & benches 23-26 Compactness/longevity 27 No. rooms 28 Internal floor/ceiling thermal conductivity 29-30 No. roofs at different levels Features where thermal 31 choices and thermal Degree of opening in each direction 32 control are in accordance Solar penetration (from s, se, sw, e, w) 33-37 Cross ventilation 38 Heating 39 Degree of transitional space 40 Table 9.6. Thermal Features (Variables) Included in Case Study 4b: The Urban Site Comparative Case Study of Buildings. Features where thermal choices and thermal control are contradictory

overall pattern of MCS change with that of only the features that operate only in accordance, for the same reasons as those outlined above in Test 3 of Case Study 2.

term pattern of change in the MCS of classes of buildings with respect to their culture and climate. Generally, the pattern of MCS change is similar to that of Case Study 2 with the very early Naqada 1 buildings in Egypt appearing as outliers to the gradual shift away from that of the early buildings in each region towards a common region shared by the vernacular buildings of both Egypt and Pakistan. However, whilst there is a discernible and gradual change over time in the character of the MCS of the buildings in all regions, the Harappan buildings do not follow this trend but form a tight cluster that does not vary over time. The subsequent phase, represented by the buildings of Pirak, c. 10th to 13th centuries B.C., does however vary slightly, in the direction of the vernacular buildings.

ARCHAEOLOGICAL BUILDING FEATURES: THE VARIABLES The full set of variables (the room-weighted building features) used in this comparative case study is shown in Table 9.6, but as it is the same as that used in the regional case study of buildings, for a description of the thermal operations involved in producing thermal choices and thermal control at this scale of whole archaeological buildings, refer to Case Study 2: Tables 6.11 to 6.30. For the calculated values of each variable for each of the buildings (the MVA spreadsheet) refer to Appendix F.

This overall pattern of change is reflected in the results of the Correlation Analysis, which shows that there is a high statistical correlation between the MCS of the buildings and the regions and periods to which they belong (r = 0.818), a result that is statistically significant (p = 0.000).

RESULTS OF THE URBAN SITE COMPARATIVE CASE STUDY The results of this case study show the pattern of change in MCS at Mohenjo-daro in a wider cultural and climatic context and at the scale of room-weighted buildings.

The correlation is again slightly stronger at this scale of room-weighted buildings compared with that performed at the scale of individual rooms in Case Study 4a above (cf. r = 0.796). This supports the findings of Case Studies 2 and 3, which indicated that MCS was best studied at the scale of whole room-weighted buildings, rather than at the scale of individual rooms. However, because this constitutes a comparison of a single site with a crosscultural long-term dataset, these results would be strengthened with further testing.

Test 1. Buildings at Mohenjo-daro in a Wider Context The results of the Discriminant Analysis are shown visually on the scatterplot, where the entities (the MCS of room-weighted buildings) were grouped according to their region and period (Fig. 9.18). Consequently, as in Case Study 2, conclusions can be made about the long-

171

The Evolution of the Built Environment

Figure 9.18. Discriminant plot of MCS of buildings in case Study 4b The overall change in MCS appears to be characterised by minimal inter-building thermal diversity, particularly at Mohenjo-daro and the other Harappan sites, up until the pre-industrial era when there was an ultimate increase in the inter-building thermal range of the vernacular buildings of both Egypt and Pakistan (there are no vernacular examples for the Palestinian highlands). This is illustrated by the relative sizes of the scattergram clusters. This reinforces the notion of conservative building practices at Mohenjo-daro and other contemporary Harappan sites, which presumably also experienced conservative building practices. That is, very little thermal variability appears to have existed within the confines of the Harappan settlements generally compared with that of a wider cultural and temporal context, but particularly when compared with that of the later pre-industrial buildings of each region, which present a relatively wide MCS range.

again shows a very similar arrangement of points to that of the plot that uses the full array of the variables (Fig. 9.19). This visual similarity is supported by similar Correlation Analysis results (r = 0.744), which are equally statistically significant (p = 0.000). This thus again indicates that the pattern of change in the MCS of classes of buildings over extended periods of time appears to have been strongly influenced by the thermal contradictions operating with the thermal system, regardless of cultural and climatic differences.

Test 3. Buildings at Mohenjo-daro and Features in Thermal Accord in a Wider Context The discriminant plot generated from only those variables that operate in accordance with other features shows a random arrangement of points (Fig. 9.20). This visually random spread of points is again confirmed by the Correlation Analysis results, which show no statistical correlation (r = 0.537), a result that is equally statistically significant (p = 0.000). This correlation observed at the scale of whole room-weighted buildings is again slightly weaker than the analysis performed at the scale of individual rooms (cf. r = 0.365 for Case Study 4a above). This result reinforces the findings that, whilst the pattern of change in the MCS of classes of buildings appears to have been uninfluenced by the operation of those features in which thermal choices and thermal control are mutually enhanced, when observed at the scale of individual rooms these features have a slightly greater level of influence. Therefore, whilst the evolution of the built environment occurs at the scale of whole roomweighted buildings, it is intrinsically linked to thermal operations at the scale of individual rooms

At this scale of analysis of room-weighted buildings, there is again no discernible relationship between the MCS of the buildings and change over time with respect to either climate or culture. It can, therefore, be stated that a statistically meaningful trend has occurred in the longterm change in the MCS of classes of buildings over time. This trend is not related to either climatic or culture, but it is characterised by a gradual increase in intrabuilding thermal complexity and inter-building thermal diversity.

Test 2. Buildings at Mohenjo-daro and Features in Thermal Contradiction in a Wider Context The discriminant plot generated from only those variables that operate in contradiction to their converse feature .

172

An Urban Site Example: Mohenjo-daro, Pakistan

Figure 9.19. Discriminant plot of MCS of buildings in case Study 4b using only thermally contradictory variables. another variable for why these settlements were ultimately abandoned for a mobile and/or rural lifestyle. The restricted inter-room thermal diversity at Mohenjodaro is observable at the scale of individual rooms, but the restricted inter-building thermal range is observable within a wider cultural and temporal sample of buildings at a scale of analysis of whole room-weighted buildings. It is particularly noticeable in comparison with that of the later pre-industrial buildings, which present a relatively wide inter-building thermal range. The pre-industrial classes of buildings appear to be characterised by enhanced levels of MCS, produced via intra-building thermal adjustability and inter-building thermal diversity, as a result of having possessed a high level of material adjustability and complexity that is most apparent at the scale of thermal operations within individual rooms.

Figure 9.20. Discriminant plot of MCS of buildings in case Study 4b using only variables in thermal accord.

CONCLUSIONS The earliest structures for which there is archaeological data at Mohenjo-daro had a high thermal mass and a high likelihood of having had upper storeys, but they had few openings. That is, there would have been a high thermal variability both within the buildings themselves and compared with the outside. They would have had relatively high thermal choices and thermal control. Over time, however, these buildings were gradually superseded by structures with a lighter thermal mass, a low likelihood of having had upper storeys, still with few openings, and that were densely packed together, restricting the capacity for the buildings to interface with the outside. That is, whilst the internal thermal variability increased slightly over time in association with the later, lighter structures, both thermal choices and thermal control actually diminished overall due to the decreased presence of upper storeys, the consistently small number of openings and the locking-off of the option to interface with the outside environment either at base ambient level or selectively. This restricted MCS at Mohenjo-daro, and other contemporary Harappan sites, is consequential of the great structural inertia of the buildings coupled with very conservative building practices. This introduces

Both thermal choices and thermal control are necessary to satisfy the thermal preferences of a wide range of occupants and a wide range of functions, and the longer a building is occupied for, the wider the range of occupants and functions it will inevitably have to accommodate. Over very long periods of time the dense urban systems that have prevailed have been those that managed to maximise both thermal choices and thermal control in the face of urban saturation, despite thermal contradictions within the system. Vernacular forms of building have produced high thermal choices and thermal control through having possessed numerous and diverse types of spaces that were thermally variable and selectively thermally alterable.

DISCUSSION The declining MCS at Mohenjo-daro relative to the wider context raises a number of issues. The first issue is that it appears that contextual changes are more likely to destabilise those built environments (and the social systems they accommodate) that are thermally (and materially) inflexible, due to their reduced capacity to 173

The Evolution of the Built Environment contradiction in buildings is likely to lead to a reduced level of evolutionary robustness in widespread classes of buildings that utilise industrialised thermal systems, or to a completely different trajectory of change in buildings altogether, making them (and their societies) more sensitive to sudden changes in circumstance.

accommodate changes in social circumstances. The massive inertia in the built environment and conservative building practices at Mohenjo-daro combined to create a system where, whilst thermal control may have declined only slightly over the lifetime of the settlement, the potential for thermal choices was persistently low. The system seems to have fallen too far towards the ordered regime and the built environment thus became incapable of accommodating changes in contextual circumstances. As Kauffman has stated, “suppose that the total landscape changes because external conditions alter. Then the detailed locations of local peaks will shift. A rigid system deep in the ordered regime will tend to cling stubbornly to its peaks” (Kauffman 1995: 266).

The study has focused on the MCS produced by individual rooms and individual buildings. This is, however, only two scales at which MCS can be examined. It can also be examined at the scale of whole settlement systems for, just as the components of rooms and buildings interact to create the MCS of the rooms and buildings, so too do whole structures interact to create the MCS of settlements. Towns and cities also possess emergent properties that arise spontaneously and, whilst this issue has not been directly addressed here, the hypothesis might be made that the MCS of settlements is also likely to have evolved to the ‘border between chaos and order’. There is a possibility, however, that patterns of operational change observed within the built environment are a factor of larger scale contextual environmental circumstances. An evolutionary model, termed ‘plus ça change’, proposed by Peter Sheldon (1990, 1996) has indicated that ‘the more things change, the more they stay the same’. The model, based on a study of evolutionary attributes of Ordovician trilobites, proposes that highly fluctuating, rapidly changing environments (such as temperate, hot-arid and cold climates) will show a pattern of stasis interspersed with occasional punctuations. Conversely, narrowly fluctuating, slowly changing environments (such as hothumid climates) will show a pattern of continuous, gradual evolution (Fig. 9.21). At first glance this model appears to be counter-intuitive with regard to the evolution of the built environment. After all, whilst buildings in hot-humid environments inhabit a stable environment, they have shown minimal morphological or thermal change up until the 20th century, when they have generally experienced a rapid evolutionary phase shift towards enhanced MCS brought about by the introduction of mechanical cooling systems. Buildings in more variable environments, on the other hand, have shown gradual and systematic change over time, as illustrated by the examples in the study. However, Sheldon’s theory is strongly supported when buildings are examined at a finer temporal scale. The very nature of the traditional hot-humid buildings has been their capacity to undergo rapid, immediate and consistent change, due to their maximal material adjustability and minimal structural inertia (Fig. 9.22). Traditional buildings of more complex, variable environments, whilst being more difficult to characterise, have generally possessed a markedly reduced capacity for change at this temporal scale due to their relatively reduced material adjustability that has resulted from their generally higher structural inertia. Mobile structures in complex climates are an exception to this, being as equally low inertia and highly materially adjustable as lightweight hot-humid buildings. An interesting issue for future research would be a comparison of the rate and degree of immediate and

The second issue raised by the pattern of MCS change at Mohenjo-daro is the implication that there has been a relationship between the degree of thermal-material interconnectedness associated with classes of buildings and the overall evolutionary robustness of the societies that the classes of buildings have accommodated. The hypothesis might thus be made that, where the level of interconnectedness has been high, the level of contradiction within the building system would have been high and finding ever-more-unobtainable solutions to the contradiction would have required ever-greater diversity of built solutions. With this in mind, it is interesting to note that the earliest large scale, complex societies generally developed in various hot-arid regions of the world where the level of interconnectedness between the thermal and material systems in the buildings would have been high as a result of the reliance on passive systems to cool the buildings, both inside and out. Innovative techniques that were able to compensate for the increasing urban density developed and proliferated and, as a result, both the urban environments and the classes of buildings developed into highly complex and correlated thermal-social systems. Conversely, classes of buildings and settlements in cold regions that have utilised fires as the primary source of heating, in which the material and thermal systems are separate and uncorrelated and in which the level of contradiction between the social and the material is consequently very low, did not develop to an equivalent level of complexity until the advent and incorporation of large areas of glazing into the facades of buildings. The incorporation of large areas of glazing into buildings in cold climates, which commenced in the 15th century, had the effect of replacing the former active thermal heating system with a passive thermal system. This consequently connected the buildings’ thermal systems with the material systems and increased the level of thermal-social contradiction in classes of buildings. This situation predominated until the industrial revolution, when mechanical heating and cooling systems have been gradually incorporated into buildings worldwide. The incorporation of mechanical heating and cooling had the effect of introducing active thermal systems into buildings in all climates. This development is too recent in evolutionary terms to ascertain what the ultimate outcome will be. The conjecture can, however, be made that the reduced level of thermal-social 174

An Urban Site Example: Mohenjo-daro, Pakistan longer term temporal change in buildings in, for example, hot-arid and/or temperate climates relative to those in a hot-humid climate.

Figure 9.21. The ‘plus ça change’ model of evolutionary chnage (Sheldon 1996: 214)

Figure 9.22. Flexibility of the lightweight, low-inertia traditional Southeast Asian hut (Cofaigh et al. 1996: 43. Reproduced courtesy of the Estate of James MacGibbon)

175

CHAPTER 10 –Microclimatic Selection in the Built Environment: Implications

impossibility due to the contradictory nature of thermal systems. It can, however, approach this if it has the capacity to create enhanced thermal choices concurrently with enhanced thermal control and some classes of buildings have come closer to this than others through having possessed enhanced levels of thermal diversity and adjustability. For example, Hovenweep Castle, Square Tower Canyon, Colorado, c. 1200-1300A.D. (Fig. 10.1) would have possessed greater MCS than a La Plata pithouse of Colorado, c. 500-700A.D. (Fig. 10.2) and a vernacular house or haveli of Old Delhi or Rajasthan (Fig. 10.3) would be inherently more thermally diverse and adjustable than the room assemblage at Çatal Hüyük, Anatolia, c. 6000-5000B.C., (Fig. 10.4).

ENHANCED MICROCLIMATIC SELECTION Both humans and animals possess a capacity to exercise thermal choices by utilising the thermal microclimates that exist within the natural environment. Animals in the wild move freely about the landscape, their thermal choices being limited only by the climate and the range of microclimates provided by their physical environment. Domestic animals do likewise, although their thermal choices are more limited as a result of their generally more restricted range of available microclimates. On the other hand, wild animals that build nests or dig burrows can selectively extend their thermal choices by having extended their range of microclimates. Humans, however, have gone one step further in this regard. The thermal choices of humans are a product of their combined natural and built environments. The latter possess the capacity to produce enhanced levels of thermal choices plus the additional capacity for thermal control. However, some built environments possess a greater capacity for producing thermal choices and thermal control than others, thus possessing the capacity to accommodate a wider range of social options.

Maximum MCS is inherently difficult to create in buildings, partly because contradictions exist naturally within thermal systems and partly because contradictions can exist between the thermal system and the social system that the space accommodates. Both types of contradiction might potentially be alleviated through the creation of spaces that are either thermally adjustable, or spatial complexes that are made up of conjoined thermally discrete spaces that may or may not be also individually thermally adjustable. Enhanced thermal diversity and adjustability is possible via numerous thermally diverse and thermally adjustable conjoined spaces at a range of scales, from that of individual rooms to buildings to settlements. Note that over time both buildings and settlements have become generally more complex and internally diverse and that the overall number of components and the diversity of the components have increased.

Studies performed under Adaptive Comfort theory have demonstrated that the thermal experiences, responses, expectations and preferences of humans are climatically and temporally variable, both between and within societies, between and within groups, and between and within individuals. A building can thus satisfy some of the people all of the time, or all of the people some of the time, but it can never satisfy all of the people all of the time. Creating maximum thermal choices and maximum thermal control within a single space is an inherent

Figure 10.1. Reconstruction of Hovenweep Castle, Square Tower Canyon, Colorado, c. 1200-1300A.D. (Ferguson & Rohn 1987: 139)

176

Figure 10.2. Pithouse, La Plata district, Colorado, c. 500-700A.D. (Nabakov & Easton 1989: 357 Reproduced with kind permission of Oxford University Press, Inc)

Microclimatic Selection in the Built Environment: Implications

Figure 10.3. Shahjahanabad Haveli, Old Delhi, Anatolia, c. 1648-1960 (Prasad 1998: 186)

Figure 10.4. Reconstruction of Çatal Hüyük, c. 60005000B.C. (Acar 2001: 15)

Spatial complexes that are composed of thermally adjustable spaces will, however, potentially possess a higher level of thermal contradiction than that of either a single thermally adjustable space, or that of a spatial complex composed of thermally discrete spaces. This is because there will be an overall increase in the number of thermal interactions. As the thermal state of one room is adjusted there will be a flow-on effect to other adjoining spaces etc.: no room is an island. Thus the overall level of thermal contradictions within the building will increase. Note, though, that a building that is wholly thermally adjustable, whilst possessing a more complex and inherently contradictory thermal system, will be more thermally diverse and adjustable overall. It will thus possess a greater capacity for accommodating unforseen changing thermal and social circumstances. It will thus be more evolutionarily robust compared with less thermally diverse or adjustable systems.

ways, some of which will have increased the fitness of the building class and some of which will not have. However, those that have increased both will generally have enhanced the overall level of fitness of the class of building. For example, ‘pithouses’ produced higher thermal choices than the preceding more rudimentary structures, but ‘pueblos’ produced higher thermal choices and thermal control than the preceding ‘pithouses’. In the North American Southwest this was due in part to the retention of the use of pithouses in the form of kivas concurrently with the use of the compartmentalised aboveground pueblo buildings. This is exemplified at Site 820 Mesa Verde, Colorado, c. 1000-1100A.D., where there were five pithouses and where the rear section of rooms stood possibly three storeys high (Fig. 10.5). Complexity theory states that “there is a relationship between the richness of contradictory constraints in a system and the ruggedness of the landscape over which it must evolve … By tuning [the degree of interconnectedness between the entities] selection also tunes landscape structure from rugged to smooth. Changing the level of contradictory constraints in the construction of an organism from low to high tunes how rugged a landscape such organisms explore” (Kauffman 1995: 187). In other words, the level of contradictory features within a complex system sets the level of evolutionary fitness. That is, for a system to evolve it must first contain contradictory features. The built environment contains a high level of operationally contradictory features and building complexes possess higher levels of thermal contradiction that isolated

The use of fire enhanced the thermal choices of humans, but without the integrated use of other structures fire does little to enhance thermal control. However, when the use of fire was integrated with rudimentary structures, such as windbreaks or morphologically simple huts, both thermal choices and thermal control were enhanced. More complex classes of built structures possess the capacity to further enhance both thermal choices and thermal control, although some classes of buildings will, by their nature, possess the capacity to enhance either one aspect or the other and not both concurrently, due to the inherent contradiction in thermal systems. Different classes of buildings have resolved the contradiction in different 177

The Evolution of the Built Environment

Figure 10.5. Site 820, Mesa Verde, Colorado, c. 1000-1100A.D. (Ferguson & Rohn 1987: 89) spaces. Ultimately, however, it is the way in which the contradiction is resolved that influences the level of relative evolutionary fitness. Different classes of buildings have resolved the contradiction in different ways over time, some poorly and some well. Note that contradiction resolution is possible irrespective of whether or not the built system has been increasingly constrained by inertia, which would have reduced the capacity to be readily thermally adjustable. Mohenjo-daro is an example of a settlement that experienced high degrees of system-level inertia (Fig. 10.6) and that, due to the conservative building practices, resolved the contradictions between the material and the social in a way that reduced the system to that of a stable and ordered state. That is, the level of overall fitness was reduced.

the selection process. For the evolution of complex systems to evolve at the ‘border between chaos and order’ there must be a definable mechanism that can account for the self-organisation in the system, for selection alone cannot account for the long-term macro-scale operational consistencies and trends. “Most biologists have believed for over a century that selection is the sole source of order in biology [but] it is not, after all, the sole source of order, and organisms are not just tinkered-together contraptions, but expressions of deeper natural laws [operational consistencies]” (Kauffman 1995: 8). With regard to the evolution of the built environment, the source of independent, undirected variation is human variability (thermal and social) and the mechanism for the selforganisation of the thermal systems associated with classes of buildings is Microclimatic Selection (MCS).

The spatially segregated and thermally discrete protostructures of the Lower Palaeolithic would have possessed a low level of thermal contradiction. Spatial complexes, on the other hand, can be comprised of either thermally discrete spaces or thermally adjustable spaces. Complexes composed of thermally discrete spaces will possess a lower level of thermal contradiction, regardless of the number of conjoined spaces, than even a single thermally adjustable space due to the reduced level of thermal interconnectedness between the built parts. However, a spatial complex composed of thermally adjustable and thermally conjoined spaces will be subject to high levels of thermal contradiction. It will also be subject to high levels of thermal-social contradiction, unless the spaces serve a single function. However, single purpose spaces have generally constituted a very restricted class of buildings (Rybczynski 1986) and have generally been used in association with multi-purpose spaces.

Figure 10.6. Block 5, DK-G South area, Mohenjo-daro (Jansen 1997: 303)

MCS AS MECHANISM FOR SELFORGANISATION “A momentary limited change of level in thermal stimulation, in either a positive or negative direction, can result in a positive effect, but change, being an essential part of life, should revolve about a fixed or neutral point, or else it degenerates into chaos.” (Gerlach 1974: 15)

THE EVOLUTION OF BUILDINGS: HUMAN AGENCY AND THERMAL PERFORMANCE For the evolution of complex systems to occur there must be a definable mechanism by which the random, undirected variation is generated that is independent of 178

Microclimatic Selection in the Built Environment: Implications Despite the great variability that has been present in the built environment, the long-term behavioural pattern of MCS has shown a general trend towards a common point or a common thermal signature, albeit one within which there is increased thermal diversity. This trend derives from selection operating on the various ways in which built systems (classes of buildings) have resolved the contradictions, both the inherent thermal contradictions and the thermal-social contradictions, such that MCS has gradually been enhanced within classes of buildings over time. The various built features that operate in accordance with other features have had minimal influence on the long-term pattern of MCS change. However, features in buildings that have increased in prevalence over time have done so, not simply because they might function well in buildings, but because the way in which they function as a component of the built system has helped to resolve the contradictions within the system in a way that has enhanced MCS. These features have thus given the whole system an evolutionary ‘edge’ over other built systems that have either not resolved the contradictions or have resolved them in a way that has not created an equivalent level of MCS.

performed that consisted of a re-run of Case Study 4b Test 2 with the addition of two extra entities, one that equated to a chaotic regime and one that equated to an ordered regime. The two additional entities are shown in Table 10.1 below, that for the purposes of this test should be read as appended to the bottom of Table 9.5. The variables used are identical to those used in Case Study 4b Test 2 that included only features that operate in thermal contradiction to their converse feature. The numerical values for variables pertaining to the two hypothetical extreme regimes (chaotic and ordered) are diametrically opposed to each other. That is, for example, the numerical value for ‘floor level’ for the ordered regime is calculated at 1 (subterranean) and, because the value for the chaotic regime is diametrically opposed, it is therefore calculated at 3 (elevated above ground level). That is, the sites listed in Table 9.1 represent only approximate equivalents and are included only for the purposes of visualisation, not quantification.

Kauffman argues that the ‘zone’ of evolution lies at the ‘edge of chaos’, a zone that lies between the chaotic and the ordered regimes, just inside of the chaotic boundary (Kauffman 1995: 26-29, 86-92). If this is true for thermal systems, then the ‘zone’ of evolution for the MCS of buildings should also have been present at the ‘edge of chaos’, as Gerlach has hinted at (Fig. 10.7). In thermal systems, order can be equated to an homogeneous, static, thermally neutral and centrally controlled regime, where there are minimal thermal choices but maximum thermal control, and chaos can be equated to an unpredictable, uncontrollable regime, where there is minimal thermal control but maximum thermal choices. These two regimes represent the two possible thermal extremes or boundaries and possess characteristics that are diametrically opposed to each other. In other words the variables of which they are composed will either contribute more to thermal choices and less to thermal control, or vice versa, but not both together (Fig 10.8)

Figure 10.7. Thermal response relative to discrepancy from thermal stasis (after Gerlach 1974: 15)

Therefore, a test for where the evolution of MCS lies with respect to the chaotic regime and the ordered regime should be limited to only those variables that operate in contradiction with their converse feature. Variables that operate in accord should be excluded. Such a test was

Figure 10.8. The ‘Zone’ of Evolution for the built environment.

