Making Visible: Three-dimensional GIS in Archaeological Excavation 9781407314723, 9781407344263

Table of contents :
Front Cover
Title Page
Copyright
TABLE OF CONTENTS
List of figures
List of tables
List of hardware and software used
Preface
Acknowledgements
Chapter 1. Introduction
Chapter 2. Three-dimensional GIS and Excavation
Chapter 3. Fundamentals of 3D Modelling and Visualisation within a GIS Environment
Chapter 4. Making Visible: Archaeological Excavation in Three Dimensions
Chapter 5. Conceptual Design and Operational Framework
Chapter 6. Making Practical: Examples of Intra-site 3D GIS
Chapter 7. Conclusions and Further Research
Glossary
References

Citation preview

Making Visible: Three-dimensional GIS in Archaeological Excavation Stefania Merlo

BAR International Series 2801 2016

First Published in 2016 by British Archaeological Reports Ltd United Kingdom BAR International Series 2801 Making Visible: Three-dimensional GIS in Archaeological Excavation

© Stefania Merlo 2016 The Author’s moral rights under the 1988 UK Copyright, Designs and Patents Act, are hereby expressly asserted. All rights reserved. No part of this work may be copied, reproduced, stored, sold, distributed, scanned, saved in any form of digital format or transmitted in any form digitally, without the written permission of the Publisher.

ISBN 9781407314723 paperback ISBN 9781407344263 e-format DOI https://doi.org/10.30861/9781407314723 A catalogue record for this book is available from the British Library

Cover Image: Fragmented archaeology in a 3D space – an elaboration upon Cornelia Parker’s “Cold Dark Matter” by Chiara Merlo (March 2016)

All BAR titles are available from: British Archaeological Reports Ltd Oxford United Kingdom Phone +44 (0)1865 310431 Fax +44 (0)1865 316916 Email: [email protected] www.barpublishing.com

Table of contents

List of Figures

vi

List of tables

viii

Preface

ix

Acknowledgements

x

1

1

Introduction

1.1 1.2 1.3 1.4

Research motivation Research objectives Scope of research Methodology 1.4.1 Conceptual level 1.4.2 Operational level 1.4.3 Implementation level 1.5 Structure of the book

1 2 3 4 5 5 5 5

2

7

Three-dimensional GIS and Excavation

2.1 3D GIS modelling of complex environments and processes: the state of the art 2.1.1 Geological modelling and the petroleum industry 2.1.2 Environmental modelling 2.1.3 Urban planning 2.1.4 Advantages and potentials of using 3D GIS for modelling complex phenomena 2.2 3D GIS: a working definition 2.3 Modelling and three-dimensional GIS for archaeological excavation 2.3.1 Models and modelling: a terminological clarification 2.3.2 Abstraction processes: from the field to the computer 2.3.3 Modelling archaeological subsurface data with 3D GIS: terms of reference 2.3.4 The construction and meaning of the archaeological data model 2.3.5 Multiple representations as a solution to model complex data 2.4 The state of art in the development of three-dimensional intra-site GIS: archaeological challenges and technical solutions 2.4.1 Early case studies of three-dimensional modelling for archaeological excavation 2.4.2 Two-and-a-half-dimensional modelling 2.4.3 Three-dimensional GIS for excavation: two procedures for reconstructing stratigraphy 2.4.4 From explorative visualisation to spatial patterning 3D GIS with a view 2.4.5 Towards dedicated 3D GIS architectures 2.4.6 Conclusion 2.5 Summary 3

Fundamentals of 3D Modelling and Visualisation within a GIS Environment

3.1 GIS models 3.1.1 What do GIS model? The representation of spatial data 3.1.2 Data structures: raster and vector. A debate or reconciliation? 3.1.3 Spatial and non-spatial attributes 3.1.4 GIS functions

iii

8 8 8 10 10 11 12 13 13 14 14 15 15 16 19 22 27 30 31 32

33 33 33 34 36 36

Making Visible: Three-dimensional GIS in Archaeological Excavation

3.2 Three-dimensional modelling in GIS 3.2.1 Review of representations and data models for 3D GIS 3.2.2 The construction and structuring of three-dimensional spatial models 3.2.3 Three-dimensional spatial models and their semantics – from geometry to topology 3.2.4 Three-dimensional GIS: analytical functionality requirements in a three-dimensional model 3.2.5 Summary 3.3 Three-dimensional visualisation 3.3.1 The role of visualisation in a 3D GIS environment 3.3.2 Visualisation and archaeological excavation 3.3.3 Visualisation requirements and technical issues 3.3.4 Summary and remarks 3.4 Conclusion 4

Making Visible: Archaeological Excavation in Three Dimensions

4.1 What is left to say? 4.1.1 Geometry and the space of excavation 4.1.2 Material and immaterial 4.1.3 Many places, many spaces 4.2 The production of archaeological space 4.3 Space in archaeological GIS 4.4 Critique and reassessment of some concepts used in excavation practice 4.4.1 On stratigraphy 4.4.2 On surface and depth 4.4.3 On lines, grids and objects 4.4.4 On mapping 4.4.5 On mimics 4.5 Reasoning about 3D archaeological space in a GIS framework 4.6 Concluding thoughts 5

Conceptual Design and Operational Framework

5.1 Designing and implementing data models in GIS: concepts and terminology 5.1.1 Design phases in modelling 5.1.2 The design process of the project 5.2 The conceptual framework 5.2.1 Conceptual framework: rationale of the system 5.2.2 The 3D spatio-temporal framework: underlying principles 5.2.3 The framework illustrated 5.3 The data model schema: design principles 5.3.1 Design concept 1. Use of a three-dimensional GIS 5.3.2 Design concept 2. Matters of scale: modelling sitescapes through time 5.3.3 Design concept 3. Information integration 5.3.4 Design concept 4. Representation of field-based and object-base excavation 5.3.5 Design concept 5. Expressing and extracting different information 5.3.6 Design concept 6. Connecting GIS and process/simulation models 5.4 Data model workflow and system architecture 5.4.1 Notes on the chosen system architecture 5.4.2 Primary observations database and data pre-processing 5.4.3 Creating three-dimensional primary and derived models 5.4.4 Model visualisation and assessment 5.5 Conclusions

iv

40 40 42 43 47 50 50 50 51 53 53 54 55 55 55 56 59 59 61 61 62 62 64 64 65 66 68 69 69 69 72 72 73 74 75 77 77 77 80 81 82 83 83 83 84 86 96 96

Contents

6

Making Practical: Examples of Intra-site 3D GIS

6.1 The Kouphovouno project 6.1.1 Background 6.1.2 Excavation procedures 6.1.3 Data collection: procedures for recording three-dimensional shapes and relationships 6.2 The building of the excavation three-dimensional base model 6.2.1 Geometric model of contexts 6.2.2 Geometric modelling of post-holes 6.3 Analytical potential and limitations of context-based excavation in 3D 6.4 Considerations on three-dimensional GIS and stratigraphic excavations 6.5 The rescue excavation at Hoge Vaart 6.5.1 Background 6.5.2 Data collection 6.5.3 Palaeo-geological setting and chronology 6.5.4 Spatial processes examined at Hoge Vaart 6.6 Taking Hoge Vaart into the third dimension: the approach 6.6.1 Reassessing Hoge Vaart within a three-dimensional framework: data retrieval and preliminary evaluation 6.6.2 Formation processes and GIS modelling at macro-scale 6.6.3 Three-dimensional GIS analysis and visualisation at meso- and micro-level 6.7 Summary of the Hoge Vaart case study 6.8 Summary 7

Conclusions and Further Research

99 99 99 101 101 106 106 108 111 113 114 114 115 119 119 121 121 123 131 149 150 151

7.1 Summary 7.2 Conclusions 7.3 Further research

151 152 154

Glossary

155

References

161

The appendix (colour figures) is available as a download from: www.barpublishing.com/additional-downloads.html

v

Making Visible: Three-dimensional GIS in Archaeological Excavation

List of figures Figure 1.1. Main stages of the research methodology Figure 2.1. Two-, two and 1/2 and three-dimensional archaeological context Figure 2.2. Stereo-scattergrams of artefacts at La Cotte de St. Brelade Figure 2. 3. Excavation boxes and find distributions at St. Veit-Kinglberg Figure 2.4. Vertical slicing of the Grafland model Figure 2.5. Stratification of deposits and 3D solid library of finds Figure 2.6. Stratigraphic unit 168 of Schwarzenbach Figure 2.7. Domus della Pescatrice, Pompeii. Three-dimensional visualisation Figure 2.8. Palace of Herod the Great Figure 2.9. DTMs showing the topography of the archaeological deposits at Knossos Figure 2.10. Cross section of the Knossos Tell Figure 2.11. Shamakush VIII site Figure 2.12. Volumetric representation of Shamakush VIII site Figure 2.13. 3D density of chemical gradients Figure 2.14. TIN surfaces and polygonised volume of stratigraphic layer at Montale Figure 2.15. Closed polygon of Montale context in ArcGIS Figure 2.16. Voxel and tetrahedron model of trench TEW at Tell ‘Acharneh Figure 2.17. Script query applied to the 3D model of trench TEW Figure 2.18. Low oblique perspective of the Loiyangalani site Figure 2.19. Waterworn pebbles at Swartkrans and database query Figure 2.20. Spheric buffer implemented for the Swartranks site Figure 2.21. The five sections of the City Gate excavation Figure 2.22. The sections of the City Gate excavation in AutoCAD Figure 2.23. Volume calculation in Arc View Figure 2.24. Visualisation of strata and finds in the STRAT tool Figure 2.25. The ARCHAVE project prototypes Figure 3.1.Entities and fields in vector and raster GIS Figure 3.2. Comparison of entity based objects and fields Figure 3.3. 1990 version of the MoLAS context recording sheet Figure 3.4. Examples of surface-based representations Figure 3.5. Examples of volume-based representations Figure 3.6. Representation of the various dimensions of geometric objects Figure 3.7. Topological relationships between two 3D topological regions Figure 3.8. A reconstructed pot inside its context Figure 3.9. Symbolised three-dimensional excavation Figure 4.1. Exploding excavations Figure 4.2. Cold Dark Matter: an exploded view Figure 4.3. Before, during and after: Cold Dark Matter: an exploded view Figure 4.4. The trialectic production of space Figure 4.5. Final stratigraphic sequence for a site Figure 4.6. Operational data spaces Figure 5.1. Data modelling levels Figure 5.2. High level overview of the spatiotemporal information system Figure 5.3 The spatiotemporal framework Figure 5.4. Palaeogeographic development of the Flevoland polders Figure 5.5. Stratigraphic reconstruction of the Hoge Vaart pedology Figure 5.6. Two- and three-dimensional distribution of charcoal at Hoge Vaart Figure 5.7. Collage of images of and from excavations Figure 5.8. System architecture Figure 5.9. Organigram of the data model Figure 5.10. MultiPatch geometry parts Figure 5.11. Extruded archaeological features at the Hoge Vaart Figure 5.12. Multipatch representation of context 801 at KE2003

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Contents

Figure 5.13. Procedure for the creation of a 3D geometric object Figure 5.14. Solid 01 and solid 02 Figure 5.15. 3D context 801 Figure 5.16. Horizontal and vertical sections of a 3D geological model Figure 5.17. Trench IX of the Akroterion excavation at Kythera Figure 5.18. Process of creating a solid model using the GRASS Figure 5.19. Creating sections by slicing the solid volume model Figure 6.1. Location of the Kouphovouno site Figure 6.2. Plan of the areas excavated in 2001-2003 Figure 6.3. Close up of sounding C Figure 6.4. The hardware and software equipment Figure 6.5. AutoCAD project Figure 6.6. Uppermost post-holes in trench C Figure 6.7. Traditional 2D recording of a post-hole Figure 6.8. Three-dimensional vector TIN model Figure 6.9. Three-dimensional vector based model Figure 6.10. Three-dimensional voxel model Figure 6.11. Packing stones of context 815 Figure 6.12. Comparison of 2D and 3D representation of post-hole Figure 6.13. Three-dimensional MultiPatch Figure 6.14. Three-dimensional visualisation of contexts and single finds Figure 6.15. The solid stratigraphic model of context Figure 6.16. Geographical location of the Hoge Vaart-A27 Figure 6.17. Subdivision of the Hoge Vaart-A27 excavation Figure 6.18. 5 meter excavation unit Figure 6.19. Automatic recording Figure 6.20. Digital Terrain Model of the Hoge Vaart site Figure 6.21. Chronological subdivision of archaeological activities Figure 6.22. Hoge Vaart northern concentration Figure 6.23. 3D visualisation of the Hoge Vaart-A27 single flints Figure 6.24. Overview of the location of bore hole lines Figure 6.25. Database log of the bore holes Figure 6.26. Two- and three-dimensional visualisation of the bore holes Figure 6.27. Complex cross-cutting of the 66 soil types Figure 6.28. Schematic representation of soil stratigraphy Figure 6.29. Three-dimensional soil model Figure 6.30. Interpolated reed and humic concentrations Figure 6.31. Interpolated reed and humic concentrations Figure 6.32. Sequence of visualisations of the total weight of flint Figure 6.33. Slicing of the 3D volume of the flint fine fraction Figure 6.34. Overview of the unburnt flint fine fraction Figure 6.35. Overview of the burnt flint fine fraction Figure 6.36. Two-dimensional overview of density distribution Figure 6.37. Three dimensional representation of burnt and unburnt flint Figure 6.38. Two-dimensional and three-dimensional view of features Figure 6.39. Distribution of hearths Figure 6.40. Three-dimensional distribution of hearths Figure 6.41. Three-dimensional visualisation of hearths Figure 6.42. Quantity of flints in features Figure 6.43. Quantities of pottery in features Figure 6.44. Quantities of charcoal in features Figure 6.45. 3D point attribute query Figure 6.46. Selected trapeze flints Figure 6.47. Spherical 1 m radius buffer around surface hearth Figure 6.48. Northern concentration. A cylindrical buffer of 8 m Figure 6.49. 3D buffer operation at the Swartranks site (RSA)

vii

Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 6.50. Data exploration in 3D space Figure 6.51. Hoge Vaart-A27 northern concentration. Occurrence of flints Figure 6.52. Sequence at the Hoge Vaart (Northern concentration) Figure 6.53. Hoge Vaart-A27 northern concentration. Density distribution Figure 6.54. Location of high ratio of burnt/unburnt flints Figure 6.55. General view of the ratio burnt/unburnt flint knapping debris Figure A1. GRASS 6 architecture Figure A2. Diagram of GRASS database Figure A3. Vector line and area feature geometries in GRASS Figure A4. Möbius strip visualization to show 3D vector capabilities of GRASS 6 Figure A5. ArcGIS geodatabase Figure A6. Datasets in ArcGIS geodatabase Figure A7. Comparison of feature and raster elements in ArcGIS geodatabase

List of tables Table 2.1. Areas of environmental modelling and GIS requirements Table 2.2. Levels of abstraction from reality to computer file structures Table 3.1. Mathematical operations for transforming attribute data Table 3.2. Summary table of representation techniques in solid modelling Table 3.3. Three-dimensional topological models Table 5.1. Levels of abstraction from reality to computer file structures Table 5.2. GIS 3D geometric primitives and 3D archaeological elements Table 6.1. Hardware and software used for data collection and editing Table 6.2. List of main categories of finds at the A27 Hoge Vaart excavation Table 6.3. Hoge Vaart A27: MapInfo graphic files list Table 6.4. Hoge Vaart-A27: overview of data tables

List of hardware and software used Hardware Intel Core Duo 2.13 GHz, 2GB RAM, 2MB cache. ATI mumble video card Intel Core 2 Duo 2.2 GHz, 2 GB RAM DDR2 SDRAM. GMA X3100 Software Category Operating system

GIS CAD Visualisation 3D Meshes creation 3D Modelling Data conversion

Commercial software

Open Source software

MacOSX 10.5.8 Windows XP

Linux pc039 2.6.20-16-generic GNU/Linux: Ubuntu 7.04 Feisty Fawn with updates. Kernel 2.6.mumble

ArcGIS 9.2 and 9.3 MapInfo AutoCAD 2004 ArcScene 3D Analyst Autodesk 3ds Max CAD2SHAPE

GRASS 6.x∗ QCad Paraview Meshlab Blender -

*In this research various developing versions of GRASS were used running on three platforms: OSX, Ubuntu and Windows XP. Modules r.vol.dem and v.crossbones were compiled and built for GRASS versions 6.1 and 6.4 respectively. The source code for v.crossbones

is

available

at

http://oadigital.net/software/xbones.

R.vol.dem

was

provided

by

Benjamin

(http://oadigital.net/aboutus/benjaminducke). Neither modules have been incorporated in any stable version of the software.

viii

Ducke

Preface

This book represents an intellectual exercise aimed at filling the theoretical and practical lacuna of GIS applications for intra-site analysis. It has long been argued that this lacuna is principally the consequence of the inability of GIS to manipulate three-dimensional data, where three-dimensionality is the main characteristic of the excavated archaeological record. The research is therefore devoted to the study of the role and potential of three-dimensional GIS modelling for understanding excavation data in theory and in practice. The premise of the research is that unless one attempts to critically engage with the nature of archaeological excavation and with contemporary archaeological practice (i.e. the operations of recording archaeological data and transforming them by creating the archaeological record), it becomes very difficult to assess the effectiveness of analytical systems to represent and interpret archaeology. For this reason, this research seeks to address the following questions: 1. To what extent do archaeological data and GIS structures parallel one another and how can GIS represent an archaeological excavation? 2. Can three-dimensional dynamic GIS improve our understanding of depositional and postdepositional phenomena? If so how might this operate? The thesis discusses the first question in critical detail before going on to argue that the answer to the second is positive. Three-dimensional data and the development and coupling of modelling techniques to GIS allow for a better and more faceted understanding of the excavation record. This research concentrates on experimenting and evaluating three-dimensional GIS for the study of archaeological excavation as a process that generates archaeological data at many different levels, in variously forms and across different spaces (from the site to the computer). Whereas the research focuses more on theoretical concepts that contribute to defining the nature of archaeological data in exploratory multidimensional complex environments, the practical examples presented narrow down to the application of three-dimensional representations in two specific excavation scenarios: the Neolithic site of Kouphovouno in Greece and the Hoge Vaart excavation in the Netherlands. Results of this study outline a conceptual framework for representing spatial (and temporal) excavation information, and provide a blueprint for creating a model for storing, manipulating and analysing archaeological excavation data. In addition to the framework, some procedures are defined for outlining the core data needed for representing excavation data in three dimensions.

ix

Acknowledgements I thank my supervisor Colin Shell for believing that, one day, I would complete the PhD dissertation around which this book is based. I am grateful to my advisor Charly French for his scholarly support throughout my work. I also thank my PhD examiners Fraser Sturt and John Robb for their insightful comments that allowed me to improve my research work greatly. My PhD work would not have been possible without the financial support of the following bodies: the Board of Graduate Studies and Newnham College (Cambridge), Università degli Studi di Padova and Università Statale di Milano, the Cambridge European Trust and the British Federation of Woman Graduates. I am most profoundly grateful to Hans Peeters for providing the data of the Hoge Vaart case study. For his support, help, discussion and for sharing his enthusiasm. I wish to thank the directors and team of the Kouphovouno project, Laconia (Greece): Chris Mee, Bill Cavanagh and Josette Renard. Moreover, the people that helped me during the recording of excavation trench C sounding: Thomas Laughlin, Chloe Duckworth and Marie Saulnier. My thank you goes to Matthew Fitzjohn who invited me to participate in the project. For their invaluable technical support I thank David Redhouse, Giovanna Falcone, Tiziano Ghisu and Benjamin Ducke. Mark Gillings, Andrea Balbo and Susanne Hakenbeck were my reviewers and Mncedisi Jabulani Siteleki helped me formatting the document for publicaton. Finally, I thank my friends and colleagues and my family for the constant support along the very long way.

x

publications and therefore does not encourage a critical assessment of IT by archaeologists involved in every day practice. The use of IT and of GIS in contemporary archaeological practice is not neutral and calls for an exploration of the position of the archaeologist in creating the archaeological record within a digital framework (conceptualised by Reilly (1991) as ‘virtual archaeology’) (Merlo 2004).

Chapter 1. Introduction This study is an intellectual exercise aimed at filling the theoretical and practical lacuna of GIS (Geographical Information Systems) applications for archaeological intra-site analysis. It has long been argued that this lacuna is principally the consequence of the inability of GIS to manipulate three-dimensional data, where threedimensionality is the main characteristic of the excavated archaeological record. The research is therefore devoted to the study of the role and potential of three-dimensional GIS modelling for understanding excavation data in theory and in practice.

The inspiration of exploring the role of and use GIS in contemporary excavation derives from the researcher’s own interest in the use of IT in the humanities. GIS were introduced in archaeology in the late 1970s as a tool to better understand the spatial and temporal nature of the archaeological record. This information technology is designed to store, analyse and display both spatial and non-spatial data (Burrough and Mc Donnell 1998). Despite its early application, archaeologists have failed to transform GIS from a simple tool into a proper research instrument, with methodological and theoretical implications, in particular in the field of intra-site analysis (excavation level). Some authors (Biswell et al. 1995, Huggett 2000b, Wheatley and Gillings 2002) have listed a number of reasons to explain this failure: 1. the nature of GIS dissemination in archaeology, which privileged landscape archaeology and CRM; 2. the nature of archaeological excavation data: the expense of collecting and processing site-based spatial data and the lack of consensus among archaeologists about what constitute minimal data collection requirement; 3. the quality of data collected in the context of modern excavation practice: inconsistency of resolution and inadequacy of recording; 4. the nature of GIS analytical modules, designed for big scale analyses; 5. the lack of availability of true three-dimensionality in routinely available GIS packages.

Archaeological excavation with the complexity and interrelations between different types of data (3D spatial and non-spatial) that characterises it presents a challenge in being handled and analysed in its entirety with current information systems. This work describes an attempt to integrate available technology components and develop new concepts to attain a system that better meets the requirements of archaeologists to approach the archaeological record in a more integrated and multifaceted manner. The study concentrates in a limited area of the 3D GIS development, the concept of creating a conceptual framework that reflects the structure and characteristics of data from archaeological excavations. The focus is on three-dimensional spatial representations of the archaeological record and its potential to elicit alternative approaches to the use of intra-site GIS. The framework is practically applied in two specific excavation scenarios: the Neolithic site of Kouphovouno in Greece and the Hoge Vaart rescue excavation in the Netherlands. 1.1

Research motivation

The publication of a number of manuals dedicated to excavation in the early 2000s (Collis 2001, Drewett 1999, Roskams 2001) highlighted a renewed interest in this subject after nearly a decade of silence. Following the post-processual claims of Ian Hodder, a discussion on related issues in monographs and papers animated the academic debate on how to best excavate and record archaeological sites (Andrews et al. 2000, Chadwick 1997, Hassan 1997, Hodder 1997, 1998, 2000, Lucas 2001a). Nevertheless, attention focused mainly on the merits (or otherwise) of scientific versus interpretative approaches, and subjective versus objective methods. As a consequence, considerations of the practicality and applicability of the different approaches was a secondary issue, when mentioned at all.

Efforts have been made to overcome some of these problems, in particular on the aspect of data collection. Although platforms have been developed for excavation data management since the 1980s (Arnold 1985), a more consistent use of electronic equipment is documented from the beginning of the 1990s, with a vision to collect data in three dimensions. Unfortunately, despite claims of the profound three-dimensional character of archaeological data (Harris and Lock 1996) - here from the strong connection between intra-site GIS and 3D only a few projects experimented with an overall threedimensional data collection system. GIS main capability, ‘the ability to store not only locational and attribute data for each spatial entity, but also the topological relationships between them’ (Lock and Harris 1992) allows for a series of analyses: data integration, reclassification, evaluation (through spatial queries, ‘what if’ queries, statistics, Boolean operations) along with modelling, visualisation, output flexibility. Some of these operations were performed on twodimensional excavation data with good performances in

With the exception of the work of Roskams (2001) and Hodder (1999), the existing reference literature still largely fails to address one of the major elements contributing to innovation in excavation practice during the past two decades years: the use of information technology (IT). Much of the literature on this topic remains confined to specialist journals and dedicated

1

Making Visible: Three-dimensional GIS in Archaeological Excavation computerized environment. Although rapidly increasing, archaeological literature in this area is rather limited and tends either to underestimate problems related in particular to error detection and evaluation, or to regard information systems environments as negative constraints (Huggett 2000a). Moreover, the notion that multidimensional GIS analysis and visualisation are better than conventional ones, and even more suitable for use in intra-site exploratory environments is, so far, an opinion that has not been experimentally proven. A tight discussion of the role of GIS in archaeological field research is definitely needed.

terms of interpretation and predictive modelling. Nonetheless, when using three-dimensional data, the archaeologists produced, at best, 2½ dimensional visualisations in the form of surface series and cross sections (see, for example, Levy et al. 2001, Schryver 2002, Vullo et al. 1999, Zhukovsky 2002). In order to perform the above-mentioned spatial analyses, using three-dimensional data, a fully 3D volume environment is needed (Harris and Lock 1996). As a matter of fact, to date, a very limited number of projects can be considered to have conceived a comprehensive GIS 3D philosophy and methodology for excavation and data analysis (Katsianis et al. 2006, Lieberwirth 2008, Losier et al. 2007, Vote et al. 2001, 2002).

Overall it appears that the attention of the archaeologists has been focused on fragments of what should be intended as an integrated process. Theory is not separate from practice and archaeology itself could be considered as a theoretical practice (Shanks and Tilley 1987). The proposed approach to excavation brings together a variety of issues that have never been discussed as a whole and is intended to overcome the inertia that has characterised archaeological excavation in the last years.

Moreover, a further fundamental problem to the development of intra-site GIS has been identified in the lack of a truly temporal dimension to GIS. ‘We might think of excavated data (if not all archaeological information) as varying in potentially 4 dimensions’ (Wheatley and Gillings 2002: 235). The need is felt to treat archaeological information as multidimensional and dynamic, in order to represent archaeological phenomena correctly. A lack of responsive software is often advocated as a cause of underdevelopment in the area. Although Huggett and Ross argue that ‘the use of information technology and information processing methods has changed the practice in archaeology’ (2004: http://intarch.ac.uk/journal/issue15/inf_index.html), this work argues that the intra-site GIS crises is due to the unchanged practice of conducting excavation and postexcavation in the field and is linked to the lack of an overall evaluation of specific functionalities and operations needed at a micro scale level. In fact whereas the extensive requirements regarding computer capacity and the lack of appropriate spatial data, even at a micro scale level, have been partly eliminated owing to better access to such data and the rapid advances in computer technology, the development of spatiotemporal micro theories is still a major challenge to archaeologists.

Firstly, the nature of contemporary archaeological excavation practice and recording needs to be elucidated and issues around the translation of the record into digital systems considered. Only once this is done the assessment of the effectiveness of digital models and analytical systems in eliciting the understanding of complex spatiotemporal patterns and relationships will be possible. For this reason, this research seeks to address the following questions: 1. to what extent do archaeological data and GIS structures parallel one another and how can GIS represent an archaeological excavation? 2. can three-dimensional dynamic GIS improve our understanding of depositional and post-depositional phenomena? If so how might this operate? The book discusses the first question in critical detail before going on to argue that the answer to the second is an emphatic yes. Existing data and the development and coupling of modelling techniques to GIS allow for a better and more faceted understanding of the excavation process. It is clear, nonetheless, that attempts to respond to innovation in tools used in other disciplines and in archaeology does not necessarily lead to certain interpretation. Ultimately, once the tools have been examined, appraised and applied it is the archaeologists’ task, structured by a set of clearly defined research questions, to use the results gained to furnish richer explanations of the excavated data.

Archaeology as a discipline that is rich in spatial representations and used to manipulating data at different levels can play a unique role by providing relevant knowledge of the production and uses of complex data sets. The need is to come up with a more systematic approach that though theoretically driven, would account for multiple ways of producing and interacting with excavation models. Without such a framework, it would continue to be a blind use of tools that are poorly known and data structures that poorly represent archaeological phenomena, with the consequence of producing aesthetically appealing but substantially misleading models to understand patterns in material culture that could tell stories of the past.

1.2

Research objectives

The specific objectives of this research are: A literature review (presented in chapter 2) highlighted a fundamental lack of serious underlying discussion and hermeneutic consideration of the implications of totally new ways of collecting and organizing data in a fully

a) To review the role that GIS plays in all stages of excavation (from data collection to presentation) and the potential of spatiotemporal data exploration and analysis;

2

Introduction them, it is important to emphasise that this work does not simply represent my personal view. Rather, it uses these datasets as a springboard for a much broader analysis of three-dimensional GIS in archaeological excavation and archaeological thought. Such analysis involves an attempt to see the systems as particular to archaeological data whilst acknowledging that the spatial and temporal characteristics of archaeological data might be very different from geocoded environmental and social data. It therefore addresses questions such as: What is the peculiarity of spatiotemporal data and in particular is it important to emphasise the spatial and temporal nature of archaeological data by using this particular technology? Are archaeological data also something else and has this technology helped me to understand and make this explicit?

b) To define the structures and operations needed to enable spatio-temporal analysis in an exploratory environment. Achieving this objective will encompass: i. Studying typical GIS structures and their suitability for archaeological data representations with a particular focus on visual thinking, insight and/or knowledge discovery; ii. Identifying the core spatiotemporal structures and processes that characterise archaeological modelling at present and identify potential new ones that might stimulate alternative analyses; iii. To design a conceptual framework that incorporates the operations and functionalities identified in order to create a three-dimensional dynamic environment for archaeological analysis; c)

1.3

This study has chosen to be specific to archaeology insofar as this discipline deals with a variety of specific problems in term of spatiotemporal data that other disciplines tend not to deal with or do so in a different manner. Building an archaeology-specific GIS conceptual framework and data model might be of use to archaeologists first but to others as well (from the developers of the systems to other disciplines). The choice to build prototypes based on the data model also raises the observation that the use of technology is neither neutral nor natural and, although the accusation that GIS modelling for archaeology tends to be positivistic and deterministic is well known to researchers, very little work has been dedicated to the study of the processes of formalisation and the methodology of applying GIS to archaeology at intra-site level. Reasons for rejection of these studies might also be that GIS are thought of as inadequate to describe archaeological data, as they are simplistic. So, whether too difficult and complex, or too simplistic and static, they are applied (albeit rarely) without any discussion of what they mean, might mean and do. This study seeks to rectify this situation through an intellectual exercise that designs, examines and analyses excavation.

To implement the conceptual framework with the use of practical examples and available software. Scope of research

Whilst both two and three-dimensional GIS approaches have been applied in a diverse range of fields, these have concentrated almost exclusively on inter-site analyses. As a consequence, particularly at intra-site level, design methods have not been adequately tailored to the type of data and processes that they are meant to represent and the techniques employ poorly understood graphic and analytical algorithms and methods. A breakthrough for effective intra-site analysis will entail the careful orchestration of a suite of approaches from a diverse range of disciplines, encompassing: reassessment of knowledge in the field of excavation, GIS terminology and technicalities, modelling in environmental and social sciences, Human Computer Interaction (HCI), and the specific disciplines within which the product is to be applied (e.g. archaeology itself, its sub-disciplines, conservation, planning, etc.). This study will, in a generic manner, look at the various relevant contributions from these diverse backgrounds and integrate the useful functionalities in order to define a GIS for intra-site studies. My position will be to ascertain the usefulness of the functionalities from an archaeological perspective. Some of the concepts in GIS and geological, environmental and social modelling will be adopted and the reader will be referred to relevant background literature where detailed studies have been undertaken. A specific case in point will be in delineating tools that enhance knowledge discovery for the archaeologist in a creative rather than deterministic manner, although within the framework of an accountable methodology.

The field of research is wide and the research has no intent to solve all aspects of the matter. As a result the main concern is to determine the potentials of multidimensional spatial studies in archaeology in theory and practice. The research does not study the specifics of individual contributing parameters related to GIS construction. For example, it does not address the details of the additional computer and human memory processing demands that may be required when using such complex systems, both in terms of memory overload and the possible strain on the human visual and cognitive system that may arise when interacting with dynamic sequences of complex graphic images involving simultaneous variation of several parameters. This research focuses instead on the underlying theoretical concepts that contribute to defining an effective exploratory environment. In particular it acknowledges that there is no one single representation of an archaeological excavation and plays around the concept of excavation models as mental maps. Whilst, on the one

Whereas the research focuses more on theoretical concepts that contribute to defining the nature of archaeological data in exploratory complex environments, the practical examples narrow down to the application of three-dimensional representations in two specific excavation scenarios. Nevertheless, although based on two datasets and my personal experience with

3

Making Visible: Three-dimensional GIS in Archaeological Excavation archaeological problem solving and their possible integration.

hand, it is acknowledged that the excavation record is a model, a representation of reality, on the other the process of model construction is here emphasised, and the fact that the end product is a new artefact which owes to the evolving imagination of one and/or many individuals. The research also studies the possible contributions that a conceptual framework provides in enabling users to explore data sets in the anticipation that new or unexpected patterns or structures will emerge leading archaeologists to construct new hypotheses.

To conclude, the study aims at the development of concepts and their validation by exploring key aspects of three-dimensional approaches to the archaeological record. 1.4

Methodology

In line with the objectives, three levels of research are pursued. These are: conceptual, operational and implementation levels (fig. 1.1).

In summary, this research concentrates on experimenting and evaluating three-dimensional GIS for the study of archaeological excavation as a process and for the interpretation of the archaeological data it generates. Several fundamental considerations outline the area of research as follows: • Since the integration of computer graphics and GIS achievements is still insufficient, the research aims at a conceptual and operational model that fully takes into account the critical aspects of a 3D GIS in terms of visualisation, topology and analysis; • Since knowledge of retrieving and handling 3D geometry and spatial relationships is still rather limited and poorly integrated with the archaeological definitions domain, the investigation focuses at clarifying these key concepts; • Since different excavation methodologies are reflected by different data structures (raster versus vector, arbitrary spits versus stratigraphic excavation) the research explores both structures, their efficacy for

From a practical perspective, the study progressed simultaneously, with a continuous feedback of areas into one another. This process was, at the same time, intended and inevitable. The nature of the research is an enquiry into areas of archaeology, computing and complex systems intended to avoid a focus on technical issues to the detriment of overall evaluations (Lock and Harris 2000) and the accusation that archaeologists are unable to transfer theoretical ideas into practice. As a consequence, the methodological chain implied inevitably an iterative process, where the formulation of ideas lead to practical operations that were tested against the software, assessed and, when necessary, reformulated. For sake of clarity the levels have been represented as separate.

Figure 1.1. Main stages of the research methodology

4

Introduction 1.4.1

the case studies are used to support arguments developed in Chapters 4 and 5.

Conceptual level

Initial phases of the research looked at the use of computer technology and GIS in excavation projects. A great deal of earlier GIS-based projects were static, focusing on the equation: better system created by better data retrieval techniques. The research explores these projects in relation to their effect on archaeologists’ interpretative and judgmental improvement during data retrieval and post-excavation analytical tasks. It also reviews conventional GIS techniques and highlights how these inhibit the incorporation of more dynamic functionalities.

1.5

The work presented here falls basically into the three parts identified above. First, an introduction to the research problem and a review of GIS application to archaeological intra-site analysis. This is followed by discussion of the development or derivation of essential concepts that ought to characterise a multidimensional dynamic GIS when used for intra-site exploratory analysis. The application, evaluation and conclusions of these concepts in a conceptual framework as applied in two case studies concludes the last two chapters of the book.

In relation to archaeological data characteristics, this work outlines an approach that defines analytical procedures within a three-dimensional dynamic environment. Analytical operators are those functions that are at the core of complex data set analysis. The research looks at processes that are typical of space-time data sets and draws from previous results and experiences in temporal geospatial typologies (ie. database classes that incorporate both spatial and temporal aspects of recorded data). Not only do these processes characterise the nature of the data, but they depict the range of queries about the data and processes that users will want to explore. A distinct feature of the methodology is the incorporation of knowledge about processes and structures that lead to knowledge discovery or stimulate insight. This research makes use of these considerations corroborated by empiricallybased intuitions to define typical processes involved in knowledge discovery and proposes a series of theoretical, methodological and practical concepts to be conveyed in the conceptual framework and implementation prototype. 1.4.2

Chapter 1 gives an overview of the entire book structure, highlighting the motivation, problem definition, methodology and how the results of the research seek to contribute to excavation theory and practice. Chapter 2 explores the role of 3D GIS in advanced modelling giving a cross-section of the state of art in various disciplines including the sphere of excavation and intra-site analysis. It then reviews the distinguishing aspects in the design of GIS-based excavation projects. It discusses each of these aspects and the ways in which they can be influenced and improved in order to enrich excavation data analysis capabilities within a multidimensional GIS environment. The chapter also introduces the need to imbue dynamism in GIS as a means of enhancing complex data sets. Chapter 3 outlines various definitions as used in the book. It gives a detailed description of 3D model building and the types and functionalities encountered in the commonly used GIS projects. Potentials and difficulties of transferring structures typical of 2D GIS to three and multidimensional approaches are also discussed.

Operational level

The main interest here is to highlight the GIS functionalities, both those previously identified by other researchers and those that are found to be useful from the studies undertaken at the conceptual level. They are then linked to the specific exploratory and analytical tasks that they could perform. This is the core of the research effort in bridging the gap between archaeological research and information systems development. It encompasses the creation of a conceptual framework that incorporates the structures and functionalities identified and allows dynamic multidimensional analysis for archaeological phenomena elicitation. 1.4.3

Structure of the book

Chapter 4 turns to concepts and terms typical of archaeological field practice. The aim is to give an account of archaeological reasoning and test the suitability of GIS data models and structures to represent and possibly challenge such reasoning. Chapter 5 then discusses the conceptual framework designed for the application of GIS and spatiotemporal modelling to archaeological intra-site representation. It outlines a prototype environment where the previously derived concepts in chapters 3 and 4 are developed.

Implementation level Chapter 6 is the presentation of two excavation datasets. Dataset one draws from two months of fieldwork at the Neolithic site of Kouphovouno, in the Sparta basin, Greece. It is an example of the process of collecting and creating vector data structures in the context of a stratigraphic excavation carried out using single context planning. At Hoge Vaart (The Netherlands) a volumetric approach to gridded excavation is used to perform traditional and innovative analysis on a vast and complex

In the third phase the conceptual framework is used to explore two excavation datasets: the Hoge Vaart project in the Netherlands and the Kouphovouno excavation in the Sparta basin (Greece). The suitability of the developed framework and the functions for archaeological intra-site analysis are tested and assessed using readily available GIS software. Although most primary data are discussed in this part, considerations on

5

Making Visible: Three-dimensional GIS in Archaeological Excavation data set, collected during a rescue archaeology project in the mid 1990s. The integration of 3D vector and voxel structures is assessed in its capability of exploring, modelling and visualising the data set. Their limits and potentials in allowing multidimensional analysis are discussed. Chapter 7 outlines the main contributions and conclusions of the research. Recommendations for improving the attained results and for pursuing further work in this and similar research fields are presented.

6

GIS

analysis, presentation) are performed through this specific computer-based environment.

Archaeology deals with three-dimensional data. Archaeologists are concerned with three-dimensional spatial observations, measurements, and explanations of a great variety of phenomena that are combined to create multifaceted narratives of the past. The three-dimensional spatiality of the excavated archaeological record is, at the same time, a strength and a weakness in terms of both technical and theoretical aspects. If, on the one hand, the link between the vertical dimension of deposition and chronology is the principle which allows us to conduct archaeological research in the first instance, at the same time this aspect is also the most problematic to record and analyse with traditional methods that combine twodimensional plans and sections in order to render volumetric spaces. The essence of three-dimensional recording was acknowledged long ago by archaeologists such as Pitt-Rivers and Wheeler (1956). Nonetheless, the representation of data in three dimensions has always been a problem, in particular in the realm of excavation. As a solution prior to computers, graphical displays involved thematic maps, cross-sections, geometric constructions and reconstructions. All were designed to portray three-dimensional relationships on twodimensional paper, and developed in a detailed and institutionalised procedure of documentation we all are acquainted with: scaled plans and sections, photographs, textual descriptions. It not a surprise, therefore, that the search for new methods to record, analyse and visualise the excavated record in 3D has been a priority in archaeological excavation since modern excavation techniques came into practice.

The application of two-dimensional GIS to the field of archaeology matured rapidly in the 1980s and became widely accepted. Borrowing techniques developed for the areas of land-use management and resource assessment, GIS was used in archaeology for a variety of research and development questions, spanning from predictive modelling to view shed and cost surface analysis.

Chapter 2. Three-dimensional and Excavation

Nonetheless, in particular in the area of intra-site analysis, the reception of GIS has been slower. Some authors (Biswell et al. 1995, Huggett 2000, Wheatley and Gillings 2002) have listed a number of reasons to justify this, as summarised in the introduction of this book (section 1.1). Amongst these, the lack of availability of true three-dimensionality in routinely available GIS packages has been thoroughly addressed by Harris and Lock (1996). The authors stress that the development of GIS analysis at inter-site (landscape) level is linked to the fact that it is in this sphere that standard two-dimensional or 2½-dimensional GIS functionalities used to identify distribution patterns and explore the relationship between sites and environment are strong. The two-dimensional emphasis in archaeology is due, in the authors’ opinion, ‘to the continuation of traditional manual and 2-D CADbased approaches to the handling of archaeological spatial data in the forms of maps and plans.’ (ibid.: 307) This has influenced the development of GIS functionalities from the beginning and has subsequently been influenced by the fact that GIS does not offer multidimensional manipulation capabilities. They also stress the lack of time and ‘change through time’ management capabilities of GIS. All these points, already problematic at a landscape level, become real limitations when dealing with ‘the three-dimensional world of stratigraphy’ (ibid.: 307). As a consequence, whereas much experience was gained with two-dimensional geographic information systems (GIS), which were applied to replicate standard forms of recording, the same cannot be stated for 3D exploration.

The spatial character of excavation data, which is also the key to the representation of its temporal nature, led to early attempts to use GIS for intra-site analysis. Different definitions of GIS1 have been given, of which the most popular in the archaeological literature is the one formulated by Burrough (1986): a powerful set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes (ibid.: 11)

Until recently, computers were of very little assistance to three-dimensional data handling and representation problems. Memory was too expensive to handle the huge amount of data required for three-dimensional representations; computational speeds were slow to perform the necessary calculations within a reasonable timeframe; and graphical displays had a resolution far too low or were too expensive to produce useful visualisations. The advent of modern computer workstations, with their enhanced memory and graphical capabilities at affordable prices, has largely overcome earlier constraints in developing three-dimensional technology. Early commercial three-dimensional GIS packages were developed between the late 1980s and the beginning of the 1990s and modelling applications were announced and demonstrated, in particular in the field of geology and environmental science (Raper 1989b, Turner 1992b). As a consequence, three-dimensional technology has matured significantly in the past years, making it

The fundamental concept is therefore that the system deals with spatial data. Spatial data represent phenomena from the real world in terms of their position, their attributes and their topology, which refers to their spatial interrelation with each other (Burrough and Mc Donnell 1998). These data are implemented in a GIS, whose fundamental components are the hardware and the software, and all the operations on the collected information (capture, modelling, manipulation, retrieval, 1

Summary tables of definitions are presented in Burrough and McDonnell (1998), Maguire and Dangermond (1991), Nyergers (1993).

7

Making Visible: Three-dimensional GIS in Archaeological Excavation analysing geological data (cross-sections and contour maps). In fact, being able to easily manipulate a large, complex dataset provides the geoscientist with the opportunity to detect and visually analyse spatial correlations between different types of data, thus leading to an increased understanding of the data. A threedimensional model of geology is constructed from sample data obtained from field measurements, which are usually scattered, normally in the form of boreholes. To create a volume model from scattered data, interpolation between points is required. Three-dimensional interpolation routines ‘fill out’ the data in places where the data are scarce and generate a three-dimensional continuous volume. In this way the shape and size of reservoirs can be determined. In the case of mining, the final phase is often the development of a three-dimensional mining plan to guide construction of the mine and removal of the ore. Data needed are incorporated in a GIS and then passed to dedicated software for the modelling.

possible to integrate GIS and advanced modelling and analysis. The initial constraints, in particular in the area of visualisation, have been largely surpassed and the ability to rapidly create and manipulate three-dimensional features is now helping real-time understanding of a series of phenomena in disciplines such as geology, mining, marine and atmospheric studies, and environmental and urban modelling. Thanks to software advances, many disciplines are now taking advantage of a 3D GIS that ensures spatial acquisition and manipulation accuracy and allows for complex three-dimensional queries. 2.1

3D GIS modelling of complex environments and processes: the state of the art

As in the popular 2D GIS for 2D spatial data, 3D GIS are used for managing 3D spatial data. 3D spatial systems are considered to offer a better chance to represent and analyse the ever-increasing quantity and complexity of information available to understand real-world phenomena. The emphasis in spatial analysis and semantics typical of GIS has therefore led to recent attempts to push this application into the realm of three dimensions, in the search for specific solutions in a range of disciplines. Some examples of the implementations, problems tackled and results achieved are given in the following sections. In particular, this overview sets out to establish a link with problems and solutions that are relevant to the sphere of archaeology.

2.1.2

Environmental modelling

“Environmental processes in the real world are three dimensional, time dependent and complex, frequently involving non-linearity, stochastic components, and feedback loops over multiple space-time scales” (Bivand and Lucas 1997, 5). Environmental modelling therefore requires more than a 2D system to capture truly volumetric 3D and temporal 4D phenomena. Volumetric phenomena that often occur in nature (e.g. smog movements and dispersal of pollutants in rivers and soils) cannot be fully represented with a 2D system. Twodimensional systems also lack the ability to calculate volumes and show interaction of objects in true 3D space. In the area of environmental modelling, some of the areas listed in table 2.1 have been recognised as better represented and modelled in 3D, whereas areas such as surface modelling and landscape ecological modelling are still considered to be 2 and 2½ D spheres of action.

2.1.1 Geological modelling and the petroleum industry The importance of three-dimensional characterisations in subsurface geology studies has long been recognised (Raper 1989a, Turner 1989). Turner (1989) stressed the need for accurate three-dimensional data to describe depositional systems and aquifer heterogeneity in order to accurately simulate hydrodynamic flow. This need has led to the development of a unique branch of GIS specialised in three-dimensional modelling for geoscience applications called Geoscientific Information Systems (GSIS). An example of a GSIS system is the GOCAD system.2 GSIS is mainly differentiated from GIS by its ability to describe complex three-dimensional spatial representations, either by using volume elements or surface representations.

Amongst the other areas, those that have attracted particular attention not only in terms of simple 3D visualisation but also in terms of numerical modelling and predictions are hydrological and atmospheric and ocean models. Hydrological modelling

The assessment of oil reservoirs and the management of hazardous waste are fundamental requirements in the development of the petroleum industry. Geological characterisation of the site is needed in order to provide information for carrying out these tasks. This involves analysis of the spatial distribution of lithology, porosity, chemical characteristics of soils, etc. Geoscientists are nowadays developing three-dimensional models of geology, recognised as better suited to the integration of many different types of data and as a representation of a site, in contrast with 2D traditional visualisation for

The correlation and synthesis of various threedimensional datasets is recognised as a fundamental requirement for developing regional ground-water flow models in areas of complex geology. The solution is envisaged in a close interfacing of existing analytical techniques for subsurface characterisation, groundwater modelling and statistical assessment. In this context, a 3D GIS becomes the platform where all the data are grouped and correlated (Turner 1992a). Recent developments in the area have therefore been moving in the direction of integrating modelling of erosion, deposition and flooding from a simple surface perspective to the integration of subsurface data, in particular in terms of hydrological modelling.

2

Refer to website http://www.gocad.org (accessed 10/05/2014) for a detailed description of the project.

8

Three-dimensional GIS and Excavation Table 2.1. Summary of the areas of environmental modelling and GIS requirements. Source: Bivand and Lucas 1997, 6.

Discipline

Model objective

Typical modelling variable

Data model requirement

Typical GIS -related problems

Hydrologic models

Predict flow of water and constituents over land and through upper layer

Momentum, acceleration, depth, friction

2.5D plus time

- requires quality digital elevation model

Predict flow of materials (soil, surficial deposits) where flow is contained by the medium through which it flows

Momentum, acceleration, depth, friction

2.5 plus time

Land subsurface models

Predict behaviour of material where flow is contained by the medium thorough which it flows (groundwater)

Momentum, acceleration, depth, friction

3D

Same as above

Ecological models

Predict distribution number or population

Migration/diffusio n, density, birth/death/growth, resources (nutrients, light, etc.)

2D plus time

- entities difficult to bound - fundamental theories often qualitative

Land surface models

resource and size of

-input data (precipitation, evaporation) and stream gauges usually have limited coverage

- little substantive theory; models based on a few empirically-based equations - high demand for accurate spatial data - poorly defined spatial variables (eg. slope, surface roughness)

- non linearities involved and complex interactions between factors

Landscape ecological models

Predict flows between points and mechanisms of change in a spatial pattern

As for ecological models plus density, transition, habitat, mass turbulence

2D plus time

- uses complex, non-linear mathematics - hierarchic processes need to be linked at multiple scales

Atmospheric and ocean models

Predict velocity, mass and direction of flows in atmosphere and/or ocean systems

Momentum, turbulence, temperature, moisture (air), salinity (oceans), density, pressure

3D plus time

- very dynamic: high rate of change for conditions - fully 4D entities - sparse observations relative to volume of model-produced data –requires supercomputer servers

9

Making Visible: Three-dimensional GIS in Archaeological Excavation sewage plans, and so on. Models and animations are used to illustrate and explain the accumulation of certain substances, for example in trials against developers on behalf of conservation departments. 3D GIS is also used to determine the position of humans in the built environment and model spaces inside the city such as fire escape routes inside and outside buildings. This modelling is impossible without a 3D representation of space that simulates the real environment of human navigation. The multifunctional use of space is explored to understand buildings above roads and railway bridges and tunnels: this is only possible using 3D GIS. Transportation and air traffic control are also benefiting from 3D GIS modelling, which helps in planning routes and networks. Civil engineering uses this approach to manage underground pipelines, electrical wires, and any kind of networked system that is distributed across subsoil, soil and air. 3D GIS for urban development (Zlatanova 2000) and the creation of 3D cadastre (Stoter 2004) are examples of recent developments in the subject. Moreover, disaster management in cities, where it is crucial to reduce the speed of emergency responses and therefore represent the complex internal structures of built environments and traffic bottlenecks to plan escape and rescue, seems to find a response in the construction of fully functional 3D spatial analysis (Kolbe et al. 2008, Kolodziej 2008). 3D data models for geometric and topological representation of 3D objects are being conceptualised and experimented within this area, following the GIS-based Intelligent Emergency Response System (GIERS) proposed and evaluated by Kwan and Lee (2005) in response to the 9/11 events (Lee and Zlatanova 2008).

GIS functionalities are often linked to numerical modelling (performed in external software). Conceptual data modelling is fundamental when datasets coming from different disciplinary areas are collated in a computer platform. Recent work has been dedicated to this, specifically in the area of groundwater modelling (Strassberg 2005). Atmospheric and ocean studies National services are increasingly asked to provide vital information for issuing warnings for hazards such as tornados, thunderstorms and high winds. The need to issue warnings that are focused on a particular territory and take into account the infrastructures of the area and the impact of the natural events on the region have led to the development of GIS platforms where cities and landscapes are represented as highly detailed rendered elements (mainly reconstructed from remotely sensed imagery, such as LIDAR data) and the atmospheric events are moving volumes characterised by different levels of intensity. GIS provides the geographic base that is coupled with modelling software operating in a 3D space within a spatio-temporal framework. The availability of 3D in GIS has also been used to analyse phenomena depending on landscape features, such as pollution dispersion in the atmosphere and snow warming on mountainsides due to the circulation of winds (Ciolli et al. 2002). This knowledge is used to model and predict hazards and events such as avalanches. Measurements of conductivity, temperature and depth to compute salinity and determine temperature of water masses are used to determine ocean properties that may explain fish and sea mammal behaviour (Carette et al. 2007, Ledoux and Gold 2004). Volumetric visualisation is used to better understand processes along the physical features. Fishery ecologists and wildlife biologists attempting to understand the relationship between main mammal foraging, prey density and physical environment may overlay foraging data with movement of animals tracked by satellite and radio to understand behaviours through time and calculate volumes. 2.1.3

2.1.4 Advantages and potentials of using 3D GIS for modelling complex phenomena As is clear from the examples above, with threedimensional GIS capabilities more realistic modelling is possible. Coupled with increased research and advances in computer technology, they offer great potential. A primary advantage is identified in the fact that threedimensional data are more manageable than their 2D correspondents. They in fact are easier to access (if used as spatial indexes) as they offer access to data that are normally compacted in 2D in a much more user-friendly manner. Reduction of abstraction, in order to open up representations to specialists outside GIS and mapping and to the public, is also highlighted with the possibility of easier identification of the shape and characteristics of objects in 3D. Ways of browsing information are also easier in a 3D environment. This does not necessarily mean that representation and modelling in 3D is easier, rather the contrary, as geospatial 3D objects are known for their complexity. The ‘business value’ of 3D approaches is highlighted by software vendors in this sense, whereas researchers and independent developers underline other advantages of 3D GIS, listed below. Advantages of using GIS-based threedimensional geovisualisation and analysis to explore human activity patterns have been highlighted and are summarised in Kwan and Lee (2004) as follows:

Urban planning

Urban models using computer simulations have been employed to inform the planning process since the mid 1990s. Innovative forms of visualisation and presentation of architecture are conceived as more efficient tools for planners and architects, which can deliver better their ideas to developers and general public than traditional two-dimensional plans and sketches. Initial 3D visualisations mainly used for virtual tourism developed into more sophisticated tools for planning processes, integrated with other data (Hudson-Smith and Evans 2003, Ranzinger and Günther 1997). 3D visualisation for studies on visual impact are increasingly integrated with information on geology to configure the impact of distribution of waste and pollutants in permeable soil layers, interconnection of these layers to former city

10

Three-dimensional GIS and Excavation been the acknowledgment, in the area of the geosciences, that 3D procedures could revolutionise scientific enquiry.

