Landscape Archaeology and GIS

Landscape Archaeology and GIS examines the ways in which Geographical Information Systems can be used to explore archaeo

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Landscape Archaeology and GIS

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Henry Chapman

Landscape Archaeology and GIS

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Landscape Archaeology and G IS examines the ways in which Geographical Information Systems can be used to explore archaeological landscapes, and summarises the most appropriate methods to use. It is structured around principal themes in landscape archaeology, and integrates desk-based assessment, data collection, data modelling and landscape analysis, right through to archiving and publication. This is the first book on GIS to focus specifically on landscape archaeology that is accessible to a wide archaeological readership. It explores the applications of GIS to a wide variety of archaeological evidence including maps, aerial photographs and earthworks. The work is well illustrated throughout with digital maps and models being used to support case studies, as well as for suggesting new hypotheses relevant to this discipline. Henry Chapman works in the Institute of Archaeology and Antiquity, University of Birmingham. He is surveyor and modeller for Channel 4 s Time Team, developing the use of GIS for the first time on the programme, and has published many articles on the subject.

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Landscape Archaeology and GIS

Landscape Archaeology and GIS

Henry Chapman

For Sam

First published in 2006 by Tenipus Publishing R e p rin te d in 2009 by T h e H istory Press T h e M ill, B rim sco m be Port, Stroud, G loucestershire, or.5 2QG w w w .thehistorypress.co.uk R e p rin te d 2011 © H en ry Chapm an, 2006 T h e right o f H en ry C hapm an to be identified as the A u thor o f this w o rk has been asserted in accordance w ith the C opyrights, Designs and Patents A ct 1988. All rights reserved. N o part o f this b o o k m ay be reprinted or reproduced or utilised in any fo rm or by any electronic, m echanical or other means, now kn o w n or hereafter invented, including pho toco pying and recording, or in any inform ation storage or retrieval system, w ithou t the perm ission in w ritin g from the Publishers. British Library C atalogu in g 111 Publication Data. A catalogue record for this bo ok is available from the British Library. ishn

978 o 7524 3603 6

Typesetting and origination by Tempus Publishing L im ited Printed and bound in G reat B ritain by M arston B o o k Services Lim ited, D id cot

Contents

Forew ord Acknow ledgem ents I

Landscape archaeology and G IS

2

Landscape archaeology and data

3 4 5

P rocu rin g data

6 7

8

9

Spatial data Processing spatial data Landscape analysis Landscape archaeology as reconstruction Landscape archaeology and theory G IS and landscape interpretation

io

Cultural resource m anagem ent

ii

G IS and illustration

12

Final words B ibliography Index

Foreword H en ry has been our main surveyor on Time Team since 2001 over the last 80 program m es and he and his G P S pole have becom e fam iliar items to those o f us regulars on the program m e. W ith his sophisticated surveying kit, H en ry enables us to fix the positions o f trenches, finds and structures and to lay out the areas for geophysical and topographical survey I occasionally take time to ask H en ry h ow m any satellites there are overhead and ho w the survey w o rk is going, but it all seems rather m iraculous to me that m ilitary satellites can be used for archaeological surveying — real ‘swords into ploughshares’ stuff. B u t o f course this is only the begin nin g o f a lon g process. O nce the data are gathered there is all the m anipulation on com puter to produce useful inform ation for us. M u ch o f this these days is related to G IS — G eographical Inform ation Systems. O lder readers w ill rem em ber ho w maps used to be drawn w ith draw ing boards, plastic film and tracing paper, and rows o f clogged up pens. N o w this w ork is done on com puters using G IS and a great deal o f additional inform ation can be delivered as a result. This b o ok is about m uch m ore than w hat m odern gism os can do to help in archaeological survey w ork. H en ry looks at w hat landscape archaeology is - I feel close to this as, w ith Trevor R o w ley , I invented the term back in 1974 - and how the study o f the landscape can be enhanced w ith G IS. This discussion is w ide and m any related aspects o f landscape archaeology are exam ined. This bo ok w ill provide the best one-stop resource for current practice in G IS for archaeology for som e time to com e. I am sure, for Time Team fans and others w ishin g to learn m ore about practical archaeological m ethods, this b o o k w ill enhance kn ow ledge o f G P S, G IS and landscape archaeology practice, and I recom m end it enthusiastically. Professor M ick Aston

6

Acknowledgements This b o o k is the result o f a decade o f research and consultancy w o rk on a w ide range ot archaeological landscapes throughout the U K and abroad. D u rin g this time I have been fortunate to w ork w ith a vast range o f individuals w ithout w h o m this b o ok w ould not have been possible. T h e m ajority o f the examples outlined have resulted from projects generously funded by English H eritage, Time Team and English N ature during m y em ploym ent at the Universities o f H ull and Birm in gham . Discussions w ith Professor M ick Aston and G u y de la Bedoyere provided the im petus for this bo ok and thanks are due to T im Taylor (Series Producer) and Philip Clarke (Executive Producer) o f Time Team for involving m e in the program m e and for their continued support over the past six years, providing m e w ith the opportunity to w ork on a w ide variety o f sites and w ith a broad range o f individuals. I am also indebted to E S R I , Trim ble and the O rdnance Survey w h o have been extrem ely supportive over the years. I am particularly grateful for the support from and discussion w ith a num ber o f past and present colleagues and other individuals including Stewart A insw orth (English H eritage), R aysan A l-K u baisi (Time Team), R u th Atkinson (H um ber A rch aeology Partnership), D r T im B ellerby (U niversity o f Hull), D r N ora B erm ingham , D r Jam es Brasington (U niversity o f Cam bridge), D r Jan e Bunting (U niversity o f Hull), Ian Carstairs (Carstairs C oun tryside Trust), K eith Challis (U niversity o f Birm ingham ), D r Jam es C heetham (Wessex A rchaeology), D r M ark D innin, D r Steve Ellis (U niversity o f Hull), N eil Em m anuel (Time Team), H elen Fen w ick (U niversity o f H ull), D r Graham Ferrier (U niversity o f Hull), W illiam Fletcher (Suffolk C o u n ty C oun cil), D r Benjam in G earey (University o f Birm ingham ), D r C h ris G affney (G S B Prospection), Professor V in ce Gaffney (U niversity o f Birm ingham ), Jo h n G ater (G S B Prospection), D r M ark Gillings (U niversity o f Leicester), K eith H ofgartner (Trim ble), D r H elen K eeley (English H eritage),T im K ohler (English N ature), R o y Lam ing (E S R I U K ), D r M alcolm Lillie (U niversity o f Hull), D r M arcos Llobera (U niversity o f W ashington), D r

Landscape Archaeology and G I S

G ary L ock (U niversity o f O xford), D ick M iddleton (U niversity o f Hull), K eith M iller (English H eritage), D r H eike N eum ann, Ian Panter (English H eritage), D r Julian R ichard s (U niversity ofY ork), D inah Saich (South Yorkshire Archaeology Service), G avin Thom as (R S P B ), Professor R o b e rt Van de N o o rt (University o f Exeter), D r N ick i W hitehouse (Q u een ’s U niversity Belfast) and Steve W ilkes (U niversity o f Birm ingham ). I have also benefited from w orkin g w ith numerous other individuals from a range o f institutions and field units across the country. Additional m aterial for illustrations was supplied by G S B Prospection and Time Team (figures 10, 17 and 27). T h e aerial photograph presented in figure S was taken by N eil M itchell (A PS U K ). C o lo u r plate 2 was provided by Keith Challis and figures 12 and 16 w ere provided by Em m a W ood. Colour plate 25 and the texture for colour plate 24 were generated by R aysan A l-K ubaisi.

1

Landscape archaeology and GIS IN T R O D U C T IO N There can be no question that Geographical Inform ation System technology (also know n as Geographical Inform ation Science or GIS) has already made a tremendous impact on archaeology and that this impact continues to increase. Its use has both influenced, and been influenced by, all areas o f archaeological research and practice. G IS is com m only used w ithin data repositories internationally, at both the regional and national levels, since they provide a spatial com ponent to the more traditional database structures. It is also com m on w ithin archaeological research, both as a data m anagement tool and as a m ethodology in its ow n right. Clearly, G IS is important to archaeological research but to w hat extent does it remain a , basis for holding and sorting the inform ation gleaned from field archaeology, and to w hat extent does it provide a new archaeological m ethod in itself? This b o o k addresses these questions in relation to landscape archaeology. It considers the ways in w h ich the excitin g ‘to o lb o x’ offered by G IS technology can be used to provide n ew ways o f addressing the questions o f landscape archaeology, testing old hypotheses and generating new ones. It is focused towards a general archaeological audience. This audience includes those w ho already use G IS , but w ish to expand their landscape archaeology functionality, and those landscape archaeologists w h o require a tool to assist in som e o f the difficulties encountered, such as the visibility o f archaeological remains on the ground due to later activity. This bo ok differs from m any on the subject in that it is structured around the particular needs o f landscape archaeology, rather than around the tools provided by G IS. These needs include elements o f cartography and map analyses (including the pitfalls encountered w hen w orkin g w ith spatial data), in addition

y

Landscape Archaeology and G I S

i Som e o f the sites m entioned in the text, i) G iant’s Grave, Feltar, Shetland, 2) Loch M igdale, Sutherland, H ighlands, 3) Applecross, Wester R o ss, 4) D ru m lan rig, D um fries and Galloway, 5) R u d ston , East Yorkshire, 6) H olderness, East Yorkshire, 7) N o rth Ferriby, East Yorkshire, 8) T h o rn e M oors, Sou th Yorkshire, 9) Sutton C o m m o n , South Yorkshire, 10) H atfield M oors, South Yorkshire, 11) Carsington Pasture, Derbyshire, 12) C o n flu en ce o f the rivers Trent and Soar, N ottingham shire, 13) G len don H all, N ortham ptonshire, 14) Beaudesert Castle, H enley in Arden, W arwickshire, 15) C o n d erto n Cam p, Gloucestershire, 16) Chesham B ois, Hertfordshire, 17) Bath, 18) Esher, Surrey, ly) M eare, Som erset, 20) D inn in gton , Som erset, 2 1) Bream ore, H ampshire, 22), C o c k H ill, Blackpatch, West Sussex, 23) Shornclifte R e d o u b t, Folkestone, K ent, 24) G reen Island, Poole H arbour, D orset, 25) M errivale, D artm oor, D evon , 26) G ear Farm , C o rn w all

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Landscape Archaeology and G IS

to rem otely-sensed data (particularly aerial photography) and features on the ground such as earthworks. It draws on varied exam ples (i) to provide practical solutions for obtaining relevant data efficiently, and for processing this data whilst avoiding som e o f the potential errors that may be encountered. Effectively then, this is a handbook for the use o f G IS w ithin landscape archaeology, providing best practice inform ation, case studies and ideas for developing n ew methods.

W H A T IS LANDSCAPE ARCHAEOLOGY? T h e term ‘landscape archaeology’ means m any things to m any people, and consequently requires som e clarification w ithin the context o f this book. A ccord in g to the C ollins Dictionary o f Archaeology (Bahn

1992) landscape

archaeology may be defined as:

... an approach, especially in archaeological survey, where the unit of analysis is the artefact rather than the site ... [It] recognises that many of the material consequences of human behaviour are ephemeral and will not conform to standard definitions of sites, and documents the distribution of humanly-modified materials across the landscape. Fundamentally, landscape archaeology is a term com m only used to characterise those areas o f archaeological research and interpretation that consider the landscape as opposed to the site, the interrelationship betw een sites, and the physical spaces separating them (2). In essence, inform ation from all areas o f archaeological research m ay be used to exam ine archaeological landscapes, though the methods that are most com m on ly used include cartographic study, docum entary

research, field w alking and survey. H ow ever, three principal,

though overlapping, philosophical approaches have been applied to landscape archaeological research. T h e first has m irrored landscape history, approaching the subject from the perspective o f regression by the removal o f later, datable layers o f hum an activity in order to reveal earlier ones. T h e methods by w h ich this has been approached include the identification o f clues w ithin the present landscape, such

as idiosyncrasies

o f field

m orphology, in

addition

to

cartographic

evidence. H ow ever, other archaeological skills have served to enhance research, including earthw ork survey, aerial photographic analysis and fieldw alking. T h e com bination o f methods has often supplied the key to providing a w ide range o f clues, identifying the com p lex palimpsest o f past activity that characterises

II

Landscape Archaeology and G I S

2 M errivale stone rows, D artm oor. Landscape archaeology is about exp lo rin g sites and their landscape setting, but also the relationship betw een different landscape features

our landscape today (e.g. Aston 1985, B o w d en 1999, H oskins 1955, M u ir 2000, O rdnance Survey 19 73, R ack h am 1986, R ile y 1980, Stoertz 1997, Taylor 1984, W ilson 1982, W ood 19 6 3 ).T h is approach may be term ed as ‘landscape analysis’ , based upon considerations o f observable data and investigating trends w ithin it. A second approach has also concentrated on the physical remains o f the past, but through the scientific reconstruction o f the changing environm ent through time based on the exam ination o f botanical remains (3). M u ch o f the history o f this discipline extends to the w ork o f H arry G o d w in w ho, based in Cam bridge during the 1960s, produced som e o f the earliest innovative w ork in the reconstruction o f past landscapes and environments using microfossil remains o f plants, particularly ot pollen (G odw in 1975). M ore recently a com plex range o f microfossils and macrofossils have been studied in order to obtain a picture o f earlier landscapes, including pollen, plant macrofossils, seeds, diatoms, testate amoebae and insects. Samples for analysis may be extracted from a particular on-site deposit or as part o f a sequence w ithin the landscape. Furtherm ore, once dated, analysed samples can begin to give various levels o f inform ation about the local environm ent and the types o f activities that m ight have been going on at different periods.

12

Landscape Archaeology and C I S

j C o llectin g samples for palaeoenvironm ental analyses using co rin g equipm ent

13

Landscape Archaeology and G I S

A m ore recent developm ent has concentrated upon the interpretation o f the qualitative aspects o f archaeological landscapes. T h e approach has focused on elements o f experience, the point o f departure being that maps and plans o f landscapes are an abstraction o f the w orld and consequently cannot be relied upon alone w hen attem pting to interpret w hat it is to be w ithin a landscape. T h rou gh narrative approaches and other techniques borrow ed from the social sciences, the exploration o f archaeological landscapes has been enabled through the study o f the interrelationship betw een m onum ents themselves, and betw een m onum ents and natural features (e.g. B en d er 1993, B radley 1993, 2000, T illey 1994). Typically, sensual perception o f landscapes has been param ount to interpretations, w ith plurality often being favoured. So, landscape philosophies

archaeology is a m ulti-faceted

discipline w ith

and contrasting m ethodologies. T h e

differing

various paradigms

that

fall under the landscape archaeology label provide various avenues to both archaeological interpretation and the m anagem ent o f the past landscapes. In this book, landscape archaeology is taken to include all o f these themes in the broadest sense, draw ing 011 these three principal philosophical approaches and w ithin the various spatial resolutions at w h ich landscape m ay be exam ined; from the extra-site locale, to the w ider, m ultiple-site region.

W HAT IS GIS? As m entioned previously, G IS stands for G eographical Inform ation Systems and, m ore broadly, for G eographical Inform ation Science. T h is is the name given to a range o f software program m es featuring spatial databases. G IS is part o f a w ider bracket o f technologies know n as Spatial Inform ation Systems, or SIS, and increasingly the boundaries betw een G IS and other software, such as C o m p u ter A ided D esign (C A D ), are becom in g blurred. H ow ever, let us begin w ith some definitions.

An organized collection of computer hardware, software, geographic data, and personnel designed to efficiently capture, store, update, manipulate, analyze, and display all forms of geographically referenced information. ESRI 1995 (glossary) A GIS is a sophisticated database management system designed for the acquisition, manipulation, visualisation, management and display of spatially referenced (or geographic) data. A ld en d erfer 1996, 4

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Landscape Archaeology and G I S

4 Layers ot data w ithin a C'dS incorporating both rasters and vectors

... G IS is increasingly b ein g seen as m uch as a place to think as a sim ple data m anagem ent and m ap ping tool. G illings and G o o d ric k 1996.

G IS can be interpreted in a num ber o f different ways though, fundamentally, the name G IS describes a range o f software packages displaying the com m on abilities to store, manage, manipulate, analyse and display spatially referenced inform ation (4). Traditionally, G IS software packages have been divided into two types on the basis o f data m anagem ent: vector systems and raster systems. T h e form er stores data in relation to points (or nodes), lines (or arcs) and closed polygons. T h e storage ot such data reflects the mathematical discipline o f topology, w hereby the relationship betw een points is im portant rather than their true positions. T h e latter considers space in terms o f cells, dividing a digital surface into regular blocks that can each be given a value or attribute that describes it. For example, cells may be labelled w ith inform ation attributing to colou r or shade, as is often used for tw o-dim ensional images or maps, or by num erical values, such as heights, to provide a third dim ension. W ithin m ore advanced G IS packages the

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Landscape Archaeology and G I S

representation of height in addition to Cartesian data means that topographic maps may be created. T h e nature o f data storage, however, means that the third dim ension is stored as an attribute representing height, unlike the continuous scales in the other two dimensions. As a result such m odels are considered as 2.5 dim ensional (2 .5 D ).T h e value o f cell-based m odelling o f this kind is that different types o f surfaces m ay be generated and com pared mathem atically in order to create n ew m odels. In application, the differences betw een raster and vector G IS are not always so clearly defined, w ith models typically containing data o f both types. B o th types o f data m anagem ent provide the potential for generating, m anipulating and analysing digital landscape surfaces, or D igital Elevation M odels (D EM s), w ith the main lim itation being the com plexity o f the software itself. Further, they both have the potential for being overlain w ith other multiple layers o f data o f different types. H ow ever, raster data structures require greater com puter m em ory, although they have the advantage o f bein g m ore flexible for further analysis. It is possible to convert data betw een the two data types, although the potential for generating error w ithin such procedures has been highlighted (e.g. Van der K napp 1992). D igital m apping was first introduced by W aldo Tobler w ith the ‘map in map ou t’ (M IM O ) m odel that first applied com puters to cartography (Centre for Spatial Analysis, U niversity C o llege

Lon don — h ttp ://w w w .casa.u cl.ac.uk/

gistim eline).The first true G IS was created in 1962 in Canada and was called the Canadian G eographic Inform ation System (C G IS ). It was developed in response to two factors —the grow th in electronic com puter technology, and the increased com plexities o f life that needed to be considered w ithin the fram ew ork o f public and private sector decision-m aking. Tw o years later a similar system was created in the U S A called M ID A S that began processing data on natural resources. A t this tim e the focus ot G IS was to m anage developm ents and infrastructure expansion, and to aid in the planning process (Bernhardsen 1992). B y the end o f the 1960s the developm ent o f G IS had led to the creation o f com m ercially based software com panies, begin nin g w ith the Environm ental Systems and R esearch Institute (E S R I), follow ed by others (Centre for Spatial Analysis, U niversity C o llege London — http ://w w w .casa.u cl.ac.uk /gistim elin e).T h e developm ent o f the m icroprocessor in 19 7 1- 2 increased the functionality o f com puters. A gradual replacem ent o f manual cartographic w ork and the developm ent o f G IS follow ed this during the 1970s and 1980s (Bernhardsen 1992). In 1981 A R C / I N F O was first launched by E S R I . T h rough o ut the 1980s new products and the creation o f research initiatives reflected the grow th in G IS. In 1987 the publication o f the ‘ C h o rley R e p o r t ’ (C om m ittee o f enquiry into the handling o f geographic inform ation 1987) appears to have fostered the developm ent o f G IS w ithin the

Landscape Archaeology and C I S

U K , although it never led to the establishment o f a national initiative as it had done in the U S A . R ath er, it led to a series o f regional research program m es funded by the E con om ic and Social R esearch C o u n cil (E S R C ) (Centre for Spatial Analysis, U niversity

C o llege

London — h ttp ://w w w .casa.u cl.ac.uk/

gistim eline). It was in the same year that the International Journal o f Geographical Information Systems (Taylor and Francis) was first published.

ARCHAEOLOGY AND GIS H arris and L o ck (1990) provide a docum entary o f the early developm ent o f G IS w ithin archaeology. T h e first use o f G IS w ithin an archaeological context was in the 1980s in N orth A m erica w here it was used to predict archaeological site location w ithin a m anagem ent context (K ohler and Parker 1986, cf. Gillings and W ise 1998). Interest in G IS and its potential w ithin archaeology appears to have em erged in 1985 at conferences organised by the Society for A m erican Archaeology. T h e first included papers relating to the analysis o f intra-site distribution using G IS (presented by G ill and Howes), and the use o f G IS for regional archaeological research (presented by K vam m e).T h e second conference saw papers on methods and principles.T h e follow ing year a third conference, the National Workshop on Microcomputers in Archaeology, saw papers 011 the potential o f G IS for archaeological research and data m anagem ent (presented by Kvam m e) and 011 the availability o f appropriate software (one presented by Ferguson and another by M iller) (reported 011 in H arris and L ock 1990). W ithin the U K the genesis o f interest in G IS appears to have occurred at the same time as in the U S A , but the uptake was faster w ithin geographical rather than archaeological study T h e initial interest in relation to archaeology was in the m id-1980s and was focused upon the com puterisation o f spatial data at a regional level for archiving, education and research, and to aid decision-m aking in the planning process (Harris 1986). B y the later 1980s the potential o f G IS w ithin archaeology in the U K had been broadened to include 2 .5 D m odelling w ith archaeological data draped over digital terrain models (Harris 1988). T h e developm ent o f G IS continued into different archaeological applications over the succeeding years (Harris and L ock 1990). M ore recently, such developm ents have m erged the distinctions betw een C o m p u ter A ided Design (C A D ) and G IS, and the w ider issues o f general data m anagem ent, com patibility and storage (e.g. Gillings and W ise 1998) have com e to the fore. T h e use o f G IS w ithin archaeology has becom e w idespread at all levels and w ithin all sub-disciplines. This has been reflected over the past few years, for exam ple, by the growth o f the G ISarch e-m ail discussion list (http://w w w .jiscm ail.ac.11k/lists/gisarch.