ENTITIES USED IN THE URBAN SITE COMPARATIVE CASE STUDY OF BUILDINGS Region

Time Period

Time Period Group in Database

Entity (Building) No. in Database

Approximately Equivalent Sites

Chaotic regime Ordered regime

n/a n/a

18 19

159 160

The outside environment An internal HVAC environment

Table 10.1. Entities (Buildings) Included in an Extension of Case Study 4b: The Urban Site Comparative Case Study of Buildings with Chaotic and Ordered Regimes/Entities.

179

The Evolution of the Built Environment The results of this extended re-run of Case Study 4b Test 2 show an arrangement of points approximately similar to that of the initial Case Study 4b Test 2 with the exception that the two extreme entities (max choices/min control and max control/min choices) form two distant outliers (Fig. 10.9). There is an equally strong correlation between the buildings and their region and period (r = 0.760 compared with the 0.744 of the Case Study 4b Test 2) and an equally high correlation significance (p = 0.000). The Naqada 1 buildings again form an outlier group, which occupies a region between the chaotic and the ordered regimes, and the majority of the buildings form a relatively tight cluster, which occupies a region slightly towards the ordered regime. Within the general cluster, however, some change over time in the nature of the thermal signatures of the archaeological buildings can be observed relative to the thermal chaotic and ordered boundaries. For example, whilst the MCS of the Egyptian buildings varies somewhat over time, the vernacular buildings are ‘poised’ between the ordered and the chaotic regimes. They are also relatively sparsely separated, thus indicating that the buildings are generally thermally diverse. Most significantly, however, the MCS of the buildings of Mohenjo-daro shows a trend contrary to the long-term trend observed for Pakistan. The buildings of the final occupational phases of Mohenjodaro occupy a position further towards the ordered regime, as anticipated, than that of the earlier phases or the later post-urban and vernacular phases. That is, it can be seen that, whilst the MCS at Mohenjo-daro moved gradually towards the ordered regime over time, the vernacular buildings in Pakistan are ‘poised’ between the ordered and the chaotic regimes. They are also again

relatively sparsely separated, thus indicating that the buildings are generally thermally diverse. The implications of this are that classes of buildings that have maintained a position ‘poised’ between chaos and order have had an evolutionary advantage over classes that have significantly favoured either thermal choices over thermal control, or vice versa. It also thus appears that classes of buildings that have possessed an enhanced level of inter-spacial thermal diversity and adjustability have had an evolutionary advantage over classes that have been too thermally restricted. Such classes of buildings will have been able to accommodate a wider range of possible social changes and will have been robust to changing social circumstances. They will also have been more robust to changing climatic circumstances. Note, however, that this does not mean that it is possible to predict the exact characteristics of classes of buildings (the building features) that are more likely to fare better in the future than others because, whilst at the larger scale the built environment appears to have tended towards the region ‘between chaos and order’ (tending more towards order) at the level of detail of individual building components the behaviour of thermal systems is non-predictive.

THE EVOLUTION OF THE BUILT ENVIRONMENT For evolution to be considered to have occurred, there has to have been an evident pattern of long-term change in behavioural regularity, not just change, because change can be random. Evolution is the result of Darwinian processes, of selection acting on random, undirected

Figure 10.9. Discriminant plot of MCS of buildings in extended Case Study 4b with max choices/min control (outside environment) and max control/min choices (inside environment).

180

Microclimatic Selection in the Built Environment: Implications variation. Study of the archaeological record has revealed that regularities and boundary conditions have been present within the evolution of settlements (Fletcher 1995). These long-term trends, evident in the limits of settlement growth, are the non-deterministic outcome of contradictions that exist between the material and the social operating at the scale of settlements and social life. This study has revealed that contradictions also exist at more finite scales of detail, at the scale of the thermal experiences and expectations of individuals and their immediate thermal environment, and that this has larger scale non-deterministic outcomes. Regularities and boundary conditions have been present within the evolution of the built environment at a range of scales, from rooms to buildings to whole settlements.

Figure 10.10. Threads and randomly connected buttons (Kauffman 1995: 55)

That these regularities and boundary conditions generate the long-term change of built environments is evident from the pattern of change in the MCS associated with classes of buildings over time. Buildings are thermal machines. Alterations made to a building will change the way it performs thermally. The emergent properties of complex systems, such as buildings and thermal systems, cannot be studied as the result of only single building features or traits because they arise spontaneously as the holistic result of the interactive operation of the features and traits. Emergent properties of buildings equate to the capacity of a building to create MCS (to produce thermal choices and thermal control). The pattern of long-term change in the MCS or thermal signatures of buildings has been regular, consistent and directional, within definable boundaries (between the extreme chaotic and ordered environments) and at ascertainable levels of detail (thermal capacity for MCS). That is, whilst between the boundaries the thermal operation of buildings has been non-determinate and non-predictive at a fine level of detail, the holistic thermal performance of classes of buildings has shown a pattern of directional consistency. There has been a general trend in the long-term pattern of behaviour of MCS towards enhanced adjustability and diversity. Buildings have evolved because their emergent properties have evolved.

Figure 10.11. Phase shift of threads and connected buttons (Kauffman 1995: 57) Kauffman has argued that the Neo-Darwinian evolution of biological species has been autocatalytic, with biological life having begun in a ‘chemical soup’ and becoming exponentially more complex over time (Fig. 10.12). The path of evolution has not been chaotic, however, because selection has favoured those organisms and species that were robust to small perturbations and that have been able to “strike an internal compromise between malleability and stability” (Kauffman 1995: 73, see also 71-80). Culture also appears to have been autocatalytic and evolutionarily robust. For example, modes of transportation have become increasingly more complex over time as mechanical devices have gradually replaced manual modes of transport. Mechanical vehicles have become more complex over time due to the increase in the number of interactive operational components (Fig. 10.13). The built environment also appears to have been autocatalytic and evolutionarily robust. If a hypothetical analogy is made in which Kauffman’s buttons are equated to individual buildings or built components (which possess thermal characteristics) and the threads are equated to modes and avenues of cultural transmission, the analogy can be used to explain why the level of complexity and thermal diversity in the built environment, and in culture generally, has increased

“The tools we make help us to make tools that in turn afford us new ways to make the tools we began with. The system is autocatalytic” (Kauffman 1993: 288). An autocatalytic system is one in which output feeds back into the system to provide the raw material that the system uses to continue operating: it feeds on itself to grow and propagate. Stuart Kauffman has made an analogy with buttons and interconnecting threads to illustrate this concept, whereby a large number of buttons are randomly connected by an increasing number of threads. As the ratio of threads to buttons increases, the likelihood that particular buttons will become interconnected increases. When the ratio of threads to buttons passes 0.5 a phase shift occurs. A ‘giant cluster’ forms whereby most of the buttons will have become interconnected (Kauffman 1995: 54-58) (Figs. 10.1010.11).

181

The Evolution of the Built Environment over time, possibly exponentially. Proto-structures can be equated to the first few buttons and subsequent buildings r with subsequent buttons. Modes and avenues of transmission can then be equated to the threads that interconnect the buttons. As more buttons were created and connected with more threads the system grew in size and complexity until it reached the point when it became autocatalytic. The distribution of buttons (buildings) and threads (modes of transmission) has not, however, been uniform in time or space. Some regions have possessed either more or less buttons and threads than other regions, and the pace at which the buttons and threads have become interconnected can be characterised by periods of stasis punctuated by moments of change. “Diversity probably begets diversity; hence diversity may help beget growth” (Kauffman 1995: 292). Kauffman was making the point that rather than attempting to identify individual buttons or threads (individual variants and their source), it is more important to note that statistically at some point a phase shift will occur and transitional change (from one class to another) will occur, although it is impossible to know beforehand when the phase shift will occur and which buttons or threads will combine to create it. By this logic it is possible to explain why diversity and complexity beget diversity and complexity, and systems that occupy the position ‘poised’ between too little complexity (order) and too much complexity (chaos) have an evolutionary advantage (Fig.10.14). The inherent spatial and temporal variability of thermal sensations, reactions, past experiences and future expectations within individuals and between people will have generated inherent contradictions in buildings. This has been resolved in a myriad number of ways throughout the world and over time. Without this variability present for selection to have acted on, evolution could not have occurred and built systems would have remained static and unchanging. At the same time, if variability had existed but in such a way that the variable forms had not interacted with each other in some way, evolution could not have occurred and the built systems would have remained static and unchanging. For evolution to occur there must be both variation and interaction, for this is the means by which the contradictions that drive the system are generated.

THE NULL HYPOTHESIS INVALIDATED The aim of the study was to test the hypothesis that the thermal performance of buildings has been evolutionarily neutral (natural selection has not and does not act upon it). The hypothesis was deemed to be strengthened if it could be demonstrated that MCS has changed in an irregular or random manner over time and invalidated if it could be demonstrated that MCS has changed in a consistent and definable manner (within boundary conditions) over time. After having performed a series of tests, using engineering-analysis and statistical analysis, and mapping the thermal signatures (the capacity to

Figure 10.12. Increased biological operational complexity over time (after Weinberg 1994: 24-25)

182

Microclimatic Selection in the Built Environment: Implications

Figure 10.13. Hypothetical percentage occurrence of modes of transportation in Ohio (Phillips et al.: 1951. Reproduced courtesy of the Peabody Museum of Archaeology and Ethnology, Harvard University) only becomes evident when viewed at the scale of either rooms or room-weighted buildings. The macro-scale pattern of MCS change has resulted from the nondeterministic way in which the contradiction that is inherent in the system at all levels of detail, both within the thermal system and between the social and the thermal systems, has been resolved. Social action has and does produce the undirected variation upon which selection operates, but it cannot dictate the long-term outcome. There is, however, some indication that, whilst the macro-scale pattern of MCS change has not been significantly influenced by climatic context, the detailed pattern of change (in rooms during the ‘pithouse’-to’pueblo’ transition) shows a slight undirected correlation with the type of climate. That is, the macro-scale pattern of MCS change appears to be non-deterministically related to levels of operation at finer levels of detail. Figure10.14.The evolutionary advantage of systems ‘between chaos and order’ (Kauffman 1995: 127)

Overall there has been a general long-term trend in the behaviour of MCS towards a common position, which is characterised by increasing thermal diversity (greater inter-building thermal variability) and adjustability (greater intra-building variability). Buildings that have possessed these characteristics have had a selective advantage over alternate classes of MCS over time. That is, whilst evolution of the built environment occurs at the scale of classes of buildings it is non-deterministically linked to more detailed scales of thermal operation. Put another way, selection may act at the scale of the detail, but it is manifested at the scale of the class (Cohen & Stewart 2000: 379) because the whole is greater than the sum of the parts.

create MCS) over time in various regions and over various periods of time, the hypothesis can be deemed to be invalidated. The pattern of change in the MCS of classes of buildings over time tended towards a common position, a position characterised by relatively high levels of both thermal control and thermal choices, in the form of inter-spatial and intra-spatial thermal diversity. The apparent tendency for buildings to evolve towards ‘the border between chaos and order’ (albeit favouring more thermal control than thermal choices) is nonpredictive and non-deterministic at a fine level of detail. This pattern of change is not apparent when thermal systems are viewed at the scale of generic buildings, but 183

The Evolution of the Built Environment thermal adjustability will have been robust to the inevitably changing circumstances (climatic and social) and will thus have been more likely to have endured.

IMPLICATIONS FOR ARCHAEOLOGY: MATERIAL ADJUSTABILITY “Stability is not immobility.”

Selection has not operated alone and buildings are not just ‘tinkered-together contraptions’. Buildings and classes of MCS have behaved as self-organised complex systems. MCS in buildings appears to have evolved towards the ‘edge of chaos’. This is most parsimoniously explained as having happened, not as the result of human cognition or learning, but as the result of selection consistently culling the variable classes of buildings that are less capable of producing high levels of MCS compared with alternate classes. That is, classes that are more able to meet the immediate thermal preferences of more occupants for more of the time by possessing the ability to absorb future environmental and social change. “The best exploration of an evolutionary space occurs at a kind of phase shift between order and disorder, when populations begin to melt off the local peaks they have become fixated on and flow along the ridges towards distant regions of higher fitness” (Kauffman 1995: 27) because at the ‘border between chaos and order’ systems will be “orderly enough to ensure stability, yet full of flexibility [adjustability] and surprise” (Kauffman 1995: 87). Thus it seems that MCS in buildings increased over time as the result of thermal and social interaction, whether the process was socially comfortable or not. One of the key means by which classes of buildings have created high levels of MCS appears to have been via the generation of diversity and variation within the built environment because, as good contradiction resolution becomes exponentially more difficult, it becomes possible only where a greater number of options are available such that the system retains adjustability. Good thermal conflict resolution has thus been most easily obtained via thermally adjustable systems, because it is easier to resolve the contradiction well in (controllably) adjustable systems than in rigid or chaotic systems.

“Any plan conceived in moderation must fail when the circumstances are set in extremes.” (Two quotes of Prince Klemens Von Metternich (1773-1859), leading activist for an alliance of the European powers after Napoleon's defeat) Buildings are complex systems and, as such, possess emergent properties. These properties arise spontaneously as the result of the workings of the parts of the thermal machine and cannot be reduced to the action of isolated components. Emergent properties equate to the functional capacity of the entity, by which they can be defined, and, the thermal capacity of buildings can be defined in terms of thermal choices and thermal control (microclimatic selection). The action of human agency has produced variability both within and between buildings, variability that has generated inherent contradiction both within thermal systems, and between thermal systems and their social systems. This relates non-deterministically to finer scales of individual, social, seasonal and climatic thermal variability amongst humans. Built environments that provide thermal choices and thermal control (or perceived control) to their occupants thus appear to have had a selective advantage over thermally neutral environments. The creation of structures that provide both thermal choices and thermal control is, however, inherently difficult due to the contradictory nature of the means by which both are produced within buildings. Thermal choices equate to thermal diversity and thermal control equates to thermal homogeneity. These contradictory factors have generated undirected variability in the built environment, variation that selection has acted on.

The level of evolutionary robustness to changing circumstances has been as much a factor of the level of contradiction present within the system as it has been the way in which the contradiction was resolved. The implication of this is that, in the case of those systems where the level of social and material contradiction were to drop off, so too would the level of robustness. Buildings in which the thermal and material systems are independent of each other will experience minimal contradiction between the two systems. However, minimal selective pressure will therefore be exerted on the material system to enhance its level of thermal adjustability, and ergo its material adjustability, and a materially inadjustable built system is less likely to be able to accommodate changing circumstances. A class of buildings in which the thermal and material systems are independent, such as buildings in cold climates that rely on fires as their sole source of heating, are therefore less likely to be evolutionarily robust to changing social and/or environmental circumstances in the long term.

Humans have made choices about their built environments, within various constraints, but the consequences of those choices have been played out over such long periods of time that they could have had no foresight of and no control over the outcomes. This is because humans do not possess the foresight to calculate either the long-term success or decline of either the classes of buildings they build or the classes of thermal systems associated with it. However, built environments in which the ‘pool’ of variable forms is diverse, variable and random will have had an advantage over built environments in which the ‘pool’ of variable forms has been more restricted, because there will have been an increased likelihood that the resultant outcome has increased the evolutionary robustness and adjustability of the system. Humans have resolved the inherent contradictions between their thermal and social systems sometimes well, sometimes poorly and sometimes hastily, and will continue to do so. But ultimately only those systems that have exhibited both material and 184

Microclimatic Selection in the Built Environment: Implications It would seem then that a correlation exists between classes of buildings that have encompassed high levels of both material and thermal contradiction in conjunction with thermally diverse and materially adjustable solutions to the contradiction. The ‘pithouse’-to-’pueblo’ transition and the long-term pattern of MCS change in classes of buildings generally is characterised by increasing thermal choices and thermal control, created by increasing building complexity, intra-spatial adjustability and interspatial diversity. This correlation has resulted in built environments that are generally more thermally flexible and robust overall. Such systems are likely to have possessed the capacity to produce diverse random

variants and to have incorporated system-level operational adjustability, such that satisfactory compromises between contradictory operations are possible. They will have been able to tolerate varied social organisation and to absorb the contextual changes that ensued when social and/or environmental circumstances changed without the system becoming unstable. At a broader scale, the types of settlements and societies that have persisted, and will persist, are likely to have been those that have resolved the inherent operational contradictions within the system in a way that generates microclimatic organisation that can be defined in evolutionary terms as robust self-organisation.

185

EPILOGUE

The built environment can possess great inertia to change relative to the verbal and the active components of social life, especially if that environment is composed of substantial and durable structures. This can lead to noncorrespondence between built environments and the more rapidly changing social life. Accommodating the faster changing social within buildings that possess great inertia is in the long-term inherently difficult. Buildings that are readily materially adjustable, however, possess a greater capacity to accommodate greater and faster social change than inflexible buildings. Buildings can meet the thermal preferences of all of the occupants some of the time and some of the occupants all of the time, but never all of the occupants all of the time. Buildings that are thermally diverse and adjustable, however, will meet the thermal preferences of more people for more of the time. Communities that possess thermally adjustable buildings will be less constrained in the long-term than those that occupy inflexible buildings and are therefore likely to endure for longer, because they can undergo more social transitions within the existing building milieu.

have occurred because adjustable systems are more likely to be evolutionarily robust to changing circumstances than non-adjustable systems. That is, they are capable of absorbing perturbations or changing contextual circumstances without becoming unstable at the level of the whole system. This is due to their enhanced capacity to resolve contradictions that inherently exist within and between the different scales of operation within the system, as proposed by Kauffman. Both social action and thermal performance are complex systems and possess inherent operational contradictions at diverse scales of operation. Contradictions between the social and the material arise due to the variability of thermal experiences and the differences in expectations of humans, from individual to individual, from society to society, and over time. People operate locally, but their actions have consequences that become evident only over time scales that they cannot possibly foresee. This has led to a pattern of evolutionary change in built environments that has tended towards the prevalence of adjustable systems, because they generate diverse thermal environments that more easily accommodate more diverse and changing social options. This unrelenting evolutionary selection of adjustability over inflexibility is as evident in the past record of built remains as it is likely to be in the future.

The prevalence of adjustable systems is explained by Complexity theory as a consequence of the evolutionary advantage that they possess relative to inflexible systems. Large-scale consistencies of operational adjustabilility

186

CHAPTER 10 –Microclimatic Selection in the Built Environment: Implications It is necessary to provide a glossary of some of the evolutionary and architectural terms used in this study for two reasons. First, scientists and architects generally use either very specialised technical terms or they use jargon and, secondly, archaeologists differ in their interpretations of evolutionary terms. Below is a glossary of various terms and their meanings as used in the study. Adaptation

A feature or trait of an organism or entity that has been shaped by selection such that the organism or entity is better suited to its environment as a direct result of having it.

Adaptive Comfort theory

A theory from within architectural studies and based principally on field studies that states that humans have a universal preference for having thermal choices available to them and the ability, or perceived ability, to make selective adjustments to their immediate thermal environment.

(ACT)

Adaptive Opportunity

A term used in Adaptive Comfort theory to refer to a person’s capacity to make adjustments to their surrounding thermal environment.

Boundary Conditions

The limits within which complex systems are self-organising and evolutionarily robust, and outside of which they are susceptible to ‘shock’ and destabilisation.

British Thermal Unit

The amount of heat required to raise the temperature of 1 lb. of water through 1oF.

Classes of Buildings

Statistically large sets of buildings that possess similar characteristics irrespective of their location in space and time. In the study this refers to similar emergent properties, specifically that of thermal capacity.

Chaotic Regime

The state occupied by a complex system when the number of connections between the components nears the number of components itself such that a change in one component alters the other components. The number of conflicting constraints is infinitely high, such that resolutions are never found or never have the opportunity to feed back into the system. The landscape on which it must evolve is wholly random.

Complex Systems

Systems with numerous interacting parts such that the emergent properties cannot be reduced to the operations of the details: the whole is greater than the sum of its parts. The system exhibits sensitive dependence on initial conditions and is therefore computationally intractable at a detailed scale of operation.

Complexity Theory

A theory from within the sciences and based principally on mathematical modelling that states that complex systems evolve when poised between chaotic and ordered regimes.

Contingency

A sequence of events such that the events are dependent on other uncertain events.

Culture

The material and social actions characteristic of a particular society or human group.

Cultural Evolution

Successive changes in the material and social characteristics of a particular society or human group that shows gradual change in the proportional representation of adaptive features and traits under the action of selection. A selective and/or transformational process.

Cultural Transmission

The process by which material and social characteristics are passed on from one social group to another over time. It is manifested as occurrence and frequency seriation. Transmission within a social group is called heritable continuity and transmission between social groups is called cultural continuity.

187

The Evolution of the Built Environment Drift

Random changes in the relative frequency of features and traits through successive generations of organisms or societies due to the inaction of selection on those particular features and traits.

‘Edge of Chaos’

The position poised between chaotic and ordered regimes. It is the position occupied by evolving self-organised complex systems.

Emergent Properties

The high-level result of the interaction of numerous parts of a complex system. Emergent properties exhibit sensitive dependence on initial conditions and are computationally intractable. That is, they cannot be reduced to an algorithm because the shortest algorithm that captures their behaviour would run in real time.

Engineering-Analysis

Experimentation and mathematical modelling by which emergent properties of complex systems can be understood.

Entropy

A law of thermodynamics that states that isolated systems will always tend towards a state of entropy or randomness. Based on this law, if the universe is treated as a closed system, the consequence must be perpetually increasing entropy leading ultimately to absence of structure and temperature uniformity. If, however, the universe is treated as a complex system, this law is counteracted by the theory of self-organised criticality.

Feedback

The modification of a process or system via the system’s results or effects ‘feeding back’ into the system.

Genetic Transmission

The process by which genetic information contained within DNA is passed on from one organism to its offspring.

Groups of Buildings

Sets of buildings that are proximally located in space and time.

Lamarckian Evolution

Successive generations of organisms that shows gradual change in the proportional representation of adaptive features and traits through the inheritance of characteristics acquired in order to survive. A deterministic process.

Microclimatic Selection (MCS)

The combination of both thermal choices and thermal control, not necessarily in equal proportions.

Selection

The process by which organisms, entities or systems that possess certain features and traits that enable them to survive and reproduce increase in number – relative to organisms, entities or systems that do not possess those features and traits – thereby exhibiting greater relative ‘fitness’.

Neo-Darwinian Evolution

A process by which successive generations of organisms, entities or systems show change in the proportional representation of adaptive features and traits under the action of selection. A selective process, not a transformational or anticipatory process.

Ordered regime

The state occupied by a complex system when the number of connections between the components nears zero such that a change in one component is independent of the other components. The number of conflicting constraints is infinitely low, such that resolutions are found too rapidly and are likely to be poor, and the landscape on which it must evolve is wholly random.

Reductionism

The assumption that high level structure is a logical outcome of low-level structure and operation. The whole is equal to the sum of its parts.

Robust

The capacity for complex systems to absorb contextual change without becoming structurally unstable.

Self-Organisation

The ability possessed by complex evolving systems to hold a position poised between the chaotic and ordered regimes. This position at the ‘edge of chaos’ is ascertainable in space and time only to within predefined boundary conditions, in which the system exhibits self-similarity. 188

Glossary Self-similarity

Behaviour that is nonpredictive and nondeterminate but which is repetitive in appearance at different scales implying pattern within pattern.

Strain

The deformationary reaction of an object under the action of stress.

Stress

A force exerted on an object, either in the direction of the object or away from the object.