1. a more complex and realistic representations of the human environment than conventional methods, which facilitates exploratory spatial data analysis and the identification of spatial relations in the data. Results can be then exported to software packages to conduct formal analysis and perform modelling; 2. a dynamic and interactive environment: viewing, querying and seeing results is more immediate; 3. the ability to retain and reflect the complexity of original data more than is the case in traditional twodimensional abstraction; 4. the creation of a ‘virtual world’ of high realism.

2.2

3D GIS: a working definition

A detailed conceptualisation and definition of 3D GIS finds space in chapter 3. At the moment, in order to offer a framework of reference for the following discussion, a working definition of 3D GIS is provided, together with the basic concepts linked to this modelling system. As is true in any area of emerging technology that encompasses quite a broad range of applications (see section 2.1), the term 3D GIS may have a variety of definitions. People have very different concepts about 3D GIS. Some think that it is the display of attribute data in 3D, others conceive it as terrain visualisation, cityscape modelling, or virtual reality. And others think of it as the analysis of complex spatial data. 3D GIS is certainly this and much more, as will emerge in the course of the following discussion. Nevertheless, the working definition used in this work is oriented mainly at distinguishing between true 3D descriptions from other conceptualisations of ‘objects’. In fact, many representations called commonly 3D GIS are in fact 2½ D systems.

Specifically, in the realm of archaeological excavation, the advantages are those of being able to study spatial problems at different scales and to integrate data on soils and their properties with various classes of finds. Improved imaging (but not necessarily extreme realism – as will be discussed in chapter 4) is also an advantage. In fact, mapping complex structures from a grid of 2D data is a subjective process that archaeologists are trained to learn in order to interpret density of information. The archaeologist has to make decisions about how to join up features seen on lines that might be metres apart. This means that establishing a pattern in a complicated area will be time-consuming, and the resulting map will always have significant uncertainties. 3D data, with their dense grid of reference, allow features such as stratigraphy to be followed and mapped with much greater assurance of closeness to the direct source of data, if not of interpretation. In this manner, the recorded data can be manipulated and further studied out of the field in different scenarios of model construction.

Two-and-a-half-dimensional systems go a step further than just describing the real world on a plane by assigning a z attribute value to a set of x and y locations. Examples of 2½ dimensional representations are Digital Elevation Models (DEMs) and Triangulated Irregular Networks (TINs). These data structures are commonly used to represent surfaces. Their z coordinates are attributes of points described by x and y coordinates in the TIN or by cells in the DEM. It is obvious that these surfaces, although very useful for describing the horizontal dimensions of simple stratigraphic contexts – in geology and in archaeology alike (see section 2.4.2) are inadequate for the representation of complex volumes, where a point x and y might have multiple vertical coordinates (e.g. overhanging and hollow shapes).

The possibility of picking up 3D patterns otherwise lost in the noise of the data is also offered. Consistent changes across a 3D dataset stand out from the noise much more than changes along a 2D line. In a 3D scenario, volumes can be inspected as a whole to get a general impression of features of interest. This might vary based on the excavation approach used, as in stratigraphic excavation most interpretation is done during the digging, whereas in grid excavation distribution analysis and statistics are used to recognise patterns (refer to chapter 4 for further discussion). Still, in both cases, 3D visualisation for inspection can give unexpected results, in particular when trying to write up and go back to the excavation several months and years afterwards.

A ‘true’ 3D system allows multiple z’s at any x,y location, but it has more than that. It is important to realise that simply storing an x,y,z location is necessary, but it is not sufficient for a true 3D system. In a 2D or 2 and ½ D GIS, users can ask ‘what is the area of the intersection of layer 202 and pit 398?’ or ‘how many flint chips are within 10 metres of fireplace 278?’ But in 2D and 2 and ½ D, users can not ask ‘what is the volume of the polyhedron created by the intersection of layer 567 and cut 356?’ or ‘how many Corinthian pots are below the Roman floor in room 203?’. In summary, true threedimensionality is critical whenever the depth dimension is crucial in analysis and interpretation. Quantitative and accurate property characterisations are important here in all spatial dimensions.

In conclusion, the potential of employing threedimensional GIS techniques not only to record but also to represent, analyse and model archaeological processes is almost self-explanatory if we recall the similarities of archaeological problems to those of disciplines such as geology, petroleum industry, hydrology and marine science that have been at the forefront of this direction of development. The reason for an early development of three-dimensional GIS modelling in these disciplines has

11

Making Visible: Three-dimensional GIS in Archaeological Excavation

Dimension

Representation

2D

2 and ½ D

3D

Figure 2.1. Comparison of two-, two-and-a-half and three-dimensional representations of an archaeological feature pit. The two-dimensional representation depicts finds as points and a series of 5 cm. interval contour lines to represent the pit. Note that in the 2 and ½ representation the pit can only be expressed through a surface by its cut, whilst in the 3D one the pit will be composed by cut and fill together.

The data types that make 3D analytical and visual operations possible are different from, but related to, those of the 2D world. Like planimetric geospatial data ‘fields’ (i.e. grids/rasters), TINs and ‘vectors’, true 3D data come in three basic forms: voxels, tetrahedrons and ‘bounded surfaces’. Voxels are the 3D version of pixels or raster cells, and tetrahedrons are the 3D equivalent of TINs. The ‘bounded surface’ is the 3D equivalent of the 2D vector, and it is usually described as a boundary representation system (B-rep). Similar to the way in which 2D has points, edges and faces (i.e. points, lines and polygons), the 3D B-rep world has points, edges, faces and ‘solids’ or ‘volumes’ (different systems use different terms). The upper part of a deposit would be a face, and the envelope that defines the entire context is a solid (or volume). Figure 2.1 shows the differences between two, two-and-a-half and three-dimensional representations of an archaeological feature, in this case a pit containing flints.

spatial data can be queried, manipulated and represented in a meaningful manner, so as to provide insight into [archaeological] problems (de Kemp 2004 in Sprague et al. 2006, 398) 2.3

Modelling and three-dimensional GIS for archaeological excavation

Introducing three-dimensional GIS in archaeological excavation practice requires exploration of the unique concepts of space (and time) in excavation and their representation within a computer environment. Whilst chapter 4 develops in more detail issues of space, time and place in contemporary excavation practice and their implications for the understanding of the past, I wish to clarify here some fundamental concepts for understanding the role of 3D GIS in field archaeology. Although the position of this work is that, in contrast to a popular understanding of the role of computing in archaeology, the computational requirements do not determine the procedures of archaeological research, it is true that the specific structuring of data within a computer

In conclusion 3-D GIS is an interpretive environment in which 3-D

12

Three-dimensional GIS and Excavation next chapter, by building a connection between digital data models and archaeological data (the theoretical basis for the use of spatial data modelling in archaeology).

environment influences them. At the junction between the understanding of this structuring and the performing of archaeological field practice is the potential of 3D GIS modelling for exploring and explaining complex phenomena.

The development and construction of geometric, mathematical and statistical models is an essential prerequisite of GIS building. The term model as conceived in this study is a representation of the archaeological excavation (no matter if it is obtained using an analogue or digital approach) in all its aspects and following the principles of morphism as specified in the definition below.

2.3.1 Models and modelling: a terminological clarification The term model is problematic in all disciplines; nonetheless it is widely used to signify a wide set of things. In general, archaeologists do not share a common idea of the term, which is very often related to concepts developed by the New Archaeology in the late 1960s (Clarke 1968, 1972). For New Archaeologists a model was directly linked to the hypothetical-deductive method and was therefore defined in terms of the link between observations and theory or hypotheses. Subsequent attempts to define models and their definitions in archaeological research are summarised in Orton (2004). What emerges from the definitions is that ‘a model is a simplified representation of some sort of reality’ (Orton 2004, http://intarch.ac.uk/journal/issue15/6/co1.html). This concept somehow limits the potentials of a GIS approach that is totally based on modelling.

A model is an artificial construction in which parts of one domain, termed the source domain, are represented in another domain, the target domain. The constituents of the source domain may, for example, be entities, relationships, processes, or any other phenomena of interest. The purpose of the model is to simplify and abstract away from the source domain. Constituents of the source domain are translated by the model into the target domain and viewed and analyzed in this new context. Insights, results, computations, or whatever has taken place in the target domain may then be interpreted in the source domain. The usefulness of a particular model is determined by how closely it can simulate the source domain, and how easy it is to move between the two domains. The mathematical concept behind this is morphism. A morphism is a function from one domain to another that preserves some of the structure in the translation.

The model considered in this research is seen more in terms of what geographers define as a mental map (Muir 1999). Concerned with locational and spatial characteristics, the ‘whereness’ content of the total data store, mental maps constitute the skeletal framework of the more rounded phenomenon of the image, some parts of which are clearly aspatial in nature. In this sense, it acknowledges that part of our response to environment may remain not only unmappable but incommunicable in any medium.

(Worboys and Duckham 2004: 135) The model is technically achieved with the use of a threedimensional GIS approach in an attempt to respond to the necessity of finding interactive and creative ways when engaging with archaeological material.

The mental map, unlike the atlas page map, is an everevolving construction into which new information is frequently incorporated. It is both a spatial and temporal map. In this sense the concept of model goes beyond the classical definition of simplification of reality to be understood as a transformation, where concepts are translated from one sphere to another: in this case, from the space of excavation to that of the GIS platform.

2.3.2 Abstraction processes: from the field to the computer Within a GIS, reality must go through an abstraction process whose ultimate result is a binary representation. The abstraction process as intended by Peuquet (1990) and Egenhofer and Herring (1991) is composed by four main stages that are summarised in table 2.2.

As a consequence, development of a consistent and understandable modelling terminology and language is extremely important to all participants in the archaeological process, from the excavator, to the pottery analysis specialist, to the public. Normally the person who developed the model would know what assumptions were made and are important. In effect, it is then the analyst or the team of analysts using the model and modelling who will feel the effects of these assumptions, in particularly in terms of unawareness of biases and model-driven problems. The need to clarify assumptions and modelbuilding frameworks then becomes essential. In the following chapter, this need is addressed by creating a common language for communicating the structure and the behaviour of a model. The exercise continues in the

During the process, the human perception of reality (or spatial conceptualisation in Egenhofer and Herring’s terminology), which differs depending on the observer’s experiences and the context in which a person views a situation, is successively defined and formalised by spatial data models. Spatial data models are formalisations of the concepts humans use to conceptualise space. Such formalisations of spatial concepts are necessary because computer systems are essentially formal systems that manipulate symbols according to formal rules.

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Making Visible: Three-dimensional GIS in Archaeological Excavation

Table 2.2. Levels of abstraction from reality to computer file structures. Source: Peuquet 1990, 252

DEFINITION

DESCRIPTION

Reality

Phenomenon as it actually exists including all aspects which may or may not be perceived by individuals

Data model

Abstraction of the real world which incorporates only those properties thought of as relevant to the application or applications at hand, usually a human conceptualisation of reality

Data structure

Representation of the data model often expressed in terms of diagrams, lists and arrays designed to reflect the recording of the data in computer code

File structure

Representation of the data in storage hardware and natural features expressed in GIS data structures within a geodatabase.3 This integrates geospatial and temporal information into a defined structure and, based on this, analysis and modelling tools can be applied. The data model provides a common characterisation and understanding of the system and this description can be utilised by multiple models, analysis tools and decision support systems all referring to the same underlying data structure. At this first level of modelling we have the conceptual description of the system (excavation and excavated data), its digital implementation and the way different elements in the system interact (for example, soil with finds and features). The data models for this system provide the basis for the modelling of cycles (for example, post-depositional events such as erosion, water logging and marine transgressions), analysis of the relationships of different elements and configuration of interpretative practices and scenarios via data exploration or simulation. These last operations are a second level of manipulation of the data.

Egenhofer and Herring (1991) highlight the importance of human abstraction capabilities for two main reasons. Firstly, spatial data models help in making spatial concepts implementable on computer systems, without the need to describe actual implementation details. In fact, during the process concepts get separated from the actual implementations, thus implementations of certain parts the large GIS system become more independent and may be updated without affecting the remaining software parts. Secondly, the authors state that ‘the formalism may serve as a means for users to verify that implementations of operations concur with their expectations’ (Egenhofer and Herring 1991, 229). The data model is translated into data structures and file structures, which are the fully computerised representations of the initial ‘reality’. The basic concepts of spatial data models and file structures are further discussed in chapter 3. Data models, as clarified by the brief discussion above, are fundamental for the translation of concepts from one domain to another, in this case from archaeological data to their digital configuration. The major development of this work is dedicated to the formulation of an archaeological excavation data model.

2.3.4 The construction archaeological data model

and

meaning

of

the

The excavation data model describes the space that encapsulates archaeological information at large (from features to finds to chemical compounds) and the relations that are fundamental for understanding phenomena of the present and of the past. Some aspects of this space are captured with quantitative measurements; others with qualitative evaluations (as further discussed in chapter 4). This type of information can be described by GIS that contain representations for continuous surfaces and discrete space objects (points, lines, areas and volumes) whose location can be considered fixed in space at any given point in time.

2.3.3 Modelling archaeological subsurface data with 3D GIS: terms of reference In the realm of GIS, we can summarise the field of data modelling in two major areas: the construction of spatially referenced models to represent the world (base or descriptive model creation) and the use of such representations to perform simulation and prediction of phenomena, behaviours, scenarios of the present and of the past (sometimes also called modelling, hence the confusion). This division applies to all areas that make use of three-dimensional GIS and therefore also to the modelling of archaeological subsoil contexts.

Designing a data model is a complex process that can include a variety of software development tasks, starting

At one level, therefore, is a conceptualisation of excavations that describes entities such as deposits, cuts

3

A geodatabase is a database designed to store, query, and manipulate geographic information and spatial data. It is also known as a spatial database.

14

Three-dimensional GIS and Excavation Modelling can place some of the heaviest challenges on GIS development in archaeology, because of its needs to handle many kinds of data in both space and time in a more dynamic way than has been demonstrated to date. These requirements will help GIS applications in archaeology evolve, perhaps in ways not fully envisioned at present. The current state of GIS is therefore seen here not so much as hindrance, but as an opportunity that needs directions and steps for progress. Realising this opportunity involves putting GIS to use in creative ways.

with defining object classes, through analysis of use cases, to activity diagrams that show the flow of activities within a system. In the realms of 3D GIS the construction of the geometric model and the linked database to represent the data are fundamental and are based on a conceptual design. To give but one example of the implications of this, archaeologists often assume that the volumes they deal with have well-defined boundaries. This emerges in particular when the method of excavation is based on stratigraphy (see chapter 4 for a discussion that further challenges these concepts). Nonetheless, other vital information for the understanding of an archaeological deposit might come from volumes with undefined boundaries or from sampled data that are scattered and not conducible to stratigraphy in a direct manner. Gridding and sampling are used to collect this kind of information. There are not only convenient ways of representing these data graphically in 3D GIS but also possibilities of integrating approaches normally considered incompatible. The conceptual design aims to create this environment and influence the way archaeologists reason about it.

2.3.5 Multiple representations as a solution to model complex data The above discussion highlights how the description of the same piece of three-dimensional space is relatively complex, and typically includes several inter-related models defining different aspects of the same reality. The exploration of subsoil archaeology (not limited to the excavation process but comprehending different scales of sampling from borehole campaigns to micromorphology) constantly creates new information that must be incorporated into a platform that contains various models (views) of reality. Moreover, 3D space may be represented at multiple times, or at the same time with different data, or even by different interpretations. Lastly, the result of process and simulation modelling on the data at hand (which would be models themselves) need to find their place in the platform. The system thus relies on, and requires, constant updating. In order to appropriately account for the existence of multiple representations of reality (many models in one model) rather than referring to 3D GIS, on some occasions it is more appropriate to refer to multidimensional GIS.

A further line of extension of three-dimensional modelling tools is towards modelling of processes important in archaeology. A static three-dimensional model view and exploration is helpful for archaeologists, but it appears that most reasoning is in terms of processes that change geometry and make use of data other than just geometric descriptors. A major improvement, dealing with the study of the excavated record, involves the linking of GIS to process modelling (of environmental, social and other data). Although analytical models have been part of GIS for some time and have been linked to GIS, the link between modelling and GIS is only starting to be explored and exploited in disciplines such as environmental science (Goodchild 1993, Kemp 1993), geoscience (Frank and Buyong 1992), urban and transport planning (Benenson and Torrens 2004, Koncz and Adams 2002, Kwan and Lee 2004). Landscape archaeology studies have also given some attention to this area (Peeters 2005, Whitley 2005). Nevertheless, the importance of integrating GIS and modelling/analytical procedures, in particular in intrasite studies, has not been explored.

Chapter 5 discusses ways of taking into account these requirements in what is a conceptual framework that pushes into an operational sphere the concepts outlined here. 2.4

The state of art in the development of threedimensional intra-site GIS: archaeological challenges and technical solutions

Many authors have advocated the need for true threedimensional digital modelling in archaeological excavation (e.g. Allen et al. 1990, Barceló et al. 2003, Cattani et al. 2004, Harris and Lock 1996). The application of 3D recording and modelling to excavation is certainly the most innovative area of technology applied to archaeological data for its challenges in terms of theory and practice. The following sections will present a selection of case studies that highlight the consideration given to 3D modelling for archaeological excavation.4

The use of a base model to test hypotheses and perform simulations (which are the main areas of process modelling) is particularly relevant for archaeological applications in order to achieve: 1. modelling of depositional and post-depositional processes (process-based external model) 2. modelling of past behaviours: intra-site modelling in 3D using GIS functionalities such as view shed and other experiential modules (internal and external model).

4 The selection is derived from a detailed examination of case studies published from 1985 to 2010. The main sources of published material were the Proceedings of the CAA Computer Applications and Quantitative Methods in Archaeology and Archeologia e Calcolatori, both published annually. Contributions from journals such as Internet archaeology, Journal of Field archaeology and Science and Archaeology

15

Making Visible: Three-dimensional GIS in Archaeological Excavation find a student to take on the registration and analysis of stratigraphic data with the aid of Titan, the University of Cambridge prototype Atlas II machine. It soon emerged that the optimistic view taken by McBurney, that computers would be able to solve stratigraphy after, rather than during, excavation, was not supported by the techniques in use during the earlier 1960s and new samples needed to be obtained. Nonetheless, his ideas were developed from 1979 by Callow. Computers had a role in four areas of the project: stratigraphy; cataloguing; artefact studies; environmental studies and dating. Without devoting too much space to the detailed description of the rationale behind the management of these areas of the project, it is interesting to note here that Callow considers the ‘extent to which the use of computers made it feasible to pursue lines of research which could once have been too labour-intensive’ (ibid.: 465) the more striking aspect of the project. The maintenance of an analytical rather than just a retrieval database used in conjunction with multivariate statistics, horizontal, vertical and 3-dimensional plots of artefact distribution helped to ‘pull together superficially disparate lines of thought’ (ibid.: 465). To give but one example, he notes that the extent to which 3D artefact distributions gave insight into geomorphological processes was totally unexpected and it gave an insight into problems and possibilities of working with artefact assemblages from a complex site, in particular through the use of stereoscattograms (the isometric view representation of finds, fig. 2.2), for which code was written as part of the project. This case study is not only a fine example of 3Doriented techniques for intra-site analysis, but also an interesting case where the development of ideas about excavation methodology influenced the application of computing in the field, which I will further discuss in chapter 4.

Although early examples tended to concentrate more on reconstruction and visualisation, since the late 1980s the analytical potential of three-dimensional geometric models linked to databases was recognised (Reilly 1991, 1992, Reilly et al. 1988, Reilly and Walter 1987). 2.4.1 Early case studies of three-dimensional modelling for archaeological excavation The importance of three-dimensional recording of excavation, recognised by Wheeler (1956), has been a fundamental element in the use of computing in archaeology since the early days of research in this area. In the late 1980s a number of projects were involved in the design and implementation of computer programs dedicated to the plotting, visualisation and analysis of three-dimensional excavation point data (Callow 1988, Dibble and McPherron 1988, Nelson et al. 1987, Ryan 1988). These excavations were mainly prehistoric and were characterised by an approach that took particular care in the three-dimensional positioning of finds. The primary aim was to identify occupation surfaces from artefact distributions, whether stratigraphy was clearly visible or not, under the following assumption: If an occupation surface is defined as having a depth as well as extent (in other words, both vertical and horizontal dimensions), and consisting of a scatter of human-manufactured debris (artefacts, debitage, bone waste, etc.) on approximately the same plane surface, it is possible to reconstruct such surface after excavation by plotting the locations of these items in three-dimensional space, providing that threedimensional location data were collected during excavation. (Nelson et al. 1987: 353)

In the same period, projects carried out by Reilly (1989) at IBM resulted in the development of two systems: the Winchester Graphics System (WGS) and subsequently the WINchester Solid Modeller (WINSOM) (Reilly 1989, 1991). The WGS system consisted of a database that contained the three-dimensional position coordinates, orientation and attributes of excavation finds (length, width, weight and fabric). These could be retrieved from the database using standard relational database operations (join, select, union and so on) and logically associated to a wide range of three-dimensional markers and lines that could be displayed to examine spatial distributions. The data could therefore be viewed from various positions and perspectives. WGS was used, for example, to analyse a Late Bronze Age midden at Potterne in Wiltshire (Reilly et al. 1988, Reilly 1989). Here, the top and bottom of the midden were visibly defined, but the intermediate parts did not present a clear stratigraphy. The strategy adopted for excavation was therefore to use a system of arbitrary context recording units consisting of one metre squares excavated in 10 centimetre spits. A database was created, consisting of the following data: layout of the excavation, the three-dimensional position of the excavation units, the distribution of various artefact categories and the section drawings. WGS was then used to identify distinct

Exemplary, in this context, is the project at the Palaeolithic site of La Cotte de St. Brelade in Jersey, the Channel Islands (Callow 1988). Extensive excavation of the site had been carried out over several periods of time, starting from its discovery in 1881. From 1961 to 1978, Charles McBurney was in charge of the project, concluded in 1980-82 by Paul Callow. Extraordinarily rich in terms of finds (100,000 stone artefacts were recovered), the site also presented a very complex stratigraphy. This combination created problems for the routine archaeological methodology of the 1960s. For this reason McBurney decided to attempt to solve these problems with the help of computing, although he had no personal familiarity with this technology. The idea was to have also been incorporated. A web-based search was conducted by simply Googling the key words GIS and excavation, 3D GIS and excavation, computing and archaeology, archaeological excavation. The papers collected were classified according to the main tool used (GIS or DBMS or graphic software), the recording techniques, the modelling approach, the analysis technique, the discussion of the assumptions and the presence of a discussion at all. At the same time information was gathered on the use of GIS-specific terminology.

16

Three-dimensional GIS and Excavation

Figure 2.2. ‘Put them back where you found them’. Stereo-scattergrams showing location of artefacts at La Cotte de St. Brelade. Source: Callow 1988, fig. 32.5.

Figure 2. 3. Outline of excavation boxes and colour coded find distributions at St. Veit-Kinglberg. Source: Reilly 1991, plate 7.1

developments in the formation of the rubbish dump as follows. A bridging program read the tables from the database and translated them into a picture segment that represented absence, presence, and relative abundance of various artefacts (in particular those identified as chronological markers). An ‘investigative loop’ was used to analyse the data by examining a simple hypothesis, in this case the pace of midden formation. This was explored by interaction with the graphic representation of data such as the distribution of pottery with the same chronology. Transparency and opacity values were used to highlight vertical and horizontal distributions, exploiting dynamic and real-time display characteristics. The 3D analysis allowed definition of chronological horizons and demonstrated that, in some cases, instead of exploiting the exact three-dimensional position of each object, aggregations in 3D can be useful to determine the pace and significance of formation processes. A limitation of the system was that the presentation of the data was restricted to two-dimensional plans and sections, sliced representations of the volumetric data.

Reilly attributes the failure to the fact that ‘even though the excavators used the highest current standards of archaeological excavation, survey and recording, it could not be said that they produced a true three-dimensional record’ (Reilly 1991, 135). Nonetheless Reilly contends that three-dimensional analysis was still possible, in particular to address a specific problem, such as the relationship of the material in the spatially extensive deep layers to that of intrusive features cut into deposits immediately below them. In light of this, WINSOM methods were introduced to investigate the Early Bronze Age settlement. WINSOM models through a set of primitives (including planes, cubes and spheres) and employs simple Boolean operators such as union, difference and intersection to join, intersect or cut up shapes. Other operators can also be used to define colour, lighting and viewer position of the model on the screen (Reilly 1992, 152). This solid modeller differs from WGS insofar as the database system is used ‘to retrieve the coordinates of vertices of the required contexts together with the value of some property of interest e.g. absence/presence of pottery type x’ (Reilly and Shennan 1989). Eight triads of coordinates are read by the program in a fixed order and are used to define the solid geometry of each box context. By this method different solid models are constructed to show the distribution of box contexts containing a combination of selected material. The visualisation and the delivery of the analytical results

Similar methodologies were also applied to the site of St. Veit-Kinglberg in Austria (Reilly and Shennan 1989). Here the site was recorded using traditional methods of planning and sectioning features such as postholes and pits. Attempts to build three-dimensional models of the deposits from the recorded data using the WGS failed.

17

Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 2.4. Vertical slicing of the Grafland model. Source: Reilly 1991.

defined initially as the volume between the measured surface and an arbitrary datum plane at some depth below. The top of the layer(s) immediately underneath formed the bottom of the previous and defined its other side. Layers were isolated using constructive solid geometry (CSG) operators. The logical stratigraphic order of the deposits was largely implicit in the model definition and linked to a Harris Matrix. Solid model animation was used for slicing and sequential exposure of deposits using an animation that would open on a flat green open space which gradually fell away leaving a block of ground (the simulated excavation volume) floating in the space. Slices were then cut away showing sections through pits and other cuts into the layers within the formation (fig. 2.4).

are therefore fully three-dimensional. Animations were used to help spatial analysis. One of them was designed to ‘investigate whether or not the distribution of material in the conspicuous layers reflected what was found in the archaeological features underlying them’ (Reilly and Shennan 1989, 160). The procedure was carried out as follows: the variables under examination were extracted directly or calculated from the database based on the features identified (i.e. the cuts of postholes and pits). Although the perfect shape of features could not be extrapolated from the traditional record (outline of top of feature and section), the planned outlines were digitised and extruded to form prisms, which would be attributed a colour on the base of the property under study (e.g. average sherd size). These prisms were then unioned with the modelled box contexts from the area of interest and shaded according to the same set of colour conventions (figure 2.3).

In another animation each layer is taken away in the sequence that would be followed by the excavator. The author highlights that Grafland demonstrates both the feasibility of solid modelling for representing archaeological excavations and the potential for presenting large quantities of complex data to a wide audience. Moreover, the exploratory value of this kind of visualisation is highlighted, in particular the possibility of using attributes via isolation and association from the database to detect patterns and analyse processes. An example given is ‘a model in which all the cut feature(s) between layer α and layer β are isolated and displayed in order to study the different routes by which residual material could have travelled in getting from α to β’ (Reilly 1991, 136). Similar approaches are still used to represent archaeological excavations in the case studies discussed below (see in particular sections 2.4.2 and 2.4.3).

Once the model was prepared, slices of the excavation could be removed by clipping, thereby enabling the researcher to see how the distribution of properties in the features compared to that in the larger layers overlying them. At St. Veit-Kinglberg no obvious patterning was observed. It is clear, nonetheless, how the principles and implementation choices of the analysis are very similar to those that form the basis of any 3D GIS. Reilly, convinced that methods not only for performing but also for teaching the three-dimensional nature of archaeological excavation are necessary to produce good archaeological data, was involved, in 1989, in the design and implementation of Grafland (Reilly 1991, 1992). This is ‘a three-dimensional model of a realistic, but simulated archaeological formation, containing layers, pits, post holes, cuts, recuts, and so forth’ (Reilly 1991, 135). The simulation consisted of a series of layers with various cut features. Layers had been created using hypothetical profiles that were digitised, in a similar procedure to surveying along a transect. The layer was

These projects show an early understanding of the potentials of 3D modelling for representing and analysing archaeological excavations. The analytical potential was envisaged in the coupling of solid modelling and hypertext technologies, whereas in following years GIS characteristics and functionalities were recognised as

18

Three-dimensional GIS and Excavation In the end shape information of contexts was not integrated.

fundamental in this area of development. Unfortunately, the software was tailored for specific projects and often designed by computer programmers for the archaeologists. As a consequence, it did not spread beyond the completion of the projects themselves. The main focus was the visual recognition of patterns and the demonstration of the potentials of the techniques. The development of the concepts put forward by the authors was forgotten or abandoned for nearly ten years. 2.4.2

The aim of standardising archaeological recording of ‘all aspects that are necessary for a good stratigraphic interpretation of the data’ (Doneus et al. 2003, http://cipa.icomos.org/fileadmin/papers/antalya/123.pdf), characterises the development of the system experimented by Doneus and Neubauer at the Neolithic site of Schwarzenbach in Austria (Doneus and Neubauer 2004, 2005, 2006, Doneus et al. 2003). Single surface planning is the concept leading the development. The procedure implies recognition and recording of the upper surface of the deposit, excavation and description of the deposit, and recording of the lower part of the deposit. Boundary polygons and topography of surfaces of each unit of stratification are measured digitally. The measurements of the boundary polygon are stored as 2D polygon shapes and as a 3D polyline shape. The surface is recorded as 3D mass points using a total station and/or laser scanner. Moreover, the positions of finds are 3D recorded and connected to a database that stores attribute data such as location, chronology and nature of the finds.

Two-and-a-half-dimensional modelling

The use of Digital Elevation Models (DEM) for representing topography to address archaeological questions about past landscapes is, nowadays, common practice. DEM generation is a standard ‘click of the button’ function of any commercial GIS, and it is often described as three-dimensional. This is in fact an incorrect definition of the function, from a computing point of view: DEMs are 2½ D structures that allow the creation of surfaces that can be draped with textures and photographs, not to be confused with true threedimensional functionality. Although the limitations of these structures to represent volumetric three-dimensional data are clear, their ability to represent archaeological contexts as surfaces needs to be acknowledged.

Digital photographs are also taken. The data are all imported into a GIS (in this case ESRI ArcView). Points, breaklines and 3D boundaries are used to create either a TIN or a DEM of the surfaces, photographs are rectified to provide texture for the DEM and planar detail to adjust the boundary of the surface recorded and the shape of finds. The finds are mapped as registered within the volumes defined by top and bottom surfaces of the corresponding single deposits, classified by stratigraphic position or material characteristics. The dynamical mapping of single surfaces or the creation of composite maps (phase or period maps, sections at any position etc.) based on the recorded data can be created within the GIS after the analysis of the stratigraphic sequence. A script was designed by the authors to automatically create sections at any part of the excavated area. Lines can be drawn which are then intersected with the TIN of each underlying surface along a perpendicular plane and the resulting stratigraphic section is displayed as a separate picture. Results are displayed in ArcView through 3D Analyst (fig. 2.6).

Surface creation is often used to represent the excavated site, in particular when the method of excavation is stratigraphic. The underling assumption of excavation representation through the use of surface sequences is that of the principles of stratigraphy. Data acquisition is carried out using electronic measurements of context tops and bottoms. The outlines of features are sometimes registered in order to provide information for the reconstruction of their shape. This is normally carried out by stopping the excavation at regular intervals in order to perform the recording. At the Viking site of Gnezdovo in Russia, Zhukovsky (2002) in an attempt to renew excavation techniques and recording methods, launched a research project in the summer of the year 2000. The underlying assumption of the project was to substitute the principles of 3D information projection in sections and plans with a direct measurement of 3D data to create a 3D virtual model in a digital environment. Digital recording of 3D spatial data in situ through manual and optical theodolite measurements was carried out and the stratigraphic method introduced for excavation. The principle of recording is exactly the same as for other case studies: upper and lower surfaces are measured. When proceeding in spits, the outlines of stratigraphic units were traced every 10 cm (pretending a cutting plane was slicing the deposit). This gives detail of the shape of the feature between the upper and bottom surfaces of containment (not done in other cases). The point data were then processed in AutoCAD and ArcView to create a series of DEMs, which represent the stratification of deposits. Finds were represented using a 3D solids library (fig.2.5).

Whereas the above case studies fail to recognise that a surface is only one aspect of the full solid identity of contexts, other scholars seem fully aware of the flaws of such an approach. The use of GIS to analyse stratigraphic contexts, exploiting to the full the limited capabilities of commercial software to render the three-dimensionality of archaeological data, is explored in the case studies discussed below. At the Domus della Pescatrice in Pompei, Laurenza and Putzolu (2002) elaborated a strategy for surface data acquisition using a combination of electronic total station point collection and digital photography to record the surfaces of the deposition of room VII. The authors considered the volumetric value of the stratigraphic unit to be the space between its surfaces:

19

Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 2.5. Meshes representing the stratification of deposits on the left, and 3D solid library of finds, on the right. Source: Zhukovsky 2002, figs. 13 and 15.

Figure 2.6. Stratigraphic unit 168 of Schwarzenbach on the left and on the right procedure for cutting a cross section of a feature. (a) defining the line fo the cross-section, (b) calculating the intersection lines with each underlying surface, (c) edited cross-section. Source: Doneus and Neubauer 2004, fig. 2 (extended CD version).

recording surfaces would therefore achieve the aim of rendering the volumetric aspect of the deposits. Point data to represent the surfaces were recorded with an average of 100 points per square metre. The upper and lower surfaces of stratigraphic units were rendered using TIN construction and texturisation within the 3D Analyst extension of ESRI ArcView. The TINs have a number of points in common along the contour line that defines the shape of the surfaces: this allowed for the visualisation of a unique solid. The volume of the unit was then calculated using the cut fill function of ArcView, which computes the difference in height between two surfaces. An attempt was made to extend the modelling to standing structures (the walls of the house) using the same software. The problematic nature of 3D modelling within a 2½ D modeller such as 3D Analyst became apparent when the authors realised that the z coordinate is not treated by the software as an independent variable but as an extrusion value. The positioning of objects along the z

axis is therefore impossible and, as a consequence, standing elements cannot be integrated in the GIS as structural elements, but just as extruded plans.5 The authors considered satisfactory the representation through extrusion of the simple architectural structure of the case study, but highlighted that ‘in case of a more complex sequence (with Wall SU (Stratigraphic Unit) one above the other) could be hardly visualised by a simple extrusion process.’ (Laurenza and Putzolu 2002, 97). In conclusion, the limitations of the software and the unclear perception of the structures needed for three-dimensional modelling at the beginning of the project led to a limited developed strategy of management of stratigraphic data (figure 2.7). 5

Extrusion is the process of assigning a vertical z value to a planar object, which is consequently visualised as a solid with a fixed height. The object, in fact, remains two-dimensional in its topological characteristics. Refer to chapter 3 for a thorough discussion of true 3D modelling and topology.

20

Three-dimensional GIS and Excavation

Figure 2.7. Domus della Pescatrice, Pompeii. Three-dimensional visualisation of the extruded and exploded deposits on the left and, on the right, three-dimensional wall reconstruction and queries. Source: Laurenza and Putzolu 2002, figs 13 and 18.

Figure 2.8. Palace of Herod the Great. Visualisation of individual trenches and general layout of the excavation in ArcView 3D Analyst. Source: Schryver 2002, 416.

Figure 2.9. DTMs showing the topography of the archaeological deposits at Knossos by period. Source: Katsianis 2004, fig. 5 (extended CD version)

21

Making Visible: Three-dimensional GIS in Archaeological Excavation stratigraphy from the bedrock upwards (the bedrock data came from the Evans archive) as shown in figure 2.9. The assessment of the resulting surfaces in defining the actual shape of the deposits was carried out by extracting profiles from the DEMs with dedicated software (Profile Extractor) and the modelled stratigraphic sequence was displayed as sections (fig. 2.10). These were used to gain insight into the slope of the surviving deposits and the depositional relationships.

At the Palace of Herod the Great in Caesarea Maritima (Israel) the 3D capabilities of ArcView’s 3D Analyst were tested by Schryver (2002) with the objective of analysing the relationships among individual trenches and between these trenches and the site as a whole. The excavation had in fact been conducted over 50 different and disconnected trenches from 1976 to 2000: a site-wide analysis of the stratigraphy had been attempted using traditional methods, but proved unsuccessful. Threedimensional modelling and visualisation seemed to provide the solution to the problem. AutoCAD was used to draw the plans of individual trenches that were subsequently imported in ArcView and linked to a database of all loci (stratigraphic units) classified using ArchEd, a Windows-based Harris Matrix program. The excavated loci were then visualised in their position and the architectural elements positioned and extruded (fig. 2.8). This allows, as in the previous example, visual representation of the site. Other querying functions were limited by the nature of the software and by the fact that the database contained only chronological data.

2.4.3 Three-dimensional GIS for excavation: two procedures for reconstructing stratigraphy Two recent projects made use of commercial software to develop a true three-dimensional model of excavation. Modules developed for use mainly in a geological context are used to render volumetric excavation data. Both case studies present data retrieved using the stratigraphic method. A base model of excavation was developed by Barceló et al. (2003) for the Shamakush VIII cave site in the Beagle Channel (Tierra del Fuego, Argentina). Shamakush VIII is a shell midden deposit generated by Yamana, ‘Canoe’ Indian hunter gatherers, living in the region until soon after the arrival of the Europeans in the 17th century (Estevez et al. 2001). The characteristic of the site is to present non-consecutive deposition phases, intermitted by natural soil formations. The rate of sedimentation of these deposits is very rapid and there are significant differences within the same occupation units. For the excavation of this shell midden, contact surfaces are considered site components and stratigraphical information is checked by micromorphological, biochemical and spatial analysis, in particular using refitting and use-wear analysis of lithics and bone fragments. The 2002 field season was designed to use such a rationale and build a model of site formation processes with the help of a geometric representation of the measured spatial variables believed to be indicative of the site during excavation.

In York, archaeologists used GIS software to examine the cross-chronological sequence of the archaeological deposits of the city, which extend vertically for more than 10 metres below the modern street level, in order to understand the life of the city through time (Miller 1995, 1996). Moreover, the project aimed to assess the effect of groundwater on the preservation of archaeological sites beneath modern buildings. Hydrological modelling was used to map the known extent of waterlogged deposits and to predict unexplored areas with potential for preservation. Hydrological modelling could also be used to monitor flooding and understand the impact it had in Roman times and at present. In order to perform such analysis, the modern topography of the city was reconstructed from 1412 manhole cover heights provided by Yorkshire Water and York City Council. The Roman topography was derived from borehole logs and data from excavated areas of the city (archival material). Using point data on the location of lived levels at different points in the past, a series of DEM depicting the topography of the city through time were constructed. This allowed the creation of a map representing the accumulated deposition across the city. By subtracting the Roman from the modern surface, zones of significant deposition and negative accumulation were highlighted.

RockWorks2002 (© RockWare, Inc.), a widely distributed software for geological applications, was chosen to create a voxel model. Rockworks allows visualisation and management of data from boreholes by computing stratigraphic sections, profiles, models and interpolated reconstruction combining stratigraphic points with lithologies. The model is based on the interpolation of points within a regular grid, where x and y values remain the same and z changes, being measured every time there is a change in the layer, following the principles and practice of the bore-holes interpolation method of the software. From the points, interpolated 2½ surfaces are obtained (GRID upper and lower, fig. 2.11), which can be closed to represent a volume (principle similar to the case studies discussed above).

The latter were found to be accurate depictions of recent civil engineering excesses, with consequences for future urban planning. The elevation models of York were used to simulate the flooding regime through time, a fundamental step in understanding the dynamics of settlement patterns and future planning. Katsianis (2004) uses a similar approach to present a diachronic synthesis of the early history of the site of Knossos before the construction of the palace. The extent and thickness of deposits were modelled on the basis of the site ceramic sequence. The absolute height of the upper surface for each period was used to reconstruct the

Sedimentary units are represented as volumes between two contact surfaces as thickness representations of the accumulated material between surfaces. The volume is the voxel representation of a uniform characteristic between two surfaces.

22

Three-dimensional GIS and Excavation

Figure 2.10. Cross section of the Knossos Tell showing the thickness of each deposit accumulated through time at the site. Source: Katsianis 2004, fig. 6 (extended CD version)

Figure 2.11. Shamakush VIII site. Contacting surface correlation using the software RockPlot 3D. Source: Barceló et al. 2003, fig. 9.

An isopach model or thickness geometry model is created by computing the spatial difference between upper and lower surfaces (the difference between elevation at the top of the first component and elevation at the top of the second component for each surface point). The results are stored in a new z value (for example SC1-SC2 is SC1 thickness; SC2-SC3 is SC2 thickness). The 3D model consists of stacked thicknesses. It represents the site components computed from a database of thickness values. Figure 2.12 shows the visualisation of the model. The authors argue that on this model grid area and volumes can subsequently be calculated and filters used to exclude areas of the site component with certain characteristics such as no trace of fire (with a rule such as burning = 0) or areas with a ‘great number’ of shells

(shell > 35). These operations are nevertheless not demonstrated in the paper. The Bronze Age ‘terramara’ site of Montale (Italy) consisted of a 3-metre stratigraphy (encompassing a chronological sequence of 400 years) with a very limited horizontal extent (40 m2), as the majority of the site had been used to extract fertilising material in the 20th century. The excavation was conducted using a stratigraphic methodology, documentation was carried out through the combination of Electronic Total Station point recording and digital photography (Candelato et al. 2003) and data were combined in a 2D GIS platform since 1996. The limitations of GIS commercial software in dealing with the manipulation of three-dimensional data are fully acknowledged by Cattani et al. (2004).

23

Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 2.12. Volumetric representation of Shamakush VIII site components in Rockworks 2002 consisting of stack thicknesses between surfaces. Source: Barceló et al. 2003, fig.12.

Figure 2.13. 3D density of chemical gradients across the site at different levels. Source: Cattani et al. 2004, fig. 12 (CD extended version)

Nonetheless, the authors proposed some routines for the management of excavated data in a three-dimensional context. A base model for three-dimensional GIS analysis was therefore built, starting from the material gathered. Two different ways of manipulating data are used and compared: a voxel and a vector modelling approach. RockWorks2002 (© RockWare, Inc.) was tested as voxel modeller. Instead of using the stratigraphy model facility, as in Barceló et al. (2003), the authors experimented with the solid model module. Text files containing x, y and z coordinates to represent the topography of the excavation surfaces were imported and filtered using a vector polygon, defining the limits of the stratigraphic unit. Single and cumulative solid models were created to represent either the single stratigraphic unit (eventually combined using the function APPEND to render the whole excavation) or the combined stratigraphy in one interpolation operation. In either case the volume is composed by the voxels contained in the space between

the upper and lower surfaces delimiting the context. Every voxel cube represents a single value that is replicated across the whole volume (in this case the stratigraphic unit number). Advantages and problems of the two different approaches are discussed. Once created, the base model can be sliced and profiled; volumes and layers can be isolated and exported to ArcGIS as isosurfaces. Thus far, the representation created does not really have any analytical potentials - it is just a visualisation. The experiment with the distribution of metallurgical slag through interpolation of geochemical properties distributed in space adds to the analytical meaning of working in 3D. In this case, voxels represent chemical gradients that can give indication of areas of higher activity not only across the site but also at different depth levels (figure 2.13). Here, we are in the realm of 3D spatial analysis, fundamental for the understanding of site dynamics and chronology.

24

Three-dimensional GIS and Excavation

Figure 2.14. Single TIN surfaces (left) and closed polygonised volume (right) of one stratigraphic layer at Montale. Source: Cattani et al. 2004, figs. 14-16 (CD extended version).

Figure 2.15. The closed polygon is imported in ArcGIS where it is overlaid with a photograph representing the upper surface of the stratigraphic layer. Source: Cattani et al. 2004, fig 18 (CD extended version)

Modelling with vectors was conducted with SDRC ® Imageware Surfacer, a specific software for threedimensional modelling, mainly used for rapid prototyping. The vector modeller allows the user to create a closed surface with multiple z values for the same x and y location and therefore to obtain a closed solid. The x, y and z coordinates of every stratigraphic layer were imported in the modeller as upper and lower surfaces. A TIN was created of each surface and then the surfaces were merged to obtain a single closed object (figure 2.14). Imageware Surfacer geometric vector entity Closed Polygonized Point Cloud describes the shape of objects as a closed collection of bi-dimensional triangles printed in a 3D space.

In conclusion, it is clear that three-dimensional visualisation of stratigraphic contexts can be achieved with software dedicated to three-dimensional modelling, the condition being the three-dimensional recording of contexts and finds during excavation which is, in the majority of modern archaeological projects, performed routinely either with the help of digital equipment (EDM or Total Station) or manually. A number of papers published recently have emphasised the necessary steps for fast and accurate performance of such recording (Bradley 2006, Costa et al. 2005, Zhukovsky 2002). Dedicated software such as 3D Studio Max has been used in a variety of projects to model and visualise stratigraphic units in vector format (Uotila and Tulkki 2002, Viti 2002). Although resulting in appealing 3D representations of the excavated sites, these models do not have any further analytical potential in terms of querying. They are, in fact, not linked to the attributes traditionally recorded in context sheets. The limitations of such approaches are evident.

The vector objects were then imported in ArcGIS for slicing and other visual manipulation. A transformation from the Imageware Surfacer format to shapefiles through dxf had to be performed to allow import of the files in ArcGIS (figure 2.15).

25

Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 2.16. (A) Voxel and (B) tetrahedron model of trench TEW at Tell ‘Acharneh. Source: Losier et al. 2007, fig. 10.

Figure 2.17. (A) Script query applied to the 3D model of trench TEW and (B) results of the query in red. Source: Losier et al. 2007,

Figure 2.18. The image combines a low oblique perspective of the Loiyangalani site, the underlying excavation units and the artefacts within. Different artefact colours represent different artefact types or faunal material. Source: http://www.serengetigenesis.org/arch_GIS.php.

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Three-dimensional GIS and Excavation regional environmental changes (sea level fluctuations and sand dune formations) that may have influenced the adaptive and behavioural patterns of early Homo sapiens (Fisher 2005a, 2005b). During the 2004 excavation of cave 13B, over 12,000 archaeological features and nearly 6,000 points representing 139 different geological strata were recorded. The high resolution analysis of these data allowed the identification of geological slumping patterns based on the spatial distribution of archaeological feature point data. The ability to quickly visualise and comprehend spatial relationships of this magnitude is considered to be unattainable through conventional 2D GIS systems. In conclusion the author states:

More recently, the GoCAD modelling tool (discussed in section 2.1) has been used at the site of Tell ‘Acharneh in Syria to geometrically model excavation units from top and bottom surfaces of the units’ boundaries (Losier et al. 2007). Both a voxel and tetrahedral approach were experimented. Figure 2.16 shows the results of the modelling. GoCAD, differently from the systems discussed above, allows for some basic operations to be carried out internally. These are volumetric calculations, and intersection computations. Since qualitative and quantitative properties can be added to the 3D objects created, spatial analysis can also be performed on the models. Nevertheless, in order to perform the queries, the user needs to be familiar with the querying language of the software. The visualisation of the models and the results of queries are all visualised within GoCAD. Figure 2.17 shows the script and the result of a query aimed at selecting excavation units of the Early Bronze Age which have more than a set number of sherds and between X1 and X2 flint flakes. 2.4.4 From explorative visualisation patterning: 3D GIS with a view

to

Although much of the current software must still be modified in order to properly model archaeological deposits, this is expected to change as more projects are introduced to the superior analytical and visualization capabilities of 3D. In the end, multidimensional GIS clearly provides a more realistic and dynamic environment to view complex spatial details that enhance the analysis and understanding of a site by not only those who excavate it but those who study the site in the future

spatial

(Fisher 2005a, 50) Commercial GIS software has also been used to explore archaeological questions that emerged out of scenarios that specifically required a three-dimensional approach in order to make sense of particularly challenging excavation data in terms of quantity and/or complicated stratigraphy.

This product is considered by the author to be a multidimensional GIS but it is in fact little more than a visualisation. The author acknowledges this limitation and the fact that current software needs to be modified to accommodate for true multidimensional models of archaeological deposits.

At the Loiyangalani archaeological project (Tanzania), Middle Stone Age archaeological deposits along the Loiyangalani River, Serengeti National Park, were uncovered in test excavations in 2000 and 2003 (Fisher 2005b). A digital land surface was created by constructing a one metre square grid over the entire site (roughly sixty by ten metres in area) and then measuring each point in the grid using a surveyor’s transit. Every artefact found on site was plotted in three dimensions and then entered into the computer database. In 2005, 3,300 artefacts and faunal remains at the site were surveyed and plotted using a total station. ESRI ArcGIS and ESRI ArcScene were used to create the 3D and 2D GIS databases for this project. Geo-referenced photographic images of each excavation level, specific artefacts and a photo mosaic of the site area were assembled to produce a visualisation of the land surface model, excavation units, subsurface levels, and geologic strata (figure 2.18). The attempt to combine excavation units and artefact densities in a three-dimensional visualisation characterises the efforts of this case study. If, on the one hand, the rendering of the stratigraphy is crude, on the other, the distribution of colour coded artefacts through the levels is a clear effort into rendering not only horizontal but vertical patterns.

The Lower Pleistocene fossil site of Swartkrans (South Africa) has been excavated periodically since 1948 by Robert Broom and John Robinson and, most recently, by C.K. Brain (Nigro et al. 2002, Nigro et al. 2003). This cave site yielded the single largest sample of the early hominid species Paranthropus robustus, and several specimens referred to as Homo erectus. The final sevenyear period of excavation, initiated in 1979, was conducted in a meticulous fashion and has produced an abundance of carefully recorded geological, fossil and artefact data. More than 20,000 fossils, along with a number of bone and stone tools, were recovered from members (strata) 1, 2, and 3 alone. The excavation was carried out largely in 10 cm elevation increments with a metal grid erected over the site to allow more accurate provenancing. A 3D GIS was built with data from a variety of sources: 1) a 1999 survey using a laser theodolite to record spatial coordinates for remaining geological features; 2) the digitisation of C.K. Brain’s field diagrams from the final seven years of excavation to reconstruct the original geology of the site; and 3) a relational database including information on more than 20,000 vertebrate fossils (macrofauna) and artefacts from members 1, 2, and 3. In the authors’ view this system allows the evaluation of artefact and fossil distributions, and the exploration of the taphonomic nature of the site taking into consideration its geological framework. It also facilitates the process of bone refitting, and contributes to

Fisher was also involved in the excavation of a series of caves along the southern coast of South Africa near Mossel Bay, Western Cape Province, to study the

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Making Visible: Three-dimensional GIS in Archaeological Excavation data and assess the rate of soil accumulation at the gate. The underlying idea of the procedure was that interpolation between the two vertical stratigraphic sequences would have replaced the missing parts of the layer, destroyed by machinery, and given a picture of the general trend of the stratigraphy. Five separate sections were digitised in AutoCAD, representing portions of the eastern and western faces of the trench (fig. 2.21) and elaborated into a 3D model in AutoCAD (fig. 2.22).

the development of a protocol for similar reconstructions at other South African fossil-bearing cave sites. Mapping and 3D reconstruction of sites such as Swartkrans present challenges when using traditional GIS approaches because they cannot interpolate data from overhang features (there must be only one z-value for each x-y coordinate). This study uses a new 3D approach combining Intergraph’s Voxel Analyst and ESRI’s Arcview 3D Analyst to overcome this limitation. Results present an accurate three-dimensional model of the site and its contents for data storage and analysis (fig. 2.19).

Once imported in ArcView, they were connected using a link function via the stratigraphic context identifier. The identified side faces of each context were then the base for helping the positioning of the top and bottom surfaces (upper and lower face of the solid) through the creation of linking 3D polylines (performed in AutoCAD, as ESRI software does not support 3D polylines). The polylines are necessary to create TINs in ArcView. These identified, once again, the upper and lower surfaces of deposits. Volumes were calculated using a ‘Cut Fill’ function (fig. 2.23).