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Landscape Archaeology and G I S

html). It has also been reflected in an expansion o f archaeological G IS into other com puter disciplines (cf. Gillings and G o o d rick 1996). A rchaeological research m ay be split into

three

main

areas: m ethods,

interpretation and m anagem ent. Th ese have been used to identify the main three themes o f research into G IS w ithin the archaeological context, even though m any projects com bine different aims together (e.g. Chartrand et al. : 993)-A. further them e o f spatial statistics has also been developed that has been closely linked to m anagem ent, but dom inated to a greater extent by predictive m odelling, particularly o f site location (Harris and L o ck 1995).

G I S procedure and archaeology T h e first them e, relating to m ethods, is often referred to as G IS procedure (e.g. Savage 1990), and is prim arily concerned w ith the accuracy o f results from studies using G IS , often at an algorithm ic level (e.g. K vam m e 19 9 0 ).T h e sphere o f G IS procedure is relatively indistinct in relation to archaeology, as it extends into other analyses using G IS. For exam ple, studies exam in in g the potential error w ithin G IS algorithm s w ill apply to archaeology and other disciplines, providing a w id er literature base. Them es o f research have included m ethods o f raw data sam pling in relation to accurate surface generation (Fletcher and Spicer 1988), processes o f surface generation from various data sources and using different algorithm s, and m easuring accuracy (e.g. K vam m e 1990, H aigh 1993, Carrara et al. 1997, G ao 1997, Lopez 1997, Voigtm ann et al. 1997), and the error w ithin the algorithm s used for further analyses and ways o f approaching it (e.g. Fisher

1993. De

Floriani and M agillo 1994, D e Floriani et al. 1994, Puppo and M arzano

1997). O th er w o rk has been m ore am biguous, falling w ith in the spheres o f G IS procedure and other themes such as interpretation (e.g. W heatley and Gillings 2000). M an y such papers present a n ew m ethod, using an exam ple to present the m ethod. For exam ple, Lake et al. (1998) outlined a G IS procedure that tailored the basic software in order to speed up analyses. W hile this m ay be considered to fall w ithin the sub-heading o f G IS procedure, the process was developed and presented as a w ay o f quantifying archaeological landscape interpretation. It seems that the boundaries, particularly betw een procedure and the other two main spheres o f G IS w ork outlined below, are extrem ely blurred.

G I S and landscape archaeology T h e need to obtain m eaning from data is fundamental to the study o f archaeology and so a level o f interpretation is always required. Approaches to understanding archaeological landscapes m ay be considered in relation to three main trends, loosely reflecting historical developm ents in theory. T h e first o f these m ay be term ed normative; essentially classifying data according to com m on patterns;

Landscape Archaeology and G I S

5 D istribution map o f R o m a n -p e rio d villa sites along the Fosse Way, Som erset

creating ‘cultures’ that reflect ‘ n orm al’ behaviour (e.g. Piggott 1968). W ithin landscape studies the principal traditional tool for this has been the distribution map (5), used to describe and interpret spatial patterns o f similar artefacts or assemblages, and ho w they change through time. Interpretation from such sources is typically carried out by creating theories to explain the boundaries o f these ‘cultures’ in order to understand changes seen in the archaeological record (e.g. Piggott 19 6 8 ).T h is approach m ay be criticised for being descriptive and determ inistic (e.g. Bin ford 1964), especially as the classification o f the ‘cultures’ form s essentially false boundaries betw een them , lim ited to the chosen parameters and w ithout consideration o f anom alous features. T h e creation o f ialse boundaries typically determ ines interpretations o f change based upon either a m igration o f population or diffusion o f ideas, rather than upon other theories such as evolution. Consequently, conclusions typically take the form o f descriptions. T h e second generalising them e in landscape interpretation relates to som e o f the developm ents ot the Neu> Archaeology, and is often referred to as processual (cj. Clarke 1978). Them es include the requirem ent to becom e ‘scientific’ in approach, to construct m odels to explain the archaeological record rather than

Landscape Archaeology and G I S

to describe it, and to add other levels o f data including those from anthropo­ logical analogies and environm ental w ork. B o rro w in g from the other social sciences, popular approaches stem from the creation and testing o f theories to construct robust conclusions. M u ch o f this w o rk has grow n from positivist reasoning — testing hypotheses to create new ones on the basis o f generalisation. W ithin landscape archaeology, the main developm ents have been in relation to environm ental factors and the integration o f ‘scientific’ m ethods o f palaeoecological reconstruction such as palynology.This type o f approach may be criticised for a num ber o f reasons, prim arily that the problem o f inferring interpretation from archaeological data still required a ju m p from the results to generalisation (cf. Popper 1992). Sim ilarly the parameters that could be studied w ere still a construct o f their survival and were therefore not real. M an y critics o f this type o f approach indicate that the systems created are determ inistic, particularly environmentally, in relation to archaeological landscapes (e.g. B radley 1984). T h e third them e is com m only referred to as theoretical, in contrast to ‘scientific’ . It reflects a m ore hum anistic v iew o f the archaeological record. This has been applied to landscape archaeology in such a w ay that it has developed into a discipline in its ow n right (e .g.T illey 1994). C entral to this has been the presum ption that landscapes are im bued w ith m eaning (cf. C osgrove 1989), and that this m eaning transcends econom ics and filters into all activities. As such, landscapes are seen as m ore than m erely a surface that archaeology rests upon (or w ithin), but rather as interactive platform s for hum an exp erien ce.T h e landscape is constantly recreated through physical and metaphysical constructions that constantly alter the relationship betw een it and those people w h o engage in activities w ithin it. T h e physical constructions m ay be measured, at least in part, through archaeological investigation, and it is on the basis o f these features that m eaning is sought. H ow ever, it has been argued that the same landscape can be perceived in different ways by different people or from different perspectives (M einig 1979, Tuan 1979, B end er 1992). Landscapes have been classified in a num ber o f ways. Fundam ental to m any o f these has been Tuan’s (1977) distinction betw een place (where activity occurs) and space (the area betw een places w here paths reside).The central themes w ithin landscape theory are underpinned by the definition o f landscape (cf. O lw ig 1993). T illey (1996) sum m arised the relationship betw een archaeology and landscape in four ways: (1) as ‘ ... a set o f relationships betw een nam ed locales’ (p. 16 1); (2) to be ‘ ... experien ced and know n through the m ovem ent o f the hum an body in space and through tim e’ (p. 162); (3) as ‘ ... a prim ary m edium o f socialisation’ (p. 162); and (4) creating ‘self-identity’ by controlling kn ow ledge and thereby influencing pow er structures (p. 162). T h e key principle is that o f experience, and thus studies o f archaeological landscapes have been based upon attempting

20

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to replicate the experience o f ‘B e in g -in -th e-w o rld ’ w hile trying to reconstruct the dialectic o f the existential ‘B e in g ’ (Tilley 1994: 12). T h e prim ary m ethod o f m easuring experience (if m easuring is a suitable word) is through analysing visibility patterns. For exam ple, Thom as (1993) investigated the visual impact o f m onum ents, particularly around Avebury, suggesting themes o f inclusion and exclusion (similar to T ille y ’s fourth point, m entioned above). D evereux (1991) analysed the spatial relationships betw een m onum ents and topography at Avebury by investigating their visual relationships. Similarly, T illey (1994) investigated three archaeological landscapes through a narrative and photographic essay and by recording patterns o f intervisibility betw een m onum ents. W ork involving the interpretation o f past landscapes revolves around what m ay be defined as the two areas o f perception and cognition (van Leusen 1999). Perception can be defined in terms o f landscape awareness and includes feelings o f bein g w ithin the landscape. C ogn ition w ithin landscape archaeology can be defined as know ledge o f the landscape that influences perception (Z u brow 1994). For exam ple, cultural or social im plications o f a certain place m ay influence its significance w ithin a landscape and may form a com pletely different perception o f the environm ent; the im buing o f m eaning upon landscape and extracting m eaning from it. G IS provides a forum that enables multiple disciplinary studies and can therefore assist in interpretation (e.g. Potts et al. 1996). T h e potential for reconstructing past landscapes w ithin

a digital fram ew ork allows for the

possibility o f stripping back the tem poral layers to provide a basis for quantifiable analysis. H ow ever, the specific application o f G IS to landscape archaeology lacks the historical developm ent o f the archaeological discipline, being a relatively n ew technique; ‘in G IS the concept o f theory is less m ature’ (Z u brow 1990a: 69). Perhaps as a consequence o f this the divisions betw een approaches are less pronounced and studies tend to span two or m ore o f the paradigms described above. Interpretation w ithin G IS may, however, be divided in terms o f levels o f sophistication. T h e least sophisticated interpretative models act in a similar way to the distribution plots seen w ithin w ork characterised as norm ative archaeology. H owever, the advantage o f G IS is its capability to display m ultiple layers o f data together such that correlation w ith other form s o f data is often used as a tool for understanding (e.g. M iddleton and W instanley 1993,Van de N o o rt and Ellis 1995,

1997,

1998, 1999, 2000). M ore sophisticated models use algorithm s to provide

inform ation about the topographic surface, thereby allow ing the G IS to generate data rather than m erely to correlate external datasets. For exam ple, distance to resources may be calculated in order to understand relationships betw een different data sources (e.g. van Leusen 1993), or sites may be exam ined in relation

21

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6 Vievvshed functions calculate w h ich cells w ithin a D E M can be seen from a given location or locations. H ere, the visible areas are show n in a darker grey

to elements such as aspect or slope angle. Such analyses are often descriptive rather than directly interpretative, form in g a com m on basis for locational predictive m odelling (m entioned above), but also acting as a foundation tor other methods o f interpretation. M ore sophisticated approaches tend to be based around two main elements: visual analysis and cost-surface analysis. T h e most com m on approach is through the creation o f viewsheds (6), and the analysis o f w hat is visible from a given position in m uch the same way as is done by T ille y (1994) and others on the ground (e.g. E x o n et al. 2001). Part o f the reason for this is the potential for G IS to quantify visibility (cf. W heatley 1995, Lock and H arris 1996, Fisher et al. 1997, Lake et al. 1998, van Leusen 1999, see chapter 5). Som e approaches com bine the viewsheds from several positions in order to analyse distributions o f m onum ents. For exam ple, Lock and H arris (1996) argued that N eolith ic lon g barrows in the D anebury area represented highly visible territorial markers because their calculated viewsheds did not overlap. Cum ulative view shed analysis has grow n as a sub-context ot visual analysis and this was characterised by W heatley (19 9 5).T h is is a technique that com bines the view sheds from several m onum ents, investigating recurrence o f visible areas in order to assess visual qualities o f m onum ents from a statistical perspective. This type o f approach has been expanded to assess the validity o f

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em pirical interpretations by the introduction o f random points from w hich to generate view sheds (Fisher et al. 1997). In this way interpretations based on m onum ent positions (chosen places) may be com pared to those from other areas (random space). This approach was extended by Lake et al. (1998) w h o achieved such an analysis by ‘tailoring’ a G IS software package to enable automated analyses. This process allowed for the com parative quantitative investigation o f viewsheds from a large sample o f cells w ithin the m odel, in order to assess w hether an archaeological site m ight have been located due to its significantly higher visibility. A second approach towards providing insight into the interpretation o f past landscape from a cognitive perspective is the creation and analysis o f ‘costsurfaces’ (W heatley 1993, Stead 1995, Gaffney et al. 1996, M aschner 1996, van Leusen 1999, Bell and L ock 2000, D e Silva and Pizziolo 2001). This technique is based upon the possibility o f m easuring the effort it takes to cross an area o f the D E M . For exam ple, an area o f flat land w ould be easier to cross than a steep area. A cost-surface generates different values for each o f the cells m aking up the surface based on w hichever parameters are chosen; though com m on ly slope is a significant factor. A cost-surface can then be used to find the path betw een two points on the surface that encounters the least effort. Such approaches have been used to investigate how past landscapes m ay have been perceived (e.g. W heatley 1993, Stead 1995), and to define and explain routes, such as the R id g ew a y extending across the southern chalklands o f England (Bell and L o ck 2000). A com bination o f both visual and cost-surface analyses has been used on a num ber o f occasions to provide a broader basis for interpretation (e.g. G affney et al. 1996, L o ck and H arris 1996, Chapm an 2003). Each o f these m ethods outlined here in relation to the themes o f G IS and landscape interpretation is expanded upon in chapters 5 and 6. A disadvantage o f past and current approaches to interpretative landscape archaeology is that there is a general desire to produce testable models and theories (e.g. Z u b ro w 1990b, Lake et al. 1998). A lthough repeatable and testable experim ents are useful in terms o f positivist theory (e.g. Bell

1994), the

fundam ental reasoning o f post-processual interpretation lies beyond the need for them , w ith criticisms levelled towards such processes. For exam ple, past populations could not analyse a landscape to find the most suitable position in terms o f visibility and so there may have been better, unknow n sites. Further, such theories exclude the influence o f cultural factors that may equally determ ine the positioning o f a site. G IS has also been criticised for its abstract nature, in terms o f being environm entally determ inistic (cf. Gaffney and van Leusen 1995). It w orks by assessing measurable aspects such as slope, aspect and distance, and so it may be argued that this capability w ill determ ine interpretative processes.

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H ow ever, as Llobera (1996) noted, such a criticism may lie in the semantic confusion o f the words ‘environm ental’ and ‘determ inistic’ , and may therefore be over-sim plifying the role o f the user. R ath er, Llobera argued that G IS should be seen as a heuristic tool for explorin g the possibilities o f social theory, w ith the abstract social theories ‘translated’ into G IS approaches. This conform s to trends w ithin non-archaeological G IS approaches elsewhere that aim for increased conceptual sophistication by adding a social perspective to m odels, including the subjectivity o f the observer (Couclelis 1999).

G I S and Cultural Resource Management (C R M ) C R M is also referred to as archaeological resource m anagem ent (van Leusen 1995) and is com m on ly associated w ith site location m odelling (Savage 1990). CRM

covers the m ajority o f professional applications o f G IS and is often

publicly funded. T h e many processes and activities encountered w ithin this broad title m ay be categorised into three m ain themes: recording, protection and managem ent. R e c o rd in g archaeological resources encompasses both the classification o f sites and landscapes and their archiving. In relation to G IS, this them e includes aspects o f database design, creation and m anagem ent (e.g. Stine and Lanter 1990), site categorisation and the digital archive (W illiams ct al. 1990, cf. Gillings and W ise 1998). Such w ork is typically focused on the values and caveats o f using G IS as a data curation, m anagem ent and display tool (e.g. G uillot and Leroy 19 95).Trends w ithin this area o f C R M highlight the various ways o f approaching these three themes, but acknow ledge the com m on value o f G IS for the m anagem ent o f large quantities o f data. T h e second area o f C R M , w hich may be term ed ‘protection’ , is a broader concept encom passing m itigation strategies for threatened archaeology such as preservation by record, preservation in situ, and rescue excavation, and is governed largely by docum ents such as P P G 16 (D epartm ent o f the Environm ent 1990). This aspect o f C R M involves the decision-m aking processes by w hich a threatened site is managed. G IS studies addressing this aspect o f C R M often involve m ore com p lex m odelling techniques, typically exploiting their ability to m odel possible outcom es, such as site distributions, in one area based upon trends identified from em pirical observations in other areas.The most frequently used aspect o f this type o f C R M

analysis involves the predictive m odelling

o f site location. T h is technique has been com m only used in areas w here the m anagem ent o f the archaeological resource is restricted by either a poorly docum ented record, or lim ited inform ation regarding the location o f sites. This type o f deductive approach is often centred on observed correlations between the distribution o f archaeological material and a range o f environm ental factors

24

Landscape Archaeology and G I S

such as slope, aspect, elevation and proxim ity

(if.

Kuna and Adelsbergerova 1995).

T h ere have been a num ber o f outlines (e.g. M arozas and Z ack 1990, Warren 1990a) and case studies (e.g. Hasenstab and R esn ic k 1990, W arren 1990b) that have approached these issues in this way. Altchul (1990) offered a philosophical alternative w hen presenting a critique o f this approach, arguing that such models were capable only o f revealing w hat was already know n. Instead he argued that an approach based upon ‘red-flaggin g’ was preferable. R ath er than ju st providing ‘accurate’ distributions, extrapolating patterns o f data already recognised, the G IS could be used to highlight (or ‘ red-flag’) areas w here increased archaeology may be encountered by a developer and w here m itigation m ay be m ore costly. Further, he w ished to expand the technique to highlight anomalies o f significance, rather than m erely show ing patterns. T h e basis for this type o f m odelling lies in the protection o f the unknow n archaeological resource through its statistical identification. T h e value o f this technique is clear in relation to the activities o f developers and the need for archaeological m itigation strategies. H ow ever, a num ber o f m ore fundamental criticisms have arisen over this technique. Firstly, the results from it are essentially difficult to test w ithout high expenditure, and so the results w ill lack robustness. Secondly, the prevailing requirem ent to m odel archaeological patterns in relation to environm ental factors may be considered to be determ inistic (see G affney and van Leusen 1995 for conflicting views). As such the technique remains as a large discipline in itself, w ith its ow n conflicting paradigms and discourse. T h e third issue relating to C R M is m anagem ent. This concerns archaeology as a know n resource w ithin the landscape, w ith its ow n considerations, but as one o f m any other landscape interests. M anagem ent issues typically involve financial and political decision-m aking in addition to directing research aims. Such approaches to G IS have been particularly prevalent w ithin areas w here w id e-ran gin g interests, each w ith large datasets, lie together, such as w ithin N ational Parks. For exam ple, the G IS for E xm o o r N ational Park consists o f data covering archaeology and m any other environm ental themes. I11 relation to G IS, applications relating to m anagem ent extend from basic data storage and display to m ore com plex issues.

W H Y TH IS BOOK? T h e aim o f this book is to provide a background to the ways in w h ich landscape archaeology problems, themes and questions m ay begin to be addressed by G IS. In contrast to m any on the subject, the focus here is the principal issues regarding landscape archaeology in practice and the ways in w h ich G IS can be

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Landscape Archaeology and G I S

used to address these issues and provide n ew approaches, new answers and new questions. Consequently, the follow in g chapter begins w ith a consideration o f the types o f data currently being generated w ithin the broad sphere o f landscape archaeology. It also considers these pre-existing sources in relation to ho w G IS may be applied to w hat is being done currently, and also introduces som e o f the themes o f G IS w ithin the context o f ho w they m ight be used in archaeology. This is follow ed in chapter 3 w ith a consideration o f spatial data, addressing the definitions and themes o f such data w ithin landscape archaeology generally, and in relation to G IS applications particularly. In chapters 4 and 5, the methods o f procuring data to input into the G IS is exam ined, including com m ercially available data, archives such as record offices and data gathered through field survey. Chapters 6, 7 and 8 begin to consider the ways in w hich G IS can be used to address the questions and issues o f landscape archaeology in the three areas o f landscape analysis, reconstruction and theory respectively. In each chapter, the key themes o f that area o f landscape archaeology are considered and addressed through exam ples. C hapter 9 continues this them e, brin gin g the overall issues o f interpretation together, considering ho w approaches com binin g each o f these three landscape archaeology themes becom e possible w ithin a G IS, and how this provides increased value to the archaeologist. T h is is follow ed in chapter 10 by a consideration o f ho w these techniques and others m ay be used to address issues o f archaeological landscape m anagem ent. C hapter 11 considers the ways in w h ich G IS m ay be used as an illustrative tool and how, through the visual reconstruction o f past landscapes, n ew opportunities o f interpretation and presentation are generated. T h e final chapter, chapter 12, brings the principal themes o f the b o o k together and offers som e concluding remarks for those addressing landscape archaeology through the spatial sciences.

2

Landscape archaeology and data IN T R O D U C T IO N I f the point o f departure lies w ithin landscape archaeology, the first them e to consider is w hat types o f data are norm ally collected w ithin the fram ew ork o f ‘landscape archaeology’ . Furtherm ore, how m ight these data be fruitfully engaged w ith by using G IS? This chapter considers the ways in w h ich G IS can be used to enrich the pow er o f datasets already obtained w ithin landscape archaeology. In later chapters, the ways in w hich G IS can address m ore specific issues w ithin landscape archaeology w ill be considered. Taking the definition in chapter i (Collins 1992), ‘the unit o f analysis is the artefact rather than the site’ . In practice, landscape archaeology norm ally begins w ith the analysis o f maps and other sources from record offices including aerial photography and records o f previous w ork, follow ed by field m ethods such as fieldw alking and w alkover surveys, in addition to earthw ork and geophysical survey. T h e variability in data sources m ay be reflected in the variability in their spatial resolution, w ith data collected at a variety o f scales. In this chapter, the different traditional sources of landscape archaeology data are considered in relation to the issues they each present w ith regards to G IS.