Thermal Capacity

The range of selective thermal states that a thermal environment is capable of producing. This equates to the level of thermal choices and thermal control that the thermal environment is capable of producing.

Thermal Choices

The range of thermal states that potentially exist within a thermal environment.

Thermal Contradiction

Mutually opposed thermal states whereby the accentuation of one makes the production of another inherently difficult.

Thermal Control

The capacity to selectively replace one thermal state with another within a thermal environment.

Thermal Environment

A thermal system that exists within predefined boundaries and that is composed of the interactions between temperature, humidity and air movement.

Thermal Performance

The characteristics of the way in which a thermal environment changes in time.

Thermal State

A single set of thermal conditions. A thermal system fixed in time.

Thermal Signature

The thermal properties shared by the buildings within a class of buildings.

Uniformitarianism

Concerns constancy and uniformity of processes (not forms or specifics) over space and time that are likely to exist within definable boundary conditions.

Variation

Morphological and compositional diversity present between entities and systems.

189

APPENDIX A - Graphs of Temperature Differentials for Field Experiments (Simple Huts) Category 1: The raw outside and inside temperatures for the various degrees of exposure – Graphs 1-6 Graph 1: Lightweight Semi-subterranean Domed Hut:

Graph 2: Lightweight On-ground Domed Hut:

190

Temperature Differentials for Field Experiments Graph 3: Lightweight On-ground Rectilinear Hut:

Graph 4: Heavyweight Semi-subterranean Domed Hut:

191

The Evolution of the Built Environment Graph 5: Heavyweight On-ground Domed Hut:

Graph 6: Heavyweight On-Ground Rectilinear Hut:

192

Temperature Differentials for Field Experiments

Category 3: The outside-inside temperature differences (outside temperature minus inside temperature) for the various degrees of exposure – Graphs 7-12 Graph 7: Lightweight Semi-subterranean Domed Hut:

Graph 8: Lightweight On-ground Domed Hut:

193

The Evolution of the Built Environment Graph 9: Lightweight On-ground Rectilinear Hut:

Graph 10: Heavyweight Semi-subterranean Domed Hut:

194

Temperature Differentials for Field Experiments Graph 11: Heavyweight On-ground Domed Hut:

Graph 12: Heavyweight On-Ground Rectilinear Hut:

195

The Evolution of the Built Environment

Category 4: T he closed-open temperature differences (closed temperature minus open temperature) for the various degrees of exposure – Graphs 13-18) Graph 13: Lightweight Semi-subterranean Domed Hut:

Graph 14: Lightweight On-ground Domed Hut:

196

Temperature Differentials for Field Experiments Graph 15: Lightweight On-ground Rectilinear Hut:

Graph 16: Heavyweight Semi-subterranean Domed Hut:

197

The Evolution of the Built Environment Graph 17: Heavyweight On-ground Domed Hut:

Graph 18: Heavyweight On-Ground Rectilinear Hut:

198

APPENDIX B – Datasets for Case Study 1 Test 1: Ethnographic Buildings and Culture Cultural Affiliation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

Khoisan Khoisan Khoisan Mbuti Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Micellaneous African Micellaneous African Micellaneous African Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Uralic Uralic Uralic Uralic Uralic Japanese/Korean Japanese/Korean Far North Asian Far North Asian Far North Asian Far North Asian Dravidian Dravidian Mon-Khmer/Austroasiatic Mon-Khmer/Austroasiatic Mon-Khmer/Austroasiatic

Culture

Nama Kung Hadza Mbuti Lozi Mbundu Bemba Nyakyusa Thonga Luguru Kikuyu Ganda Tiv Nkundo Banen Ashanti Ibo Fon Azande Mende Wolof Otoro Songhai Masai Shilluk Fur Teda Kaffa Konso Somali Tuareg Riffians Amhara Egyptians Rwala Gheg Neapolitans Irish Russians Kurd Basseri Vedda Saramacca Lapps Yurak Turks Kazak Khalka Koreans Japanese Ainu Gilyak Chukchee Yukaghir Gond Toda Santal Lamet Vietnamese

Grouping

1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 5 5 6 6 6 7 7 8 8 8 8 9 9 9 9 9

1 Communal Structures 3 3 3 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 2 3 3 1 3 1 3 1 3 2 1 1 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 1 1

199

Variables 2 3 4 Floor Plan Floor Level Wall Material 5 3 2 5 3 2 5 3 2 5 3 2 5 3 2 5 3 2 5 3 2 5 3 4 5 3 2 3 3 3 3 3 4 3 2 5 5 3 4 3 3 3 2 3 2 2 3 4 3 3 2 3 3 4 5 3 4 5 3 2 5 3 2 5 3 2 3 3 2 3 3 2 5 3 4 5 3 2 3 3 4 5 3 2 5 3 3 5 3 2 3 3 4 2 3 4 5 3 2 3 3 4 3 3 2 3 2 3 3 2 4 3 3 4 3 2 3 3 3 4 3 3 2 5 4 5 3 3 2 3 3 2 5 3 2 3 3 4 5 3 2 4 3 2 3 2 3 3 2 3 3 3 2 3 4 3 5 3 2 5 3 2 3 3 3 3 3 2 2 3 4 3 1 3 2 2 3

5 Roof Shape 2 2 2 2 5 5 5 5 5 4 5 1 5 4 4 4 4 1 5 5 5 5 4 4 5 5 5 5 5 2 5 5 5 5 4 4 5 4 4 5 5 5 4 4 5 4 2 5 1 1 4 5 5 5 4 4 4 3 4

6 Roof Material 1 2 2 2 2 2 2 2 2 2 4 2 2 2 1 2 2 2 2 2 2 2 1 3 2 2 2 2 2 1 4 4 2 1 1 2 2 2 2 4 1 5 2 1 2 2 1 1 2 2 2 4 1 2 2 2 2 2 2

The Evolution of the Built Environment Cultural Affiliation

60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121

Mon-Khmer/Austroasiatic Mon-Khmer/Austroasiatic Mon-Khmer/Austroasiatic Tibetan Tibetan Tibetan Tibetan Tibetan Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Nadene Nadene Nadene Nadene Nadene Nadene Eskimo Eskimo Macro-Algonkian Macro-Algonkian Macro-Algonkian Macro-Algonkian Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Uto-Aztecan & Zuni Uto-Aztecan & Zuni Uto-Aztecan & Zuni Uto-Aztecan & Zuni Uto-Aztecan & Zuni Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean

Culture

Semang Nicobarese Andamanese Lolo Lepcha Garo Lakher Burmese Siamese Rhade Tanala Javanese Balinese Iban Badjau Alorese Manus New Irelanders Trobrianders Tikopia Ajie Maori Marquesans Samoans Gilbertese Marshallese Trukese Yapese Palauans Ifugao Atayal Orokaiva Kimam Kapauku Kwoma Siuai Tiwi Aranda Ingalik Slave Kaska Eyak Haida Chiricahua Aleut Copper Eskimo Montagnais Saulteaux Gros Ventre Yurok Bellacoola Twana Havasupai Eastern Pomo Omaha Hidatsa Wadadika Comanche Papago Huichol Zuni Klamath

Grouping

9 9 9 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 14 14 14 14 15 15 15 15 15 15 16 16 16 16 16 17

1 Communal Structures 3 1 3 3 1 3 3 1 1 3 1 3 1 3 3 1 1 1 2 1 1 1 3 1 1 3 1 1 1 3 3 1 3 3 1 1 3 3 1 3 3 1 3 3 3 3 3 3 3 3 3 1 3 1 3 3 3 3 1 1 3 3

200

Variables 2 3 4 Floor Plan Floor Level Wall Material 3 3 2 4 1 2 3 3 1 3 3 3 3 3 2 3 2 2 3 1 2 3 1 3 3 1 3 3 1 3 3 2 2 3 3 2 2 3 3 3 1 2 3 3 3 3 1 2 3 1 2 3 3 3 3 3 3 3 3 2 4 2 2 3 3 3 3 2 2 3 2 2 3 3 1 3 3 2 3 3 2 3 2 2 3 1 2 3 1 2 3 3 3 3 1 3 4 3 2 3 3 3 3 3 3 3 1 2 3 3 2 3 3 2 3 4 3 4 3 3 4 3 4 3 3 3 3 4 3 4 3 2 3 4 4 4 3 4 4 3 2 4 3 2 4 3 2 3 4 3 3 4 3 3 3 3 4 3 2 4 3 2 4 4 3 4 4 3 4 3 2 2 4 3 4 4 2 3 3 4 3 3 4 4 4 4

5 Roof Shape 3 1 3 4 4 4 4 4 4 4 4 4 4 4 5 4 4 4 4 4 5 4 4 4 4 4 1 4 4 1 4 1 1 4 4 4 4 3 2 5 5 4 4 2 2 2 5 5 5 4 4 3 2 2 2 2 2 5 2 1 5 1

6 Roof Material 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 4 2 4 1 2 2 4 4 1 1 1 2 2 2 2 2 4 4 1 1 4 2 4 4

Datasets for Case Study 1 Cultural Affiliation

122 Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean 123 Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean 124 Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean 125 Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean 126 Mayan & Cariban 127 Mayan & Cariban 128 Mayan & Cariban 129 Mayan & Cariban 130 Mayan & Cariban 131 Mayan & Cariban 132 Mayan & Cariban 133 Equatorial 134 Equatorial 135 Equatorial 136 Equatorial 137 Equatorial 138 Equatorial 139 Ge-Panoan 140 Ge-Panoan 141 Ge-Panoan 142 Ge-Panoan 143 Ge-Panoan 144 Andean 145 Andean 146 Andean 147 Andean

Culture

Grouping

Variables 2 3 4 Floor Plan Floor Level Wall Material 3 3 2

Lake Yokuts

17

1 Communal Structures 3

Kutenai

17

3

3

4

2

4

1

Pawnee

17

3

4

4

4

2

4

Creek

17

1

3

3

3

4

1

Quiche Black Carib Carib Miskito Cuna Warrau Cayapa Goajiro Mundurucu Cubeo Jivaro Siriono Trumai Amahuaca Nambicuara Timbira Aweikoma Lengua Aymara Mapuche Tehuelche Yahgan

18 18 18 18 18 18 18 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20

1 3 3 3 1 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3

3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 1 3 3 3 4

3 3 3 1 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4

4 2 1 2 3 1 1 2 3 3 3 2 2 1 2 2 2 2 4 2 2 2

4 4 4 4 4 4 1 4 4 1 1 1 4 4 1 1 4 3 4 1 4 2

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2

201

5 Roof Shape 4

6 Roof Material 1

The Evolution of the Built Environment

Test 2: Ethnographic Buildings and Climate Cultural Affiliation Culture

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

Khoisan Khoisan Khoisan Mbuti Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Micellaneous African Micellaneous African Micellaneous African Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Uralic Uralic Uralic Uralic Uralic Japanese/Korean Japanese/Korean Far North Asian Far North Asian Far North Asian Far North Asian Dravidian Dravidian MonKhmer/Austroasiatic MonKhmer/Austroasiatic MonKhmer/Austroasiatic

Bearing

K-T Climate Classif

3 2 Wint Hum Temp 2 6 2 7 3 7 1 8 3 6 3 7 3 6 3 6 3 6 3 7 3 6 1 7 3 8 1 8 1 7 3 8 1 8 3 8 3 8 3 8 2 8 2 8 2 8

Nama Kung Hadza Mbuti Lozi Mbundu Bemba Nyakyusa Thonga Luguru Kikuyu Ganda Tiv Nkundo Banen Ashanti Ibo Fon Azande Mende Wolof Otoro Songhai

27s,17e 20s,21e 3s,34e 1n,28e 16s,23e 12s,16e 11s,31e 9s,34e 26s,32e 7s,38e 1s,37e 0,32e 7n,9e 0,18e 5n,10e 7n,2w 5n,7e 7n,2e 5n,28e 8n,12w 14n,15e 11n,31e 17n,1w

GBwal GBsab GAwab Araa GAwal GAwbb GAwbl GAwbl GAwbl GAwab GAwbl GArbb Awha Araa Arab Awaa Araa Awha Awha Amha Bwha Bwha Bwia

1 Sum Temp 8 8 8 8 8 7 7 7 7 8 7 7 9 8 8 8 8 9 9 9 9 9 10

Masai

3s,36e

GAwab

8

7

Shilluk

10n,32e

Bsha

9

Fur Teda Kaffa Konso Somali Tuareg Riffians Amhara Egyptians Rwala Gheg Neapolitans Irish Russians Kurd Basseri Vedda Saramacca Lapps Yurak Turks Kazak Khalka Koreans Japanese Ainu Gilyak Chukchee Yukaghir Gond Toda Santal

13n,25e 21n,17e 7n,36e 5n,37e 9n,47e 23n,6e 35n,3w 13n,37e 25n,33e 33n,38e 42n,20e 41n,13e 53n,10w 53n,41e 36n,44e 29n,54e 8n,81e 3n,56w 68n,22e 68n,51e 39n,34e 48n,70e 47n,96e 38n,126e 34n,134e 43n,143e 54n,142e 66n,177e 70n,145e 19n,82e 11n,76e 23n,87e

GBwhb GBwhl GAwbl GAwha GBwhb GBwhl Csak GAwab Bwhl GBwhk Crak Csak Dolk Dcbc Csik GCshk Arha Amaa FTkc FTkc GCsao GDcbc GBSld DCao Crak Dcbo Dclc FTkd ECld GAwhb Amha Cwhl

Lamet

Variables

Smplfied K-T Climate Climate PC1

K-T Climate

2 3 4 5 6 1 Com Flr Flr Wall Roof Roof Bldg Plan Lvl Mat Shpe Mat 5 3 2 2 1 3 5 3 2 2 2 3 5 3 2 2 2 3 5 3 2 2 2 3 5 3 2 5 2 3 5 3 2 5 2 1 5 3 2 5 2 2 5 3 4 5 2 3 5 3 2 5 2 3 3 3 3 4 2 3 3 3 4 5 4 3 5 3 2 1 2 3 5 3 4 5 2 3 3 3 3 4 2 3 2 3 2 4 1 3 2 3 4 4 2 3 3 3 2 4 2 3 3 3 4 1 2 3 5 3 4 5 2 3 5 3 2 5 2 3 5 3 2 5 2 3 5 3 2 5 2 3 3 3 2 4 1 3

0.102 0.469 -0.036 1.340 -0.403 -0.570 -0.936 -0.936 -0.936 -0.036 -0.936 0.440 0.864 1.340 0.974 0.330 1.340 0.864 0.864 0.864 1.369 1.369 1.902

Temp Temp Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Hot Arid Hot Arid Hot Arid

3

-0.036

Tropical

3

3

3

2

4

3

8

2

1.369

Hot Arid

3

5

3

4

5

2

9 9 7 9 9 9 8 8 9 9 8 8 6 7 10 9 9 8 5 5 8 7 6 8 8 7 6 5 6 9 9 9

7 6 6 8 7 6 5 7 6 5 5 5 5 3 5 5 8 8 3 3 4 3 2 4 5 4 3 2 2 7 8 6

2 2 3 3 2 2 3 3 2 2 1 3 3 3 3 3 1 3 2 2 3 3 2 3 1 3 3 2 3 3 3 3

1.002 0.636 -0.936 0.864 1.002 0.636 -0.769 -0.036 0.636 0.270 0.241 -0.769 -1.836 -2.035 0.298 -0.235 1.874 0.330 -2.597 -2.597 -1.135 -2.035 -2.430 -1.135 0.241 -1.669 -2.569 -2.963 -2.935 0.497 0.864 0.131

Hot Arid Hot Arid Tropical Tropical Hot Arid Hot Arid Temp Tropical Hot Arid Hot Arid Temp Temp Temp Cold Temp Temp Tropical Tropical Cold Cold Temp Cold Cold Temp Temp Temp Cold Cold Cold Tropical Tropical Temp

1 3 3 2 3 3 1 3 1 3 1 3 2 1 1 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3

5 3 5 5 5 3 2 5 3 3 3 3 3 3 3 3 5 3 3 5 3 5 4 3 3 3 3 5 5 3 3 2

3 3 3 3 3 3 3 3 3 3 2 2 3 2 3 3 4 3 3 3 3 3 3 2 2 3 4 3 3 3 3 3

2 4 2 3 2 4 4 2 4 2 3 4 4 3 4 2 5 2 2 2 4 2 2 3 3 2 3 2 2 3 2 4

5 5 5 5 2 5 5 5 5 4 4 5 4 4 5 5 5 4 4 5 4 2 5 1 1 4 5 5 5 4 4 4

2 2 2 2 1 4 4 2 1 1 2 2 2 2 4 1 5 2 1 2 2 1 1 2 2 2 4 1 2 2 2 2

20n,101e Awhb

9

7

3

0.497

Tropical

1

3

1

3

3

2

Vietnamese 20n,105e Cwhl

9

6

3

0.131

Temp

1

2

2

3

4

2

202

Datasets for Case Study 1 Cultural Affiliation Culture

61 MonKhmer/Austroasiatic 62 MonKhmer/Austroasiatic 63 Tibetan 64 Tibetan 65 Tibetan 66 Tibetan 67 Tibetan 68 Thai-Kadai 69 Thai-Kadai 70 Thai-Kadai 71 Thai-Kadai 72 Thai-Kadai 73 Thai-Kadai 74 Thai-Kadai 75 Thai-Kadai 76 Thai-Kadai 77 Thai-Kadai 78 Thai-Kadai 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105

Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Nadene Nadene Nadene Nadene Nadene Nadene Eskimo Eskimo

106 107 108 109 110

Macro-Algonkian Macro-Algonkian Macro-Algonkian Macro-Algonkian Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan

111 112 113 114 115

Bearing

K-T Climate Classif

K-T Climate

Nicobarese 7n,94e

Arha

1 Sum Temp 9

Andamanes e Lolo Lepcha Garo Lakher Burmese Siamese Rhade Tanala Javanese Balinese Iban Badjau Alorese Manus New Irelanders Trobriander s Tikopia Ajie Maori Marquesans Samoans Gilbertese Marshallese Trukese Yapese Palauans Ifugao Atayal Orokaiva Kimam Kapauku Kwoma Siuai Tiwi Aranda Ingalik Slave Kaska Eyak Haida Chiricahua Aleut Copper Eskimo Montagnais Saulteaux Gros Ventre Yurok Bellacoola

12n,93e

Awha

9

8

29n,103e 27n,89e 26n,91e 22n,93e 22n,96e 14n,101e 13n,108e 22s,48e 8s,112e 8s,115e 2n,112e 5n,120e 8s,125e 2s,147e 2s,151e

Cwak HBSlo Cwhl GAmhl Awhb Awha Awha Arab Araa Araa Araa Araa Awaa Araa Arha

8 6 9 9 9 9 9 8 8 8 8 8 8 8 9

9s,151e

Araa

12s,168e 21s,166e 35s,174e 9s,140w 14s,172w 3n,172e 6n,169e 7n,152e 9n,138e 7n,134e 17n,121e 24n,121e 8s,148e 7s,138e 4s,136e 4s,143e 7s,155e 12s,131e 24s,134e 62n,159w 62n,122w 60n,131w 60n,145w 54n,132w 32n,109w 54n,167w 69n,110w 48n,72w 51n,95w 49n,109w 41n,124w 52n,126w

K-T Smplfied Climate Climate PC1

2 3 Wint Hum Temp 8 1

Variables 1 2 3 4 5 6 Com Flr Flr Wall Roof Roof Bldg Plan Lvl Mat Shpe Mat 1 4 1 2 1 2

1.874

Tropical

3

0.864

Tropical

3

3

3

1

3

2

5 4 6 6 7 8 8 7 8 8 8 8 8 8 8

3 2 3 3 3 3 3 1 1 1 1 1 2 1 1

-0.769 -1.697 0.131 0.131 0.497 0.864 0.864 0.974 1.340 1.340 1.340 1.340 0.835 1.340 1.874

Temp Temp Temp Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical

3 1 3 3 1 1 3 1 3 1 3 3 1 1 1

3 3 3 3 3 3 3 3 3 2 3 3 3 3 3

3 3 2 1 1 1 1 2 3 3 1 3 1 1 3

3 2 2 2 3 3 3 2 2 3 2 3 2 2 3

4 4 4 4 4 4 4 4 4 4 4 5 4 4 4

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

8

8

1

1.340

Tropical

2

3

3

3

4

2

Araa Arab Crbl Araa Araa Arhh Araa Araa Arha Araa Arha Crhl Awha Araa GArab Arha Arha Awha GBWha EClc ECld GEClc EOlo DOlk GBWak EOlo FTkd

8 8 7 8 8 9 8 8 9 8 9 9 9 8 8 9 9 9 9 6 6 6 6 6 8 6 5

8 7 6 8 8 9 8 8 8 8 8 6 8 8 7 8 8 8 8 3 2 3 4 5 5 4 2

1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 3 2 3 3 3 3 3 2 3 2

1.340 0.974 0.074 1.340 1.340 2.240 1.340 1.340 1.874 1.340 1.874 1.141 0.864 1.340 0.974 1.874 1.874 0.864 1.369 -2.569 -2.935 -2.569 -2.202 -1.836 -0.264 -2.202 -2.963

Tropical Tropical Temp Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Temp Tropical Tropical Tropical Tropical Tropical Tropical Hot Arid Cold Cold Cold Temp Temp Temp Temp Cold

1 1 1 3 1 1 3 1 1 1 3 3 1 3 3 1 1 3 3 1 3 3 1 3 3 3 3

3 4 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 4 4 3 3 4 3 4

3 2 3 2 2 3 3 3 2 1 1 3 1 3 3 3 1 3 3 4 3 3 3 4 3 4 3

2 2 3 2 2 1 2 2 2 2 2 3 3 2 3 3 2 2 2 3 3 4 3 3 2 4 4

4 5 4 4 4 4 4 1 4 4 1 4 1 1 4 4 4 4 3 2 5 5 4 4 2 2 2

2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 4 2 4 1 2 2 4 4

Dcbc Ecbc GDCbc DObk GDolc

7 7 7 7 6

3 3 3 5 3

3 3 3 3 3

-2.035 -2.035 -2.035 -1.302 -2.569

Cold Cold Cold Temp Cold

3 3 3 3 3

4 4 4 3 3

3 3 3 4 4

2 2 2 3 3

5 5 5 4 4

1 1 1 2 2

Twana

47n,123w Dobk

7

5

3

-1.302

Temp

1

3

3

3

3

2

Havasupai

36n,112w GBWhk

9

5

2

0.270

Hot Arid

3

4

3

2

2

2

Eastern Pomo Omaha

39n,123w Csbk

7

5

3

-1.302

Temp

1

4

3

2

2

2

41n,96w Dcao

8

4

3

-1.135

Temp

3

4

4

3

2

4

Hidatsa

47n,101w GDcbc

7

3

3

-2.035

Cold

3

4

4

3

2

4

203

The Evolution of the Built Environment Cultural Affiliation Culture

116 117 118 119 120 121

122

123

124

125

126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147

Uto-Aztecan & Zuni Uto-Aztecan & Zuni Uto-Aztecan & Zuni Uto-Aztecan & Zuni Uto-Aztecan & Zuni Macro-Penutian, Kutenai, Caddoan, Iroquoian & Natchez-Muskogean Macro-Penutian, Kutenai, Caddoan, Iroquoian & Natchez-Muskogean Macro-Penutian, Kutenai, Caddoan, Iroquoian & Natchez-Muskogean Macro-Penutian, Kutenai, Caddoan, Iroquoian & Natchez-Muskogean Macro-Penutian, Kutenai, Caddoan, Iroquoian & Natchez-Muskogean Mayan & Cariban Mayan & Cariban Mayan & Cariban Mayan & Cariban Mayan & Cariban Mayan & Cariban Mayan & Cariban Equatorial Equatorial Equatorial Equatorial Equatorial Equatorial Ge-Panoan Ge-Panoan Ge-Panoan Ge-Panoan Ge-Panoan Andean Andean Andean Andean