The most interesting aspect of the project consists in the development of scripts in Avenue (the programming language of ArcView) to obtain three-dimensional spherical buffers around specifically selected finds. This 3D buffer was used to study finds dispersal and taphonomic patterns not only horizontally but vertically, helping with the understanding of depositional practices and post-depositional processes as shown in figure 2.20. Putzolu et al. (2004) worked on the reconstruction of volume information from sections at the City Gate complex in Tell Leilan, Syria, where a bulldozer had damaged the east site of the gate in 2001. The phasing and stratigraphy were therefore visible in a small slice of the entire area (9 metres long and one metre apart). The aim was to provide volumetric measurements to contextualise archaeobotanical and soil micromorphology

Volume data was used to investigate site formation processes in combination with radiocarbon dates, through Bayesian statistics. The accumulation rate of the abandonment phase was compared and contrasted with phases of occupation and other sequences of occupation/abandonment across the site.

Figure 2.19. Viewing waterworn pebbles (yellow) in the semi-transparent cave environment of Swartkrans (left) and database query (right). Source: http://www.cast.uark.edu/research/research_theses/swartkrans3d/thesis_web28.html.

Figure 2.20. Spheric buffer implemented for the Swartkrans site by Nigro. Source: http://www.cast.uark.edu/research/research_theses/swartkrans3d/thesis_web24.html.

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Three-dimensional GIS and Excavation

Figure 2.21. The five sections of the City Gate excavation digitised in AutoCAD. Source: Putzolu et al. 2004, fig. 6 (extended CD version).

Figure 2.22. The sections of the City Gate excavation placed in their vertical position in AutoCAD. Source: Putzolu et al. 2004, fig. 7 (extended CD version).

Figure 2.23. Demonstration of volume calculation through the use of the Cut Fill function in Arc View. Source: Putzolu et al. 2004, fig. 11 (extended CD version)

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Making Visible: Three-dimensional GIS in Archaeological Excavation 2.4.5

This is a customised system that seems to combine raster and vector modules. STRAT is based on stratigraphical recording and seems to be oriented to visualisation. It is not clear how much ability the tool has in terms of analysing layers and finds in combination.

Towards dedicated 3D GIS architectures

3D Murale (Cosmas et al. 2001) is a CEE funded project dedicated to designing and implementing a set of tools for 3D recording, reconstructing, visualising and database searching/querying that operate on buildings, building parts, statues, statue parts, pottery, stratigraphy, terrain geometry and texture at the site of Sagalassos, in Turkey. The tools are loosely linked by a common database in which they all have the facility to store and access data within a multimedia architecture system.

The SHAPE lab was created in 1997 to develop scientific tools for 3D archaeological excavation and artefact reconstruction and analysis using data coming from the Great Temple excavations in Petra, Jordan (Leymarie et al. 2001). One of the key projects of the lab was to develop ARCHAVE, a software system consisting of a virtual reality interface for archaeological analysis (Vote et al. 2001, 2002). The goals were to provide tools to support navigation and interaction within a virtual representation of the site, and store and display the spatial relationships between the elements of the excavation (finds and contexts). Visualisation and interaction techniques were experimented in four prototypes. Prototype 1 was a conceptual model. Prototype 2 was developed using ESRI software (ArcView and 3DAnalyst) to display bulk concentrations of pottery in the site trenches (fig. 2.25). The lack of threedimensional measurements and the inability of the software to handle the z dimension resulted in an inadequate tool to examine multiple artefacts in combination.

Stratigraphic Visualisation tool (STRAT tool) is one of the components of MURALE (Green 2003). It enables 3D visualisation, manipulation and the storage and querying of archaeological material (building elements, artefacts, stratigraphy, plan and profile drawings and photographs), and visualisation of the site in the form of a Harris Matrix. In addition a variety of 2D information, such as plan/profile drawings or Polaroid photographs that are often recorded about a site, can also be entered into the STRAT tool. Artefacts discovered in a layer can be represented by a symbol or by a 3D representation of the found artefact. Each layer can have an arbitrary shape. Information on the area of the site excavated, extent and description of the layer of interest are entered in the dialogue box of STRAT. Layers are represented as cuboids tied to a grid. Photogrammetric models can be linked to the stratum at that position (fig. 2.24).

For the construction of prototypes 3 and 4, a straight GIS approach to the problem was therefore abandoned in favour of a customised data visualisation environment within an immersive virtual reality interface (VR) offered by a CAVE (Cave Automatic Virtual Environment). Interaction was the main goal of the prototypes. The system supports multiple queries and display of various combinations of data characteristics and quantities. Special finds can also be displayed in their location within the trenches. This environment clearly lacks the absolute topology characteristics of a GIS, and therefore presents the drawback of not being scalable.

In this manner the user can visualise the relative positioning of the stratigraphic layers, selectively choose a particular group of artefacts to be visualised within the stratigraphic layers for the purpose of analysis, visualise each stage of the excavation using a time slider thus establishing the chronological sequence of stratigraphic layers, visualise user-defined cross sections of the Stratigraphy, and highlight through visualisation the inconsistencies in the dimension of adjacent stratigraphic layers. A link between the data analysis capabilities of Harris Matrix and the visual stratigraphic model can be performed. Querying is also possible, but not in 3D form.

Figure 2.24. Visualisation of strata and finds in the STRAT tool. Source: Green 2003, fig. 1

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Three-dimensional GIS and Excavation

Figure 2.25. On the left, the Arc View 3D Analyst based prototype and on the right the CAVE environment (prototype 4) of the ARCHAVE project. Source: Vote et al. 2002, figs. 4 and 10.

tool that allows the user to aggregate stratigraphic units into higher level entities. Although the tools developed are clearly an advancement for three-dimensional intrasite spatial analysis, the limitation of the project is that it is not at all clear how they were created. Moreover, as it has been the case in the past, the wider community of archaeologists gets little benefit from tools that are not eventually either incorporated into the proprietary software platform or released in the public domain.

For this reason two models were created: a life-scale, for local interaction; and a miniature model for a synthesis of global site features (in order to explore relationships between trenches). The evaluation of the final prototype by archaeologists from the project demonstrated that the tool has great potential in terms of interactive queries that easily display finds patterns and anomalies that can be used to test hypotheses on site formation and postdepositional processes. The lack of three-dimensional information on bulk finds and the inability of the system to retrieve additional attributes from finds categories via querying were noted. Unfortunately, the project did not develop a further prototype to deal with these problems. Nonetheless, it certainly provides a series of ideas about useful methods for 3D GIS-oriented exploration of excavation. The spatial paradigm employed and the problems highlighted point towards this kind of solution.

2.4.6

Conclusion

The case studies presented demonstrate that the state of accomplishment and knowledge in the field of intra-site and, in particular, 3D GIS applications is exponentially growing. From the first case studies, dedicated at the production of appealing and realistic visual models, the research is now directing itself towards the translation of the excavation record into 3D GIS structures and the analysis of the so obtained data. Although it is true that there has been a growth in knowledge related to the topic, there are still various issues to be resolved in a variety of areas.

More recently, Katsianis et al. (2008) designed a formal data model and digital workflow for the documentation of a stratigraphic excavation in three-dimensions at the prehistoric site of Paliambela Kolindros in Greece. An object oriented approach was used for the design of the data model which defines both object classes and spatial relations of a stratigraphic excavation. Temporal class diagrams were employed to code stratigraphic relations. The data model presented is very structured and by nature rigid as it only caters for stratigraphic excavation approaches. Nevertheless, it must be highlighted that this study is a unique case in the examined literature insofar it presents an analysis of excavation practice and recording and a possible translation of it into a digital domain.

Firstly, the majority of the solutions presented are selfreferential, personalised and specific. This problem, highlighted in the context of excavation databases in Crescioli et al. (2002) and equally applicable to 3D GIS, makes it impossible to develop common procedures or analyse common problems encountered when processing the data. This is a fundamental obstacle to the diffusion of solutions for excavation data recording systems. The issue is therefore to systematise and integrate single case studies in more generalised conceptual frameworks that could become the platform for discussion of several types of excavations.

Esri’s ArcGIS was chosen as the software platform for the implementation of the system (coupled with the use of external software for geometric modelling). Since the analytical functionalities of this platform are limited in the 3D domain, the authors developed three tools to support more complex queries: a 3D point-to-point distance tool for spatial queries, a routine for extracting and storing user-defined sections of the 3D model and a

Secondly, the studies presented in the literature review often neglect to discuss the specific steps used to obtain the three-dimensional models on which analysis is performed. Moreover, the software at times developed for

31

Making Visible: Three-dimensional GIS in Archaeological Excavation However, since the beginning of the 1990s, scholars working in the area of the geosciences, in particular, have developed both data models and dedicated software for handling real 3D data (Abdul-Rahman and Pilouk 2008, Abdul-Rahman et al. 2006, van Oosterom et al. 2008). Unfortunately, the majority of the projects developed prototypes. The main limitation is nowadays the fact that the major software proprietary developers have put very little effort into commercialising such systems and distribute them at very high prices. This seriously limits groups such as archaeologists with scarce economic resources and limited computational background. They have to limit themselves to using what is available.

the modelling and analysis is generally not available to the general public. This study aims therefore at elucidating in detail the thinking behind the modelling and the practical steps followed to obtain the results presented in an attempt to fill the gap emerged in the literature. Thirdly, a general weakness is that the translation of excavation data into GIS structures concentrates on the study of stratigraphic, single-context excavation with the result of neglecting the evaluation of other approaches to the record, such as those depicting the continuous variation of subsoil characteristics (Cattani et al. 2002). Lastly, an overall discussion of whether 3D GIS has reshaped or should at all reshape the way we carry out excavation is lacking. This is another area that the present project aims at evaluating. 2.5

Summary

Four topics are covered in this chapter: the application of GIS in representing and modelling complex phenomena; the state of development of three-dimensional GIS research; an introduction to the terminology of GIS modelling with particular reference to excavation; and a review of archaeological excavation modelling with 3D GIS. The literature review highlights an existing gap in the representation of information considered relevant for archaeological research. There is no current data model that provides a broad conceptual description of the archaeological record within a 3D GIS. Base models tend to describe one aspect of the archaeology (namely the stratigraphy), eluding the data model incorporation of other aspects of the record. Moreover, the specific characteristics so far identified as descriptive of the record are not implemented into a public domain design. In addition to the needed data model there is a lack of procedures to create, edit and display three-dimensional features within a GIS system, and the development of such procedures as part of the data model design will advance the incorporation of GIS technology into archaeological studies. It is clear from the other disciplines where 3D GIS is more developed and used that the fundamental step is to build conceptual frameworks to bridge archaeological and GIS concepts in a useful manner for analysis and modelling. This might have implications in the way we approach excavation, even in the field. The following chapters are aimed at addressing these issues more in detail. The advocated constraints of GIS software, which allows the so-called 2.5 modelling and analysis but not real 3D modelling, are now frequently mentioned in passing by researchers exploring the feasibility of 3 and 4D modelling in archaeology and in other disciplines.

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development of intra-site modelling and analysis.

Chapter 3. Fundamentals of 3D Modelling and Visualisation within a GIS Environment

3.1.1 What do GIS model? The representation of spatial data

This chapter presents an overview of GIS-based threeand multidimensional data modelling, analysis and display and aims to familiarise the reader with the fundamental concepts and principles used in this thesis. The overview is not intended to be an exhaustive introduction to all the concepts of GIS data and modelling1. It is rather a presentation of issues specific to three- and multidimensional modelling with GIS that are relevant to their application in an archaeological excavation environment.

Research dedicated to the process of representing geographical phenomena in a GIS environment has led to the identification of two principle descriptors for representation, corresponding to the questions what is present and where (Burrough and Mc Donnell 1998). Depending on the nature of geographic phenomena, object- or field-based data models have been used to represent discrete entities or continuous fields in a GIS, respectively (Coucleis 1992, Laurini and Thompson 1992).

The chapter is divided into three main sections. First, an introduction to the basic principles of GIS representation and their relevance for archaeological modelling is given. The basic principles of 3D GIS are then discussed within the specific semantic and functional domain of GIS. Definitions, characteristics and spatial relationships of archaeological objects are explored. The last part of this chapter devotes some attention to the visual aspect of three-dimensional modelling, starting from the appraisal of the state of the art and considering aspects of this crucial requirement for data delivery to specialists and the general public. All these issues are closely related to the system architecture and data model conceptual design presented in chapter 5 and exemplified within chapter 6. 3.1

Discrete phenomena are spatially homogenous entities with distinct locations and boundaries, such as rivers, highways, and buildings. They hold relatively permanent identities and are identified as individuals prior to any recognition of their attributes (Coucleis 1992). Many GIS researchers applied such a feature-based (or entitybased) approach to handle geographic data (e.g. Mark 1993, Tang et al. 1996, Usery 1993, 1996). In contrast, continuous phenomena are distributed continuously across space with undetermined boundaries. They are distributions of single-value geographic variables (called fields), such as temperature, terrain, and soil type. Such a field-based approach is frequently used in thematic mapping.

GIS models

The object-based representation corresponds to a container (empty box) view of space, which exists independently and is occupied by entities that are described by their properties and whose position can be given in relation to a geometric coordinate system. In this representation, position and shape are given priority over other characteristics of the object. The main characteristic of an entity-based approach is that it assumes an isotropic space with the same properties in all directions and treated equally in all directions. This presents some advantages, in particular when analysing landscape data. Object- (or entity-) based models are created by partitioning the space into a set of mutually exclusive and collectively exhaustive volumes, based on a chosen parameter; in the case of archaeological excavation the parameter is the archaeological context.

GIS have been used since the 1960s to portray spatiotemporal phenomena found in the real world within a computer environment (Coppock and Rhind 1991). This need to model the real world has led to the development of the study of semantics and ontology, which are used to capture more of the meaning as well as the structure and behaviour of data than in traditional models. Various abstraction mechanisms are studied in an attempt to improve information systems design methodologies, providing structures with which to describe phenomena and mimicking the ways humans themselves utilise abstraction to categorise what is perceived. A discussion of model and modelling processes has been presented in section 2.3. Here, a more detailed description of the way GIS are structured in terms of data models and data structures is given, in an attempt to provide tools for understanding the potential and the limitations of ‘unlocking’ archaeological data within a GIS framework. Moreover, the computational aspects of multidimensionality are discussed, as they present some of the major difficulties (in computational terms) that have been indicated as one of the main obstacles to the

Conversely, fields (or, better, continuous fields) describe the continuous variation of an attribute through space, with the use of a mathematical function. Space is conceived in terms of continuity of a Cartesian two- or three-dimensional box, where the attribute represented is given more importance than the shape of the entity or phenomenon to be represented. It is in fact the attribute that defines the position and the shape of the phenomenon under study. As a consequence, the fieldbased representation reflects a plenum perspective of space. Hence, whilst the object-based representation allows empty space, the field-based representation requires that all space be exhausted (i.e., every location must have one and only one value in a field).

1

For this, manuals have been published both in the disciplines of geography and archaeology. Amongst others: Burrough (1986), Burrough and McDonnell (1998), Worboys (1995), Worboys and Duckham (2004), Wheatley and Gillings (2002), Connolly and Lake (2006).

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Making Visible: Three-dimensional GIS in Archaeological Excavation coordinates (the vector), and the polyline (or arc in technical terminology) is a series of straight line segments that are connected by consecutive points. An area is defined in terms of its boundary, represented as a collection of vectors, where the start and end point are the same. A volume (or body) is defined by boundaries, in this case the boundary is a discrete surface created by the union of planar polygonal facets (Worboys 1995). Whilst Euclidean geometry defines the absolute spatial reference system, within which points exist, vector GIS is also characterised by the use of topology to abstract space. Topology adds a structure to the points by defining neighbourhood in a qualitative way and by allowing the capturing of relationships between points, rather than just encapsulating their absolute location (Apel 2004). Both systems of spatial definition are necessary to address the nature of spatial relationships, as geometry allows for making measurements of space and objects in space, and topology for identifying spatial relationships between objects; for instance, which are the potsherds inside context A and which inside context B? As is clear from the example, archaeological queries are often topological. The establishment of relationships between archaeological strata, often represented in a Harris matrix, is another example of archaeological topology (Wheatley and Gillings 2002). Vector data structures are very efficient in terms of computer data storage, as only points of interest need to be stored.

An interesting corollary of this conceptualisation (which was initially aimed at representing a two-dimensional map-based world) is that subsoil volumetrics, and amongst these archaeological deposits, occupy the entirety of the subsoil space. In the subsoil there are (in theory) no empty spaces. It is clear that the relevance of both representations of the real world in the realm of archaeological excavation needs to be contextualised and discussed. 3.1.2 Data structures: raster and vector. A debate or reconciliation? The choice of conceptual model determines how information is gathered to be put in the systems, as well as how it will be retrieved and analysed within them. Spatial data, conceptualised as fields and objects, are traditionally represented using two different classes in computing: raster and vector. Another categorisation of representations of the real world is that presented by Raper (1989a) that defines objects with known or welldefined spatial extent, location and properties and those with unknown or not well-defined spatial extent, location and properties. These seem to correspond, again, to the divide vector and raster. Traditionally, rasters are the computer translation of continuous fields by the use of grid-based representations (fig 3.1, right). Raster data is structured as an array or grid of cells, referred to as pixels2. The three-dimensional equivalent is a three-dimensional array of cubic cells, called voxels. Each cell in a raster is addressed by its position in the array (row and column number). Rasters are able to represent a large range of computable spatial objects (Worboys and Duckham 2004, 17)

An appraisal of the rationale behind the construction of such data structures for the representation of geographical (and in our case archaeological) spatial data goes beyond the scope of this research. A good example of this exploration is given in Raper (2000) and in numerous contributions by environmental scientists and geographers that studied the cognitive links between geographical phenomena and their possible digital representation (Goodchild 1993, Kemp 1993, Mark 1999, Worboys 1996).

Entity based data models are represented with the use of the vector data structure (fig. 3.1, left).

Here the structures are explored to inform the decisionmaking processes of building a link between them and archaeological data. Each structure is tied to a specific set of manipulation and analysis operations that at times cannot be carried out using another structure. Careful choice of the data structure is crucial in these terms as data structure transformation is possible, but not without consequences. Conversion from raster to vector data and vice versa is one of the thorniest problems of GIS (Worboys and Duckham 2004).

A vector is a finite straight-line segment defined by its end-points. The locations of the endpoints are given with respect to some coordinatization of the plane or higher-dimensional space (Worboys and Duckham 2004, 17) Euclidean geometry normally regulates the form of this space and of the primitives (points, lines and polygons) that are positioned in this space. Therefore a point is defined by a given coordinate (x and y in biplanar spaces and x, y and z in volumetric ones). A line is characterised by a start and end point of known 2 An acronym for picture element. The smallest picture element that can be manipulated by software. The individual "bit" in bitmapped, where the bit is the measure of data stored in a computer system. This is allowed two states: 0 (off, false) and 1 (on, true). Pixel in GIS and graphics can also indicate the single grid unit with variable resolution (in measure units such as meters and inches)

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment

Figure 3.1. Conventional representation of entities and fields in vector and raster GIS. Source: Burrough 1996, fig. 1.2

Figure 3.2. Comparison of entity based objects and fields and their structures (vector versus raster). Left: a simple object structure with topology and attributes for each polygon. Right: simple continuous fields (discretised as regular grids) with a separate layer for each attribute. Source: Burrough 1996, fig. 1.1.

Raster structures rely on an intrinsic database for representing space whereas vector structures are formed by a more complex and structured database where spatial and attribute data are related but stored separately (fig. 3.2). As a consequence, relations and functions are expressed differently in the two structures and the two structures better represent different sets of data. Nevertheless, Raper and Maguire (1992) emphasise that Goodchild (1992), Burrough (1992) and Frank and Buyong (1992) all draw attention to the hazards of equating raster (tesseral) and vector representations with pixel- or line-oriented models of real-world phenomena: both raster and vector representations can implement equivalent (but not identical) models of the real world at a specified spatial resolution.

representing different views of reality, with the result that two separate kinds of systems have been developed. It is now recognised that separate modelling of the two representations (or types of objects) tends to contradict reality and leads to difficulties in representing relationships between objects and different representations of phenomena. This contradiction becomes more prominent when a three-dimensional view of the world is considered. Abdul-Rahman and Pilouk (2008) point out, for example, the impossibility of answering the question: ‘how many of the people working in a 50-storey office building are affected by polluted air generated by vehicles in nearby streets during rush hour?’ until the two separate models are combined. Modelling this scenario requires modelling together entities and fields, maintaining an accurate representation of their relationships.

Whichever the case, traditional GIS used to manage independently the two data models and data structures

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Making Visible: Three-dimensional GIS in Archaeological Excavation database, and uploading Electronic Distance Measurer (EDM) or laser measurements, photographs and any form of elaboration of post-excavation analysis (finds, environmental, dating databases, for example) to the digital platform.

3.1.3 Spatial and non-spatial attributes In a GIS, spatial data structures are mainly aimed at expressing spatial properties, namely geometry and topology, in other words describing and graphically displaying the real world. Nevertheless, the system also takes into account that spatial form and location are not the sole characteristics of spatial data. In fact, much analysis, modelling and querying is based on attributes that are non-spatial (Laurini and Thompson 1992).

Structuring/verification This is a fundamental stage in developing the system. It determines the range of functions which can then be used for manipulation and analysis. Different systems have different structuring capabilities, which range from simple to complex topology for vector data models to relational, object-oriented, regular or irregular tessellation for raster data. It concerns the data structures used for implementing the data model and internal encoding of data.

Attributes are stored differently in raster and vector models. In raster representations the attribute is intrinsic of the space represented. In vector based representations the attribute is attached to the entity represented. The raster structure is the spatial representation of the variation of an attribute: what defines the space is the attribute itself. The vector structure is the space that contains a pre-defined attribute or set of attributes. Attributes are defined characteristics of an entity, which are linked to it by an identifier. Attributes could be conditions that are not used in the original definition of entities, may refer to behaviour of the entity in time (as in space an entity is, by definition, static) and finally, some recorded characteristics may represent functions of an entity (Laurini and Thompson 1992). Non-spatial attributes are generally stored in a database.

Manipulation Among other important manipulation operations there are generalisation and transformation. Generalisation is used to reduce data complexity or to make data more legible. Transformation includes coordinate transformation, scaling, cleaning of the data and, most importantly, interpolation methods. Interpolation identifies ‘the procedure of predicting the value of attributes at unsampled sites from measurements made at point locations within the same area or region’ (Burrough and McDonnell (1998, 98). It is used in GIS mainly to convert data from point observations to continuous fields, the best known result of interpolation being the Digital Terrain or Digital Elevation Model (DTM or DEM). For a thorough discussion of interpolation and interpolation methods refer to Burrough and McDonnell (1998), Gold (1989), McLaren and Kennie (1989), Weber and Hellen (1991).

In archaeology, the raster data structure has the potential of making visible characteristics that are not necessarily visible in the field, whereas the vector structure easily expresses, in stratigraphic excavation, the boundaries of contexts, linked via a unique identifier (the context number) to the context sheet which describes that specific context through a series of attributes. This fundamental parallelism between GIS data models and structures and the conceptualisation and representation of archaeological excavations is further elaborated in chapter 4.

The Triangular Irregular Network (TIN) designed by Peuker and co-workers in 1978 is a vector-based structure to represent elevation models avoiding the redundancies of altitude matrices (Peucker et al. 1978). It is a sheet of continuous, connected triangular facets based on Delaunay triangulation of irregularly spaced nodes or observation points which are related to each other topologically. ‘TINs provide efficient, accurate data storage of elevation data at the expense of introducing a triangular discretization that may hinder some kinds of spatial analysis, such as the derivation of surface geometry and topology’ (Burrough and McDonnell 1998, 124).

3.1.4 GIS functions Functionality as defined by Raper and Maguire (1992) refers to the operations that a GIS can perform on spatially related data accessible to the system. It therefore encompasses data capture and storage functions, spatial query and analysis, as well as tools used in presenting output. It is clear that the functionality of a GIS is related strongly to the system architecture, particularly the structuring of the spatial data employed and the organisation of the spatial database. Harmon and Anderson (2003) clarifies and indicates how specific types of GIS implementation (GIS design models) condition the range of functionalities available within such GIS.

Analysis Spatial analysis can be thought of as a general ability to manipulate spatial data and extract additional meaning as a result (Fotheringham and Rogerson 1994).

Data capture/input These operations consist of capturing and inputting the spatial and non-spatial data into the system. Different techniques and devices can be used to perform this operation. In archaeological excavation it traditionally consists of transferring context sheet information into a

Analysis can limit itself to basic operations such as creating new attributes that can be attached to the original entities, increasing the size and value of the database or

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment Attribute operations

creating new spatial entities, requiring the database to be expanded to include these new items. In this sense, Bailey (1994) proposes the use of the term summarisation, which refers to basic functions for the selective retrieval of spatial information within defined areas of interest. The basic summaries of such operations can be computed, tabulated and mapped. Analysis proper is therefore more concerned with the investigation of patterns in spatial data, in particular seeking possible relationships between such patterns and other attributes or features in the study region, and with the modelling of such relationships. Results are then visualised (Bailey 1994). Spatial analysis can be statistic (in the sense of stochastic) or deterministic, where areas of functionality go from network analysis, routing, transportation, location/allocation modelling, site selections and projection or cartographic algebra. Besides exploratory and explanatory analysis, also described in the literature as descriptive and declarative approaches, the power of GIS is also that of allowing modelling either internally or externally to the system (Kemp 1993).

Attributes are properties that define what entities are. They can either refer to the location of the entity, they can be attached to an entity as qualitative or quantitative descriptors of a non-spatial property, or they can be derived form the spatial properties of the entity (Burrough and Mc Donnell 1998). Many of these attributes are present in any standard recording form of an excavation (see, for reference, the MoLAS form in figure 3.3). For example, the attributes of context number, name of excavator, and soil characteristics describe non-spatial properties of a stratigraphic context. The length and width of a context, the area, shape and contiguity are attributes derived from the shape of the context. The relationship of one context with others is a clear example of topology. Simple data retrieval by attribute query is a standard GIS operation. Nevertheless, the major contribution of a GIS approach to excavation conducted using a stratigraphic approach and single context registration is that of identifying new attributes that can be attached to entities (amongst others, Francovich and Valenti (2000), Fronza et al. (2001)). The process of selection of data or creation of new attributes makes use of various types of logical, mathematical and statistical operations, as summarised in table 3.1. For example, a new attribute can be computed for contexts with concentrations of a particular ceramic type or stone tool higher than a given number, or those having a particular ceramic shape. The new attribute can be displayed as a colour ramp or new symbol chosen to represent the entity (in this case the new context) on the excavation plan to create distribution maps. The results of transformation or reclassification of attributes, usually displayed by re-shading or re-colouring of the entity, do not change the spatial properties of the entity, except in the case where neighbouring entities are discovered to be the same and generalisations take place (context 201= context 202).

It is clear that ‘the kind of data analysis is governed by the data types used in the data model’ (Burrough and McDonnell 1998, 28). For example, whereas logical operations (derived from Boolean algebra and aimed at manipulating ‘truth values’ of concepts) can be carried out with all data types, algebraic operations can only be performed on real and integer data types (and therefore raster structures). It is important to emphasise, for clarity, that in the case of entities, data retrieval and analysis are based on attributes, location and relationships (topology) of the entities and measures of spatial distribution, whereas in the case of continuous fields, data analysis concerns the properties of the fields and their variation through space (Burrough and Mc Donnell 1998). This core functionality of a GIS is very little discussed in the realms of applications of GIS to archaeological excavation, in particular in terms of the different and distinctive operations that can or cannot be performed using a raster rather than a vector data model. A discussion of the basic analytical operations for entitybased and continuous field GIS is provided in the following sub-sections, accompanied by archaeological examples. Whereas the discussion in the following paragraphs is applicable mainly to 2 and 2½ D GIS, section 3.2. will review whether the operations can be carried out in a 3D environment and how a 3D environment would benefit intra-site spatial analysis in archaeology.

Point data encoded in a vector system can also be very simply retrieved from the database or reclassified by using the operations listed in the table above. At landscape level these points normally represent sites, whereas at intra-site level they represent single artefact locations (Wheatley and Gillings 2002). The structure of groups of point locations is the simplest patterning one can consider and visualise as a distribution map, whether the attribute explored is one or a combination of many characteristics of the artefact (Biswell et al. 1995, D’Andrea et al. 2000, Meffert 1995, Miller 1996, Moscati 1999, 2000, Mytum 1996, Powlesland 1991, Quesada Sanz et al. 1995, Rains 1995, Reilly and Thompson 1993, Semeraro 1993, 1996, Vote et al. 2001, Vullo et al. 1999). Formal statistics methods are used to verify the reliability of the distributions.

Operations for spatial analyses of discrete entities The basic classes of operation for spatial analysis with entities, as discussed in Burrough and McDonnell (1998), are divided into attribute, geometric and topological. By using one of these three fundamental characteristics of an entity at a time or using them in combination, a range of queries and other exploratory operations can performed.

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Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 3.3. 1990 version of the MoLAS context recording sheet. Source: Chadwick 1997, fig. 3.

Table 3.1. Mathematical operations for transforming attribute data. Source: Burrough and McDonnell (1998)

Mathematical operations for transforming attribute data • • • •

•

Logical (Boolean) operations Simple and complex arithmetical operations and numerical models Univariate statistical analysis Multivariate statistical methods or Bayesian statistics for classification and discrimination Multicriteria methods, AI-based methods: neural networks and fuzzy logic

Other vector analysis operations include operations that consider attributes from two or more entities that completely or partially occupy or cover the same space (Burrough and Mc Donnell 1998). This set of operations finds little space in a GIS created exclusively to represent single context excavation, as the principle behind this approach to excavation inherently excludes the space of one context from that of another.

Nevertheless, these operations are worth mentioning as they allow us to perform inclusion, entity overlap and intersection. These operations have wide potential in a GIS that incorporates not only an exclusively stratigraphic dataset but also alternative descriptions of the archaeological deposit (for example, soil geochemistry, single finds descriptions disconnected from the contexts and so on).

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment and relationships are known exactly (Burrough and Mc Donnell 1998). Some questions arise: how useful is this for archaeological excavation and moreover for single context recording systems? How much has GIS offered to intra-site analysis that is different from what we already knew from traditional recording and analysis? The case study discussed in chapter 6 offers some answers to these questions.

In this case re-manipulation of the originally identified contexts can be carried out and the operations become relevant in a study that includes fuzzy logic3 operations and reclassification of entities after post-excavation analysis. Distance/location operations Distance and location operations use the inherent geometry of the GIS project (and the entities that populate it) to locate entities with respect to simple Euclidean distance or location criteria and to create buffer zones around one or more entities.

Operations on continuous fields There is a large range of analytical products that can be derived from continuous surfaces represented as regular grids, where each attribute is denoted by a separate overlay and each cell is allowed to take a different, scalar value – which could range from altitude to any other characteristic (Burrough and Mc Donnell 1998). Single numbers for each cell of gridded data allow for the application of algebraic techniques to build models for spatial analysis. The operations performed are either point-based or spatial. Whereas point operations compute a new attribute value for a location with coordinates (x,y) from the attribute values in other maps at the same location (x,y), spatial operations compute the new attribute value of a location from the attribute values in the same map, but at other locations within a certain neighbourhood (Conolly and Lake 2006). Spatial operations include:

These operations are generally less used in intra-site archaeology than in landscape studies, perhaps as measurements and locations are traditionally inferred and estimated by visual inspection of maps (no matter whether digital or analogue). These types of operation allow for area and distance calculations (Craig and Aldenderfer 2003, Fronza et al. 2001, Fronza et al. 2003) and moreover for performing buffering (Nørbach 1999) in particular within refitting studies (Bollong 1994). A buffering query involves the selection of a subset of the dataset based on its distance to (or from) a defined point, line or polygon feature (Conolly and Lake 2006). In this manner, for example, the proportion of all stone flakes found within a certain distance from a core can be figured out and put in relationship with the refitted sequence, or formation processes can be linked to patterns of pot sherd dispersion.

• Interpolation: the prediction of the value of an attribute at an unsampled location based on a measurement done at a sampled ones located within a given neighbourhood. It is generally used to create discretized continuous surfaces from sparse observations (Burrough and McDonnell 1998); • Spatial filtering: the passing of a square window (the filter) over the continuous surface to compute a new value of the central cell as a function of the cell values covered by the window (Burrough and McDonnell 1998); • First and higher-order derivatives: these derivatives are generally used for continuos surfaces that represent terrain models. Slope is defined by a plane tangent to the modelled DEM at any given point and it comprises two components: the gradient (the maximum rate of change of altitude) and the aspect (the compass direction of this maximum rate of change) (Burrough and McDonnell 1998); • The derivation of surface topology through the application of algorithms to calculate drainage network routes, and remove pits; • Clumping: the assessment and grouping of contiguous cells to a unit distinct from others (Burrough and McDonnell 1998); • Non linear dilation: a similar function to buffering (which is used on entities) but with heterogeneous rather than isotropic spreading to reflect the variations in resistance during the process (Burrough and McDonnell 1998); • Viewshed, shaded relief, and irradiance: three strongly related methods concerned with the

Operations using in-built spatial topology These operations can exploit both the geometry and the topology of the dataset. The entities are in any case directly linked in the database: the linkage can be spatial (contiguity) or topological. Topological queries are then used to characterise the relationships between entities. In an excavation GIS where contexts are represented by polygons and finds by points, a question such as “select all potsherds inside context 205” is a topological query. This question is answered using a point-in-polygon operation (Conolly and Lake 2006). Polygon-overlay queries are, in many cases, used to explore both the relative and absolute chronology of an archaeological deposit by exploiting the concept of the Harris matrix to elucidate stratigraphic relationships. None of these methods of analysis pay attention to data quality or errors. There is an underlying assumption, when dealing with entity-based data models, that all data 3

Fuzzy logic is a term that emerged in the conceptualisation of the theory of fuzzy sets developed by Zadeh (1965) and it is applied, in particular in computing, to the processing of imprecise and variable data. Instead of using traditional binary values, such as true and false, fuzzy logic operates with a range of variables that offer greater flexibility. For a discussion of geographic objects with indeterminate boundaries see papers in Burrough and Frank (1996).

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Making Visible: Three-dimensional GIS in Archaeological Excavation computation of the paths of light between a light source on or above a DEM and its relationship with other locations (Burrough and McDonnell 1998).

3.2

Three-dimensional modelling in GIS

As discussed in chapter 2, three-dimensional GIS is considered to be more suited than 2 and 2½ D systems to represent a series of spatial objects, regions and phenomena which exist in or enclose 3D space. A true three-dimensional GIS model requires multiple x,y,z observations to be structured into a solid domain. As a consequence, all locations are defined within a x,y,z coordinate system (generally within a Euclidean space), where multiple z values can be defined for a single x,y location in the plane (Raper 1989a, Turner 1992a). A system that deals with spatial objects restricted to single values of z for any x,y position is therefore not considered three-dimensional for the purpose of this research and in much literature on 3D GIS. This is in fact a 2½-dimensional one.

Various combinations of these spatial operations and their products (primary and derivative) have been successfully used to explore archaeological landscapes, in particular using procurement (catchment) and visibility analysis (Aldenderfer and Maschner 1996, Gillings et al. 1999, Lake and Woodman 2003, Lake 2007, Llobera 2003, Peterson 1998). At an intra-site level, the examples of use of map algebra and continuous spatial operations for analysis are less frequent. These include rare cases of visualisation of density distribution patterns from the interpolation of point data or extrapolation of gridded excavation databases (Biswell et al. 1995). Peeters (2007) worked on the characterisation of site-specific spatial structures of activity areas, using percolation analysis on continuous surfaces representing find distributions. Simulations of cluster aggregations are also performed, based on the elevation data of the activity surfaces reconstructed for a particular period of the archaeological palimpsest. Miller (1996) performed hydrological modelling in a case study that predicts waterlogging rates and plans piling in York. The derivative layers used for the modelling are all products of operations performed on continuous surfaces.

3D GIS is not mere three-dimensional representation, but it fundamentally aims at providing the same functionality as 2D GIS and both software companies and research groups are making increasing efforts in developing GIS functionality in 3D. These functionalities constitute, in the opinion of GIS specialists, the requirements for the usefulness of any given system. A consensus has been achieved in the arena of 2D GIS (Nyergers 1993, Openshaw 1991, Rhind and Green 1988) and, even if the complexities associated with data capture, system implementation and visualisations are greater in three dimensions, the requirements are assumed to be little different. Papers, and dedicated publications, provide an assessment of the status of 3D GIS development in this sense (Abdul-Rahman and Pilouk 2008, Abdul-Rahman et al. 2006, Pouliot et al. 2006, Stoter and Zlatanova 2003, Zlatanova 2002, Zlatanova et al. 2002a, 2002b). In particular regarding the review of vendor systems, it is concluded by Zlatanova et al. (2002a) that

Visibility analysis can also be relevant for the study of domestic social spaces, as proven in the studies of Anderson (2005) and Moscati (2000). The single major contribution of continuous surface operations at an intra-site level remains the reconstruction of archaeological surfaces or the shape of cuts and other features via interpolation of height points both from newly acquired and legacy data (Doneus and Neubauer 2004, Laurenza and Putzolu 2002, Viti 2004, Zhukovsky 2001). Many interpolation algorithms are inbuilt in current GIS software packages and the nature of the terrain determines the best algorithm to be used. Not much research has, to date, been dedicated to the specific nature of archaeological surfaces and features and the appropriate algorithms to be used for their geometric reconstruction. This discussion remains relevant as it is mainly through the assembling of 2½-dimensional continuous surfaces obtained with interpolation of spot heights that 3D objects are built, as illustrated in the following sections.

all systems revealed little provision of 3D GIS functionality in terms of 3D structuring, 3D manipulation and 3D analysis but most of them can handle efficiently 3D data in the 3D visualization aspect. A fully integrated 3D GIS solution has yet to be offered by general purposed GIS vendors. (ibid.:http://www.tudelft.nl/live/binaries/2faaf567465a-48c7-b2044944195b6b6c/doc/Sisi%203DGIS%20Ottowa.pdf) The following section summarises such considerations and provides an overview on the status of 3D GIS by considering its ability to respond to the fundamental functions discussed in section 3.1 and in relation to their relevance for 3D GIS in archaeology.

Presentation This is the final task in a GIS. At this stage, all generated information or results are presented in the form of maps, graphs, tables and reports. With the development of more sophisticated analysis and visualisation techniques in a GIS environment, presentation is now an integral part of various stages of the whole GIS process and is increasingly becoming interactive and Internet based.

3.2.1 Review of representations and data models for 3D GIS Three-dimensional GIS requires three-dimensional representations of the real world. Similarly to the manner in which two-dimensional space is modelled, threedimensional space can be broadly conceptualised as field-

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment and 3D space when defining the spatial identity of the object under investigation. He distinguishes between: 1. geo-objects which are believed to have a discrete spatial identity (e.g. a perched acquifer or fault block in archaeology, a context and feature); 2. geo-objects which vary in identity in space which can be visualised by choosing threshold parameter values for inspection (e.g. ore grades or a sedimentary facies - in archaeology: geochemical distributions, flint and/or pottery counts and other characteristics).

based and object-based. In the field-based approach the spatial framework will be a portion of three-dimensional (generally Euclidean) space. Fields will be functions from locations in a 3D spatial framework to attribute domains; variations of the attributes will be detected and represented in such space (Worboys 1995). A threedimensional space can also be populated by spatially referenced 3D objects. In a 3D world the definition of possible forms of spatial objects is non-trivial and various approaches have been conceptualised and experimented with, to achieve a representation that ultimately allows a fully functional GIS. The main challenge has been that of taking into account the fact that 3D object reconstruction and modelling has grown independently of GIS. It was initially created as a means for representing objects and landscapes, in particular in architecture, reverse engineering and realistic 3D graphics for gaming and animation. These disciplines have traditionally been more concerned with 3D photorealistic reconstruction of shapes and are less concerned with the semantics of the objects per se. In archaeology, 3D graphic modelling has been mainly used in reconstructions of sites and ancient building complexes (Chalmers and Stoddart 1996, Daniels, 1997, Huggett and Go-Yuan 2000), recording and conservation of historic architecture (Salonia 2000, Wood and Chapman 1992) and for use in museums, public displays and websites (Berndt and Teixeira 2000). Virtual reality in archaeology makes large use of 3D reconstructions and has grown as a specialised area in archaeological computing (Barceló 2000, 2001, Forte 2000, Forte 2008, Forte and Guidazzoli 1996, Gillings and Goodrick 1996, Goodrick and Gillings 2000).

3D spatial modelling of the first kind of geo-objects is sampling limited, and a minimum of database operation is required to assemble the spatial data, which will consist of a boundary type definition. The 3D modelling of the second type of geo-object is definition limited and is governed by the establishment of the data model used to formulate a database query. Therefore, ‘with the relative paucity of information often available to carry out such definition limited 3D spatial modelling, the importance of the data model used in the analysis increases’ (Raper 2000, 136). The discussion of spatial identity definition of both types of objects is crucial both in a 2D and 3D environment. It becomes even more crucial in a 3D environment as the quality of data introduced needs to be constantly checked. This is due to the fact that this type of modelling mainly applies to subsurface data, which are not verifiable at any given location. Appropriate 3D measurements in the study domain are necessary to determine the 3D spatial form of the object or phenomenon under investigation. In the majority of cases, the collection of the measurements is a sampling based one and the variability of representation techniques of the measurements is far higher than in a 2D domain. As a consequence, any spatial representation obtained is a probability of the object to exist in a particular form (Raper 1989). Differently from the geological domain, where on very rare occasions the sampled 3D object can be verified, in archaeological excavation there is a possibility of re-negotiating the stratigraphy built from vertical sampling with the one sampled to recreate excavation surfaces and shapes or represent other soil characteristics (via arbitrary excavation or sampling of variables such as chemistry of soil, etc.).

Conversely, the additional challenge for 3D GIS, which is also concerned with the 3D conceptualisation of spatial modelling, not only the construction of 3D shapes, is that this is quite different to that used in two-dimensional cases as there is no equivalent to conventional cartography as a source of information. Therefore, threedimensional conceptualisation has to proceed from first principles in each case. Different conceptualisation methodologies have developed in each application area through time and these are often incommensurable (Raper 2000). The domain of interest significantly influences the three-dimensional conceptualisation, be it the ocean, the atmosphere, the subsoil or an urban environment. As a consequence, even the data model domain will vary and adjust based on the phenomena to be represented. Given the characterisation of archaeological deposits in the sphere of subsoil sciences, the parallel with the ontology used in this discipline to conceptualise strata, faulting and other phenomena can be usefully applied. In chapter 4 the challenges and potentials of archaeology as opposed to geology in 3D GIS representations of the real world will be further elaborated.

As for the equivalent data structures in 2D GIS, vector and raster data structures are generally used to deliver respectively sample-limited and definition-limited objects (or entities). Three-dimensional vector data models and structures The representation of 3D entities through vector structures involves choosing geometric primitives that can mimic their forms, structures and properties (Raper 2000). The primitives used are the same available for 2D modelling i.e. points, lines and polygons but they are, in this case, defined by a triplet of coordinates (x, y and z) in a Euclidean space. In order to mimic complex objects, primitives are generally aggregated to form complexes.

Raper (1989a) underlines that there is an important qualitative difference in the modelling of geology in 2D

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Making Visible: Three-dimensional GIS in Archaeological Excavation structure is that calculations, computations and simulations are well developed and relatively straightforward on such volumetric structures, and they work very similarly to the raster-based computations. The two most popular versions of volume representations are cuberille and octree (Gargantini 1992). Traditionally these structures were used for acquired information, but they are now emerging as effective means to integrated large amounts of data acquired or created using vector approaches (Gargantini 1992).

In entity-oriented approaches, space is generally empty and filled with individual objects. An explicit mechanism is needed to establish the boundaries of the objects, as these will define what is part of the object (inside the boundary) and what is not (outside the boundary). Whereas in everyday life the volume within which we travel is empty (the air) and what we normally experience are surfaces (the floor, the pavement, the surface of the desk where we sit and of the keyboard that we press), archaeology is mainly concerned (and so is geology, and other disciplines that deal with fluid dynamics) with deposits that occupy the entirety of the space under consideration. Moreover, what is of importance is what is inside the volume, more than just its mere boundaries. In geology, some authors recommend that the entire space should be partitioned because geological systems have indiscernible boundaries or many neighbouring relationships; others (Pouliot et al. 2008b) consider that ‘since geologists usually view the subsurface as a collection of distinct objects (faults, geological units, ore deposits…), it is more appropriate to use the objectoriented modeling approach for the construction of 3D GeoModels’ (ibid.: 532). The following advantages are listed to support the choice: database management ease, less storage requirement, easier representation of topological analysis and more flexible adjustments to projections systems.

From the discussion above it is clear that in a 3D GIS environment, like in a 2D one, a simple choice between vector versus raster data structure to conceptualise real world objects and phenomena is neither useful nor possible. Therefore, it is the statement of this research project that both structures will be considered useful for the representation of archaeological data, depending on the aspect of the real world to be represented, as highlighted in Raper (1989a). 3.2.2 The construction and structuring of threedimensional spatial models The construction of three-dimensional spatial models is referred to as solid modelling. Although at present this type of modelling is used on a daily basis in all disciplines that deal with volumetric data, from atmospheric modelling and disaster management, to cadastre and urban planning, to medical imaging and surgical planning, to gaming and animation, its roots are in computer-aided design and manufacture in mechanical engineering. As a consequence, the modelling approaches available at present to reconstruct solids are not necessarily all appropriate for use in a 3D GIS environment, as clarified in this section.

Despite the advantages listed above, to date there has not been a successful way of geometrically representing a packed complex volume, as validation operations such as clean and build (that clean errors and build topology in a 2D environment) cannot be conducted automatically whilst constructing geometry in 3D. Three-dimensional raster data models and structures

Geometric modelling of solids copes with the problem of building a numerical model for lines, surfaces and volumes (Mallet 1992) and as such it is based on different conceptualisations of a solid, which can be constructed using principally: • surface-based modelling; • volume-based modelling.

A different approach to volume discretisation is the use of volume pixels, otherwise known as voxels, obtained through the operation known as spatial enumeration. These cubes are fundamentally pixels with a fixed length and width, exploded to the third dimension, that add up to create a volume, with a common resolution throughout it. This results in a cubic growing memory demand, the main disadvantage of such a structure. Spatial enumeration techniques are useful when objects are fairly regularly distributed so that large portions of the 3D space can be stored efficiently. Moreover, the disadvantage is nowadays solved by the increasing amount of memory found in modern workstations. The major advantage of voxels is the simple internal structure with implicit topology, similarly to what we have seen for raster data. The voxel format reduces the topological complexity of the data and no mesh needs to be established. The problem of resolution definition must be addressed before starting the modelling process, according to the resolution needs. Any time the needed resolution has to be changed, the whole volume structure must be recomputed (this issue can be addressed in the conceptual framework via the concept of multiple resolution). The user must consider the optimum between performance and accuracy. Another advantage of this

An object is surface-based when it can be represented by surface primitives. Volume-based, conversely, identifies the object’s interior as solid information (Li 1994). Surface-based representations are grids, shape models, facet models and boundary representations (B-reps) (fig. 3.4) whereas volume-based representations are: 3D array, octree, constructive solid geometry (CSG) and 3D TIN (or TEN) (fig. 3.5). All approaches have advantages and disadvantages depending on the specific application that is making use of them as summarised in table 3.2 (Abdul-Rahman and Pilouk 2008, Bak and Mill 1989, Stoter et al. 2004). The choice of one over the other, in a design and graphic environment, is generally determined by ease of use, quality of rendering and interoperability.

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment

Figure 3.4. Examples of surface-based representations. Source: Abdul-Rahman and Pilouk 2008, fig. 3.8.

Figure 3.5. Examples of volume-based representations. Source: Abdul-Rahman and Pilouk 2008, fig. 3.15.

world objects in isolation or combination. The combinations available in a three-dimensional environment, as opposed to 2 and 2½ ones, increase in number and complexity and are aimed at becoming closer to the appearance and behaviour of real-world objects. Figure 3.6 graphically summarises the representation of geometric primitives in 3D. The specific representations conceptualised for use in archaeological excavation for the purpose of this research are discussed in chapter 5.

Conversely, in a 3D environment that has at its core spatial data processing and analysis, the geometric model of choice becomes fundamental, as it will influence even the simplest operation such as volume calculation - e.g. estimate of the volume of an object from a facial model would require specific algorithms, whereas it would be a simple mathematical counting for a 3D array (Pouliot et al. 2008b). Moreover, it has been noted that ‘solid modelling systems should not be confused with other graphics systems which produce pictures of solid looking objects’ (Reilly and Shennan 1989, 158). One example of the latter are face-models from sets of rendered polygonal panels and B-reps. In this case, although the object has the appearance of being solid, it does not actually conform to a truly enclosed solid object. This has significant consequences in terms of the analytical potential of the model created, let alone the semantics that reflect the way the 3D space has been captured.

A further challenge characterises 3D reconstruction for 3D GIS use. In fact, whilst numerous operational sensors for 3D data acquisition are routinely employed (optical, laser scanner, radar, thermal, GPR, photogrammetry, etc.), 3D reconstruction software still offers predominantly manual and semi-automatic tools (e.g. Leica Photogrammetric Suite, PhotoModeler, GoCAD, Microstation, ThinkDesign and Rapidform, amongst others). 3D automatic reconstruction algorithms remain the subject of intense but uncompleted research (Zlatanova 2008).

If the concern of 3D GIS is with the ability to capture the semantics and variations of volumes, it should therefore be appropriate for it to use mainly volume-based constructed objects (as argued by Pouliot et al. 2008b). Nevertheless, as 3D GIS is not only a series of objects, but an environment, a conceptual universe, it is probably more appropriate to conceive it as the 3D framework which is populated by 3D elements variously represented depending on the aspect to be captured. In this environment, three-dimensional primitives represent real-

3.2.4 Three-dimensional spatial models and their semantics – from geometry to topology Solid GIS components are unique as they represent not only a shape, but also the volume enclosed by it and all the characteristics of this volume. In order to enhance these characteristics a step needs to be made from the construction of a geometrical model (discussed in the

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Making Visible: Three-dimensional GIS in Archaeological Excavation Table 3.2. Summary table of representation techniques in solid modelling suitable for use in 3D GIS. The material is compiled from Abdul-Rahman and Pilouk (2008), Bak and Mill (1989) and Stoter et al. (2004)

Method

Summary

Advantages

Disadvantages

Grid

It is a structure that specifies height values at regular locations

Simple to generate. Topology information (in terms of position) is implicitly defined.

It cannot be used for sources of multiple heights, e.g. vertical walls, overhangs

Shape

This model describes an object surface by using surface derivatives (e.g. slopes) of surface points. Each point grid has a slope, instead than a z value.

Experimented for sea bed surface mapping

Data acquisition problems

Facet

It describes an object’s surface by planar surface cells, which can be of different shapes and sizes. The most popular version is a TIN (Triangulated Irregular network). The surface is described by a network of triangle facets. Each node of the triangles has a set of x, y and z coordinates.

Widely used for Digital Terrain Models (DTM) for its structural stability and terrain surface adaptability, object visualisation.

High resolution datasets require much storage memory and computation of TIN can be a lengthy process

The object is defined by its bounding surface, this being represented by a set of Euler operators or a set of coordinates and their connectivity. Typically this is described by polygonal facets, each of which is

Optimal for representing real life objects.

Boundary representation

The original observation data are reserved.

Very popular CAD based design, most of the rendering engines are based on B-reps.

defined by its edges, which are in turn defined by their vertices. In the case of sculptured (freeform) surfaces, spline functions can be used to fit patches through the vertices of each polygonal face

Considerable amount of geometric and complex computation in order to represent irregular objects. They are not unique and constraints (rules for modelling) may get very complex to implement

CGS (constructive Slid Geometry)

This represents an object by combining primitive point sets using Boolean operations (union, intersection and difference). Examples of primitives are spheres, cubes, cylinders, cones or rectangular solid.

It is very appropriate for CAD manufacturing because object creation can be completed interactively with a simple modelling language. Straightforward construction of composite objects.

Real-world objects (irregular) are very complex and CSG does not deal with this complexity very efficiently

3D ARRAY Voxels

This represents an object by the union of a set of cells where each cell is a primitive shape (either regular or irregular). Cells are adjacent, connected and do not intersect.

Appropriate for the modelling of continuous phenomena such as geology, soil, chemistry of the atmosphere

High resolution data require large volumes of computer space. The surface is not regular by nature.

Octree

It refers to a hierarchical data structure that specifies the occupancy of cubic regions of the object space. Conceptually, the area of interest is enclosed by a cube represented by voxels. Recursive decomposition is at the base of the structure and it is used to encode 3D objects.