MAPS Perhaps the first source o f data used for projects in volvin g a landscape archaeological elem ent is the map. M aps can take m any different form s and can display varying types o f inform ation at a variety o f scales, survey accuracy and depiction. N orm ally, a m odern map will provide inform ation regarding

^7

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Landscape Archaeology and G I S

basic location against w h ich to plot sites and finds, in addition to providing inform ation regarding topography. This includes the positions o f watercourses and the shape o f the landscape through the contours. U sin g just these very fundamental elements it is possible to begin to interpret site locations and to explore h ow sites relate to their local landscape. Furtherm ore, maps, norm ally those at 1:2 5,0 0 0 scale or higher, w ill provide a plan o f field shapes w hich may provide m orphological clues that may in turn both assist in identifying new sites and providing context to those sites that are already know n. In addition to m odern maps, historical maps can be extrem ely valuable to landscape archaeologists (7). For exam ple, in the U K the early O rdnance Survey m apping can be extrem ely useful for providing a picture o f a landscape around the year 1900, depending on the area being studied. H ow ever, earlier maps, such as tithe maps and estate maps, can often push back the dating to m uch earlier, providing snapshots into the past that w ould not be possible in any other way. It is the com bination o f m apping evidence that is arguably o f most use to the landscape archaeologist.

7 Above: H istorical maps are fundam ental to landscape archaeology, such as this nineteenth-century O rdnance Survey map o f the Stonehenge area on Salisbury Plain, W iltshire 8 Opposite: Aerial photograph — earthw ork remains of Ellerton Priory, on the R iv e r D erw en t south­ east o fY o rk

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RECORD OFFICES A second resource com m on ly used by landscape archaeologists are the databases held by record offices. In the U K these include the Sites and M onum ents R e c o rd (S M R ) and H istoric Environm ental R e c o rd (H E R ) offices, the N ational M onum ents R e c o rd (N M R ) and other regional record offices. C o m m o n ly data are held either digitally or on index cards, though norm ally relating to sequential n um bering on a map base. T h e y provide inform ation regarding previously discovered sites, norm ally for use w ithin the planning process in advance o f developm ent, but o f obvious value to landscape archaeologists.

AERIAL PHOTO G RAPHY A m ethod ubiquitous to the landscape archaeologist is aerial photography (8) and the identification o f features from shadows, soil marks or crop marks

Landscape Archaeology and G I S

w ithin the fields (R ile y 1944, 1982, W ilson 1982). A erial photography may be categorised into two principal types - vertical and oblique. N orm ally, though not exclusively, vertical photographs are used for activities such as m apping and are often printed at a given scale, whereas archaeological oblique photographs, also know n as specialist photographs, are taken by an archaeologist whilst in flight using less sophisticated equipm ent. W hilst num erous archives o f both types o f aerial photography exist w ithin record offices and on the internet, their applicability w ithin G IS requires som e understanding. T h e form ats o f aerial photographs are norm ally as prints that may be digitised through scanning.Vertical photographs w ill not norm ally need m uch rectification, although lens distortion towards the edges o f a photograph m ight w arp the features. In the case o f oblique photographs, rectification is always needed before they m ay be applied w ithin a G IS. In both cases, but particularly in the case o f oblique photographs, it is com m on for any archaeological feature to not be contained w ithin a single print, but be spread across several prints. In m any cases these features may have been transcribed, either by hand or digitally (see W ilson 1982 for m ore detail). R e a d ily available vertical photographs or transcriptions m ay be digitised through scanning to provide a raster image. In some cases such digital formats are available from national or regional bodies and record offices. Som etim es, though m ore rarely, transcribed features are available as digitised vector files consisting largely o f lines. In these cases it may be possible to im port the data directly into the G IS. W ith digitised raster photographs or transcriptions, data can be brought into the G IS for further analysis, com parison w ith other data formats, or digitisation into vectors. B rin g in g a raster image, w hether it is a vertical photograph or a transcription, requires a num ber o f considerations. Firstly is resolution, or the num ber o f pixels that represent the image, as this w ill influence the type o f detail that m ay be digitised as vectors from it. Secondly is georeferencing — the process o f placing the image in its correct location. In other words, cells w ithin the image (pixels) need to be matched to geographical locations w ithin the GIS. Th ere are a num ber o f ways o f achieving this w ithin the various G IS software packages, but the ultimate result is an image that fits in geographical space, thus w ith pixels that maintain a specific geographic size, dependent on the resolution o f the original scanned image. T h e advantages o f transform ing the results from aerial survey into a G IS environm ent are num erous. Firstly, it becom es possible to overlay these w ith other data, such as the point data generated from fieldw alking or w alkover survey. Secondly, it becom es possible to consider the photograph or transcription in relation to other form s o f data such as topography or geology, dependent on other layers that m ay be available w ithin the G IS. This m ay be achieved through

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Landscape Archaeology and Data

draping the raster images over D E M s o f the topography.The georeferenced image also provides the basis for digitising lines, or vectors, relating to archaeological features. In the same way as for point data, these can be ascribed attributes w ithin the database, perhaps according to type o f feature, likely date and so forth. H ence, w ithin the G IS it becom es possible to overlay different types o f data together to assess correlations between archaeological features identified 011 the photographs and any other natural or archaeological features. It also enables the creation o f new data including interpretative transcriptions.

FIELD DATA O n e o f the earliest approaches to studying a landscape w ill be to w alk over the area. T h e w ay this is approached may be determ ined by other data sources, such as previously know n finds or perhaps features identified on aerial photographs. B o th fieldw alking and w alkover surveys can be conducted in a num ber o f m ethods, though these are principally either systematic or non-system atic. In the case o f systematic surveys, the landscape m ay be divided into a grid for walking, particularly in the case o f fieldw alking (Gaffney et al. 19 91), or fieldworkers will be required to w alk across the landscape in transects set at specific intervals apart (e.g.Van de N o o rt and Ellis 1995). I11 contrast to this, and often the case w ithin walkover surveys, assessment o f the landscape m ay be m ore qualitative. In the case o f pastoral upland landscapes, it m ight not be feasible to attempt any intense or systematic survey and so a m ore subjective approach is used. W hatever the approach to fieldw alking or w alkover surveys, the results com m on ly share the form at o f dots on maps, or a list o f coordinates, each relating to the finding o f an artefact or a site. In terms o f G IS approaches, this is referred to as ‘point data’ , and this w ill be covered in m ore detail later on. Point data at its simplest form consists o f ju st a position that can be plotted w ithin the G IS as a dot, perhaps against a map, though not necessarily. Additional inform ation m ay be held for each point, such as type o f find, date, m aterial and so forth as a database or list. O n ce input into a G IS, most sim ply as a list o f data, it becom es possible to p erform a num ber o f display and analytical processes. At the m ost basic level it becom es possible to display the positions o f the data points, perhaps in relation to other form s o f data already held in the G IS, but again this w ill be covered later. Secondly, it becom es possible to interrogate the point data in order to identify groups. For exam ple, the data m ay be exam ined and displayed in relation to date, type, material, or any other factor that is w ithin the database or listing associated w ith each position (9).

31

Landscape Archaeology and G I S

g Fieldw alking data represented in a G IS

3^

Landscape Archaeology and Data

M ore sophisticatedly, point data such as those from fieldw alking and w alkover surveys may be exam ined in term s o f clustering (colour plate /) .This can be achieved at a range o f com plexities in order to define groups. O w in g to the nature o f artefact survival, it is often easier in landscape archaeology to look to the bigger picture, and thus procedures such as density analysis becom e extrem ely useful. In chapter i the themes o f vector and raster data structures w ere introduced. In this context, point data m ay be considered as vector data —just points in virtual space. A density m odel converts this vector data into a continuous raster format, or grid o f ‘cells’ at a given resolution,perhaps each cell being i x in i, 10 x iom or 50 x 50m. In any case, each cell w ithin the raster is the same size in spatial terms. W ithin the raster, each cell contains an attribute w hich may relate to any factor including elevation, colour, or num ber o f artefacts. In converting the vector point data to a raster density plot, the com puter determ ines a value for each cell based upon the density o f point data. W hilst there are num erous parameters that may be adjusted in such an approach, ultimately the resulting m odel shows a raster w ith each cell value being equivalent to a value such as the num ber o f finds per square kilom etre. T h e advantage o f an application o f density analysis to point data is that it may be used to generalise the data. T h e point data obtained from fieldwalking, for exam ple, w ill be determ ined by a num ber o f factors such as ploughing regim e, depth o f archaeological burial, damage to the archaeology and so on. Furtherm ore, often certain areas o f the landscape m ight not currently be available for access to fieldwalkers, such as w ood ed coverts or built-011 areas. In these cases it is im portant to obtain a m ore general picture o f the data, highlighting the concentrations o f data. Sim ilarly it is im portant to identify w hether there are patterns in the data, perhaps indicative o f foci o f activity, or w hether artefacts are spread m ore generally, in the case o f activities such as m anuring perhaps. Such generalisations o f the data can be extrem ely useful w ith regards to m aking decisions over the m anagem ent o f resources w hen conducting future fieldw ork. Follow ing the principle o f ‘from the know n to the u n kn o w n ’ , the density plots provide a centre for activity that can form the starting points for later research that can then be extended into the w id er landscape. H owever, whilst this m ethod addresses som e o f the issues o f non-system atic data recovery on the ground, it should always be considered that the m ore systematic a study, the better the results.

33

Landscape Archaeology and G I S

-y .

W

m

10 G eophysical raster data - G ear Farm Iron A ge fort interior, C o rn w all

34

m

ts M

Landscape Archaeology and Data

GEOPHYSICAL SURVEY As for aerial photographic data, the results from the variety o f methods o f geophysical survey are com m on ly output as raster images (10). In the same way as for aerial photography, these images can be georeferenced and transform ed so that they occupy geographical space w ithin the G IS. From here it becom es possible to correlate anom alies w ith other form s o f data, or to drape the geophysical im age over topography or any other surface. Furtherm ore, it is possible to digitise vector lines and polygons as interpretative layers, generating new data from the raster image. Additionally, further functionality o f G IS enables other analyses o f geophysical data. As a raster, it becom es possible to p erform im age analyses to differentiate betw een trends in the data. O th er formats o f geophysical data m ight include x, )', ~ form ats, w here x and y provide a position in geographical space for each cell o f the image, and z provides values, such as positive or negative nanotessla (nT). U sin g interpolation techniques it can be possible w ithin the G IS to convert these data to different form ats and to analyse them to highlight variations and subtleties, w ith the potential o f identifying m ore subtle features.

C O N CLU SIO N S T his chapter has exam ined some o f the data that are com m on ly collected in n o n -G IS led landscape archaeology. It has exam ined how these data may usefully be exam ined w ithin a G IS environm ent, w ith the possible types o f other approaches that may be used in order to obtain m ore inform ation from the data. It has also provided an application-based introduction to G IS, and leading on from the introduction in the previous chapter, has identified som e o f the key themes. These include different types o f raster and vector data structures, and concepts such as georeferencing and digitising. This leads us into chapter 3 w here the nature o f spatial data w ill be exam ined in m ore detail.

35

3

Spatial data IN T R O D U C T IO N Before considering die role o f G IS w ithin the various disciplines characterised by the term ‘landscape archaeology’ there should be some appreciation o f the fundamentals o f spadal data. Essentially, a G IS is a ‘spatial database’ which means that it is a database with the facility to store, manage and analyse data in terms o f its position in the world, albeit in a virtual sense. This is where some element o f discrepancy arises. T h e G IS only simulates the physical world and, in the case o f archaeology, it can demonstrate how sometimes this simulation is the closest way o f approaching the unknown, past realities being studied. This presents something o f a problem as the simulation becomes the more important but its value is reliant on spatial data. Hence issues o f spatial resolution and scale, the curvature o f the earth, map projections and National Grids all provide the need for consideration and the possibility o f error. In this chapter the nature o f spatial data is considered in relation to the history o f m apping and the theoretical considerations o f maps. T h e different ways o f obtaining spatial data are also discussed w ith a view to the ways in w hich such data may be considered and prepared for use w ithin the G IS.

WAYS OF C O N SID ER IN G SPACE At this juncture, it is profitable to consider different ways o f conceptualising space and the m apping o f the E arth ’s surface. Ethnographic evidence and the evidence from anthropology reveal other m ethods o f m apping that are o f im portance to archaeology, at least at a conceptual level. T h e function o f maps is o f som e significance here; essentially, w hy m ake a map in the first place? T h e principal reasons for creating maps may be categorised as for navigation, resource quantification, for strategic m ilitary reasons or for engineering.

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Landscape Archaeology and G I S

11 H alf-scale replica o f one o f the Bronze A ge Ferriby Boats under sail. N avigation during prehistory is likely to have relied upon mental m apping o f the relationship betw een waypoints

N avigation provides perhaps the most fundamental need for maps, but this has developed the most diverse cartographic responses (11). N avigation, or pathfinding, particularly through the translation o f a route to a third party, is a concept that m ight take place on several levels. Essentially it is about waypoints that may be described and follow ed as a narrative. It has been argued that the history o f navigation reflects a change from ‘non-instrum ental’ (N eedham 19 7 1), also referred to as ‘environm ental’ (M cG rail 1987) in earlier periods, relating to pre-com pass methods. Such approaches to navigation relied upon the identification o f m orphological characteristics that m ight be seen or otherwise experien ced du ring passage. ‘M ental m apping’ is a concept that has been used to explain how this process o f navigation w ithout physical maps m ight have w orked in practice, w ith pathfinding achieved through the creation o f a dynam ic, changing and continually updated mental im age o f w aypoints (O akley 1977). G ell (1985) has further dem onstrated ho w m uch o f this process w ou ld have relied upon a level oF p ractical lo g ic ’ , providing structure to B o u rd ie u ’s (1977) concept o f ‘practical m astery’ o f the route, involving personal experien ce before a route could be know n and subjected to the ‘mental m ap’ .

Spatial Data

‘ Q uantitative navigation’ is the name given to approaches to w ayfinding that use instruments, such as the compass, as a w ay o f m easuring space. It is at this stage o f navigational history that cartography began to becom e established in Europe. For exam ple, the creation o f M ercator’s Projection o f the E arth ’s surface onto a flat chart (see below) was undertaken to ensure that shipping routes could be accurately m apped. This process o f navigation relates to the use o f other measurements too, including stellar observation and the m easurement o f distance ‘ over groun d ’ . A ccord in g to N eedh am (19 71) ‘ quantitative navigation’ was supplanted by ‘mathem atical navigation’ . T h e latter process related to a higher level o f m apping calculation and it is w ithin this system that the fundamentals o f G P S navigation and G IS themselves lie. T h e second principal m otivation for m ap-m aking may be classified as ‘resource quantification’ . T h e vast m ajority o f early maps w ere created in order to address issues such as taxation.T he tithe maps in the U n ited K in gd om , for exam ple, were drawn up in order to calculate acreage o f land so that people could be taxed appropriately for funding the parish clergy. This m ethod was so im portant that legislation in 1831 established T ith e Districts. Similarly, estate maps w ere usually drawn up to serve a quantification exercise, either related to taxation or for general m anagem ent purposes. T h e third m otivation for the creation o f maps lies w ith m ilitary tactics and strategy. T h e origins o f the O rdnance Survey lie in the need to generate maps ot areas w here potential uprisings m ight have occurred in order to be used for strategic purposes (see below for a full discussion o f the O rdnance Survey). Finally, maps may be generated for engin eering purposes. Indeed, m any o f the decisions relating to the scales used on O rdnance Survey m apping reflected the requirem ents o f the engineers o f the Industrial R evolu tion , and particularly the building o f railways (see below ). Ultim ately, in these cases a quantification o f the landscape is undertaken in order to calculate w here to build, the quantity o f raw materials required and so forth. Essentially, therefore, the ontogeny o f m apping, particularly in the Cartesian sense (as view ed from above), extends back over several centuries in order to fulfil a range o f requirements. These requirements have influenced the types o f maps created, including their scales and their projections. It is w ithin this trajectory that G IS has com e into existence.

SPACE A N D LA N D SC A PE A R C H A E O L O G Y T h e role o f space w ithin landscape archaeology w ou ld at first appear to be very obvious. Landscape, by definition, w ould involve space. H owever,

39

Landscape Archaeology and G I S

w ithin archaeology there have been a num ber o f different approaches to the consideration o f space, bo rrow in g on a range o f broader academ ic disciplines. Fundam entally the consideration o f space from the perspective o f landscape archaeology can be divided betw een practical and theoretical approaches. Practical approaches to landscape archaeology have been focused on the identification o f archaeological sites (e.g. O rdnance Survey 19 73, W ood 1963, W ilson 1982), distribution maps and the interpretation o f sequencing and phasing w ithin the landscape (e.g. Hoskins 1955, Aston 1985, M u ir 2000). In addition to physically identifying sites, either through remains visible on the ground or through aerial photography, the approach has been to identify diagnostic patterns w ithin the existing landscape that provide clues to the past landscape. B y incorporating other sources including place names, historical docum ents and cartographic material, it can be possible to build up a picture o f how the landscape has physically changed through time, in conjunction w ith a pragmatic consideration o f potentially influential factors such as watercourses and topography. In addition to these approaches, other considerations o f the landscape have been made through the investigation o f palaeoenvironm ental factors. Theoretical interpretations o f space consider it to be a cultural construct. In other words, distinction is made between particular locations o f activity (places) and areas between them (spaces —c/lTuan 1977). Furthermore, consideration o f space (and/or places) has been influenced by the recognition that landscape has cultural meaning that falls outside the limits o f pragmatic interpretation. To take a m odern example, council planning policy guidance w ill influence w here new settlements may be built probably m ore so than considerations o f the physical features o f the area. In the past cultural considerations w ill have manifest m eaning and symbolism in the landscape that w ill have influenced h ow and w here different activities might have taken place. O ne key text that began to examine these theories in relation to landscape archaeology specifically was A phenomenology of landscape by Christopher Tilley (1994) w hich exam ined the interplay between places and spaces, and the role o f the individual as a vehicle for experiencing the landscape. H e and others have subsequently argued that many traditional approaches to landscape archaeology, including distribution maps and site plans, are o f lim ited value since past landscapes were about the people and how they view ed their world. Instead there has been a greater emphasis on the ‘em bodied’ landscape, assessing factors such as intervis­ ibility between monum ents and the experience o f m oving through the landscape. These various considerations o f space from the perspective o f landscape archaeology provide som ething o f a problem for the landscape archaeologist w ishing to take a m ore holistic view. In addition to these themes, the advent o f G IS-based approaches has resulted in an increased interest in quantitative approaches to understanding landscape archaeology.

40

Spatial Data

PRIN CIPLES OF SPATIAL DATA Fundamentally, spatial data refer to any inform ation

that has a location

com ponent. It is abstract insofar as it simulates chosen elem ents o f the real w orld, whilst rem aining ‘virtual’ . Certain features may be recorded, such as trench positions or archaeological features, whilst others m ight not be recorded, including perhaps m odern or tem porary structures. Furtherm ore, certain features m ight be recorded at a very high resolution, such as the positions o f small finds w ithin an excavation trench, whereas other features m ight dem and a low er level o f accuracy, such as the recording o f w ide banks. All maps, w hether paper or digital (as in the case o f G IS), represent the elements o f the real w orld using some type o f tw o - or three-dim ensional approach. T h e tw o-dim ensional approach, based on X and Y, refer to the ‘plan’ ot the ground, in other words representing it as a Bat im age as i f seen from above. T h e third dim ension, Z , refers to elevation or height, and m ay be expressed as contours, spot heights or m odelled three-dim ensionally. T h e principal two dim ensions that represent the w orld from above, or in plan, are variously labelled as longitude and latitude, X and Y or Eastings and N orthings. Effectively they each place a grid over the w orld so that the positions o f all features can be expressed in terms o f either angles or distances. For each type o f grid, positions are related to a datum or starting point. H ence positions are m easured as so far east o f this point, and so far north. In the case o f N ational G rids the datum point or origin is norm ally a point towards the south-west corn er o f the area being m apped, from w hich positions are measured in both directions. Spherical grids, such as longitude and latitude, m easure these distances in terms o f degrees o f arc, again starting from a given datum point, such as the equator for latitude and G reen w ich for longitude. Angular measurements reflect the spherical shape o f the planet; whereas distances from the datum consider the w orld to be at least locally flat (see below ). For the purposes

o f most m apping, and particularly w ithin

the

G IS

environm ent, grids expressed in terms o f distances from the datum are the norm al m ethod o f defining position. C om m only, these expressions are given 111 relation to a N ational G rid (see below) that provides a position relative to grid north, although local grids can also be used, w ith an arbitrary datum point, norm ally expressed as o-East and o-N o rth , or 100-East and lo o -N o r th , and so on, so that a find that is im to both the north and east o f this datum may be at lo i-E a st and 2 0 1-N o rth .