Bearing

K-T Climate Classif

GDcao GCwhk BWhl GAwhl GBWak Dobk

K-T Climate 1 Sum Temp 8 9 9 9 8 7

K-T Smplfied Climate Climate PC1

2 3 Wint Hum Temp 4 3 5 3 6 2 6 3 5 2 5 3

Wadadika Comanche Papago Huichol Zuni Klamath

43n,119w 33n,100w 32n,112w 23n,105w 36n,109w 43n,122w

Lake Yokuts

35n,119w Csll

6

6

Kutenai

49n,117w GDobo

7

Pawnee

42n,100w GBsao

Creek

Quiche Black Carib Carib Miskito Cuna Warrau Cayapa Goajiro Mundurucu Cubeo Jivaro Siriono Trumai Amahuaca Nambicuara Timbira Aweikoma Lengua Aymara Mapuche Tehuelche Yahgan

Variables 1 2 3 4 5 6 Com Flr Flr Wall Roof Roof Bldg Plan Lvl Mat Shpe Mat 3 4 3 2 2 1 3 4 3 2 5 1 1 4 4 2 2 4 1 3 3 4 1 2 3 3 3 4 5 4 3 4 4 4 1 4

-1.135 -0.235 0.636 0.131 -0.264 -1.302

Temp Temp Temp Tropical Temp Temp

3

-1.470

Temp

3

3

3

2

4

1

4

3

-1.669

Temp

3

3

4

2

4

1

8

4

3

-1.135

Temp

3

4

4

4

2

4

33n,85w Crak

8

5

1

0.241

Temp

1

3

3

3

4

1

15n,91w 16n,89w 7n,60w 15n,91w 9n,78w 9n,62w 1n,79w 12n,72w 7s,57w 1n,71w 3s,78w 14s,63w 12s,54w 10s,72w 12s,59w 6s,45w 28s,50w 23s,59w 16s,66w 38s,73w 46s,70w 55s,69w

7 9 8 7 8 8 8 9 8 8 6 8 8 8 8 9 8 9 8 6 6 5

6 8 8 6 8 8 8 8 8 8 6 7 7 8 7 8 6 7 7 5 5 5

3 1 1 3 1 3 1 2 3 1 3 3 3 1 3 3 1 3 1 3 2 2

-0.936 1.874 1.340 -0.936 1.340 0.330 1.340 1.369 0.330 1.340 -1.470 -0.036 -0.036 1.340 -0.036 0.864 0.607 0.497 0.974 -1.836 -1.331 -1.864

Tropical Tropical Tropical Tropical Tropical Tropical Tropical Hot Arid Tropical Tropical Tropical Tropical Tropical Tropical Tropical Tropical Temp Tropical Tropical Temp Temp Temp

1 3 3 3 1 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3

3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 1 3 3 3 4

3 3 3 1 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4

4 2 1 2 3 1 1 2 3 3 3 2 2 1 2 2 2 2 4 2 2 2

4 4 4 4 4 4 1 4 4 1 1 1 4 4 1 1 4 3 4 1 4 2

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2

GAwbl Arha Araa GAwbl Araa Awaa Araa BSha Amaa Araa HAwll Awab Awab Araa Awab Awha Cral Awhb Arab Dolk BSlk FTkk

204

Datasets for Case Study 1

Test 3: Ethnographic Buildings and Sedentism/Mobility Cultural Affiliation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57

Khoisan Khoisan Khoisan Mbuti Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Niger-Kordofanian Micellaneous African Micellaneous African Micellaneous African Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Afroasiatic Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Indo-European Uralic Uralic Uralic Uralic Uralic Japanese/Korean Japanese/Korean Far North Asian Far North Asian Far North Asian Far North Asian Dravidian Dravidian MonKhmer/Austroasiatic 58 MonKhmer/Austroasiatic 59 MonKhmer/Austroasiatic 60 MonKhmer/Austroasiatic

Culture

Murdock Grouping Sedentism/ Mobility

Variables

Nama Kung Hadza Mbuti Lozi Mbundu Bemba Nyakyusa Thonga Luguru Kikuyu Ganda Tiv Nkundo Banen Ashanti Ibo Fon Azande Mende Wolof Otoro Songhai Masai Shilluk Fur Teda Kaffa Konso Somali Tuareg Riffians Amhara Egyptians Rwala Gheg Neapolitans Irish Russians Kurd Basseri Vedda Saramacca Lapps Yurak Turks Kazak Khalka Koreans Japanese Ainu Gilyak Chukchee Yukaghir Gond Toda Santal

B B B B R P I I I P P P P P P P P P P P P P P B P P B P P B B P P P B T P P P P P B S P B B P S S P P T S S B I R

1 1 1 1 3 6 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 1 6 6 1 1 6 6 6 1 4 6 6 6 6 6 1 2 6 1 1 6 2 2 6 6 4 2 2 1 5 3

1 Communal Bldg 3 3 3 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 2 3 3 1 3 1 3 1 3 2 1 1 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3

Lamet

P

6

1

3

1

3

3

2

Vietnamese

T

4

1

2

2

3

4

2

Semang

P

6

3

3

3

2

3

2

205

2 Floor Plan 5 5 5 5 5 5 5 5 5 3 3 5 5 3 2 2 3 3 5 5 5 5 3 3 5 5 3 5 5 5 3 2 5 3 3 3 3 3 3 3 3 5 3 3 5 3 5 4 3 3 3 3 5 5 3 3 2

3 Floor Level 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2 3 3 4 3 3 3 3 3 3 2 2 3 4 3 3 3 3 3

4 Wall Material 2 2 2 2 2 2 2 4 2 3 4 2 4 3 2 4 2 4 4 2 2 2 2 2 4 2 4 2 3 2 4 4 2 4 2 3 4 4 3 4 2 5 2 2 2 4 2 2 3 3 2 3 2 2 3 2 4

5 Roof Shape 2 2 2 2 5 5 5 5 5 4 5 1 5 4 4 4 4 1 5 5 5 5 4 4 5 5 5 5 5 2 5 5 5 5 4 4 5 4 4 5 5 5 4 4 5 4 2 5 1 1 4 5 5 5 4 4 4

6 Roof Material 1 2 2 2 2 2 2 2 2 2 4 2 2 2 1 2 2 2 2 2 2 2 1 3 2 2 2 2 2 1 4 4 2 1 1 2 2 2 2 4 1 5 2 1 2 2 1 1 2 2 2 4 1 2 2 2 2

The Evolution of the Built Environment Cultural Affiliation

61 MonKhmer/Austroasiatic 62 MonKhmer/Austroasiatic 63 Tibetan 64 Tibetan 65 Tibetan 66 Tibetan 67 Tibetan 68 Thai-Kadai 69 Thai-Kadai 70 Thai-Kadai 71 Thai-Kadai 72 Thai-Kadai 73 Thai-Kadai 74 Thai-Kadai 75 Thai-Kadai 76 Thai-Kadai 77 Thai-Kadai 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105

Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Thai-Kadai Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Malayo-Polynesian Nadene Nadene Nadene Nadene Nadene Nadene Eskimo Eskimo

106 107 108 109 110

Macro-Algonkian Macro-Algonkian Macro-Algonkian Macro-Algonkian Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Salishan,Hokan & Siouan Uto-Aztecan & Zuni

111 112 113 114 115 116

Culture

Murdock Grouping Sedentism/ Mobility

Variables

Nicobarese

B

1

1 Communal Bldg 1

Andamanese

P

6

3

3

3

1

3

2

Lolo Lepcha Garo Lakher Burmese Siamese Rhade Tanala Javanese Balinese Iban Badjau Alorese Manus New Irelanders Trobrianders Tikopia Ajie Maori Marquesans Samoans Gilbertese Marshallese Trukese Yapese Palauans Ifugao Atayal Orokaiva Kimam Kapauku Kwoma Siuai Tiwi Aranda Ingalik Slave Kaska Eyak Haida Chiricahua Aleut Copper Eskimo Montagnais Saulteaux Gros Ventre Yurok Bellacoola

S P P P P P P I R P P I B P P

2 6 6 6 6 6 6 5 3 6 6 5 1 6 6

3 1 3 3 1 1 3 1 3 1 3 3 1 1 1

3 3 3 3 3 3 3 3 3 2 3 3 3 3 3

3 3 2 1 1 1 1 2 3 3 1 3 1 1 3

3 2 2 2 3 3 3 2 2 3 2 3 2 2 3

4 4 4 4 4 4 4 4 4 4 4 5 4 4 4

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

P P P P P P P P P P P P P I P P P P P B B R R S T T B T

6 6 6 6 6 6 6 6 6 6 6 6 6 5 6 6 6 6 6 1 1 3 3 2 4 4 1 4

2 1 1 1 3 1 1 3 1 1 1 3 3 1 3 3 1 1 3 3 1 3 3 1 3 3 3 3

3 3 4 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 4 4 3 3 4 3 4

3 3 2 3 2 2 3 3 3 2 1 1 3 1 3 3 3 1 3 3 4 3 3 3 4 3 4 3

3 2 2 3 2 2 1 2 2 2 2 2 3 3 2 3 3 2 2 2 3 3 4 3 3 2 4 4

4 4 5 4 4 4 4 4 1 4 4 1 4 1 1 4 4 4 4 3 2 5 5 4 4 2 2 2

2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 4 2 4 1 2 2 4 4

S S S B P

2 2 2 1 6

3 3 3 3 3

4 4 4 3 3

3 3 3 4 4

2 2 2 3 3

5 5 5 4 4

1 1 1 2 2

Twana

P

6

1

3

3

3

3

2

Havasupai

S

2

3

4

3

2

2

2

Eastern Pomo

S

2

1

4

3

2

2

2

Omaha

T

4

3

4

4

3

2

4

Hidatsa

T

4

3

4

4

3

2

4

Wadadika

T

4

3

4

3

2

2

1

206

2 Floor Plan 4

3 Floor Level 1

4 Wall Material 2

5 Roof Shape 1

6 Roof Material 2

Datasets for Case Study 1 Cultural Affiliation

117 118 119 120 121

122

123

124

125

126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147

Uto-Aztecan & Zuni Uto-Aztecan & Zuni Uto-Aztecan & Zuni Uto-Aztecan & Zuni Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean Macro-Penutian, Kutenai, Caddoan, Iroquoian & NatchezMuskogean Mayan & Cariban Mayan & Cariban Mayan & Cariban Mayan & Cariban Mayan & Cariban Mayan & Cariban Mayan & Cariban Equatorial Equatorial Equatorial Equatorial Equatorial Equatorial Ge-Panoan Ge-Panoan Ge-Panoan Ge-Panoan Ge-Panoan Andean Andean Andean Andean

Culture

Murdock Grouping Sedentism/ Mobility

Variables

Comanche Papago Huichol Zuni Klamath

S B R P P

2 1 3 6 6

1 Communal Bldg 3 1 1 3 3

Lake Yokuts

S

2

3

3

3

2

4

1

Kutenai

T

4

3

3

4

2

4

1

Pawnee

S

2

3

4

4

4

2

4

Creek

T

4

1

3

3

3

4

1

Quiche Black Carib Carib Miskito Cuna Warrau Cayapa Goajiro Mundurucu Cubeo Jivaro Siriono Trumai Amahuaca Nambicuara Timbira Aweikoma Lengua Aymara Mapuche Tehuelche Yahgan

T P P I P P S P P P I S P I S I B B P P B B

4 6 6 5 6 6 2 6 6 6 5 2 6 5 2 5 1 1 6 6 1 1

1 3 3 3 1 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3

3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 1 3 3 3 4

3 3 3 1 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4

4 2 1 2 3 1 1 2 3 3 3 2 2 1 2 2 2 2 4 2 2 2

4 4 4 4 4 4 1 4 4 1 1 1 4 4 1 1 4 3 4 1 4 2

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2

207

2 Floor Plan 4 4 3 3 4

3 Floor Level 3 4 3 3 4

4 Wall Material 2 2 4 4 4

5 Roof Shape 5 2 1 5 1

6 Roof Material 1 4 2 4 4

APPENDIX C – Dataset for Case Study 2

Site

Period

Bldg. No.

Variables where Thermal Choices and Control are contradictory 1 n

2 ne

3 nw

4 s

5 6 7 se sw e

12 Dpth

1

8 9 10 11 w Vert Roof Roof Shpe Shpe Rnge 1 2 2.66 3

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

2.25

4

1.00

1

5.00

4.00

1

1

1

1

1

1

1

1

2

2.50

4

1.00

1

5.00

3.00

1

1

1

1

1

1

1

1

2

2.14

4

1.00

1

5.00

4.45

1

1

1

1

1

1

1

1

2

2.00

4

1.00

1

5.00

4.34

1

1

1

1

1

1

1

1

1

1.00

1

1.00

1

5.00

3.00

1.00

13 14 Dpth Ext Rnge Wall Mat 1 5.00

15 Roof Mat 3.66

1

Safadi

2

Abou Matar Ph 1 Chalc 202

3

Abou Matar Ph 1 Chalc 108

4

Neve Noy

5

Neve Noy

Ph 1 Chalc 106/ 102 Ph 1 Chalc 109

6

Safadi

Ph 2 Chalc 528

Negev 1 Early Chalc Negev 1 Early Chalc Negev 1 Early Chalc Negev 1 Early Chalc Negev 1 Early Chalc Negev 2 Chalc

7

Safadi

Ph 2 Chalc 546

Negev 2 Chalc

1

1

1

1

1

1

1

1

1

2.00

1

1.00

1

5.00

3.00

8

Safadi

Ph 2 Chalc 303

Negev 2 Chalc

1

1

1

1

1

1

1

1

2

1.43

2

1.00

1

5.00

4.57

9

Abou Matar Ph 2 Chalc 135

Negev 2 Chalc

1

1

1

1

1

1

1

1

1

2.00

1

1.00

1

5.00

3.00

10

Abou Matar Ph 2 Chalc 156

Negev 2 Chalc

1

1

1

1

1

1

1

1

1

2.00

1

1.00

1

5.00

3.00

11

Shiqmim

Ph 2 Chalc 1-7

Negev 2 Chalc

1

1

1

1

1

1

1

1

2

1.00

1

1.00

1

5.00

5.00

12

Safadi

Ph 3 Chalc 409

2

2

2

2

2

2

2

2

1

3.00

1

2.00

1

5.00

4.00

13

Abou Matar Ph 3 Chalc 368

2

1

2

2

2

2

2

2

1

3.00

1

2.00

1

5.00

3.00

14

Shiqmim

Ph 3 Chalc A2

1

1

1

2

2

2

1

1

1

3.50

2

2.00

1

5.00

2.00

15

Shiqmim

Ph 3 Chalc E6

1

1

1

1

1

1

1

1

1

2.66

2

2.00

1

5.00

2.33

16

Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba

Ph 3 Chalc 1321

1

1

1

1

1

1

1

1

2

2.80

4

1.00

1

5.00

2.60

2

2

2

2

2

2

2

2

2

1.00

1

1.00

1

3.50

2.00

1321

Negev 3 Late Chalc Negev 3 Late Chalc Negev 3 Late Chalc Negev 3 Late Chalc Negev 3 Late Chalc Negev 3 Late Chalc Negev 4 Iron

1

1

1

1

1

1

1

1

2

3.00

4

1.00

1

5.00

2.60

1656

Negev 4 Iron

2

2

2

2

2

2

2

2

2

1.00

1

1.00

1

3.50

2.00

1665

Negev 4 Iron

2

2

2

1

2

2

2

2

1

3.50

2

2.00

1

5.00

2.00

2524

Negev 4 Iron

2

2

2

1

2

1

2

1

1

3.20

2

2.00

1

5.00

2.61

2060

Negev 4 Iron

2

1

2

1

2

1

1

1

1

3.25

2

2.00

1

5.00

2.49

2309

Negev 4 Iron

2

2

2

1

2

1

1

1

1

3.17

2

2.00

1

5.00

2.67

2358

Negev 4 Iron

1

1

2

1

2

1

1

2

1

2.66

2

2.00

1

5.00

2.00

2356

Negev 4 Iron

1

1

2

1

2

2

1

2

1

3.20

2

2.00

1

5.00

2.61

2726

Negev 4 Iron

1

2

2

2

2

2

2

2

1

3.00

1

2.00

1

5.00

3.00

2759

Negev 4 Iron

2

2

2

2

2

2

2

2

1

3.00

1

2.00

1

5.00

3.00

17 18 19 20 21 22 23 24 25 26 27

Ph 1 Chalc 616

Grouping

Ph 3 Chalc 1656 Iron I: IX Str Iron I: IX Str Iron I: VIII Str. Iron I: VII Str. Iron I: VII Str. Iron I: VII Str. Iron I: VII Str. Iron I: VII Str. Iron I: VII Str. Iron I: VII Str.

208

Dataset for Case Study 2 17 Ext Wall Ins 1.00

18 Roof Ins

1

16 Roof Mat Range 5

22 Ratio Range

23 Posts

24 Posts Range

25 Niches

3

7.00

1

5.67

5.33

1.00

9

1

30 Ceiling /Flr Conduct 3

2

5

1.00

1.00

1.75

4

4.25

2

7.00

1

6.00

6.00

1.00

8

1

3

3

5

1.00

1.50

2.00

3

5.00

3

7.00

1

6.00

6.00

1.00

10

3

3

4

5

1.00

1.00

2.14

5

3.00

5

7.00

1

5.43

5.85

1.00

6

1

3

5

5

1.00

1.00

2.00

4

3.65

5

7.00

1

5.34

6.00

1.00

7

1

3

6

1

1.00

2.00

5.00

1

6.00

1

6.00

2

6.00

5.00

1.00

11

3

3

7

1

1.00

2.00

5.00

1

6.00

1

7.00

1

6.00

6.00

1.00

11

3

3

8

3

1.00

1.15

2.50

5

5.64

6

7.00

1

6.00

6.00

1.00

5

1

3

9

1

1.00

2.00

3.00

1

6.00

1

7.00

1

6.00

6.00

1.00

11

3

3

10

1

1.00

2.00

3.00

1

6.00

1

7.00

1

6.00

6.00

1.00

11

3

3

11

1

1.00

1.00

3.20

5

5.37

3

7.00

1

5.71

5.90

1.00

5

1

3

12

1

1.00

2.00

1.00

1

4.00

1

7.00

1

6.00

6.00

1.00

11

3

3

13

1

1.00

2.00

1.00

1

2.00

1

7.00

1

6.00

6.00

1.00

11

3

3

14

3

1.00

1.50

1.50

2

4.50

2

7.00

1

6.00

6.00

2.00

10

3

3

15

3

1.00

1.66

1.00

1

4.34

2

7.00

1

5.67

6.00

3.33

9

3

3

16

5

1.00

1.00

2.20

5

4.60

5

7.00

1

5.80

5.80

1.00

7

2

3

17

1

1.50

2.00

4.00

1

5.00

1

7.00

1

6.00

6.00

1.00

11

3

3

18

5

1.00

1.00

2.20

5

4.60

5

7.00

1

5.80

5.80

1.00

7

2

3

19

1

1.50

2.00

4.00

1

5.00

1

7.00

1

6.00

6.00

1.00

11

3

3

20

3

1.00

1.50

1.00

1

4.00

3

7.00

1

6.00

6.00

1.00

10

3

3

21

3

1.00

1.80

1.00

1

3.80

4

7.00

1

6.00

6.00

3.00

7

2

3

22

3

1.00

1.75

1.00

1

4.00

5

7.00

1

6.00

6.00

2.00

8

2

3

23

3

1.00

1.84

1.00

1

4.15

5

7.00

1

6.00

5.83

2.67

7

2

3

24

3

1.00

1.50

1.00

1

3.48

4

7.00

1

5.83

6.00

2.83

7

2

3

25

3

1.00

1.80

1.00

1

3.60

4

7.00

1

6.00

6.00

2.50

7

2

3

26

1

1.00

2.00

1.00

1

5.00

1

7.00

1

6.00

6.00

1.00

11

3

3

27

1

1.00

2.00

1.00

1

4.00

1

7.00

1

6.00

6.00

1.00

11

3

3

1.00

19 20 21 Int Int Ratio Angles Angles Range 2.34 5 5.00

209

26 27 Benches Compact

28 29 No. Int. Wall Rooms Conduct

The Evolution of the Built Environment Variables where Thermal Choices and Control are in accordance 33 s

34 se

35 sw

36 e

37 w

Primary References

32 Opngs

1

31 No. Roofs 0

0.33

2.00 1.66 1.00 1.00

1.00

38 CrossVent 1.00

39 40 Heating Trans Spaces 2.99 0.33

2

0

0.25

1.25 1.00 1.00 1.00

1.00

1.00

1.75

0.25

Perrot, J. 1984; Perrot, J. 1957

3

0

0.50

1.50 1.00 1.00 1.00

1.00

1.00

2.50

0.50

Perrot, J. 1957

4

0

0.71

1.15 1.00 1.00 1.00

1.00

1.85

1.72

0.14

Eldar, I. & Baumgarten, Y. 1985

5

0

0.67

1.34 1.00 1.00 1.00

1.00

1.50

2.01

0.17

Baumgarten, Y. & Eldar, I. 1983

6

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Perrot, J. 1984

7

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Perrot, J. 1984

8

0

0.14

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Perrot, J. 1984

9

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Perrot, J. 1957

10

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Perrot, J. 1984; Perrot, J. 1957

11

0

0.09

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Levy, T.E., et al. 1991

12

1

1.00

1.00 1.00 2.00 1.00

1.00

1.00

4.00

0.00

Perrot, J. 1984

13

1

1.00

2.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Perrot, J. 1984

14

1

0.50

1.50 1.00 1.00 1.00

1.00

1.50

3.50

0.50

Levy, T.E. 1987a & b

15

1

1.00

1.66 1.00 1.00 1.00

1.00

1.34

3.00

0.33

Levy, T.E. 1987a & b

16

0

0.60

1.60 1.00 1.00 1.00

1.00

1.80

4.00

0.60

Herzog, Z. 1984

17

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Herzog, Z. 1984

18

0

0.60

1.60 1.00 1.00 1.00

1.00

1.80

4.00

0.60

Herzog, Z. 1984

19

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Herzog, Z. 1984

20

1

1.50

1.50 1.00 1.50 1.00

1.00

1.50

3.50

0.50

Herzog, Z. 1984

21

1

0.80

1.40 1.00 1.20 1.00

1.00

1.20

3.40

0.20

Herzog, Z. 1984

22

1

1.00

1.50 1.00 1.50 1.00

1.00

1.50

2.50

0.50

Herzog, Z. 1984

23

1

0.67

1.16 1.00 1.00 1.00

1.00

1.16

1.83

0.17

Herzog, Z. 1984

24

1

1.00

1.66 1.00 1.00 1.00

1.00

1.50

3.66

0.50

Herzog, Z. 1984

25

1

1.00

1.20 1.00 1.00 1.00

1.00

1.20

2.00

0.20

Herzog, Z. 1984

26

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Herzog, Z. 1984

27

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Herzog, Z. 1984

210

Perrot, J. 1984

Dataset for Case Study 2 Site

28

Period

Negev 4 Iron

2

2

2

2

2

2

2

2042

Negev 4 Iron

2

2

2

1

2

1

1

1

1

3.25

2

2.00

1

5.00

2.49

2301

Negev 4 Iron

2

2

2

1

1

1

1

2

1

3.00

1

2.00

1

4.00

3.00

1689

Negev 4 Iron

1

1

2

1

2

1

1

2

1

3.33

2

2.00

1

4.00

2.33

1651

Negev 4 Iron

1

1

2

1

2

1

1

1

1

3.33

2

2.00

1

5.00

2.34

2099

Negev 4 Iron

1

1

2

1

2

1

1

1

1

3.50

2

2.00

1

5.00

2.00

314

Negev 4 Iron

1

1

2

1

1

2

1

1

2

3.00

1

2.00

1

5.00

4.00

Iron II

314

Negev 4 Iron

1

1

2

1

1

2

1

1

2

3.00

1

2.00

1

5.00

4.00

Iron II

270

Negev 4 Iron

1

1

2

1

1

1

1

1

1

3.00

1

1.34

2

4.00

3.00

Iron II

282

Negev 4 Iron

2

2

2

1

1

1

1

1

1

3.00

1

1.34

2

4.00

3.00

Iron II

435

Negev 4 Iron

1

1

2

1

1

1

1

1

1

3.00

1

2.00

1

5.00

3.00

Iron II

430

Negev 4 Iron

2

2

2

1

2

1

1

1

1

3.13

2

2.14

2

5.00

2.76

Iron II

75

Negev 4 Iron

1

1

1

1

1

1

2

2

1

3.28

2

1.28

2

5.00

2.43

Iron II

76

Negev 4 Iron

1

1

2

1

2

1

1

1

1

3.14

2

1.42

2

5.00

2.70

Iron II

Fort

Negev 4 Iron

2

2

2

2

2

2

2

2

1

3.10

2

2.00

1

5.00

2.79

Iron II

2

Negev 4 Iron

2

2

2

2

2

2

2

2

1

3.40

2

2.00

1

4.00

2.61

Iron II

3

Negev 4 Iron

2

2

2

2

2

2

2

2

1

3.33

2

2.00

1

4.00

2.67

Iron II

Watch Negev 4 Iron tower 5 Negev 4 Iron

2

2

2

2

2

2

2

2

1

3.14

2

2.00

1

5.00

2.70

2

2

1

2

2

1

2

1

1

3.25

2

1.75

2

4.00

2.50

12

2

2

2

2

2

2

2

2

1

3.17

2

2.33

2

5.00

3.52

2

2

2

2

2

2

2

2

1

3.05

2

2.00

1

5.00

3.84

2

2

2

2

2

2

2

2

1

2.06

3

1.94

2

5.00

3.96

2

2

2

2

2

2

2

2

1

3.11

3

2.01

3

5.00

3.20

2

1

2

1

1

1

1

2

1

3.11

2

2.00

1

5.00

3.68

2

2

2

2

2

2

2

2

1

3.09

2

2.00

1

5.00

3.36

2

2

2

1

1

1

1

1

1

3.00

1

2.00

1

5.00

4.00

1

1

1

2

2

2

2

2

1

3.11

2

2.24

2

5.00

3.76

2

1

1

1

2

1

1

1

1

3.08

2

2.31

3

5.00

3.76

2

1

1

1

1

1

2

1

1

3.17

2

2.34

2

5.00

3.52

49 50

Nessana

51

Rehovot

52

Sobata

53

Sobata

Byzantine

54

Sobata

Byzantine

55

Sobata

Byzantine

56

Sobata

Byzantine

31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Variables where Thermal Choices and Control are contradictory

2072

Late Roman Late Roman Late Roman Late Roman Late Roman Byzantine

30

Grouping

8 9 10 11 w Vert Roof Roof Shpe Shpe Rnge 2 1 3.33 2

Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Tel Beersheba Horvat Haluqim Horvat Haluqim Horvat Haluqim Horvat Haluqim Horvat Haluqim Horvat Haluqim Tel Beersheba Nessana

29

Bldg. No.