Simplicity for Boolean operation and visualisation rendering algorithms

Same as above

Tetrahedron (TEN)

It is an extension of TIN. An object is described by connected but not overlapping tetrahedrons (of four vertices, six edges and four faces).

Very simple structure that supports fast topological processing and rapid visualisation. Well defined because the three points of each triangle always lie on the same plane. Easy manipulation, display and analysis.

It could take many tetrahedral to construct a factual object. Work on tetrahedral for GIS very limited

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment

Table 3.3. Three-dimensional topological models and frameworks for spatial objects (Zlatanova et al. 2004).

3D topological models (summary from Zlatanova et al. 2004) 1. 3D FDS The formal data structure was the first data structure to consider spatial objects as an integration of geometric and thematic properties. A conceptual model and 12 conventions define the structure (Moolenar 1990). The fundamental rule of 3D FDS is the concept of a single-valued map where the four primitives node, arc, face and edge can appear in the description of only one geometric object of the same dimension. In this way, space is partitioned in non-overlapping objects, ensuring 1:1 relationships between primitives. 2. TEN Acronym for Tetrahedral Network. It was introduced by Pilouk (1996) to overcome some difficulties of 3D FDS in modelling objects with indiscernible boundaries such as geological formations. It uses a simplex-oriented approach and has four primitives (tetrahedron, triangle, arc and node). The subdivision of the space is full. This model has a real 3D primitive, the node, whose relationship to the arc is defined in a relational database table. The rule for creating the model is based on the fact that each node is part of an arc, each arc is part of a triangle, which, in turn, is part of a tetrahedron. Everything must be classified into these categories as a rule of the model. 3. SSM The Simplified Spatial Model, designed by Zlatanova (2000a) for web applications, is a topological structure that focuses on visualisation aspects of the queries. It does not require a full partition of space, as all objects are embedded in 3D. The simple objects used for constructing 3D entities are four (body, surface, line and point) but the primitives are only two: node and face. The model allows for one arc to be part of more than two faces. Faces represent the 3D objects and 3D primitives as such are not maintained. A set of rules are used to create the objects, one of which is the explicit storage of the orientation of the faces and the order of the nodes describes a face. 4. UDM The Urban Data Model is based on a full partition of space and represents the geometry of a body or surface by planar convex faces (Coors 2003). Each face is defined by a set of nodes. A 1D primitive is not supported. Orientation of faces is stored implicitly. Every face composed of more than three nodes is composed of triangles, which results in a triangulation of surfaces. 5. 3D TIN based OO model Designed by Abdul-Rahman (2000) it utilises the FDS model to construct a 3D TIN based on spatial objects in an object-oriented environment. The model works with four primitives: nodes, lines, surfaces and solids. Simple topological relationships between primitives can be established. 6. SOMAS Solid Object Management System was designed by Pfund (2001) and it is based on explicitly structuremaintaining objects. It is still a conceptual model, not implemented in a DBMS. 7. OO3D Shi et al. (2003) developed an OO data model to handle complex 3D objects in GIS. The model is based on three geometric elements: node, segment and triangle, which define, accordingly, abstract geometric objects (points, lines, surfaces and volumes). Formal representations of spatial objects are provided in detail. The model is applied in the tailored-designed software Space-Info. 8. THE CELL TUPLE MODEL This type of topological model was introduced by Brisson (1990) and extended by Pigot (1991, 1995). It is viewed as a tuple model. It defines cells and cell complexes by the properties of a manifold. No clear separation between objects and primitives exists.

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Making Visible: Three-dimensional GIS in Archaeological Excavation previous section) to topological model. In fact, once the 3D solid model is built we only have its spatial appearance (constituted by the geometry and other characteristics, such as the rendering). In order to build a full 3D GIS object, we must add elements that give other insights into the described environment: these are geometric relationships and geometric behaviour determined by functionalities, algorithms, models, mechanisms and so on. In other words, we must add to the reconstructed object the element of topology. Topological models contain useful properties that are absent in purely geometric ones, such as space partitioning, orientation, connectivity, Euler characteristics, composed and boundary relationships (Wei and Ping 1998, in Pouliot 2008). Moreover, it is only through these models that validity of space objects can be created and connection relationships between objects can be managed. In fact valid objects are necessary to make sure that entities can be manipulated in a correct way, from volume calculations to more sophisticated analyses. Explicit rules are therefore needed to check spatial data (Stoter 2004).

Zlatanova et al. (2004) offer a review of advantages and disadvantages of the models, emphasising that these change depending on the research application area. Modelling of complexity, storage, speed and visualisation cannot all be guaranteed at the same time by the different models: the choice therefore must be based on the best compromise for the area of research. It is also brought to attention that, to date, the review is based on literature, as all the models have been implemented under different conditions and tested with different datasets. Although the OpenGIS consortium has suggested abstract specifications for spatial objects storage in a database with their geometric and topological representations to ensure consistency between Object Oriented (OO) and relational implementations, this does not imply that all vendors have accepted them. This is at the base of the above-mentioned problem. Although different approaches are used to encode spatial relationships in 2 and 3 dimensions, namely metric, topology and order, the best known is that formulated by Egenhofer and Clementini (Egenhofer et al. 1989, 1994) based on the 9-intersection model (fig. 3.7). The concept of topology is here considered the most appropriate mechanism to describe spatial relationships.

Spatial models that take into account topology characteristics are reviewed in Zlatanova et al. (2004) as presented in table 3.3 on page 46.

Geometric primitive

Graphical representation

Point

Polyline

Surface

Volume

Figure 3.6. Representation of three-dimensional geometric primitives. Adapted from: Bédard 2006, table 2.1.

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment

Figure 3.7. Topological relationships between two 3D topological regions according to the 9-intersection model. Source: Apel 2004, fig. 4.2

spatial identity in a fully functional GIS domain, they should be readily manipulated, displayed and analysed. In fact, although it is undisputable that three-dimensional displays are useful for many applications (i.e. in archaeology graphics and photographic records are given an overwhelming importance and much analysis is carried out visually), it is 3D formal analysis that interests disciplinary studies. Nevertheless, analysis functions in 3D require more complex algorithms than the 2D counterpart and add to the computational complexity of the system (Lee 2008). They therefore remain, despite the potentials acknowledged and the efforts made, one of the major challenges in the development of 3D GIS (Khuan et al. 2008).

Nevertheless the research on other frameworks continues, for example Billen et al. (2002) proposed the dimensional model for representing spatial relationships, built up in an affine space and convexity properties of the constructing elements (named dimensional elements). This allows larger variations in grouping spatial relationships compared to the Clementini model. Other models are reviewed in Zlatanova et al. (2004) and are: the Voronoibased spatial algebra, the uncertain topological relationship modelling and the extended topological relationships in GIS. Ellul and Haklay (2006) summarises the requirements for topology in 3D GIS in three broad categories: data modelling, upload and validation, and standard analysis and custom analysis. Various example application areas, including archaeology, are provided in their summary tables. It is clear that topology is a fundamental aspect of development in 3D GIS, with major implications in the area of analysis where questions regarding relationships and relative positions of elements, directional adjacency, topological structure of objects (how many holes, how many tunnels) and 3D buffering can almost exclusively be resolved through topology.

The following sections are aimed at providing an assessment of the analytical operations that can be carried out in a 3D environment in comparison to those explored for a 2D one in section 3.1. The concluding part is aimed at summarising key functions of a three-dimensional GIS that have been identified but not yet successfully implemented. Spatial analysis of discrete entities: 3D spatial queries and other operations

It is important to continue assessing and updating the requirements, as the authors point out; nevertheless, it must be emphasised that although some progress has been made in establishing topological DBMSs (Emgård and Zlatanova 2008, Khuan et al. 2008, Stoter et al. 2004), a geometric topological model for GIS has still not successfully been resolved and a full topological model cannot be used in any 3D GIS platform for analysis (Pouliot et al. 2008a).

Spatial analysis of discrete entities would be based, as for the 2D counterpart, on attribute operations, distance and location operations mainly exploiting the geometry of the system and operations that require the use of in-built spatial topology. Attribute operations Descriptive attribute operations provide the means to query, manipulate and transform the dataset as discussed in section 3.2. What changes in a three-dimensional environment is that the three-dimensional nature of the entities queried and the properties extracted should be

3.2.4 Three-dimensional GIS: analytical functionality requirements in a three-dimensional model Once 3D objects or fields are successfully defined in their

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Making Visible: Three-dimensional GIS in Archaeological Excavation and point, point-line, surface-line, surface-surface, linebody, body-surface and body-body, which in most cases are indicated as resolvable using the 9-intersection model. Once the relationship is established, it is possible to perform spatial analysis. Queries that require this approach can include ‘examine whether the relation between two excavated floors is Xxx’, ‘find the stratigraphic unit adjacent to SU356’, ‘check whereas the linear feature 567 goes through floor 789’, ‘is assemblage 67 outside pit 789?’. In some cases answers cannot be obtained by identifying a single topological relation, but must be derived by analysis of several sub-queries.

taken into account and therefore reflected in the results obtained. All non–spatial properties of an entity constructed using a vector-based approach to three-dimensional modelling are properties of the whole object. In order to perform the query, it is necessary to create a pointer to the object in question. This can then be selected based on its properties, with the possibility of combining query terms using Boolean expressions. The majority of GIS systems do not support pointers to complex three-dimensional entities. At present, three-dimensional attribute queries are conducted mainly on 2D objects that are then extruded to represent 3D shapes. This is not necessarily a problem in disciplines such as geology and archaeology because a geological unit or a context, even if stored in a two-dimensional database, are in fact conceptualised in 3D and occupy a 3D space, where they are identified by an ID number, the shape of the entity (at any level decided) and its thickness.

Other two categories of queries should be possible. The first aimed at establishing the relation between two noncontiguous objects. In this case the dimension of the object is known but the code of the relation is unknown. For example, ‘what is the relation between posthole 894 and posthole 895’, ‘find the type of interaction between structure 2 and house 5’. Such queries will most likely require the examination of internal closure and intersection between the topological primitives that define the entities.

Non-spatial characteristics associated with threedimensional point data are at present the easiest type of information to retrieve and manipulate in a threedimensional GIS. These are used for processes such as selection of point clouds that can easily be displayed in three-dimensional environments for inspection, explorative analysis and manipulation. A visiometric approach to analysis can be used to process information selected in a quantitative form (Silver and Zabusky 1993). An alternative manner to use properties queries of point data for further manipulation and analysis is that of transforming such point clouds into gridded volumes for further elaboration and processing via spatial enumeration or three-dimensional interpolation (Mitasova et al. 1996).

The second category of queries refers to the identification of a relation regardless of the dimension of objects. Examples of such queries are ‘find all the objects which meet’, ‘find all the common walls in the architectural complex’. The query is relevant for a consistency check in the object reconstruction phase. The query can be implemented by searching for objects with common nodes or common faces. These are only examples of relationships and relationship building between simple objects, as implemented by Apel (2004), De La Losa (2000) and Zlatanova (2000a). In reality to model and analyse the real world, and in our case excavation, simple objects may not be sufficient. To define binary relationships between complex 3D objects, further investigation is being carried out by GIS experts and computer specialists in mainly three directions: 1) definition of rules to compose complex objects, 2) definition of the topological primitives needed to describe them and 3) derivation of possible relationships between complex 3D objects. Some of these issues are discussed, for example, by Egenhofer et al. (1994), Egenhofer and Franzosa (1994) and Hornsby and Egenhofer (1998).

Operations based on in-built topology As already anticipated in the previous section, in the absence of a solution to the establishment of topology in a 3D system, topological queries remain the main obstacle in this area of GIS functionality. Although a systematised study on demand for 3D spatial relationships has not yet been published, in Zlatanova (2000a) 3D spatial relationships are defined for urban environments. It emerges that the fundamental difference between 2D and 3D spatial relationships (with major implications for the analysis) is that, whereas in 2D it is possible to establish relationships such as adjacent, inside, outside and around, it is only in 3D that under, above (also called direction relation), contains and, inside can be formalised. The totality and complexity of topological relationships in 3D spatial data is far richer than that of two-dimensional data and therefore much more complex.

It is clear that there are a high number of relationships that can be created and formalised in topological terms, when we increase the dimensions of interest and the combinations of objects that can relate to each other. Nonetheless, as noted in Zlatanova (2000a), some of the possible relations may never be needed in reality. In fact, the possible spatial relationships between some objects may be very limited. Therefore, instead of creating redundant hypothetical relationships, the semantic characteristics (‘meaning’) of the object may be used to facilitate development of a limited set of operations. The development of relationship operations is dependent on the spatial objects maintained. This implies that the needed operations have to be clarified with the user after

Relationships normally considered as important and formalised for creating topology are: disjoint, meet, contains, covers, inside, covered by, equal and overlap (Egenhofer et al. 1989, Molenaar 1992, Zlatanova 2000). These are applicable to the relationship between point

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment 2. Property queries. These return a geometrical property like length of a border, highest point of a surface, area of a surface, volume of a closed surface or model volume region, or curvature. 3. Spatial and non-spatial queries on geo-objects. 4. Orientation queries can, for example, select the set of faults with a given orientation. 5. Relative location queries, for example: ‘Select all geo-objects above, below, between, north, east, south, or west of given geo-objects.’

the specification of the objects. Under this underlying assumption, other operations based on topology are used in 3D environments such as network connectivity analysis, flow analysis and 3D navigation. These are mainly employed in urban modelling and disaster management situations to resolve problems such as the calculation of the shortest route to evacuate a multiple storey building or identify isolated networks and/or areas in a building. In the absence of topological analysis algorithms, at present, these problems are resolved by the creation and maintenance of topological models in databases such as Oracle, where the connectivity spatial relationships of the internal structure of buildings or networks of roads and underground systems are developed and abstracted (Lee and Zlatanova 2008, Stoter et al. 2008, van Oosterom et al. 2002). This indicates that it is necessary to clarify the specific relationships between archaeological data in the excavation in order to decide which ones are fundamental, if these are reflected in any established topological implemented model and if there ultimately are archaeological relationships that need to be added and/or looked for when on site.

3D buffer operations have been successfully implemented in a series of systems, such as Gocad (Apel 2004). Apel (2004) implemented three different cases of Euclidean distance buffer queries: 1. ‘Select the geo-objects which are completely within the buffer.’ In this case, all vertices of the queried entity have to fall within the buffer; 2. ‘Select the geo-objects which are at least partly within the buffer’ returns all entities, where at least one of the vertices is within the buffer; 3. ‘Select all the points of a geo-object which are within the buffer’ computes a list of vertices, which are within the buffer. This query can either return the list of vertices as points set, or visually highlight the parts of the entity, which are within the buffer. This is realised by computing a boolean value telling whether a vertex is within the buffer. This value is used for interpolating the colour opacity value α for each triangle.

Operations based on geometry Although at present it is not possible to establish relationships based on topology per se, relations such as ‘touch’ and ‘disjoint’ can be expressed in terms of metric relations, which exploit the geometric characteristics of the dataset and computation (Abdul-Rahman and Pilouk 2008). In this case, for example the relationship ‘touch’ can be defined by a distance equal to zero between two objects, whereas ‘disjoint’ may be defined so that the distance from any point of object A to any point of B is greater than zero (Egenhofer et al. 1989). A disadvantage remains, as ‘operations based on metric relationships (known as computational geometry) are time consuming’ (Abdul-Rahman and Pilouk 2008, 64).

This function has been also implemented in archaeology, firstly by Nigro et al. (2002 and 2003) who developed an Avenue script for ArcView 3.2 in order to combine a spherical graphic of a user-defined radius and a routine that used a 3D Euclidean distance calculation to measure the space from a selected point to all the points in a dataset and then by Kastianis et al. (2008) who designed a 3D point-to-point distance tool to perform spatial queries both on single and multiple entities. These latter authors claim that queries could be restricted to a specific 3D radius, a direction or a vague spatial association such as ‘nearest to’. Nearest neighbour analysis and other spatial statistics are said to be implemented in the system too. One reservation regarding these routines is that it is not at all clear how the tool works and in which system it has been implemented. It is clear that these metrical and distance functions have the potential to bring refitting and association analysis of finds to another level, allowing for a spatial search not only in the horizontal but also vertical dimension.

A three-dimensional geometric model can also be used to perform metric and position operations such as area and volume computations and distance calculations in a threedimensional space. Geometrical queries are the most used in 2D GIS, and similar functionality can be made available in a 3D GIS. The geometry of any point of a discrete entity can be defined by a location vector triplet (x, y and z) in a Euclidean coordinate system. Based on their geometrical properties, either subsets of entities (buffer queries and relative location queries) or real numbers (property queries) can be obtained as result of queries.

Analysis of continuous fields Geometrical queries on entities can be classified as follows (Apel 2004):

As already discussed in the presentation of twodimensional environments, grid models of continuous fields not only provide the possibility of fast property queries and computations but are the only structures that allow for algebraic operations and numerical modelling.

1. Distance buffer queries, for example: ‘Select all geoobjects which are situated within a certain distance of a given geo-object’ or ‘Select all points of a geoobject which are within a given distance of a given geo-object’.

One or more values describing some of the properties of

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Making Visible: Three-dimensional GIS in Archaeological Excavation the object of investigation can be associated with each cell, directly or via geostatistics. The properties will be calculated from data coming from sample measurements. Ranked values will represent the variation of these properties. A grid cell can also represent the presence/absence of a particular characteristic in a gridded area. A value of 1 will represent presence and 0 will represent absence of an element. The gridded volume will be accordingly visualised in a three-dimensional environment, ready for further manipulation.

Research on 3D GIS has mainly focused on the geometrical and topological modelling of objects to ensure data coherence and correct visualisation (Molenaar 1992, Pilouk 1994, Zlatanova 2000a). While well suited to applications where visualisation is important (e.g. urban planning and environmental impact assessment), these models are not suited to computing complex queries such as the propagation of a natural phenomenon (e.g. roof collapsing in a quarry) (Poupeau and Bonin 2006).

3D grid-based models are mostly used to support 3D volume computations such as 3D slope stability analysis. This is used for landslide hazard assessment in environmental modelling (Lee 2008).

3.3

Three-dimensional visualisation

Visualisation of 3D data is a fundamental requirement of 3D GIS. The closeness to reality of three-dimensional visualisation is advocated as a major advantage in particular for applications where inherent 3D spatial properties are the subject of study, e.g. geology and archaeology (Bishop 1994, Kennedy et al. 1995). Moreover, visualisation enables one to identify spatial characteristics and association of objects, which would be difficult to recognise without the support of such 3D spatial representation (de Kemp et al. 2005) and it helps collaboration and communication between specialists because everyone shares a same vision of the territory (Pouliot et al. 2008b).

A shortest path algorithm is also implemented for an un-indexed three-dimensional voxel space using a cumulative distance cost approach. This approach produces a set of voxels, such that each voxel contains an attribute about the cost of travelling to that voxel from a specified start point, if there is uniform friction of movement throughout the representation. (ibid.: 435) Numerical modelling leads to the possibility of process modelling and simulations of phenomena such as the dispersion of pollutants in the atmosphere, the deformation and transformation of rocks under pressure via mass balance computations (Le Carlier de Veslud et al. 2009), and geochemical and/or fluid flow simulations in a terrain body of different permeability characteristics via groundwater modelling (Feltrin et al. 2009, Bonomi, 2009). Filtering and extracting of information via threshold is also exclusively possible in a grid-based environment. Examples of the use of three-dimensional continuous gridded models for the analysis (not just the modelling) of archaeological excavations are not found in the literature.

Visualisation (and in specific scientific visualisation) has also long been recognised as the best vehicle for dealing with large and complex datasets, characterised by multiple attributes as the provision of a third dimension allows for additional information to be represented on screen (Hernshaw and Unwin 1994). This does not necessarily have to be the Euclidean vertical dimension z, but could be a characteristic of the data to be displayed in its variation across the x and y plane. Visualisation therefore deserves a dedicated consideration in the discussion of 3D GIS and its potential for intra-site spatial analysis as not only the vehicle for realistic data representation but also data exploration and analysis. In this context visualisation becomes therefore a general term to denote the process of extracting data from the model and representing them on screen, providing processes for seeing and steering the unseen and enriching analytical methods.

3.2.5 Summary To perform some of the above operations it is necessary to integrate the use of vector and raster data structures. Moreover the reasoning process needs to accomodate for a constant change of structure (from vector to raster and viceversa) as this is tightly linked to the modelling operations used.

3.3.1 The role of visualisation in a 3D GIS environment Visualisation is used at many levels in a 3D GIS environment. It can be the means to display original data and elaborated 3D models, mainly with the aim of data verification and error correction, or it can be used to graphically reproduce database queries (Khuan et al. 2008). Moreover, visualisation can be used as an exploratory tool. In particular in a three-dimensional environment, the display of realistic data can be combined with automated 3D techniques that allow rotation and change of viewing angle. This facilitates the exploration of all the aspects of the subject. Irregular surfaces (such as, for example, geological faulted surfaces but also archaeological contexts) are, in this

It is clear that even excavation/subsurface characterisation needs to consider the two approaches, as both entity-based and grid-based modelling allows the study of properties whereas geometric modelling is necessary when exploring and considering discontinuities (in geology, for example, they are represented by faults, and in archaeology by sedimentary gaps, sharp erosion surfaces and post-depositional, intrusive stratigraphic units). Metric and position operations such as area and volume calculation are realised on the geometric model, while spatial relation operations such as ‘meet’ and ‘overlap’ are performed on the topological model.

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment manner, more easily conceptualised (van Driel 1989). The combination of datasets by draping one over the other allows the examination of relationships between elements. Surface and subsurface data can be combined. The relationship between a particular above ground landform and the subsurface pedology (and archaeology) can also be shown. Relationships in 3D can be viewed that are not necessarily evident from the source data, necessarily collected at different points in time, often in the form of plans or surfaces. Patterns can be explored. Fence diagrams and sections can be obtained via manipulation of the 3D model in real time. 3D also extends the features that may be used in the generation of information-providing visualisations, e.g. the relative position of objects in 3D space or the depth of an object.

3.3.2 Visualisation and archaeological excavation It is clear that different principles and applications lead to different approaches to visualisation and interaction methods (Buttenfield and Mackaness 1991, Dykes et al. 2004, Fisher and Unwin 2002, Hearnshaw and Unwin 1994, Zlatanova 2000b). Although 3D visualisation of archaeological deposits might have much in common with geological, cadastre or urban situations, it is worth discussing how it has been used in the archaeological excavation context and which are the specific requirements still to be met in the discipline, in particular as the discussion of 3D visualisation in archaeology has mainly concentrated on landscape (Chalmers and Stoddart 1996, Forte et al. 2001), urban and architectonic reconstruction (Daniels 1997, Huggett and Go-Yuan 2000, Wood and Chapman 1992) and the application of virtual reality for the delivery of past and simulated scenarios (Barceló 2000, 2001, Forte 2000, Gillings 2000, Gillings and Goodrick 1996, Goodrick and Gillings 2000). Whilst Frisher’s overview of the role of visualisation in archaeology offers a good general synthesis (Frisher 2008), this section is dedicated to a more detailed discussion of visualisation specifically in the context of 3D GIS and excavation.

2½ D representations of surfaces through perspective cues and use of vertical exaggeration are common in most GIS systems. These systems provide the means to superimpose several surfaces that lie one above the other, giving an apparent reconstruction of a complex 3D structure. Some examples can be seen in section 2.4 specifically for archaeology. The easy navigation of these representations has become part of the majority of GIS commercial software. Nevertheless, true 3D representations of entities are, at present, not supported by GIS visualisation modules. In most GIS packages the three-dimensional visualisation and rendering is done in an external viewer or software package altogether. In ArcGIS, for example, 3D scenes are rendered in the ArcScene extension. GRASS uses NVIZ or allows conversion of data sets in *.vtk, a format that allows visualisation in Paraview, a rapid visualisation software. As a consequence, at present, data manipulation and analysis is conducted at the level of the database and/or within the GIS platform but visualising the results implies the transfer of the data into a different visualisation engine. This has implications for the interactive manipulation and direct query of the datasets and is one of the major limitations of three-dimensional GIS systems.

Traditionally, excavation data are represented in a simplified form as a stack of paper prints - plans, phase plans, cross sections and so on. So, from 3D there is a passage to 2D representations that could give a sense of depth and verticality. Different 2D sections through the 3D data volume, as inlines, crosslines, random tracks or time slices, are needed to create the impression of a 3D dataset. The only way to view more than one section at a time is to open multiple windows and view each one on a separate display. Now, largely due to the low-cost computer power and memory, it is possible to view entire datasets so that the viewer can quickly get a feel for the actual 3D nature of the patterns. The possibility of visualising information in 3D, in particular using realistic representations of archaeological contexts, strata and finds, for example through the employment of photogrammetry and photogrammetric stereovision in combination with the display of GIS-based data structures, has therefore become the centre of many visualisation pipelines specifically for excavation. Some of these dealt with legacy data, therefore with traditional 2D plans and sections to be transformed in 3D data and visualisations (Green 2003), others were designed for newly collected data (where total stations, laser scanners and photogrammetry were at the core of the data model reconstruction and visualisation pipeline) (Allen et al. 2004, Vote 2001, Vote et al. 2001, 2002). Significant areas of excavation, if not the entire excavation, are to be mapped across. Before carrying out any detailed work, it is useful to inspect the volume as a whole to get a general impression of the structural and stratigraphic features of interest. This can be done by using a variety of means, but volume visualisation techniques have a prominent role. In fact interpretation is increasingly carried out in this environment, which is considered to be the privileged mean for analysing and delivering archaeological

Despite the limitations summarised above, the potential use of workstations for 3D display and interpretation has been welcomed by analysts of 3D datasets (medical imaging, seismic prospection, urban modelling). Advantages offered in particular for subsoil studies are summarised as follows in Bacon et al. (2003): 1. ability to view sections through the data orientation; 2. automatic book-keeping of manually horizons; 3. semi-automated horizon picking; 4. calculation of attributes that can be used to additional information; 5. ability to see the data volume in 3D, not sections.

in any picked

extract just as

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Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 3.8. A reconstructed pot inside its context visualised through cross-section cutting of a three-dimensional model. Source: Bezzi et al. 2006.

enhanced by innovative forms of symbology, in order to be entirely effective.

excavations (Cattani et al. 2004, Del Grande and Rondelli 2004, Fisher 2005a, 2005b, Katsianis et al. 2006, Losier et al. 2007, Vote et al. 2002). Several different stratigraphic contexts represented as volumes can be viewed simultaneously. In some cases the stratigraphy is rendered through the use of various levels of transparency to also allow the visualisation of finds represented as point clouds or realistic reconstructed objects (Bezzi et al. 2006).

Excavation units in a 3D environment can be seen from all angles and scales and their relationships might be clear. Rotation and translation can be directed by a mouse in environments such as GoCAD. In this environment, cutting of stratigraphic sections can be performed on the fly. Certainly in ArcGIS extension ArcScene and in NViz/Paraview, all layers must be separately imported in the visualisation engine. In Paraview these can be further elaborated, cuts made transparent and so on, but this is not the case in ArcScene ad NViz.

Even in 3D computer visualisation environments, nevertheless, mainly due to high computational demand of multidimensional datasets and to the difficulty of overcrowded effects of the complexity and graphical redundancy of 3D data, these are often represented as cross sections and simplified diagrams (fig. 3.8).

A final consideration needs to be made in concluding this brief discussion of the use of 3D visualisation of archaeological excavations. Although the power of 3D visualisation is acknowledged to be explorative and interpretive even more than just a viewing tool, the display of data is to date still based on GIS thematic layers that have been previously created and manipulated in the database. This indeed limits the interrogation of the dataset. Moreover, an assumption is made in Losier et al. (2007) that realistic representation of excavations, where photo draping is combined with solid modelling, would improve the understanding of an archaeological site. Although it can be argued that seeing in 3D the position of trenches, finds and possibly the landscape context of the site is helpful, it is only through the realisation of the explorative power of visualisation through constant interrogation of the dataset and interactive manipulation of it in a 3D display that the 3D graphics can fully assist interpretation and offer more than what we already have in 2D displays.

In a vector environment, strategies are created for the display of data using appropriate symbology. Simple 3D point markers such as tetrahedral, cube cones and spheres are used to display point data and are available in most 3D GIS visualisation extensions, whereas the creation of complex custom symbols requires the use of dedicated software platforms (Katsianis et al. 2008). Colour ramps can be used to identify other characteristics of the same symbol (for example if a cone represents a stone tool, different colours could represent the type of stone tool, similarly to the use of symbology in 2D). Photorealistic textures can be draped on volumes surfaces. Katsianis et al. (2008) offer an example of the possibilities for visualisation in a 3D environment, although they do not explain which visualisation tool is used and how interactive the display is (fig. 3.9). Moreover, the authors underline how the depiction of a complex stratigraphic sequence needs to be further

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Fundamentals of 3D Modelling and Visualisation within a GIS Environment

Figure 3.9. Combination of trenches, excavation units, finds, drawings and photos with advanced symbols. Source: Katsianis et al. 2008, fig. 8.

texturing and photorealism. Manipulation and interaction happen via a mouse in virtual theatres where navigation and exploration are simulated. In AR the user explores and navigates in the real world, augmented by computergenerated data. Issues of the link between 3D GIS and VR/AR have been under discussion for some time (Verbree et al. 1999).

3.3.3 Visualisation requirements and technical issues The identification of visualisation requirements for effective 3D displays and the solution of a series of technical issues have characterised the focus of the research on the topic in the past few years. First and foremost, the quality and interaction capabilities of 3D reconstructions are still believed to be rather limited (van Kreveld 2008).

The limited dynamic range of the actual display and therefore its inability to portray the underlying data highly affects disciplines such as soil sciences and archaeology. The number of possible different attributes that can have different colours assigned to them in a screen display is typically only 256 (8-bit resolution). This gives an adequate visual impression for photorealistic views, but it could be much less than the dynamic range of the attributes we might be playing with. For example, both EarthColors, which supplies a total of 367 chips, and the Munsell chart, used to determine the colour of archaeological soils, which has 322, exceed the screen display resolution.

Another important element of 3D visualisation is the level of detail (LoD). The concept of LoD was introduced to facilitate visualisation of large scenes. The idea is to represent spatial objects that are compatible with the pixel size on the screen, relative to the observer’s distance. This is often achieved by the replacement of the original geometric representation with a lower resolution representation, which requires less time for rendering and increases the speed of the visualisation process. When the object is zoomed in and comes closer to the viewer, nevertheless, it is often represented at full resolution. Balance between the ability and suitability of working in small areas for detailed work and have overviews of data could be reached with scalability of the system, both in terms of dimensions and resolution. Nevertheless, not many systems at present exploit this possibility, neither do they support different LoDs.

Another element to be taken into consideration is that the extent of the vertical dimension (z) in archaeology and soil sciences is often smaller than that of the other dimensions (which define the horizontal aspect of the excavation). Often exaggeration or explosion are used to allow for easier understanding of the z dimension variations but this compromises the visual cues in the display.

Interaction is still an issue of 3D visualisation. At present, no internal display in any GIS software is 3D. All 3D visualisation is conducted in an external viewer or software platform altogether, as highlighted in section 3.3. Virtual reality (VR) and augmented reality (AR) are techniques for improving visualisations of and interactions with 3D data. VR aims at providing a realistic representation of data, which consists in trying to achieve a realistic representation of objects through

3.3.4 Summary and remarks Some interesting conclusions can de drawn, from a combined evaluation of the advantages and disadvantages of 2D and 3D graphic representations of spatial remains distributions. If there are definitely numerous advantages in providing 3D interactive and fast display of 3D data,

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Making Visible: Three-dimensional GIS in Archaeological Excavation and features. The entity-based representation is believed to allow a more accurate description than the raster representation and is therefore used in most archaeological projects. In vector approaches the most important characteristic of data is topology, which is the standard way of representing spatial relationships in GIS. Spatial relationships are needed to perform a large variety of GIS functions. Nonetheless, most of the crucial operations for 3D analysis (from map algebra to the description of fuzzy datasets) can only be performed on field-based data models.

there is also a need for combining different graphic representations (both 2D and 3D), because they show different but complementary aspects about the character of spatial distributions of archaeological finds. Graphic representations must be informative and accurate, eliminating, whenever possible, redundant or irrelevant data, as the priorities in interpretive environments are simplicity and clarity. In fact 3D provides grater visual realism, but is more difficult to read and, consequently, misreading happens more often. Research efforts towards expressive modelling and non-photorealistic rendering are needed to complement the use of photorealistic and often redundant information. The use of colour is an important aspect to be taken into consideration because of the abundance and the complexity of the layering of data in a 3D display, given the limited amount of colourcoding possibilities in a computer environment and some limitations in human ability to identify patterns and relations when many layers and variables are simultaneously viewed (Kwan and Lee 2004).

Major progress in 3D GIS has been made on improving 3D visualisation and animation (Zlatanova 2002). However, 3D functionality is still lacking, such as generating and editing 3D geo-objects (in particular in vector form), 3D structuring, 3D manipulation and 3D analyses (overlay, buffering, shortest route on complex objects or TIN surfaces). Specific solutions have been designed for specific projects. Moreover 3D GIS is not commercially fully realised for a number of reasons, in particular because the individual application area solutions have all been developed separately and no tool has been developed into a general cross-sector platform (Slingsby and Raper 2008).

Another challenge characterises three-dimensional data and their visualisation. In fact, whilst on the one hand, the power of the imaged model is indisputable in an immersive and interactive environment, on the other, it is extremely difficult to transmigrate it outside a digital environment. Standard publications are still on printed paper and this implies transforming three-dimensional modelling outputs into two-dimensional screenshots. Whilst, on the one hand, this is a necessity, it is indeed in contradiction with the very argument of enhanced understanding of data in 3D. On paper, the multifaceted approach to the archaeological record, characteristic of multidimensional interactive environments, becomes fossilised in that image chosen to portray all the possible versions of the excavation offered by a digital 3D environment. A printed version of a three-dimensional digital archive also has to take into account issues of resolution, as rarely a journal or a report are published in format that exceed the that of a computer screen. The selection of an effective rotation angle and scale of representation of the 3D model screenshot have to be taken into consideration for the delivery of an effective 2D image of a 3D model. Both technically and intellectually, the choice of an image for publication remains still an issue in the dissemination of the threedimensional archaeological record. 3.4

No amount of technology, however, can make up for differences in interpretation that are made before the phenomena are recorded. If scientist X perceives the landscape as being made up of sets of crisp entities represented by polygons, his view of the world is functionally different from scientist Y, who prefers to think in terms of continuous variation. Both approaches may be distortions of a complex reality which cannot be described completely by either model (Burrough and Mc Donnell 1998, 33) Chapter 4 considers this debate in the realms of different approaches to archaeological excavation and the possibility of translating them in a 3D GIS conceptual model.

Conclusion

A 3D GIS model consists of various components that have been discussed in this chapter by introducing the terminology crucial for the understanding of how the concepts will be translated to the archaeological conceptual framework. The information stored in the GIS model is related to the geometry and semantics of the entities and their data structure is fundamental for analysis and visualisation. The geometric characteristics of an entity tend to define it with respect to location in space, shape and size. These characteristics are closely related to abstractions of space

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There are two main Western philosophical ideas about the nature of space: one sees space as a container of all material objects, which exists independently of any objects that might fill it (a fixed frame of reference reflected in Euclidean, Kantian and Newtonian conceptualisations); the other conceptualises space as a positional quality of the world of material objects or events. According to the latter, space cannot exist without objects, and what matters is the relationship between these objects. This duality of absolute and relative is also reflected in the conceptualisation of time in archaeology.

Chapter 4. Making Visible: Archaeological Excavation in Three Dimensions The spatiotemporal nature of archaeological processes is given as an indisputable paradigm in the discipline and it lies at the core of archaeological analysis. Due to the particular capability of GIS in handling spatiotemporal data, such systems were therefore very rapidly integrated into archaeological practice. However, the characteristics of archaeological spatiotemporal data and the suitability of GIS structures for their representation have remained almost unexplored, even though the use of Cartesian concepts of space and time in archaeology and, in particular, in GIS-based archaeological interpretation have been strongly criticised (Thomas 2004). A gap remains between the theoretical formulation of complex and multifaceted ideas of space and place in the past, and empirical methodologies to match them in the present. 4.1

Absolute space is the Cartesian frame of reference of the archaeological site, which allows us to measure all elements in reference to the site Temporary Bench Mark (TBM) and eventually to locate the site at a fixed point on the earth’s surface. Relative space is the relationship between layers and contexts. Vertically, if primacy is given to establishing temporal sequences; horizontally, if the focus is the relationship between finds and primacy is given to analysis of patterns. Although different, these two conceptualisations of space have much in common, as they both use the language of geometry, Euclidean geometry and topology respectively. In fact, they express logico-mathematical spaces and therefore obey a series of rules pertaining to the particular language of the geometric domain.

What is left to say? Much, if not all, of the data that archaeologists recover is spatial in nature, or has an important spatial component (Wheatley and Gillings 2002: 3)

Spatial patterns, spatial relationships, spatial configurations, spatial distributions. Neutral spaces and quantified relationships in space, meaningful spaces and sense of place indicate that, through time, and despite paradigm shifts, the notion that spatial is special is embedded in archaeological research. With time, it is in fact at the heart of archaeology and, as such, it is often taken for granted. At this point, I would like to pose the same question Lucas (2005) poses in the introduction of his work on the archaeology of time ‘- what is left to say?’ And similarly to him I would like to answer: ‘Well, quite a lot actually.’ (ibid.: 1). Although it is true that the discussion of space in archaeology has a long history, from the 1970s volumes by Hodder and Orton (1976) and Clarke (1977) to more recent contributions by Wheatley and Gillings (2002) and Robertson et al. (2006), it has concentrated more on space as an analytical instrument or space as place (the sociological interpretation of space and place in the past). Less attention has been paid to space as a theoretical concept, in particular to how it is understood and employed in contemporary archaeological practice. The way we conceptualise space in every stage of archaeological research affects the way we do archaeology and, most importantly for this study, it affects the way we develop digital frameworks of doing archaeology. In the following sections I will present some considerations regarding the concepts of space used in contemporary archaeological practice.

Measurements of Euclidean spatial parameters of a site (or on site) using tapes, plumb bobs, a level, a total station or a laser scanner are routinely employed in the practice of recording an archaeological excavation. Contexts are related to each other through a physical matrix (below, above, inside, covers) that helps create a temporal one. Artefacts are located either individually by x, y and z coordinates to a good level of accuracy or traditionally through so-called imprecise location of artefacts (bulk finds) (Roskams 2001). However, bulk samples can be located within a grid system and can therefore be analysed spatially to a satisfactory level of accuracy. Nevertheless, the space of excavation can also be captured in analogue or digital photography, through the graphic representation of shapes and colours that may or may not be scaled and referenced to geometric space systems. Video representations of archaeological excavations can also create spaces that are more surreal than real and linked to art1. Surface and subsurface imaging via photography and geophysics also represent and interpret space. Whilst they are normally studied in reference to a geometric system, they do not necessarily just reflect such a concept of space.

4.1.1 Geometry and the space of excavation Absolute and relative space are concepts discussed in Conolly and Lake (2006) as the predicates on which spatial analysis in archaeology is based.

1

See, as an example, the work of Shanks et al. http://documents.stanford.edu/traumwerk/Home [Accessed: 05/04/15].

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Making Visible: Three-dimensional GIS in Archaeological Excavation practice of materialisation. The materialising activity of archaeology brings new things into the world and reconfigures it by doing so. Excavation is a creative enterprise that is both a process of making - drawings, notes, photographs - and of translation from object to image and text. Lucas further argues that this is not simply a case of representation, but rather a double materialisation, where both excavation and translation constitute each other. The process of making and translating is enacted at the site, where sections of the soil are cut, features are created, and objects are observed and manipulated in preparation for the recording, but it continues outside the excavation space, when the site is displaced, both physically – soil samples, ceramics, lithics, carbons end up in the laboratory and in the museum - and representationally – in the record. Whilst I will further discuss the physical displacement in the second part of this section, looking at Jones’s work, I wish here to further elaborate on the concept of the archive as the place of displacement, as discussed in Lucas (2001b, 2004).

4.1.2 Material and immaterial In this section, I will explore some ways in which archaeological excavation has been conceptualised in the more recent past. The focus is on how archaeologists today perceive the space of archaeological excavation and how the information collected during excavation is used to produce archaeological spaces and times. What Tilley (1989) and Lucas (2001b) identified as the rhetoric of destruction is a persistent paradigm in the conceptualisation of the process of archaeological excavation. The speed, quality of documentation and data processing has been identified by many authors as the main contribution offered by information technology to the preservation of archaeology by record, seen as the only possible constructive act in the action of excavation (Craig and Aldenderfer 2003, Dancey 1996a, 1996b, Doneus and Neubauer 2004, 2005, Doneus et al. 2003, Drewett 1999, Roskams 2001, Thomas 1998, Valenti 2000, to cite but a few). The conceptualisation of excavation as destruction has recently been challenged by new ideas such as excavation as displacement (Lucas 2001b) or as explosion (Jones 2002). These alternative concepts, in different (and, at times, diametrically opposite) ways, account for a more articulated reading of archaeological practice and, by challenging the rhetoric of destruction and preservation by record, offer digital technologies a more interesting role in the practice of archaeology.

The archive is the location to which the site is displaced. Drawings, photographs, and texts are our excavation in another place and another form. As a consequence, archive and archiving are crucial as ‘the only medium through which a site in all its detail is perceived by the archaeological community at large’ (Lucas 2001b, 43). The archive stands for the site but is different from it. It is not a copy of an original but its substitute. The term ‘substitute’ is not intended to be pejorative, but is intended to emphasise the positive aspect of the relationship as metaphorical and not simply representational. The archive allows iterability (we can always go back to it); it has a life of its own. This is why we can re-examine old sites and offer new interpretations for them. Lucas sees the archive as the response of archaeologists to the scientific experiment. Nevertheless, the archive also poses some problems, such as the tendency to be self-referencing and to become homogenous through increased standardisation intended to allow cross-referencing. The risk is that ‘a site begins to look like another’ (Lucas 2001b, 44).

Lucas (2001b) sees excavation as an act of displacement and transformation rather than destruction. By emphasising that, when we excavate a site or a feature, we do not annihilate matter (as it does not vaporise or disappear) but merely displace it, he provides a more positive platform for the discussion of archaeological practice and its consequences (for example in contract excavation) for the general public. He also places particular importance on a practice of archaeology where the constitution of the archaeology and the archaeologist are given primacy over the emphasis on preservation by record. Lucas specifically emphasises that the way a site is excavated is largely determined by the way it will be recorded. This point is particularly relevant to my further discussion on GIS and excavation. To add to Lucas, I argue that there is great fluidity in the performance of excavation. As much as the way we want (or hope to) record a site influences the way we excavate it, the opposite should equally be considered. Sometimes the nature of a site only allows for a particular approach to its recording (e.g. where sites without features require grid and spit recording). Tensions arise when performers of the two different approaches to excavation (feature-based versus grid-based) come to work together, as it is difficult for one group of practitioners to understand and visualise the other. Moreover, ‘different fieldwork practices in different parts of the world or from different periods produce different sites and therefore different understanding’ (Lucas 2001, 43).

Lucas concludes the paper with a series of considerations: How do we avoid an archive (or excavation) becoming self-fulfilling? How do we maintain all the marginalia that tend to disappear from the archive? How do we maintain the open-process character of the excavation in the archive? And how do we guard the heterogeneity and uniqueness of sites against the homogenising tendency of the archive? By way of an answer, he points to the need to maintain a balance between generalisation and specificity in the excavation/archiving of a site and the necessity to re-evaluate constantly the relationship between our excavation and archiving practices. Although Lucas does not really provide a practical answer to his own questions, he successfully breaks the dichotomy of subjective/objective. With the concept of displacement, he allows us to focus our attention on the fact that when we materialise excavation we create, we transform and displace. In other words, we experience,

At the core of archaeology, Lucas (2001b, 2004) sees the

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Making Visible: Archaeological Excavation in Three Dimensions The paper (or digital) records will be stored somewhere, the photographs might find a different home, samples and artefacts will be sent to various specialists for classification and analysis. The more ramified the operations on the material from excavation become with specialist analysis, the more the excavation is pulled apart. Specialist reports crystallise this separation, and eventually publication reflects this same compartmentalisation. Jones’s particular preoccupation is de-contextualisation and the creation of hierarchies. The way in which the post-excavation analysis has been conceptualised and designed dislocates the archaeological finds from their context and it encourages highly structured and narrow analysis (ceramics on their on, metallurgy on its own, lithics on their own). As a result, some problems might be examined systematically, but others are constantly ignored. In particular, the association of different classes of materials in their spatial and temporal context is often lost. Moreover, certain classes of materials or certain types of analyses assume more importance than others. Hierarchies are created, that, in Jones’s view, often reflect the social hierarchy of the organisation of archaeological practice. Ultimately, not only are the space of excavation and post-excavation analysis separated and separate, but the final interpretation is in the hands of the site director/supervisor rather than the excavators and/or the specialists who performed the post-excavation analysis.

use and make different spaces. It is the element of creativity that therefore finds its way into excavation as an act of materialisation: the archaeologist is a craftmaker (Shanks and McGuire 2000) and she is also an artist (Lucas 2004). Craftmakers, artists and archaeologists have a particular way of thinking about space. I will explore this further in the next section. Jones (2002) situates his examination of archaeological practice and interpretation in the realms of postexcavation analysis, his area of expertise. In particular he criticises its linearity in the realms of archaeology and scientific practice. The linearity of archaeological practice is created by three consequential steps: excavation, post-excavation and publication. While this linearity is paramount in archaeological practice, Jones (2002) points out that it also produces two further structuring principles with important effects: fragmentation and hierarchy. These two principles operate at all levels of the archaeological process. During excavation, layers and deposits are dissociated from one another, and objects are separated from each other and from their context. Through the process of excavation the site has become fragmented. Physically, the site now only exists in the form of the individually bagged and labelled material remains removed from the site. The recorded material created by the archaeologist who excavated the site provides information on where the material remains came from and on the spatial and temporal layout of the excavated features. However these features, and the relationship that once pertained between them, no longer exist. (Jones 2002: 41)

In principle, I do not see a problem with the systemic nature of archaeological practice (criticised in Jones 2002), if taking apart the matter is intended to elicit understanding. After all, how else would we be creating the archaeological record if not by taking it apart? However, fragmentation of excavation and fragmentation of the steps of analysis of archaeological material, and the consequent de-contextualisation, do remain a problem. Explosion as a result of archaeological practice is disturbing. I see a parallel between Jones’s illustration of ‘exploding excavations’ (fig. 4.1) and the evocative work of Cornelia Parker (fig. 4.2).

Jones (2002) argues that layers and deposits, after excavation, only have a meaning because of their representations in plans, sections or as sets of figures and measurements, and that they have no material existence beyond this. In this respect, Jones presents the antithesis of Lucas (2001) as he emphasises the immateriality of layers and deposits rather than emphasising that, by recording the excavation, we create it and materialise it.

I first saw Cold Dark Matter in a seminar by Colin Renfrew, where he was presenting his then forthcoming book Figuring it out (Renfrew 2003). The exploded view, hosted at the Tate Modern, was composed of the fragments of a garden shed, which had been blown up by the British Army in the grounds of the School of Ammunition, near Banbury. Prior to its explosion it had been assembled in the gallery and filled with objects which the artist obtained from her own and friends’ garden sheds and from car boot sales (fig. 4.3).

I believe that Lucas’s conceptualisation of the creation of the record is far more constructive. However, Jones makes an important point in stating that the archaeological record, whether in its physical form as objects or in its displaced form as pictures and text, is indeed fragmented during excavation. It is not displaced as an organic whole, but as a series of separate entities that take very different material and immaterial routes. I wish to borrow from Jones this concept of archaeological practice as a process of fragmentation, to the point of reaching explosion.

Whilst in Renfrew’s discussion, the work of Parker is used as a parallel to archaeological formation processes, my attraction to this work of art grew out of a reflection on our inability to put back together, in a creative manner, the fragments of our materialised excavation. Are we really creating cold dark matter?

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Figure 4.1. Exploding excavations. Source: Jones 2002, fig. 3.1.

Figure 4.2. Cold Dark Matter: an exploded view. Cornelia Parker, Tate Modern, London (1991).

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Figure 4.3. Before, during and after: Cold Dark Matter: an exploded view by Cornelia Parker (1991). Source: http://www.tate.org.uk/colddarkmatter/default.htm.

Lefebvre criticises this and instead presents a theory of developments of systems of spatiality in different historical periods. These ‘spatialisations’ are not just physical arrangements of things, but spatial patterns of social action and embodied routine, as well as historical conceptions of space and the world. These operate at all scales. Arrangements of objects, work teams, landscapes and architecture are the concrete instances of this spatialisation. Ideas about regions, images of cities and perceptions of ‘good neighbourhood’ are other aspects of this space. Lefebvre conceptualises space as a medium, not a category. Rather than discussing a particular theory of space (or social space) Lefebvre examines struggles over the meaning of space and considers how notions of space come together in what he calls the production of space. Space is something that is materially produced, but at the same time not separate from the process of production. Social space is ‘not a thing but rather a set of relations between [objects and products].’ (Lefebvre 2001, 83).

4.1.3 Many places, many spaces The archaeological record is at the same time the context of everyday life and the expression of social relations in the past and in the present. It encompasses objects, bodies, animals and people. It is content and shape. It is texture, consistency and colour. It is different types and scales of materialities and relations. It is landscape, it is house, it is home, it is one and many lives. It is text, photographs, drawings and paper. It is a certain group of people, it is geographic size and a collection of things. It is a node, a network point and a centre of production. It is all of these together. It is both material and immaterial. The archaeological record existed, exists and will exist in different places and reflects different spaces in the past and in the present. Our conceptualisation of the archaeological record needs to take into account these different spaces. In section 4.1 I briefly discussed the use of Euclidean conceptualisations of space in excavation practice, and I listed other, alternative means of representing the excavation graphically, which do not necessarily obey the rules of geometry. I went on to discuss Lucas’s and Jones’s articulation of archaeological practice. Both the concepts of displacement and of fragmentation/explosion are evocative of space. However, even here space is not fully considered as a practical challenge and for its analytical potential. In the following section, I use Lefebvre’s theory of the production of space to explore the production of archaeological space. I aim to locate archaeological practice in this framework in an attempt to overcome the problems discussed by Lucas and Jones and to offer a perspective on the place of digital spaces in this practice. 4.2

According to Lefebvre, space is produced by three types of practice: spatial practices of physical transformation of the environment, practices of representation of space and everyday practices of appropriation of space. The first practice produces spaces as perceived, the second produces representations of space which allow space to be conceived, and the third transforms space into what is called representational spaces, i.e. space considered as lived (fig. 4.4). Within the framework of spatial practices we can understand how environments are complicit in the ‘routines’ performed by the archaeologist, the site visitor, the supervisor, the developer, and how places are constituted by these activities of everyday practice. Representations of space, which are the conceptual depictions of space usually created, at least in Western societies, by using logico-mathematical concepts, can be understood as the highly structured products of academia and industry. They include maps, plans, coordinates, diagrams and interpretations of space to a more quantitative and constructed level that is both abstract (the conceptualisation) and concrete (the archaeological plan and section per se). The last component of the Lefebvrian trialectic is that of space of representation (or representational space). In his words this is ‘space as directly lived through its associated images and symbols,

The production of archaeological space

Lefebvre (1991) moves the analysis from simple discourses ‘on’ space to the manner in which understandings of logico-mathematical space2 and practico-sensory space are produced, i.e. the modality of their genesis. Much twentieth-century thinking on space conceived of people and things as merely ‘in’ space (influenced by the philosophy of Descartes and Kant). 2

Abstract space - see also geographical space as conceptualised in Goodchild (2001).

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Making Visible: Three-dimensional GIS in Archaeological Excavation objects, in practices and spaces that we seem to fail to describe appropriately by using written language for things that are fundamentally non-linguistic. The digital model allows us to express different spaces (in the same geographic framework of reference) without the exclusive use of written language. Unlike more traditional representational systems that are restricted to quantitative representations of dimensionality and materiality, alternative uses of digital spaces can provide a platform that transcribe qualitative and sensory aspects of experience. Digital technology can be used to produce the space of displacement. It is, in this sense, a representational space. Representational qualities can move beyond the space of representation (longitude and latitude, coordinate grid) towards connecting with other qualities of space, not necessarily geometric, not necessarily reflecting realism. In this context, digital technology can be taken from being purely a space of representation to becoming a representational space (through modelling, thinking and creating).

and hence the space of «inhabitants» and «users», but also of some artists, […] writers and philosophers, who describe and aspire to do no more than describe’ (Lefebvre 1991, 39). As such, representational space offers a framework for understanding our ever changing relationship with space and the constant tension between the experience and production of it and its perception. Any model we have for a place, in our case the archaeological site, is based upon a relational dynamic between social, physical, economic, and cultural attributes. These dynamics are created by the language of spaces and the images and symbols that construct and persuade us of different values, narratives and systems for operating within a space. A complex and heterogeneous vocabulary characterises this language and as representations of space ‘coexist, concord or interfere’ (Lefebvre 1991, 41) with representational spaces, contradiction and dissonances emerge when we try to negotiate one with the other. The relevance of the theory of production of space in the context of this research is twofold: on the one hand, it provides a useful framework for archaeologists who attempt to engage in archaeological practice within its conventions and rules (without overly criticising them) but also in its diversity and challenges. On the other hand, it creates a platform for discussing critically the role of digital technologies in the production of archaeological spaces.