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Landscape Archaeology and G I S

PR O JEC TIO N S AND NATIONAL GRIDS From as early as the sixth century B C and the w o rk o f Pythagoras it has been understood that the Earth is not flat. This fact has provided cartographers w ith a num ber o f challenges. O w in g to the fact that the Earth is broadly spherical, and that maps are generally flat representations o f its surface, som e kind ot error may be assumed from the translation from one form to the other (12). Approaches to m apping have centred on w hat is kn ow n as projections. In cartography, a projection is the means o f depicting the spherical surface o f the earth on a flat piece o f paper — essentially how a curved surface is translated to the flat map. Flem ish cartographer Gerardus M ercator developed a projection in the sixteenth century that enabled the course o f a ship steering on a constant bearing to be represented as a straight line on the map. T h is projection is still in use today and has form ed the basis o f many N ational G rid systems. H owever, w hatever m ethod is used there w ill be som e level o f error for larger areas and this w ill relate directly to the distances being covered. H istorically som e o f this error was discarded w ithin areas o f the landscape that w ere considered to be le^s likely to be measured, such as river valleys or areas o f m oorland, by effectively cutting out parts o f the m easured map to fit the grid (see below ). D ifferent G IS packages approach the subject o f projection in different ways, w ith som e developing methods o f transposing betw een different projection system s.W ithin archaeological studies two factors - ho w b ig the site is, and your w hereabouts - often determ ine the choice o f projection. I f your site is very small it is possible to w ork accurately to a local grid that m ight be tied to a N ational G rid. W here sites are bigger or w here w h ole landscapes are being studied, perhaps at the county scale, then considerations o f projection becom e im portant. M easurem ents betw een places w ithin a N ational G rid m ight be different from those on the ground due to the nature o f the projection. Y ou r whereabouts becom es im portant for a num ber o f reasons. M an y larger countries have several N ational Grids to counter the problems o f error caused by projections. In these cases the appropriate projection and grid w ill need to be sought.

ELEVATION Expressing the third dim ension on a tw o-dim ensional map requires some elem ent o f depiction. Traditionally the m apping o f height has been achieved through spot heights m arked on the map, or by using contours (13). C ontours as a m ethod o f depicting areas o f shared elevation value w ere fam ously invented by the m athem atician Charles H utton in 1774. This was really m ore o f a by-product

Spatial Data

12 C o m p arison o f spherical and norm al grids dem onstrating the need for projections

13 U sin g contours to represent elevation - G ian t’s G rave V ik in g burial, Fetlar, Shetland

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Landscape Archaeology and G I S

from a broader attempt to calculate the mass o f the Earth by m easuring the mass o f Schiehallion M ountain in Scotland to assess the relative gravitational pull o f both. T h e con tourin g becam e a means to make some sense o f the num erous spot heights across the m ountain generated by the team o f surveyors for the purpose. Essentially contours are lines o f equal elevation that enable factors such as slope and topographic shape to be visualised.Thus, w here lines depicting equal vertical intervals are closer together it is indicative o f a steeper slope and w here they are further apart the represented landscape is flatter. T h e issues relating to the depiction o f elevation com e from a num ber o f standpoints. From the perspective o f map projections, relative changes in elevation over considerable distances w ill result in loss o f accuracy as the spherical Earth effectively drops away. This needs to be accounted for. Furtherm ore, and as w ith all cartographic representation, the accuracy o f the height depiction is limited by the survey data. N orm ally only a certain resolution o f m easurements w ill be recorded w ith the areas betw een the points being estimated, or interpolated. M apping height is achieved through a num ber o f diverse methods, including ground survey, using a variety o f instruments, from aerial photography and the use o f photogram m etry, and m ore recently through the use o f other rem ote L igh t sensing techniques such as L ID A R (Light D etection A n d R an gin g). D ifferent G IS software packages address elevation in a num ber o f w ays. Som e packages do not deal w ith height in a three-dim ensional way whilst others require additional software extension packages to be purchased in addition to the basic tw o-dim ensional software. M o re generally, elevation can be addressed through graphical means, such as a scanned georeferenced im age o f contours, through the basic labelling o f features (such as X Y coordinates w ith an attached label representing its elevation) or, m ore sophisticatedly, through three-dim ensional representation o f surfaces. Tw o principal methods o f threedim ensional representation are possible although in each case it should be noted that the third dim ension is not ‘real’ . In other words the G IS provides a tw o-dim ensional plot o f X Y coordinates in virtual space, w ith the additional Z coordinate represented as an attribute o f the X Y position. This is norm ally referred to as 2.5 D as opposed to true 3D. T h e two ways o f representing elevation as a continuous surface w ithin a G IS are as a grid or as a T IN . A grid is a representation o f a surface that divides the landscape into equal square blocks. Each o f these blocks has an attribute that relates to its elevation. For exam ple, an area bein g studied m ight cover 5 x 5km , and it m ight be decided that an appropriate resolution for a three-dim ensional m odel is 10111, hence the landscape is divided into blocks each m easuring 10 x 10m (500 x 500 blocks for the w hole landscape). Each o f your blocks, or cells, w ill have an individual attribute relating to its elevation. T h e second method,

44

Spatial Data

the T I N , is an acronym for Triangular Irregular N etw o rk. This is part o f a broader m athematical concept o f Polyhedral Irregular N etw orks, but focusing on triangles. A T I N is a netw ork o f triangular planes that jo in X Y Z coordinates together. H ence, the input data m ight be a spread o f X V ' coordinates each w ith a height value, Z . T h e T I N w ill be created so that triangles jo in betw een all the points, w ith smaller triangles being created betw een points that are closer together. Each o f the resulting triangles will be a flat plane angled to jo in each o f the three input points at their attributed heights.

SCALE AND RESOLU TIO N A map is an abstract representation o f the real w orld for a given purpose. Consequently, certain

elem ents

o f the real w orld

w ill be

show n

using

representative symbols. Furtherm ore, features w ill be depicted at scale. Scale is denoted using a ratio figure, such as i: 10,000, w hich means that 1111 on the map represents 10km on the ground. At a m ore useful level, icm on the map will represent 10,000cm , or 100m , on the ground. Traditionally, cartography is undertaken at a pre-determ ined scale, and this w ill be determ ined by the requirem ents o f the final product — the map (14). For urban areas, m apping w ill norm ally need to provide greater detail than an area o f open fields. For exam ple, the highest resolution com m ercial m apping in the U K is at a scale o f 1:12 5 0 , though this is only available for built-up areas, w ith many rural areas not being surveyed at any scale greater than 1:2500. Archaeological survey, as dem onstrated by the w ork conducted traditionally by the R o y a l Com m ission on the Historical M onum ents, and n ow partially undertaken by English H eritage, came from a background in the O rdnance Survey. H ence, the m ethods used for depicting archaeological features such as earthworks follow those used tor m ore traditional m apping requirements. E arth w ork remains are surveyed at a given scale, w h ich m ay range perhaps betw een 1:250 0 to 1:10 0 , depending on the needs o f the survey. H igh-resolution analytical survey, aim ed at interpreting phasing and so forth, w ill clearly provide a very different result com pared w ith a low er resolution survey.The cartographic product is therefore at a scale w hich remains static and so the scales cannot be used interchangeably; a low -resolution survey should not be used for a higher resolution study, for exam ple.

45

Landscape Archaeology and G I S

0

_1

25

50

100 Metres

I_______ I_______ I_______ I_______ I_______ I_______ i_______ I

14 H o w the same features may be depicted w ithin different resolution m apping. T h e image on the left shows an urban area m apped at 1:50 ,0 0 0 scale, whilst the im age 011 the right shows the same area at 1:10 0 0

THE ORDN A NCE SURVEY - A CASE STUDY IN CARTOGRAPHIC HISTORY T h e nature o f scale is fundam ental to issues o f cartography, and one that provides crucial context for all m apping. So far it has been established that maps are representations o f the real w orld made to fit a particular purpose and that scale choice is essential in fulfilling this purpose. T h e case o f the O rdnance Survey demonstrates the issues relating to scale. T he need for accurate mapping was realised during the eighteenth century when George II was at w ar w ith France, but also faced Scottish rebellion. For strategic reasons, he commissioned a survey o f the Scottish highlands in 1746, w hich was undertaken by W illiam R oy.This began the process o f mapping, w hich was continued for the south coast by the end o f that same century due to fear o f revolution. Gradually additional areas w ere m apped, w ith the first map being published in 1801 - a iin to the m ile scale depiction o f the county o f Kent. B y 1820 maps at this scale covered approxim ately a third o f England and Wales, but by 1824 a new scale o f 6in to the m ile was developed w ith the com m issioning o f m apping

46

Spatial Data

Ireland. This n ew scale began to increase in im portance due to changes in the tithe m apping o f the country and the setting up o f ‘T ith e D istricts’ . Additional pressure on the requirem ent for higher resolution m apping came w ith the boom in railway construction. Fundamentally, the original Tin to the m ile scale maps w ere too generalising to be o f use to the engineers constructing the railways and, furtherm ore, th e -iin series hadn’t been com pleted. T h e need for accurate m apping lead to the developm ent o f rights for surveyors to access all areas for the purposes o f cartography in 18 4 1. F ollo w in g the fire at the Tower o f London in 1841 and the subsequent m ovem ent o f the O rdnance Survey offices to Southam pton, further decisions needed to be made relating to scales for mapping. In 1863, final decisions were made. T h e resulting choices lead to the m apping o f m oorland or mountainous areas at a scale o f 6in to the m ile (1:10 ,5 6 0 ), the m apping o f rural areas at a scale o f 25m to the mile, and urban areas at ioin to the m ile.T h e iin series (1:63,360) was also retained. B y 1895 the first m apping o f the w hole cou n try at 25 in to the m ile scale was com pleted, know n as the First Edition C o u n ty Series. A n updated m apping at the same scale follow ed this, know n as the Second Edition. U p to the 1930s m apping had been covered on a county by county basis; hence, the 25m C o u n ty Series First and Secon d Editions. H ow ever, by this time, choices relating to national m apping w ere being considered. O n e o f the problems had been that the C o u n ty Series m apping had assumed a flat surface o f the Earth. T h ey were extrem ely accurate but i f edges o f two county maps were m atched together then problem s began to arise. Essentially the errors generated by the translation o f the spherical surface o f the planet to a flat map began to be apparent w hen considering such large areas. In order to address this issue, and to begin to develop a national m apping strategy, 1935 saw the re-triangulation o f Britain and the establishment o f the N ational G rid; w hich is still used today. T h e N ational G rid for the U n ited K in gd om uses a Transverse M ercator Projection to counter the issues o f flat m apping and the pushing o f map error caused by the spherical surface to areas such as m oorland. T h e system uses Eastings and N orthings to calculate position in a consistent coordinate system and the resulting series o f maps follow scales that fit w ithin this system.

SPACE A ND GIS So far the nature o f space in its three dimensions and in relation to different coordinate systems and map projections has been considered. W ithin a G IS space is understood in two principal ways from the basis o f the tw o data formats: vector and raster.

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Landscape Archaeology and G I S

Vector data aim to represent features in a w ay that is as close to the original as possible or appropriate (Burrough 1986).Vectors m ay be considered as principally points, lines or polygons (15).

1

J5 D ifferent types o f vector data - points, lines and polygons

48

Spatial Data

A point w ill represent a position in space defined by a coordinate. A line connects a series o f ‘nodes’ . A polygon defines area and may be used to define topology; the relationships betw een different areas. T h e vector data m ay be attached to a database providing the potential for multiple attributes to be stored in relation to the spatial unit. For exam ple, a point file w ithin an archaeological record, such as the S M R , m ight contain inform ation regarding the name o f site, its period, size, type o f remains, co u n ty original finder, any published inform ation and so forth. T h us is becom es possible to use the G IS to form ulate an enquiry and display all sites o f a certain period, for exam ple. Furtherm ore, it becom es possible to interrogate the database in num erous ways in order to exam ine the data spatially. Vector data relies on topological relationships betw een different features, particularly in the case o f polygon layers. For exam ple, a polygon representation o f a geology map should be constructed o f polygons for each o f the geolo gy types, w ith no gaps betw een them, and no overlaps.Thus, anywhere w ithin the layer, the geological type may be identified. From this, it becom es possible to interrogate other vector data. Follow in g the exam ple o f the geological map, it m ight be useful to understand w hat proportion o f w etland sites lie w ithin alluvial deposits com pared to peatlands. A simple spatial interrogation o f a point file containing an attribute for w etland sites may be spatially com pared w ith the geology map to provide num bers o f sites in each area. T h e alternative to vector data structures is the raster. Simply, a raster consists o f an array o f grid cells, also referred to as pixels (16). Each cell w ithin the grid is referenced in relation to the row and colum n upon w hich it is situated, providing its location, w ith an attribute providing a value.

16 R ep resen ting elevation w ithin a raster dataset. Each cell covers a standard area and represents values according to height

49

Landscape Archaeology and G I S

This value m ay relate to a colour, as in the case o f photographs or maps, or m ight relate to elevation, as in the case o f terrain m odels. In georeferenced rasters, each cell is generated at a given size on the ground providing the raster resolution. For exam ple, each cell may relate to 10 x iom and so the attribute value for that cell covers this w h ole parcel o f land. U n like vector data, w ith rasters only one attribute may be held by each cell. H igh er resolution rasters (i.e. smaller cells) w ill result in potentially greater detail, w ith each cell representing a smaller land parcel. H ow ever, increased resolution has im plications in terms o f storage space. In the case o f images such as aerial photographs, scanned maps or the results from geophysical survey, the im age w ill need to be georeferenced before it is useable w ithin the G IS. This procedure norm ally occurs w ithin the G IS, w hereby identifiable positions on the im age are provided w ith real-w orld coordinates. This can be achieved in a num ber o f ways, w hich include the generation o f an attached table o f coordinates for the corners o f the im age, or through directly clicking on the image, perhaps on a ju n ctio n o f field boundaries, and either entering the projected coordinates directly or else dragging the image to the correct position against another m apping layer. O n ce the im age has bepn georeferenced it becom es possible to interrogate as w ith a raster that m ight display other values such as elevation. A third data structure available within G IS is the Triangular Irregular N etw ork, or T I N (Peuker et al. 1978), also referred to occasionally as Polyhedral Irregular N etworks. A T I N is a vector-based topological structure that creates surfaces normally o f elevation that uses sheets o f continuous, connected triangular facets based upon Delaunay triangulation o f irregularly spaced data w hich form the points o f the triangles. T h e advantages o f a T I N data format he in its ability to model directly from the data, particularly maintaining lines such as stream beds.The raster alternative relies on interpolation that may generate computational artefacts between input-data points w hich might lead to peaks and troughs w ithin w hat should be a flat surface. Fundamentally, a T I N is a vector-based topological structure. O n ce data are processed in any o f the G IS formats different layers can be presented, exam ined and analysed together. Vectors m ay be displayed overlying rasters or T IN s and m ay form the basis for interrogating them . T h e limitations o f rasters to hold a single attribute for each cell means that overlaying different rasters w ith different inform ation becom es im portant. I f each raster overlies the same Cartesian area and has the same cell size, then it is possible to exam ine different attributes for the same area. This m ight be com parin g elevation from a terrain raster w ith other rasters perhaps displaying slope or aspect and so forth. T h e relative usefulness o f different types o f G IS data structures are considered in m ore detail in chapter 5, w ith reference to m ethods o f generating models o f landscape, and the efficacy o f the various interpolation techniques.

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Spatial Data

C O N C L U S IO N S In this chapter the nature o f spatial data has been exam ined, including the ways in w hich it may be considered in maps, and ultim ately w hy maps have been made. T h e ways in w hich space is understood or socially constructed have been outlined, and how this process can im pact on the way that space may have been view ed in the past and how it is view ed in the present has been discussed. Furtherm ore, the principles o f spatial data have been considered, particularly in relation to themes o f map projections, elevation, scale and resolution. M u ch o f this is evident in the case study o f the O rdnance Survey. A t this juncture, and for the purposes o f G IS, space m ay be considered as a series o f geographical positions that may be understood through coordinates to give position and attributes such as elevation to provide additional detail. This is fundam ental to the nature o f G IS w hich considers any area as a grid w ithin w hich layers are plotted. H ence the x and y coordinates remain as a constant against w h ich attributes are measured, depicted and analysed. T h e follow ing chapter exam ines the ways in w h ich data may be obtained for input w ithin a G IS environm ent.

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4

Procuring data IN T R O D U C T IO N To be functional, G IS requires data. O n e advantage o f G IS is that it can generate new data from existing data, but it does require these data in the first place. In chapter 2 some o f the types o f landscape data were considered in terms o f how they m ight be better used w ithin a G IS, as a way o f introducing the functionality o f the software. C hapter 3 considered this further in relation to the nature o f spatial data in relation to G IS . T h is chapter further considers data for use w ithin the G IS, focusing on different types o f data, including archaeological, cartographic and survey data, all o f w h ich m ay be used together w ithin a single system. This is considered in conjunction w ith the themes outlined in the previous chapter such as spatial resolution, scale and depiction. Archaeological data encompasses an extrem ely broad range o f different types obtained from a num ber o f different sources, and this is outlined in part in the previous chapter. P rincipally archaeological data may reflect the positions o f sites or finds, in addition to transcriptions o f site plans or features identified 011 aerial photographs. T h e sources for these data include a range o f books and articles, in addition to archives held by m useum s and record offices. O ften the nature o f the data w ill determ ine w here it is held, although the first avenue o f investigation w ill norm ally be w ith regional or national archaeological data repositories. In the U K , these include the N ational M onum ents R e c o rd (N M R ) held by English H eritage in their offices in Sw in don, and the various Sites and M onum ents R eco rd s (S M R ) and H istoric Environm ent R ecord s (H E R ) held regionally. In addition, other sources o f data m ight be from agencies and com m ercial units. In terms o f integrating archaeological data w ithin the G IS, m uch o f the inform ation available to archaeologists is provided in im age form at, such as geophysical plots, aerial photographs, surveys and so forth. Integration o f these rasters w ithin the G IS requires a process o f georeferencing w hereby the image is

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rectified so that it lies w ithin the appropriate coordinate space, such that distances can be m easured from the im age, and coordinates o f locations on the image can be outputted. This section explores the different types o f archaeological data that are available and indicates ho w they m ight be profitably integrated w ithin the G IS in terms o f basic data input. H ow ever, it should always be considered that the value o f the resulting G IS database is only as good as the data input into it, and this includes the resolution o f the original images, the scanning resolution and the accuracy o f georeferencing, w hich m ight rely on both the person p erform ing the task, and the availability o f identifiable positions that can be used to supply coordinates to the im age, thereby georeferencing it. T h e principal types o f data relating to landscape archaeology are outlined in chapter 2. This chapter exam ines the ways in w h ich these data sources may be integrated w ith in G IS. It also covers the ways in w h ich input data may be processed to generate n ew data, such as surfaces for contextualising and analysing landscapes.

M APS M appin g provides a basis for all G IS w ork and is fundam ental to it. N orm ally som e level o f com m ercial m apping data w ill be required for any G IS project or database. A t a basic level, m apping provides the coordinate system and scaling for integrating all other spatially referenced data. C artographic data include a range o f different types o f m aterial in various form ats, and w ith the potential for converting data betw een formats, principally betw een raster and vector. As w ith any other data, different types o f m apping w ill have their ow n levels o f accuracy and there are issues regarding this. These issues are: 1. T h e intentionality o f the map regarding its accuracy. M aps are norm ally generated for a particular task.Tithe maps were generated for econom ic purposes tor calculating the taxable areas of land, as were m any estate maps w hich also had purposes in planning further developm ents such as gardens or construction w orks.T h e intentions o f the map m aker m ight not always be identifiable, although som e consideration o f the possibilities w ill have im plications for the accuracy o f the original survey and hence the map, and w hat is actually depicted. 2. T h e surveying accuracy o f the map. As m entioned in part above, different maps w ill have required differing levels o f surveying accuracy. T h e most obvious exam ple o f this is the different scales o f maps. W ithin a G IS , you have the potential ability for unlim ited ‘zoom in g in ’ . Clearly, obtaining pinpoint positions from a

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map that was originally surveyed at a m uch low er scale w ould be inappropriate. This should always be considered w hen using maps in G IS , and in turn leads to the im portance o f m aintaining metadata to ensure that any data derived from m apping layers makes a note o f the original survey scale o f the parent map. 3. T h e quality o f georeferencing. As w ith other types o f georeferenced raster, positions and m easurements taken from the im age w ithin the G IS rely com pletely on the accuracy o f the original georeferencing. This w ill be influenced by user error, as well as by the availability o f identifiable positions on the im age or map, that m ay be com pared w ith kn ow n positions 011 your base map. Alternatively they m ight be georeferenced relative to G P S or other survey positions. Again, this w ill influence the accuracy o f the resulting georeferenced image. 4. A further issue relates to the reissue o f some historical m apping. I11 chapter 3, the integration o f the N ational G rid by the O rdnance Survey was provided as an exam ple o f how m apping errors could occur w h en generating large areas o f m apping on a flat projection. In some cases, com m ercially available historical m apping is provided w ith N ational G rid superim posed over it. W hilst this becom es a useful tool for providing general position, the original errors in integrating a C o u n ty Series-based m apping to a N ational m apping system mean that the grid can often be inaccurate. Thus it is w orth considering that, in most cases, it is most appropriate to georeference from identifiable features depicted 011 the map rather than to use the superim posed grid. A t least, each case should be considered fully before proceeding w ith integrating data w ithin the G IS. 5. Finally, the quality o f digitising w ill im pact on accuracy, as w ell as h ow the landscape features are digitised as vectors. Fundamentally, the care w ith w hich the digitising has been done w ill be im portant, but only in the context o f the accuracy o f the original raster map and its georeferencing, both m entioned above. H ow ever, additional factors that influence the usefulness o f the final m apping layer lead from the nature o f digitising, such as the use o f different types o f data structure, such as polygons, points or lines, for different features.The decisions made in how to depict certain features also influence the usefulness o f the resulting product. U ltim ately any vector data w ill be derived from another source. W hilst this will often not be possible, w here it is, it is useful to obtain the original data to check ho w the derivation took place.