Iron I/II: VI Iron I/II: VI Iron I/II: VI Iron I/II: VI Iron I/II: VI Iron I/II: VI Iron II

Iron II

Fort Citadel Nth Chrch Stable Bldg Pool Hse Rm 102 Stable Hse North Hse Nth Gov Hse

Negev 5 Late Roman Negev 5 Late Roman Negev 5 Late Roman Negev 5 Late Roman Negev 5 Late Roman Negev 6 Byzantine Negev 6 Byzantine Negev 6 Byzantine Negev 6 Byzantine Negev 6 Byzantine

1 n

2 ne

3 nw

4 s

5 6 7 se sw e

211

12 Dpth 2.00

13 14 Dpth Ext Rnge Wall Mat 1 5.00

15 Roof Mat 2.34

The Evolution of the Built Environment 17 Ext Wall Ins 1.00

18 Roof Ins

28

16 Roof Mat Range 3

22 Ratio Range

23 Posts

24 Posts Range

25 Niches

2

7.00

1

5.67

5.67

1.66

9

2

30 Ceiling /Flr Conduct 3

29

3

1.00

1.75

2.50

2

5.26

2

7.00

1

5.75

6.00

3.25

8

2

3

30

1

1.00

2.00

1.00

1

6.00

1

7.00

1

6.00

6.00

4.00

11

3

3

31

3

1.00

1.66

1.34

2

1.00

1

7.00

1

6.00

6.00

2.33

9

2

3

32

3

1.00

1.66

1.00

1

6.24

4

7.00

1

6.00

6.00

2.34

7

2

3

33

3

1.00

1.50

1.00

1

3.50

2

7.00

1

6.00

6.00

2.00

10

3

3

34

1

1.00

2.00

1.00

1

3.00

1

7.00

1

6.00

4.50

2.00

8

2

3

35

1

1.00

2.00

1.00

1

5.00

3

7.00

1

6.00

5.25

2.00

10

1

3

36

1

1.00

2.00

1.00

1

2.00

1

2.00

6

4.00

5.00

2.00

11

3

3

37

1

1.00

2.00

1.00

1

2.00

1

2.00

6

6.00

5.00

2.00

11

3

3

38

1

1.00

2.00

1.00

1

3.00

1

7.00

1

6.00

6.00

2.00

11

3

3

39

3

1.00

1.88

1.00

1

3.20

4

6.88

3

5.75

5.88

2.88

6

2

2

40

3

1.00

1.72

1.00

1

3.00

4

7.00

1

6.00

5.85

3.00

6

2

3

41

3

1.00

1.85

1.00

1

2.58

3

7.00

1

6.00

5.85

3.00

6

2

3

42

3

1.00

1.90

1.00

1

3.92

4

7.00

1

5.80

6.00

3.10

5

2

3

43

3

1.00

1.80

1.00

1

3.60

4

7.00

1

6.00

6.00

2.20

7

2

3

44

3

1.00

1.80

1.00

1

5.30

5

6.83

3

5.83

6.00

2.00

7

2

3

45

3

1.00

1.85

1.00

1

4.28

4

7.00

1

6.00

6.00

2.43

6

2

3

46

3

1.00

1.75

1.00

1

4.00

1

7.00

1

6.00

6.00

4.00

8

2

3

47

4

1.00

1.00

1.00

1

4.66

5

6.50

3

5.83

5.50

2.67

7

2

2

48

4

1.00

1.00

1.00

1

5.25

5

7.00

1

6.00

5.95

4.20

3

2

3

49

4

1.00

1.98

1.00

1

5.70

3

7.00

1

6.00

6.00

2.67

1

2

3

50

4

1.00

1.11

1.11

4

4.20

6

6.58

5

5.77

5.89

5.26

2

2

3

51

4

1.00

1.00

1.11

2

5.00

4

6.66

4

5.66

6.00

6.33

6

2

3

52

4

1.00

1.00

1.00

1

5.44

4

7.00

1

6.00

6.00

6.73

5

2

3

53

1

1.00

1.00

1.00

1

5.00

1

7.00

1

6.00

6.00

8.00

11

3

3

54

4

1.00

1.00

1.00

1

5.45

2

6.66

4

6.00

5.89

2.55

6

2

2

55

4

1.00

1.00

1.00

1

5.23

3

7.00

1

5.70

5.77

3.61

5

2

2

56

4

1.00

1.00

1.16

2

5.34

2

7.00

1

5.83

5.83

4.66

7

2

2

1.00

19 20 21 Int Int Ratio Angles Angles Range 1.00 1 3.68

212

26 27 Benches Compact

28 29 No. Int. Wall Rooms Conduct

Dataset for Case Study 2 Variables where Thermal Choices and Control are in accordance 33 s

34 se

35 sw

36 e

37 w

Primary References

32 Opngs

28

31 No. Roofs 1

1.00

1.34 1.00 1.00 1.00

1.00

38 CrossVent 1.66

39 40 Heating Trans Spaces 2.67 0.67

29

1

1.00

1.25 1.00 1.00 1.00

1.00

1.25

2.25

0.25

Herzog, Z. 1984

30

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

4.00

0.00

Herzog, Z. 1984

31

1

0.67

1.66 1.00 1.00 1.00

1.00

2.00

4.33

0.67

Herzog, Z. 1984

32

1

0.83

1.34 1.00 1.00 1.00

1.00

1.34

2.67

0.33

Herzog, Z. 1984

33

1

1.00

1.50 1.00 1.00 1.00

1.00

1.50

3.50

0.50

Herzog, Z. 1984

34

0

1.00

1.50 2.00 1.00 1.50

1.00

2.00

5.50

0.00

Aharoni, Y. 1973

35

0

0.50

1.25 1.50 1.00 1.25

1.00

1.50

3.25

0.00

Aharoni, Y. 1973

36

0

5.00

1.00 1.00 1.00 1.00

1.00

2.00

1.00

0.00

Aharoni, Y. 1973; Herzog, Z. 1993

37

0

5.00

1.00 1.00 1.00 1.00

1.00

2.00

1.00

0.00

Aharoni, Y. 1973; Herzog, Z. 1993

38

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Aharoni, Y. 1973

39

2

0.63

1.13 1.25 1.13 1.00

1.13

1.25

1.75

0.13

Herzog, Z., et al. 1977

40

1

0.57

1.28 1.00 1.00 1.00

1.00

1.57

2.86

0.29

Aharoni, Y. 1973

41

1

0.29

1.15 1.00 1.00 1.00

1.00

1.15

1.72

0.14

Aharoni, Y. 1973

42

1

0.90

1.40 1.40 1.20 1.10

1.00

1.20

5.20

0.10

Cohen, R. 1976

43

1

1.20

1.60 1.60 1.20 1.00

1.00

1.60

3.40

0.40

Cohen, R. 1976

44

1

0.67

1.34 1.34 1.34 1.34

1.00

1.16

5.00

0.33

Cohen, R. 1976

45

1

0.57

1.28 1.15 1.00 1.00

1.00

1.28

4.57

0.14

Cohen, R. 1976

46

1

1.00

1.50 1.00 1.00 1.25

1.00

1.25

2.50

0.25

Cohen, R. 1976

47

1

0.83

1.16 1.34 1.50 1.34

1.16

1.50

2.66

0.17

Cohen, R. 1976

48

1

1.00

1.45 1.35 1.40 1.25

1.05

1.65

1.90

0.10

Aharoni, Y. 1973

49

1

0.86

1.19 1.19 1.06 1.02

1.00

1.13

4.27

0.03

Dunscombe Colt, H. 1962

50

3

0.65

1.46 1.07 1.04 1.07

1.07

1.34

4.38

0.35

Dunscombe Colt, H. 1962

51

1

1.67

1.22 1.34 1.11 1.00

1.00

1.78

1.78

0.11

52

1

1.00

1.37 1.00 1.00 1.00

1.00

1.46

4.73

0.18

Tsafrir, Y. & Holum, K.G. 1988; Tsafrir, Y., et al. 1988 Segal, A. 1983

53

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

4.00

0.00

Segal, A. 1983

54

2

1.33

1.22 1.34 1.22 1.22

1.22

1.78

3.78

0.11

Segal, A. 1983

55

2

1.00

1.46 1.16 1.16 1.00

1.00

1.46

2.08

0.15

Segal, A. 1983

56

2

0.83

1.84 1.00 1.16 1.00

1.00

1.50

3.34

0.17

Segal, A. 1983

213

Herzog, Z. 1984

The Evolution of the Built Environment

Site

Period

57

Sobata

Byzantine

58

Sobata

Byzantine

59

Sobata

Byzantine

60

Sobata

61

Sobata

62

Sobata

63

Rehovot

64

Rehovot

65

Rehovot

66

Rehovot

67

Rehovot

68

Rehovot

69

Rehovot

70

Rehovot

71

Tel Beersheba Hemamiya

72 73

76

Hierakonopo lis Hierakonopo lis Hierakonopo lis Elephantine

77

Elephantine

78

Elephantine

79

El Lahun

80

El Lahun

81

El Lahun

82

El Lahun

83

Elephantine

84

Elephantine

74 75

Bldg. No.

Sth Gov Hse West Gov Hse Cntrl Chrch Gov Hse Rm 204 Rm 306 Stable Bldg 221

Grouping

1 n

2 ne

3 nw

4 s

5 6 7 se sw e

Negev 6 Byzantine

1

1

1

2

2

1

Negev 6 Byzantine

1

1

1

2

1

2

1

2

2

2

1

1

2

2

1

Negev 6 Byzantine Byzantine Negev 6 Byzantine Byzantine Negev 6 Byzantine Byzantine Negev 6 Byzantine Byzantine Negev 6 Byzantine Byzantine Negev 6 Byzantine Byzantine 208 Negev 6 Byzantine Byzantine Bath Negev 6 House Byzantine Byzantine Nth Negev 6 Chrch Byzantine Byzantine Nth Negev 6 Chrch Byzantine NE rm Byzantine Nth Negev 6 Chrch Byzantine SE rm Early Arab Stable Negev 7 Arab Bldg Early Arab Fort Negev 7 Arab Naqada I Naqada I

S-II

Naqada I

S-IV

Naqada I

S-V

Old Kingdom Old Kingdom Old Kingdom Middle Kingdom Middle Kingdom Middle Kingdom Middle Kingdom Middle Kingdom Middle Kingdom

Admin Bldg Works hop West Rm type 1 type 3/B type C type 5 69 70

Variables where Thermal Choices and Control are contradictory

Egypt 1 Naqada 1 Egypt 1 Naqada 1 Egypt 1 Naqada 1 Egypt 1 Naqada 1 Egypt 2 Old Kingdom Egypt 2 Old Kingdom Egypt 2 Old Kingdom Egypt 3 Middle Kingdom Egypt 3 Middle Kingdom Egypt 3 Middle Kingdom Egypt 3 Middle Kingdom Egypt 3 Middle Kingdom Egypt 3 Middle Kingdom

12 Dpth

2

8 9 10 11 w Vert Roof Roof Shpe Shpe Rnge 1 1 3.10 2

2

1

1

1

3.11

2

2.00

1

5.00

3.68

1

2

1

2

1

2.00

1

2.00

1

5.00

3.00

2

2

2

2

1

1

3.05

3

2.26

2

5.00

3.56

1

1

1

1

2

1

1

3.00

1

2.00

1

5.00

4.00

1

1

2

2

2

1

1

1

4.00

1

2.00

1

5.00

1.00

2

1

2

1

1

1

1

2

1

3.11

2

2.00

1

5.00

3.68

1

1

1

1

1

1

2

1

1

3.20

2

2.00

1

5.00

3.40

1

2

2

1

1

2

2

1

1

3.20

2

2.00

1

5.00

3.40

2

2

2

2

2

2

2

2

1

1.75

3

1.75

2

5.00

4.00

2

2

2

2

2

2

1

2

1

2.73

3

1.84

3

5.00

3.47

2

2

1

1

1

1

2

1

1

2.00

1

2.00

1

5.00

3.00

1

1

1

2

2

1

2

1

1

2.00

1

2.00

1

5.00

3.00

2

1

2

1

1

1

1

2

1

3.09

2

2.00

1

5.00

3.72

2

2

2

2

2

2

2

2

1

3.05

2

2.00

1

5.00

3.88

2

2

2

2

2

2

2

2

1

1.00

1

1.00

1

3.00

3.00

2

2

2

2

2

2

2

2

1

3.50

2

1.50

2

2.50

2.00

2

2

2

2

2

2

2

2

1

3.00

1

2.00

1

2.00

2.00

2

2

2

2

2

2

2

2

1

3.00

1

2.00

1

2.00

2.00

2

2

2

1

1

1

1

1

1

3.11

2

2.00

1

4.00

2.78

1

1

2

2

2

2

2

2

1

3.10

2

2.10

2

4.00

2.80

2

2

1

2

2

2

2

2

1

3.00

1

2.00

1

4.00

3.00

2

2

2

2

2

2

2

1

1

3.25

2

2.00

1

4.00

2.50

1

2

2

1

2

2

2

2

1

3.14

2

2.00

1

4.00

2.71

1

2

2

1

1

2

1

2

1

3.00

2

2.00

1

4.00

2.71

1

2

2

1

2

2

2

2

1

3.06

2

2.04

2

4.00

2.72

1

1

1

2

2

2

2

1

1

3.13

2

2.00

1

5.00

3.00

2

2

2

1

2

1

2

1

1

3.00

1

2.29

2

4.00

3.00

214

2.30

13 14 Dpth Ext Rnge Wall Mat 2 5.00

15 Roof Mat 3.72

Dataset for Case Study 2

17 Ext Wall Ins 1.00

18 Roof Ins

57

16 Roof Mat Range 4

22 Ratio Range

23 Posts

24 Posts Range

25 Niches

3

7.00

1

5.90

5.90

6.30

5

2

30 Ceiling /Flr Conduct 2

58

4

1.00

1.00

1.00

1

4.68

3

6.78

3

6.00

6.00

4.66

6

2

3

59

1

1.00

1.00

1.00

1

4.00

3

6.00

4

5.50

5.75

4.22

8

2

3

60

4

1.00

1.00

1.05

2

5.04

4

6.94

4

5.97

5.95

5.28

1

2

2

61

1

1.00

1.00

1.00

1

5.00

1

7.00

1

6.00

6.00

5.00

11

3

3

62

1

1.00

1.00

1.00

1

6.00

1

7.00

1

6.00

6.00

4.00

11

3

3

63

4

1.00

1.00

1.11

2

5.00

4

6.66

4

5.66

6.00

6.33

6

2

3

64

4

1.00

1.00

1.20

2

1.80

2

7.00

1

6.00

6.00

4.40

7

2

3

65

4

1.00

1.00

1.00

1

5.00

3

7.00

1

6.00

6.00

4.60

7

2

3

66

1

1.00

1.00

1.00

1

4.75

2

7.00

1

5.50

4.75

1.50

8

2

3

67

2

1.00

1.54

1.23

3

4.47

5

6.53

5

4.69

4.69

2.46

5

2

2

68

1

1.00

2.00

1.00

1

4.00

1

7.00

1

6.00

6.00

2.00

11

3

3

69

1

1.00

2.00

1.00

1

4.00

1

7.00

1

6.00

6.00

2.00

11

3

3

70

4

1.00

1.00

1.09

2

5.20

4

6.72

4

5.72

6.00

7.81

5

2

3

71

4

1.00

1.00

1.00

1

5.29

5

7.00

1

5.95

5.76

6.37

3

2

3

72

1

2.00

2.00

5.00

1

6.00

1

7.00

1

6.00

6.00

1.00

11

3

3

73

3

2.00

1.50

1.00

1

6.00

1

6.00

2

6.00

6.00

1.00

10

3

3

74

1

2.00

2.00

1.00

1

6.00

1

7.00

1

6.00

6.00

1.00

11

3

3

75

1

2.00

2.00

1.00

1

5.00

1

7.00

1

6.00

6.00

1.00

11

3

3

76

3

1.00

1.89

1.00

1

4.22

5

7.00

1

5.67

6.00

2.33

6

2

3

77

3

1.00

1.90

1.20

2

5.10

2

7.00

1

5.90

5.90

2.60

5

2

3

78

1

1.00

2.00

1.00

1

6.00

1

7.00

1

6.00

6.00

3.00

11

3

3

79

3

1.00

1.75

1.00

1

4.50

2

7.00

1

6.00

6.00

3.50

8

2

3

80

3

1.00

1.85

1.00

1

4.57

2

7.00

1

6.00

5.85

2.56

6

2

3

81

3

1.00

1.85

1.00

1

4.78

4

7.00

1

6.00

5.93

2.93

5

2

3

82

3

1.00

1.84

1.08

3

4.32

6

6.93

3

5.89

5.96

11.46

1

2

2

83

1

1.00

1.88

1.13

2

5.38

4

6.88

2

5.75

6.00

4.25

6

2

3

84

1

1.00

2.00

1.00

1

5.57

2

6.71

3

6.00

5.85

5.14

6

2

2

1.00

19 20 21 Int Int Ratio Angles Angles Range 1.00 1 5.00

215

26 27 Benches Compact

28 29 No. Int. Wall Rooms Conduct

The Evolution of the Built Environment Variables where Thermal Choices and Control are in accordance 33 s

34 se

35 sw

36 e

37 w

Primary References

32 Opngs

57

31 No. Roofs 2

0.70

1.70 1.50 1.40 1.00

1.00

38 CrossVent 1.50

39 40 Heating Trans Spaces 7.00 0.10

58

1

0.78

1.34 1.45 1.00 1.00

1.00

1.09

5.33

0.11

Segal, A. 1983

59

2

2.00

2.00 1.25 1.75 1.00

1.00

1.75

5.50

0.75

Segal, A. 1983

60

2

0.57

1.57 1.28 1.28 1.00

1.00

1.60

3.94

0.17

Segal, A. 1983

61

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

4.00

0.00

Segal, A. 1983

62

0

1.00

2.00 1.00 2.00 1.00

1.00

2.00

6.00

1.00

Segal, A. 1983

63

1

1.67

1.22 1.34 1.11 1.00

1.00

1.78

1.78

0.11

64

1

1.00

1.20 1.00 1.00 1.00

1.00

1.40

2.00

0.20

65

1

1.20

1.20 1.00 1.00 1.00

1.00

1.80

2.00

0.20

66

1

3.00

1.75 1.00 1.00 1.00

1.00

1.75

2.75

0.00

Tsafrir, Y. & Holum, K.G. 1988; Tsafrir, Y., et al. 1988 Tsafrir, Y. & Holum, K.G. 1988; Tsafrir, Y., et al. 1988 Tsafrir, Y. & Holum, K.G. 1988; Tsafrir, Y., et al. 1988 Tsafrir, Y., et al. 1988

67

1

0.82

1.16 1.07 1.31 1.00

1.00

1.46

1.93

0.18

Tsafrir, Y., et al. 1988

68

0

1.00

1.00 1.00 1.00 2.00

1.00

1.00

1.00

0.00

Tsafrir, Y., et al. 1988

69

0

1.00

1.00 2.00 1.00 2.00

1.00

1.00

1.00

0.00

Tsafrir, Y., et al. 1988

70

1

1.36

1.28 1.28 1.18 1.00

1.00

1.63

1.73

0.09

71

1

0.90

1.34 1.34 1.34 1.28

1.05

1.38

1.76

0.10

Tsafrir, Y. & Holum, K.G. 1988; Tsafrir, Y., et al. 1988 Aharoni, Y. 1973

72

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Uphill, E.P. 1988

73

1

1.00

2.00 2.00 1.00 1.50

1.00

1.50

6.00

0.50

Hoffman, M.A. 1980; Hoffman, M.A. 1982

74

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Hoffman, M.A. 1982

75

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Hoffman, M.A. 1982

76

1

0.89

1.22 1.00 1.00 1.00

1.00

1.22

1.66

0.11

German Institute of Cairo, 1998

77

1

0.90

1.20 1.00 1.00 1.00

1.00

1.20

1.30

0.10

German Institute of Cairo, 1998

78

1

1.00

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

German Institute of Cairo, 1998

79

1

0.75

1.25 1.00 1.00 1.00

1.00

1.25

2.25

0.25

Uphill, E.P. 1988

80

1

0.57

1.28 1.00 1.00 1.00

1.00

1.28

1.15

0.14

Uphill, E.P. 1988; Schoenauer, N. 1981

81

1

0.57

1.35 1.00 1.00 1.00

1.00

1.35

1.92

0.14

Uphill, E.P. 1988; Schoenauer, N. 1981

82

2

0.32

1.13 1.04 1.04 1.02

1.02

1.20

1.68

0.14

Uphill, E.P. 1988

83

1

0.75

1.13 1.00 1.00 1.00

1.00

1.25

1.63

0.13

German Institute of Cairo, 1998

84

2

0.71

1.43 1.00 1.00 1.00

1.00

1.28

2.00

0.00

German Institute of Cairo, 1998

216

Segal, A. 1983

Dataset for Case Study 2 Site

Period

85

Elephantine Middle Kingdom

86

Elephantine Middle Kingdom Deir elNew Medina Kingdom Deir elNew Medina Kingdom Deir elNew Medina Kingdom Deir elNew Medina Kingdom Deir elNew Medina Kingdom Deir elNew Medina Kingdom Medinet New Habu Kingdom Medinet New Habu Kingdom Medinet New Habu Kingdom Amarna New Kingdom Amarna New Kingdom Amarna New Kingdom Amarna New Kingdom Amarna New Kingdom Ramesseum New Kingdom Ramesseum New Kingdom Medinet Third Int Habu Ismant elLate Gharab Roman Ismant elLate Gharab Roman Ismant elLate Gharab Roman Medinet Byzantine Habu Medinet Byzantine Habu Medinet Byzantine Habu Medinet Byzantine Habu Fustat Arab

87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111

Bldg. No.