Through Lefebvre we understand a little more about the complex constitution of spaces of archaeological practice. But, more importantly, we can differentiate between the map that defines the coordinates of a place, and the symbolic and practical engagement with an environment. Whilst the use of maps and charts is central to the traditional archaeological record, the creative use of alternative operational spaces that can be produced in a digital environment allows us to explore qualities of the record without necessarily relying on the use of maps and actual locational context.

The archaeological site is displaced in the archive: in text, in drawings, in photographs, in fragments and exploded

Figure 4.4. The trialectic production of space.

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the digital archive. The ‘digital revolution’ seems not to have challenged and changed the way we record, archive, and ultimately conceptualise the archive. Nevertheless, if we are to use GIS and its analytical potential in a creative manner (a manner that unlocks the possibility of expressing representational spaces) we have to bear in mind that different archaeological spatial analytical problems can at present only be resolved by exclusively either applying a vector approach or by using a raster approach (as outlined in chapter 3). We must challenge archaeological excavation methodology based on this consideration, rather than just deciding, for example, that grid excavation is not relevant when context boundaries are visible. On the other hand, we need to develop more critical approaches to the conceptualisation of the archaeological record in order to generate digital responses to archaeological problems that we cannot resolve by other means.

Space in archaeological GIS

When comparing the computer data structures used in GIS (and discussed in chapter 3) and the archaeological site record (textual and graphic) traditionally used to represent archaeological excavation, it is interesting to note that a parallel can be drawn between vector model and stratigraphic method, raster model and spit/arbitrary method. Single context recording systems, which are used for recording stratigraphic-method excavation, are conceptually affine to object-based data models and, similarly to these, aim to describe bounded uniform entities (contexts) that represent an area (or volume) with a single and unique characteristic. Since the primary concern is not location of the entities in the Euclidean or coordinate space, but the locations and relationships between the individual stratigraphic units (Conolly and Lake 2006), topology is used to link the entities. The Harris matrix is the synthesis of this topological sequence. It is therefore not a surprise that the majority of stratigraphic excavation recording systems transferred to a GIS platform have made use of vector data structures when translating the single context recording system and Harris matrix into digital formats.

4.4

Critique and reassessment of some concepts used in excavation practice

Various contributions (Andrews et al. 2000, Chadwick 1997a, Hassan 1997, Hodder 1997, 1998, 2000, Lucas 2001a) since the mid 1990s have renewed discussions of perspectives on fieldwork practice and conceptualisations of the archaeological record. A particular effort has been made to deconstruct traditional dichotomies such as subjective versus objective, scientific versus social, databased versus theory-laden archaeological practice. Different perspectives on how to achieve a more rounded and less structural understanding of archaeological fieldwork as a process have been put forward and they all succeed, some more and some less, in providing a more critical assessment of how field archaeology operates in everyday practice. One fundamental idea emerging from most contributions is that of archaeological excavation being a process that transforms all the participants (people and objects) through the materialising practice of creating the archaeological record. Self-reflection therefore becomes a focal point. The contribution of digital technologies to this process has received some attention (Hodder 1999, Lock 2003, Evans and Daly 2006) and this section aims to provide a further contribution.

Raster data models, which are the structures reflecting space in terms of continuous Cartesian coordinates in two or three dimensions, are defined by grid cells (pixels or voxels in 3D). They reflect excavations carried out using the spit methodology, where the explored area is divided up into a regular grid and the material is lifted in discrete units. This method, which is usually employed for sites ‘without features’ (Drewett 1999), is generally considered a necessary if undesirable alternative when the stratigraphic approach fails (Roskams 2001). It is not commonly used in British excavation where it is perceived as arbitrary excavation, and its application has been discouraged by the proponents of the stratigraphic method even in the context of North American historic archaeology (Praetzellis 1993). As a consequence, the use of a raster approach to GIS analysis of excavations is rare. The work by Meffert (1995) and Peeters (2007) in the Netherlands is an exception, influenced, no doubt, by the extensive use of grid excavation in this country. They use raster-based analysis, for example, for interpreting the distribution patterns of finds using finds densities, indices of dimensions of finds, dumping patterns and post-depositional processes to reconstruct occupation surfaces.

In line with Yarrow (2003) and in contrast to Lucas (2001b), this is not intended as a critique of conventions and methods of excavation per se. These have in fact proven to be a solid and useful framework for retrieving archaeological data, despite critiques coming mainly from post-processual archaeologies (Hodder 1999, 2000). As a confirmation of this, excavation methodology has not changed dramatically in the past 30 years, after all (for reference see Barker 1977, 1982, 1993, Drewett 1999, Francovich and Manacorda 1990, Joukowsky 1980, Roskams 2001). Wylie (2007) refuses to accept the interpretive dilemma created by an extreme emphasis, on the one hand, on theory-laden data retrieval, and on total arbitrary interpretation of the record on the other, and instead supports the traditional and consolidated procedures used to collect empirical data in the field. She

It is clear that archaeologists record and archive archaeological knowledge based on the manner in which they conceive the archaeological site and approach its excavation. The site can be understood either as a collection of contexts positioned one relative to another or as a gridded division of space. Ultimately, the conceptual model that encapsulates descriptions of archaeological contexts and spatio-temporal relationships that conform to certain archaeological entities (cut, fill, deposit) or divisions of space (the grid) are transferred in

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Making Visible: Three-dimensional GIS in Archaeological Excavation a two-dimensional representation of a much more complicated four- or five-dimensional hermeneutic into which three-dimensional archaeological deposits, time (both linear diachronic time and non-linear time), past actors and the excavators in the present are inextricably woven’ (Chadwick 1997, http://www.assemblage.group.shef.ac.uk/3/3chad.htm). Second, a matrix is usually built from many isolated sequences. Although these sequences can be assembled to represent the entire site, the archaeological record is much more than a sequence of layers. The concept of palimpsest - originally discussed by Patrik (1985) and used in Lucas (2005) - is a ‘rather messier affair’ (ibid.: 37) where multiple, overlapping activities over periods of time are intertwined. Activities happen diachronically but also simultaneously. It is clear that spatial relationships between events happening simultaneously are easily lost in the topological schematism of the matrix (fig. 4.5).

contributes two insights. The first highlights that ‘the archaeological record routinely demonstrates a capacity to subvert even our most strongly held expectations about the past’ (ibid.: 521). Archaeology may be enigmatic but it is not entirely plastic and has an ability to speak for itself. Second, ‘archaeologists can, and often do, very effectively deploy the recalcitrance of the empirical record, systematically designing archaeological research so as to elicit empirical constraints that sometimes tell quite powerfully and precisely for or against specific interpretive hypotheses’ (ibid.: 521). In fact, employing particular conventions and methods during excavation ‘expands people’s capacity by enabling them to make artefacts and features archaeologically visible’ (Yarrow 2003, 67). By representing these elements via conventions in the archaeological record, visibility, making visible, is possible. Routine ways of spatially measuring and describing the archaeological record using metrics, Euclidean geometry, and mapping reference systems are not under attack here, for the reasons stated above. Nevertheless, I wish to raise a series of question marks over the role of space in excavation. The representation of space is given prominence in the production of archaeological space, to the detriment of other complementary analytical realms, spatial practices and spaces of representation. I will do this by exploring, in particular, the effects that thinking and recording excavation in two dimensions have had on the practice of excavation. I will therefore criticise some concepts used in excavation practice and used also in the realm of a three-dimensional conceptualisation of space (in particular in a GIS environment), namely, stratigraphy and the use of Harris matrix, contexts, mapping and dimensionality and finally mimics (in the sense developed by Shanks and Webmoor (2009)).

The Harris matrix encourages us to examine strata or single contexts only vertically so that the various features across the site are not related to each other. Although topology (which is a fundamental concept in vector GIS) is useful for establishing relationships between entities, the Harris matrix almost exclusively captures temporal relationships without any spatial ones, even though the people who lived at the site dealt with an occupation surface where features were related to each other. There was a smooth transition between occupation layers and most of the features, different activities were carried out not only separately but also interconnectedly, and very rarely events would have been totally independent of each other (Lucas 2001a). In order to reconstruct the activities of the inhabitants, alternative methods of activity interpretation are necessary. Meffert (1995) suggests the reconstruction of occupation sitescapes as an integrated whole of features and layers. Topology matters, but physical location and space patterns matter as well.

4.4.1 On stratigraphy Stratigraphy, which has been employed in archaeological excavation for over two centuries as a method for excavation and description of archaeological deposits, is encapsulated in two broadly opposing views: the geological/geoarchaeological view and the Harrismatrix/unit-based one (Lucas 2001a). The latter is commonly used throughout most of field archaeology in Britain and has been adopted elsewhere since the late 1970s (Harris 1989 [1979], Harris et al. 1993). Initially intended as an interpretative diagram of the stratigraphic sequence, the Harris matrix soon became a representation of the entire process of excavation (Chadwick 1997). As such it has been widely criticised (Adams 1992, Adams and Brooke 1995, Chadwick 1997, Carver 1990, Hodder 1999, Lock 2003, Lucas 2005).

One of the interesting corollaries to this critique of the matrix is that it has been proposed that, whilst the written record has little room for allowing further dimensions to the matrix, computer graphics could have the potential to explore further representational possibilities that take into account the limitations discussed (Chadwick 1997, Lock 2003). 4.4.2 On surface and depth In Harris (1989 [1979]) stratification is the combination of strata (distinguished between natural strata, man-made layers and upstanding strata, all referred to also as deposits) and interfaces (or surfaces). The deposit, considered the material aspect of stratification, is defined by four attributes: a ‘face’ or original surface which distinguishes the original upper surface of a layer from its lower surface; boundary contours that define the unique extent of each unit of stratification in both horizontal and vertical dimensions; surface contours that represent the topographical relief of the surface of a layer (usually recorded as series of spot heights) and finally the volume

Much of this criticism has concentrated on its temporal rather than spatial inadequacy. It has been considered a tool that does not account for the duration of processes and is therefore pointillist Carver (1990) and that only shows temporalities of production and not use Lucas (2005). Nevertheless, spatial inadequacies have also been identified. First, ‘the Harris matrix is effectively

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Figure 4.5. The figure shows sequences of plans merged and combined with profile data to create a final stratigraphic sequence for a site. Source: (Harris 1989 [1979]), fig. 22. or mass (Harris 1989 [1979]). The deposit is therefore defined by its boundary surfaces, and its mass is a container of ‘finds or objects of chronological, cultural or ecological significance’ (Harris 1989 [1979], 51). During excavation, deposits can only be recorded partially, by sampling, since according to Harris, most of their mass is destroyed, and, as a consequence, it is impossible to reconstruct them. Finds and samples are isolated from the stratigraphic unit when it is excavated (Harris 2001).

2004, Doneus et al. 2003, Katsianis et al. 2008, Losier et al. 2007) and, in Harris’s words ‘the only way to see’ (Harris 2001) when using GIS in archaeological recording. GIS, applied to excavation, inherited the problems of context-based approaches to excavation. A reconsideration of the limitations of understanding archaeology as a dichotomy of surface and deposit, where one unit of analysis is given prominence above the other, is of utmost importance.

In contrast to deposits, surfaces are of the utmost importance in Harris’s stratigraphy, even though they are devoid of physical existence (and therefore immaterial) (Harris 1989 [1979]). Surfaces define volumes (layer interfaces) and so account for over 50% of the stratigraphic record. Furthermore, they indicate a series of actions in the past (cutting of ditches and pits, floors on which people lived). They thus ‘represent the use periods of a site and … account for far more time in its history, than do deposits’ (Harris 2001, 4). Harris therefore considers surfaces the stratigraphic element par excellence. He advocates recording single surfaces in their entirety in contour plans or stratigraphic platelets, since he believes them to be more appropriate than deposits for defining the stratigraphic sequence and, ultimately, the story of an archaeological site.

A first problem lies in the reliance on the concept of surface to describe anything archaeological. In a GISbased environment this has translated into a bias in favour of the use of vector-based structures to represent archaeological contexts, as this structure is particularly suited for creating lines and for constructing surface geometry. However, when we migrate to a threedimensional conceptualisation of the deposit, the representation of volumes with surfaces is particularly cumbersome (as highlighted in the previous chapter). Furthermore, its analytical potential is limited, as it lacks topological integrity. Secondly, the separation into surface as a feature and deposit as non-feature and their different treatment as units of analysis is flawed. Feature and deposit are in fact not separate concepts, they are three-dimensional signatures that can be expressed either in vector or in raster format (or both), depending on the characteristics of the signature we might be studying or on the way the excavation was carried out. There is a procedural difference, but there cannot be a conceptual difference, since a deposit is in fact a feature of the area under investigation. Due to the overwhelming importance given to surfaces in stratigraphic excavation, a cut is labelled as

This conceptualisation of units of stratigraphy has been criticised widely and effectively, both because it confuses units of observation and units of analysis (Hammond 1993), and because of its dismissive view of the deposit (Collcutt 1987, Farrand 1984). Nevertheless, it still represents a widespread understanding of stratigraphic contexts in much recent GIS literature (Barceló et al. 2003, Barceló and Vicente 2004, Doneus and Neubauer

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Making Visible: Three-dimensional GIS in Archaeological Excavation that a context defines one single event and can be conceived as such, but this is a rarity. One problem has emerged since the introduction of digital technologies in archaeology: whereas before it was possible to account for uncertainty by drawing dotted lines on paper, this is not compatible with a straightforward entity-based GIS. Polygons and volumes (in our case) are defined by continuous and closed lines and surfaces. GIS-based excavations do not account for this problem and do not allow for uncertainty, even though some boundaries are not always justified. By making exclusive use of a vector approach to excavation (lines and surfaces), the context becomes a monolithic entity. Many archaeological phenomena vary gradually in space and deposits might not be necessarily homogeneous. These situations should be modelled, and ways for incorporating uncertainty to explain and understand the contradictory nature of archaeological data need to be found.

a feature and a deposit is treated separately as a nonfeature and thus as less important. A deposit is thought of as either a natural accumulation of sediment (and thus of less interest to the archaeologist) or a dump of rubbish. The material culture associated with it is useful only for dating. However, studies of the structured deposition of sediments and objects in pits and ditches (e.g. Hill (1995) and Garrow (2006, 2007)) have demonstrated that things might not be so simple. Fragmentation and structured deposition appear to be deliberate practices of the past that can only be detected when the deposit is treated as a feature with internal variation. By emphasising the perception that surfaces provide a better representation of the archaeological site, stratigraphic excavation methods tend to underestimate the importance of deposits. Everyday life makes us experience surfaces. The volume within which we travel is empty and is usually the air. The aim of the stratigraphic method is to record and study surfaces, under the assumptions that people lived on surfaces. However, the deposit in its volumetric status has been subjected to transformations through time. These tell us stories of the past as forceful and evocative as those inferred from surfaces. Excavation is a unique opportunity to experience volume in terms of materiality, in order to infer new information about the present and the past.

Raster data are designed for mapping continuously varying phenomena and do indeed reflect very efficiently attribute variations in two- and three-dimensional space. Differently from vector data, they can accommodate fuzzy boundaries (Conolly and Lake 2006). They can cater for the definition of contexts not exclusively via the identification of surface boundaries and immutable characteristics, but through examining variations of characteristics of archaeological data in space. In an object-oriented view of excavation, finds are situated within contexts and are defined by contexts. By acknowledging that artefacts may be independent from a context mainly defined by its soil matrix, we allow them to speak for their autonomous life within and outside a deposit and, in same cases, to become the identification units of the context itself. In a three-dimensional space (and in particular a 3D GIS environment) relationships between context and finds independent of each other in every spatial dimension (x, y and z) can be more easily studied than in the traditional 2D record. Moreover spatial and other characteristics of the deposit can be combined in a more complex manner, enhanced by the possibility to manipulate and explore them in a 3D space. In this manner, those ‘meaningful actions and events [that] can and often occur both above and below the level of depositional or stratigraphic units’ (Lucas 2001a, 167) can be taken into consideration.

By emphasising depth, and not only surfaces, as part of the everyday experience of humans, the volume and what happens within it acquires importance too. The deposit becomes a feature with its own complex story to tell, as much as a surface. 4.4.3 On lines, grids and objects According to Connolly and Lake (2006), one of the disadvantages of vector structures is that ‘vector data impose properties into real-world objects that do not necessarily correspond with reality. The most important imposition is ‘boundedness’.’ (ibid.: 29). In reality objects, and in our case archaeological contexts, often have indetermined boundaries. GIS research has long incorporated the use of fuzzy sets and objects to deal with inexact concepts in a definable manner (Burrough and Frank 1996). Nevertheless GIS applications in excavation have not considered this possibility and have glossed over the problematic conceptualisation of single contexts as closed entities very effectively criticised by Lucas (2001a).

4.4.4 On mapping Spatial analysis in archaeology (whether inter- or intrasite) has traditionally been based primarily on mapping. In mapping, the space is a surface, features are graphic lines in plans and sections, and distributions are of objects and features on this flat canvas. A two-dimensional representation of excavation has proven to be a successful platform for the recording, analysis and delivery of archaeological data. However, it has several limitations.

The limitations of the entity-based approach to archaeological investigations become even more obvious when we look at it from a volumetric, three-dimensional perspective. Firstly, it assumes an isotropic space (see chapter 3), where the context is defined as an entity with the same properties in all directions, both in two- and three-dimensional environments. Secondly it requires well-defined boundaries. This is convenient for its graphical representation but also influences the way we reason about the context. In some cases it is undisputable

Archaeologists have been trained to interpret plans and section drawings as representations, either from above or from one side. Reilly (1992) identifies this strict view

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Making Visible: Archaeological Excavation in Three Dimensions During my fieldwork in Kouphouvuno (Greece) (section 6.1), I asked different undergraduate students to help me record trench C using a total station. For most of them it was impossible to follow a three-dimensional profile where they could not place the staff holding the prism on a surface. I soon realised they were thinking of a plan and could not conceptualise a three-dimensional space when it came to recording. When we excavate (in three dimensions) we fully experience the act of excavation. When we switch to recording, we invariably think in terms of cutting planes (whether horizontal or vertical). A trained archaeologist, even when making twodimensional maps, probably continues to think in terms of three-dimensional spaces where the relationships of three-dimensional contexts cross the boundaries of twodimensional drawings. However, communicating such complex understanding to others (colleagues and the public) becomes more difficult once the view retained has become fossilised in 2D. As thinking in 3D allows us to conceptualise better the ‘total sets of transformation of space’ (Gosden 2005, 202) that compose the complex history of the archaeological deposit, a three-dimensional environment for recording, analysis and communication is ideal.

dependency as the most severe limitation of a twodimensional record. Conceptually, limitations of the 2D interface are most noticeable in the section, whose purpose is difficult to understand during the course of fieldwork. On the one hand, a section drawing can convey vital information about the relative sequence of the exposed stratigraphy and about the shape of features, which are central to interpreting their function (tips, dumps, pits and truncated deposits). On the other hand, there are two problems associated with its use. First, ‘any number of intervening contexts may be involved, but do not happen to intersect with the plane of the section’ (Reilly 1992, 159). This is partially alleviated by the use of Harris matrices that convey information about the stratigraphic context. However, as mentioned above, the Harris matrix brings with it its own issues. Second, a section cut is laid with no foreknowledge of the feature under investigation. ‘Decisions about where to place sections are frequently arbitrary in relation to the archaeological context. Therefore, if the feature is not symmetrical then a section must miss details’ (159). Reilly (1992) argues that procedures for three-dimensional recording other than sectioning are required. Third, analysis and representation of two-dimensionally located data is restricted to the third dimension under study. We can either examine a horizontal pattern or a vertical sequence. Where we can represent the three dimensions of an object (x, y and z), then the space of analysis is amplified to both the horizontal and vertical axes and the exploration of a fourth characteristic is allowed. For example, finds distributions can be explored visually and quantified computationally in space and time, under the assumption that time is vertical (although we know this might not always be the case). A three-dimensional space is therefore necessary for the representation of contextual data and for allowing us to undertake exploratory modelling. Finally, representation remains a problem. As Richards (1993) has put it:

4.4.5 On mimics Three-dimensional models of excavation tend to set themselves the goal of being ‘realistic’ if they are to represent best what was ‘objectively’ on the ground. Here I would like to add a critique of 3D approaches to archaeology based on mimics. Mimesis in this context is identified as the archaeological obsession with the production of final deliverables that are accurate and true to the ‘original’ via representations that are technologically enabled through a correspondence theory of fidelity (Shanks and Webmoor 2009). If we maintain, as discussed in section 4.1.2, that the archaeological record is not destroyed, but displaced and takes a different form in its new world, it becomes obvious that the idea of archaeological record as a replica of the site before excavation is absolutely useless. It is the act of displacement and transformation itself that brings to life the information potential of the act of excavation, the act of recording and the act of revisiting the record. The archaeologist actively engages at all levels of the excavation process. The metaphor of the archaeologist as a craftmaker (Shanks and McGuire 2000) or an artist is therefore a useful one for discussing the creative transformation of the archaeological record as matter. On the one hand, the material world is taken seriously at an analytical level; on the other, the same importance is given to the transformative action performed by the archaeologist who retrieves, manipulates and represents this material world in a variety of concomitant or even contrasting manners. Ultimately, if material worlds are our channel to people of the past, we have an obligation to allow these material worlds to recount stories of the past. We can borrow here Gell’s (in Gosden 2005) view of artefacts as forming a world of their own logic somewhat independent of human intentions and combine

It is all too easy for archaeologists to represent sites and monuments as two-dimensional plans. The sites are always drawn as plans and are subsequently analysed as plans, normally in the guise of phases and artefacts distributions. Consequently, they are visualized and interpreted as plans. The unfortunate corollary of this traditional procedure is that people who originally inhabited the sites which the archaeologist excavates become difficult to accommodate and become quickly consumed in the search for interesting two-dimensional patterns. (…) a false view of the world is being projected onto the material remains. For instance, how many archaeologists think of their homes or workplaces (apart from excavations) in terms of a twodimensional plan? Presumably, very few. Like other human beings, archaeologists make sense of the world through interpretative practice. (ibid.: 543-544)

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Making Visible: Three-dimensional GIS in Archaeological Excavation In the recognition of the parallel between forms of archaeological excavation practice and recording with GIS data models and structures lies the potential not only for assessing the usefulness of these GIS structures for the representation of archaeological data, but also for questioning the suitability of the stratigraphic versus arbitrary approaches for offering a more complex representation of the archaeological record. Much of GIS application has mirrored archaeological excavation practice and recording standards and has perpetuated their limitations, unable to move beyond the traditional paper record (in terms of conceptualisation). As already discussed in chapter 2, all too often GIS projects have simply consisted of a mere digitalisation of traditional context sheets, plans and section drawings and have taken for granted that single context excavation and planning are ‘the only way to see’ (Harris 2001). Suggestions as to how to go beyond the tendency to objectify and fossilise the archaeological context or the unit have been proposed by Lucas (2001a) and need to be incorporated into a critical use of GIS as a space of archaeological practice.

it with our more axiomatic representation of archaeological space. According to Gosden (2005) this is ‘vital in demonstrating that there might be many cases in which forms of abstract thought and mental representation take the shape suggested by objects, rather than objects simply manifesting pre-existing forms of thought.’ (ibid.: 196). In order to analyse data in three dimensions we need to produce three-dimensional spaces. However, we do not have to mimic exclusively three-dimensional geometric shapes, as the aim is to stimulate thought beyond a map. The role of visualisation in archaeology has generally been conceived as being purely mimical. Visual emphasis at an analytical level is different from understanding the past merely by visual means. Scientific visualisation, as the process of creating and viewing graphical images of data with the aim of increasing human understanding (in particular the branch of visometrics – already discussed in chapter 3), is a totally different exercise from relying mostly on the analysis of visual patterns to enter the social space of the past. The interpretive result of scientific visualisation does not have to be a realistic image of the ‘original’ data recorded. Moreover, the process of making a pattern visible does not necessarily mean that the pattern was only visual in the past. Of course, in performing the exercise of making visible there is a constant risk of overemphasising two extreme conceptualisations of space: on the one hand the search for realism and the ‘real’ experience of individuals and groups in relation to this space, and on the other hand the exclusive aim to generate a schematic and logical model that can only be used for experimental theoretical interpretation. The physical and mental fields created in this manner exclude the social space not only of the past but that of the present. In some areas of GIS-based research (for example, escape route simulations in urban environments) realism is not only useful but necessary, but this is not the case in making visible an archaeological excavation. Here, what is important is the ability to manipulate data in different forms and to use volumetrics to explore more dimensions at the same time.

The production of space according to Lefebvre’s trialectic provides a critical framework within which I can locate the use of a 3D GIS as a particular space in archaeological practice. The complexity and heterogeneity of the archive could involve a synthesised yet creative engagement with the many aspects of a site; however, the introduction to a site in our publications too often still starts with a representation of space (map) and concludes with a composite sequence that is again manifested either through a plan or a table with numbers. Due to the ‘nature’ of digital systems, data can be translated into many different forms. Described as ‘liquid media’ (Phillips 2005), the empowerment of digital platforms lies not in the extension of traditional media (more sophisticated maps) but in that they allow us to move between media forms. Whilst the representation of space provides the grid of reference for our coordinates, these can be linked or hyper-linked across media types and data representations (visual, audio, quantitative, qualitative, raw and manipulated). The traditional map disappears as the attention is brought to other forms of representation of the site consisting of models not only of the raw scattered data but of interralated and transformed data, in other words mental maps.

The purpose of creating a 3D excavation platform is therefore not to create a more realistic model, but to force the archaeologists to think in 3D rather than in 2D, to allow the transition from space to place and from the present to the past. Doing this in a three-dimensional environment enhances our analytical possibilities, not because of supposedly more realistic representations, but because this enables the creation of a visually enhanced environment where more data can be displayed and manipulated synchronically. 4.5

In this framework, then, three-dimensional GIS is conceptualised as a representational space (that third spatial practice that Lefebvre incorporates in the production of space and archaeologists do not normally consider) and can therefore have the power of ‘broadening our appreciation of the richness and unfamiliarity of lives that were lived in the distant past’ (Thomas 2004, 235). The potential of a three-dimensional GIS lies in the energy [that is] created by the concrete production of representational spaces as modes for exploring the unfamiliar in different and creative manners (yet within spaces whose geometries we are familiar with) and in providing a platform for the negotiation of the archive.

Reasoning about 3D archaeological space in a GIS framework

In this section, I will discuss some central propositions that take into consideration the points that have arisen above and are at the core of the conceptual framework proposed in the following chapter.

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Making Visible: Archaeological Excavation in Three Dimensions to ‘make visible’ the displaced site is a useful interpretative exercise. Reliance on vision has been mostly criticised as a paradigm of cognition reflecting an exclusive Western modern world and therefore unable to appropriately help the understanding of different perspectives (in the past and for different cultures).

In practice, the formulation of a conceptual framework that enables us to capture the trialectic dynamics of the production of space and enhances the role of representational space in archaeological practice via digital media is guided by the following considerations. First, despite my criticism of Cartesian approaches to space and time in archaeology, a Euclidean space of representation needs to be fully acknowledged and integrated into archaeological reasoning, as it reflects the parameters of the production of much of the graphic archive of an archaeological excavation alongside many other elements of spatial practices (the direct experience of the excavation and its routines, the narrative of it, the daily engagement with its texture, its smell, its sound). Moreover, a Euclidean framework of triple coordinates (x, y and z) allows for the linking of disparate datasets in a common spatial framework. The explosion of the archaeological record is avoided by acknowledging the power of Euclidean space for putting back together displaced fragments of excavation. Physical 3D space conforming to Euclidean rules is also a pre-requisite for the analysis of phenomena governed by mechanical processes. An example is groundwater modelling, where fluxes of water are run through 3D deposits and, depending on the physical characteristics of the latter, produce different chemico-physical effects in the subsoil. We therefore cannot demonise, let alone reject, this representation of space in our engagement with the archaeological record.

Nevertheless, I argue that, not only is this an acceptable exercise, as it is part of the spaces we, as contemporary archaeologists, produce and are required to produce, but also that this critique is no longer valid once we reject realistic spaces as the only possible spaces able to deliver an insight into the production of space. The relevance of using the production of space as a framework is that it allows concurrent and contradictory spaces to exist at the same time. As archaeologists, we use different spaces to locate and create the archaeological record. At the same time, we need to combine these spaces for exploratory and interpretive analysis and delivery of results. It is not often the case for data to be, for example, normally distributed and similar. Rather, several different and sometimes contrasting spaces are used in the production of representational spaces in archaeology. These are multidimensional, not similar but coherent and sensible. In a digital platform this can be practically translated into the creation of different operational spaces that reflect the complexity of the dataset at hand (Lees 1996). The operational space is that in which analyses are carried out. Examples of operational spaces are given in Lees (1996) for environmental scenarios (fig. 4.6).

Second, while not excluding other forms of engagement, I argue that thinking through drawing, sculpturing (modelling) and manipulating three-dimensional spaces

Figure 4.6. Operational data spaces. Source: Lees 1996, fig. 7.1.

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Making Visible: Three-dimensional GIS in Archaeological Excavation more effective expressions of the qualities of archaeological data, e.g. defining a context using the colour of the sediment or by analysing its chemical composition, defining objects through a context or vice versa. These concepts also offer a practical solution to the translation of scattered raw data represented through GIS structures into base models that can be manipulated to simulate depositional and post-depositional phenomena. The apparent contradiction of using the physical laws that apply to sediments and, at the same time, imagining the life of a fragmented pot before, during and after entering the context we are drawing in the three-dimensional virtual environment gets resolved by acknowledging the possibility of the concomitant existence of different images of the same geographic space. Granularity and characterisation are at the core of the conceptual framework presented in the following chapter, since they offer an organic solution to three-dimensional GIS in archaeological practice.

Archaeological examples may be geographic, spectral or archaeological space. Taxonomic definitions are also used in archaeology. Geographical space is defined using longitude, latitude and elevation (corresponding to many but not all rules identified by Goodchild (2000)). Spectral space is created from geophysical recordings, where a data space is defined by discrete slices of the electromagnetic spectrum. Archaeological space, finally, can be defined by environmental, cultural and a number of other variables, represented singularly or in combination. These could be nutrient status of soils or soil composition but also finds distributions. Some motion in these spaces can correlate with an apparently equivalent vector in another space. For example, movement parallel to the elevation axes in geographic space often has an equivalent along the temperature in an environmental space. A movement along the soil moisture axes of environmental data space is often matched by a vector in the spectral space (the principle on which geophysical prospection is based). Other movements, such as nutrient status or finds distributions correspond to jumps in the geographic space. For example, a point in the archaeological data space can represent extensive, disjointed areas in geographic space and discrete, remote volumes in spectral space. Confusion exists because of incorrect assumptions about correlations between these spaces. Instead of seeing the analysis as a series of steps, each of which can be carried out in the appropriate operational space, we ought to use analytical approaches that make no assumptions about spatial distributions. Scalar and characteristic approaches are therefore the operational response to the conceptualisation of spaces and data discussed in previous sections of the chapter. 4.6

Concluding thoughts

While most attempts at creating 3D GIS and visual threedimensional immersive models seem to insist on ways of re-creating ‘natural’ and ‘realistic’ representations, I argue conversely that in my model multidimensionality is intended to generate the multi-faceted thinking and analysis characteristic of mental maps. Therefore visually realistic results are only of secondary importance. The aim is not photorealistic effects but the ability to explore the excavation in all directions and with a variety of perspectives, whether these have a visual correspondent or not. A full consideration of the multidimensional facets of archaeological space is therefore obtained not through the exclusive use of Cartesian objectified space but by the acknowledgement of other characteristics of space, in particular granularity and characterisation. Granularity allows me to see archaeological data in terms of scale and resolution and to accommodate varying degrees of detail and engagement with the archaeological record. Characterisation is an operation that enables us to think about archaeological data and archaeological space using different modes of expression. These include technical GIS concepts, e.g. raster versus vector, and

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Chapter 5. Conceptual Operational Framework

Design

and

visualising an excavation within a 3D GIS. The objectives of the exercise, the guiding concepts used throughout the development and the data model framework are presented in this chapter. This is in response to the advocated need for new approaches to the user-specific analysis and presentation of query results. The user must be able to explore the spatial database interactively in 3D and, at the same time, formulate new queries on the basis of the situation as it is presented. The conceptual data model is therefore query oriented and responds to the new focus brought about by the 3D conceptualisation of excavation discussed in chapter 4.

The increasingly sophisticated tools for spatial modelling and analysis provided by today’s GIS encourage the incorporation of such processes in archaeological research. However, the design aim of most widely used GIS packages has not been archaeology at all. As a result, translation of the unique spatial concepts, relationships and processes examined in chapter 3, which have grown independently of GIS, is not obvious and without misapplications. This chapter intends to present the process of construction of a framework that bridges archaeological and computational concepts in order to achieve a platform for knowledge construction. In this way I want to move away from direct descriptions of excavations (and in general of archaeological contexts) based on geometric and numeric spatiality to complex models, with uncertain spatial parameters, varied effects and inputs where full system description might become impossible, but exploration and explanation could find a place. The use of complex structures to represent and understand complex systems is directed by the principle of information amplification, rather than simplification (normally used when modelling is conceived as being a simplification of reality).

In this chapter, firstly the stages for the design of any generic GIS system are briefly summarised. Secondly, the conceptual framework for the application of 3D GIS to archaeological excavation is presented (objectives and concepts developed) and the system architecture is clarified. Lastly, the step-by-step management workflow designed to integrate heterogeneous data, store them, and construct and analyse the models in 3D using an integrated approach is presented. 5.1

Designing and implementing data models in GIS: concepts and terminology

In chapter 3, an overview of the basic concepts and terms in GIS-based spatial data modelling was presented. Here, a brief description of the different phases in data modelling is provided to familiarise the reader with the workflow used for the conceptualisation and implementation of the threedimensional spatiotemporal framework, which is the result of this research.

The general assumption that computer technology equals objectiveness has long been abandoned in other areas of research. Although it is true that IT requires some criteria for data collection, input and processing, this does not mean that there cannot be space for flexibility, exploration and interpretation. As highlighted in chapter 4, the problem lies in the practice of materialising excavation in very rigid forms of archiving, not in the use of a technology as such. In fact, the traditional record often allows practice flaws to be hidden. In this chapter, one of the many possible ways of materialising excavation through a digital approach is presented, bearing in mind the points discussed in the previous chapters.

5.1.1

Design phases in modelling

A data model is a structure to capture an abstraction of reality for a specific application. In designing a data model, Laurini and Thompson (1992) identify four levels, which directly correspond to the four representations of the world formulated when abstracting for the real world to the computer binary code (fig. 5.1):

A framework is proposed to help archaeologists examining and using these models in all their forms from raw to sophisticated with the use of an iterative process. The system is data driven and points to data integration. ‘Data driven’ does not reject the use of hypothetical reasoning, but it aims at configuring archaeological data at the centre of the analytical process allowing for direct retrieval, in forms of aggregation and scaling that can vary subject to the decision of the system user.

• • • •

External level Conceptual level Logical level Internal level

Table 5.1 summarises the correspondence between the representations and the design stages in the process of abstraction that is the design of an information system. The process discussed by the authors applies to database design, but it can be applied to the design of any information system.

The objective of this research is to conceive a platform for archiving, exploring, analysing and

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Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 5.1. Data modelling levels. Source: Laurini and Thompson 1992, 360.

Table 5.1. Levels of abstraction from reality to computer file structures. Modified from Peuquet 1990, 252. Definition

Description

Design stages

Reality

Phenomenon as it actually exists including all aspects which may or may not be perceived by individuals

External design: the real characterised according to requirements

Data model

Abstraction of the real world which incorporates only those properties thought of as relevant to the application or applications at hand, usually a human conceptualisation of reality

Conceptual design: the model is populated with spatial objects and attributes in the form of diagrams

Data structure

Representation of the data model often expressed in terms of diagrams, lists and arrays designed to reflect the recording of the data in computer code

Logical design: the diagrams are converted to schema

File structure

Representation of the data in storage hardware

Physical or internal design: considerations of software and hardware

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world is application

Conceptual Design and Operational Framework As the real world corresponds to a subset of reality that is of interest at the moment of perception, the authors use the notion of a universe of modelled phenomena to define the forms of analogical or digital encoding that are used to describe it. In this sense verbal, pictorial, aural and many other forms of representation can be integrated in the representation of the reality to be modelled.

model in which the principal types of phenomena and their associations and constraints are laid out. In this sense it only differs from the external model insofar it goes from the more narrative descriptions of phenomena, dynamics and requirements to a more schematic one. Moreover, the conceptual model is the synthesis of several external models, in particular when dealing with geometric data. The two common approaches for designing a conceptual data model are the entity relationship (ER) model and the object oriented (OO) model. The basic components of an entity-relationship model are entities (objects), relationships and attributes. These components are abstract; therefore, a variety of definitions exist to describe them. An entity is an object, event or concept in the user environment about which data are maintained. Examples of entities in an archaeological data model are contexts (in the realms of stratigraphic excavation). A relationship is a meaningful association between entities. For example, a relationship can link a context with another one to create a spatial or temporal connection, or a single special find with the context in which it belongs. Attributes are properties or characteristics of entities, such as the depth of a deposit or the pH characteristics of a soil stratum. Associated with the ER model is the ER diagram, which gives a graphic representation to the conceptual model. In the ER diagram, entities are represented as boxes, attributes as ovals connected to the boxes and relationships as diamond boxes. Recently, UML (Unified Modelling Language) has become a standard for conceptual (and logical) model design. Object oriented modelling represents the world as object classes (or simply classes). Object classes are similar to entities in the ER model but in addition to having state (attributes and relationships) they also exhibit behaviour, which represents how the object acts and reacts to events. In this sense it is considered to be a more natural representation of reality. Nevertheless, it is more difficult to implement at a logical level. In many instances, an OO conceptual design is used but the implementation is carried out using an ER diagram.

External model The very beginning of the design process is the external modelling, in which the users define their own subset of the real world, in other words what is relevant for the specific application. External models of the real world are as many as the representational needs and purposes of the universe of modelled phenomena. Conceptual model The conceptual model is used to formalise human concepts of a particular reality. It is the conceptual representation required by the computer system to represent a model of the system. In this sense it operates as the bridge between the concepts of the external model (or models) and their physical representation in a database (spatial or aspatial) by providing a synthesis of all external models. It is called this for two reasons: firstly, because it is made of very sound concepts; secondly, because it is the basis for the conception process, as it defines common information ideas and elements to communicate the needed spatial data. In this sense, the conceptual model is independent of the GIS platform or application. The focus is in fact put on semantic compatibility that pertains to fundamental issues and has profound implications in GIS representations and data modelling. Despite being an abstraction of the real world, the result of the modelling is quite concrete in nature, consisting of a schematic representation of phenomena and how they relate through the use of flow diagrams. The scheme produced deals with the information content, at this stage, not with the physical storage. In this manner, the same conceptual model may be appropriate for diverse physical implementations.

Logical model In the phase of logical design, the conceptual model is translated into a logical model. This is commonly considered to be the first step in computing. The logical model corresponds to the transformation (mapping) of the conceptual model with the tools offered by the logical model which are a set of mathematical concepts. The translation of the conceptual model is achieved by mapping the conditions of the semantic data model into the definitions, constraints and procedures of a particular DBMS (database management system). At this stage, the permanent properties of the database are clearly

The conceptual model provides the basis of schematisation and is a fundamental tool for thinking, discussion and sharing. As a consequence, a good conceptual model should be easily understandable. The model sharing may be done by means of narrative statements, but the transfer to the logical model (the next step in the design) is easier if more formal mechanisms are used. The representation of the conceptual organisation can take various forms. Nevertheless, it can be thought of as a semantic data

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Making Visible: Three-dimensional GIS in Archaeological Excavation specified and become more constrained by the choice of the database model. Often the term logical model is associated with data structure, since in this phase the structure of the database is designed. Various types of database models can be distinguished nowadays, which correspond to the conceptual design approaches, from relational to object oriented (OO) to object relational (OR). Others could be network or hierarchical models. It is not a concern of this dissertation to deal with detailed logical or internal modelling. It is nevertheless worth mentioning that the majority of commercial GIS software is constructed using a relational database.

at supporting analysis in the domain of the complex spatiotemporal characteristics of archaeological excavation data. The conceptual model domain of design is privileged. 5.2

A conceptual framework informs the GIS production process and guides the conceptualisation and implementation of the data model. It is connected to a cycle characterised by two main steps: 1. analysis of how the GIS incorporates multiple realities and competing assumptions of space and time (specifically in archaeological applications) and consequent experimentation with GIS software; 2. analysis of archaeological data and their suitability for GIS platforms.

Internal model The phase of the internal (or physical) design is concerned with the byte-level data structure of the database. Here the logical model is translated into hardware and software architecture. The design of the physical model is critical to ensure performance. It in fact enables operations for manipulating the logical model in an efficient way. At the physical level the following tasks are handled by the DBMS: - storage - access paths and indexes - query processing and optimisation - concurrency and recovery. The physical model is normally hidden from the user. 5.1.2

The conceptual framework

As there are limitations in both data models (GIS and archaeological), a circular exploration can individuate and find ways of correcting them. In this manner, not only the conceptual framework and data model are formulated but also an investigation of current GIS technology and how to best utilise it in the data model design (within the limited budget of an archaeological project) is performed. The framework incorporates users’ requirements into a coherent method of analysis. It should provide a focus and insight relative to integrating new informational requirements with existing data sources and a platform for guiding further development of techniques and approaches as additional data capabilities evolve. In this sense, the conceptual framework is a conceptual overview of the data model. The logic behind it can be implemented in any GIS software package.

The design process of the project

Designing a data model is a complex process that includes a variety of tasks, both intellectual and practical. In areas where substantial research on the applicability of GIS solutions to modelling and analysis has a long standing tradition, such a process is well documented at an intellectual level and, as a consequence, most efforts are at present put into developing efficient spatial databases. In archaeology, the development of databases for the organisation and storage of information from excavations is well documented (Andresen and Madsen 1996, Arias et al. 1996, Arroyo-Bishop 1989, 1991, Beck 2000, Courboud 2002, D'Andrea et al. 1999, Francovich and Valenti 2000, Fronza 2000, Fronza et al. 2001, Fronza et al. 2003, Guermandi 1990, Moscati 1999, 2000, 2001, Moscati et al. 1999, Nardini and Salvadori 2000, Valenti 1998). Nevertheless, as highlighted in chapter 2, these databases, designed without dedicating sufficient consideration to the initial steps of conceptualising a GIS-based data model, do not always satisfactorily support complex analysis involving space-time as well as other dimensions (attributes) of the spatiotemporal objects. Therefore, in response to this, the design process of this research has concentrated more attention into creating a conceptual framework to provide the basis for the data modelling flow. The framework is aimed

The development of the framework also takes into consideration the fact that although users may possess considerable knowledge about a particular problem domain, it cannot be assumed that they have the time and expertise needed to develop sophisticated simulation software based on complex spatial data models. The challenge is therefore to develop a framework within which users can construct digital models of complex systems without requiring them to write complex computer code. Bennett (1997) proposes a modular approach to the development, which allows the user to treat some sub-models as black boxes while focusing attention on the development of other model elements. Also, as the same model (in our case archaeological) and subsequent data structure may not be appropriate for all sub-models, integration of multiple archaeological models must be supported into a single conceptual framework capable of capturing complex archaeological systems. The aim of this kind of

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Conceptual Design and Operational Framework framework is to allow the user to concentrate and focus on the model design rather than data structures.

quantitative and qualitative considerations on the methods used and results obtained. It is a framework for reasoning within a reasonable position between the data and its existence within a computerised system. Conceptual design becomes here an exercise to create a platform for alternative interpretations.

The conceptual framework presented here is the synthesis of this research. It is not meant to be a rigid final result, but it is the result of the different iterative phases in the search for an effective way to use 3D GIS to address archaeological excavation questions. It derives from empirical and theoretical considerations emerging from literature review and assessment of GIS use (and software) in archaeology and other disciplines. It was slowly built in a trial and error process, where the two case data sets at hand have been a constant reference and test of application. The framework is a platform for questioning fieldwork and off-site analysis in the process of archaeological research. 5.2.1

The conceptual framework is based upon the exploratory analysis approach (Kemp 1993) and puts an emphasis on the requirements listed by Lee and Kemp (1998) as follows: • the user to deal with natural representations of the phenomena and relationship of interest • the application of statistical functions to the raw dataset so that flexible partitioning of the problem space is achieved • the intuitive and interactive visualisation of selected subsets to enhance exploration and analysis.

Conceptual framework: rationale of the system

The aim is to equip the archaeologist with a general ability required for identifying and specifying the patterns and aggregates that occur across several dimensions and at different levels of analysis, in other words in the different operational spaces discussed by Lees (1998) and discussed in section 4.5. This is an important issue that has been neglected up to now in the development of GIS applications, with the consequence of preventing a methodologically sound and practically useful use of intra site GIS.

An archaeological system is a set of interrelated entities and phenomena in the natural environment, such as rivers, lakes, forests, the sea and human elements that interact with these entities. These systems have multiple scales of interaction and are characterised by complex physical and social processes. The peculiarity of this system, which makes it different from environmental ones when it comes to computer modelling is that, from the physical record we infer not only natural phenomena but human behaviour, relationships and past worldviews.

In order to fully express the three-dimensional nature of archaeological sites and sitescapes as conceptualised in chapter 4, the framework aims are therefore: 1. to develop a comprehensive 3D oriented digital description of archaeological data with the idea of recomposing the fragmented archaeological record coming from excavation and related research datasets at various scales; 2. to provide archaeologists with better access to large volumes of high quality heterogeneous data; 3. to advance knowledge, providing better comprehension of data at various levels of enquiry and abstraction, from their raw form to complex representations which can be used to understand processes that influence site formation.

As a consequence, archaeological analysis and interpretation happen at different levels, touch several sub-disciplines and are carried out by a variety of experts. Nonetheless, they are characterised by a common thread: the search for interesting patterns which, in computing terminology, corresponds to the concept of knowledge discovery. The main concern is to provide the archaeologist with a general capability for specifying the patterns and aggregates that may be required across several dimensions to produce such knowledge. Generally, different data models and analytical techniques are needed of a particular spatiotemporal dataset. Moreover, views of such datasets are often different. In addition to this, in a multidimensional problem space the constraints and conditions that apply to each dimension can only be expressed in the context of the specific analytical process carried out. The aim of designing a framework for a three-dimensional archaeological information system is to inform the choice of data collection and storage strategies and of data structures to be used and integrated for an informed exploration and knowledge discovery within GIS capabilities. It provides a set of approaches to the design of pattern discovery in the dataset, allowing the user to make

To respond to these aims, rather than representing different types of information in a compartmentalised manner (like other conceptual frameworks), the proposed framework looks at combining data, models and modelling (process and simulation) to support archaeological enquiry. This is achieved by identifying a suitable conceptual approach to the description. Archaeological applications are characterised by large heterogeneous spatiotemporal datasets involving

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Making Visible: Three-dimensional GIS in Archaeological Excavation many variables. The spatial data infrastructure is the combination of a subject-oriented framework and field data used for a variety of purposes: rescue archaeology, research oriented to answer specific questions, palaeolandscape studies. In these circumstances, a computer framework is used to underpin spatiotemporal research in an exploratory and intuitive manner (Lee and Kemp 1998).

conceptual framework is that archaeological excavation encapsulates different meaning within the boundaries of the stratigraphic context to those of sedimentary modelling and interpretation. In some cases, as highlighted in chapter 4, this meaning may also be captured exploring areas above and below context boundaries. 5.2.2

The system must provide flexible analytical support. The fundamental themes of space and time, expressed by the use of three-dimensionality, provide a common thread that runs through the various perspectives that relate to any given spatiotemporal system. These perspectives can be explored at different levels of the application, which must be artificially created to help accessibility and understanding of the dataset and its levels of abstraction and complexity.

The 3D spatio-temporal underlying principles

framework:

Generally, different views of a particular spatiotemporal dataset are expressed by different requirements. Moreover, in a multidimensional platform the constraints and conditions to apply to each dimension (x, y, z and time) can only be expressed in the context of the analytical process being carried out. Therefore different ranges of requirements can be summarised for the spatial and temporal dimension.

In order to achieve this, data must be manipulated at different levels. Low–level research data (raw data) can be queried for simple questions such as What? Where? How much? When? (already a more complicated question); medium-level research data are used to retrieve summarised or aggregated information in a variety of ways. Typical queries would be total weight of flint category X in timescale Y (which implies an established chronology in level 1), summary of the weight of all categories of flints/pots between space interval 1 and 2 (identified by soil characteristics); high-level research data are used to integrate data related to human activities with environmental data, for example movements of people linked to seasonality. Examples of indications to be achieved are: establishing the ‘normal’ range of an identified phenomenon and the measurable fluctuations in this range, measuring the degree to which human material matched or is matched by certain particular sets of environmental indicators and characteristics, establish the relationship between concentrations of a particular human assemblage and spatial and temporal variations in material supply and type and climate change, measuring the possible increase of disturbance of certain human occupation patterns by natural change and vice versa, studying the spatial variability in human community structures, and selecting locations of particular activities using a combination of attributes that might indicate so.

Despite the seeming difficulty in identifying connections across dimensions, two properties can be recognised: • Multiple granularity (issues of scale in space and time) • Multiple characterisation (issues of aggregation of space and time in units different from the ones used for recording and issues of multiple representations of the same units) The two identified properties are inspired by the Analytical Abstraction Layer (AAL) discussed by Lee and Kemp (1998), used amongst others for the management of marine fisheries. The Analytical Abstraction Layer approach The Analytical Abstraction Layer (AAL) characterises how data are abstracted. It views data as a multidimensional entity and, as a consequence, supports complex analysis involving space-time as well as other dimensions (attributes) referred to in the literature as aspatial properties of a spatiotemporal object. By complex analysis Lee and Kemp (1998) refer to ‘a non-trivial combination of data extraction from some data repository, and manipulation of that data’ (ibid.: 151). Time, location, weight, densities are examples of what a dimension can represent, as discussed in chapter 4.5.

A lot of inspiration for the construction of the framework, in particular in the realms of the conceptualisaton of subsoil features during exploratory survey phases and in later stages of excavation, came from geological and hydrogeological modelling. The added challenge in constructing an implementation for the archaeological

Figure 5.2 below shows a high level overview of the spatiotemporal information system as expressed by the authors.

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Conceptual Design and Operational Framework

Figure 5.2. High level overview of the spatiotemporal information system. Source: Lee and Kemp 1998, fig. 14.1.

expressivity of such operations by allowing conceptual mobility and multiple belonging. Datasets may belong to one or more characterisation classes or groups of classes. Each characterisation level or group can have many members. Categorisation classes allow the preparation of higher level data for further analysis. Multiple characterisation also allows the same dataset to be portrayed in different graphical forms that emphasise different aspects of data. Moreover, it allows the representation and compatibility of data collected with different criteria (single contexts transformed in digital vectors as opposed to voxel interpolated samples). Certain categorisation methods will reduce the number of dimensions or the complexity of a dataset. Datasets from different classes can only be compared subject to a mapping function, used to reconcile datasets that are not directly compatible. Characterisation involves using aggregation, inter- and extrapolation, classification, generalisation, categorisation and partitioning to interactively elicit patterns and anomalies alike.

Multiple granularity and characterisation in the proposed framework The term granularity has its origin in physics, where it refers to the average metric of the size of particles; in fact, in physics granularity means subdivision that makes physical object fine (Zhao et al. 2008). In computer sciences it has come to identify a concept similar to but broader than resolution Multiple granularity is a concept that encapsulates issues of scale in space and time and is one of the parameters that determines the outcome of the analysis. When a dataset is explored at various spatial scales, corresponding degrees of detail are revealed; similarly, temporal information can also be presented at different levels of resolution (e.g. decennia, centuries, millennia), with each level showing different patterns and trends. When data are examined at a coarser spatio-temporal resolution than that at which they were recorded, they need to be aggregated. The same operations can also be used to generalise information that is considered less important for a specific level of analysis. If spatial resolution needed to be finer than the level at which it was recorded, decomposition and disaggregation could be achieved using, for example, kriging. Multiple abstraction is used for data processing and organising data meaningfully as well as expressively. Datasets may belong to one or more abstraction classes. Each abstraction class in turn can have many members. Comparability of data sets from different abstraction classes can sometimes only happen subject to mapping function (adjustments in data). For example, calibration of different coordinate systems. Multiple abstraction also allows the same dataset to be portrayed in different forms, for example raster or vector format.