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Modem mapping M o d ern m apping is available at a variety o f resolutions and qualities depending upon the region in w h ich you are w orking. Particularly in som e areas o f some countries there is little or no recent m apping o f features, requiring new data to be obtained. In the U K , current m apping is constantly bein g updated and can be purchased in a num ber o f different formats and at a num ber o f different scales, as either raster or vector formats. C o m m o n ly the vector formats are generated from an am algam o f maps, potentially o f different scales, so that different parts o f any one map tile m ight be m ore accurate than others. This inform ation is norm ally invisible once the data is in the G IS. W hilst such issues should be rem em bered, it is norm al for such vector-based data to be digitised from the most accurate maps available for any single area.W ithin the U K the introduction o f M asterM ap has produced a topologically correct polygon-based coverage w h ich has revolutionised the potential o f G IS. M odern m apping, as m entioned previously, is o f fundam ental im portance to the landscape archaeologist. M apping can be incorporated w ithin the G IS directly from com m ercially available data or, dependent upon copyright issjues, can be obtained through scanning hard copies o f maps and integrating them w ithin the G IS through georeferencing.

Historical mapping H istorical m apping covers a range o f different types o f maps, at different qualities and different resolutions. In som e cases, historical maps cannot be expected to provide m ore than a schem atic understanding o f space, such as the famous Inclesm oor map from the southern edge o f the H um ber. H ow ever, w ith m ore appropriately surveyed m apping, historical maps can be profitably utilised w ithin the G IS environm ent. In the U K the principal historical maps relating to the nineteenth and early twentieth century are the first and second edition O rdnance Survey C o u n ty Series maps (see chapter 3).These maps w ere generated at 1:2500 scale, and were conducted on a county by county basis. T h ese maps predate the establishment o f a N ational G rid and w ere thus not generated using a projection. As a result o f this, the edges betw een counties rarely m atch correctly, although locally they are accurate. H ence, it is m ore appropriate to georeference a map for a particular area relative to local detail rather than the overlying N ational G rid. T h e direct advantages o f im porting historical m apping into a G IS are numerous. Firstly it may be possible to discredit certain features that m ight be visible on the ground as being the remnants o f earlier datable field boundaries or buildings. Secondly, it is possible to begin to phase detail w ithin the landscape, such as identifying landscape features that were created at a point betw een two different edition maps - for example, a field pattern that exists on a late nineteenth-century

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map w here open fields were depicted on a map 50 years earlier.Thirdly, it becomes possible to identify oddities w ithin the landscape pattern that might indicate either diagnostic features or else point to areas o f interest. For example, a bend in a field boundary m ight indicate the presence o f an earlier feature that no longer exists, such as a barrow. Fourthly, through the comparison o f numerous historical maps it becom es possible to identify the position o f an earlier feature. For example, G eorge I l l ’s W hite House at K ew Gardens, London, demolished in 1802, is only identified 011 one eighteenth-century plan by W illiam Cham bers. There are 110 identifiable features on this plan that relate to the m odern landscape, due to the subsequent w ider landscaping o f the area. However, through the use o f georeferencing intermediate m apping together, it was possible to regress the m apping back so that the m odern mapping could be com pared with the original m apping o f the W hite House. H ence it was possible to begin setting out the positions o f the buildings 011 the ground. However, as m entioned above, any use o f mapping, particularly historical mapping, will bring levels o f error, some o f w hich are un-quantifiable (see above).

Topographical data T h e w ord ‘topography’ is a broad term referring to all natural and artificial features w ithin a district. H ow ever, for the purposes o f G IS, and specifically this book, I use the term to refer to data regarding the lie o f the land; in other words, data about the shape o f the land, particularly in three dim ensions. M any archaeological projects w ill require a three-dim ensional approach to landscape. T h e landscape may be constructed as a com puter m odel that w ill both represent it, and provide the possibility for its analysis. Topographical data m ay be acquired at a variety o f resolutions, either available comm ercially, or through survey w ork (see below ). T h e resolution o f the topographical data will influence the types o f analyses that m ay be perform ed on a surface and the reliability o f the results stem m ing from them. Data is available in a num ber o f formats, although these may norm ally be considered as either contour data or point data o f som e sort. C o n to u r data consist o f a tile o f polylines, each containing a height attribute. T h e density o f these lines is measured by the interval betw een them; i.e. im contours com pared to 10111 contours. H owever, there is a tendency to consider contour data, o f say 10m , to be o f equal value. I f you consider the nature o f different landscapes, clearly there w ill be denser contour data for a hilly or m ountainous landscape com pared w ith a flood plain. Thus the usefulness o f the con tour data in terms o f its resolution is very much dependent on the nature o f the terrain that is to be m odelled. Point data, on the other hand, norm ally consists o f points w ith height attributes at a given resolution. For exam ple, this resolution m ight be a point

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every 10 m or a point every 50m. Again, whilst the 10m resolution data w ill contain m ore inform ation, it should be rem em bered that most o f these datasets w ere derived from the same data as the contours, i f not from the contours themselves, leading us to the same issue w hereby there is generally less data for flatter areas than for topographically variable or hilly areas. Th us, different types o f data are not so directly comparable. T h e creation o f three-dim ensional surfaces from topographical data is dealt w ith later in the follow in g chapter.

Remote sensing data fo r mapping R e m o te sensing data refers to those data that may be collected rem otely and com m only involving the use o f lo w -flyin g aircraft-based technologies that use optical or near infra-red wavelengths (colour plate 2). Since the 1970s remote sensing approaches have included the use o f digital multispectral im aging sensors, therm al im aging radiom eters and im aging R A D A R . T h ese approaches produce data that may be enhanced, rectified and reclassified using software to isolate and identify particular features w ithin the landscape (B ew ley et al. 19 99).This type o f data is available internationally over the internet from providers such as Intermap (w w w .in term ap.com ) and provides a solution to the problem s identified w ith the resolution o f input data. U sin g these data, available to 5m surface resolution, it is possible to m odel even relatively flat landscapes accurately due to the high resolution o f data input. Increasingly com m on w ithin archaeology is the use o f L ID A R data, w hich can produce very high-resolution maps o f topography able to identify subtle variations created by earthworks and other features. In addition to airborne rem ote sensing techniques, the use o f satellite im agery using a num ber o f different wavelengths and at various land-surface resolutions has becom e possible, although its use w ithin archaeology remains limited. T h e advantages o f rem ote sensing data lie in the ability to collect data for large areas relatively rapidly and in the ability to identify subsurface features. For exam ple, airborne therm al im agery, or Infrared Line Scanning technology, used m ainly by the m ilitary can also be useful for detecting archaeology, although this, like aerial photography is dependent upon flyin g at the correct time o f day and w ithin the correct environm ental conditions. T h e use o f L ID A R data has becom e m ore reliable in recent years due to the use o f different bandwidths to effectively see below vegetation and trees. H ow ever, the problem s w ith any undifferentiated system o f data collection is that the data contain arguably too m uch m odern detail, including buildings and cars, that m ay detract from the m ore subtle archaeological features. H owever, its application remains in its infancy and as such provides another reliable layer o f data for integrating w ithin G IS landscape models.

Procuring Data

R E C O R D O FFIC E DATA In the case o f each data repository, different types o f recording and storage will have been undertaken w hich w ill reflect directly on how accessible data is for integration w ithin a G IS project. For exam ple, paper records w ill need to be digitised and digital records m ight need to be standardised into correct formats (see databases below ). In m any cases, archaeological data held w ithin repositories is stored w ithin G IS and m ay be directly available for use, although different G IS packages store data in different ways and so data m ight require conversion.

A E R IA L PH O T O G R A P H Y A com m on tool within landscape archaeology is the use o f aerial photography (see chapter 2). The use o f aerial photography within a G IS environment may be in raster or vector format, the form er relating to the integration o f georeferenced images, as with geophysical plots and survey plans, the latter relating to the interpreted plot that might be available or generated as a vector layer. However, the potential for integrating aerial photography within G IS is dependent upon the nature o f the aerial photograph. Principally, aerial photographs are either oblique or vertical. T h e former normally relate to specialist photography taken by archaeologists o f features that have been identified and need to be recorded. T h e problem with oblique photographs is that they require some type o f rectification, normally in another software package than GIS, to make them effectively vertical so that they can be added as a layer.The second type o f aerial photograph is vertical, which is often non-specialist. Whilst a vertical photograph is much easier to georeference within the GIS, it will normally contain less information than a specialist oblique photograph. Furthermore, it is possible for lens distortion to occur towards the edges o f any photographic image w hich should be considered prior to taking measurements from any photography.

F IE L D DATA O th er types o f data that m ight result from archaeological landscape-based fieldw ork include surveyed plans o f sites, as w ith either con tour surveys or hatchured surveys (see B o w d en 1999). For the time being these may be considered as plans that may be integrated into the G IS as images, or rasters, for storage and presentation against other layers o f data. In this case, the G IS works as a m ap-overlay tool, m uch as m any graphics packages can w ork, but w ith the added advantage o f providing scaling and coordinates.

Landscape Archaeology and G I S

Additional archaeological data that m ight relate to a landscape archaeology G IS include gathered field data, either generated from G P S positions or from sketched or triangulated positions on maps. Th ese positions m ight relate to the position o f a m onum ent in the landscape, or a find scatter derived from fieldwalking, for exam ple. Inputting these data into the G IS is achieved in one o f two ways. T h e first w ay is for the paper map containing all the data to be scanned, georeferenced and for the positions o f sites to be digitised from the image. T h e second, perhaps m ore practical way, is to type the coordinates into a database, spreadsheet or w ord processing package, w ith a colum n providing inform ation relating to each site. This can then be saved and uploaded into the G IS to provide point data for each o f the sites.

G E O P H Y S IC A L DATA G eophysical data m ay be incorporated w ithin a G IS in a num ber o f ways. T h e nature o f geophysical data can take different form s depending on the m ethpd being used and the software processing the data (see chapter 2). N orm ally, however, the results from geophysical survey are either in the form o f an image (greyscale or colour), or as an interpreted plot. Alternative outputs can be digitised, interpreted plots that m ight be im ported as vector layers, or as digital data files. In the case o f im ages, plans can be im ported into the G IS and spatially referenced to becom e a georeferenced raster layer. O n ce in this form at, the geophysical data can be correlated and com pared w ith other forms o f georeferenced data (17).

17 D raping geophysical results over a topographic IDEM derived from G P S survey - R o m a n to m edieval features at G len don H all, N ortham ptonshire (270 x 180m lo o k in g north-west)

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G A T H E R IN G N EW F IE L D DATA Som etim es, the data that are available from other sources are either inappropriate or lim ited for a study o f a particular landscape and additional data need to be acquired on the ground. In particular, the resolution o f available data might be insufficient for a single purpose. This is certainly the case for topographic m apping ot sites w ithin their im m ediate landscapes, or assessing changes to a landscape through repeated surveying over time.

Point, line and polygon data At the most basic level, the recording o f sites may be achieved from sketching positions onto a map to obtain grid references. M ore sophisticatedly, and depending on the required levels o f spatial accuracy, positions o f finds or sites m ight be recorded using a hand-held G P S or triangulated compass position. At the higher resolution end, positions m ight be recorded using sophisticated survey equipm ent such as Total Station E D M or Survey Grade Differential G P S (18). H ow ever the data are collected, the result w ill norm ally be a list o f coordinates that m ay be input into the G IS to provide a range o f positions that may be analysed directly in terms o f distribution, or in relation to other layers o f data, such as m apping. Line and p olygon data may be generated from these point data w ithin the G IS to provide a graphical representation o f features. T h e m ore sophisticated approaches enable elevation to be recorded w hich can be useful in a num ber o f ways in archaeology, such as considering w hether two floors o f an excavated building are at the same level, or the interpretation o f elevation difference w ithin an industrial com plex, to interpret rate o f w ater flow along a channel, for exam ple. Principally, w ithin G IS the point, line and polygon data w ill form the basis for most types o f analysis and can be spatially related to all other types o f data obtained from desk-based w ork.

Topographical data W hilst elevation m ight be recorded as part o f point, line or polygon data, large quantities o f elevation data m ay be used to generate surfaces w ithin the G IS through different m ethods o f interpolation, in the same w ay that surfaces may be generated from com m ercially available topographical data (see above). B y surveying a dense scatter o f three-dim ensional positions across a site or landscape, you provide the potential for creating a m uch m ore accurate m odel o f the landscape than w ould be possible through com m ercially available data. Topographical data are collected in the same way as any contour survey w ould be conducted. Principally, the challenge is to obtain num erous three-dim ensional positions across the area o f interest. This may be achieved in a grid using an

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18 A variety o f different equipm ent m ay be used for obtaining data for inputting into G IS. This figure shows T rim ble s Su rvey G rade Differential G P S equipm ent

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Landscape Archaeology alld G I S

optical levelling device, or m ore rapidly through the use o f m ore sophisticated equipm ent such as Total Station E D M or D ifferential G P S (19). T h e ways in w h ich data are collected for creating T IN s or raster models in G IS w ill profoundly influence the usefulness o f the resulting m odel. B efore considering the resolution o f data collection (i.e. ho w close together you record positions), it is first necessary to consider w hether data w ill be collected at regular intervals, or follow in g the positions o f any earthworks. Clearly, i f there are com plex earthworks on a site, these are likely to require m ore recorded positions in order to simulate them in the G IS m odel than an area o f flat land w ould need. T h e relative values o f ‘grid d ed ’ and ‘n o n -grid d ed ’ data for generating G IS models w ere considered in a paper by Fletcher and Spicer (1988) w h o applied contrasting sam pling m ethods to a virtual surface in a sim ulation study. B y recreating the surface from the sampled data the two different approaches were considered (objective and subjective, or gridded and n on-gridded). T h e results from this study dem onstrated that, w here earthworks are prom inent w ithin a landscape, they w ill need a greater num ber o f points to represent them. I f the site is gridded, this provides a very large quantity o f points, and thus has implications for survey time. Th us it was concluded that n on -grid d ed survey m ethods were most appropriate for areas o f com p lex earthworks, w hilst other areas could be approached in a m ore systematic manner. O ften it is best to use a com bination o f systematic and non-system atic survey methods. W here earthworks are not com plex on a site, it is norm ally most appropriate to collect data in transects across the landscape. T h e data collection interval should be considerably smaller than the distance over w hich features visibly change on the ground. Furtherm ore, it is norm ally m ost appropriate to align transects either across the principal alignm ent o f any earthworks (such as ridge and furrow) or at an angle to them, but certainly not straight along them. W ith m odern survey equipm ent, it is often possible to collect data autom atically at a set time or distance interval. A loss o f accuracy may be expected using this m ethod due to difficulties in keeping the detail staff level and so forth, although the advantages in terms o f time can be extrem ely positive. Variations in sample resolution along transects m ight also be different from the distance betw een transects. This is w orth experim enting w ith, although it is perhaps best to respond to the archaeology on the ground and use the most appropriate m ethod for collecting data that w ill represent the features on the com puter. As a rule o f thumb, m ore data w ill generate a better m odel, although the collecting o f points very close together does provide the potential for generating a m odel o f local variations that are not reflected on the ground due to the accuracy o f the survey equipm ent and the user. O ften this w ill lead to the need to strip dow n data to avoid localised artefacts in the resulting G IS model.

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E N V IR O N M E N T A L DATA So far this chapter has considered archaeological data and cartographic data. In addition to these it is often useful to integrate environm ental data into projects. T h e use o f geology and soils maps can be useful in understanding the distributions o f archaeological finds and, particularly in the case o f aerial photography, understanding the visibility o f archaeological features. As w ith other maps, geology and soils maps are often available digitally, though not universally, and often not at the range o f scales available for other types o f maps. Even w here they are not available digitally, and copyright perm itting, paper copies may be scanned and georeferenced. In this form at they m ay either be used as a raster layer w ith out further processing for com parative purposes, or else the different geological or soils units may be digitised to create a polygon layer. U sing the latter approach, either single types o f deposit, such as peat, can be digitised, or all features may be digitised to create topologically correct continuous coverages. A dditional environm ental data may be obtained

from num erous other

sources. These m ight include borehole data, norm ally inputted into the G IS as a database w ith different layers incorporated. In the case o f numerous boreholes, it can be possible to m odel different layers as T IN s or rasters (20).

2c> Area ot T h o rn e M oors, South Yorkshire, show ing the peatland surface and the basal clay topography derived from borehole survey (150 x 190m view ed from the east)

f>5

Landscape Archaeology and G I S

Alternatively environm ental data m ay include bathym etric data. For exam ple, w hen considering issues such as shifting coastlines or sea-level change, it can be useful to incorporate bathym etric data w ithin the G IS .T h is may be obtained from sources such as the Adm iralty in the U K as paper charts or digitally as sounding points. W hilst issues such as erosion, accretion and dredging w ill norm ally have altered the coastline and undersea surface, this still provides a useful starting point for developing m odels o f past landscapes, particularly w here a continuous raster m odel is required. W hen this is achieved, it becom es possible to m odel the im pact o f themes such as sea-level rise through time on the available land area.

C O N C L U S IO N S In this chapter, the types o f data used in landscape archaeology have been considered in relation to how they m ight be procured directly for use w ithin a G IS. In addition to the data types outlined in chapter 2, this chapter has also included approaches to collecting new field data, including both survey and palaeoenvironm ental data. B o th o f these themes w ill be explored further in the follow in g chapters.

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5

Processing spatial data IN T R O D U C T IO N Follow in g on from the previous chapter, the focus o f chapter 5 is to finalise the processing stages o f G IS, setting it up for further analyses. C hapter 2 identified the types o f data that exist for landscape archaeology. C hapter 3 explored themes o f spatial data, and chapter 4 brought these themes together by explorin g how landscape archaeology datasets m ay be specifically procured for integration w ithin a G IS environm ent. In this chapter, the methods o f processing spatial datasets w ithin the G IS are explored, including organisation, databases, metadata and the various m ethods o f interpolating from different data sources. T h e representation o f landscape surface models is explored as are the variety o f surface analysis tools and how they m ight usefully be applied w ithin landscape archaeology.

O R G A N IS IN G SPA TIA L DATA FO R G IS Different layers o f spatial data may be view ed together w ithin the G IS due to the fact that they are linked by the same coordinate system. In order to maintain spatial consistency betw een different datasets, it is necessary to maintain the same coordinate system for each layer and to ensure that each layer is spatially referenced to the same resolution. In other words, it is possible to provide grid references to a num ber o f different levels o f accuracy. W ith the O rdnance Survey it is necessary to transform the alphabetical p refix to its num erical counterpart. T h en , the num ber o f figures for each o f the N orthings and Eastings w ill provide levels o f accuracy. H ence a 12-figu re grid reference (e.g. 459225, 325123) will give accuracies to the metre, such that decim al places w ill be to the decimetre or centimetre. A shorter grid reference w ill give low er precision. For exam ple, a 10-figure reference (e.g. 45922, 32512) w ill provide precision to 10111, w ith

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decim al figures providing metre and decim etre values. A n 8-figure reference (e.g. 4592, 3251) w ill provide precision to 100m , w ith decimals at tens o f metres and metres. It is crucial that all datasets are calibrated to the same coordinate system and to the same reference value. Assum ing the spatial referencing o f the different datasets to be put into the G IS are consistent, then organising spatial data becom es possible. T h e ways in w hich a spatial G IS database m ay be organised is partially dependent on the software, w hereby different layers o f data m ay be associated in particular ways. For exam ple, data m ay be organised in groups or geodatabases, or particular layers may be referenced so that they w ill only be show n at particular scales, thus ensuring that layers are used appropriately in relation to their original scale. H owever, for the purposes o f most landscape archaeological approaches, m aintaining different layers o f data is relatively straightforward, creating maps that contain the layers bein g used. A caveat w ith m uch G IS study is the way in w hich data, generated by the G IS from the original input data, may be organised w ithin the G IS. For example, many G IS files w ill contain a num ber o f different files. In E S R I software, shapefiles relating to point, line or polygon data norm ally contain five separate files. Similarly, raster files will contain six separate files, w ith an additional file norm ally contained elsewhere. For different G IS layers to w ork correctly, all o f these files need to be present, and so the organisation o f data w ithin directories on the com puter is crucial, particularly i f files are to be copied for use on other machines. Similarly, maps that are created w ill contain inform ation regarding the locations o f each o f the files being used, in terms o f w here they are stored on the computer. Thus, copying map files can only be achieved through altering the file itself, or by recreating the map on each separate machine that is used. However, all ot these issues will be largely software specific, although it is w orth considering these types of issues before designing a G IS database to limit the problems that m ight be encountered.