Sanc of Heqaib Priest's Hse V NE VIII NE XV NE XXV NW IV SW VIII SE Inner ring Outer ring Palace se 1 n 11 n 12 Vizier Nekht P46.8 Palace Officls

1 3 4 3 4 77 78 1 A&B

Grouping

Variables where Thermal Choices and Control are contradictory 1 n

2 ne

3 nw

4 s

5 6 7 se sw e

12 Dpth

1

8 9 10 11 w Vert Roof Roof Shpe Shpe Rnge 2 1 3.20 2

Egypt 3 Middle Kingdom

2

2

1

1

1

1

Egypt 3 Middle Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 4 New Kingdom Egypt 5 Late Roman Egypt 5 Late Roman Egypt 5 Late Roman Egypt 6 Byzantine Egypt 6 Byzantine Egypt 6 Byzantine Egypt 6 Byzantine Egypt 7 Arab

1

1

1

2

1

2

1

2

1

3.20

2

2.00

1

5.00

2.60

2

2

1

2

1

2

2

2

1

3.13

2

1.88

2

4.00

3.01

2

2

1

2

1

2

2

2

1

3.20

2

2.00

1

5.00

2.60

2

2

1

2

2

2

2

2

1

3.28

2

1.72

2

4.00

3.01

2

2

1

1

2

1

2

1

1

3.33

2

2.00

1

4.00

2.33

2

2

1

2

1

2

2

2

1

3.25

2

2.00

1

4.00

2.50

2

2

1

2

1

2

2

2

1

3.20

2

1.80

2

4.00

3.00

2

1

1

2

1

1

1

1

1

3.22

2

2.00

1

4.00

2.55

2

1

1

2

1

1

1

1

1

3.00

1

1.63

2

4.00

3.00

2

1

2

1

1

1

2

1

1

3.00

1

2.00

1

5.00

4.00

2

2

2

1

1

1

1

1

1

3.20

2

2.00

1

4.50

2.80

2

2

2

2

2

2

1

1

1

3.25

2

2.00

1

4.00

2.50

1

1

1

2

2

2

1

1

1

3.20

2

2.00

1

4.00

2.60

1

1

1

1

1

1

1

1

1

3.00

1

2.00

1

5.00

3.00

1

1

1

2

2

1

1

1

1

3.08

2

2.00

1

4.00

2.69

1

1

1

1

1

1

1

1

1

3.00

1

2.00

1

5.00

4.00

1

1

1

2

2

1

2

1

1

3.00

1

2.00

1

5.00

2.71

2

2

2

2

2

2

1

2

1

3.13

2

2.00

1

5.00

2.76

1

2

2

1

2

1

2

1

1

2.56

4

2.00

1

5.00

2.79

1

2

2

1

1

2

1

1

1

2.57

4

1.73

2

5.00

2.73

1

2

1

2

2

2

1

2

1

3.28

2

2.00

1

5.00

2.43

2

2

2

2

2

2

1

1

1

2.00

1

2.01

3

4.00

4.00

2

2

2

2

1

2

1

2

1

2.00

1

1.89

3

4.00

4.00

2

2

2

2

1

2

2

2

1

2.00

1

2.00

3

4.00

4.00

2

2

2

2

1

2

2

2

1

2.00

1

2.00

3

4.00

4.00

1

1

1

1

1

1

1

2

1

2.76

3

2.00

1

5.00

3.69

2.00

13 14 Dpth Ext Rnge Wall Mat 1 5.00

15 Roof Mat 3.40

112 Fustat

Arab

1 C&D Egypt 7 Arab

1

1

2

2

2

2

1

2

1

2.70

3

2.00

1

5.00

3.65

113 Fustat

Arab

Q x1

1

2

2

2

2

2

2

2

1

2.00

1

2.00

1

5.00

4.00

Egypt 7 Arab

217

The Evolution of the Built Environment

17 Ext Wall Ins 1.00

18 Roof Ins

85

16 Roof Mat Range 4

22 Ratio Range

23 Posts

24 Posts Range

25 Niches

3

7.00

1

1.80

6.00

2.80

7

2

30 Ceiling /Flr Conduct 3

86

3

1.00

1.80

1.20

2

5.40

2

6.40

3

5.60

6.00

2.60

7

2

3

87

5

1.00

1.88

1.13

2

5.13

3

6.88

2

5.75

5.63

4.51

6

2

3

88

3

1.00

1.80

1.20

2

5.00

5

7.00

1

5.80

5.80

3.80

7

2

3

89

5

1.00

1.72

1.15

2

4.71

3

6.86

2

6.86

5.43

4.01

6

2

3

90

3

1.00

1.66

1.00

1

5.33

2

7.00

1

6.00

6.00

6.00

9

2

3

91

3

1.00

1.75

1.25

2

4.75

5

6.00

3

5.75

5.75

3.00

8

2

3

92

5

1.00

1.80

1.10

2

4.70

3

6.90

2

5.90

5.70

4.20

5

2

3

93

3

1.00

1.65

1.00

1

4.55

4

7.00

1

6.00

5.89

1.66

6

2

3

94

1

1.00

2.00

1.00

1

4.91

3

7.00

1

6.00

6.00

2.81

5

2

3

95

1

1.00

1.00

1.04

2

4.68

6

6.77

4

1.17

5.83

7.36

1

2

3

96

3

1.00

1.80

1.00

1

5.00

4

3.78

2

6.00

5.50

2.60

5

2

3

97

3

1.00

1.75

1.13

2

5.50

3

7.00

1

5.88

6.00

3.00

6

2

3

98

3

1.00

1.80

1.00

1

5.40

3

5.80

2

6.00

5.80

3.00

7

2

3

99

1

1.00

2.00

1.00

1

5.16

3

6.62

4

5.75

5.81

4.67

1

2

3

100

3

1.00

1.84

1.00

1

5.08

4

7.00

1

6.00

5.85

3.01

5

2

3

101

1

1.00

1.00

1.00

1

4.93

4

5.46

5

6.00

5.93

3.73

4

2

3

102

3

1.00

1.85

1.00

1

4.57

4

6.86

2

6.00

6.00

2.71

6

2

3

103

3

1.00

1.88

1.00

1

5.00

5

6.50

3

5.88

5.75

0.40

6

2

3

104

4

1.00

1.45

1.16

2

4.94

5

7.00

1

5.78

5.89

7.72

4

2

3

105

4

1.00

1.31

1.10

2

5.06

5

7.00

1

5.79

5.84

6.15

4

2

3

106

3

1.00

1.72

1.15

2

5.57

3

7.00

1

5.85

5.85

3.86

6

2

3

107

1

1.00

2.00

1.28

2

4.14

4

7.00

1

6.00

5.43

3.00

6

2

2

108

1

1.00

2.00

1.22

2

3.77

4

6.77

2

6.00

5.44

2.43

6

2

2

109

1

1.00

2.00

1.15

2

5.00

5

6.86

2

6.00

5.43

2.00

6

2

2

110

1

1.00

2.00

1.15

2

5.00

5

6.86

2

6.00

5.43

2.00

6

2

2

111

4

1.00

1.00

1.14

2

4.72

4

6.93

3

5.93

5.93

3.58

2

2

3

112

4

1.00

1.00

1.12

2

4.94

4

7.00

1

5.71

5.71

7.47

4

2

3

113

1

1.00

1.00

1.00

1

5.00

1

7.00

1

6.00

6.00

6.00

11

3

3

1.80

19 20 21 Int Int Ratio Angles Angles Range 1.40 2 3.80

218

26 27 Benches Compact

28 29 No. Int. Wall Rooms Conduct

Dataset for Case Study 2 Variables where Thermal Choices and Control are in accordance 33 s

34 se

35 sw

36 e

37 w

Primary References

32 Opngs

85

31 No. Roofs 1

0.40

1.20 1.00 1.00 1.00

1.00

38 CrossVent 1.20

39 40 Heating Trans Spaces 2.80 0.20

86

1

0.80

1.20 1.00 1.00 1.00

1.00

1.60

2.00

0.20

German Institute of Cairo, 1998

87

2

0.63

1.25 1.00 1.00 1.00

1.00

1.50

1.75

0.13

Bruyere, B. 1939; Bierbrier, M. 1997

88

2

0.80

1.40 1.00 1.00 1.00

1.00

1.20

2.20

0.20

Bruyere, B. 1939; Bierbrier, M. 1997

89

2

0.57

1.28 1.00 1.00 1.00

1.00

1.57

1.85

0.29

Bruyere, B. 1939; Bierbrier, M. 1997

90

1

1.33

1.66 1.00 1.34 1.00

1.00

2.00

2.67

0.33

Bruyere, B. 1939; Bierbrier, M. 1997

91

2

1.00

1.25 1.00 1.00 1.00

1.00

1.75

2.25

0.25

Bruyere, B. 1939; Bierbrier, M. 1997

92

2

0.50

1.30 1.00 1.00 1.00

1.00

1.50

1.70

0.20

Bruyere, B. 1939; Bierbrier, M. 1997

93

2

0.56

1.22 1.00 1.00 1.00

1.00

1.34

2.11

0.33

Uphill, E.P. 1988

94

2

0.45

1.00 1.00 1.00 1.00

1.00

1.18

1.00

0.00

Uphill, E.P. 1988

95

3

0.35

1.11 1.04 1.07 1.00

1.07

1.21

2.22

0.00

Badawy, A. 1968

96

2

0.80

1.50 1.00 1.00 1.00

1.00

1.60

2.30

0.20

Bierbrier, M. 1997

97

2

0.63

1.50 1.00 1.00 1.00

1.00

1.63

3.00

0.38

Bierbrier, M. 1997

98

2

0.80

1.20 1.00 1.00 1.00

1.00

1.60

2.00

0.20

Bierbrier, M. 1997

99

2

0.19

1.03 1.03 1.03 1.00

1.00

1.07

1.03

0.03

Peet, T.E. & Woolley, C.L. 1923

100

1

0.62

1.23 1.00 1.00 1.00

1.00

1.16

1.54

0.15

Peet, T.E. & Woolley, C.L. 1923

101

2

0.40

1.14 1.20 1.06 1.14

1.00

1.34

2.00

0.07

Badawy, A. 1968

102

2

1.00

1.28 1.00 1.00 1.00

1.00

1.43

2.42

0.14

Badawy, A. 1968

103

2

1.13

1.63 1.00 1.00 1.00

1.00

1.75

3.13

0.13

Lacovara, P. 1997

104

1

0.78

1.50 1.00 1.00 1.00

1.00

1.34

2.17

0.11

Hope, C.A. 1987

105

1

0.84

1.37 1.00 1.00 1.00

1.00

1.27

1.79

0.21

Hope, C.A., et al. 1989; Hope, C.A. 1987

106

1

0.71

1.43 1.00 1.00 1.00

1.00

1.57

2.57

0.29

Hope, C.A. 1987

107

1

0.43

1.00 1.00 1.00 1.00

1.00

1.00

1.14

0.00

Nelson, H.H. 1929; Badawy, A. 1978

108

1

0.33

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Nelson, H.H. 1929; Badawy, A. 1978

109

1

0.71

1.28 1.00 1.00 1.00

1.00

1.28

1.85

0.00

Badawy, A. 1978

110

1

0.57

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Badawy, A. 1978

111

1

0.69

1.31 1.00 1.00 1.00

1.00

1.45

2.28

0.31

Creswell, K.A. 1978

112

1

0.76

1.36 1.00 1.00 1.00

1.00

1.41

2.07

0.35

Creswell, K.A. 1978

113

1

1.00

1.00 1.00 1.00 1.00

1.00

2.00

4.00

0.00

Creswell, K.A. 1978

219

Habachi, L. 1985; German Institute of Cairo, 1998

The Evolution of the Built Environment Site

Period

Bldg. No.

Grouping

Variables where Thermal Choices and Control are contradictory

114 Fustat

Arab

Q x3

Egypt 7 Arab

1

1

1

2

1

2

1

8 9 10 11 w Vert Roof Roof Shpe Shpe Rnge 1 1 2.00 1

115 Fustat

Arab

3

Egypt 7 Arab

1

1

2

2

1

1

1

2

1

3.08

3

2.00

1

5.00

3.74

116 Cairo

Egypt 8 Vernacular Egypt 8 Vernacular

1

1

1

1

1

2

1

2

1

3.04

2

2.59

3

4.00

2.91

1

1

1

1

1

1

1

1

1

1.90

4

2.00

1

4.00

4.00

Egypt 8 Vernacular Egypt 8 Vernacular

1

1

1

1

1

1

1

1

1

2.63

4

2.05

2

5.00

4.00

1

1

1

1

1

1

1

1

1

1.91

4

2.17

2

5.00

4.00

Egypt 8 Vernacular Egypt 8 Vernacular Palestine 1 EB

2

2

1

1

1

1

2

1

1

2.82

4

2.32

2

5.00

2.88

1

1

1

2

1

2

1

2

1

2.75

4

2.30

2

5.00

3.00

2

1

1

2

2

1

2

1

1

4.00

1

2.00

1

5.00

3.00

123 Ai

Vernacular Kritliy ya Vernacular Fathy Hamed Said Vernacular Fathy Msque Vernacular Fathy Girls School Vernacular Fathy Hse 1 Vernacular Fathy Hse 2 EB I Tower C west EB I 238

Palestine 1 EB

1

1

2

1

1

1

1

1

1

4.00

1

2.00

1

5.00

3.00

124 Ai

EB I

MN

Palestine 1 EB

2

2

2

2

1

2

1

2

1

4.00

1

2.00

1

5.00

3.00

125 Ai

EB I

Palestine 1 EB

2

1

2

2

1

2

1

2

1

4.00

1

2.00

1

5.00

3.00

126 Ai

EB I

Palestine 1 EB

2

2

2

2

2

2

1

2

1

4.00

1

2.00

1

5.00

3.00

127 Ai

EB I

Palestine 1 EB

1

1

1

1

1

1

1

1

1

2.50

4

2.00

1

5.00

2.00

128 Ai

EB II

Palestine 1 EB

2

1

1

2

2

1

2

1

1

4.00

1

2.00

1

5.00

3.00

129 Ai

EB II

Palestine 1 EB

1

1

2

1

1

2

1

1

1

4.00

1

2.00

1

5.00

3.00

130 Ai

EB II

Palestine 1 EB

2

1

2

2

1

2

1

2

1

4.00

1

2.00

1

5.00

3.00

131 Ai

EB II

Palestine 1 EB

2

2

2

2

2

2

1

2

1

4.00

1

2.00

1

5.00

3.00

132 Ai

EB II

Palestine 1 EB

1

1

1

1

1

1

1

1

1

3.25

4

2.25

2

5.00

2.50

133 Ai

EB III

Palestine 1 EB

2

2

2

1

2

2

2

1

1

3.00

4

2.00

1

5.00

2.33

134 Ai

EB III

Sanct C AII Sanct C AIII Palace C Tower C west 238198 Sanct B AII Sanct B AIII Palace B 211222 75-76

Palestine 1 EB

1

1

1

2

2

2

1

2

1

4.00

1

2.00

1

5.00

3.00

135 Ai

EB III

Palestine 1 EB

1

1

1

1

1

1

1

1

1

3.40

4

2.20

2

5.00

2.60

136 Ai

EBIII B

Palestine 1 EB

2

1

2

1

1

2

1

2

1

3.00

4

2.00

1

5.00

2.33

137 Ai

EBIII B

138 Khirbet edDawwara 139 Khirbet edDawwara 140 Ai

117 Marg 118 New Gourna 119 New Gourna 120 New Gourna 121 New Gourna 122 Ai

1 n

2 ne

3 nw

4 s

5 6 7 se sw e

12 Dpth 2.00

13 14 Dpth Ext Rnge Wall Mat 1 5.00

15 Roof Mat 4.00

Palestine 1 EB

1

1

2

2

2

2

1

2

1

2.50

4

2.00

1

5.00

2.00

Iron I

Palace A Sanct AII-III Sanct AIV 103

Palestine 2 Iron

1

1

1

2

2

1

1

1

1

3.25

4

2.00

1

5.00

2.50

Iron I

118

Palestine 2 Iron

2

1

2

2

2

2

1

2

1

3.40

4

2.00

1

5.00

2.60

Iron I

155

Palestine 2 Iron

1

2

1

1

2

1

2

2

1

3.25

4

2.00

1

5.00

2.50

141 Ai

Iron I

166

Palestine 2 Iron

2

2

2

2

2

1

2

2

1

2.50

4

2.00

1

5.00

2.00

142 Ai

Iron I

188

Palestine 2 Iron

1

2

2

2

2

2

2

2

1

3.50

4

2.00

1

5.00

2.67

220

Dataset for Case Study 2 17 Ext Wall Ins 1.00

18 Roof Ins

114

16 Roof Mat Range 1

22 Ratio Range

23 Posts

24 Posts Range

25 Niches

4

7.00

1

6.00

6.00

3.00

8

2

30 Ceiling /Flr Conduct 3

115

4

1.00

1.00

1.13

2

4.79

5

7.00

1

5.90

5.83

4.73

3

2

3

116

3

1.50

2.00

1.16

2

4.39

6

7.00

1

5.76

5.63

2.74

1

2

2

117

3

1.00

1.00

1.10

2

5.20

3

7.00

1

5.70

5.00

2.80

5

2

3

118

3

1.00

1.42

1.37

2

4.57

6

6.53

4

5.48

5.73

2.58

4

2

3

119

3

1.00

2.08

1.12

2

4.66

5

6.94

3

5.59

5.59

4.18

1

2

3

120

3

1.00

1.82

1.13

2

4.76

5

7.00

1

5.75

5.57

3.32

4

2

2

121

3

1.00

1.75

1.15

2

4.35

5

7.00

1

5.70

5.50

3.65

3

2

2

122

1

1.00

2.00

3.00

1

5.00

1

7.00

1

6.00

4.00

2.00

9

3

3

123

1

1.00

2.00

1.00

1

4.00

1

5.00

1

6.00

5.00

1.00

11

3

3

124

1

1.00

2.00

1.00

1

4.00

1

6.00

1

6.00

6.00

2.00

11

3

3

125

1

1.00

2.00

1.00

1

5.50

2

7.00

1

6.00

5.50

2.00

10

2

3

126

1

1.00

2.00

1.00

1

5.50

2

7.00

1

6.00

6.00

2.00

10

2

3

127

3

1.00

1.50

1.00

1

4.00

1

5.00

3

6.00

6.00

2.00

10

2

3

128

1

1.00

2.00

3.00

1

5.00

1

7.00

1

6.00

4.00

2.00

9

3

3

129

1

1.00

2.00

1.00

1

4.34

2

5.66

3

6.00

5.67

1.00

9

2

3

130

1

1.00

2.00

1.00

1

6.00

1

7.00

1

6.00

6.00

2.00

11

3

3

131

1

1.00

2.00

1.00

1

5.50

2

7.00

1

6.00

3.00

2.00

10

2

3

132

3

1.00

1.75

1.50

3

3.75

6

6.00

3

6.00

5.50

1.75

8

2

2

133

3

1.00

1.66

1.00

1

5.33

2

7.00

1

6.00

6.00

3.67

9

2

3

134

1

1.00

2.00

1.00

1

6.00

1

7.00

1

6.00

6.00

3.00

9

2

3

135

3

1.00

1.80

1.20

2

4.00

6

6.20

3

5.80

5.60

1.60

7

2

2

136

3

1.00

1.66

1.34

2

5.33

2

7.00

1

5.33

4.33

2.66

9

2

3

137

3

1.00

1.50

1.00

1

4.50

4

7.00

1

5.50

6.00

3.00

10

2

3

138

3

1.00

1.75

1.00

1

3.00

1

7.00

1

6.00

6.00

2.50

8

2

3

139

3

1.00

1.80

1.00

1

3.80

3

7.00

1

6.00

6.00

2.40

7

2

3

140

3

1.00

1.75

1.00

1

4.00

3

7.00

1

6.00

6.00

5.00

8

2

3

141

3

1.00

1.50

1.00

1

4.50

2

7.00

1

6.00

6.00

4.00

10

2

3

142

3

1.00

1.84

1.00

1

4.66

4

7.00

1

6.00

6.00

5.00

7

2

3

1.00

19 20 21 Int Int Ratio Angles Angles Range 1.00 1 4.25

221

26 27 Benches Compact

28 29 No. Int. Wall Rooms Conduct

The Evolution of the Built Environment Variables where Thermal Choices and Control are in accordance 33 s

34 se

35 sw

36 e

37 w

Primary References

32 Opngs

114

31 No. Roofs 1

0.25

1.00 1.00 1.00 1.00

1.00

38 CrossVent 2.00

39 40 Heating Trans Spaces 4.00 0.25

115

1

0.57

1.26 1.00 1.00 1.00

1.00

1.26

2.30

0.22

Creswell, K.A. 1978

116

2

1.66

1.31 1.16 1.17 1.07

1.07

1.44

1.97

0.08

Toulan, N.A. 1980

117

1

0.80

1.20 1.00 1.00 1.00

1.00

1.60

1.60

0.10

Fathy, H. 1973

118

2

1.05

1.42 1.21 1.21 1.10

1.16

1.73

2.89

0.26

Fathy, H. 1973

119

2

1.18

1.56 1.56 1.47 1.03

1.12

1.67

1.94

0.09

Fathy, H. 1973

120

2

1.19

1.32 1.07 1.07 1.07

1.07

1.75

2.07

0.25

Fathy, H. 1973

121

2

1.45

1.35 1.05 1.00 1.05

1.10

1.80

1.95

0.20

Fathy, H. 1973

122

1

0.67

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Callaway, J.A. 1980

123

1

2.00

2.00 2.00 2.00 2.00

2.00

2.00

1.00

0.00

Callaway, J.A. 1980

124

1

2.00

1.00 1.00 1.00 1.00

1.00

2.00

4.00

0.00

Callaway, J.A. 1980

125

1

1.50

1.00 1.00 1.00 1.00

1.00

2.00

3.00

0.00

Callaway, J.A. 1980

126

1

1.50

1.00 1.00 1.00 1.00

1.00

2.00

1.00

0.00

Callaway, J.A. 1980

127

1

1.00

1.50 2.00 1.00 1.00

1.00

1.50

4.00

0.50

Marquet-Krause, J. 1949; Garstang, J. 1931

128

1

0.67

1.00 1.00 1.00 1.00

1.00

1.00

1.00

0.00

Callaway, J.A. 1980

129

1

1.33

2.00 2.00 2.00 2.00

2.00

2.00

1.00

0.00

Callaway, J.A. 1980; Marquet-Krause, J. 1949

130

1

3.00

1.00 1.00 1.00 1.00

1.00

2.00

1.00

0.50

Callaway, J.A. 1980

131

1

1.00

1.00 1.00 1.00 1.00

1.00

2.00

1.00

1.00

Callaway, J.A. 1980

132

2

1.25

1.25 1.75 1.25 1.25

1.25

1.75

2.75

0.25

Marquet-Krause, J. 1949; Garstang, J. 1931

133

1

4.33

1.34 1.00 1.00 1.00

1.00

2.00

2.67

0.67

Marquet-Krause, J. 1949

134

1

4.33

1.00 1.00 1.00 1.00

1.00

2.00

1.00

0.33

Callaway, J.A. 1980

135

2

0.80

1.75 1.75 1.20 1.20

1.20

1.60

2.40

0.20

Marquet-Krause, J. 1949; Garstang, J. 1931

136

1

1.33

1.34 1.00 1.00 1.00

1.00

1.66

2.67

0.67

Callaway, J.A. 1980

137

1

1.00

1.50 1.00 1.00 1.00

1.00

2.00

3.50

1.00

Callaway, J.A. 1980

138

1

0.75

1.50 1.00 1.00 1.00

1.00

1.75

4.50

0.75

Finkelstein, I. 1990

139

1

1.00

1.20 1.00 1.00 1.00

1.00

1.60

3.60

0.60

Finkelstein, I. 1990

140

1

0.75

2.00 1.00 1.00 1.00

1.00

1.50

2.25

0.50

Marquet-Krause, J. 1949

141

1

1.00

1.50 1.00 1.00 1.00

1.00

1.50

3.50

0.50

Marquet-Krause, J. 1949

142

1

0.83

1.16 1.00 1.00 1.00

1.00

1.66

1.83

0.67

Marquet-Krause, J. 1949

222

Creswell, K.A. 1978

Dataset for Case Study 2 Site

143 144 145 146 147 148 149 151 152 153 154 155 156 157

Ai Ai Shiloh Shiloh Tell enNasbeh Tell enNasbeh Tell enNasbeh Tell enNasbeh Tell enNasbeh Tell enNasbeh Tell enNasbeh Tell enNasbeh Khan alLubban Khan alLubban

Period

Bldg. No.