5.2.3

The framework illustrated

Taking into consideration the issues discussed in the previous sections, the framework proposed (figure 5.3) is a system in which multiple granularity and characterisation are adopted to create micro-, mesoand macro-base models of the excavation data. These reflect a conceptualisation of excavation as a localised unit (or trench), as a site and within its landscape. The combination of raw data and their manipulated forms in terms of granularity and characterisation implies possibilities of aggregation, classification, generalisation, specification and partitioning to enable pattern and anomalies to be elicited. The principle is that of a nested hierarchical structure where analytical operations (map algebra, extrapolation, interpolation, etc.) are used to produce new datasets suited to proceed to further phases of the analysis and visualisation. The elements of the system are connected by a double directional flow, to avoid the

In the proposed framework, the concept of abstraction is amplified in terms of characterisation. This variation stems from the need of archaeologists to classify and categorise data in a complex manner. In addition to the properties of abstraction, therefore, characterisation enhances the dynamism and

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Making Visible: Three-dimensional GIS in Archaeological Excavation determinism typical of some hierarchical structures. As indicated above, the framework considers data multidimensionally: time, location, aspatial attributes and inheritance from previous levels of interpretation are examples of what a dimension can represent. Data as such are stored in the multidimensional database. The dimension of interest can be one or many at a time and data are explored using the pathways of multiple resolution and multiple characterisation, as shown in figure 5.3. These properties, common to all archaeological data, are the foundations of the system and between them they provide support for a wider range of analytical procedures. Multiple granularity and multiple characterisation become the axes of the multiple model building. Changing them can change the interpretation of the spatiotemporal phenomenon and can create different interpretative scenarios. These operations are used both for time and space to

suppress or enhance detail, differentiate or generalise the components both for display and analysis. The conceptual framework is designed to be platform independent. The intended purpose is in fact to allow the sharing of archaeological information independent of logical and physical implementations. It is a preliminary logical step, which precedes the creation of the data models, where archaeological concepts and the relationships between them are made explicit. The actual working platform used in this research to both conceive and experiment the framework with the use of two datasets was a combination of free/open source software released under the GNU General Public Licence and ArcGIS, a popular proprietary software employed by several archaeological research and contract units. The details of the system architecture are provided in section 5.4.1.

Figure 5.3 The spatiotemporal framework. Conceptual scheme of an integrated archaeological information and modelling system.

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Conceptual Design and Operational Framework 5.3

through time and depending on the requirements of different users 2. modelling of surfaces and volumes, as this has major implications on geostatistical and process modelling. This is necessarily tailored to every single application, notwithstanding the fact that geometric characterisation requires representation of any irregular three-dimensional shape with efficiency and precision 3. continuous testing and assessment of tools and functionalities to support analysis of 3D dataset 4. three-dimensional interactive and dynamic visualisation

The data model schema: design principles

Whilst the conceptual framework indicates the general workflow and the principles at the basis of the concept of representing an archaeological excavation within a digital environment, the data model is the formalised representation of it. It takes into account not only the excavation but also the process of formation and post-formation events of an archaeological deposit. It describes features such as contexts but also events such as flooding and erosion. It serves as the basis for an ‘archaeological information system’ which is a synthesis of the spatial and temporal data supporting archaeological analysis and modelling. Spatial and temporal information are integrated and extracted in a defined but flexible structure that is aimed at merging the different archaeological datasets that have been fragmented and exploded with the act of excavation. The structure and its mechanisms influence analysis and further modelling. The data model provides a platform for the understanding of the archaeological system proposed. This description can be utilised by multiple models and analysis tools and can support different excavation approaches and methodologies. The process of formulation of the data model workflow has been guided by a series of design considerations that have been identified as the response to the main principles a three-dimensional representation of archaeological excavation should incorporate.

5.3.2 Design concept 2. Matters of scale: modelling sitescapes through time In order to place the excavation trench or open area within its wider landscape context it is necessary to employ, in the representation, different scales of enquiry and different resolutions. The extent of the wider catchment system of a past landscape is usually large and its modelling requires a conceptual simplification mainly due to the type of data available at a macro- and meso- territorial scale. As a consequence, models of territorial systems may be large in extent but remain simple both in surface and even more in subsurface representation. Figure 5.4 shows an example of a two and ½ dimensional territorial system, which offered the macro level of basis of analysis of the Hoge Vaart excavation. The landscape system needs to be conceptualised as either 2 and ½ - (as in the example above) or 3-dimensional. Issues of differences in the horizontal and vertical scale of the data must be taken into consideration. For a meso-scale system in which to situate the site, the model should provide a set of features to define crucial elements of the landscape. These could be features to define the topology of the present day surface, hydrological system boundaries and drainage features (springs, wells, streams), etc. The process of defining a subsurface stratigraphy that incorporates natural and cultural deposits at this scale consists generally of extracting cores from boreholes and analysing geophysical logs to define absence or presence of archaeologically significant deposits and characterise the subsoil. The results can be categorised into geological or pedological units and a common practice is to create cross sections from this data (fig. 5.5).

5.3.1 Design concept 1. Use of a three-dimensional GIS Although the three-dimensional capabilities of current GIS might be still somewhat limited (as discussed in chapter 3), the 3D nature of archaeological data is the fundamental notion developed in the proposed approach. The nature of archaeological data and the assumption that three-dimensional capabilities and tools will improve over the years have led to the conclusion that a data model based on threedimensional characteristics and processes provides the foundation for representing archaeological excavation within GIS, as follows from the discussion presented in chapter 4. The data model translates classic excavation concepts into GIS structures taking advantage of their ability (although at present somehow limited) to represent 3D data, offering an alternative to the flat two-dimensional domain of traditional documentation.

Construction of solid models that can be converted to solid grids and meshes for numerical modelling has still not been incorporated into most archaeological representations of these data.

This approach will have to take into consideration some fundamental needs in a 3D GIS for archaeological excavation: 1. storage of true 3D data types (points, lines, surfaces and volumes – both vector and voxel based) and representations that can change

Stratigraphy measurements can be documented as field logs on paper or in digital format.

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Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 5.4. Sequence showing the palaeogeographic development of the Flevoland polders in the Netherlands between 6800 and 5300 BP. The sequence was created using a DEM of the Pleistocene surface of the area (extracted from borehole data) and a water level trend curve. Legend: 1 woodland, 2 reed, 3 grassland, 4 marsh, 5 water, 6 Hoge Vaart. Source: Peeters and Hogestijn 2001, figs. 65 to 69.

Figure 5.5. Stratigraphic reconstruction of the Hoge Vaart pedology for the period 5700/5000 BP. Legend: 1 Old Coversand ii; 2 Young Coversand; 3 Allerød soil; 4 soil profile (E, B, and C-horizons); 5 gyttia/detritus; 6 eroded sand; 7 washed sand; 8 reed peat ; 9 unconsolidated Claais II clay; 10 clayish reed peat; 11 wood peat. Source: Peeters and Hogestijn 2001, fig. 10a and b.

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Conceptual Design and Operational Framework Figure 5.6a displays a commonly used twodimensional representation of various archaeological indicators of borehole samples. Quantities are assumed to be constant in the vertical dimension and evaluation of areas to conduct more intensive excavation is only based on horizontal indicators of presence/absence of a find category or quantities of a category. Figure 5.6b shows measurements displayed in 3D and therefore enhances the horizontal and vertical variations of the category under examination.

They are represented as vertical measurements referenced by depth down the core whose spatial location is defined by the x and y coordinates of the borehole. The workflow for constructing a three-dimensional georeferenced stratigraphy description of a mesoscale area and interpolating sections and solids commonly includes the following steps: 1. analysing the cores and geophysical logs, categorising the stratigraphy into geological/pedological units and unifying all measurements into a common spatial coordinate system (either global or local); 2. creation of cross sections by connecting the geo/pedological units between boreholes (as in figure 5.5). Another possibility is the interpolation of the points identifying a boundary between two geological units in order to create surfaces (2 and ½ D units). This can be achieved with different types of interpolation, yielding different results, as discussed in Burrough and McDonnell (1998); 3. from cross sections or surfaces, solid models of the subsurface can be interpolated; 4. new cross sections can be created by cutting through the solid model.

Although regional systems have in the past been modelled as two-dimensional, this is now changing and as computation capabilities increase, models are being built to try and more accurately understand and predict distributed continuous phenomena at mesoscales. These ideas need to be incorporated by archaeologists in an effort to study sites in their larger landscape setting not only in two but in three dimensions. In fact, whilst geoarchaeologists have demonstrated to be able to grasp and represent complex surface and subsurface phenomena even through two-dimensional schematic models - as their reasoning remains fundamentally three-dimensional(Butzer 1982, French 2003, Peeters 2005, Sturt 2006) the same phenomena might escape other specialists that are used at conceptualising space in two-dimensions and mainly at a site scale.

The subsoil at a regional level can therefore be represented, and analysis of past and present landscape dynamics can be performed, by using simulation numerical models such as MODFLOW1.

At an intra-site level the type of description of the deposits changes. The interest is no longer to capture a whole catchment; rather the purpose is to describe in detail a small section of interest, generally picked for finer analysis subsequent to meso-scale survey in order to address specific research questions. The process of excavation and the methodologies applied influence the manner in which the site is defined. The archaeological deposits can be categorised in contexts or in fixed units (see chapter 4).

A similar process of interpolation in 3D can be performed to identify both the horizontal and vertical distribution of particular classes of evidence considered to be indicators of human occupation and activity (ceramics, flint, bone, charcoal or chemical signatures such as phosphates). Although these types of subsoil measurements are commonly conceived as three-dimensional, they are usually represented as two-dimensional and the linkage of the horizontal and vertical distribution is often neglected. As a consequence, the presence/absence of the material or of a particular chemical signature from a soil sample is seen often as representative of the concentration either of the entire depth of the deposit or, conversely, of exclusively that point in the sequence. These point measurements are, in reality, three-dimensional objects. They have x, y and z coordinates and in order to store, analyse and display them one must construct appropriate three-dimensional data objects. Figure 5.6 shows the difference between the two representations.

When constructing a context-based and stratigraphic representation of the excavated area, one might want to follow a modelling workflow similar to that discussed in the preceding paragraphs. What changes is the resolution of data collection and the consequent measures of interpolation employed, as further discussed in section 5.4.3. Other representations of the collected data can be created in three dimensions when a gridded excavation is to be represented. In this case, the Hoge Vaart dataset discussed in chapter 6 is a typical example. Again, the modelling procedures to be followed present similarities to those discussed for data at a meso-scale above, with the difference that at a microscale the finds are analysed and categorised not only in terms of detection of presence/absence but also by incorporating results from post-excavation analysis.

1

MODFLOW is a 3D finite-difference groundwater flow model originally designed in the early 1980s by the USGS (US Geological Survey) to solve groundwater flow equations and simulate the flow of groundwater through aquifers. The code is open source and free and can be compiled and run in a variety of operating systems (McDonald and Harbaugh 2003).

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Making Visible: Three-dimensional GIS in Archaeological Excavation

(a)

(b) Figure 5.6. (a) Above, two-dimensional distribution of charcoal in the bore hole samples within the excavation area at Hoge Vaart.; below (b), three-dimensional distribution of charcoal locations and humic material at the site. The excavation area is displayed as a 3D wireframe.

A fundamental requirement of the design concept discussed is the ability of the system of storing and easily allow retrieval of data at all the different scales of enquiry. Only in this case the fragmentation of the archaeological record, critiqued by several authors and discussed in chapter 4, can be avoided, or better, it can be recomposed to offer a comprehensive and exhaustive contextualise understanding of all elements of an excavated site.

most attracted not only archaeologists but all the disciplines characterised by use of large and nonuniform datasets (Shepherd 1991). Shepherd (1991) defines integration as: the synthesis of geographical information in a computer system which depends for its effectiveness on information linkage (i.e. for spatial and attribute data) within a coherent data model. This involves bringing together diverse information from a variety of sources (information interchange), requires the effective matching of supposedly similar entities in these sources, and

5.3.3 Design concept 3. Information integration Integration is one of the characteristics of GIS that

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Conceptual Design and Operational Framework demands information consistency across the source data sets (ibid.: 338)

kept in mind that there is no magical solution to the creation of such system and to designing a platform where heterogeneous information is made compatible.

The benefits of information integration are as follows: • a broader range of operations can be performed on integrated information than on disparate datasets; • by linking datasets together, spatial consistency is imposed on them through the integration of data which were previously the domain of individual disciplinary specialists; an interdisciplinary perspective to archaeological problem solving is encouraged; • users benefit from a perception that they have access to a seamless information environment, uncomplicated by the need to consider differences in data sources, information types, storage devices, computer platforms; • shared information is good for cooperation and sharing.

5.3.4 Design concept 4. Representation of fieldbased and object-base excavation Modern excavation data (as demonstrated in chapter 4) are generated by an extremely varied array of instruments and conventions, all with different formats, resolutions, and sets of attributes (figure 5.7). Not only does the model need to deal with a variety of data sources, but it also needs to deal with a variety of data structures (e.g. tables of chemical concentration versus raster images of soil types versus gridded material distributions versus vectorised features, etc.). It is becoming increasingly obvious that a comprehensive data model is needed to support a wide and flexible range of archaeological conceptualisations, and that this is essential not only for advanced management of data, but also and moreover for facilitating analytical tasks. The archaeological data model presented here seeks to identify and organise such entities and their levels.

Information integration is a benefit offered by many 2D GIS platforms and it has been used to assemble a variety of evidence which in combination acquires more value than it does individually (Richards 1996). Incorporation of records derived from different types of survey and excavation at different scales allows to put a site in its context and to predict and/or confirm interpretive hypotheses (for example, in Richard’s work, patterns of metal detector finds were analysed in combination with aerial photography, geophysics and further tested with excavation). In the case of intra site analysis information integration in three dimensions also enhances the ability of understanding archaeological sites. It is common for surface and subsurface archaeological data to be modelled separately: nonetheless, the interaction of surface and subsurface systems has shown to be significant in many issues relating to water interaction between soils and archaeological material, influencing erosion and preservation of deposits in terms of quality of the terrain and development of aerobic or anaerobic environments. The behaviour of water in formation and post-depositional processes is in fact an interaction between surface and subsurface fluxes. The integration of stream networks and subsurface hydrological modelling is now possible through the use of hydrological models that combine the systems previously separated and can offer great potential to the understanding of archeological formation processes, in particular in buried and inundated landscapes.

Similarly to many geographic phenomena, archaeological data may exhibit discrete features yet possess distributed properties (the case of the context discussed in chapter 4 is one of many cases) and therefore do not fit well into either of these conceptual models. Data models that adhere to just one worldview are unable to provide a complete representation of archaeological phenomena. Likewise, it is inadequate to model distributed phenomena that possess both field- and object-like characteristics simply as exact objects because information on the distributed nature of the phenomena will be lost. In a modern excavation scenario, both continuous and discrete forms of data are collected. Therefore a framework and strategy by which the user can work at different levels, either with the discretised data or with continuous (or semi-continuous) elaborations of them, in the same system, with the possibility of combining them and with a clear knowledge of the difference between the two forms, is necessary. Archaeological data and processes present three major challenges to GIS representation: (1) to capture both field- and object-like characteristics; (2) to provide semantic flexibility in support of application-specific boundary requirements; and (3) to calculate and maintain geometry and spatiotemporal relationships among identified object-like features.

To conclude, integration is a goal to be achieved, for excavation archaeological platforms, if we consider the strive for multi-user multi-data approaches that are becoming more common in the discipline. It must be

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Making Visible: Three-dimensional GIS in Archaeological Excavation

Figure 5.7. Collage of images of and from excavations in Ranaka (Botswana) showing operations conducted and formats of data recorded before and during excavation of site 1, trench B in May 2008. Source: The Ranaka project.

To meet these representation challenges for archaeological phenomena dual, hybrid, and objectoriented approaches have been proposed (Mcintosh and Yuan 2005). Hybrid approaches allow vector and raster representations to be created and stored within the same system, within which conversion to each other is also possible. The proposed framework therefore employs this approach to allow multiple representations of archaeological features. At present, such approach is not fully supported in a single three-dimensional GIS platform, but it should be a priority in any chosen system architecture.

of archaeological contexts and characterise the surrounding natural soils are closely connected to sedimentary processes occurring at various scales of alluvial, fluvial and marine environments. These are known to influence depositional and postdepositional phenomena that effect also archaeological deposits. In archaeological excavation this information is often expressed in a qualitative form through a nominal description of the soil in question (e.g. loose clayish sand). Nevertheless a quantitative estimate becomes necessary in cases such as using hydrogeological modelling to simulate landscape evolution and other site formation processes. Databases for the threedimensional modelling of textural variation in heterogeneous and unconsolidated aquifers have been developed for the benefit of geological studies (Bonomi 2009). A degree of flexibility in transforming parameters such as conductivity and porosity of material into quantifiable parameters to be used for simulation and modelling (e.g. differing percentages of the materials such as gravel, sand and clay in fluvial, glacio-fluvial or marine deposits) is required in a framework that is aimed at studying a variety of dynamics at intra- and inter-site level.

Maintaining multiple representations increases storage requirements. Nevertheless, only by storing multiple representations at different resolutions or in different data models, systems can better support complex analysis (McIntosh and Yuan 2005). 5.3.5 Design concept 5. Expressing and extracting different information The framework must accommodate both quantitative and qualitative reasoning and analysis. Archaeological data are often expressed in a qualitative form, but converting them into a quantitative one may be necessary. For example, the textural properties of sediments that form the matrix

On the other hand, the expression of quantitative data, such as counts, and other characteristics which

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Conceptual Design and Operational Framework come from sampling (from bulk to microscopic) and analysis, can be a challenge for interpretation, if left in numeric form. Three-dimensional representation and visualisation of such data is necessary for the exploration of patterns in any given dimension. The shape of the attribute distribution and its localisation can be obtained by either extrapolation or interpolation of point data. Once this is created, it becomes easier to perform exploratory and qualitative analysis on the phenomenon under study. An example of such procedure is the interpolation of quantities of phosphate coming from core samples to predict the presence/absence of human activity across a vast site previous to choosing areas for excavation.

5.4

Data model architecture

workflow

and

system

The developed data model includes the basic data types for describing archaeological excavation and the workflow used to transform basic data into secondary data through several iterative steps. It takes into consideration the design concepts which allow for multiple granularity and characterisation of archaeological data. Some steps of the workflow can eventually be automated to speed up data processing and allow easier model updates. Other steps, like data reinterpretation or validation, require user interaction. The main phases of the workflow aimed at creating and maintaining the data model are: • creation of a primary observations database and data pre-processing • creation of derived models • model visualisation and assessment

5.3.6 Design concept 6. Connecting GIS and process/simulation models Numerical modelling of sediment variability, erosion, transport, inundation and other typical depositional and post-depositional processes is an important aspect of the interpretation of archaeological deposits. There are various types of approaches that could be applied to simulation of these processes, from simple one- and twodimensional analytical solutions to complex threedimensional numerical schemes that solve variations of flow, transport, rate equations. The conceptual model presented acknowledges the need for integrating simulation of such processes in analysis of archaeological deposits, and it focuses on developing object classes that will enable the representation of structures of simulation models and their inputs and outputs within a GIS. Enabling structures, inputs and outputs of various simulation models to be geospatially referenced and managed within the same environment allows for better integration of data between models. The spatial analysis and querying capabilities of GIS also enhance the analysis of model input data and results.

5.4.1

Notes on the chosen system architecture

The developed conceptual framework is based on experience with different excavation conditions, methodologies, concepts and datasets, and different GIS software (mainly MapInfo, ArcGIS and GRASS). Although the design is generic and platform-independent in order to allow diverse (and not necessarily GIS-oriented) researchers to perform the implementation with different GIS platforms, it has been implemented within a system oriented towards the use of Open Source free software. This is one of the major contributions of the research, which responds to the limited availability of funding, characteristic of archaeological research and contract units and takes advantage of the power of customisation of the Open Source development community (Bezzi 2005, Duke and Reeves 2009, Hall and Leahy 2008, Neteler and Mitasova 2008). The decision to use such software is a consequence of careful evaluation of 3D vector and raster data analysis capability (to date, GRASS is the only GIS environment that fully expresses true threedimensional point data), together with the choice of turning my attention to Open Source software for ethical and pragmatic reasons. Contacts with the developers and the chance to access the source code make it easier to discuss potentials and problems of applying particular sets of operation within environments that are not designed for archaeologists. Moreover, the study and selection of components was limited to the software and hardware currently available at the institutions where I conducted my research, namely the Department of Archaeology at the University of Cambridge and the Archaeology Unit at the University of Botswana. Freeware modules and

There are a number of approaches for integration of information with simulation models. The main approach is to read and write information from the model files into GIS. In this case the numerical model can be run regardless of the GIS, and GIS is used only for pre- and post processing of model data. Another approach is to integrate GIS in the model execution process, this requires the development of an Interface Data Model (IDM) which stores the information of the simulation model within GIS data structures. In the IDM approach the simulation model is executed from GIS data, either directly or by writing the simulation model files (Strassberg 2005).

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Making Visible: Three-dimensional GIS in Archaeological Excavation easily positioned relative to it. Various data collection phases of the project are therefore integrated at different scales. The primary observation database is the repository of the project. Data stored at this level are not manipulated after the phase of pre-processing. The database can be internal or external to the GIS platform. In any case, data need to be transformed into the native format of the GIS platform of choice before being imported into it for model creation and processing. An internal database repository is present in any GIS, independent of the platform. At this level the link between spatial and a-spatial attributes is created and maintained.

evaluation versions of commercial software, such as ArcHydro Groundwater Modelling Tools were also used to gather an understanding of certain approaches to three-dimensional modelling and analysis. A short overview of the important features motivating the choice is presented below. The system architecture, illustrated in figure 5.8, is based on GRASS as the geographic information system and principal modeller. Meshlab, and CADbased software were used for the elaboration of vector data, Paraview as a volume visualisation engine and a simple external database as primary data repository. ArcGIS was also employed for modelling, querying and visualisation of 3D vector objects, as these operations are more user friendly in this environment2. Ideally all the operations should be performed in one system, nevertheless this is, at present, not possible due to the lack of voxel support in ArcGIS and the limited 3D vector manipulation functions in GRASS.

A detailed account of all data to be included in the primary database goes beyond the scope of this discussion. Nevertheless, some examples are provided here in order to clarify aspects of the requirements for the construction of a threedimensional platform for archaeological excavation.

The data transfer between software components is operated through file exchange. This is one of the most problematic areas of the loosely coupled type of architecture, in particular when the raw data come from proprietary software transfers. Generally, the data formats used have been .dbf for geometry and attribute point data, and .dxf for graphics as these are generic formats compatible with almost all software.

The aim is not to give a complete overview of data acquisition and processing. Rather, I will concentrate on the differences between 2D and 3D elements and how they contribute to the construction of the data model. An important aspect of 3D data processing is to ensure spatial consistency of parameters and time animation applicability to a volume set.

The prototype system architecture here proposed was used to verify the feasibility of a threedimensional GIS for both context based and grid based excavations within the limitations of the software available at present.

Various types of data useful for building a multidimensional and multi-scaled model are routinely collected during archaeological excavation. Once the data are organised in a 3D oriented structure, they can be employed for the construction of the three-dimensional data model. Data to be included go from measurements at the early exploration stages of a project (from geomorphology to subsoil coring to geophysics), to data processed in the laboratories in the phase of post-excavation analysis (chemical analysis of bulk and other samples, detailed finds analysis). Clearly these datasets are collected and processed at different levels of detail (different granularity). The framework allows the user to combine all these scales in the construction of the data model for exploratory purposes. The element that allows the use of multiple granularity and multiple characterisations of the data is the three-dimensional georeferencing of all the elements of the database. The spatial (location) information of all elements of the dataset is therefore, as in any GIS, the crucial element of the organisation. In this case, the presence of a triplet of coordinates (x, y, z) is mandatory to register all data types.

5.4.2 Primary observations database and data pre-processing The creation of the database includes collecting, structuring and interpreting data. The workflow has been experimented with using digitally collected data but, as long as the data present threedimensional characteristics (for example a series of plans and sections and 3D points coordinates for spot finds), even non-digital or unstructured formats can be digitised, encoded and structured. Data is then referenced to a spatial coordinate system. In the case of several excavation areas (and the case studies here presented are no exception), setting up an arbitrary x,y,z coordinate system is the common practice. Once the projected (or non-projected) coordinate system is set up, observations can be

2

Refer to the documentation on the characteristics, advantages and limitations of the two GIS platforms used in the respective websites (www.grass.osgeo.com and www.arcgis.com).

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Conceptual Design and Operational Framework

Figure 5.8. System architecture based on three principal software components: a database, the GIS systems and the visualisation software. Supplementary software is to be used for vector 3D modelling which at present, is not fully supported internally in any GIS platform. Table 5.2. Correspondence between the GIS 3D geometric primitives and the 3D archaeological elements (data types) of an excavation. Figures taken from Bédard 2006, table 2.1.

3D spatial elements

Example of archaeological element -

Single find (pot, stone tool, metal) Chemical analysis sample Point of coring Point representing a grid unit Spot height

Line

-

Line of coring Context boundary

Surface

-

Archaeological horizon Archaeological context (cut) Sedimentary horizon Topographic surface

Volume

-

Archaeological context (deposit/fill) Sedimentary unit Chemical concentration Finds concentration

Point

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Figure

Making Visible: Three-dimensional GIS in Archaeological Excavation Similarly, different representations and therefore models of the primary archaeological dataset are necessary for archaeological interpretation. The conceptual framework has highlighted that all these models can concurrently contribute to an effective representation of the different aspects of the excavation. Nevertheless, it is crucial for the user to acquire and develop knowledge about the level and stages of manipulation of the models, as compared to the primary database. In particular, as modern commercial systems contain hundreds of algorithms for model elaboration, the analyst may have to try several different techniques before being satisfied that any particular step is optimised. Moreover, the user should be aware of the substantial differences between models created with different software and different algorithms.

Point grids and point clouds are the basic element of construction of the data model and the point remains the constituent element of the structure. Point clouds are employed for the reconstruction of threedimensional volumes representing single contexts through the creation of closed surfaces. Three-dimensional point arrays are used to store the characteristics of three-dimensional excavation units (counts of finds based on specific characteristics, soil composition and chemical characteristics). The primary observations database would normally only contain point and line elements, as surfaces and volumes would be the result of further elaboration during the modelling process. Eventually all elements would be contained into the internal GIS database, used for storing the various models of the excavation (as in figure 5.9).

In practice, from one consistent primary observation database, a set of different models can be created, derived and maintained to represent the various aspects of and approaches to archaeological excavation. Some examples are: • geometric and topological models obtained by manipulation and interpolation; • models obtained by stochastic simulation; • models representing conceptually different archaeological scenarios; • models accustomed to users’ needs.

Although the present research deals specifically with data collected with a three-dimensional framework in mind, the challenge posed by legacy data remains. Rarely legacy excavation documentation consists of three-dimensional point arrays and clouds. Nevertheless, the graphic archive normally contains a certain number of levelled plans and sections which can be used to reconstruct three-dimensional geometries (Putzolu et al. 2004, Lieberwirth 2008, 2009). Three-dimensional vertical alignments of points (borehole logs) are the basis for creating the surfaces and volumes that represent the subsoil of the excavation area at a meso- and micro-scale.

From a construction point of view, models can be distinguished primarily into two different categories: geometric models and property models. The first are aimed at describing the shape of archaeological elements prior to incorporating their characteristics, the second at creating a volumetric shape using particular properties of the archaeological deposits. The following paragraphs elucidate how these various models are created and integrated in the 3D data model using the principles of the conceptual framework.

Table 5.2 presents a schematic view of the fundamental 3D spatial elements used to represent a series of archaeological elements. 5.4.3 Creating three-dimensional primary and derived models As highlighted in Apel (2006):

The GIS modelling architecture geomodels may differ in their semantics and in their spatial and non-spatial properties. Such geomodels act as repositories of geo-objects, define their own semantics, and take care of explicit topological relationships and selfconsistency. A geomodel stores information about itself, particularly a description of the geological situation and concepts, general model parameters, a hierarchical legend, and also meta data like a description of the raw database, the data quality, and technical details about the model generation. This will result in a synthesis of the spatial data model and the conceptual geological model. (ibid.: 224)

The database repository (which can grow with the development of the project and be constantly updated) offers the source for the various modelling operations aimed at representing the archaeological excavation data. Data can be manipulated both internally and externally and will be imported into the database once 3D processed. Differently from many other GIS which allow surface modelling only, GRASS is able to handle 3D data and most of the 3D modelling required can be performed internally (both point, surface and volume modelling). The point, polyline and all other data in the database are used to model surfaces and volumes, both in the process of creating geometry and property models. The modelling is distinguished

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Conceptual Design and Operational Framework in the developed workflow in two main stages: primary and secondary modelling (fig 5.9). This is done in order to differentiate the levels of manipulation of the data.

better represent different characteristics of the archaeological dataset. Therefore both vector and raster modelling are used and combined to be represented and analysed and on the type of queries and analyses to be performed. A raster approach will convey 3D continuous characteristics of the excavation, whilst a vector approach will portray 3D discrete entities, such as contexts. Combining of the various representations allows for enhanced query and visualisation operations.

After modelling (internally and externally to the GIS platform), primary and secondary models are stored in the GIS database under distinguished directories and alongside the raw data. Primary modelling entails the creation of base models. The manipulation of data is kept at a minimum level and it is mostly aimed at creating 3D graphic representations of the dataset. Examples of primary models are: • subsurface models for exploration: geoarchaeological sampling via boreholes and sections studies and geophysics are used to create • horizons and volumes that describe the subsurface deposits; • geometric models of the excavation contexts and units: surfaces will represent cuts and negative interfaces, volumes will represent deposits and fills; • property models of excavation units: arbitrary excavation units will be represented by the property of the cell under study (distribution of stone tools or ceramics inside a gridded spit, chemical characteristics of excavated soil from baulk samples or point readings).

A set of procedures has been developed to create three-dimensional representations of data within the proposed system architecture. These include modelling for the primary and secondary levels. The principles at the basis of creating surface and volume features in a standard GIS have been discussed in chapter 3. The basic steps of the modelling of 3D solids in GRASS and ArcGIS were conducted using the data from the two excavations explored in this research and are summarised here. They represent the state of the art development and limitations of 3D geometric modelling, the fundamental step which precedes any possibility of further 3D GIS data manipulation, analysis and presentation. Modelling vector geometric volumes Methods for representing three-dimensional volume objects are discussed in detail in section 3.2. Currently, three-dimensional meshes are the basic structures of a 3D solid compatible with any GIS platform. The main approaches used to create this type of solid are: 1. boundary representation consisting of: • simple extrusion of a polygon; • surface reconstruction of top and bottom of, for example, an archaeological context and clipping of such surfaces (generally by extrusion or lofting); • creation of a closed surface defined by a point cloud; 2. TEN: creation of a tethrahedral mesh, composed by not overlapping 2D triangles connected trough their vertices in a 3D space.

Secondary modelling, conversely, identifies a further level of manipulation of the data, where the visualised models are the result of operations such as queries and map algebra using one or more primary models to obtain the final result. These derivative datasets serve as proxies for unavailable variables and are further complex elaborations of the primary models. Examples of secondary models are: • fuzzy set 3D map series; • simulated density distributions; • simulated water inundation through time; • time series. Many secondary modelling procedures can be performed internally but some have to be carried out externally. One example being the use of modelling software such as MODFLOW for the simulation of groundwater movement and its action of archaeological deposits. Procedures models

for

creating

The fundamental difference between the two approaches when modelling an entity such as an excavation context is that whilst the first defines it as an empty space enclosed by a surface, the second creates a network of tetrahedrons that portrays it even in between the encapsulating surfaces. In this latter case, therefore, some elementary operations such as geometric deformations in 3D can be performed by pulling the nodes that connect the triangles which define the TEN.

three-dimensional

Three-dimensional models of an excavation have been, to date, created using mainly a vector approach. This research argues that a hybrid approach to the modelling should be used in order to

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Figure 5.9. Organigram of the data model and the modelling workflow. Data are collected so as to structure and store them in a GIS database. They are then manipulated into primary and secondary modelling environments (either in the GIS platform or by external modellers) and visualised in a visualising environment.

Figure 5.10. MultiPatch geometry parts. Source: ESRI White Paper 1998, fig. 3.

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Conceptual Design and Operational Framework In order to create 3D vector object (volume) based on geometric characteristics of archaeological contexts, the present work has employed a number of procedures which are briefly summarised below. Although the logical steps of the procedures are fairly simple, it is not possible to perform them all in the same software within the proposed architecture. Transformation and conversion of data was necessary and elucidated in the following paragraphs.

creates a closed 3D object and it can be employed to generate a 3D context of known thickness. This method, developed for the construction of buildings, does not take into account any irregularities of the enclosing upper and lower surfaces of an object. Figure 5.11 shows a series of archaeological features created by extrusion of the lower surface footprint to the registered height of the feature. The z coordinates of both surfaces are stored and retrieved from the feature database. These extrusion techniques are very simplistic and can only be used for representing basic volume objects, preferably regular in shape. The top and bottom surface (z plane) are always parallel to the x/y plane and flat as it is clear in the figure.

The primary data for the geometric construction of an archaeological deposit is a set of threedimensional points defined by x, y and z coordinates. These coordinates are used to create meshes that define the upper and lower horizons of the deposit. A 3D polyline is often necessary for defining the boundary along which the upper and lower surfaces touch and therefore allow for the closing of the context. Slightly different approaches to the modelling are to be used for the GRASS and ArcGIS platforms. This is due to their different degree of support of vector and data structures and to the level of development of their respective internal routines for the creation and maintenance of 3D objects. In particular, whereas ArcGIS offers internal routines for creating three-dimensional shapefiles from flat surfaces or TINs, in GRASS all the modelling and visualisation needs to be performed externally.

It is also possible to extrude a base geometry between two functional surfaces in the form of TINs, which allows to take into account complex topology of context surfaces, as opposed to the example above. This latter extrusion principle, which defines boundary constraints using linear barriers, was used to generate MultiPatch representations of the contexts of the Kouphovouno sounding. An example is shown in figure 5.12. Here, the MultiPatch of context 801 was produced by the ArcGIS command Extrude Between TINs and represents the bounding volume for a given area between two surfaces (in this case the outline of context 801). The surfaces provide the vertical constraint and the polygon provides the horizontal one.

Creation of a MultiPatch in ArcGIS ArcGIS 3D Analyst extension supports threedimensional objects known as MultiPatches. The MultiPatch data format, developed by ESRI in 1997, but not fully incorporated in ArcGIS until the more recent releases, is a geometry used as a boundary representation. MultiPatch features can be composed by triangle strips, triangle fans, triangles or rings (fig. 5.10).

Although MultiPatch geometry does not support topological operations, and therefore cannot be cleaned of errors and built, representing contexts in the manner outlined above guarantees a consistency between the surfaces representing interfaces and/or cuts and the MultiPatch volumes, as they are originated from the same functional surfaces. The analytical potential of MultiPatchs is mainly linked to the ability of linking the created geometry to a database through the MultiPatch unique identifier. Once this link is established, basic thematic mapping and simple querying of the volume can be performed (examples are given in chapter 6.1).

3D MultiPatches can be constructed in ArcGIS or can be imported from software such as Sketchup and CAD based software such as AutoCAD and 3ds Max® (ESRI White Paper 2008). In this latter case a third party converter (CAD2Shape) needs to be used to transform CAD objects into shapefiles (Caprioli et al. 2007).

Through the routine CAD to Shapefile, it is also possible to use an external modeller to create an archaeological context either from point clouds or using a combination of plans and sections) and import it into ArcScene. Management of more complex shapes can only be guaranteed by the use of this approach which is further clarified below.

The main method of MultiPatch construction within ArcGIS is extrusion of lines or polygons. For example a 2D polygon can be extruded to a set height to generate a 3D object via the command Construct Extrude From To. This type of extrusion

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Figure 5.11. Extruded archaeological features at the Hoge Vaart excavation. The pit in the bottom right corner shows in pale blue the triangulated structure characteristic of MultiPatches.

Figure 5.12. Multipatch representation of context 801 of the KE2003 sounding obtained by extrusion between the two functional surfaces representing the top of context 801 and 802.

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Conceptual Design and Operational Framework Creation of a surface solid in a mixed environment

The creation of a 3D solid through this routine presents several challenges and limitations both in terms of the number of steps and file format conversions to be performed and in terms of final results. The type of feature obtained is described in computer graphics as a convex hull. Convex hulls are suitable for the representation of simple archaeological deposits but do not provide a detailed accurate representation of formations that can be irregular and hollow. Specialised proprietary threedimensional software packages (such as GMS, Gemcom GEMS and GoCAD) allow for the creation of this type and also more complex (non-convex) geometric volumes. Nevertheless, it was a choice for this project to use non-proprietary and free software for the implementation of the framework. Moreover, the geometric modelling of archaeological features per se is not the main interest of the present work. The aim of modelling some basic archaeological context remains that of demonstrating how they can be integrated in a three-dimensional GIS and how useful they can be for archaeological analysis and interpretation. For an evaluation of these aspects, the reader is referred to chapter 6.

Meshlab, Paraview, GRASS and a CAD modeller (in this case AutoCAD 2004 and 3ds Max®) were used in combination to create the final product using the following steps, graphically summarised in figure 5.13: 1. Import the three-dimensional points of the surfaces into GRASS to make them permanent features of the internal database and to perform a preliminary validation and assessment; 2. export the 3D points as a .vtk file; 3. in Paraview, open the .vtk file, apply a Delaunay triangulation to obtain a 2 and ½ triangulated mesh and save the result as a .ply file; 4. in MeshLab, open the file and perform all the necessary editing (as Delaunay triangulation does not take into account hollow shapes and corrections need to be made to the obtained meshes). Save as .dxf; 5. the so obtained upper and lower surfaces can then be imported into a 3D modelling software (in this routine 3ds Max®) with the boundary line to help the operation of snapping of the features.

Figure 5.13. Procedure for the creation of a three-dimensional geometric object using Open Source software

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Making Visible: Three-dimensional GIS in Archaeological Excavation The clipping of triangulated meshes in AutoCAD (version 2004) the software utilised for the data preprocessing and cleaning, proved impossible as this software does not allow for solid creation from meshes. It was therefore necessary to edit the data in 3ds Max®.

single object which was converted in a mesh (fig. 5.14a). Creation of solid 02 The 3D Studio Max Shell command allows extrusion of an irregular surface (which is not possible with the command Extrude of AutoCAD). The surface of context 01 was extruded downwards to meet the surface of context 02. In this case the whole surface becomes a mesh that encloses a volume. It is not possible to add points to this mesh (fig. 5.14b).

Two approaches were used to close the TINs, as follows. Creation of solid 01 From the perimeter of surface 01, 4 splines were created that join every side of this perimeter to surface 02. The four splines were united to for a

Figure 5.14. (a) Solid 01, representing context 801, created by joining surfaces 801 and 802 of the Kouphovouno dataset through a spline function using approach 1 and on the right (b) solid 02 created by extrusion of the upper (801) onto the lower context surface (802).

Figure 5.15. 3D context 801 created with the procedure illustrated in figure 5.13 and visualised in Paraview

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Conceptual Design and Operational Framework It needs to be noted that both operations led to the loss of topographic detail of the lower and upper surfaces. In other words the TIN surface data was lost in the transformation. When detail is achieved of the sides of a context, this results in the loss of detail of planar surfaces. Both solid 1 and 2, saved in .dxf format, had to be transformed into a series of independent triangular faces (within AutoCAD) and could then be successfully imported into GRASS through the command v.in.dxf.

the character of the resulting volume from thin plate to membrane. Higher values of tension parameter reduce the overshoots that can appear in volumes with rapid change of gradient. With the smoothing parameter set to zero (smooth=0) the resulting volume passes exactly through the data points (Neteler and Mitasova 2008). Figure 5.6b is an example of regularised spline interpolation of humic values from bore hole point data at the Hoge Vaart site.

Here, the command db.connect allows for linking of the 3D surface object with the project database. GRASS does not, nevertheless, allow for internal visualisation of 3D vector objects. The solids therefore had to be converted into .vtk and could then be visualised in Paraview (see system architecture), as shown in figure 5.15.

Extrusion is a simpler, yet effective method for constructing three-dimensional volumes. Within GRASS there are a number of options for extruding vector data and transform them into voxels. A 3D vector point can be converted into a 3D cell volume, where every single cube cell in the matrix represents the characteristic under study. In GRASS this is done by running the command v.to.rast3, which performs a discrete transformation of a 3D vector point to a 3D raster grid. This is a useful approach for representing element counts in arbitrary excavations. For example, every 3D cell will represent the quantity of a particular class of finds in that volume of excavation. This simple but visually effective representation has been widely employed, in this study, to represent excavation data at the Hoge Vaart (chapter 6).

The same models can also be imported into ArcScene through the procedure discussed at the beginning of this section. Modelling voxels A regular 3D grid of points, representing a specific characteristic of the site, can be converted in a 3D voxel volume mainly using two methodologies: interpolation and extrusion. Within the chosen system architecture, these operations are only possible within GRASS as ArcGIS does not, at present, offer any support for voxel data structures.

Complex volume objects can be represented by a further extrusion method used, with variations, both by Masumoto et al. (2002) for subsoil modelling and Lieberwirth (2008, 2009) for archaeological deposits. In the first case, geological boundary surfaces were calculated from drilling data; they were then classified as upper and lower layers, based on a proposed geological function and finally the space between the surfaces was filled. Although this function is not described thoroughly in the paper, it is more likely to be r.to.rast3elev, which creates 3D volume maps based on 2D elevation and value raster maps (fig. 5.16).

Point data, representing a quantified property under study (chemical, physical or other), can be interpolated to a 3D grid continuous volume represented by voxels using trivariate interpolation algorithms (Neteler and Mitasova 2008). In GRASS, inverse distance weighing – command v.vol.idw and regularised spline with tension - command v.vol.rst can be used to create 3D volumes that represent numerical values such as concentrations. V.vol.idw (originally developed by Hofierka in 1999 as s.vol.idw) fills a 3D raster grid with interpolated values generated from a set of irregularly spaced data points using numerical approximation (weighted average) techniques. In comparison with other methods, numerical approximation allows representation of more complex volumes (particularly those with anomalous features), restricts the spatial influence of errors and generates the interpolated volume from the data points (Neteler 2001). A much more sophisticated tool is v.vol.rst, which uses regularised spline with tension in three dimensions (Hofierka et al. 2002, Mitasova and Hofierka 1993, Mitasova and Mitas 1993, Mitasova et al. 1995). The tension parameter tunes

Lieberwirth (2008, 2009) employs a script designed by Ducke but not incorporated in released versions of GRASS - r.vol.dem - to reconstruct the stratigraphy at the Akroterion excavation (Greece). The module is described as taking advantage of the flood-filling process to calculate voxel maps between at least two digital elevation models. The flood-filling algorithm assigns the same label (wvalue) to each voxel per 3D unit. The principle is the same as the one described by Masumoto et al. (2002). The results can be viewed in figure 5.17.

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Figure 5.16. Examples of horizontal and vertical sections of a 3D geological model created using a flood filling algorithm. (a) Horizontal section (top = DEM, middle = 0 m., bottom = -500 m.) and (b) – (e) vertical sections. Source: Masumoto et al. 2002, fig. 9.

Figure 5.17. Trench IX of the Akroterion excavation at Kythera, vertical slice of all stratigraphic units visualised in Paraview. Source: Lieberwirth 2008, plate 12.

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Conceptual Design and Operational Framework

Figure 5.18. Process of creating a solid model of stratigraphy using the GRASS module r.vol.dem. The model is visualised in Paraview.

Figure 5.19. Creating sections by slicing the solid volume model of the Kouphovouno excavation in Paraview. Above the whole model and below the sections along the x and y normals of the model.

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Making Visible: Three-dimensional GIS in Archaeological Excavation offers a wide variety of exploratory analysis tools in a three-dimensional environment.

This methodology uses context surfaces (both top and bottom) transformed into DEMs and fills in the gaps between them with voxels. The flood filling process is repeated for each surface in an upwards or downward direction (this is selected when running the script). The stratigraphy is built up incrementally.

In conclusion, within the system architecture here presented, the visualisation of the data models can happen internally, in ArcGIS with ArcScene and in GRASS through NViz. This latter visualisation environment has limited capabilities of displaying vector volumes. A more sophisticated visualisation and manipulation set of operations are only possible when using an external visualisation engine. The steps carried out consisted of transformation of the data from the native GRASS format to .vtk to allow not only visualisation in Paraview (or MayaVi or Vis5D, depending on the user’s choice) but also transformation and manipulation of the data (as discussed in the preceding sections and detailed discussion in the following chapter).

Figure 5.18 shows this process as applied to the construction of solid stratigraphy within this project: first surfaces were created by interpolating point clouds defining the topography of the upper part of a context. Polylines were used to identify context boundaries as polygons. DEMs were computed from the point clouds. Each surface represented the upper surface of a context. These surfaces were visualised and checked for errors (in particular overcut due to the nature of excavating thin contexts such as floors). In the case of a cut, a surface may represent the cut (and therefore the lower boundary of a fill context. A single solid model was then created using the GRASS module r.vol.dem. The resulting volume contained the various solid contexts labelled by context number and accordingly colour coded. The solid model created and stored as a 3D voxel element was visualised in an external visualisation software (in this case Paraview) after transformation into the appropriate format (.vtk).

5.5 Conclusions The problems and intricacies of analysis in the spatiotemporal domain of an archaeological excavation have been incorporated in the construction of the proposed framework. An important element of the framework is the modularity of the system, which provides better management and understanding of spatiotemporal problems. Data in the nested system are multidimensional, where this multidimensionality is not only a measure of space and time components but also multiplicity of attributes and characterisation of layers of analysis. This means that we can view data at various levels of detail and in a multitude of representations and elaborations. In turn, the information we retrieve can be used for complex analysis and for constructing higher or lower order patterns of spatiotemporal processes.

Spatial and topological analysis of solids created in this manner is not possible within a visualising environment. As in all raster data structures, attributes of voxel models are stored intrinsically in the geometry of the feature. Whilst this is a useful characteristic of property-based volumes, as it allows display and exploratory analysis of data and further quantitative analysis through map algebra, in the case of geometry-based volumes (as the description of contexts) the usefulness of the representation does not really go beyond visualisation and volume calculation. In fact, excavation elements such as contexts are better represented by the vector model, to which a database of attributes can be attached. 5.4.4

At the core of the framework is not the data model per se, but its representation in three dimensions. The 3D nature of archaeological deposits introduces a new level of complexity in the design. On the other hand it helps resolving the temporal dimension of archaeological investigation and offers the possibility of adding further dimensions to the interpretation. The framework is particularly effective at exploratory analysis whereby different types of data are used interactively and collaboratively by archaeologists from different areas of expertise. In this manner a platform is created that allows for the re-composition of the fragmented record in the same environment. Here alternative interpretations of the record can be taken into consideration through the voices of more than one archaeologist. Although analytical techniques have been traditionally associated with statistics, the complex and diverse nature of archaeological data calls for an approach to analysis resembling a process of knowledge discovery. Knowledge discovery is fundamentally the search for

Model visualisation and assessment

The aim of 3D visualisation is, in the first instance and during the phases of model construction, to validate the models, and secondly to explore patterns of the data in 3D. GIS analytical operations on a three-dimensional model are therefore highly dependent on the possibility of appropriately visualising and interrogating both the primary and secondary models in combination. Useful visualisation operations can be performed on solid models once they are created. These can vary from thresholding, transparency rendering and slicing, all operations that help exploratory analysis. Figure 5.19 shows the process of creating stratigraphic sections by slicing the volume shown in figure 5.18. The operation was here conducted in Paraview, which

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Conceptual Design and Operational Framework interesting patterns. However, in order to support knowledge discovery, it is important for different data types to be able to exchange information in a standardised (meaningful) manner. The mechanisms proposed for organising the search process ensure that any discovered knowledge during analysis is consistently accounted for and stored in the GIS database. This also helps to constrain the search process with domain knowledge not easily available within the system.

From the proposed framework, the next step is to illustrate the examples that were used to incorporate the data, the processes and some tools to test and enhance the approach. Here the fundamental concepts discussed in the framework and implemented in the data model are used for representing the excavation situation of two datasets and simulate scenarios of past human and ecological activities.

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context recording methodologies. Although elements of this approach are already present in papers such as Reilly and Richard’s (1988) on Sutton Hoo and further discussed in more recent publications (Barceló et al. 2003, Cattani et al. 2004, Katsianis et al. 2006, Losier et al. 2007), this presentation attempts to critically reflect on software advancement in the past few years. Fundamental limitations linked to the analytical potential of 3D analysis of volumes representing single contexts are discussed.

Chapter 6. Making Practical: Examples of Intra-site 3D GIS This chapter presents some practical steps in the implementation of the framework for an excavation 3D GIS introduced in chapter 5. Whilst the conceptual design presented in chapter 5 proposed a comprehensive and generalised solution to the requirements of a threedimensional excavation approach to data collection, storage, analysis and visualisation, here the two excavation datasets that allowed the conceptualisation of the framework in the first instance are presented. The first dataset, collected using a stratigraphic excavation approach, is more oriented at experimenting with the real possibility of open and commercial GIS software to model full three-dimensional geometry for the representation of archaeological contexts. Methods of 3D objects construction have already been described in chapter 3 and, more in detail, chapter 5. Here, the case study data were used to test the feasibility of vector versus raster methods for the reconstruction of archaeological contexts as conceptualised in a stratigraphic excavation. The analytical and visualisation potential of these approaches to excavation representation are tested. The second dataset is characterised by a grid excavation approach with elements of feature representation using vector structures and is used to create meso-scale threedimensional stratigraphy, solid models and cross section from bore hole observations. Finally, at a micro-scale resolution, it evaluates the results of 3D GIS analysis and visualisation of grid-based and object-based data.

6.1.1

The Kouphovouno tell lies 2.5 km south-west of Sparta, 3.7 km. east of Taygetos and outside the distal margin of a large alluvial fan that issues from a mountain-front gorge at Parori. It lies 5 m. above the Sparta basin, at approximately 200 m. above sea level (fig. 6.1). The site has been described by Waterhouse and Hope Simpson (1960, 77) as ‘the most important Neolithic site in Laconia’. It was briefly excavated by Von Vacano in 1941. Most of the finds have subsequently disappeared, but those stored in the Sparta Museum, were published by Josette Renard in Le site néolithique et Helladique ancien de Kouphovouno (1989). The excavations at the site are part of a scientific project by a team of three Universities, led by C. Mee (Liverpool), W. Cavanagh (Notthingham) and J. Renard (Clermont Ferrand) and it is aimed at investigating the evolution of complex societies in the Aegean from the Middle Neolithic and understand the basis of evolution of later Early Helladic complex societies (5800-2300 BC) (Cavanagh et al. 2007).

The different nature of excavation approaches and data collection techniques at the two sites allowed for experimentation with different aspects of 3D data construction and analysis. The experimented routines of 3D geometry construction and data manipulation respond to the philosophy and requirements of the different datasets. The geometric 3D data are maintained as ascii, dxf and csv files, transformed in to shapefiles, GRASS native and vtk files in the elaborations. A database of attribute information was never really maintained at either site. Both ArcGIS 9.2 and 9.3 and Grass 6.3 and 6.4 were used as main GIS platforms for data elaboration and transformation. Depending on the aspect of data under scrutiny, data were visualised in Paraview or ArcScene. As a consequence, the first part of the chapter presents the two excavations separately. Considerations about the response of the two different platforms to the analytical needs of the datasets will follow. A discussion of limitations and potential of 3D GIS for archaeological excavation under the criteria discussed for the framework in chapter 5 concludes the chapter. 6.1

Background

In 1999 a grid of 250x250 m. was investigated through systematic fieldwork, short of actual excavation (Mee 2001). In 2001 four trial trenches were opened. Two of them were extended in 2002, when four more trials explored other parts of the tell. During the 2003 summer season four areas were further explored. Figure 6.2 indicates the locations of the explored areas in the three seasons of excavation. The fieldwork was carried out for six weeks between August and September 2003, and I worked on the deep sounding of trench C (figures 6.2 and 6.3). Area C lies at the summit of the tell at 199.4-199.5 asl. Immediately below the plough zone a number of distinct contexts of different dates, ranging from Middle Neolithic to Middle Helladic, were revealed since the first season of excavation (2001). In 2002 a Middle Neolithic house was identified in the area. The aim of the deep sounding was to allow more rapid investigation of the stratigraphy of the house, with the aim of reaching a depth of at least 2.6 m. (the depth of the archaeological sediment in this area of the site, as established from a core taken in 1999) and possibly use a pottery sequence derived from the deep sounding for the general dating of the site.