DATABASES G IS is essentially a spatial database that enables new data to be generated from existing data, such as from com paring different layers o f data, or through the interrogation o f a particular database. T h e design o f G IS databases follows similar principles to all other databases and it is w orth som e consideration o f how databases w ork so that the best can be made from them .W ithin G IS software two types o f spatial database exist; relational databases and object-oriented databases. R elational databases are the most com m on form o f data structure, both w ithin and outside o f G IS (21).

68

Processing Spatial Data

Code

X -coord in ate

Y-coordinate

M aterial

Period

Class

100589 566504

521101

Bronze

MBA

Spearhead Metal detector

100590 >f>733°

567880

Silver

ROMAN Coin

Metal detector

100591 581000

543000

-

1A

Hillfort

Aerial photograph

100592 578930

566380

PM

Windmill Document

100593 591100

586630

MED

Village

100594 598700

553380

-

p

Crop mark Aerial photograph

100595 591200

592100

Bronze

LBA

Axe

Antiquarian

100596 585443

521002

-

MED

Church

Standing building

100597 581000

564000

-

7

Enclosure Aerial photograph

100598 592350

563460

Pottery

ROMAN Scatter

C o n text

Earthworks

Fieldwalking

21 A typical archaeological database o f sites and finds

6y

Landscape Archaeology and G I S

A relational database is essentially hierarchical, w hereby different elements o f the database are related to other sections. To clarify this point, consider the exam ple o f a database o f locations o f pottery found du ring fieldwalking. T h e principal spatial database m ay be a unique identifier, an .v-coordinate and a y coordinate, thus providing locational inform ation. O n ce the pottery has been analysed, there m ight be a separate database consisting o f the same unique identifier as the first database, but additional fields for the type o f material, the type o f artefact and the date from w hich it was m anufactured. In this simple exam ple, it is possible to see that the databases m ay be jo in e d in relation to the unique identifiers in each table such that, w ithin the G IS, the locational database may be interrogated in relation to the second database. H ence it is possible to provide an illustration o f pottery distribution from a particular date or o f a particular material. R elatio n al databases are hence based upon relationships, w hereby different tables, perhaps constructed as a spreadsheet, a simple table or w ithin database software, are linked or jo in e d together through shared fields, thus creating a m uch larger database. T h e advantages o f this type o f database ar^ that it is a relatively transparent m ethod o f organising data and is the most com m only used m ethod. T h e drawbacks o f this type o f database are that the data may only be interrogated on the basis o f the ways in w h ich it is structured. T h e alternative m ethod o f database construction is O b ject-O rien ted G IS (O O -G IS ). W hilst this remains relatively underused w ithin the archaeological com m unity, it w orks on the basis o f object-orien ted program m ing (see Tschan 1999). U sin g the traditional data structures o f rasters and vectors, archaeological sites need to be significantly sim plified to be useful w ith in the G IS. This means that a site w ill either be represented by a cell w ith a value o f 1, perhaps, w ithin a raster o f os, or else as a point, line or polygon in a vector form at. Thus levels o f inform ation about the site are om itted follow in g the required sim plification o f the site to its abstract form . Sites becom e reduced to sim plified cells or vectors in space, linked to attribute tables providing inform ation about them. In O bjectO rien ted systems, the approach is to consider the archaeological site or find as closely as it is by an archaeologist, treating each as an object. To take W heatley and G illings’ (2002) exam ple, one m ay consider a Bron ze A g e bell-barrow. From an archaeological perspective, such a m onum ent m ay be considered to be w ithin a broader class o f ‘Bron ze A g e burial m ounds’ w hich m ay be divided into sub-classes o f bell-barrow, dish-barrow, saucer-barrow and so forth. In other words, the bell-barrow is a sub-class o f Bronze A ge burial m ounds, w hich shares similarities w ith other burial m ounds from that period, but differs in terms o f shape. H ence, the site is seen w ithin the O b ject-O rien ted database as an object w hich, in addition to having its ow n attributes relating to size and so forth, also has non-hierarchical relationships w ith a range of other m onum ents and features.

70

Processing Spatial Data

Thus, whilst the use o f O b ject-O rien ted G IS w ithin archaeology is in its in fan cy it provides the potential for the G IS to simulate archaeology in a m uch m ore realistic way, albeit a m ore com p lex one.

M ETADATA M etadata has already been m entioned in part, although it is w orth considering in detail here. So far, a num ber o f potential problems that m ight occu r w ithin a G IS through user error have been noted. An obvious exam ple o f this is the use o f m apping recorded at a low resolution for calculations at a higher resolution. For exam ple, polygons digitised from 1:50,0 0 0 scale m apping m ight get exported to another user, w h o m ight be then using them to m ake calculations at a m uch higher scale, w ithout the know ledge that they were originally generated at a low er scale, and thus providing the potential for generating large errors. In m any cases, such as w here data have been collected for a single purpose, such limitations o f data can norm ally be appreciated and com plied w ith. H ow ever, a potential problem arises w hen a spatial database is likely to be used for a secondary purpose by a third party. W hen this happens, know ledge o f the limitations and original intentions o f the data capture might not follow the dataset and so unknow n errors or uncertainties can creep in surreptitiously. O ne w ay o f avoiding this w ould be to standardise data formats and scales, although this w ould be arguably inappropriate for archaeological w ork. T h us a second m ethod o f addressing this has been developed; metadata. Effectively, the role o f metadata is to provide a security net for such a situation, to ensure that subsequent users o f a dataset are aware o f its limitations (M iller and Greenstein 1997, W ise and M iller 1997). T h ere has been som e standardisation o f the structure o f metadata files. T h e D ublin C o re was developed follow in g three years o f international consultation (Gillings andW ise 1998).This m odel provides 15 principal elements w ithin w hich to record inform ation. T h ese are: Title, Creator, Subject, D escription, Publisher, C ontributors, Date (o f creation or dissem ination),Type (i.e. text, im age), Form at (i.e. book, C D - R O M , w eb page), Identifier, Source, Language (i.e. English or French), R elatio n (i.e. i f part o f a w ider study), Coverage (spatial and temporal), and R igh ts (copyright inform ation). R e c e n tly som e G IS software packages have begun to provide an integrated m ethod for recording metadata, w hereby files held w ithin the G IS m ay be tagged w ith inform ation recorded on pro-form a sheets.

71

Landscape Archaeology and G I S

22 C o m p a rin g the results o f raster interpolation and T I N creation on the Iron A ge hiUfort site o f C o n d erto n Cam p, Gloucestershire (280 x 160m view ed from the south-west)

T R IA N G U L A T E D IR R E G U L A R N ET W O R K S / In chapter 3, the different types o f data structure for creating, representing and analysing three-dim ensional data w ere considered, particularly w ith reference to T IN s and rasters. T IN s , or m ore strictly, Triangular Irregular N etw orks (but also sometimes referred to as Polyhedral Irregular N etw orks), consider threedim ensional data in a three-dim ensional w ay (22). Essentially, a T I N is a vector-based interpolation w h ich creates a solid surface from input data, either from points (containing som e type o f elevation attribute) or from contour lines, w hereby nodes are used as the key input points. It calculates elevation o f a plane passed through the closest data points o f know n value by jo in in g them w ith their nearest neighbours by arcs to form polygons. W ith T IN s these polygons are always trianglular. T IN s have an advantage insofar as they can generate surfaces from m ultiple data sources that can include breaklines (w hich define sudden changes in height). T I N models can be generated directly from either regularly or irregularly spaced input data, so that greater detail m ay be obtained from one part o f a surveyed site than another. T h e resulting T I N surface is com prised o f triangular facets or planes w ith the input point data at each o f points o f the triangle. For irregularly spaced data, the sizes o f the triangles w ill thus v a ry T h e resulting surface is a m esh o f triangles held in three-dim ensional space dependent on the elevations o f the input data. In addition to generating a T I N surface, it is possible to add breaklines into the T I N w hen it is created. A breakline is any G IS line w h ich represents a direct break in the data. This m ight relate to a c liff edge, for exam ple. B y inputting a breakline into the T IN , the surface w ill be m odelled from each side o f the line just up to it, enabling com plex landscape features to be m odelled accurately.

72

Processing Spatial Data

In addition to the obvious advantages o f using breaklines, one o f the principal advantages o f the T I N data structure is the ability to use a variety o f input data together, such as com binin g point and line data. It is also a useful interpolation m ethod since it only uses the input data and w ill not generate errors or interpolation artefacts (such as deep pits or peaks) that other methods o f interpolation can -generate. Similarly, the input data can be o f a variety o f spatial resolutions, w hich is extrem ely useful i f you are m odelling data resulting from an earthw ork survey w hich contains high-resolution point coverage for areas o f earthworks and a low er resolution for the flatter areas. This is because the T I N itself does not assume a particular resolution o f its ow n, in contrast to raster data structures. O n ce created, the T I N occupies a single file w hich is extrem ely useful for simple copying o f data files betw een com puters or betw een different spatial databases. A T I N m ay be used for direct surface representation or for surface analyses. For exam ple, T IN s can be interrogated directly to obtain inform ation regarding elevation, aspect and slope, or analysed in terms o f visibility, determ ining an observer’s view o f a landscape from a particular position. H ow ever, further than interrogation o f the T IN , analyses w ill generate outputs in the form o f rasters, at a given cell resolution (see chapter 3 and below for a discussion o f raster formats). For exam ple, a T I N can be used to generate m odels o f slope, aspect, hillshade, view shed or cut-fill analysis, but the results from these analyses w ill be rasters. There are two principal limitations o f T I N data structures. Firstly, the surfaces generated from the input data form straight lines and planes betw een the input points, rather than providing som e elem ent o f sm oothing. This presents a potentially unrealistic representation o f a landscape or site, although it can be com pensated for through sm oothing in som e software packages i f the T I N is converted or interpolated into a raster data form at. T h e second limitation o f T IN s is that they lack the mathem atical potential for rasters. For exam ple, com parin g surfaces mathem atically, such

as adding surfaces

together, or

com binin g surfaces (see below ) cannot be achieved w ithin conversion. These mathem atical capabilities are am ong the most pow erful tools available w ithin G IS and so often a T I N w ill ultim ately need to be converted to a raster for indepth analysis. A T I N ’s accuracy is dependent upon the process o f triangulation that is em ployed to form a continuous surface. C om m only, an interpolation m ethod know n as Delaunay Triangulation w ill be used w hich follows a criterion that a circle drawn through the three nodes o f each triangle w ill contain 110 other data points, thereby requiring smaller triangles in areas o f greater point density (tf. Voigtm ann et al. 1997).

7.3

Landscape Archaeology and G I S

O ther than the ability to generate surfaces from regularly and irregularly spaced data and from m ultiple sources (including both contours and points), there are three m ain benefits to using T IN s in the generation o f surfaces from point-based data (G ouch er 1997, 249-50). Firstly, the D elaunay Triangulation process means that the triangles form ed are as equiangular as possible, providing a better geom etry than i f the angles w ere able to becom e m ore acute. This means that the surfaces are potentially m ore accurate w h ich is helpful for further analyses. Secondly, the D elaunay process ensures that every interpolated area is as near as possible to a triangle node so a m inim um o f interpolation is required. Finally, this process gives each survey point equal priority so that the order in w hich the points are processed w ill not affect the resulting surface (G oucher 1997: 249). H ow ever, although T I N

generators have becom e increasingly

popular over the past decade due to their apparent efficien cy and flexibility, they are m ore reliable w h en the data source consists o f irregularly distributed spot heights. W h en contour data is used they are less reliable, form in g a less realistic landscape representation, and can generate errors w h en m odelling valley bottom m orphology, requiring subjective breaklines to be added, although som e research has been conducted to overcom e such errors (e.g.Voigtm ann et al. 1997). It was also noted that the potential o f surface sm oothing provided a further paradox insofar as it can reduce artefacts o f the interpolation process but w ill also lose some m icro-relief inform ation (Voigtmann et al. 1997). There are two principal ways o f converting T IN s to raster formats. T h e first is by direct conversion, w hereby a grid o f a given surface resolution (or cell size) is effectively draped as a rubber sheet over the T IN , and each cell is given an elevation value taken from the T I N itself. This is the most direct m ethod o f generating a raster from a T IN , whilst m aintaining the detail that is available w ithin the T I N itself. T h e second m ethod is through interpolation, w hereby the nodal values o f the T I N form the basis for generating a m athem atically derived surface o f cells. T h e relative m erits o f the different m ethods o f interpolating surfaces are outlined below.

IN T E R P O L A T IO N O f principal im portance to G IS cell-based m odelling is the D E M and a num ber o f studies have investigated the applications, values and limitations o f different raw data sources, ways o f creating the D E M , and ways o f m easuring errors w ithin the D E M (e.g. B u rro u gh 1986, H utchinson and Gallant 1999). A num ber o f studies have addressed the accuracy o f models resulting from the use o f different types o f data and through different approaches.

74

Processing Spatial Data

2 } T h e results from different interpolation methods o f an earthw ork survey: (A) T riangular Irregular N etw ork , (B) R egu larised Spline, (C) Tension Spline, (D) Inverse Distance W eighting, (E) O rdinary K rig in g , (F) Universal K rig in g - M igd ale henge, H ighlands (area measures 20m across, lookin g north-west)

Different G IS software packages provide different options for the interpolation o f D E M s, although the range m ay be classified into three m ain categories —linear interpolation (drawing straight lines through data points), Cubic Spline (drawing a curve through the data points) and statistical interpolation (using algorithm s to generate values for cells betw een know n data points based on the values o f them) (Gillings and W ise 1998: 35-6). A n outline o f these various m ethods is provided in chapter 8 o fB u rro u g h (1986).T h is text describes the m athem atical fram ew ork o f the various techniques, and som e o f the applications o f these methods and their effects. D ifferent interpolation m ethods w ill have varying appropriateness for dealing w ith different types o f data and w ith providing different types o f results (23).

Trend Trend describes ‘gradual lon g-ran ge variations’ o f the z-valu e o f a m odel (Burrough 1986: 149). It uses polynom ial regression to generalise the surface from a statistical approach to the raw point data. As such the surface rarely passes through the survey points, but rather provides a m odel o f trend. Defaults norm ally use a linear trend, although options for non-linear surfaces using quadratic and cubic regression are possible. T h e effects o f these are outlined in B u rrou gh (1986). This m ethod o f surface interpolation is therefore different from the other in that it does not attempt to recreate a landscape, but rather it generalises the data. T h e usefulness o f trend surfaces lies in the possibility o f m easuring

75

Landscape Archaeology and G I S

deviation from the general trend. It is therefore not n orm ally considered as an interpolation m ethod (Burrough 1986). W ithin the present study, trend has been included as a baseline for generalising the dataset and a basis for com parisons w ith other data.

Spline Spline interpolation creates a surface from point data by fulfilling two criteria. Firstly, the surface must pass exactly through all data points rather than generalising them. This means that the resulting D E M w ill retain the m ore subtle features unlike w ith the trend function (Burrough 1986). Secondly, the surface must display m inim um curvature, creating a continuous and sm ooth surface (E S R I 1995). A n advantage o f this technique is that calculations do not require high processing power. Splines can be generated w ithin two principal parameters: either regularised or w ith tension.T he two approaches produce different m odels, although tension norm ally w ill reduce the num ber o f interpolation artefacts from the processes. This m ethod is som etim es referred to as R egu larised 'Sp lin e w ith Tension (R S T ).

Inverse distance weighting Inverse distance w eighting (IDW ) is a m ethod o f interpolating a D E M by calculating the averages o f the data around each input-data point. W ith IDW, this averaging is w eighted inversely w ith distance. For each cell in the D E M , those input-data points closer to the cell are given greater influence on its value than those further away. T h e effectiveness o f this function is dependent upon the size o f influence for each cell (B urrough 1986). In other words, changes in the radius used w ill have a direct effect upon the value o f each cell point as m ore or less data is considered. Greater radii w ill also increase the need for com puting effort. C om m only, the default uses the nearest 12 points to the cell for the calculation, w ith a m axim um radius o f five map units.

Kriging K rig in g (or ‘optimal interpolation’) is an interpolation m ethod that uses spatial covariance to create a statistical surface from point data, based upon factors m ore com plex than the single function o f distance demonstrated by spline (Burrough 19 8 6 ).T h e technique is most useful w h en interpolating surfaces from data that is less anom alous for the creation o f the D E M . K rig in g assumes that regional spatial variation is statistically reflected by the rest o f the data and is thus not appropriate for datasets displaying vast local vertical variations, as the surface w ill be treated as uniform . T h is is the most advanced interpolation m ethod, relying on a m ultiple factors rather than just distance.

76

Processing Spatial Data

Generating rasters from T IN s For m any purposes, raster data w ill be required and so a T I N m odel w ill need to be converted. Different software packages enable this in different ways. In m any G IS packages there is an option to p erform a direct conversion from the T I N to the raster. Such a conversion interpolates a continuous grid using the T I N as a reference from w h ich gridded elevations are calculated. T h e function places a grid o f cells at a pre-determ ined density across the area covered by the T I N w hich are referenced in terms o f x and y coordinates. A height attribute for each cell is then interpolated from the T IN . A rcln fo presents a num ber o f different ways o f converting a T I N to a lattice using this function, the prim ary two being linear and quintic interpolation. Linear interpolation treats the T I N s arcs betw een nodes as straight lines that are reflected directly in the heights o f the cells across it. T h e areas enclosed by the T I N ’s triangles are then treated as flat plates. Surfaces constructed using linear interpolation often look faceted and unnatural, though the potential for interpolation inaccuracies is reduced. T h e second m ain m ethod, quintic interpolation, applies sm oothing to the areas inside o f the T I N triangles. This m ethod appreciates that the surface being represented is sm ooth w ith out the harsh breaks in slope represented through linear interpolation. Instead a continuous sm ooth surface is created w hich runs through the nodes and form s a sm ooth interpolation through the areas between. T h e resultant surface is not only m ore aesthetically realistic, but has the potential for being m ore accurate w ith a curved, rather than faceted, surface. Sim ilar processes are provided in A rcln fo using Tinlattice, in A rcG IS using the ‘ convert’ function, or in ID R IS I using Tinsurf.

D E M R E SO L U T IO N T h e relationship betw een the data source and the D E M extends further than the choice o f interpolation/generation m ethod to be used. D E M resolution is crucially im portant and may be defined as the size o f each cell in x and y (24). Principally, the D E M resolution directly influences the degree to w hich it is suitable for a specific application (Veregin 1999). T h e value o f each m ethod ot interpolation is dependent on the input-data source and the desired cell size ot the resulting grid. It has been shown, however, that general trends exist. For exam ple, a paradox exists w ithin grid interpolation techniques such that com puter processing pow er and data storage can be a problem i f a large area is analysed at high resolution (i.e. small cell size). I f a low er resolution is chosen then the D E M can becom e inaccurate and over-generalised (Carrara et al. 1997), and m ay negatively affect analyses (M adry and R ak o s 1996).

77

Landscape Archaeology and G I S

24 T h e results from interpolating a D E M at the different surface resolutions o f 10m , 5m and 0.5m - B ream ore, Ham pshire (260 x 150111 area lo o k in g south-west)

Studies have also highlighted the effect o f the relative resolution o f the data source (cf. Jo ao 1998) and the desired cell size w ithin cell-based D E M s. G ao (1997) chose three landscapes displaying the different topographic characteristics o f a valley, a peak and a ridge, and generated D E M s from digitised contours interpolated into a grid through kriging. T h e accuracy o f each resulting D E M was analysed in relation to the real landscape that was bein g represented by the m odel. T h e tests also investigated the im pact o f interpolating at different cell resolutions. In order to standardise the results o f the experim ents, each D E M was analysed to explore the effects o f the variable contour resolution and D E M cell size through exam ining gradient in different areas o f the m odels. T h e results o f this research dem onstrated the m inim al effects o f resolution on gradient and provided a m ethod for achieving optimal surface resolution from a given contour density.