Grouping

Variables where Thermal Choices and Control are contradictory

Iron I Iron I Iron I Iron I Iron I

207 Palace 312 335 583

Palestine 2 Iron Palestine 2 Iron Palestine 2 Iron Palestine 2 Iron Palestine 2 Iron

2 2 2 1 2

2 2 2 2 1

2 2 1 1 2

2 1 2 2 1

1 2 2 2 1

2 2 2 2 1

1 1 2 2 1

8 9 10 11 w Vert Roof Roof Shpe Shpe Rnge 2 1 2.50 4 2 1 4.00 1 1 1 4.00 1 1 1 3.40 2 2 1 3.40 2

Iron II

390

Palestine 2 Iron

2

2

2

1

1

1

2

2

1

3.96

2

2.14

2

4.00

2.70

Iron II

Gate

Palestine 2 Iron

1

2

2

2

1

2

2

2

1

3.00

1

2.00

1

5.00

4.00

Iron II

327 later 594

Palestine 2 Iron

2

2

2

2

2

2

2

1

1

3.00

2

2.00

1

5.00

2.34

Palestine 2 Iron

2

1

2

2

2

2

2

2

1

3.40

2

2.20

2

4.00

2.61

Palestine 3 Babylonian Palestine 3 Babylonian Palestine 3 Babylonian Palestine 4 Ottoman Palestine 4 Ottoman

1

1

1

1

1

1

1

1

1

4.00

2

2.16

2

5.00

2.67

1

1

1

1

1

1

1

1

1

3.40

2

2.20

2

5.00

2.61

1

1

1

1

1

1

1

1

1

3.64

2

2.14

2

5.00

2.76

1

1

1

1

1

1

1

1

1

1.85

2

1.76

2

5.00

3.56

1

1

1

1

1

1

1

1

1

1.74

2

1.76

2

5.00

3.40

Iron II

Babylonian 1 Babylonian 2 Babylonian 3 Mamluk Ottoman

Carava nsarai Carava nsarai

17 Ext Wall Ins 1.00 1.00 1.00 1.00 1.00

18 Roof Ins

143 144 145 146 147

16 Roof Mat Range 3 1 1 3 3

148

3

1.00

1.85

1.14

2

149

1

1.00

1.00

1.00

150

3

1.00

1.50

151

3

1.00

152

3

153

2 ne

3 nw

4 s

5 6 7 se sw e

2.00 2.00 1.20 0.83 2.20

13 14 Dpth Ext Rnge Wall Mat 1 5.00 1 5.00 2 5.00 1 4.00 2 4.00

28 29 No. Int. Wall Rooms Conduct

15 Roof Mat 2.00 3.00 3.00 2.61 2.61

23 Posts

24 Posts Range

25 Niches

1 2 4 4 3

7.00 7.00 6.50 7.00 7.00

1 1 4 1 1

6.00 5.75 6.00 6.00 5.80

6.00 6.00 6.00 6.00 5.80

3.00 1.00 1.83 1.80 4.00

10 8 7 7 7

2 2 2 2 2

30 Ceiling /Flr Conduct 3 3 2 3 2

4.56

4

7.00

1

5.72

6.00

5.28

5

2

2

1

3.00

1

7.00

1

4.00

6.00

1.00

11

3

3

1.00

1

3.50

2

7.00

1

5.50

6.00

2.00

10

3

3

1.66

1.00

1

4.50

2

7.00

1

5.83

5.83

5.67

7

2

3

1.00

1.80

1.20

2

4.00

1

7.00

1

5.80

5.80

3.00

7

2

2

3

1.00

1.84

1.16

2

2.88

3

7.00

1

5.83

6.00

1.79

7

2

2

154

3

1.00

1.80

1.20

2

3.40

2

7.00

1

5.80

6.00

1.80

7

2

2

155

3

1.00

1.88

1.12

2

4.52

4

7.00

1

5.75

6.00

2.26

6

2

2

156

4

1.00

1.00

1.00

1

4.62

6

7.00

1

6.00

6.00

3.00

7

2

3

157

4

1.00

1.00

1.00

1

4.38

6

7.00

1

6.00

6.00

2.70

7

2

3

223

26 27 Benches Compact

12 Dpth

22 Ratio Range

1.50 2.00 2.00 1.80 1.80

19 20 21 Ratio Int Int Angles Angles Range 1.00 1 4.00 1.00 1 5.00 1.00 1 5.56 1.00 1 4.40 1.20 2 4.59

1 n

The Evolution of the Built Environment Variables where Thermal Choices and Control are in accordance

Primary References

32 Opngs

33 s

34 se

35 sw

36 e

37 w

143 144 145 146 147

31 No. Roofs 1 1 2 1 2

1.00 0.25 0.67 1.00 0.80

1.50 1.00 1.00 1.40 1.80

1.00 1.00 1.00 1.00 1.00

1.00 1.00 1.34 1.40 1.20

1.00 1.00 1.00 1.00 1.00

1.00 1.00 1.34 1.40 1.00

38 CrossVent 1.50 1.00 1.16 1.80 1.80

39 40 Heating Trans Spaces 3.50 0.50 1.00 0.00 3.66 0.00 5.20 0.20 2.60 0.60

148

2

1.14

1.43 1.00 1.15 1.00

1.00

1.43

2.00

0.29

McCown, C.C. 1947; Zorn, J.R. 1993

149

1

2.00

2.00 1.00 1.00 1.00

1.00

2.00

2.00

0.00

McCown, C.C. 1947; Zorn, J.R. 1993

150

1

0.50

1.50 1.00 1.00 1.00

1.00

2.00

3.50

0.50

McCown, C.C. 1947; Zorn, J.R. 1993

151

1

0.83

1.34 1.00 1.00 1.00

1.00

1.66

2.67

0.33

McCown, C.C. 1947; Zorn, J.R. 1993

152

2

0.80

1.40 1.00 1.00 1.00

1.00

1.40

2.20

0.40

McCown, C.C. 1947; Zorn, J.R. 1993

153

2

1.00

1.50 1.16 1.00 1.00

1.00

1.34

2.17

0.17

McCown, C.C. 1947; Zorn, J.R. 1993

154

2

1.20

1.40 1.20 1.20 1.00

1.00

1.40

2.20

0.20

McCown, C.C. 1947; Zorn, J.R. 1993

155

2

1.00

1.25 1.13 1.13 1.00

1.00

1.50

1.75

0.38

McCown, C.C. 1947; Zorn, J.R. 1993

156

1

2.30

1.15 1.28 2.00 1.00

1.00

1.85

5.14

0.14

Hawari, M. 2001

157

1

2.00

1.40 1.40 1.60 1.00

1.00

2.00

5.60

0.20

Hawari, M. 2001

224

Marquet-Krause, J. 1949 Marquet-Krause, J. 1949; Garstang, J. 1931 Finkelstein, I., et al. 1993 Finkelstein, I., et al. 1993 McCown, C.C. 1947; Zorn, J.R. 1993

APPENDIX D – Dataset for Case Study 3

Site

Period

Room No.

Group

Variables where Thermal Choices and Control are contradictory Exposure 1 2 3 4 n ne nw s

1

Wet Leggett

Cochise

2

SU

Pinelawn

3

SU

Pinelawn

4

SU

Pinelawn

5

SU

Pinelawn

6

SU

Pinelawn

7

SU

Pinelawn

8

SU

Pinelawn

9

SU

Pinelawn

10

SU

Pinelawn

11

SU

Pinelawn

12

SU

Pinelawn

13

SU

Pinelawn

14

SU

Pinelawn

15

SU

Pinelawn

16

SU

Pinelawn

17

SU

Pinelawn

18

SU

Pinelawn

19

SU

Pinelawn

20

SU

Pinelawn

21

SU

Pinelawn

22

SU

Pinelawn

23

SU

Pinelawn

24

SU

Pinelawn

25

SU

Pinelawn

26

SU

Pinelawn

27

SU

Pinelawn

Dwelling Pinelawn Valley 1 A Pinelawn Valley 2 B Pinelawn Valley 2 C Pinelawn Valley 2 D east Pinelawn Valley 2 D west Pinelawn Valley 2 E Pinelawn Valley 2 F Pinelawn Valley 2 G Pinelawn Valley 2 H Pinelawn Valley 2 I Pinelawn Valley 2 J Pinelawn Valley 2 L Pinelawn Valley 2 M Pinelawn Valley 2 N Pinelawn Valley 2 O Pinelawn Valley 2 P Pinelawn Valley 2 Q Pinelawn Valley 2 R Pinelawn Valley 2 S Pinelawn Valley 2 T Pinelawn Valley 2 U Pinelawn Valley 2 V Pinelawn Valley 2 W Pinelawn Valley 2 X Pinelawn Valley 2 Z Pinelawn Valley 2 SH1 Pinelawn Valley 2

1

1

1

1

1

1

1

1

1

10 Roof Shape 2

1

1

1

1

1

1

1

1

1

1

3

4

3

2

1

1

1

1

1

1

1

1

1

5

5

3

3

2

1

1

1

1

1

1

1

1

1

5

4

3

3

2

1

1

1

1

1

1

1

1

1

5

4

4

3

2

1

1

1

1

1

1

1

1

1

4

4

3

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

5

4

3

3

2

1

1

1

1

1

1

1

1

1

5

4

3

3

2

1

1

1

1

1

1

1

1

1

1

3

4

3

2

1

1

1

1

1

1

1

1

1

5

2

4

3

2

1

1

1

1

1

1

1

1

1

1

3

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

1

4

4

3

2

1

1

1

1

1

1

1

1

1

1

4

4

3

2

1

1

1

1

1

1

1

1

1

5

4

3

3

2

1

1

1

1

1

1

1

1

1

1

4

4

3

2

1

1

1

1

1

1

1

1

1

4

4

3

3

2

1

1

1

1

1

1

1

1

1

1

2

4

3

2

1

1

1

1

1

1

1

1

1

1

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

5

5

3

3

2

225

5 6 7 se sw e

8 9 w Vert

11 Dpth 6

12 13 Wall Roof Material Material 2 2

14 Wall Insulat 3

The Evolution of the Built Environment

15 Roof Insulation

16 Internal Angles

17 Ratio l/w

18 Posts Type

19 Niches

20 Benches

1

1

5

6

7

6

6

21 No. Connected Rooms 5

2

2

5

6

4

6

6

3

2

5

6

4

6

4

2

4

6

4

5

2

5

6

6

2

2

7

2

8

22 23 24 25 No. Upper No. Lower Nearest Plan Area Storeys Storeys Neighbour 3

3

8

11

5

3

3

6

4

6

5

3

3

4

8

6

6

5

3

3

4

7

3

6

6

4

3

3

1

5

6

6

6

6

4

3

3

1

9

5

6

4

6

6

5

3

3

2

7

2

5

6

4

6

6

5

3

3

3

7

9

2

4

6

4

5

6

5

3

3

3

7

10

2

4

6

5

5

6

5

3

3

4

8

11

2

4

5

4

4

6

5

3

3

2

8

12

2

5

6

3

6

6

5

3

3

5

7

13

2

5

6

7

5

5

5

3

3

4

9

14

2

5

6

3

6

6

5

3

3

4

8

15

2

3

6

2

6

6

5

3

3

7

6

16

2

5

6

4

6

6

5

3

3

5

7

17

2

5

6

1

6

6

5

3

3

5

7

18

2

5

6

4

6

6

5

3

3

6

11

19

2

5

6

4

6

6

5

3

3

6

8

20

2

5

6

4

5

6

5

3

3

3

9

21

2

5

6

4

6

6

5

3

3

3

6

22

2

5

6

4

5

6

5

3

3

6

7

23

2

5

6

3

6

6

5

3

3

5

4

24

2

3

6

3

6

6

5

3

3

6

8

25

2

5

6

4

6

6

5

3

3

5

7

26

2

5

6

4

6

6

5

3

3

6

8

27

2

5

6

3

6

6

5

3

3

6

8

226

Dataset for Case Study 3

1

Variables where Thermal Choices and Control are in accordance Solar Access Wind Access 26 27 28 29 30 31 32 33 34 No. s se sw e w Cross Vent Cnr Vent Heating Openings 1 1 1 1 1 1 0 0 1

References 35 No. Trans Spaces 1 Martin, P.S. & Rinaldo, J. 1950b

2

1

1

2

1

1

1

0

1

3

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940

3

1

1

2

1

1

1

0

1

3

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940

4

1

1

1

1

2

1

0

1

3

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940

5

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940; Wills, W.H. 1996

6

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940; Wills, W.H. 1996

7

1

1

2

1

1

1

0

1

3

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940

8

1

1

1

1

2

1

0

1

3

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940

9

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940

10

1

1

1

1

2

1

0

0

2

1

Martin, P.S. 1943

11

1

1

1

1

1

1

0

0

1

1

Martin, P.S. 1943

12

1

1

1

1

2

1

0

0

2

1

Martin, P.S. 1943

13

1

1

1

1

1

1

0

0

1

1

Martin, P.S. 1943

14

1

1

1

1

1

1

0

0

1

1

Martin, P.S. 1943

15

1

1

1

1

2

1

0

0

2

1

Martin, P.S. 1943

16

1

1

1

1

1

1

0

0

1

1

Martin, P.S. 1943

17

1

1

1

1

2

1

0

0

2

1

Martin, P.S. 1943

18

1

1

1

1

2

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1947

19

1

1

1

1

2

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1947

20

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1947

21

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1947

22

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1947

23

1

1

2

1

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1947

24

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1947; Wills, W.H. 1996

25

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1947; Wills, W.H. 1996

26

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1947; Wills, W.H. 1996

27

1

1

1

1

1

1

0

0

2

1

Martin, P.S., Rinaldo, J. & Kelly M. 1940

227

The Evolution of the Built Environment Site

Period

Room No.

Group

Variables where Thermal Choices and Control are contradictory Exposure 1 2 3 4 n ne nw s

28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57

SU

Pinelawn

SH2

Pinelawn Valley 2 SU Pinelawn SH3 Pinelawn Valley 2 Promontory Pinelawn A Pinelawn Valley 2 Promontory Pinelawn B Pinelawn Valley 2 Promontory Pinelawn C Pinelawn Valley 2 Promontory Pinelawn D Pinelawn Valley 2 Promontory Pinelawn E Pinelawn Valley 2 Three Pines Pinelawn Pithouse Pinelawn Valley 2 Starkweather Georgetown A Pinelawn Valley 2 Starkweather Georgetown C Pinelawn Valley 2 Starkweather Georgetown F Pinelawn Valley 2 Starkweather Georgetown Q Pinelawn Valley 2 Turkey Foot Georgetown G Pinelawn Valley 2 Starkweather Early San I Pinelawn Fran Valley 3 Starkweather Early San K Pinelawn Fran Valley 3 Starkweather Early San N Pinelawn Fran Valley 3 Starkweather Early San O Pinelawn Fran Valley 3 Starkweather Early San P Pinelawn Fran Valley 3 Starkweather San Fran E Pinelawn Valley 3 Starkweather San Fran G Pinelawn Valley 3 Starkweather San Fran R Pinelawn Valley 3 Turkey Foot San Fran C Pinelawn Valley 3 Turkey Foot San Fran E Pinelawn Valley 3 Turkey Foot San Fran F Pinelawn Valley 3 Turkey Foot San Fran H Pinelawn Valley 3 Turkey Foot San Fran I Pinelawn Valley 3 Turkey Foot San Fran J Pinelawn Valley 3 Turkey Foot San Fran K Pinelawn Valley 3 Turkey Foot San Fran L Pinelawn Valley 3 Turkey Foot San Fran M Pinelawn Valley 3

1

1

1

1

1

1

1

1

1

10 Roof Shape 5

1

1

1

1

1

1

1

1

1

5

6

3

3

2

1

1

1

1

1

1

1

1

1

2

4

3

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

1

1

1

1

1

1

1

1

1

2

5

3

3

2

1

1

1

1

1

1

1

1

1

4

5

3

3

2

1

1

1

1

1

1

1

1

1

2

5

3

3

2

1

1

1

1

1

1

1

1

1

2

4

3

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

2

4

4

3

2

1

1

1

1

1

1

1

1

1

5

2

5

3

2

1

1

1

1

1

1

1

1

1

5

2

4

3

2

1

1

1

1

1

1

1

1

1

5

2

5

3

2

1

1

1

2

1

2

1

1

1

5

3

4

3

2

2

2

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

5

4

3

3

2

1

1

1

1

1

1

1

1

1

1

2

4

3

2

1

1

1

1

1

1

1

1

1

1

4

4

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

228

5 6 7 se sw e

8 9 w Vert

11 Dpth 6

12 13 Wall Roof Material Material 3 3

14 Wall Insulat 2

Dataset for Case Study 3

15 Roof Insulation

16 Internal Angles

17 Ratio l/w

18 Posts Type

19 Niches

20 Benches

28

2

5

6

3

6

6

21 No. Connected Rooms 5

29

2

5

6

1

5

6

30

2

5

6

6

6

31

2

5

6

7

32

2

4

6

33

2

2

34

2

35

22 23 24 25 No. Upper No. Lower Nearest Plan Area Storeys Storeys Neighbour 3

3

4

6

5

3

3

4

6

6

5

3

3

6

8

6

6

5

3

3

5

4

7

6

6

5

3

3

7

8

6

6

4

6

5

3

3

5

9

3

6

5

4

6

5

3

3

5

11

2

4

6

4

6

6

5

3

3

8

8

36

2

5

6

5

6

6

5

3

3

4

9

37

2

5

6

5

6

6

5

3

3

4

10

38

2

5

6

5

6

6

5

3

3

6

10

39

2

5

6

5

6

3

5

3

3

4

8

40

2

4

6

3

6

4

5

3

3

8

9

41

2

4

6

4

6

6

5

3

3

6

10

42

2

5

6

4

6

6

5

3

3

7

10

43

2

4

5

5

6

6

5

3

3

2

11

44

2

5

6

5

6

6

4

3

3

1

12

45

2

4

4

5

6

6

4

3

3

1

11

46

2

2

6

5

6

6

5

3

3

5

11

47

2

1

5

5

6

6

5

3

3

5

9

48

2

4

5

5

6

6

5

3

3

6

11

49

2

2

6

4

6

6

5

3

3

5

9

50

2

2

5

4

5

6

5

3

3

6

7

51

2

5

6

3

6

3

5

3

3

4

8

52

2

2

6

4

5

6

5

3

3

4

9

53

2

2

6

4

6

6

5

3

3

5

9

54

2

4

6

3

6

3

5

3

3

4

9

55

2

3

6

3

6

4

5

3

3

4

6

56

2

3

6

4

6

4

5

3

3

4

8

57

2

4

6

4

6

3

5

3

3

4

8

229

The Evolution of the Built Environment

28

Variables where Thermal Choices and Control are in accordance Solar Access Wind Access 26 27 28 29 30 31 32 33 34 No. s se sw e w Cross Vent Cnr Vent Heating Openings 1 1 1 1 1 1 0 0 1

References 35 No. Trans Spaces 1 Martin, P.S. 1943

29

1

1

1

1

1

1

0

0

1

1

Martin, P.S. 1943

30

1

1

2

1

1

1

0

1

3

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

31

1

1

1

1

2

1

0

0

2

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

32

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

33

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

34

1

1

2

1

1

1

0

0

2

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

35

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

36

1

1

1

1

2

1

0

0

2

1

Nesbitt, P.H. 1938

37

1

1

1

1

2

1

0

0

2

1

Nesbitt, P.H. 1938

38

1

1

2

1

1

1

0

0

2

1

Nesbitt, P.H. 1938

39

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

40

1

1

2

1

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950a

41

1

1

1

1

2

1

0

0

2

1

Nesbitt, P.H. 1938

42

1

1

1

1

1

1

0

0

2

1

Nesbitt, P.H. 1938

43

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

44

2

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

45

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

46

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

47

2

1

1

1

2

1

0

1

3

1

Nesbitt, P.H. 1938

48

1

1

1

1

2

1

0

0

2

1

Nesbitt, P.H. 1938

49

2

1

2

1

1

1

0

1

4

1

Martin, P.S. & Rinaldo, J. 1950a

50

1

1

2

1

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950a

51

2

1

1

1

2

1

0

1

3

1

Martin, P.S. & Rinaldo, J. 1950a

52

2

1

1

1

2

1

0

1

4

1

Martin, P.S. & Rinaldo, J. 1950a

53

2

1

1

1

1

1

0

1

3

1

Martin, P.S. & Rinaldo, J. 1950a

54

2

1

1

1

2

1

0

1

3

1

Martin, P.S. & Rinaldo, J. 1950a

55

1

1

1

1

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950a

56

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950a

57

2

1

1

1

2

1

0

1

4

1

Martin, P.S. & Rinaldo, J. 1950a

230

Dataset for Case Study 3 Site

Period

Room No.