The Kouphovouno project

In this case study procedures for data collection of three dimensional spatial objects are explored, together with the operations to be performed and relations to be established in order to create a 3D GIS environment to reflect single

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Figure 6.1. Location of the Kouphovouno site in Greece and GoogleEarth view of the landscape in the Sparta environs

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Making Practical: Examples of Intra site 3D GIS The sounding was identified as the most suitable area for me to work in, as the nature of a research excavation is usually to be rather slow and a 2x2 m. trench was the only guarantee I had to be able to record a good depth of stratigraphy, necessary to experiment on threedimensional recording and processing of archaeological contexts in a GIS environment. The deep sounding excavation was supervised by Thomas Laughlin. Excavation was carried out by Chloe Duckworth, Marie Saulnier and myself. The electronic recording of the trench was conducted by Matthew Fitzjohn and myself for the first two weeks. Subsequently, all members of the team and occasionally students working in other trenches helped me with the recording. By the end of the season the sounding had reached the depth of 1.30 m from the surface, only half of the initially planned depth. The operations of excavation and recording were slowed down by the complex stratigraphy encountered, characterised by shallow and intertwined contexts. These were a total of 42, of which 8 cuts and 36 deposits. 6.1.2

Bearing in mind the above considerations that influence the reconstruction of a context from recorded points, the procedure for geometry data collection was as follows. The equipment used is described in table 6.1. Recording of single contexts A context consists both of a discrete archaeological entity, and its interfaces with other contexts (Harris 1979). A specific deposit of soil will be above something, below something (unless the top layer), and beside any number of other deposits. At each of the junctions there is an interface. By isolating interfaces, sequences – the stratification of the site – can be established.

Excavation procedures

Excavation and recording procedures at the site followed the stratigraphic method. The progress of excavation was recorded on context forms. A sketch plan and levels were added at the back of each form. Composite drawings were made of multiple contexts at arbitrary chosen levels, generally when either a new context was discovered or one had been totally removed (post-excavation plans). Finds and samples were recorded on a separate sheet for each context; they were given a single running series of numbers, which covered pottery, bones, chipped stone tools as well as individual special finds such as polished stone tools and samples. Individual finds were given three-dimensional coordinates (not necessarily reflected, nevertheless in the site database made available to me in January 2009), but grouped finds were accorded to context alone (Cavanagh et al. 2007). 6.1.3

et al. 2005). These are largely based on photogrammetry and total station 3D points and polygons measurement. Lately laser scanners have also been used to register 3D point clouds and recreate the shape of archaeological surfaces and features (Doneus and Neubauer 2004, 2005, 2006). Laser scanning provides hundreds of readings that allow for approximation of surface topography to the accuracy of a few millimetres. It is obvious that the use of point readings from a total station would not produce the same accuracy of surface definition. Nevertheless, the procedures for reconstructing three-dimensional shapes from points are identical.

Data collection: procedures for recording three-dimensional shapes and relationships

Since the beginning of the project a decision was taken to concentrate on the digital collection of three-dimensional context data, without interfering with the project practice of traditional paper recording (drawings, context sheets, sample sheets) for subsequent transfer into a database. The stratigraphic method of excavation was not questioned as the aim of the recording was to test the suitability of such an approach for three-dimensional data representation. Since the early 2000s, a number of ways of collecting true three-dimensional geometric information of archaeological contexts have been experimented (Barceló et al. 2003, Barceló and Vicente 2004, Bezzi et al. 2006, Burgess et al. 2000, Cattani et al. 2004, Fiorini 2004, Katsianis et al. 2006, Laurenza and Putzolu 2002, Losier et al. 2007, Main et al. 1995, Putzolu et al. 2004, Zabulis

(Drewett 1999, 107) It is clear that the paramount operation in this approach is that of defining the interfaces that encapsulate contexts and therefore surface information capturing and reconstruction is the aim of context based excavation. Whilst in a two-dimensional environment what is normally recorded is the top surface of a context (and measures recorded in the context sheet are taken for defining the thickness of a context), in a threedimensional environment not only the upper and lower interfaces (surfaces) of the context but ideally even its sides need to be recorded in order to capture sufficient data to reconstruct a three-dimensional shape which defines a volume. As already discussed in chapter 5 (table 5.2), in a three-dimensional context therefore single finds will be three-dimensional points, cuts will be surfaces and deposits would be surface defined volumes. During excavation no contexts were identified as cuts except for the post-holes that are discussed in the following section. All contexts identified were treated as deposits both in the traditional and in the 3D recording systems. For the recording of single context deposits, once the context was identified in plan, a first point data collection of the topography of its upper surface was conducted by: - delineating the external visible boundary of the feature/context by recording points at regular intervals of 10 cm (or at any break of line), which were recorded as multidimensional lines; - following a grid (normally 10 cm intervals) superimposed on the feature;

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Figure 6.2. Plan of the areas excavated 2001-2003 and the boundaries of the plot of land purchased for excavation. In red the areas excavated in 2001, green 2002 and blue 2003. The black line indicates the boundary of terrain purchased by the project.

(a)

(b) Figure 6.3. Close up of sounding C at the start (a) and finish (b) of excavation season 2003. Photos: A. Frémont.

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following a criterion where ‘important’ points are considered the ones that indicate a change in topography, normally points where the line passing through them breaks horizontally or vertically.

Subsequently, in particular when the context exceeded in the plan the boundaries first identified, more points were registered to better approximate the topography and limits of its upper surface. After this operation was complete, the team would proceed excavating the context in its entirety (unless the context was extending beyond the boundary of the trench). Once the context was completely removed the topography of the upper surface of the following context was recorded as it served to depict the interface between the two contexts and therefore define the sides and lower surface of the context just removed. In many occasions the bottom interface of a context corresponds to more than one upper interface of the contexts below. An average of 100 points would be collected for the upper surface of a feature with an area of approximately 2m2. The system had been set up to register each threedimensional context directly into a GIS platform through PenMap (fig. 6.4). This is a pen computer system software that allows the linking of a laptop computer to a total station (or GPS) for real time data survey and recording. Within the software it is possible to create a GIS based series of layers (one for every context identified) and then proceed to dynamic real time recording of contexts and single finds in 3D. In fact x,y,z coordinates are recorded for points and polylines. In this manner, the graphic elements of a context excavated in multiple sessions during several days are easily retrieved and updated. Unfortunately the system did not successfully work throughout the season and a manual recording of the contexts to document and organise the point clouds was necessary. The data collected in the field was daily logged into a laptop computer. Data were edited daily in the form of Excel spreadsheets. Notebook annotations were added to the readings of x, y and z values, to be used for further graphic elaboration. The daily data was eventually remanipulated to collect all points and lines defining the surfaces of single context in one Excel spreadsheet, transformed in txt for direct importation in AutoCAD. Undoubtedly, carrying out the recoding and processing of the data without the help of a dedicated software such as PenMap impacts not only on the time of data collection but on that of data elaboration as it requires manual combination of data belonging to the same context yet collected in different recording sessions. This type of recording situation is therefore not ideal. Point cloud elaboration started with AutoCAD, where as many layers as are the number of single contexts were created and the points and lines defining the upper surface of each context were imported as text files. The result was a multi-layered dxf project defined by 3D point clouds and polylines (fig. 6.5). A limitation of this approach is that a context number was attributed exclusively to the

defining upper surface. Only at a later point, when combining upper and lower surfaces to create a 3D context, could a single solid object be identified as a single context. Only in the case of features such as pits, the lower interface of the context was fully recorded, numbered and elaborated as a separate element. In fact, in this case, stratigraphic excavation registers the feature cut. Recording of the post-holes: stone arrangements The trench was characterised by the presence of a series of post-holes located in the north west corner and centre of the trench (fig. 6.6). These occurred at different levels and were located in roughly the same area, one post-hole below the other, in a sequence of three pairs. The construction technique of the post-holes was also similar. The post-holes were identified as arrangements of packing stones, some of which were particularly large in size. There was not always a clearly visible distinction that would allow one to distinguish the fill and surrounding deposits, and to see a clear cut of the feature. It was therefore assumed that, in most cases, the packing stones could be considered to be the elements defining the external container of the post. In other cases, when the cut of the pit-hole was visible, it appeared to define an area just external to the packing stones. In stratigraphic excavation post-holes are generally considered to be composed by a minimum of two stratigraphic contexts, where a cut indicates the hole excavated with the purpose of creating a location of the post and one (or more) fill context number is assigned to the material filling the post-hole. This reflects the minimum numbers of actions required to insert posts in the ground where a hole larger than the diameter of the post is initially dug and a post is then inserted sometimes in the corner and others in the centre of the hole (Barker 1993). In some cases, when the subsoil or underlying layers are soft, the bottom and/or the sides of the pit are lined and/or packed with stones. The post-hole is then backfilled with soil and rubble to hold it firmly. Packing stones and backfill become, in the record, the fill of the post-hole and are indicated, as highlighted above, by one or more context numbers. Stake-holes are generally considered as a different kind of feature, characterised by cut and fill but the result of a different formation process, where the post is inserted directly in the terrain. Here the cut reflects directly the shape and depth of the post or stake and the fill is often the remains of the in situ rotten post. Different scenarios of formation processes of post-holes are discussed in Barker (1993). In the Kouphovouno excavation the post-holes consisted of stone arrangements of three to more than 15 stones of various sizes, generally rounded in shape. Whilst the identification in plan of the post-holes was relatively easy due to the presence of the packing stones, the irregularity and large size of the stones made these features difficult to record. The excavation and recording of the post-holes presented therefore a particularly interesting challenge,

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Table 6.1. Hardware and software used for data collection and editing

Hardware and software

Function

Leica TC600 EDM

3D points and lines collection

Laptop

Direct connection with EDM

PenMap1

Real time visualisation and processing of 3D data in GIS environment

Figure 6.4. The hardware and software equipment. The Leica TC600 EDM, a laptop computer (Penmap, TC tools and Excel loaded) and me in action. Photo: M. Fitzjohn.

Figure 6.5. AutoCAD project containing the point cloud and 3D polyline data of the deep sounding at Kouphovouno. In blue the outline of the expected portion of excavation, in various colours the 3D point and line data collected and elaborated in AutoCAD

1

Refer to http://www.penmap.com/ [accessed 10/07/10] for further details.

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Figure 6.6. Uppermost post-holes in trench C (contexts 814/815 and 816/817). Photo: A. Frémont.

Figure 6.7. Traditional 2D recording of a post-hole (digitised plan and section: S. Merlo, photo: A. Frémont).

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Making Visible: Three-dimensional GIS in Archaeological Excavation both when using traditional and digital recording approaches. In Barker (1993) the excavation of a structural feature such as a post-hole is considered as a special problem area. In fact, as already highlighted above, although postholes might appear as shapes differentiated from the surrounding area by colour or texture or both, not every such feature is in the end a post-hole and the feature must be dissected in order to establish its character. The traditional record of the post-hole in the sounding consisted of a plan of the excavation trench at the level of identification of the post-holes and a section for each one of them. A corresponding photograph was taken (fig. 6.7). Every post-hole was described by two context sheets, one for the cut and one for the fill. The packing stones were considered to be part of the fill in all the post-holes recorded. In this manner, very little attention was paid to the position, shape and arrangement of the packing stones and their relevance to the post-hole arrangement. Moreover the drawing and the photograph presented the post-holes from a single viewpoint (one from above and the other in section) limiting the description of the feature, in particular the visual one. The 3D oriented digital recording of the post-holes proceeded in a slightly different manner. The position and approximate shape of all the single stones composing the packing of post holes was recorded through the measurement of x, y and z coordinates of a minimum of six points (one for the top and bottom and four for a cutting plane arbitrarily chosen but assumed to be the one indicating the upper level of the post-pit) in order to obtain at least an octahedral or more sophisticated representations of the stones. A similar procedure had been employed in the recording of a Bronze Age fissure burial at Torbyran, Devon (Main et al. 1995). At Kouphovouno, a total station was used and data were logged into the database stone by stone. The top point and profile of the cutting plane in the middle of the stone were recorded before lifting the stone, one bottom point (or more) were recorded using the negative impression left by the stone in the soil. 6.2

6.2.1

The building of the dimensional base model

excavation

three-

Geometric model of contexts

Spatial objects (i.e. topographic and fictive objects) of interest in a stratigraphic excavation include layers, deposits, cuts (pit holes, post holes), fills, other artificial boundaries created during excavation. Considering the nature of the collected data (3D point clouds), the first step is the creation of 3D elements representing the conventional stratigraphic elements (deposit, cut, fill). Depending on the data at hand and on the analytical tasks to be performed, different steps to convert them into the appropriate three-dimensional

vector or raster structure for further processing in the model have to be followed. This is also dependent on the 3D modelling capabilities of the 3D GIS software at hand, whose limitations have already been discussed in chapter 3. The import operations into a GIS platform for point data are relatively straightforward. In GRASS, for example, data collected in the field can be imported as ASCII or .dxf and be linked to the relevant database. Every point is characterised by a triplet of coordinates that represent its true three-dimensional position. In ArcGIS points are equally easily imported using their x and y location. The z dimension remains an attribute of the point but this does not present particular problems for display and simple manipulation of data. Whilst point data for example representing a single find do not need further elaboration in the model, other sets of points are used to create objects representing threedimensional features in the form of surfaces (floor deposits, interfaces, cuts) or volumes (deposits, fills). The geometric construction of surfaces and volumes will require a set of operations to be performed in order to reflect the complexity of the objects being modelled and allow for checking of errors and final readability of the data (in particular in terms of visualisation) as a 3D volume. Most current research efforts are directed towards automatic or semi-automatic methods for the reconstruction of archaeological contexts based on software and procedures originally constructed for geology and subsoil modelling. A number of publications have presented results of such reconstructions using proprietary software such as SDRC Surfacer Imageware (Cattani et al. 2004), Environmental Visualization System – EVS (Tsipidis et al. 2005), GoCAD (Losier et al. 2007). (Katsianis et al. 2008) mentions a programming routine to create MultiPatches from individual measurements but is not clear what this consists of. It is important to underline that the 3D reconstruction of contexts in the mentioned case studies is performed using 3D modelling software. Only the finished models are then imported into a GIS platform (in most cases ArcGIS). Most GIS software can only import and handle polygonal (generally triangular) meshes for the representation of both 2 ½ and 3D surfaces and volumes. For example, GRASS can import and allows elaboration of 3D face objects through the module v.in.dxf. ArcGIS handles a structure called Multipatch, which can be created by extruding TINs in ArcScene or imported from external software through conversion models (as discussed in chapter 5.4.3.2). Importantly neither ArcGIS nor GRASS can handle a closed volume entity. Any such structure needs to be decomposed in single 3D triangular connected faces (for example in AutoCAD by using the command Explode) before being imported in such environments.

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Figure 6.8. Three-dimensional vector based model of the Kouphovouno stratigraphy obtained by extrusion between two TINs (command Extrude Between) and visualised in ArcScene. The legend captures context numbers. The upper contexts are shown at different degrees of transparency to allow the vision of the lower contexts.

Figure 6.9. Three-dimensional vector based model of the Kouphovouno stratigraphy obtained by extrusion of context footprints and visualised in ArcScene. The legend captures context numbers.

This is no trivial shortcoming, as it has major implications in the linkage of a database to archaeological contexts. In fact the database needs to be linked to every single face of the depicted object rather than to the object representing the context. Although the overall effect in terms of spatial representation is the same, the amount of time spent to organise the data in this manner is overwhelming compared to the analytical potential developed under the section Modelling vector geometric volumes in chapter 5. Figure 6.8 shows the results of the TIN based vector based modelling efforts visualised in ArcScene. The

extrusion of footprints approach was also used as it is a much less cumbersome and problematic manner of creating 3D solids compared to the creation of triangulated volumes from TINs. The shortcoming of this method, already discussed in chapter 5 and quite visible in figure 6.9, is that the topography of context interfaces is always flat. Lastly, a voxel approach to the modelling of contexts was employed. In this case volume models were generated through the use of the voxel flood filling algorithm developed by Benjamin Ducke and discussed in chapter 5.4.3. Figure 6.10 summarises the results.

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Figure 6.10. Three-dimensional voxel model of the Kouphovouno contexts visualised in Paraview. Voxel resolution: 2.5 cm.

6.2.2

create 3D GRASS GIS models and export data for visualisation.

Geometric modelling of post-holes

Appreciation of spatial relationships from all angles can help identifying typologies of different post-holes and their function in the structure. The difficulty of representing post-holes in a two-dimensional environment (discussed in section 6.1.3) has led to the decision of dedicating part of the research to explore three-dimensional alternatives that might offer insights into the modelling of this particular type of archaeological feature. Bearing this in mind, the modelling of the Kouphovouno post-holes consisted mainly in that of the packing stones, which were determining the shape and overall direction of the features (as discussed in section 6.1.3). Two approaches were used for the reconstruction of the geometry of the packing stones of the post-holes. One based on 3D modelling in AutoCAD and vector modelling software, the other using a GRASS add-on originally developed for the display and analysis of human bone assemblages.

Crossbones provides a simple process by which a user can convert raw point data from site into a file containing 3D entities which can be represented in a viewer. The internal algorithm for converting individual points into 3D entities is described in Isaksen et al. (2009). The principle of identification of a single element by a top and bottom point was applied to the representation of single packing stones. The original .csv files containing IDs and coordinates for every point recorded for single stones was manipulated to maintain a triplet of readings (x, y and z) for the uppermost and lowermost position of the stone. In order to provide a direction to the stone, the uppermost point was given an odd number (proximal end) and the lowermost an even one (distal end). The add-on produces data in GRASS native format, easily exportable for visualisation in Paraview. Once the data were elaborated, it was possible to verify the superposition and alignment of the post-holes (fig. 6.11).

The second prototype was developed using Crossbones, a GRASS add-on. Crossbones was originally developed by Leif Isaksen for Oxford Archaeology as a rapid method for surveying and visualising dense skeletal assemblages. In April 2009, Benjamin Ducke of OA Digital released the GRASS implementation of the tool, which allows to

Whilst the shape of the packing stones in the representation is far from naturalistic, this approach to their modelling is fast and efficient, allowing the rendering of the depth of the feature as a whole and in relation to others, differently from the traditional section which tends to isolate the context (fig. 6.12).

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(a)

(b) Figure 6.11. Packing stones of context 815 visualised in Paraview. The above (a) and rotated (b) views are shown. Legend: red: context 816, purple: context 814, yellow: context 821, blue: context 823.

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Figure 6.12. Comparison of traditional two-dimensional record (above) and 3D representation (below) of post-hole 822/823. The traditional record captures a view from above and one side of the post-hole, whilst the 3D record enables the recording of all components of the feature within context 825 (in yellow). These are shown in Paraview through the use of transparency, maintaining the same section line as in the drawing above.

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Making Practical: Examples of Intra site 3D GIS distribution of data classes in well-defined areas such as excavation contexts. Choropleth maps do three things very efficiently (Conolly and Lake 2006): 1. they show a quantitative or qualitative value associated with a geographic area; 2. they give the reader a sense of patterning of the mapped variables over a larger area; 3. they provide a basis for comparing other values mapped using the same geographic boundaries

6.3 Analytical potential and limitations of context-based excavation in 3D Although GIS in their application to stratigraphic excavation in 3D have often been used primarily as tools to display spatial data, it is their ability to produce analytical outputs that offers a potential that should justify the need for modelling 3D geometries despite the difficulties that it presents. This section explores threedimensional analytical functions in a context-based vector 3D GIS.

The characteristics listed above clearly show that this type of thematic mapping is the most suitable for enhancing the understanding of stratigraphic features expressed in a GIS via a vector data structure. Figure 6.13 below illustrates a thematic map representing contexts 800 to 812 at Kouphovouno. Ceramics weights are displayed through ranked colour classes. The threedimensional contexts can also be queried one by one through selection.

The absence of topological functions in any GIS platform is, as discussed in chapter 3, the single most severe deficiency that hinders the capability of transforming a vector GIS into a fully operational analytical environment. The geometric reconstruction of contexts presented in the preceding sections is, in this sense, still problematic for two main reasons. Firstly, the cleaning of topological errors and automatic building of threedimensional objects is not possible. As a consequence, the models created always present inconsistencies such as, for example, the intercutting of three-dimensional contexts, a serious topological error which undermines the integrity of vector objects. Moreover, the absence of topology makes the link of the context database information to the complex geometries representing the context virtually impossible. In fact, the created 3D context is an assemblage of 3D triangulated facets registered singularly and composing the object. A database link is still possible, as illustrated in the discussion below, but it requires a connection to every single facet rather than one single object (one object could be composed by a few or more than a few hundreds facets).

Point in feature analysis is another area of potential in the exploration of the relationship between finds and context in a stratigraphic excavation, as it allows the inspection of artefact patterning not only within but between contexts. Unfortunately, most stratigraphic excavation records (and Kouphovouno is not an exception) only record threedimensional coordinates (if any) for the category of finds referred to as ‘special finds’. The rest of the artefacts are collected, stored and subsequently analysed as part and parcel of a context and therefore do not possess a spatial identity independent of it. This limitation needs to be addressed at the level of recording before the potential of independent analysis of finds and features can be properly evaluated. Figure 6.14 shows single finds displayed as threedimensional points and contexts as MultiPatches. The single finds where here generated as random threedimensional points to demonstrate the potential of visualisation, query and finds in feature analysis that could be performed once a decision is made on site to record finds (pottery, lithics or and other classes) independent of the context in which they may be found by simply plotting their x, y and z coordinates.

As previously discussed in chapter 5 and above, MultiPatch is the shapefile format that allows visualisation and manipulations of 3D objects in ArcGIS. Commercial GIS software does not allow the execution of complex spatial queries on MultiPatch or any other vector objects. Such limitation is due to the lack of 3D spatial operators. Spatial data analysis is therefore limited to intra-object measurement such as the calculation of volumes between two TINs, whereas inter-object measurements which depend of full 3D geometry topology are not possible. Despite limitations, once the MultiPatch is linked to the corresponding database, basic attribute data analysis is possible in the form of thematic mapping.

Other operations such as slicing and clipping through the excavation at any point and in any direction which should be routinely incorporated in the exploration of threedimensional excavation data are, at present, exclusively possible in an environment such as Paraview. Figure 6.15 is a demonstration of clipping through a number of contexts in the case study to make evident the relationship between the contexts and the packing stones characteristic of the post-holes at the site.

Various types of thematic mapping techniques are available as routine in most GIS programs. Nevertheless, the choropleth map is the best way to depict the

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Figure 6.13. Three-dimensional MultiPatch representation of contexts 801 to 803 at the Kouphovouno site. Weight of pottery for each context is displayed by colour coding the contexts. Interrogation of the attribute table of each context is possible by selection.

Figure 6.14. Three-dimensional combined visualisation of contexts and single finds in ArcScene. Finds are displayed as 3D points and contexts as semi-transparent Multipatches.

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Figure 6.15. The solid stratigraphic model of context is displayed in transparency and clipped to show the relationship between the packing stones of post-holes and the deposits at Kouphovouno. Visualisation performed in Paraview.

6.4.

Considerations on three-dimensional GIS and stratigraphic excavations

Several important points emerged from modelling in this area of the research. Considerations on the procedures used, the software response to the needs of the framework and considerations on single context planning are here summarised. The value of building the base model of an excavation is that it forces the archaeologist to define explicitly every single element in it as well as relationships and other associated components during and after excavation (whilst performing the reconstruction exercise). It forces to think clearly about the data and it produces a visual summary. Nevertheless, the work highlighted also that there are severe limitations in representing 3D archaeological contexts as objects some of which technical, others conceptual. Firstly, the lack of fully automatic routines with inbuilt topological operations such as clean and build to create three-dimensional geometries of complex and overlapping elements such as contexts results in primary models of excavation that are by necessity always approximate and simplistic. Whilst on the one hand, realism is not necessarily the aim of three-dimensional GIS modelling and analysis, this shortcoming hampers the ability to conduct analysis operations such as volume calculations and accurate check of context relationships.

Moreover, it must be emphasised that, in all the approaches to geometric modelling presented, a clear understanding of stratigraphic relationships between context needs to be achieved prior to the modelling and in fact this guides the modelling itself. It remains unclear therefore how much the geometric reconstruction of contexts helps stratigraphic interpretation if not as just a post-excavation verification of data already interpreted at the site. Secondly, 3D analysis once the context is created and linked to an attribute database (operation that, at present, still presents technical difficulties as discussed in section 6.3) remains fairly limited due to the limitation of analytical operations available in state-of –the-art GIS software. The area that benefits is certainly 3D visualisation of thematic maps. Nevertheless, benefits must be questioned against the time overload needed to produce a full 3D representation of single contexts. The data collection time was 5 weeks. The data processing (including preliminary editing and experimentation) for the data sets took several months to develop and implement. The data collection therefore forms only a small portion of the entire process, which is an important consideration for survey timing and design. Aside from thematic mapping and single finds queries, very little can be achieved in a full three-dimensional environment without the use of topological operators. Once this area of GIS developments gets resolved more

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Making Visible: Three-dimensional GIS in Archaeological Excavation sophisticated analyses will be possible in a vector-based environment. Examples would be, amongst others: • 3D point, line and solid in and from buffer operations allowing for the identification of vertical and horizontal taphonomic processes; • 3D spatial queries such as interactive distance betweeen points or along a surface and complex volume calculations allowing for the calculation of rates of erosion and accumulation both at macro- and micro-level; • 3D intersection, union or overlay between objects expressed in one, two and three dimensions allowing for the post-excavation examination of stratigraphic • 3D geostatistics for the application of threedimensional spatial autocorrelations and density analysis is also an area that is still underdeveloped but would be of benefit for the study of finds distribution in an excavation site. The use of a three-dimensional GIS approach to model and analyse stratigraphic excavation has also resulted in an evaluation of the validity of this method when one wants to consider the excavation space above and beyond two dimensions. It has emerged that rarely, at present, the practice of stratigraphic excavation, from data collection to post-excavation analysis takes into consideration a record that goes beyond a flat representation of archaeological material. It is therefore the practice of excavation itself that needs to open up to alternative and multidimensional visions for the benefit of a shift in the way we conceptualise spaces in archaeology, both theoretically and practically. In fact, in the literature reviewed (chapter 2) it seems that generally the effort of 3D modelling of stratigraphic excavation and what I, myself, thought was relevant has been that of creating an exact representation of every single context and its neighbours, in a repetition of what is usually done in 2D. On the one hand this might still be a necessary step for volume calculations and for eventually resolving topological relations but, if we want to take our thinking beyond creating contexts that exclude one another, then shape and boundaries might become of secondary importance. To some extent, therefore, the challenge is that of re-thinking the representation of stratigraphic excavation from a direct and accurate representation of what was excavated to a mental map. In this sense, whilst it remains important to continue the exercise of recreating contexts as three-dimensional shapes, these do not necessarily need to be precise and accurate but they can be seen as interpretive building blocks that can somehow change their aspect through time and be, for example, represented as squared shapes or simple triangles (as shown in the representation of packing stones in section 6.2). Despite the limitations of the case study dataset, in particular the lack of three-dimensional information on point data, it is clear that the separate collecting of data on the geometry of contexts and the location of finds is a fundamental requirement for the reconstruction of processes that happen above, below and outside a context.

Such data collection strategy offers the possibility of reconstructing both depositional and post-depositional events at a post-excavation level in a manner that would not have been possible during excavation. For example the detailed study of pottery and/or flints could allow for a re-assessment of context boundaries at a later stage than at the trowels’ edge. Re-assigning of context bundaries or, in certain cases, the creation of voxel volumes identified by particular characteristics of finds rather than sediments could be carried out and displayed againt the traditionally intepreted startigraphy of the site. Moreover artefact point cloud distributions could be queried for a particular property (date, re-fitting parameters, etc.) and used to define a surface not necessarily visible during excavation and later cut through by other features such as pits or post-holes. Re-deposition of broken pots in pits across a landscape, studies by necessity during the post-excavation phases of a project (such as that discussed in Garrow (2006, 2007)) would find in a three-dimensional environment the ideal visualisation platform. 6.5 6.5.1

The rescue excavation at Hoge Vaart Background

The Hoge Vaart project in the Netherlands (fig. 6.16) was a research looking into the Mesolithic and Early Neolithic of the Flevoland province (Horgestijn and Peeters 2001, Jonkers 1995, Peeters 2004b, 2005, 2007). The investigation was conducted by ROB (Rijksdienst voor het Oudhiedkundig Bodemonderzoek, the Netherlands National Service for the Archaeological Heritage) and it took place between 1994 and 1997 within the framework of the completion of motorway A27 between Blaricum and Almere, after Exaltus found archaeological evidence in the area during the 1993 coring survey conducted in anticipation of the construction of the A27 (Exaltus 1993). The project, funded by the Executive Ijsselmeergebied, can therefore be considered a rescue archaeology situation. This was at the time and still is, the most extensively excavated Mesolithic and Early Neolithic site in the Netherlands. The main interest of the research team rested in formation processes and landscape dynamics in the transition between the Mesolithic and Early Neolithic, which are barely represented in the regional literature. Therefore, not only archaeological data in a strict sense, but also geological and palaeoecological material was collected. Furthermore, excavation was not limited to the zones with high density of archaeological material, but also to the low-density and ‘empty’ zones that had been identified in the preliminary phases of sampling. The excavation recovered a vast quantity of occupation remains and anthropogenic features. Identified features consisted of nearly 120 surface hearths and several hundred small stake holes in which wood was sometimes preserved. Approximately 100 deep hearth pits (essentially containing charcoal) were also identified. Extremely well preserved fish traps were also recovered.

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Making Practical: Examples of Intra site 3D GIS The rationale behind the excavation methodology and excavation results were published in 2001 in the extensive report De mesolithische en vroeg-neolitische vindplaats Hoge Vaart-A27 (Flevoland), RAM 79. The relevant parts regarding aspects concerned with my research were translated from Dutch to English and are here summarised briefly and combined with the further analyses carried out by Peeters for his doctoral research, which was published in January 2007 (Peeters 2007). 6.5.2

Data collection

The project consisted of three phases, one aimed at identifying the areas of main concentration of archaeological remains, the second consisting of systematic excavation of approximately 8600 m2 and the third, conducted in the years that followed aimed at preserving and studying all collected material. Phase 1 – survey and coring During the first phase of the project, after isolating by sheet-piling an area of ca. 8600 m2, the peat deposit was stripped by machine to ca. 10 cm. above the coversand (estimated by gauging at 1342 sampling locations positioned at a regular grid of 2x2 m). Subsequently, samples were taken from the top of the coversand with sampling locations of a diameter of 20 cm. These were wet sieved using a 2 mm mesh and examined for macrofossils and other remains to assess the potential of the site (Hamburg et al. 2001) Based on geomorphological characteristics and the number of flints recovered in each bore hole, the excavation area was divided into three zones (fig. 6.15): • the main concentration zone, defined by the top of a south-north running coversand ridge on which a highdensity zone of occupation debris and anthropogenic features was located ( 20x50 m); • the peripheral zone, defined by the sloping flanks of the coversand ridge and characterised by a smaller concentration of material and a thin scatter of archaeological remains; • the gully zone, defined by the low-lying area infilled with peat detritus and clay to the east of the coversand ridge, with evidence of gullies and specific remains associated with human activities (Hamburg et al. 2001, Peeters 2007). Phase 2 - excavation Excavation was conducted using the so-called arbitrary method (Lucas 2001, 163) based on a grid system, as it is usual in the Netherlands for Neolithic sites (Peeters, p.c.). The underlying idea was the consideration that threedimensional recording of individual objects is time consuming and total stations are expensive.

At the same time the deposit presented ecologically very fragile material (with the consequent risk of destruction). As sieving gives back ecological material in good conditions and grid spatial research is appropriate for finding structures where the limits of units are invisible, a combination of grid and spit systems was used to gather the data (Peeters, p.c.). main concentration and parts of the periphery zones of the site were excavated at a finer resolution (50x50 cm squares) by hand and shovel, whereas the gully zone was excavated by hand and machine at various resolutions, from 1 to 5 meters (fig. 6.16). Methods of assessment and adjustment of grid resolution were constantly used. Vertically the excavation was conducted in principle stratigraphically, which means that, where possible, the shape and the cross-section of different sedimentary and/or pedological layers were followed. No use was made of Harris Matrix. In fact, as the majority of the stratigraphy was located within 15/20 cm and the character of the sediment was chaotic this, in the eyes of the excavation team, would have required an enormous investment of time (Peeters, p.c.). The main guiding principle of vertical excavation remained in any case the use of spits. Initially a maximum depth unit of 2 cm was used. Rather quickly, during the research, it became clear that most of the material came from the upper 10 to 15 cm from the humic sand present under the peat. Moreover, the amount of processing appeared so time consuming that it would have been impossible to examine the area within the available two years. For this reason it was decided to double the maximum cross-section of the depth unit to 4 cm (Hamburg et al. 2001). The research strategy gave fundamental importance to the use of automation in order to improve data quality, efficiency and monitor errors in real time. During the fieldwork an effort was made to collect, when possible, all data digitally. To achieve this, two parallel systems were used: • ‘automatic’ 3D location definition by means of a tachometer (total station) with a radio link to a handheld computer and by means of altitude measurements with laser theodolite or water level in parallel with the assigning of work pit and section numbers. The find numbers were read in by means of a bar code scanner, the feature data input by means of a menu on the hand terminal (fig. 6.17). • a part of the field administration was performed in analogue form. This applied particularly to the recording and descriptions of the anthropogenic features. Feature descriptions were later stored in a digital file. The field drawings were made in the traditional manner ‘by hand’ (Haanen et al. 2001).

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Figure 6.16. Geographical location of the Hoge Vaart-A27 excavation near Almere, The Netherlands. Source: Google Earth.

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Figure 6.17. Subdivision of the Hoge Vaart-A27 excavation area in three zones after consideration of sampling campaign. Legend: 1 the main concentration zone, 2 the peripheral zone, 3 the gully zone. Source: Peeters 2007, fig. 4.4

Figure 6.18. 5 meter excavation unit subdivided in 50x50 grid units on the left. Shovel excavation of the 50x50 units on the right. Note the sheet piling in the background. Source: Hamburg et al. 2001, figs. 9 and 10.

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Figure 6.19. Automatic recording through the combination of a total station, a data logger and a portable computer (left) and bar code label (right). Source: Haanen et al. 2001, figs. 2 and 3.

Table 6.2. List of main categories of finds at the A27 Hoge Vaart excavation Code

Description

Cha

charcoal

Sto

stone different from flint

See

seeds including nuts and fruit

Bon

bone, teeth, horns and possible bone (generally splinters)

Fli Pot

flint pottery

She

shell

Ree

burnt reed

Woo

wood

Oth

remaining (other interesting material, such as resin, concretions and ochre) no material

Not

Phase 3 – sieving, sorting and analysis The excavation of every unit took place mainly by shovel (fig. 6.18), unless vulnerable material was identified in situ such as fish nets, in which case the trowelling technique was used too. The soil was transferred from the grid box in a plastic barge, which was transported to the sieving area, after being provided with a find ticket (Hamburg et al. 2001). Twelve fixed sieves were used during excavation, they had been positioned on the area between the excavation pits on concrete platforms. Sieving was facilitated by the use of a hose pipe. The screen barges had dimensions of approximately 1 x 1 m., the wire netting was simply of metal and could be removed to be hosed. The mesh

amplitude was 2 mm, and the soil was stirred on it with metal and hard plastic tool (particularly building trowels and spatulas). By changing the measure of the mesh, these sieves could also be used as flotation tanks (Hamburg et al. 2001). All activities concerning the find processing and the material storage took place in the archaeological centre arranged for this aim at Zeewolde, at about 17 kilometres from the excavation. Here, the material was washed, dried and sorted. Moreover, finds numbers were created and the relevant category database was adjourned. Finds were sent for further analysis to various specialists and eventually delivered to the provincial deposit in Lelystad (Beestman 2001). The main categories of finds created are listed below in table 6.2.

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time span are absent. Four activity phases were distinguished on the basis of stratigraphic evidence, C14 dates, dendrochronology and archaeological characteristics. Figure 6.21 presents a summary of these phases. At least two Mesolithic and two early Neolithic occupation phases were distinguished (Peeters 2004a).

Palaeo-geological setting and chronology

The Hoge-Vaart site is located approximately 3 meters below the present day land level on a north-south running coversand ridge along a low-lying valley area, which was dissected by various tidal gullies, also known as the Eem system (figure 6.20).

Neolithic activities identified occurred in the context of a gradually inundating landscape and involved the firing of surface hearths, flint knapping, tool maintenance, occasional pottery production and food consumption. These resulted in at least 120 surface hearths and large amounts of flint, quartz, granite, pottery and bone accumulated on the surface over approximately 300 radiocarbon years (6000-5700 BP). Activity on the sand ridge came to an end between 5700 and 5600 BP when it was permanently inundated and was covered by reed. A second Neolithic phase involved fishing activities in the tidal gully, but at this point, the sand ridge was presumably hardly visible and no other activities were conducted in the inundated area (Peeters 2004a).

The ridge is characterised by gradual slopes in northward and westward directions, on the east it is flanked by a low-lying gully zone, which probably developed during the Weicheselian under the influence of the drainage system of the Gelderse Valei and its surrounding icepushed ridges further to the south. To the west, the Hoge Vaart coversand ridge develops into a coversand plain Towards the east, several small tributary streams appear with relatively little relief. To the north, the ridge forms a promontory into the valley of the Eem system. This part of the valley also contains several other ridges and dunes. to have joined the main stream. Further eastward, the Pleistocene surface shows some ridges and gradually rising plains. Prior to the deposition of clay and the formation of peat, the elevation difference between the highest and lowest parts of the Pleistocene surface was around 10 m in the area (ca 3 to 13 m –NAP) (Peeters 2007, 81). Geomorphologically this is therefore a relatively diverse landscape.

6.5.4

Spatial processes examined at Hoge Vaart

The site of Hoge Vaart has been used by Hans Peeters to discuss the Mesolithic and Neolithic dimensions of landuse in relation to palaeoenvironmental dynamics in the Flevoland polders (Peeters 2007).

C14 dating of the site spans a chronology between ca. 7800 and 5300 BP. Human activities before and after this

Figure 6.20. Digital Terrain Model of the Hoge Vaart site, showing the Pleistocene surface characterised by a north-south coversand ridge and the tidal gully to the east. The DEM, which shows the area within the boundaries of the sheet piling, was obtained using point data from the core database of the site and kriging ordinary interpolation.

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Figure 6.21. Chronological subdivision of archaeological activities in relation to the local paleaoenvironmental developments at Hoge Vaart-A27. Source: from Peeters 2007, fig. 4.3.

Figure 6.22. Hoge Vaart northern concentration. Interpretation in terms of activity zones identified through analysis of features and material. Source: Peeters 2007, fig. 4.20.

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Making Practical: Examples of Intra site 3D GIS post-depositional phenomena is crucial in situations such as the one at the Hoge Vaart excavation. The extent of the excavation and its topographic characteristics offered a platform for enhancing the understanding of formation processes in three dimensions. Based on the indicators used by Peeters (2007) for the detection of patterns and identification of activities, the aim of using 3D GIS and scientific visualisation was to explore the data in threedimensions in order to correlate not only horizontal but also vertical distributions into a comprehensible whole.

Spatial processes in the northern concentration (square numbers 329, 330, 349 and 350 in fig. 6.17) were first explored as a possible comparative platform for the main concentration. This area covered an area of 100 m2 and yielded almost 7 kg of archaeological material. A surface hearth was identified in unit 329, with a radiocarbon date from charred hazelnuts of 5820 ± 50 BP. The first step of the spatial analysis of the northern concentration was aimed at verifying the stratigraphic integrity and single versus multiple occupation on the basis of information on flint, hearths and charred hazelnut. Vertical and horizontal distribution patterns of burnt versus unburnt flint were visually explored and related to the soil horizons of the area. The second step focused on understanding the horizontal patterns in terms of human activities (flint knapping, tool use and maintenance, discard, firing, clearing, etc.) through the chaine operatoire approach. The third step concerned the combining of pattern interpretations, resulting in the overall interpretation of the site in terms of depositional and postdepositional processes and the subsequent definition of a site model to be used for the analysis of the main concentration zone (fig. 6.22 summarises the interpretation of the Northern concentration).

GRASS was selected as the best basis for the modelling and analysis of this excavation. In fact, its new version 6.0 has now developed a library that manages both voxel and three-dimensional vector data. The combination of the two seems to offer major advantages for 3D applications in archaeology. Moreover, it allows for integration of all recorded elements of the dataset, gathered using a mixed approach (grid excavation accompanied by features geometric description). Nevertheless, certain vector operations such as thematic mapping were performed within the ArcGIS module ArcScene, which still offers the advantage of a userfriendly and highly interactive graphic interface.

Refitting and raw material identification were employed. Distribution patterns of category finds other than flint were also visually explored. In this case horizontal distributions only were examined (Peeters 2007).

6.6.1 Reassessing Hoge Vaart within a threedimensional framework: data retrieval and preliminary evaluation

Spatial processes in the main concentration and gully zone were then explored through flint artefact characteristics study and visual inspection of distribution patterns of these and other material culture categories (pottery, faunal remains, non-flint stone tools). The distribution of hearths was studied in relation to elevation of the Pleistocene surface. Finally, computer modelling of phase 3 site percolation and analysis of the assemblage’s spatial structure were performed. Fuzzy set and percolation theory were used to quantitatively address the comparability of density patterns in a grid-based collection of material. Variations and factors of uncertainty were therefore integrated in the spatial identification of comparable structural entities. The results of the simulations are discussed in detail in Peeters (2007).

The Hoge Vaart data was provided in the form of Map Info files which had to be converted into dxf format for importation in the GIS platforms used for this study (table 6.3) and a series of database tables containing information on the archaeological remains recovered at the site and the documentation catalogued (table 6.4). The transfer of the data to both GRASS and ArcGIS platforms was straightforward, through the import into native format of the graphic elements, after the creation of the appropriate environment (a non-projected system based on the excavation’s arbitrary x,y grid and NAP level). Point data, such as single flints and bore holes are readily imported into GIS in 3D and are representable in a 3D view (figure 6.23). The preliminary visualisation allowed for visual inspection of the database and correction of errors, such as points with 0 measuring x, y or z coordinates.

All analyses were conducted using a 2D GIS approach, but it is clear, from the data presented, that a 3D framework was kept in mind, in particular in the study of stratigraphic integrity and post-depositional movements. For this reason, this was considered to be an ideal dataset where using a 3D GIS framework to conduct similar or more complex analyses of the dataset provided could benefit the overall interpretation of the site. 6.6

The database did not store coordinate information for all the material recorded, as visible in table 6.4. However, it was possible to link certain 2D vector objects, such as features (hearths, pits, stake holes stored in the MapInfo files CONT01 et al.) to their z location via a database join using the combination of PITNR and FEATRNR (a new column called PITFEAT was created), further enhancing the three-dimensional data structure available.

Taking Hoge Vaart into the third dimension: the approach

As already demonstrated with the 2D GIS analysis (Peeters 2007), the assessment of depositional and

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Making Visible: Three-dimensional GIS in Archaeological Excavation Table 6.3. Hoge Vaart A27 (Almere, the Netherlands): MapInfo graphic files list Name of the file

Description

Dam

Excavation limits

Putten

5x5 m grid

Totgrid

0.50x0.50 m excavation units

Cont01,02,03,04

Excavation features (hearths, stakes, posts,etc)

Table 6.4. Hoge Vaart-A27 (Almere, the Netherlands): overview of data tables (datasets not provided shaded) Name A27anto

Description deep hearth pits

A27bota

macrobotanical remains

A27bone A27bug A27C14 A27ceram A27core

bone assemblage arthropods Results of C14 dating ceramics coring

A27feat

features

A27feem

features from Eem river zone field registration

A27field A27flifi A27fliba A27flimi A27fliup A27fli1 A27fli2 A27grspg A27micro A27seed A27sort A27stak A27stone A27wood

detailed flint analysis, fine fraction detailed flint analysis, basal level detailed flint analysis, middle level detailed flint analysis, upper level total flint, fine fraction total flint, individual pieces aggregated features file use analysis of flint seeds find numbers sorted by category stakes and posts stone tools other than flint wood

Coordinates x-y z_top and bottom of feature Not provided

Location identifiers PITNR, VLAKNR, SQRNR, FEATRNR PITNR, FINDNR,SQRNR, FEATRNR

x, y and z (point)

FINDNR, TRENCHNR, SPNR

Not provided x, y and z of every soil horizon along a vertical line x-y z_top and bottom of feature x, y, z top, mid and bottom

PITNR, FINDNR

x, y and z (point)

x, y and z (point) x, y and z (point) x, y z_top and bottom of feature

PITNR, FINDNR,VLAKNR, SQNR, FEATRNR PITNR, FINDNR,VLAKNR,SPNR, FEATRNR PITNR, FINDNR,VLAKNR,SPNR, FEATRNR

PITNR, FINDNR,VLAKNR,SQNR, SPNR, FEATRNR PITNR, FINDNR,VLAKNR,SQNR, SPNR, FEATRNR PITNR, FINDNR,VLAKNR,SQNR, SPNR, FEATRNR FINDNR

x, y z_top and bottom of feature

PITNR, FINDNR,VLAKNR, FEATRNR

Z only

PITNR, FINDNR,VLAKNR, SQNR, FEATRNR

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(a)

(b) Figure 6.23. Three-dimensional visualisation of the Hoge Vaart-A27 single flints (A27fli2) in ArcScene before (a) an after (b) correction.

6.6.2 Formation processes and GIS modelling at macro-scale

could equally be useful for study past landscape dynamics in an archaeological context.

Within the conceptual framework of multiple granularity and multiple characterisation, the entirety of the vast datasets was explored at first to achieve a full understanding of the site in its landscape. In contrast with the procedure used by Peeters (2007), who, having been involved in the excavation at Hoge Vaart and therefore familiar with the site, studied the Northern concentration before exploring other areas of excavation, it was felt that exploration at macro-scale was necessary before zooming into smaller areas.

Three-dimensional data handling This part of the data handling and modelling implied exploring the potentials of using bore-hole data for volume model creation for predictive use (coarser resolution) and comparison with the excavated area (finer resolution). Bore holes were drilled in the area prior to excavation (figure 6.24). The data were then analysed and stored in a file called A27core.dbf which consists of a series of entries (figure 6.25) which record both qualitative (soil type and corresponding archaeologically classified feature number) and quantitative data (point system indicating the presence of elements such as humic material, charcoal, peat, roots and the grain size of the sediment, amongst others). The depth of the different soil types (and consequently features) is registered by providing x, y and z coordinates of the top and bottom of the soil horizon. The vertical depth of the horizon is also indicated by its beginning and end in centimetres.

This section explores the creation of 3D volume models from bore-hole relatively sparse data. These models, still quite rare in archaeology (although extremely common nowadays in geological subsoil exploration) can offer a basis for the prediction of site location, in particular in deeply buried deposits, and for the study of depositional and post-depositional events at a landscape level. In fact, modelling of surface and volume subsoil data in threedimensions rather than in the form of stratigraphic columns and/or sections on the one hand requires the use of less sparse data (and therefore demands more investment in data collection) on the other it enables the visualisation and computation of soil characteristics (reflecting human environment interactions and natural events) that produce intuitive overviews on the landscape at hand. Moreover, three-dimensional subsoil base models are fundamental to compute erosion, percolation and flooding events on the volumes and estimate the post-depositional events affecting the units considered. Although these types of models have not been used in archaeological contexts, they have proved to be a fundamental and successful platform for studying subsoil phenomena such as groundwater dynamics in past and present environments (Cohen 2003, Strassberg 2005) and

The bore holes file was imported in GRASS and ArcGIS for visual verification and processing. Whilst in two dimension the location of the point of coring only can be displayed, in the 3D visualisation engines the bore hole columns can be displayed as a series of vertical points (fig. 6.26) that can be colour coded using the database information. Data cleaning of this file, consisting of 6514 entries (for a total of 537 bore holes) corrected inconsistencies both in terms of coordinate repetition and incorrect measurements and/or parameters definitions, which were easily detectable through visual inspection of the 3D dataset.

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Figure 6.24. Overview of the location of bore hole lines inside and outside the excavation area. Source: Hamburg et al. 2001, fig. 16.

Figure 6.25. Database log of the bore holes at the Hoge Vaart A27, file A27core.

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Figure 6.26. Two- and three-dimensional visualisation of the bore hole lines in ArcMap and ArcScene. Whilst in two dimensions it is only possible to visualise bore hole locations, in three dimensions visualisation of data characteristics (such as soil type) is possible through their vertical display.

any other subsurface volumetric entity) horizons need to be connected and closed, as each unit consists of the space between two horizons. The data used for the creation of the model were extracted from the bore holes database discussed in 6.6. A selection of point data was performed on the dbf core file using the archaeological feature classification provided by Ridderhof (database metadata file), corresponding to numbers 0 to 66 of column SPNR. All points classified as the same soil horizon were saved as separate files, carrying the name of the relevant soil feature and used to create 66 raster horizons through interpolation (DEMs).

Three-dimensional GIS modelling tools at macro- and meso- scale Surface and volume models of the stratigraphic sequence need to be constructed from the point data in order to obtain a 3D representation of the soils at the site. As the procedure for the creation and discussion of the limitations of a vector based approach to the modelling of contexts has already been put forward in chapter 5, here the discussion focuses on raster and voxel based modelling of three-dimensional stratigraphy. Two approaches can be used that differ both in the conceptualisation of what the volume represents and in the modelling procedure. Moreover, the results obtained will be employable for different types of exploration of the dataset. As a consequence, the two modelling approaches are not mutually exclusive but can both be employed to differently characterise the data under investigation and respond to different queries. They are both discussed in the following sections.

Initially, all horizons were taken into consideration for the construction of the soild model. Several problems were encountered, mainly linked to the cross cutting of the horizons in a stratigraphy that is extremely shallow and presents several variations (fig. 6.27). Moreover, when considering all horizons, the result obtained is overcrowded and difficult to interpret visually, in particular since most visualisation software do not allow zooming in the z dimension to the same degree it can be done on the x and y plane. This is a problem generally not encountered when the approach is applied to structural geology, where the geological units are several meters thick and the only problem encountered is that of modelling faults (problem solved in various manners by dedicated software).

Horizon method to create solid stratigraphy The steps illustrated in chapter 5.4.3, figure 5.18 were used to create the solid model of soils using the concept of horizons. The term ‘horizon’ refers to the top of each stratigraphic unit (pedological and/or archaeological) that will be represented in the model. Using this method, in order to construct 3D subsurface soil layers (the same applies to excavation units, as illustrated in chapter 5, or

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Figure 6.27. Complex cross-cutting of the 66 soil types identified at the Hoge Vaart. The image shows the result of slicing all horizons in Paraview along an arbitrary y normal at x coordinate 520. Horizon 20, corresponding to feature 20, is displayed in full (dark grey) to allow the viewer to have a perspective on the data.

Figure 6.28. Schematic representation of the soil stratigraphy at Hoge Vaart. Source: Peeters 2007, fig. 4.10.

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(a)

(b) Figure 6.29. Three-dimensional soil model of the area covered by the coring, reflecting the 9 horizons identified by Peeters (2007). Above (a) the entire model is show. In white the excavation limits. Below the sections running along the excavation limits are shown

Legend: 1

, 2-3

,4

, 12

, 13

, 14

, 19

127

, 20

, 63

Making Visible: Three-dimensional GIS in Archaeological Excavation representing a lot of the component under consideration. Grain size was similarly classified. These measurements represent 3D scattered data and to model, analyse and visualise their spatial distribution it is necessary to interpolate these data to a 3D grid.

To simplify the dataset, a selection of the stratigraphy was performed, reflecting that recognised on site during the study of a number of profiles and illustrated in figure 6.28. Stratigraphic features numbers are grouped in what are called horizons (Peeters 2007). A description of each horizon is provided which reflects single or aggregated stratigraphic layers (figure 6.28).

As discussed in chapter 5.4.3, GRASS offers two different algorithms that allow three-dimensional interpolation of this type of data: Inverse Distance Weighting (IDW) and Regularised Spline with Tension (RST). The mathematical description of these methods and the equations used to compute associated grades and curvatures are fully documented in Neteler and Mitasova (2008).

The sequence was interpreted as follows. Immediately above the sand profile was a layer of reed peat or reed sedge peat (1C - horizon, feature 1). Directly below, therefore at the top of the sand profile, a thin layer of eroded and deformed coversand (2C-horizon, features 23) that was very heterogeneous in composition at different locations. Under these erosion layers a natural soil profile that lied in the young coversand had formed. From above to below were distinguished a light grey 3Ehorizon (feature 4), a dark brown 3B-horizon (feature 12) and pale brown passage layers (3BC-horizon, feature 13) grading into the yellow-white mother material (3Clhorizon, feature 14). Below laid a distinguished greengrey loamy layer of Young Coversand (3C2-horizon, feature 19), a grey white layer of Young Coversand (3C3horizon, feature 20) and finally a green-grey loam layer of Old Coversand (4C-horizon, feature 63). Figure 6.29 shows the three-dimensional model of the site, reflecting the classification in 9 horizons rather than 66 features. To easily visualise the stratigraphy, cutting and slicing of the volumetric model is necessary. The entirety of the model can be employed for understanding formation dynamics of the general area, whereas subsets of the model can be used for putting into context different smaller areas of the excavation.