Processing Spatial Data

25 D ifferences betw een different interpolation techniques. From a dataset w ith .111 elevation variation o f 1.0 1111, the difference betw een Tension Spline and O rdin ary K rig in g is 0.07111 (on the left), whereas the difference betw een R e g u la r Spline and ID W is 0.29111 (011 the right)

M E A SU R IN G ERRO R A distinguishing feature o f G IS is its ability to create new data from input data, achieved by applying algorithm s to the data. For exam ple, a D E M may be created from raw variable resolution input data, and the resulting D E M w ill fo rm the basis for further com putations and analyses. Therefore, the accuracy o f results from all later levels o f analysis is dependent upon the accuracy o f the creation o f the D E M in the first instance. T h e possibilities o f error may becom e com pounded as further levels o f analysis are conducted (25). It is for this reason that the accuracy o f the original data, the intrinsic data, is as high as possible. It is also im portant to scrutinise the validity o f the algorithm s and processes that are used to generate the n ew data. This may be referred to as operational error (see Marozas and Z ack 1990 for error definitions, cf. H euvelink 1999). Finally, it is im portant to be able to assess w hether any errors have been encountered, particularly w ithin the operational process, since these can be corrected. W hile the abstraction o f the landscape into G IS models is advantageous, allow ing analyses to be perform ed on the m odel w hich w ould be impossible on the ground, this same principle means that models cannot be accurately com pared to real landscapes in order to check for error. Therefore other methods have been proposed by different researchers to establish ways o f checking the accuracy o f D E M s and the analytical processes, and thereby their reliability w ithin further analyses. For exam ple, a m ethod using progressive

Landscape Archaeology and G I S

thinning o f the input data was applied to assess changes in the D E M in relation to both surface area (com pared to its simple tw o-dim ensional planim etric area) and volum e (Brasington et al. 2000). It is im portant to understand w here errors occur w ithin a D E M due to the surface’s fundam ental influence on the results o f further analyses (Kvam m e 1990). Fisher (1999) highlighted the range o f errors that m ay occu r w ithin G IS, classifying error, vagueness and ambiguity, relating error w ith uncertainty.T h is w ork demonstrated the need for users to be aware o f these issues in order to provide confidence in their decision-m aking. T h e main problem s o f such analyses lie in the lack o f benchm arks by w hich to com pare abstract data m odels. T h e first obstacle is that a m odel can only be as good as the raw data from w h ich it is derived, and therefore its accuracy can only effectively be com pared w ith this original dataset. Such a com parison has been attempted statistically (e.g. Li 1994) and visually (e.g.W ood and Fisher 19 9 3).T h e overall quality o f the resulting D E M has been exam ined in a num ber o f ways. G ao (1997), for exam ple, m easured the accuracy o f 200 random ly chosen points from a given terrain to enter into a statistical equation to calculate root-m eansquare-error. H aigh (1993) analysed his resulting D E M s in relation to reliability, robustness and realism. R eliab ility was m easured through the application o f the technique to a num ber o f different sites. Robustness was seen in relation to the required jo b o f the m odel, in this case the rectification o f aerial photographs, and realism was m easured by a com parison betw een the m odel and the perceived surface it represented. H e also noted that large changes could be identified w hen surfaces w ere interpolated from contours depending upon the num ber o f points used to digitise them , and through the addition o f extra data points. Am ongst other m ethods, Carrara et al. (1997) analysed a D E M by com parin g the contours that form ed the source data for the m odel and the contours generated from it. In this study five parameters w ere identified for assessing accuracy. Firstly, cells near to contours should be o f similar height; secondly, betw een contours all cells should be w ithin the vertical range displayed by them ; thirdly, in these areas heights should vary linearly betw een elevations; fourthly, in areas o f low relief variability the cell values should reflect a ‘ realistic m o rp h o lo g y’ ; and lastly, the distribution o f interpolation artefacts should be small (Ibid.: 453). Similarly, a further dim ension to assessing D E M accuracy was provided by Lop ez (1997) w ho demonstrated that two types o f errors were present w ith in D E M s: systematic, resulting from the m ethod used to generate the D E M , and random , resulting mostly from the user (cf. Beard and Buttenfield 19 9 9 ).This research presented a new m ethodology tor identifying random errors by using Principal C om p on en t Analysis that was tested by analysing user-generated random errors. A statistical approach to m easuring the probability o f error across a D E M has been suggested

So

Processing Spatial Data

(Kyriadis et al. 1999). This m ethod produced alternative representations o f other possible outcom es for areas not covered by w hat was referred to as ‘hard’ data, or input data.

SU RFA CE R E P R E SE N T A T IO N T h e representation o f digital surfaces can provide inform ation regarding the nature o f the landscape and has the potential to provide inform ation about m icro-topography

that m ay reflect archaeological and

non-archaeological

activity. Surfaces can be represented in two ways, centred on w hether or not elevation is displayeci.

Elevation-based surfaces Elevation-based surfaces display m odels such that their vertical scale is represented (1colour plate 3). Typical m ethods o f display include contours and contour bands w hereby ranges in vertical drop are generalised by a given sym b o l.T h e advantage o f such m ethods is that they display features that highlight vertical drops such as river valleys and assist in the understanding o f the landscape. O n a basic level, elevation surfaces may be represented as con tour lines or bands. C o n to u r lines are usually positioned at given intervals in order to provide a linear representation o f height. C o n to u r bands can fulfil the same jo b , but can also be scaled m athem atically from the data. Archaeological features are norm ally identified as being anom alous com pared w ith the surrounding landscape. Linear contour bands generalise a landscape and therefore lim it the visibility o f surface detail. W ithin landscapes w h ere archaeology lies upon a relatively flat plane, it is possible that features w ill lie w ithin a single contour band and as such w ill not be visible. In these cases, a second m ethod o f contour banding may be applied based upon percentiles o f the frequency o f different cell values.

Non-elevation-based surfaces N on -elevation surface representations display detail o f topographic change, highlighting subtleties o f the surface itself rather than in changes in elevation. It has the added advantage o f representing details that may be missed, lyin g between contour values, for exam ple. T h ere are several methods o f non-elevation-based surface representation. T h e first illuminates the surface from a given lighting position (an analytical function, though considered here under the heading o f representation, but w hich could equally be considered w ithin surface analysis). Hillshading is a m ethod o f im proving the visual quality o f a map (colour plate 4). It calculates areas o f light and shade by determ ining reflectance o f the source based

Landscape Archaeology and G I S

upon elements o f aspect and slope (see below, B u rrou gh 1986).T h e result o f such analysis is a surface that is similar aesthetically to aerial photographs, highlighting earthworks, for exam ple. B y altering the position o f the light source it is possible to highlight alternative features. T h e second m ain m ethod included w ithin the them e o f non-elevation surface representation is perspective m odelling, using elements such as elevation exaggeration and draping o f vector and raster features on

a w ire-fram e

mesh. Principally, this is achieved through identifying a D E M as the surface, m ultiplying the values o f each cell by a given exaggeration value, and draping a mesh (or other data) over it. This is represented from a given angle, w ith an observer position and a target position, providing an oblique perspective image. T h e advantage o f this representation m ethod is that the less pronounced features w ill be highlighted w ith the altered ratio betw een the Cartesian coordinates and the height values. Further, the perspective m odel provides a backdrop for draping other archaeological features, providing a clearer visual understanding/of the relationship betw een topography and archaeology.

SU RFA CE A N A L Y SIS TO O LS A D E M surface can be analysed in a num ber o f ways before any layers o f data are added to it, but also in relation to other layers o f data (such as archaeological distributions). Surfaces can be generalised by calculating slope or aspect, or m ore com plex analyses o f visibility can be used. Surface analyses such as slope and aspect can form a basis for understanding the surface, particularly in relation to D E M s based on survey data o f archaeological earthw ork sites. T h e latter, m ore com plex types o f analysis using visibility are often used for the interpretation o f archaeological landscapes, but have also been used in relation to assessing the impact o f developm ent. Approaches involving surface analyses are com m on, particularly w ithin studies using multiple techniques to understand terrain form in relation to archaeological distribution (e.g. Kvam rne 1992).

Slope Slope has been defined as the com bination o f both gradient (the m axim um rate o f change in altitude) and aspect (compass direction o f gradient) (cf. Bu rrou gh 1986: 50). T h e calculation o f slope creates a new raster w ith values relating to the gradient for each cell (colour plate 5). T h e output grid is defined either in terms o f percentage or degree and is calculated from the greatest change in height betw een the cell in question and the eight cells im m ediately surrounding it. Slope, along w ith elements o f elevation, has been used to investigate location

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preference in past occupation distributions (e.g. K una and Adelsbergerova 1995), providing both C R M

and interpretative results. Calculations o f slope have

been a principle aspect o f m ultiple surface analyses, particularly those involving predictive m odelling (e.g. K oh ler and Parker 1986, C arm ichael 1990, Marozas and Z a c k 1990, Kuna and Adelsbergerova 1995) and interpretative cost-surface analyses (cf. van Leusen 1999, Bell and Lock 2000, D e Silva and Pizziolo 2001).

Aspect T h e calculation o f aspect w ithin a D E M identifies the direction o f the m axim um rate o f change in values o f each cell (colour plate 6). Ascribed cell values are given in positive degrees from north. Archaeologically, it may be argued that elements such as south-facing slopes w ithin the northern hemisphere m ay be preferentially settled or farm ed due to increased solar exposure. Therefore an exploration o f correlation betw een aspect and site distribution may present interesting results in relation to this hypothesis. A n exam ple o f the use o f aspect m odelling was provided by Llobera (1996) w ho generated an aspect m odel o f the Wessex chalklands and com pared it to the distribution o f linear ditches. This, coupled w ith other analyses identifying hillcrests, was used to explore the relationship betw een anthropogenic and natural boundaries. It was noted that there was a positive correlation betw een changes in aspect and turn in the ditches. As w ith slope analysis, the generation o f aspect models has been used as one o f several layers o f data w ith in m ultiple surface analyses o f C R M predictive m odelling (e.g. Altschul 1990, K vam m e 1992).

Visibility Visual analysis on a D E M can be conducted in two ways. Lines o f sight (LO S) can be calculated in the binary sense, investigating w hether tw o points are intervisible, or com plete directional or non-directional viewsheds can be calculated from a given position (26). T h e form er measures w hether obstructions lie betw een two given points, and the latter calculate all cells that have a clear line o f sight from the observation point. T h e G IS calculates a view shed for a given position from w hich rays are sent out to all cells o f the D E M . T h e output o f the process is a binary map show ing w hich cells are obstructed, and w hich are visible. G IS view shed analysis has been criticised in a num ber o f ways, including assessments o f the nature o f accuracy at an algorithm ic level (e.g. D e Floriani and M agillo 1999). O therw ise, the main focus o f criticism has been w ith the unrealistic binary output that does not reflect the com plexities o f reality (e.g. Fisher 1992, 1993). In response to this problem , other methods have been applied in order to provide a m ore realistic understanding o f visibility, particularly

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26 C o m p a rin g view sh ed (grey areas) and line o f sight on a D E M . T h e lines o f sight are divided into grey (invisible) and black (visible) areas

in relation to visual im pact assessments for n ew developm ents. For exam ple, N ackaerts et al. (1999) dem onstrated an approach that com bined a surface representing error w ith in the parent D E M to provide a m ore realistic calculation o f the likely visibility from each cell using probability. T h e application o f visibility analysis w ithin landscape archaeology is well founded (e.g. D evereu x 19 9 1, Thom as 1993, T ille y 1994). T h e principles upon w hich visual analyses o f this type rest are those o f existence and perception. C o ntem p orary studies in the social sciences have suggested that visibility is the principal way in w h ich humans relate to and interpret their landscapes (e.g. Punter 1982). Equally, it has been argued that such interpretations are only possible w ith

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a p rior understanding o f the landscape so that the signs m ay be interpreted (cf. M ein ig 19 7 9 ,Tuan 1979, C osgrove 1989). Archaeologically, visibility has been an elem ent that may be investigated and understood (e.g. Ben d er et al. 1997) and has been used frequently w ithin interpretative approaches. For the purposes o f most studies a height o f 1.7 m is chosen for the height o f the observer w hen p erform in g visual analyses. W hilst this is the norm al default height used for visibility analyses, it may be corroborated through the analysis o f skeletal remains. For exam ple, on the Yorkshire Wolds a num ber o f prehistoric cem eteries have been excavated providing a large population sample, particularly for the early Iron A ge (e.g. Stead 1991). A statistical analysis o f these skeletal remains was presented by Leese (1991) w h o has com pared the results from this region to broadly contem porary sites elsewhere in the country. T h e results demonstrated that, in the Yorkshire region, mean heights o f individuals represented in the cem eteries were 1.7 1 m for males and 1.5 8 m for females, based upon excavated cem etery populations o f 68 and 48 individuals respectively. This figure is slightly high com pared to the other sites o f W etwang on the Wolds w hich produced figures o f 1.6 7m and 1.5 6 m from populations o f 12 2 and 168 individuals, and M aiden Castle w hich produced figures o f 1.65111 and 1.52111 from populations o f 19 and 9 individuals. T h e figure o f 1.7111 has been rounded up from the data for males. C learly other influencing factors including clothing and vegetation w ill influence the accuracy o f level o f sight, and so a m ore accurate determ ination m ay not be appropriate.

Combining surface analyses T h e results from different surface analyses in isolation can provide valuable information regarding questions relating to landscape archaeology. However, most often the most significant results stem from a process o f com bined analyses, integrating what each o f the different surface analyses techniques show together (27). There are a num ber o f ways o f achieving such a com parison o f results w ithin the G IS whilst m aintaining the original rasters. O n e m ethod is to exam ine the different resulting models w ithin a three-dim ensional view er, although a m ore straightforward m ethod is to explore transparency functions w ithin the software. In most G IS packages it is possible to add a level o f transparency to one or m ore layers o f data. This allows num erous surfaces to be view ed simultaneously. For exam ple, for m any purposes it w ill be im portant to v iew elevation data in conjunction w ith surface analysis data such as slope or hillshade. B y m aking the latter models semi-transparent they may be view ed over the top o f the elevation surface.Thus, additional details can be identified through the com bination w hich m ight not have been visible otherw ise. This type o f approach is particularly useful for exam ining high-resolution surface data derived from survey or L ID A R .

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27 C o m b in in g topographic m odelling (from G P S survey) w ith the results from geophysical survey at D ru m lan rig R o m a n fort in D u m fries and G allow ay (250 x 220111 view ed from the east. T h e area o f the fort measures approxim ately 190 x 120111)

C O N C L U S IO N S In this chapter issues o f data have been considered. T h ese have included the nature o f archaeological data and ho w this m ay be integrated usefully w ithin a G IS. M ethods o f dealing w ith G IS data, and particularly the use o f landscape topographical data, have been considered. D ifferent types o f input data have been outlined, and the ways in w h ich these may be processed in order to generate surfaces from them has been covered, including both vector-based surfaces as T IN s and raster, cell-based surfaces. This chapter has also considered the relative merits o f different ways o f representing surface data, and the different ways o f analysing it, to generate landscape models o f slope, aspect or visibility. In this and the preceding chapters, the ways in w h ich G IS works w ith different types o f data have been addressed, including ho w they m ay be processed and analysed, the issues involved in terms o f error, and the various methods o f generating n ew data. T h is provides a foundation for addressing direct issues o f landscape archaeology. This is the first section o f the bo ok, addressing m ethod and procedure. T h e follow in g section o f the bo ok addresses these themes, considering questions and case studies relating directly to landscape archaeology.

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In the follow in g chapters, the types o f analysis already discussed w ill be considered directly in relation to themes w ithin landscape archaeology. In the next chapter, methods o f analysing landscape archaeology are considered from both tw o-dim ensional and three-dim ensional approaches. This is follow ed in chapter 7 by a consideration o f landscape reconstruction, including themes o f palaeovegetation as w ell as archaeological reconstruction. C hapter 8 then considers themes o f theoretical approaches to landscape archaeology, assessing the ways in w h ich G IS users have addressed themes o f archaeological theory, and presenting case studies o f how theoretical concepts may be addressed using G IS. Chapter 9 brings together m any o f the separate themes from chapters 6-8, but addressing the interpretation o f archaeological landscapes m ore holistically. C hapter 10 then considers these themes in relation to cultural resource m anagem ent (C R M ) , and the ways in w hich G IS is, and can be, used for addressing the needs o f curators. In chapter 11 some new m ethods for presenting the results o f G IS analyses are discussed in relation to the variety o f end users o f G IS technology.

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Landscape analysis IN T R O D U C T IO N In chapter i, landscape archaeology was defined as being an am algam ation o f three principal branches. T h e first o f these was ‘landscape analysis’ , essentially based in the realm o f analysing the data that exists for a landscape, com m only perform ed through activities such as map regression. In other words, the landscape archaeologist in this field w ill typically be lo o kin g for themes and patterns w ithin the landscape, such as field m orphology, to assess how the landscape has developed through time and w h ich elements may be attributed to a particular period. In the previous chapters, themes including space, procuring data, processing data, and ways in w hich space is considered w ithin a G IS have been exam ined. This chapter begins to consider ho w G IS may be used in practical scenarios drawing on a num ber o f case studies and examples. Traditionally the broad them e ot landscape archaeology, w hich m ay be term ed ‘landscape analysis’ , has represented those areas o f archaeology that have been concerned w ith maps and the recognition o f the m orp h o logy o f archaeological features. W hilst this area o f landscape archaeology will be considered, additional themes w ill be drawn upon that lie w ithin w hat may be term ed a positive archaeology, including predictive m apping o f archaeological features based on observed data.

T R A D IT IO N A L A P P R O A C H E S TO LA N D SC A PE A N A LY SIS Landscape analysis is a broad term encom passing a range o f activities. For the purposes ot this book, landscape analysis is taken to include those areas o f archaeology that focus on the interpretation o f features in the landscape including identification, m apping and the consideration o f principal themes. These include

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assessing the density o f sites and their distribution, considering this distribution in relation to other archaeological or natural features, or analysing archaeological site positions in relation to the archaeological and topographical landscape. Traditionally such landscape analysis has centred on map w ork, norm ally in tw o-dim ensions. Input data have consisted largely o f aerial photographs, m odern and historical maps, and archaeological data gathered on the ground or obtained from record offices. Approaches to analysing this data have norm ally consisted o f identifying and m apping features, such as crop marks, considering phasing in the landscape betw een different editions o f m apping, and through exam ining w here sites are in relation to natural features. T h e outputs from m uch o f this type o f landscape analysis have been in the form o f distribution plots o f sites in an area and textual descriptions o f trends and findings. W here it has been approached, further analysis m ight have involved considering the territorial boundaries based upon distributions or exam ining spheres o f influence, although the types o f approach are linked closely w ith trends w ithin archaeological theory.

M A P S, R E G R E S S IO N A N D G IS O n e o f the principal tenets o f any landscape study is the use o f maps. As m entioned in chapter 4, cartographic data includes m odern m apping, historical m apping and environm ental m apping, such as geolo gy or soils maps. M aps are clearly a good point o f departure for any study using a cartographic software package such as G IS. As m entioned in chapter 1, one m ethod o f approaching landscapes is to regress them. In other words, the approach is to identify datable features w ithin an area, such as a R o m a n road, and rem ove them gradually to reveal the earlier landscapes beneath them . C o m m o n ly this is approached by analysing the stratigraphic relationships betw een features, such as a motorway, interrupting the field pattern. W ithin G IS, this type o f approach m ay be addressed by generating them ed or dated layers based on vector formats. H o w this is achieved depends largely on the nature o f the input data. For vector-based maps, it is norm ally possible to select features o f a given data and to export them to a new file relating to a given period. For raster-based map formats, it w ill be necessary to digitise the field boundaries and other features o f interest. C om m only, a regression analysis aim ed at identifying different phases o f landscape developm ent w ill be based upon m ultiple maps, including both m odern and historical maps. In these cases a com bination o f vector and raster formats w ill be required, necessitating an approach using georeferencing, digitising o f features and vector selection.

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ID E N T IF IC A T IO N OF FEATU RES A second branch oflandscape analysis involves the identification and interpretation o f m orphologically distinct features.This process can also involve the identification o f different phases o f activity on a site and is com m on ly undertaken through survey w ork. For exam ple, the w ork o f the R o y al C om m ission on the H istorical M onum ents have perfected the process o f hachured survey (e.g. B o w d en 1999) aim ed at identifying features, phasing etc. T h e advantages o f this approach lie in the interpretative approach that involves a high level o f archaeological skill.

Hachure plans are a useful and well understood notation within archaeology and can be used to create a clear visual representation of terrain form that would be difficult to represent using contours (Wheatley and Gillings 2002, 108). H ow ever, limitations exist w ith this traditional m ethod o f notation. Firstly, they are a purely qualitative approach, representing only relative variation in terrain rather than absolute height. Secondly, they tend to focus on interpreted detail at the expense o f broader topographical trends. Thirdly, hachure plans can becom e so densely drawn and com plex that the results are rendered effectively meaningless (W heatley and Gillings 2002). Consequently, in the context o f G IS, hachured plans are lim ited to their use as a georeferenced im a g e .‘H achure plans are best regarded as a fo rm o f interpretation or presentation rather than as a form al m ethod o f recording’ (W heatley and Gillings 2002, 110). In contrast to the hachured survey is a con tour survey. I f the hachured survey is subjective, then the contour survey m ay be considered objective, although the realities o f this are m ore variable. T h e idea is to record three-dim ensional positions across a site in order to reconstruct a contour representation. W ithin a G IS , and through various surface interpolation techniques, it is possible to generate a ‘solid’ surface m odel rather than, or in addition to, contours (2cS’).W ith the developm ent o f survey equipm ent, and particularly G P S , the capturing o f data has becom e extrem ely rapid enabling high resolutions to be achieved. In chapter 4, the advantages and problems involved w ith data capture using such methods w ere outlined, highlighting the need to respond to the local topography, choosing the appropriateness o f systematic or irregular recording o f positions. In addition, different interpolation m ethods w ill produce different results from different types o f data. C om m ercially available data, from either the O rdnance Survey, or from rem otely-sensed data, is often at a resolution that is too low for the representation of archaeological features and particularly earthworks. T h us through accurate survey aim ed at obtaining three-dim ensional positions across the area o f study,

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28 C o ck h ill, Blackpatch, West Sussex. M od el o f a B ronze A ge enclosure identifying m orphologically diagnostic features

it is possible to generate D igital Elevation M odels (D EM s) o f these features. This m ight be undertaken for a num ber o f reasons. A t the most basic level, the approach m ight be to record features three-dim ensionally, w ith the added advantage o f generating a plan that may facilitate interpretation (cf. B o w d en 1999). I f the m orp hology o f a site has been com prom ised, perhaps through erosion, ploughing or environm ental changes (e.g. peat grow th, alluviation or even vegetation growth), the features are com m on ly less visible on the ground, and so a second them e o f identifying the m ore subtle features m ight be appropriate. Thirdly, additional detail m ay be required for interpretation purposes. It m ight be im portant to identify subtle features through surface analysis that m ight assist in the interpretation o f a site. M an y archaeological sites w ill have various needs in terms o f interpretation. W h ile spatial areas m ay require a com bination o f approaches, here those approaches w ill be addressed separately.