Group

Variables where Thermal Choices and Control are contradictory Exposure 1 2 3 4 n ne nw s

58

Turkey Foot San Fran

59

Turkey Foot San Fran

60

66

Starkweather Late San Fran Starkweather Late San Fran Starkweather Late San Fran Starkweather Late San Fran Starkweather Late San Fran Starkweather Late San Fran SU Three Circle

67

Starkweather Three Circle

68

Turkey Foot Three Circle

69

Turkey Foot Three Circle

70

Turkey Foot Three Circle

71

Turkey Foot Three Circle

72

Turkey Foot Three Circle

73

Turkey Foot Three Circle

74

Turkey Foot Three Circle

75

Turkey Foot Three Circle

76

Turkey Foot Three Circle

77

Turkey Foot Three Circle

78

Turkey Foot Three Circle

79

Twin Bridges Three Circle

80

Twin Bridges Three Circle

81

Twin Bridges Three Circle

82

Twin Bridges Three Circle

83

South Leggett Three Circle

84

Three Pines

Reserve

85

Three Pines

Reserve

86

Three Pines

Reserve

87

Three Pines

Reserve

61 62 63 64 65

N

Pinelawn Valley 3 O Pinelawn Valley 3 B Pinelawn Valley 3 D Pinelawn Valley 3 H Pinelawn Valley 3 J Pinelawn Valley 3 L Pinelawn Valley 3 M Pinelawn Valley 3 Y Pinelawn Valley 3 J Pinelawn Valley 3 A Pinelawn Valley 3 B Pinelawn Valley 3 C Pinelawn Valley 3 D Pinelawn Valley 3 E Pinelawn Valley 3 I Pinelawn Valley 3 K Pinelawn Valley 3 L Pinelawn Valley 3 M Pinelawn Valley 3 N Pinelawn Valley 3 O Pinelawn Valley 3 A Pinelawn Valley 3 B Pinelawn Valley 3 C Pinelawn Valley 3 D Pinelawn Valley 3 Pithouse Pinelawn Valley 3 Jacal Pinelawn Valley 4 Rm Jacal Pinelawn Valley 4 Porch A Pinelawn Valley 4 B Pinelawn Valley 4

1

1

1

1

1

1

1

1

1

10 Roof Shape 4

1

1

1

1

1

1

1

1

1

1

3

4

3

2

1

1

1

1

1

1

1

1

1

6

2

5

1

1

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

5

4

3

3

2

1

1

1

1

1

1

1

1

1

5

4

3

3

2

1

1

1

1

1

2

2

1

1

2

4

4

3

2

1

1

2

1

1

1

1

2

1

5

4

4

3

2

1

1

1

1

1

1

1

1

1

5

2

5

3

2

1

1

1

1

1

1

1

1

1

5

4

3

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

1

3

4

3

2

1

1

1

1

1

1

1

1

1

1

2

4

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

1

1

1

1

1

1

1

1

1

1

4

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

1

1

1

1

1

1

1

1

1

2

3

4

3

2

1

1

1

1

1

1

1

1

1

1

3

4

3

2

1

1

1

1

1

1

1

1

1

4

3

4

3

2

1

1

1

1

1

1

1

1

1

4

5

3

3

2

1

1

1

1

1

1

1

1

1

1

4

4

3

2

1

1

1

1

1

1

1

1

1

5

3

4

3

2

1

1

1

1

1

1

1

1

1

2

2

5

3

2

1

2

1

1

1

1

1

1

1

5

6

3

2

2

1

1

1

1

1

2

1

1

1

5

6

3

2

2

1

1

1

1

1

1

1

1

1

5

6

4

3

1

1

1

1

1

1

1

1

1

1

5

6

4

3

1

231

5 6 7 se sw e

8 9 w Vert

11 Dpth 3

12 13 Wall Roof Material Material 4 3

14 Wall Insulat 2

The Evolution of the Built Environment

15 Roof Insulation

16 Internal Angles

17 Ratio l/w

18 Posts Type

19 Niches

20 Benches

58

2

3

6

3

6

4

21 No. Connected Rooms 5

59

2

4

6

4

6

6

60

1

5

6

7

6

61

2

2

6

5

62

2

1

5

63

2

1

64

2

65

22 23 24 25 No. Upper No. Lower Nearest Plan Area Storeys Storeys Neighbour 3

3

5

10

5

3

3

5

10

6

5

3

3

5

3

6

6

5

3

3

5

11

5

6

6

5

3

3

5

10

6

5

6

6

5

3

3

5

9

4

6

4

6

6

4

3

3

1

11

2

2

6

5

6

6

4

3

3

1

10

66

2

1

4

4

6

6

5

3

3

5

10

67

2

1

6

5

6

6

5

3

3

8

9

68

2

1

6

4

6

6

5

3

3

4

10

69

2

5

6

2

3

6

5

3

3

4

7

70

2

2

6

4

6

6

5

3

3

4

9

71

2

4

5

4

6

6

5

3

3

4

10

72

2

2

5

4

5

6

5

3

3

4

7

73

2

2

6

4

6

6

5

3

3

5

9

74

2

3

6

3

6

4

5

3

3

4

6

75

2

3

6

4

6

4

5

3

3

4

8

76

2

4

6

4

6

3

5

3

3

4

8

77

2

3

6

3

6

4

5

3

3

5

10

78

2

4

6

4

6

6

5

3

3

4

10

79

2

2

6

5

5

6

5

3

3

5

10

80

2

3

6

6

6

6

5

3

3

5

11

81

2

2

6

6

6

6

5

3

3

5

11

82

2

2

6

4

6

6

5

3

3

5

9

83

2

3

6

6

6

3

5

3

3

8

7

84

2

1

4

5

6

6

5

3

3

1

7

85

2

1

3

7

6

6

5

3

3

1

12

86

2

1

6

7

6

6

5

3

3

4

10

87

2

1

5

7

6

6

5

3

3

4

11

232

Dataset for Case Study 3

58

Variables where Thermal Choices and Control are in accordance Solar Access Wind Access 26 27 28 29 30 31 32 33 34 No. s se sw e w Cross Vent Cnr Vent Heating Openings 1 1 1 1 2 1 0 0 2

References 35 No. Trans Spaces 1 Martin, P.S. & Rinaldo, J. 1950a

59

1

1

1

1

1

2

0

1

3

1

Martin, P.S. & Rinaldo, J. 1950a

60

1

2

2

2

1

1

0

0

2

1

Nesbitt, P.H. 1938

61

1

1

2

1

1

1

0

0

2

1

Nesbitt, P.H. 1938

62

2

1

1

1

1

1

0

1

2

1

Nesbitt, P.H. 1938

63

2

1

1

1

2

1

0

1

2

1

Nesbitt, P.H. 1938

64

1

1

1

1

1

1

0

1

4

1

Nesbitt, P.H. 1938

65

1

1

1

1

1

1

0

0

4

1

Nesbitt, P.H. 1938

66

1

1

2

1

1

1

0

1

4

1

Martin, P.S. & Rinaldo, J. 1947; Wills, W.H. 1996

67

2

1

1

1

1

1

0

1

1

1

Nesbitt, P.H. 1938

68

1

1

1

1

2

1

0

1

4

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

69

1

1

1

1

2

1

0

0

2

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

70

2

1

2

1

1

1

0

0

4

1

Martin, P.S. & Rinaldo, J. 1950a

71

2

1

1

1

2

1

0

1

4

1

Martin, P.S. & Rinaldo, J. 1950a

72

1

1

2

1

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950a

73

2

1

1

1

1

1

0

1

3

1

Martin, P.S. & Rinaldo, J. 1950a

74

1

1

1

1

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950a

75

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950a

76

2

1

1

1

2

1

0

1

4

1

Martin, P.S. & Rinaldo, J. 1950a

77

1

1

1

1

2

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950a

78

1

1

1

1

1

2

0

1

4

1

Martin, P.S. & Rinaldo, J. 1950a

79

1

1

1

1

2

1

0

1

3

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

80

1

1

1

1

1

1

0

0

2

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

81

1

1

1

1

1

1

0

1

4

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

82

1

1

1

1

2

1

0

1

4

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

83

1

2

1

1

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950b

84

1

1

1

2

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950b

85

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

86

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

87

1

2

1

1

1

1

0

0

2

1

Martin, P.S. & Rinaldo, J. 1950b

233

The Evolution of the Built Environment Site

Period

Room No.

Group

Variables where Thermal Choices and Control are contradictory Exposure 1 2 3 4 n ne nw s

88

91

Sawmill / Fox Farm Sawmill / Fox Farm Sawmill / Fox Farm Oak Springs

92

Oak Springs Reserve

B

93

Oak Springs Reserve

C

94

Oak Springs Reserve

D

95

Oak Springs Reserve

E

96

Oak Springs Reserve

F

97

Oak Springs Reserve

Un

98

Wet Leggett

Reserve

A

99

Wet Leggett

Reserve

B

100 Wet Leggett

Reserve

C

101 Wet Leggett

Reserve

D

102 Wet Leggett

Reserve

E

103 Wet Leggett

Reserve

F

104 South Leggett Reserve

A

105 South Leggett Reserve

B

106 South Leggett Reserve

C

107 South Leggett Reserve

D

108 South Leggett Reserve

E

109 Starkweather Tularosa

5 early

110 Starkweather Tularosa

6 early

111 Starkweather Tularosa

7 early

112 Starkweather Tularosa

8 early

113 Starkweather Tularosa

10 early

114 Starkweather Tularosa

11 early

115 Starkweather Tularosa

12 early

116 Starkweather Tularosa

3 mid

117 Starkweather Tularosa

4 mid

89 90

Reserve

A

Reserve

Early Kiva Late Kiva A

Reserve Reserve

Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4

1

1

1

1

1

2

1

2

1

10 Roof Shape 5

1

1

1

1

1

1

1

2

1

4

2

5

3

2

1

1

1

1

1

1

1

2

1

4

2

5

3

2

2

2

2

2

2

2

2

1

1

5

6

4

3

1

1

2

1

2

2

1

1

1

1

5

6

4

3

1

2

2

2

1

2

1

2

1

1

5

5

4

3

1

2

2

2

1

2

2

2

2

1

5

5

4

3

1

2

1

2

2

2

2

1

2

1

5

5

4

3

1

1

1

1

2

2

2

1

1

1

5

6

4

3

1

1

1

2

1

1

2

1

2

1

5

6

4

3

1

1

1

2

1

1

2

1

2

1

5

6

4

3

1

2

2

2

1

1

1

1

2

1

5

6

4

3

1

1

1

1

2

2

2

2

2

1

5

6

4

3

1

2

2

2

1

2

1

2

1

1

5

6

4

3

1

1

1

1

2

2

2

2

1

1

5

6

4

3

1

1

1

1

1

2

1

2

1

1

5

6

4

3

1

2

2

2

1

1

1

1

1

1

5

6

4

3

1

1

1

1

2

2

2

1

2

1

5

6

4

3

1

1

1

1

2

2

1

2

1

1

5

6

4

3

1

1

2

2

1

2

1

1

1

1

5

6

4

3

1

2

2

1

1

2

1

2

1

1

5

6

4

3

1

1

2

1

2

2

2

2

1

1

5

6

4

3

1

2

2

2

2

2

2

2

1

1

5

6

4

3

1

2

2

2

2

2

2

2

1

1

5

6

4

3

1

2

2

2

1

2

1

2

1

1

5

6

4

3

1

1

1

2

2

1

2

2

2

1

5

6

4

3

1

2

2

2

2

1

2

2

2

1

5

6

4

3

1

2

2

2

1

1

2

1

2

1

5

6

4

3

1

2

2

2

1

2

1

2

1

1

5

6

4

3

1

1

2

1

2

2

2

2

1

1

5

6

4

3

1

234

5 6 7 se sw e

8 9 w Vert

11 Dpth 4

12 13 Wall Roof Material Material 3 3

14 Wall Insulat 1

Dataset for Case Study 3

15 Roof Insulation

16 Internal Angles

17 Ratio l/w

18 Posts Type

19 Niches

20 Benches

88

2

1

6

6

6

6

21 No. Connected Rooms 5

89

2

1

6

4

6

6

90

2

1

6

4

6

91

2

1

5

7

92

2

2

5

93

2

1

94

2

95

22 23 24 25 No. Upper No. Lower Nearest Plan Area Storeys Storeys Neighbour 3

3

1

8

5

3

3

4

4

6

5

3

3

4

4

6

6

4

3

3

1

11

7

6

6

4

3

3

1

9

6

7

6

6

5

3

3

1

11

1

5

7

6

6

4

3

3

1

11

2

1

5

7

6

6

4

3

3

1

11

96

2

1

6

7

6

6

5

3

3

1

11

97

2

1

4

7

6

6

5

3

3

1

11

98

2

3

6

4

6

6

4

3

3

1

7

99

2

1

3

5

6

6

3

3

3

1

10

100

2

2

6

5

5

6

4

3

3

1

11

101

2

2

5

5

6

6

4

3

3

1

11

102

2

1

4

5

6

6

4

3

3

1

11

103

2

1

5

7

6

6

5

3

3

1

12

104

2

1

4

7

6

6

5

3

3

1

8

105

2

2

6

6

6

6

5

3

3

1

11

106

2

1

6

7

6

6

4

3

3

1

9

107

2

2

6

6

6

6

5

3

3

4

9

108

2

1

6

7

6

6

4

3

3

1

10

109

2

1

5

5

6

6

4

3

3

1

11

110

2

1

4

5

6

6

4

3

3

1

9

111

2

1

4

5

6

6

5

3

3

1

11

112

2

1

5

5

6

6

5

3

3

1

10

113

2

1

5

7

6

6

5

3

3

1

9

114

2

1

4

5

6

6

5

3

3

1

8

115

2

1

5

7

6

6

5

3

3

1

11

116

2

1

5

5

6

6

4

3

3

1

10

117

2

1

5

6

6

6

4

3

3

1

11

235

The Evolution of the Built Environment

88

Variables where Thermal Choices and Control are in accordance Solar Access Wind Access 26 27 28 29 30 31 32 33 34 No. s se sw e w Cross Vent Cnr Vent Heating Openings 1 1 1 1 1 1 0 1 2

References 35 No. Trans Spaces 1 Bluhm, E.A. 1957

89

1

1

2

1

1

1

0

1

4

1

Bluhm, E.A. 1957

90

1

1

2

1

1

1

0

1

4

1

Bluhm, E.A. 1957

91

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

92

2

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

93

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

94

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

95

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

96

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

97

1

1

1

1

1

1

0

0

1

1

Martin, P.S., Rinaldo, J. & Antevs, E. 1949

98

2

1

1

1

1

1

0

1

5

1

Martin, P.S. & Rinaldo, J. 1950b

99

2

1

1

1

1

1

0

1

4

1

Martin, P.S. & Rinaldo, J. 1950b

100

1

1

1

1

1

1

0

0

3

1

Martin, P.S. & Rinaldo, J. 1950b

101

2

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

102

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

103

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

104

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

105

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

106

2

1

1

1

1

1

0

0

3

1

Martin, P.S. & Rinaldo, J. 1950b

107

2

1

1

2

1

1

0

1

6

1

Martin, P.S. & Rinaldo, J. 1950b

108

1

1

1

1

1

1

0

0

1

1

Martin, P.S. & Rinaldo, J. 1950b

109

2

1

1

1

1

1

0

1

1

1

Nesbitt, P.H. 1938

110

4

1

1

1

1

2

0

2

5

1

Nesbitt, P.H. 1938

111

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

112

1

1

1

1

1

1

0

0

2

1

Nesbitt, P.H. 1938

113

2

1

1

1

2

1

0

1

5

1

Nesbitt, P.H. 1938

114

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

115

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

116

2

1

1

1

1

1

0

0

2

1

Nesbitt, P.H. 1938

117

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

236

Dataset for Case Study 3 Site

Period

Room No.

Group

Variables where Thermal Choices and Control are contradictory Exposure 1 2 3 4 n ne nw s

118 Starkweather Tularosa

5 mid

119 Starkweather Tularosa

6 mid

120 Starkweather Tularosa

7 mid

121 Starkweather Tularosa

8 mid

122 Starkweather Tularosa

9 mid

123 Starkweather Tularosa

10 mid

124 Starkweather Tularosa

11 mid

125 Starkweather Tularosa

12 mid

126 Starkweather Tularosa

1 late

127 Starkweather Tularosa

2 late

128 Starkweather Tularosa

3 late

129 Starkweather Tularosa

4 late

130 Starkweather Tularosa

5 late

131 Starkweather Tularosa

6 late

132 Starkweather Tularosa

7 late

133 Starkweather Tularosa

8 late

134 Starkweather Tularosa

9 late

135 Starkweather Tularosa

10 late

136 Starkweather Tularosa

11 late

137 Starkweather Tularosa

12 late

138 Beidha

Natufian

hut

139 Beidha

PPNB A1

18

140 Beidha

PPNB A1

49

141 Beidha

PPNB A1

48

142 Beidha

PPNB A1

50

143 Beidha

PPNB A1

57

144 Beidha

PPNB A1

33

145 Beidha

PPNB A1

53

146 Beidha

PPNB A1

29

147 Beidha

PPNB A2

38/21

Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Pinelawn Valley 4 Jordan Highlands 1 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2

1

2

2

2

2

2

2

2

1

10 Roof Shape 5

2

2

2

2

2

2

2

1

1

5

6

4

3

1

2

2

2

2

2

2

2

1

1

5

6

4

3

1

2

2

2

2

2

2

2

1

1

5

6

4

3

1

2

2

2

1

1

1

1

1

1

5

6

4

3

1

1

1

2

2

1

2

2

2

1

5

6

4

3

1

2

2

2

2

1

2

2

2

1

5

6

4

3

1

2

2

2

2

1

2

1

2

1

5

6

4

3

1

2

2

2

1

2

1

2

1

1

5

6

4

3

1

2

2

1

2

2

2

2

1

1

5

6

4

3

1

2

2

2

2

2

2

2

1

1

5

6

4

3

1

1

2

1

2

2

2

2

1

1

5

6

4

3

1

1

2

2

2

2

2

2

2

1

5

6

4

3

1

2

2

2

2

2

2

2

2

1

5

6

4

3

1

2

2

2

2

2

2

2

2

1

5

6

4

3

1

2

2

2

2

2

2

2

2

1

5

6

4

3

1

2

2

2

1

1

1

1

2

1

5

6

4

3

1

1

1

2

2

1

2

2

2

1

5

6

4

3

1

2

2

2

2

1

2

2

2

1

5

6

4

3

1

2

2

2

2

1

2

1

2

1

5

6

4

3

1

2

1

1

1

1

1

1

1

1

2

6

2

2

3

2

1

2

2

1

1

1

1

1

2

4

4

3

1

2

2

1

1

2

1

1

1

1

2

4

4

3

2

1

1

1

2

2

2

2

2

1

2

4

5

3

2

2

2

2

1

2

1

2

2

1

4

4

4

3

1

2

1

2

1

1

1

1

1

1

2

4

4

3

1

1

1

2

2

2

2

1

2

1

4

4

5

3

1

1

1

1

2

1

2

1

1

1

2

4

5

3

1

2

2

2

1

1

1

1

1

1

2

4

4

3

1

1

2

1

1

1

1

1

1

1

2

4

4

3

1

237

5 6 7 se sw e

8 9 w Vert

11 Dpth 6

12 13 Wall Roof Material Material 4 3

14 Wall Insulat 1

The Evolution of the Built Environment

15 Roof Insulation

16 Internal Angles

17 Ratio l/w

18 Posts Type

19 Niches

20 Benches

118

2

1

5

5

6

6

21 No. Connected Rooms 4

119

2

1

4

5

6

6

120

2

1

4

5

6

121

2

1

5

5

122

2

1

5

123

2

1

124

2

125

22 23 24 25 No. Upper No. Lower Nearest Plan Area Storeys Storeys Neighbour 3

3

1

11

4

3

3

1

9

6

5

3

3

1

11

6

6

4

3

3

1

10

5

6

6

4

3

3

1

8

5

7

6

6

5

3

3

1

9

1

4

5

6

6

5

3

3

1

8

2

1

5

7

6

6

5

3

3

1

11

126

2

1

3

5

6

6

5

3

3

1

11

127

2

1

6

5

6

6

5

3

3

1

8

128

2

1

5

5

6

6

4

3

3

1

10

129

2

1

5

6

6

6

4

3

3

1

11

130

2

1

5

5

6

6

4

3

3

1

11

131

2

1

4

5

6

6

4

3

3

1

9

132

2

1

4

5

6

6

5

3

3

1

11

133

2

1

5

5

6

6

4

3

3

1

10

134

2

1

5

5

6

6

4

3

3

1

8

135

2

1

5

7

6

6

5

3

3

1

9

136

2

1

4

5

6

6

5

3

3

1

8

137

2

1

5

7

6

6

5

3

3

1

11

138

1

5

6

4

6

6

5

3

3

8

11

139

2

2

6

6

6

6

5

3

3

1

11

140

2

3

6

6

6

6

5

3

3

1

10

141

2

3

6

6

6

6

4

3

3

1

9

142

2

1

5

7

6

6

4

3

3

1

12

143

2

3

5

6

6

6

5

3

3

1

11

144

2

2

6

7

6

6

5

3

3

1

11

145

2

3

6

7

6

6

4

3

3

1

11

146

2

5

6

7

6

6

4

3

3

1

12

147

2

4

5

6

6

6

5

3

3

1

9

238

Dataset for Case Study 3 Variables where Thermal Choices and Control are in accordance Solar Access Wind Access 26 27 28 29 30 31 32 33 34 No. s se sw e w Cross Vent Cnr Vent Heating Openings 2 1 1 1 1 1 0 1 1 118

References 35 No. Trans Spaces 1 Nesbitt, P.H. 1938

119

4

1

1

1

1

1

0

2

4

1

Nesbitt, P.H. 1938

120

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

121

2

1

1

1

1

1

0

1

2

1

Nesbitt, P.H. 1938

122

2

1

1

1

1

1

0

1

1

1

Nesbitt, P.H. 1938

123

2

1

1

1

2

1

0

1

5

1

Nesbitt, P.H. 1938

124

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

125

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

126

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

127

1

1

1

1

1

1

0

0

2

1

Nesbitt, P.H. 1938

128

2

1

1

1

1

1

0

0

2

1

Nesbitt, P.H. 1938

129

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

130

2

1

1

1

1

1

0

1

1

1

Nesbitt, P.H. 1938

131

4

1

1

1

1

1

0

2

4

1

Nesbitt, P.H. 1938

132

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

133

2

1

1

1

1

1

0

1

2

1

Nesbitt, P.H. 1938

134

2

1

1

1

1

1

0

1

1

1

Nesbitt, P.H. 1938

135

2

1

1

1

2

1

0

1

5

1

Nesbitt, P.H. 1938

136

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

137

1

1

1

1

1

1

0

0

1

1

Nesbitt, P.H. 1938

138

1

1

1

1

1

1

0

0

1

1

Kirkbride, D. 1968a & b; Byrd, B.F. 1989; Byrd, B.F. 2005

139

2

1

1

1

1

1

0

0

1

2

Kirkbride, D. 1967; Byrd, B.F. 1994; Byrd, B.F. 2005

140

2

1

1

1

2

1

0

0

2

2

141

1

1

1

1

1

1

0

0

1

1

Kirkbride, D. 1967; Byrd, B.F. 1994; Kirkbride 1968b; Byrd, B.F. 2005 Kirkbride, D. 1967; Byrd, B.F. 1994; Byrd, B.F. 2005

142

2

1

1

1

1

1

0

0

1

2

Kirkbride, D. 1967; Byrd, B.F. 1994; Byrd, B.F. 2005

143

1

1

1

2

1

1

1

0

2

1

Kirkbride, D. 1968b; Byrd, B.F. 2005

144

1

1

1

1

1

1

0

0

1

1

Kirkbride, D. 1967; Byrd, B.F. 1994; Byrd, B.F. 2005

145

2

1

1

1

2

1

0

1

2

1

Byrd, B.F. 1994; Byrd, B.F. 2005

146

2

2

1

1

1

1

1

0

2

1

Byrd, B.F. 1994; Byrd, B.F. 2005

147

4

1

2

1

1

2

2

4

2

1

Byrd, B.F. 1994; Byrd, B.F. 2005

239

The Evolution of the Built Environment Site

Period

Room No.

Group

Variables where Thermal Choices and Control are contradictory Exposure 1 2 3 4 n ne nw s

148 Beidha

PPNB A2

74

149 Beidha

PPNB A2

83

150 Beidha

PPNB A2

33

151 Beidha

PPNB A2

37

152 Beidha

PPNB B

32

153 Beidha

PPNB B

37

154 Beidha

PPNB B

61

155 Beidha

PPNB B

60

156 Beidha

PPNB B

47

157 Beidha

PPNB B

25

158 Beidha

PPNB B

40

159 Beidha

PPNB B

160 Beidha

PPNB B

161 Beidha

PPNB C

sctr E6 north sctr E6 south 13 lower

162 Beidha

PPNB C

13 upper

163 Beidha

PPNB C

14 lower

164 Beidha

PPNB C

165 Beidha

PPNB C

166 Beidha

PPNB C

167 Beidha

PPNB C

168 Beidha

PPNB C

14 upper west 14 upper east 12 north lower 12 north upper 12 south

169 Beidha

PPNB C

19 lower

170 Beidha

PPNB C

19 upper

171 Beidha

PPNB C

10 lower

172 Beidha

PPNB C

10 upper

173 Beidha

PPNB C

75

174 Beidha

PPNB C

w

175 Beidha

PPNB C

sw

176 Beidha

PPNB C

s

177 Beidha

PPNB C

se

Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 2 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3 Jordan Highlands 3

1

2

1

2

1

2

1

1

1

10 Roof Shape 2

2

1

1

2

1

2

1

1

1

2

4

5

3

1

1

1

1

2

2

2

1

1

1

4

4

5

3

1

1

1

1

1

1

1

1

1

1

2

4

5

3

2

2

2

1

2

2

2

2

2

1

6

6

5

1

1

1

2

2

2