The RST module v.vol.rst was used to compute the 3D grid of data, after setting the project region to the appropriate 3D settings with g.region. The grid was then exported to Paraview for visualization. Parameters were here made visible with the use of thresholding. The following figure 6.30 shows the concentration of reed and humic material extracted from the core database. Figure 6.31 shows the same dataset in combination with a semitransparent terrain model for easier spatial orientation and appreciation of the relationship between the archaeological signatures under study. It is easily visible that the reed volumes are all above the Pleistocene surface and represent the period of emergence of a submerged landscape at the Hoge Vaart, whilst the presence of humic material is more likely to indicate the presence of wood in the archaeological deposits (for example fish traps, as it was confirmed by excavation at the site).

From point data to volumes Considerations on macro- and meso-scale modelling of a buried landscape

Exploration of the subsoil for the identification of archaeological deposits cannot limit itself to the geometrical description of stratigraphic units. Physical properties such as density, porosity, chemistry are very important parameters to associate with any area of the investigated subsoil for prediction of archaeological occurance. Moreover, this type of model, which considers quantitative characteristics of soil components, can be created to serve as the base model for a wide variety of further modelling functions ranging from groundwater flow to deformation of deposits, and reverse modelling of dispersal of natural and cultural material, all functions that operate on quantitative measures.

Surface and volume models are important analytical tools for the understanding of landscape dynamics at a macroand meso-scale. They provide useful pictures of general patterning and combinations of variables. They also offer base models for the study of modifications of soils and palaeohydrology dynamics, amongst other sets of analysis that can be carried out. It must be emphasised, nevertheless, that, as already highlighted for their two-dimensional counterpart (Conolly and Lake 2006), methods for creating such models are far from straightforward and verification of accuracy of original data combined with a careful study of which models can be useful at which scale to enhance the understanding of the archaeological landscape under study are necessary.

At Hoge Vaart, concentrations of humic material, peat, roots, reed, charcoal were measured in bore-holes at various elevations. Values from 0 to 5 were attributed to the concentrations with 0 representing none and 5

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Figure 6.30. Interpolated reed and humic concentrations at Hoge Vaart interpolated in GRASS from bore hole data and visualised in Paraview. In white the limits of the excavation.

Figure 6.31. Interpolated reed and humic concentrations at Hoge Vaart interpolated from bore hole data to 3D volumes in GRASS and visualised in Paraview. The simultaneous visualisation of the Pleistocene coversand allows the understanding of the relationship of the various materials with the formation of the landscape at the Hoge Vaart (reed and reed peat above and humic material below the surface).

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Figure 6.32. Sequence of visualisations of the total weight of the flint fine fraction at the Hoge Vaart A27. In white the 0.50x0.50 excavation grid.

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Making Practical: Examples of Intra site 3D GIS 6.6.3 Three-dimensional GIS analysis visualisation at meso- and micro-level

The overall total, total burned flint and total unburned quantities of the flint fine fraction were visualised in Paraview after conversion to a 3d grid in GRASS and exportation to a vtk file. Exploratory analysis was possible through visualisation with thresholding, a simple but effective tool to separate objects from the background. This technique is a segmentation procedure used in image processing to convert a gray scale image to binary format, where only two values are possible for the pixel, zero or one (Shapiro and Stockman 2001). Its further elaboration (available as an analysis tool in Paraview) consists in the assignment of ranked colour values to pixels or voxels representing particular properties of the image under study.

and

This part of the study presents a series of operations that were carried out on the dataset at meso- and micro-scale. These were aimed at generating a three-dimensional representation of the excavation area at various levels of characterisation. Data on distributions of various find classes (flints, pottery, charcoal) were used in combination with stratigraphy information and the threedimensional shapes of identified features to analyse the dataset. A comparison with the two-dimensional approach to the analysis and visualisation, presented in Peeters (2007) was deemed crucial for assessing the potential of a three-dimensional approach to excavation data and is incorporated in the discussion.

Figure 6.32 shows a sequence of visualisations of the total weight of the flint fine fraction extracted using different threshold values. The general patterning and distribution of the material can be visually inspected in this manner.

Since the analysis benefited from mixing operations carried out on discrete entities and continuous fields, rather then describing them separately as in chapter 3.2.4, the presentation proceeds from elucidate analytical operations from simple (creation of primary models) to more complex (combination of the dataset characterised in different manners). From points to data volumes: simple exploratory visualisation of flint distributions

Horizontal slice cutting of the 3D volume of flints provides the user with the ability to better visualise and therefore understand the vertical distribution of the fine fraction at any particular point in the explored area. Figure 6.33 shows the results of a horizontal slicing operation.

Two methods are available for the transformation of 3D point data to volumetric data (Neteler 2001): − rendering of a full volume via 3D interpolation (discussed above) − direct conversion of 3D points to their 3D voxel representation restricted to existing data values.

A DEM of the top of the Pleistocene coversand is visualised (in semi-transparent form) in figures 6.34 and 6.35 to allow the viewer to situate the material in its landscape context. The 3D representations are compared to the equivalent two-dimensional ones from Peeters (2007) shown in figure 6.36.

Of these two methods the most appropriate for the representation of data collected using grid excavation is the second. In this manner the specific value attached to the excavation basic sampling unit, represented in the database by a point defined by x, y and z coordinates, is extruded to a cube representing its original spatial position. Direct conversion of the database was performed using the GRASS module v.to.rast3. Before running this process, the 3D region was defined with g3.setregion. The resolution chosen for the region definition was identical to that of the excavation units (0.50 x 0.50 x 0.04 m). In GRASS the voxel created represents exactly the grid of excavation as the value to be extruded to a cube, which in the database was located at the centre of the excavation grid, is registered at the left bottom corner.

It is clear that the three-dimensional visualisations allow not only to better perceive the characteristics of the landscape (in this case the position and depth of the coversand channel) but also to consequently appreciate the patterns of dispersion of the flint fine fraction away from the areas of activity at the site and down the flank of the ridge into the channel. Moreover, working in three-dimensions and in an interactive environment, allows the combination of differently classified classes of material thanks to the added manipulation dimension (figure 6.37 shows the combined visualisation of burnt and unburnt flint fine fraction).

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(a)

(b) Figure 6.33. Slicing of the 3D volume of the flint fine fraction at Hoge Vaaart 27. Above (a) the horizontal cutting plane is shown and below (b) the results of the slicing.

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Figure 6.34. Overview of the unburnt flint fine fraction at A27 Hoge Vaart over the digital terrain model of the Pleistocene coversand surface after mechanical removal of the peat.

Figure 6.35. Overview of the burnt flint fine fraction at A27 Hoge Vaart over the digital terrain model of the Pleistocene coversand surface after mechanical removal of the peat

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Figure 6.36. Two-dimensional overview of density distribution of unburnt (left) and burnt (right) fine knapping debris. Source: Peeters 2007, figs. 4.21 and 4.22.

Figure 6.37. Three dimensional representation of the combined burnt and unburnt flint data at the Hoge Vaart site. The opportunity of exploiting a further dimension allows the visualisation of the data in combination, differently from the more restricted situation in a two-dimensional environment.

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Making Practical: Examples of Intra site 3D GIS identifying a single feature and the objects is then extruded to the value of top z. The solid created needs to be exploded in order to become a mesh of 3D faces, which can be imported in GRASS and visualised in Paraview; 2. A bottom DEM can be created interpolating the bottom z value of all features in the features database A27GRSPR. The contexts are then assigned the DEM height as their base height and they are extruded to the z top value retrieved from the database.

Three-dimensional thematic mapping and querying of features As already highlighted in section 6.6.1, although shape information on features at Hoge Vaart had been provided in 2D format, it was possible to create 3D objects exploiting the feature footprints and the knowledge of their thickness. Various methods are available for achieving an approximate 3D shape for the 2D contours, all involving extrusion of the feature exploiting the top and bottom z values stored in the database. The routines investigated were: 1. Import of context data into AutoCAD in dxf format. A bottom z value is attributed to the polyline

These two methods create real three-dimensional features as shown in figure 6.38 below.

Figure 6.38. Above, two-dimensional and below three-dimensional view of the features present in the Northern concentration and shown in relation to the Pleistocene coversand layer. The features were created using routine 1 and are fully three-dimensional

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Making Visible: Three-dimensional GIS in Archaeological Excavation An alternative manner to rapidly create pseudo threedimensional features for visualisation and thematic interrogation is to use the ArcScene routine Convert Features to 3D, which allows to assign a base height to 2D features from an attribute table and to extrude this newly created shapefile to a desired z value (again extracted from the attribute table).

or in Paraview. The following figures compare the twoand three-dimensional visualisation of surface and deep hearths at the Hoge Vaart (figs. 6.39 to 6.41). It is evident that exploration of patterns and relationships in three dimensions enhances the understanding of the excavation features in relation to each other and to elements such as paleosurfaces.

Once the contexts are created in three-dimensions as vector entities, they can either be visualised in ArcScene

Figure 6.39. Distribution of hearths in relation to elevation of the Pleistocene surface. Legend: 1 surface hearth, 2 deep hearth pit. From Peeters 2007, fig. 4.34

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Figure 6.40. Three-dimensional distribution of hearths in relation to elevation of the Pleistocene surface. Surface hearths are represented in green and deep hearth pits in red and visualised in ArcScene with a semitransparent DEM of the Pleistocene surface. The features consist of extruded two-dimensional footprints and are, as such, not fully 3D.

(a)

(b) Figure 6.41. Three-dimensional visualisation of surface (green) (red) hearths at the Hoge Vaart. By simple navigation it is possible to identify which features are on top (a) and under (b) the Pleistocene coversand.

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Making Visible: Three-dimensional GIS in Archaeological Excavation Thematic mapping Choropleth mapping of contexts and features, as already highlighted in section 6.3 is a simple but extremely effective type of thematic mapping that allows to convey quantitative information on the distribution of data classes across a boundary defined dataset. This type of mapping is fairly well developed in three-dimensional symbology menus in ArcGIS, whilst other forms of thematic mapping such as proportional symbols, dot density and chart maps (equally useful to explore data classes in combination) are still only possible when quantifying categories contained in two-dimensional objects. As a demonstration of the data exploration value of this GIS technique, three-dimensional choropleth mapping was used to explore the features dataset at the Hoge Vaart. Figure 6.42 shows the quantities of flints and figure 6.43 of ceramic sherds contained in the Hoge Vaart surface hearths and deep hearth pits. Three-dimensional visualisation provides a means for determining patterning in the whole area of excavation at various depths. Differently from two-dimensional displays, it in fact allows the simultaneous visualisation of features that are one on top of the other. Moreover, the three-dimensional environment allows the display of the features in relation to specific soil horizons, linked, from interpretation, to particular moments of landscape formation. Here the features are displayed in relation to horizon 2C (refer to figure 6.28 for a detailed description), the re-deposited

coversand that characterises the higher level of the coversand ridge, just below the peat deposit (horizon 1C). Patterns in the shape of the features (shallow surface hearths versus deep hearth pits), their position in the stratigraphy and their content can be easily detected. Comparison between the quantities of different classes of material in the features is also straightforward. For example, by simple visual inspection of figures 6.42 and 6.43 it is evident that shallow hearths contain more flint and pot sherds than hearth pits, although the latter are volumetrically bigger. This is no surprise, to some extent, when linked to the dating of the features that locate the surface hearths to Early Neolithic and the deep pits to Mesolithic activities. Simple calculations such as percentage of the total can also be used to create choropleth maps of material such as charcoal (fig. 6.44). Both quantitative and qualitative data can be used for this type of mapping. In the case of the former, the data must either be a total (for example, number of artefacts) or a ratio (such as the number of the artefacts divided by the area of the enumeration unit). Again, simple visual inspection of the assemblage shows that the percentage of charcoal in deep hearths is higher than in surface hearths. This is an observation that, although simply reflecting that presented by Peeters’ analysis, has the added power of showing in the same image both types of features and enhancing the understanding for the archaeologist that has not seen the site or the database.

Figure 6.42. Quantity of flints in features. Note that most shallow hearths are above stratigraphic layer 2_3 (horizon 2C) and contain more flints than hearth pits (that are situated below horizon 2C). Horizon 2C is displayed with transparency value 20%. Features above it appear bright and features below appear slightly shaded.

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Figure 6.43. Quantities of pottery in features. Note that, similarly to flint quantities, shallow hearths contain more pot sherds than hearth pits. In fact hearth pits contain no pottery except for a few exceptions (see 190.0908) indicated by an arrow.

Figure 6.44. Quantities of charcoal in features (percentage of total). Quantities of charcoal are higher, in percentage, in deep hearths.

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Figure 6.45. 3D point attribute query selecting trapeze shapes from the flint coarse fraction assemblage. The points are encapsulated by a gridded cube.

As highlighted in (Conolly and Lake 2006) both quantitative and qualitative forms of choropleth mapping are based on the assumption that the data are evenly spread throughout the unit under consideration. This issue, has been addressed by Lock et al. (1999) at a landscape level where it is obvious that very rarely this assumption can be justified. Nevertheless, statistical measures such as coefficient of variation can be applied to account for intra-enumeration unit variability (Conolly and Lake 2006). It is interesting to notice that absence of internal variation is in fact the inherent characteristic of the whole concept of context based excavation already criticised in chapter 4. It is important to bear in mind, therefore, that such mapping, whilst useful for exploring the overall pattern of finds in features across the dataset, should ideally be combined with another independent measure of finds distribution (such as the one described below). Attribute queries In the absence of the ability of three-dimensional GIS to perform topological queries, the selection of spatial data on the basis of their attributes is still an effective form of basic exploratory analysis aimed at pattern recognition

and interpretation (Conolly and Lake 2006). This can be performed on any three-dimensional geometric element (point and 3D objects in the case of excavation) that is linked to a more or less articulated database which stores the descriptive characteristics of the elements under inspection. In the case of the Hoge Vaart excavation, the database containing information on the detailed analysis of the flint coarse fraction (single pieces) was explored in this manner. This operation was spatially possible in 3D as every single flint had been registered through a triplet of coordinates. The example above (figure 6.45) shows the result of a selection of flints from the site single finds database, executed through simple attribute query. It is clear by navigating the 3D view that almost all trapezes are above horizon 2C and above or around the surface hearths (dated to phase 3 – Neolithic). They cannot therefore be associated to any earlier phases, dated to the Mesolithic. Moreover, through point in feature inspection (possible via the transparent rendering of the features) it is possible to notice that almost no trapezes are in fact inside any of the hearths or pits, an observation that confirms Peeter’s interpretation of the assemblage as episodes of use of the hearths and episodes of their clearing (fig.6.46).

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(a)

(b) Figure 6.46. The selected trapeze flints (yellow dots) are displayed in combination with the 3D extruded vector features recorded at the site. In green the surface hearths and in red the deep hearths. Stakes and stake holes are in brown. A DEM of horizon 2C (features 2 and 3) is used to enhance the three-dimensional understanding of the perspective view.

dimensional buffering as implemented in projects such as (Nigro et al. 2002, Nigro et al. 2003) was based on creating spheres around points. Such approach presents limitations when we consider that horizontal and vertical patterns in archaeological contexts are not of isotropic nature and horizontal and vertical displacement, as studies in formation processes have demonstrated, differ greatly in magnitude with the horizontal dispersal of archaeological material around features as a hearth being far bigger than the vertical one (Janes 1989, Stevenson 1985).

3D buffering An operation of the type ‘A is within/beyond distance D from B’ is carried out in a GIS through buffering commands. Buffering is used to draw a zone around the initial entity where the boundaries of the zone or buffer are all at distance D from the coordinates of the original entity. In 2D the buffer is in effect a new polygon that can be used as a temporary aid to spatial query. It can also be added to the dataset as a new entity. A selection of a subset of a dataset can then be performed based on its inclusion within the buffer area through an overlay operation (Burrough and Mc Donnell 1998). The geometric construction of a simple 3D buffer is an easy operation, as it can be performed by creating a sphere or a cylinder around a 3D point. For example, three

In the case of the northern concentration at the Hoge Vaart site, for example, the relationship between surface hearth 349.0901 (10 cm. thickness) and the flints surrounding it, was studied through the use of cylindrical

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Making Visible: Three-dimensional GIS in Archaeological Excavation buffers as an example of the potential of this type of analysis. A series of cylindrical buffers of varying diameter and thickness rather than spheres were created in AutoCAD and imported in Paraview at the location of the feature under examination (see figures 6.47 and 6.48 for a comparison between a spherical and cylindrical buffer).

for a vertical extent of 4 to 10 cm above the surface hearth might indicate activities even after the hearth ceased to be used. Whilst similar considerations are made in Peeters (2007) it was only through the use of the buffering function around the surface hearth in three-dimensions that the absence of post-deposition vertical (and with high probability horizontal displacement) could be proven.

A cylindrical buffer of 10 cm (same vertical extent of the feature) showed that no flints were identifiable below the bottom of the surface hearth (fig. 6.48). All flints were actually distributed either inside (a small part) or above the hearth. The horizontal spread was of a maximum radius of 4 meters around the hearth. In this manner the vertical and not only horizontal position of finds in relation to the feature was examined Considerations about the extent of post-depositional movements can be made. No vertical post-depositional displacement below the surface hearth is detected indicating that the flint distribution above and around the hearth is probably in its primary context. It seems obvious from the pattern detected that the activities of flint knapping/use and those associated with the use of the hearth are contemporaneous. The presence of flints

In the routine implemented in this study, which employed standard functions and did not proceed to programme ad hoc overlay operations, the buffer could only be used for visual inspection of the dataset. Conversely, in Nigro et al. (2002, 2003) the buffer had been designed to operate as a selector within the system (fig. 6.49). Another case of implemented distance based spatial operation is from the study by Katsianins et al (2008), where a routine was designed to select finds and excavation units by 3D distance and nearest neighbour operations are also applied (fig. 6.50). The add-on for such operations is not available for public use.

Figure 6.47. Spherical 1 m radius buffer around surface hearth 349.0901 showing a cut off of the total weight of flint fine fraction and the surface earth feature itself

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Figure 6.48. Northern concentration. A cylindrical buffer of 8 m of diameter and 0.10 m of thickness was created around surface hearth 349.0901. The total flint weight indicates the major areas of activity around the hearth as identified even in Peeter’s analysis (see figure 6.22) for comparison.

Figure 6.49. 3D buffer operation used as selector of 3D point data at the Swartkrans site (RSA)

Figure 6.50. Data exploration in 3D space: (a) selection of finds by 3D distance, (b) selection of excavation units by 3D distance, (c) applied nearest neighbour analysis. Source: Katsianis et al. 2008, fig.10.

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Making Visible: Three-dimensional GIS in Archaeological Excavation and 2C (sandy peat) and well above the other soil horizons. Presence and/or absence of flint material in other stratigraphic layers needs to be interpreted in the light of vertical, and not only horizontal relationships. Interactive visual inspection of the surface rendered stratigraphic layers sequence and three-dimensional features in the Northern concentration allowed for these preliminary observations, which were followed by the integration in the model of flint material as discussed next.

Mixed environment exploratory analysis In the presence of a dataset such as the Hoge Vaart one, it is possible to inspect finds and feature patterns as independent of one another and in relation to elements of stratigraphy. For example, questions such as the relative position of burned and unburned fine fraction of flints as compared to the hearths can be readily investigated through visual inspection. The results of a 3D approach will be here compared to those of 2D visualisation. The stratigraphic layers of the Northern concentration were represented, in a two-dimensional environment, as flat gridded units on which flint distributions are presented as present and/or absent (figure 6.51). The position of the surface hearth feature 329.0901 is represented by a black dot. Whilst, on the one hand, this sequence of flat slices through the stratigraphy allows for a rough understanding of the relationship between finds, feature and stratigraphic layers, in reality, across the site, the stratigraphic layers vary in shape, orientation and are some times presenting inversions in deposition as it emerged clearly from the creation of the surface sequence model discussed in section 6.6.2. Once one starts correlating, for example, the stratigraphic layers and surface hearth feature in the Northern concentration, it emerges that firstly, at least in the area under investigation, stratigraphic layers 4 and 12 are inverted compared to the expected sequence. Secondly, it is incorrect to directly related the feature with stratigraphic layers 4 and 12, being the hearth embedded in layer 1 and 2_3, corresponding to horizons 1C (peat)

Only a three-dimensional model of all the elements that compose the archaeological record can offer an overall understanding of their relations. In the case of the Northern concentration, the relevant portion of stratigraphic horizons was extracted from the overall model through selection of the boundaries of units 349, 350, 329 and 330. Sub-setting of the model is a necessity when working in three- dimensional environments to make visualisation and exploration of relationships between stratigraphy and features and/or finds easier. Once this operation was performed, the stratigraphic subset was used to visualise the occurrence of flints across horizons and the relationship of features with the general stratigraphy in the area. Figure 6.52 is a sequence created by peeling off the succession of horizons and highlighting, by rotating, the relationship of surface heart 329.0901 with the stratigraphy above, around and below it.

Figure 6.51. Hoge Vaart-A27 northern concentration. Occurrence of flints in the various stratigraphic layers of the excavation units 329,330,349 and 350. The circle indicates the position of surface hearth 329.0901. Source: Peeters 2007, fig. 4.11.

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Figure 6.52. Sequence showing the relationship between surface hearth 329.0901 and stratigraphic layers at the Hoge Vaart (Northern concentration)

Through three-dimensional dynamic display the statigraphy and the features can be studied in relation to one another and compared with the observations made by Peeters (2007). The interpretation that can be reached by examining this sequence is not necessarily different to the one offered by Peeters (2007) but some new elements seem to emerge if the sequence is studied closely. Peeters (2007) argues a possible contemporaneity of surface hearths and stake holes based on the fact that both are

generally found within horizon 2C (features 2 and 3). Whilst this is true for the surface hearth and the stake hole in the Northern concentration as they are both embedded in horizon 2C, the surface hearth is visible above it, indicating that it was probably in use when horizon 1C (reed peat) was already starting to form. The stake hole, conversely, is embedded only in horizon 2C and cuts trough the ones below. Even if we can assume a general contemporaneity of use of hearths and structures linked to

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Making Practical: Examples of Intra site 3D GIS the presence of stake holes, in the specific case of the Northern concentration it is plausible to state that there is no stratigraphic relationship between the surface hearth and the stake hole. The surface hearth was in use after the stake and the structure related to it had been abandoned and covered by horizon 2C sediments. Moreover, the stratigraphic relationship of the surface hearth to horizon 1C seems to indicate that episodes of use of surface hearths continued even when the marine transgression was starting to cover the upper area of the coversand ridge. In an environment such as Paraview, simultaneous visualisation of stratigraphic horizons, features and flints

distributions is possible. Figure 6.53 shows the relationship of stratigraphic layer 2-3 (horizon 2C) with surface hearth 329.0901. It is clear that most flints are above and within this horizon and the feature, confirming and graphically enhancing the interpretation offered by Peeters (2007) of a contemporaneity of activities of flint knapping and usage of the hearth. Figure 6.54 shows, with a view from below, the higher quantity of burnt as opposed to unburnt fine fraction of flints in the area above of the southwestern corner of surface hearth 329.0901, offering the possibility of interpreting the relationship of the surface hearth and the distribution of burnt material at the site.

Figure 6.53. Hoge Vaart-A27 northern concentration. Density distribution of total fine fraction of flint in relation to stratigraphic layer 2_3 (2C horizon) and the surface hearth 329.0901. Both the stratigraphic layer and the flint density distribution are rendered in semitransparent mode to allow visibility of the features.

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Figure 6.54. Location of high ratio of burnt/unburnt flints in a grid square in correspondence of the surface hearth 329.0901 in the Northern concentration.

Figure 6.55. General view of the ratio burnt/unburnt flint knapping debris across the site and in relation to the various identified features. The calculation was performed using simple map algebra.

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Making Practical: Examples of Intra site 3D GIS Map algebra operations of flint distributions Spatially dependent analysis can be performed on voxel data with a 3D map calculator which allows to employ common algebraic, trigonometric and binary functions and operators (Neteler 2001). In this case, a simple ratio of burnt/unburnt flint knapping debris was calculated and displayed in Paraview in combination with the location of identified features at the site (surface hearths, deep hearths, stakes) in fig. 6.55. This is one of the many possible map algebra analyses in a voxel environment, which allows for more sophisticated enumeration techniques and simulations due to the characteristic data structure of voxels, which express data in a numerical form. 6.7

Summary of the Hoge Vaart case study

The Hoge Vaart case study, differently from the Kouphovouno one, was characterised by a vast quantity and variety of data. Moreover, both grid and context excavation strategies were used in combination at the site. This made it an ideal ground for the experimentation of the proposed framework. In fact, the framework itself, in its conception, was highly influenced by considerations made on handling a complex dataset of this type. The potential of a 3D voxel approach for 3D import, manipulation, analysis and visualisation was demonstrated. It is clear, when compared and contrasted with the previous case study that it is exactly in this area that, at present, 3D GIS offers its best potential for archaeological excavation. It is therefore in the area of data collection that particular attention should be paid to a recording not exclusively based on the principles of archaeological stratigraphy. The gathering of point sampled data as well as the use of integrated approaches that consider gridding strategies for an exploration of finds and other elements distributions is mandatory in a three-dimensional approach that aims at exploiting the analytical potential of continuous data. Nevertheless, even the area of three-dimensional vector based exploratory spatial analysis in the form of thematic mapping, point and feature queries and buffering, although not fully developed in the available GIS software has shown great potential in particular in the area of exploratory analysis of large datasets. The approach chosen demonstrates the utility of mixing vector and raster data structures. The data collected enables the creation of three-dimensional objects for describing various aspects of the archaeological record (find clusters, soil properties, etc.). These can be combined with simply extruded 3D representations of recognised features. The aim of such combination is therefore not realism per se but the ability of combining different ways of conceptualising not only volumes but the archaeological record. The reconciliation of the vector versus raster debate seems to offer a way of enhancing a

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mixed approach to excavation rather than a context versus arbitrary grid one. Since the strategy of excavation highly influences the possibility of using 3D GIS analysis, this needs to be considered when the excavation recording process gets started. This is a cautionary note further supported by the demonstrated difficulty of transforming legacy data collected for two-dimensional analysis into three-dimensional ones (Lieberwirth 2007, 2010). In summary, by a combination of 3D vector and voxel approaches the following operations were possible: 1. in the area of modelling: • creation of surface and solid stratigraphy of deep sediments through the interpolation of bore hole data; • creation of solid representations of soil properties. These types of models are considered, for the purpose of the framework presented, base models. Secondary models can be created from the base ones via the application of process algorithms. This area of application was not experimented with in the present project and the potential of simulation scenarios such as volume erosion or water inundation for the understanding of the landscaoe under study was not assessed in practice but is certainly possible once the base model is created and evaluated. 2. in the area of explorative analysis: • interactive display of three-dimensional finds distributions and three-dimensional vector features and visual inspection of their relationship; • three-dimensional data classification and inspection of qualitative quantifications through thematic mapping; • attribute querying of feature and point data. 3. in the area of spatial analysis: • 3D buffering for the exploration of taphonomic process and the study of finds movements in relation to features and topography of the site; • 3D map algebra for the calculation of the relationship between data characteristics (e.g. ratio of burnt and unburnt flints). Further three-dimensional analyses for the study of 3D vector data (for example correlation, cluster analysis, cluster membership, density analysis and predictive modelling) would be ideal for the study of distribution of finds represented as points. At present 3D geospatial analysis is not an available option within the software packages used in this research but the possibility of processing the data in 3D analysis statistical packages such as R and display it in the three-dimensional GIS platform exists. The three-dimensional modelling, display and analysis of the Hoge Vaart record was aimed mainly at exploring this

Making Visible: Three-dimensional GIS in Archaeological Excavation large dataset in three-dimensions in order to correlate not only horizontal but also vertical distributions into a comprehensible whole and therefore enhance the interpretation of depositional and post-depositional patterns at the site. Fundamentally the overall understanding and interpretation of the various activities at the site did not drastically changed compared to the original interpretation presented by Peeters (2007). As it has been highlighted before the analyses conducted by Peeters (2007) were directed by a conceptualisation of the site phenomena in three dimensions and successfully managed to come to a complex interpretation of the record even without the use of a 3D GIS. Nevertheless the 3D GIS platform created by the author of this research allowed for the easier manipulation of the dataset as a whole rather than subsets of it. For example Peeters only studied in detail the Northern concentration as the amount of point data and their relationship to features were overwhelmingly large and almost impossible to be studied within a 2D GIS. In a 3D environment it is also easier to study vertical relationships or lack of relationships as clearly documented by the re-examination of the stratigraphy of the Northern concentration discussed in section 6.6.3. Moreover, the basic application of 3D GIS spatial analysis such as buffer operations also enhance the possibility of studying post-depositonal displacement of finds and features as illustrated in section 6.3.3. Several other analytical modules for the study of excavation in three-dimensions can be exploited once the base model of the excavation is created. Without drastically changing the interpretation that the expert archaeology would make of the site under study, these would enhance the results and through the power of image would allow a wider variety of experts and the general public to enjoy the possibility of appreciating the site recomposed and interpreted in a variety of manners. 6.8

Summary

The conceptual framework and data model architecture proposed in chapter 5 were constructed and, at the same time evaluated, with the help of the two excavation datasets presented above. This chapter presented some of the basic GIS model construction routines and sets of analyses possible in a three-dimensional environment within the present potentials and limitation of developed GIS platforms. From a first series of queries and modelling processes a second stage of the model is reached and new queries can be performed (iterative process). Although much time is spent in creating the base models, the possibility of combining them in further stages of analysis (from primary to secondary models) offers certainly a rich scenario for an enhanced evaluation and understanding of complex sites.

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Chapter 7. Research

Conclusions

and

other disciplines and was followed by an analysis of the state-of-the-art in the application of 3D GIS in intra-site archaeology (chapter 2). This revealed that, although the state of accomplishment and knowledge in the field of intra-site 3D GIS is exponentially growing, there are still various issues to be resolved in a variety of areas as follows: 1. the lack of clarity concerning the specific steps used to obtain the three-dimensional models on which analysis is performed. Moreover, the software at times developed for the modelling and analysis is generally not available to the general public; 2. the concentration on stratigraphic excavation as the only way to see; 3. the lack of a discussion of whether 3D GIS has reshaped or should at all reshape the way we carry out excavation; 4. the failure to systematise and integrate single case studies in more generalised conceptual frameworks to accommodate the conceptualisation of several types of excavations and excavation methodologies in 3D; 5. the severe limitations that the discipline of archaeology get when using dedicated sowftare in terms of return for the money.

Further

Archaeological excavation with the complexity and interrelations between different types of data (3D spatial and non-spatial) that characterises it presents a challenge to current information systems in how it is handled and analysed in its entirety. This book describes an attempt to integrate available technology components and to develop new concepts to attain a system that better meets the requirements of archaeologists to approach the archaeological record in a more integrated and multifaceted manner. The research, which stemmed from the author’s interest in the application of IT in the humanities, was probably a more ambitious project than envisaged at the beginning. The idea of representing archaeological excavations through a three-dimensional GIS seemed at the time an obvious response to the inadequate application of GIS to intra-site analysis. Moreover, in the early 2000s, a major shift in GIS development was happening, following the acknowledgment of the need to model 3D data and phenomena. Nevertheless a number of difficulties were encountered very soon along the path, not last the fact that the development of a full 3D GIS was and is highly problematic and in fact still not completed. Several of the theoretical and more technical limitations of such an approach to excavation have been discussed through this book and are summarised in the sections that follow. The research work conducted in the end came to concentrate on a limited, yet crucial, area of three-dimensional GIS development: the creation of a conceptual framework that reflects the structure and characteristics of data from archaeological excavations. The focus is on threedimensional spatial representations of the archaeological record and their potential to elicit alternative approaches to the use of intra-site GIS. 7.1

Chapter 3 introduced the fundamentals of 3D GIS modelling and visualisation. Here the differences between raster and vector data were highlighted and the potential of the two approaches for analysis, in particular in an archaeological context was discussed. The detailed study of the two GIS data structures was crucial for establishing their feasibility to represent excavation data and it allowed the realisation of an important parallelism: that between grid excavation and raster data structures and between single context excavation and vector ones. The translation of the excavation into a GIS platform is very much reliant on this parallelism, which needs to be considered in its potential and limitations in particular in the realms of analysis.

Summary These observations were followed by a discussion of contemporary excavation practice and the production of the archaeological record seen under the light of Lefebvre’s theory of the production of space. The key objective of this research was then outlined in chapter 5, i.e. a GIS framework able to conceptualise the elements routinely used in archaeological excavation in a manner compatible with a GIS system architecture. The framework suggested was designed and evaluated with the use of two main case studies (and the experience of several excavation projects).

Full development of a 3D GIS for archaeological excavation is a complex task requiring extensive research, which exceeds the scope of this book, in particular as the implementation of 3D GIS structures and operations has yet not reached the standards of the correspondent 2D approach at the level of commercial and/or Open Source software. Despite this, this work proposes a conceptual framework as the fundamental step in the direction of creating 3D GIS models capable of both analysis and visualisation of excavation data.

The study ends with a review of all the necessary fundamental concepts of GIS modelling incorporated in its design (chapter 5 and 6). By relating different theories and concepts of spatial theory in both GIS and archaeology, it identifies parallels and potentials for the development of both three-dimensional GIS and a threedimensional conceptualisation of excavation. Some key aspects clearly emerged from the study of past literature, cutting-edge research in three-dimensional GIS and theoretical considerations of the topic of contemporary

At the beginning of this study two questions were posed: 1. To what extent do archaeological data and GIS structures parallel one another and how can GIS represent an archaeological excavation? 2. Can three-dimensional dynamic GIS improve our understanding of depositional and post-depositional phenomena? If so how might this operate? To answer these questions, the book begun with an overview of the achievements of a 3D GIS approach in

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Making Visible: Three-dimensional GIS in Archaeological Excavation I propose a system in which multiple granularity and characterisation are adopted to create micro-, meso- and macro-base models of the excavation data. An important element of this framework is the modularity of the system, which provides better management and understanding of spatiotemporal problems. In this nested system data are multidimensional, and this multidimensionality is not only a measure of space and time components but also of the multiplicity of attributes and characterisation of layers of analysis. This means that we can view data at various levels of detail and in a multitude of representations and elaborations. In turn, the information we retrieve can be used for complex analysis and for constructing higher or lower order patterns of spatiotemporal processes.

archaeological practice. This project has structured their articulation around the concept of three-dimensional space. 7.2

Conclusions

This research clarifies the existence of a parallel between stratigraphic excavation and object-oriented models (vector structures) in GIS and grid excavation and fieldoriented models, reflected in a raster structure. Chapter 4 in particular discusses not only the inadequacy of these approaches applied to excavation, when taken on their own and without questioning archaeological practice once the record is transferred into a digital environment. It then proposes that the complexity and heterogeneity of the excavation archive should be reflected in a threedimensional approach to excavation, where 3D GIS is conceived as a representational space within the Lefebvrian theory of the production of space. The potential of a three-dimensional GIS seen from this perspective lies not in the power of producing ‘realistic’ representations of the archaeological excavation but in the energy that is created by the concrete production of representational spaces as modes for exploring the unfamiliar in different and creative manners (yet within spaces whose geometries we are familiar with) and in providing a platform for the negotiation of the archive.

This framework comprises a number of design concepts to be followed when conceptualising any ad hoc GIS system for the representation and analysis of the space of excavation: 1. the use of a three-dimensional GIS as the foundation for representing archaeological excavation within GIS; 2. the use of different scales of enquiry and different types of resolution in order to locate any excavation into its wider context; 3. information integration, in other words, the ability of the system to bring together diverse information from a variety of sources. This demands consistency across the source data sets; 4. the representation of both field-based and objectbased and the incorporation of both vector and raster data structures in order to allow for the incorporation of different excavation approaches (grid and stratigraphic); 5. the ability to express and extract different information from the same data set through modelling in order to accommodate for both quantitative and qualitative approaches to archaeological excavation; 6. the necessity of connecting the GIS base models to process and simulation models as a concept to be developed in future work.

The theoretical discussion of contemporary archaeological excavation concluded that a threedimensional approach to excavation can overcome issues of fragmentation and explosion of the archaeological record and can offer a complex platform for the representation of the excavation. This is possible when 3D GIS is used to model the record as a series of mental maps, i.e. different representations of the same geographic space that allow the recomposition of the fragmented record and at the same time effect the way we perceive the site and its present and past history. The next step was therefore to clarify how to conceptualise theoretically and model and analyse practically archaeological excavations in three-dimensional spaces to improve our understanding of the production of archaeological space. This would ultimately provide insights into the 3D properties of the archaeological record. Based on the findings of a review of the background literature, the study of technological possibilities and the experiments with the case studies, it can be concluded that a full 3D archaeological GIS excavation platform that both registers 2D and 3D data and allows for the analysis of such data in 2 and 3D offers great potentials and is possible.

In a technical sense this research contributes to the creation of a workflow that takes into consideration design concepts and allows for multiple granularity and characterisation of archaeological data. The main phases of the workflow are: 1. creation of a primary observations database and data pre-processing; 2. creation of three-dimensional models; 3. model visualisation and assessment; 4. 3D GIS exploratory and advanced spatial analysis.

Despite the seeming difficulty in identifying connections across dimensions, two properties can be recognised: 1. Multiple granularity (issues of scale in space and time); 2. Multiple characterisation (issues of aggregation of space and time in units different from the ones used for recording and issues of multiple representations of the same units).

For practical use, workflows for the construction of threedimensional spatial models within the current limitations of available and affordable software were designed. Whereas the creation of three-dimensional voxel models from sparse or gridded data has been well developed, no simple method for 3D object reconstruction is available.

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Conclusions and Further Research 3D a multifaceted understanding of a 2D-based record is still possible, on the other hand, the understanding of a complex dataset is made easier by its 3D representation. This became clear when studying the Hoge Vaart dataset where I did not participate in the excavation and was provided with the excavation reports and the raw data. Through the creation of the 3D model of the excavation it was possible for me to comprehend horizontal and spatial relationships of finds and features and contextualise them within their landscape of deposition and post-deposition. The rich digital archive represented by the rows and columns of the database could be displayed in composite images of the site by displaying each time a different recorded aspect of it (from the quantity of bunrt and unburnt flints to the shape and depth of features – se figures in chapter 6). A three-dimensional GIS is indeed a place where the fragmented archaeological record can be put back together in a meaningful manner.

This research showed and implemented major preconditions to establish a full 3D excavation environment within a theoretical and practical framework. However, many technical limitations still need to be addressed before we can accomplish a full 3D approach to excavation. First, we need not only commercial but open source tools to support a full 3D GIS platform for excavation. Second, we need to improve excavation recording. In particular, I propose that excavation procedures should adjust themselves to account for both object-based and field-based conceptualisations of archaeological data, as this has major consequences for the ability to model and analyse data in 3D. Certain issues and limitations, mainly linked to current development of 3D GIS in common GIS platforms emerged while pursuing this study. 1. There is a lack of an environment that fully integrates raster and vector 3D structures. GRASS allows the handling of such structures. However, the absence of a GIS-based visualisation environment is a serious problem, and much vector 3D visualisation still needs to be carried out in ArcScene, which, conversely, cannot handle 3D voxel data. Switching between the various environments is possible but not ideal; 2. There is a total absence of topological operations for three-dimensional GIS. This seriously hampers the possibility to construct and analyse three-dimensional objects; 3. And, finally, there is a lack of user-friendly routines for the performance of spatial and attribute 3D analysis.

The conceptual framework designed is generic as it acknowledges that a narrow approach to the representation of excavation would be counterproductive and would fail to account for the very diverse approaches to excavations that have developed through time in different geographic areas of the world. Decisions on what to record and how are taken every time a part of the archaeological record is produced. The level of detail of the recording is also very much dependant on the strategy of excavation employed which is often influenced by time, money and other constraints. Acknowledging these aspects is fundamental and it was indeed taken into consideration when the conceptual framework was designed. Some minimum requirements need nevertheless to be fulfilled to allow for three-dimensional analysis. Firstly the data are to be recorded in 3D , i.e. measurements in a three-dimensional geometric space are necessary. Secondly, for certain analytical operations to be possible, the data have to be collected in a particular manner, i.e. vectorised single contexts cannot be reclassified, merged or split during post-excavation analysis if the finds within them have not been recorded as point clouds or gridded units. Strategic choices need to be made based on the research questions at hand and bearing in mind practical constraints that characterise excavation.

The major implication of these limitations is that the extent to which depositional and post-depositional process can be examined is still rather limited. Whilst in fact in theory complex queries, process models and simulation scenarios are possible in a three-dimensional environment, this research was not able to fully assess their capability to offer enhanced interpretative scenarios for the sites and landscapes under study. Despite the limitations, the research carried out brought about a better understanding of the work processes involved in model building, particularly the difficulties in moving data between software applications. The research in the end does not offer innovative technical solutions to the problems inherent in 3D GIS and it is deficient in particular in the area of providing a straight forward manner to create thee-dimensional base models of entities. It rather provides a systematic approach to account for multiple ways of producing and interacting with excavation models.

In summary, archaeological data and GIS structures do parallel one another, more specifically excavation methodologies that use a single context approach produce records that are reproducible in GIS with a vector structure whilst arbitrary gridded excavation can be represented by raster structures. GIS has the potential to represent an archaeological excavation and can better do it in a three-dimensional space. Here depositional and post-depositional phenomena can be studied not only horizontally but vertically. Moreover, the multidimensionality of a 3D space can enhance the understanding of the site dynamics as it allows for the combination of the study of phenomena determined by the laws of physics and the more perceptive value of seeing finds and features in a modelled volume rather than a plan and section. Although limited in the array of

The notion that multidimensional GIS analysis and visualisation are better than conventional ones, and even more suitable for use in intra-site exploratory environments, as often argued by the literature but not fully explored is the central theme of the book. The results show that, whereas on the one hand threedimensional approaches to archaeological excavation are not necessary because when the excavator is thinking in

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Making Visible: Three-dimensional GIS in Archaeological Excavation the analyses presented, the case studies discussed in chapter 6 demonstrate the potential of the approach discussed. There will continue to be much debate and much research to be done regarding the role of GIS in general and multidimensional GIS in particular for archaeological spatio-temporal modelling. Paraphrasing Fotheringham (2000, 29): Sceptics will continue to ask, ‘Is a GIS necessary for excavation spatio-temporal modelling?’, although the relevant question is really, ‘Is GIS useful for excavation spatio-temporal modelling?’ The answer to the former will continue to be ‘no’, whilst the answer to the latter is ‘yes’ in the context of the framework of exploratory analysis proposed in this research. In fact, a three-dimensional approach to excavation in a GIS environment does not necessarily change the way we interpret an archaeological site. As it is clear in both the excavations discussed, it is the view on the site that influences the way it is seen and interpreted. Once a three-dimensional sensibility is developed (even without GIS), certain questions emerge that a 3D GIS can help to better answer, in particular through the development of enhanced analytical domains. 7.3

Further research

The proposed conceptual framework is the basis for developing an organic approach to the use of threedimensional GIS in the management and analysis of archaeological excavation. It is meant to assist archaeologists in implementing the concepts of an excavation data model, especially the creation of threedimensional features and environments for the analysis of excavation data. Future areas of further investigation that will enhance the approach proposed are: 1. the continuous assessment of developing analytical approaches in three-dimensional GIS in order to improve the functionality of the system in a manner tailored for and determined by the archaeological agenda, rather than the other way round; 2. the incorporation of time series in the modelling workflow; 3. the development and incorporation of subsoil process modelling as a routine in the study of depositional and post-depositional phenomena that influence the developmental history of landscapes and sites.

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Glossary

A Accuracy 1) a quantitative estimate of data error; 2) a measurement of faith in data validity (see precision). Attribute 1) a characteristic of a feature described by numbers or text, generally stored in a tabular format and linked to the feature by an identifier 2) a column in a database table B Bezier curve (see also Spline) a spline using polynomial approximations. B-spline a spline using a piecewise polynomial approximation exactly passing through a set of points. C CAD Computer Aided Design. An automated system for the design, drafting and display of graphical information. Choropleth map A thematic map showing a quantitative or qualitative attribute value associated with a bounded geographic region. Colour ramp A gradual change of colour used to represent ordinal scale, interval scale or ratio scale attribute values. D Database A logical collection of inter related information, managed and stored as a unit, usually on some form of mass storage system. A GIS database includes data about the spatial locations and shape of geographic features recorded as points, lines, pixels, grid cells or tins as well as their attributes. Data model Abstraction of the real world which incorporates only those properties thought of as relevant to the application or applications at hand, usually a human conceptualisation of reality (Peuquet 1990). Data stucture Representation of the data model often expressed in terms of diagrams, lists and arrays designed to reflect the recording of the data in computer code (Peuquet 1990). Dbf DataBase File. It is the extension used to indicate a database file.

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DBMS DataBase Management System, a software that is used to store, manage and query data in a database. It can be relational and object oriented. Delaunay triangulation A tessellation of triangular polygons constructed such as the closest vertex to any point within a given traingule is one of the vertices used to construct that triangule. Mostly used to create TINs and TENs. Direct data Data directly measured; location and properties of direct data can be observed and described (examples: core geochemical composition, geometric profile of a section). DEM Digital Terrain Model, a digital map that represents a model of the elevation of a surface generally through a raster array where every pixel represents a hight value. DXF Data Exchange Format, for storing vector data in ASCII or binary files. Originally designed by AutoCAD and used by other CAD software for data interchange. F Feature 1) In archaeology and in particular in excavation practice a feature indicates a recognisable object and/or element (natural or human made). In stratigraphic excavation this is normally a context (cut, deposit, fill); 2) In GIS it is a continuous area (it may have holes in it) that does not touch any other area of the same type. (When two features of the same type touch, they immediately become a single larger feature.) Features may represent anything the user chooses to isolate and identify. Functional Surface A surface representation which stores a single Z value (as opposed to multiple Z values) for any given x, y location. TIN represents data as functional surface. Functional surfaces are also referred to as 2 and ½ dimensional surfaces. Fuzzy data Data with some significant uncertainty linked to its measured value (example: soils boundary). G Geodatabase A database designed to store, query, and manipulate geographic information is also known as a spatial database.

and spatial data. It

GUI Graphical User Interface, a facility for running computer programs, specifying operations and choosing options by using a pointing device (mouse) to select the choices presented on the screen menu. I Indicative or indirect data 1) Data about any domain gained from indirect measurements such as remote sensing or geophysics; 2) Data from models or process simulations.

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Glossary

Interpolation Interpolation is a collective term for various mathematical techniques of determining an approximate value of a function at a point in the domain between given points at which the function values are known. Bilinear interpolation, for example, is based on the presumption that the value being sought lies on or near a straight line joining two known values. IDW Inverse Distance Weighting. One of the most commonly used techniques for interpolation of scatter points. It is based on the assumption that the interpolating surface should be influenced most by the nearby points and less by the more distant points. The interpolating surface is a weighted average of the scatter points and the weight assigned to each scatter point diminishes as the distance from the interpolation point to the scatter point increases. K Knowledge base Collection of data expressing rules about or behaviour of a phenomenon or process which can be interrogated by rule-based operators. Kriging A form of interpolation that relies on geostatistics to calculate the distance weighting of surrounding values to that under study. L Level of detail (LoD) It is the amount of information (detail) or complexity of any object that is displayed at any particular time. The LoD is usually a function of the distance of the object from the viewer (Remondino 2003). M Map algebra Manipulations and functions that operate on raster objects cell by cell. Any raster object can be used as a variable, or operand, in a raster algebraic expression. The result of a map algebra operation is stored in a new raster object. Metadata Literally, data describing other data. Additional information attached to a database, such as structure, source, accuracy, etc. Mesh It is a collection of triangular (or quadrilateral) contiguous, non-overlapping faces joined together along their edges. A mesh contains vertices, edges and faces. Its basic unit is a single, generally three-dimensional, face. The TIN is a type of mesh. Finite element methods are generally used to generate a surface mesh. Multipatch A 3D geometry used to represent the outer surface, or shell, of features that occupy a discrete area or volume in three-dimensional space. Multipatches comprise 3D rings and triangles that are used in combination to model a three-dimensional shell. Multipatches can be used to represent simple objects such as spheres and cubes or complex objects such as isosurfaces, buildings, and trees (ESRI White paper 2008).

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N nearest neighbour interpolation Interpolation method that computes the distance between the center of each cell in the output dataset and the 4 nearest cells (or origin points) in the input dataset. The data value for the closest input cell is assigned without alteration to be the data value of the output cell. Therefore, the input value of one input cell may be assigned to more than one output cell. NURBS 1) Non-Uniform Rational B-Splines: a spline using rational functions (polynomial divided by polynomial) approximations; 2) The ratio between two B-splines allowing description of regular objects with use of non-uniform spacing of points to allow more complex, natural-looking objects to be represented. O Octree A data organization (indexing) scheme in three dimensions based on regular recursive subdivision of each boundary coordinate interval by half. Open GL A 3D graphics programmer interface, initially designed by SGI and now developed by several companies, to improve performance of graphical hardware supporting the Open GL standard (Remondino 2003). Open Source Software It is a computer software that is available in source code form for which the source code and certain other rights normally reserved for copyright holders are provided under a software licence that permits users to study, change and improve software. P Pixel 1) a “picture element” 2) a two dimensional matrix, or grid, element whose geographic (spatial) position is inherently defined by its position in the matrix and whose value represents the condition found at that location (a descriptive property expressed by a number: height, chemical quantity, codified soil descriptor).

Precision The maximum potential of a system to express a metric subdivision (compare with accuracy). Q Quadtree A data organisation scheme in two dimensions based on regular recursive subdivision of each boundary coordinate range by half. Query The selection of spatial features according to their attribute values and/or spatial and geometric properties.

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R Raster An array of pixels in which spatial position is implicit in the order of the pixels. Conventionally, in GIS systems, the origin coordinate position is located at the bottom left corner of the array. The size of the pixel determines that of the array. Representation Specific abstraction of a real world object or phenomenon in the computer. Resolution 1) The detail with which a map depicts the location and shape of geographic features. The larger the map scale, the higher the possible resolution. As scale decreases, resolution diminishes; 2) The dimensions represented by each cell or pixel in a raster; 3) The smallest spacing between two display elements, expressed as dots per inch, pixels per line, or lines per millimeter. S Spline A mathematical (generally polynomial) function used to approximate curves and surfaces to any degree of precision required. A class of parametric curves and surfaces is the Non-Uniform Rational B-Spline (NURBS) curve or surface. These are the generalisation of non-rational B-splines, which are the basis of a polynomial function based on rational Bezier curves. T Table A collection of rows that have attributes stored in columns. Tension A parameter that can be used to increase the roughness of a surface interpolated using splines so that it better fits the attribute values at the sample locations (Conolly and Lake 2006). TEN TEtrahedron Network, it is an extension of TIN. A volumetric object is described by connected but not overlapping tetrahedrons (of four vertices, six edges and four faces). TIN Triangulated Irregular Network, a vector elevation model that represents a surface as a triangular tessellation, mostly using Delaunay triangulation principles. Topology The branch of geometry concerned with those spatial properties of an object that are independent of distance and absolute position but concern the relative position of the object and its relationship to others. Total station An electronic survey instrument that combines an angle measurer and a laser beam in order to record horizontal and vertical angles and linear distances, therefore located points in space by their x, y and z coordinates. Transparency Use of visual cues to generate the illusion of a surface or volume with partial light transmission quality.

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Three-dimensional (3D) A representation with multiple z values for a given x-y coordinate. Thresholding segmentation technique used in image processing to convert a gray scale image to binary format, where only two values are possible for the pixel, zero or one (Shapiro and Stockman 2001). It can also be the assignment of ranked colour values to pixels representing particular properties of the image under study. Two-dimensional (2D) A planar representation of space with one z value available for a given x-y coordinate. (Example: contour lines or choropleth maps) Two-and-a-half dimensional (2 and ½ D) A surface with one z value for a given x-y coordinate displayed with depth cues to produce the appearance of three dimensions. V Vector data Geometric data expressed as sequences, arrays or functions of coordinate points. Visualisation Graphical display of a representation. Voxel 1) “volume element”; 2) a 3D pixel. W wireframe A graphical representation of a two-dimensional or three-dimensional solid or surface by means of regularly-spaced, connected line segments. The wireframe surface looks as if it had been molded by a flexible wire mesh. Computer systems use wireframe representations for many intermediate 3D renderings because they take much less processing time to create and manipulate than a continuoussurface representation. X XML Extensible Markup Language (XML) is a simple flexible tagged text format, which is a common standard for information exchange.

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ARNOLD, C. J. 1985. Computing and the archaeological excavation. Archaeological Computing Newsletter, 5: 23.

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