Recording morphology A high-resolution survey o f an area o f upstanding earthworks w ill provide data for m odelling a surface w ithin the G IS, in addition to generating a p oin t-in ­ time record o f the site. H ow ever, this w ill be dependent upon the usefulness o f collected points. As m entioned previously, a study o f ‘ C lo n eh e n g e’ identified the need for exam ining the usefulness o f regularly spaced and non-regularly space survey data (Fletcher and Spicer 19 88).This study dem onstrated that, w ithin an

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Earthwork remains of earlier excavations

29 H illshaded D E M o f the h en giform m onum ent at L och M igdale, H ighlands, show ing the position o f an earlier excavation trench (zo x 20m lo o k in g east-south-east)

area o f archaeological earthw orks, local variations in topography can be high over a small area. T h e recording o f regular survey positions for generating a D E M w ould therefore require an exceptionally high resolution, and thus a high num ber o f points. Consequently, it was argued that a varied approach should be used, responding to the local needs o f the archaeology to ensure that breaks in slope are fully represented. For the purposes o f recording m orp h o logy o f the earthworks o f a site, this may be taken as a useful rule o f thumb. T h e aims o f recording m orp h o logy m ay be reduced to two basic elements. There is the need to generate a record o f the site that m ay have archival value and w hich can assist in the interpretation o f a site through seeing it in plan, as som e archaeological sites are very difficult to fully com prehend and interpret on the ground. A n exam ple o f this is the h engiform m onum ent from the northern edge o f Loch M igdale, near Tain, in the Scottish Highlands (29). Here, a small prehistoric m onum ent was already know n and was visible on the ground, and had been partially excavated previously. A rapid differential G P S survey o f the site generated a plot o f positions from the surface in advance o f excavation that w ould lose this surface inform ation, nine hundred and sixty-tw o points were recorded across the site, at a variable surface resolution o f betw een o.2-2m , becom ing m ore w id ely spaced in the area outside the m onum ent. T h e total area ot the site m easured approxim ately 20m in diameter, providing an average survey point density o f 3.2 points per square metre. T h e data were

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processed w ithin the G IS using a spline interpolation technique to generate a D E M relative to absolute heights. T h e resulting D E M form s a useful record o f the site p rior to losing surface inform ation from excavation. It also provided a position for the site (which deviated considerably from the O rdnance Survey position in this rem ote area) and a plan, although in this case som e interpretation o f the site was possible due to the earthworks bein g highly visible on the ground. In addition, however, the resulting D E M highlighted the positions o f the previous archaeological trenches across the interior o f the feature as subtle depressions that w ere not recognised on the ground.

Identifying subtle features Som etim es archaeological features may be masked by vegetation or simply by the density o f later archaeological features around them. In these cases it is possible to use the high-resolution survey o f three-dim ensional positions, m entioned previously in relation to the recording o f features, w ith the addition o f surface analyses and exaggeration to identify features o f interest. For exam ple, Bronze A ge features in the Peak D istrict near Carsington reservoir cover large swathes o f the landscape, but are masked by the activities o f post-m edieval miners w ho excavated num erous bell pits to extract lead (jo). O ne exam ple dem onstrated a close relationship betw een an earlier Bronze A ge barrow and a m iddle-late Bronze A ge field boundary. These subtle features w ere overw helm ed by a m ine that had generated dramatic earthworks w hich dom inated the landscape rendering the Bronze A ge features less visible on the ground, and even harder to translate to others in any visual way. Effectively, you can’t see the w ood for the trees, or rather the Bron ze A g e features for the bell pits! Again, a G P S survey o f the site and surrounding area was undertaken at a varying surface resolution to respond to the local variations in topography generated by the different earthw ork features. T h e resulting D E M o f the site provided a m ore convenient illustration o f the site, but rem ained plagued by the same problems o f interpretation as on the ground. H ow ever, a series o f surface analyses made the Bronze A ge features m ore visible. C o n to u rin g the m odel provided the shape o f the barrow, distinct from the later features. This showed the shape o f the barrow and the difference in m orp hology com pared w ith the other m onum ents. T h e addition o f a light source, positioned perpendicular to the alignm ent o f the Bron ze A ge field boundary served to highlight this feature, despite its being cut through by the later bell pits. T h e resulting m odel again provided an accurate record o f the site p rior to its excavation and also served as an illustration o f the different p eriod features and their com p lex archaeological stratigraphy.

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j o Features from a variety o f periods represented in a D E M o f an area o f Carsington pastures, D erbyshire (90 x 65m view ed from the south-east)

Highlighting additional detail In som e cases, a site cannot be fully understood 011 the ground. Tree coverage, sheer scale and physical damage to a site, such as from ploughing, w ill render archaeological features invisible 011 the ground. H owever, as before, highresolution surface survey m ay be used to generate a D E M in the G IS w hich can then be manipulated in order to obtain m axim um inform ation from it.Tw o exam ples w ill be considered here. T h e first is a m edieval m otte and bailey site at Beaudesert, in H enley in Arden, W arwickshire, w here construction com m enced in the twelfth century (ji). This site has previously been considered to be unusual archaeologically due to the presence o f two baileys rather than the standard one. This interpretation had been based upon previous surveys o f the site (Salter 1992). A topographic survey using a com bination o f G P S and E D M total station equipm ent to cope w ith dense tree coverage in some parts o f the site, was undertaken providing 4508 points over an area o f 4.4311a.These points w ere then m odelled to generate a D E M w ithin the G IS. D ue to the position o f the site on the top o f a steep hill, all elevation-based rendering o f the D E M proved to be o f little value. T h e spread o f colour contours for different elevations generalised m uch o f the archaeology, although this was assisted through light shading. H owever, the best results for the interpretation o f

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31 Slope m odel o f Beaudesert Castle, W arwickshire, w ith darker shades representing steeper slopes (450 x 130m lo o k in g east)

this m odel came from slope analyses. U sing standard slope-based surface analysis o f the D E M , a m odel displaying steepness by colour shades was generated. This had the advantage o f highlighting sudden changes in topography that are com m on w ith anthropogenic earthwork features, w ithout the directionality assumed through light-based m odelling and shading. From this analysis a num ber o f conclusions were made. Firstly, the slope m odel highlighted the difference between the steep hillslopes and the flatter hilltop, w hich w ould have been occupied by the castle features. Secondly, it showed the positions o f archaeological features.These included the ditches across the site and a track running around its western side. Thirdly, the slope m odel revealed details about the structure o f the ditches surrounding the motte area (although it should be noted that the motte in this instance was a separated area o f land, rather than being a m ound). Part w ay dow n the ditches, a line on the slope m odel indicated a change in slope betw een the upper section and the lower section. U p o n closer examination, this was interpreted as a second phase o f ditch digging, w ith the earlier ditch being shallower and U -shaped and the later ditch being m ore steeply cut into the base o f the earlier one. T h e later ditch was also steeper in profile. Finally, and perhaps most significantly, the outer bailey was demonstrated to be a natural feature. It was not a second bailey as it had at first

Landscape

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32 Sutton C o m m o n , South Yorkshire, from the ground. T h e remains o f the enclosures are extrem ely difficult to identify

appeared to be due to its flat plateau (a function o f geology) and the position o f a pathway running around its western side. There were no earthworks form ing its eastern side. This was later verified and corroborated through excavation. A second example is the wetland Iron A ge site o f Sutton C om m on, South Yorkshire, where bulldozing and ploughing have removed the majority o f the upstanding earthworks such that m uch o f the site is not visible on the ground (32). T h e site consists o f a pair o f enclosures occupying islands 011 opposite sides o f a palaeochannel (W hiting 1936, Parker Pearson and Sydes 1997). O f the two enclosures, the smaller is largely intact, surviving as earthworks, whereas the larger enclosure has been com pletely rem oved by the bulldozing and ploughing such that it cannot be seen on the ground. A G P S survey o f the site provided the data for G IS m odelling o f the surface. T h e basic elevation-based surface highlighted the shape o f the ploughed-out enclosure. However, through the application o f hillshading from the north-w est, subtle features were identified, particularly along the western edge o f the larger enclosure (33). Slight changes in gradient had occurred through the shrinkage o f organic sediment w ithin the ditch causing a tall in the height o f the overlying land surface.This subtle difference was identified as a linear ditch by hillshading o f the D E M (Chapm an and Van de N o o rt 2001).

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33 H illshaded D E M o f Sutton C o m m o n , South Yorkshire, show ing the upstanding earthworks o f the smaller enclosure and the plou gh ed -o ut features o f the larger enclosure. W hilst the features o f the larger enclosure w ere not visible on the ground, they could be identified from the G IS m odel derived from high-resolution G P S survey

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Landscape Analysis

LO C A L LA N D SC A PE C O N T E X T OF FEATU RES As explored in chapter i , the definition o f ‘landscape’ w ithin the discipline o f landscape archaeology is diverse, particularly in terms o f spatial scale. A site may be exam ined in isolation, w ith in its locale (w hich itself w ill be variable) or w ithin its w id er region, including other sites. W hen considering the local landscape context o f a site, one may refer to the direct physical or anthropogenic features that define it. For exam ple, this m ight be a slope into w hich a house platform is cut w hich w ill provide the necessary context in order to understand the m orp hology o f the platform itself. In the previous section, reference was made to the wetland Iron A g e site o f Sutton C o m m o n . This provides a good exam ple o f a site w ithin its local landscape context, m odelled from G P S data. H ere the archaeology is defined by earthworks, both upstanding and levelled. W ithin the w etland landscape, the archaeology only really makes sense in relation to the local topography and palaeo-landscape features. T h e two enclosures occupy areas o f higher land, effectively islands above the surrounding marshy wetlands. R u n n in g betw een the two ‘islands’ is a relict palaeochannel, w hich again w ou ld have been m uch w etter during the Iron A ge and the p eriod o f occupation. T h e survey o f the site covered the local landscape surrounding the enclosures. In addition to identifying the subtleties o f the earthworks (see above), the G IS m odelling o f the site also demonstrated how the m orphology o f the earthworks reflected the natural m orphology o f the ‘islands’ , and also highlighted the surrounding wetland landscape, including the palaeochannel, other islands and a deep w etland area to the east o f the archaeology (Chapm an and Van de N o o rt 2001) (colour plate 7). A plan o f the site w ithout this three-dim ensional elem ent o f the landscape could not convey the detail o f the site, w hich equally remains hard to understand on the ground. Such an understanding is crucial before the analysis o f the landscape archaeology o f the site becom es possible.

TH E W ID E R LA N D SC A PE C O N T E X T OF FEATURES M o v in g outwards from the local context o f features, the w id er context may include almost any resolution o f study that seems appropriate for a given question. This m ight range from local to national concerns, though norm ally, particularly w hen dealing w ith three-dim ensional landscape m odelling, the w ider landscape context of features m ay for the present purposes be defined as an area o f betw een 5 x $km and 30 x 30 km .T h is convenient, though arbitrary, division betw een the local landscape and the w id er landscape may m ore appropriately be defined by the nature of the landscape archaeology, or perhaps the input data being used for

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the study. Typically, though not always, the input data for local landscape studies w ill be generated by ground survey, whereas data for the w ider landscape is m ore likely to be purchased com m ercially, such as from national m apping agencies (e.g. the O rdnance Survey) or from rem otely-sensed data (e.g. L ID A R ). From these types of data it becom es possible to explore the relationships betw een different sites rather than the context o f a single site, though not exclusively. T h e questions that can be asked from a w id er landscape study w ill be different from those relating to the site locale. Fundamentally, the questions are likely to relate to the relationships betw een num erous sites rather that ju st the site in its local context. H ow ever, this w ill be in part determ ined by the type o f site. For exam ple, a linear site such as a routeway, road or cursus w ill necessitate a w id er consideration than a m ore focused type o f site. T h e direct questions relating to the site m ight include themes such as distribution patterns, territorial considerations or site location analyses. These are discussed below.

A N A L Y S IN G D IS T R IB U T IO N Site distribution is one o f the earliest m ethodological approaches used by archaeologists (e.g. C h ild e 1925). D istribution m ay be considered at a num ber o f different spatial resolutions, depending upon the questions being asked. This approach was used to define ‘ cultures’ based upon distinctive assemblages o f artefacts and m onum ents and to explore how people and ideas m ight have m oved across E urope at different times. W hilst such approaches are based on cartographic analysis and can be conveniently addressed w ithin the G IS, the functionality o f the software enables m uch m ore com plex analyses to be perform ed. A t a tw o-dim ensional level distribution patterns m ay be addressed from the perspective o f distance to certain resources, such as rivers. U sin g buffering algorithm s, it is possible to calculate the num bers o f sites o f a given type w ithin a given distance o f a river system, for exam ple. This is norm ally undertaken by creating a polyline file representing the river system, and then generating a buffer from that polyline, w ith the output bein g a raster o f cells each w ith a value relating to its distance from the nearest river. This type o f analysis can equally be p erform ed in relation to other resources, and possibly used in conjunction to address themes o f resource accessibility, although this is addressed in m ore detail below. Similarly, distribution patterns m ay be addressed in a m ore quantitative way by lookin g at spatial relationships, such as through the generation o fT h iessen polygons. This m ethod effectively considers a distribution o f sites as point

Landscape Analysis

files and calculates lines betw een them w hich can be subdivided into two. B y connecting these m id-points betw een sites it is possible to create polygons w hich tentatively reflect potential territories relating to each site. This type o f approach was popular during the 1970s w ith the developm ent o f the N e w A rchaeology (Clarke 1978), and has been criticised for not taking other themes into account, being overly quantitative (see John son 1999). H owever, such an approach does have the potential for defining areas w here sites m ight not have been discovered on the basis o f very large ‘territories’ .

T E R R IT O R IE S A N D V IE W S H E D S W ithin

analytical studies

o f archaeological landscapes, visibility functions

w ithin G IS have been used to address issues relating to the distribution o f sites, particularly in relation to them es o f territory and influence. For exam ple, Lock and H arris (1996) dem onstrated that viewsheds could be used to demonstrate socio-political units in a m ore useful way than Thiessen polygons. T h ey studied the distribution o f N eolith ic lon g barrows w ithin the D anebury landscape and found that the resulting view shed images demonstrate very little overlap. W ithin this study, viewsheds were also shown to demonstrate the defensive prospect o f hillforts w ithin the region, but the authors noted that they were more im portantly positioned to m axim ise visual dom inance over valleys and other smaller settlements. Similarly, visual relationships form ed the basis o f conclusions made by Gaffney and Stancic (1991) about the siting o f R o m a n towers. A slightly m ore com plex approach by Fisher et al. (1997) demonstrated how view shed analysis could be enhanced by statistical analysis o f the results. In a study o f Bronze A ge cairns on M ull it was shown that it was possible to use view shed analysis w ithin a hypothesis-testing environm ent such that analyses m ay be question-led. H owever, the w idespread use o f visibility analysis w ithin landscape interpretation has been criticised. Lake et al. (1998), for exam ple, argued that the fact that intervisibility m ay be dem onstrated betw een tw o locations may not be considered to be a reason for the location o f one o f the tw o points w ithout analysis o f other areas that m ay also provide the same results. Similarly, W heatley (1996) highlighted that the results from any single analysis, such as line o f sight, may only be significant relative to themselves and that other variables from other analyses may be a ‘causal factor’ . W heatley and Gillings (2000) sum m arised m any o f the criticism s o f visibility analysis.This w ork highlighted the main limitations and critiques o f the approach using three categories: pragmatic, procedural and theoretical critiques. Pragm atic critiques covered issues such as environm ental factors, eyesight, mobility,

Landscape Archaeology and G I S

sim plification and causation. Th ese critiques covered the practical problems and oversights that underlie visual analysis. Procedural critiques reflected Marozas and Z a c k ’s operational error (1990), reflecting upon those problems encountered through the m ethod. Listed items included problem s w ith the D E M , view shed algorithm s, the bin ary nature o f view sheds and overall robustness. T h ey also added the concept o f the ‘edge effect’ highlighting the problem o f viewsheds that often extend beyond the edges o f the D E M , or the influence o f viewsheds from outside o f the D E M that have the potential to extend onto it. Finally, theoretical critiques highlighted further problems. These included elements such as the ‘ unquestioned p rim acy’ placed upon vision above other sensory factors, and the abstracting nature o f technological determ inism and, particularly, the w ay in w hich viewsheds are generally presented in plan. T h e need for a qualitative approach to view shed analysis was argued in response to these lim itations.W heatley and Gillings (2000) proposed an approach to view sheds using a ‘H ig u ch i’ m odel. This classified three depths o f vision,' along w ith a range o f other physical and social characteristics. It was argued that short, m iddle and lon g distance view s w ould be relevant for different perceptual experiences and may therefore be m ore useful in interpretative studies. A n additional level o f G IS-based visibility functionality has been term ed ‘m ultiple-view shed analysis’ (W heatley 1995), w hereby the viewsheds from multiple positions are considered together. This m ethod was first introduced in order to investigate ho w intervisibility w ould be used w ithin archaeology in a m ore robust way, particularly in the context o f unknow n environm ental factors that could influence and restrict visibility. B y generating a binary grid representing each view shed and adding these together for each o f the lon g barrows w ithin his study area, it was possible to understand w h ether their locations displayed exceptional intervisibility. A similar study by L o ck and H arris (1996) explored the relationship betw een different m onum ents w ithin the D anebury landscape. This study dem onstrated a lack o f overlap from m ultiple view sheds derived from the positions o f lon g barrows in the area. This lack o f overlap was interpreted to indicate that they acted as highly-visible markers defining territories. R u g gles et al. (1993) used a m ethod o f cumulative view sheds, in conjunction w ith other tools, as a basis for generating determ inistic and predictive m odels in relation to the locations o f stone rows in northern M ull, Scotland. Th ese models w ere then used to determ ine significant visual foci (particularly in terms o f astronomical alignments) and predictively to highlight areas w here other stone rows may be found. Interpreting the purpose o f cursus m onum ents provides a useful illustration o f the use o f visibility analyses using G IS. T h e function o f N eolith ic cursus m onum ents m ay be considered as an enigm a, given their length, w ith numerous

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possible interpretations bein g posited from astronomical function (Penny and W ood 1973) to the original concept o f race track (cf. Chippendale 1996). An analysis o f the cursus com plex at R u d ston , East Yorkshire, showed one o f the cursuses to be different from the other three, having a ‘d o g -leg’ bend. Cum ulative view shed analysis o f this m onum ent highlighted a num ber o f themes that assisted in its interpretation (Chapm an 2 0 0 3 ).T h e cumulative view shed demonstrated a constant horizon v iew o f two lon g barrows to the west; m onum ents that predate the construction o f the cursuses and w hich appeared to form the focus for the architecture o f the site, m aintaining a link w ith the earlier landscape defined by the burial m onum ents (34).

M O D E L L IN G A R C H A E O L O G IC A L SIT E LO CA TIO N S T h e archaeological record is both finite and incom plete.Traditionally approaches to studying landscapes and larger areas have focused on the distribution o f sites or artefacts as a w ay o f assessing trends in past activity. In a quantitative sense, this type o f approach is hard to deal w ith as the results are determ ined through variable quantities o f field w ork and investigation in different areas. In som e cases, large areas have not been studied and so the pattern o f activity at different periods cannot be established. Despite these limitations w ithin the archaeological record, it is som etim es appropriate to consider the w ider archaeological landscape in such uninvestigated areas, for a num ber o f reasons. A developm ent such as largescale forestry m ight pose a threat to the unknow n archaeological resource o f an area. Forestry can cover expansive areas w h ich w ould make traditional fieldw orkbased m itigation inappropriate or prohibitively expensive, particularly given the unknow n nature o f the resource. In such cases it can be advantageous to undertake targeted field w ork based upon models sim ulating the most probable locations ot archaeological sites in com parison to m ore com prehensively studied areas. In these cases, G IS can provide a point o f departure by generating predictive models displaying areas o f greater or lesser archaeological potential. It is often possible to postulate trends in archaeological data. For exam ple, it m ight be observed that m any prehistoric occupation sites in a given area lie on south-facing slopes, presum ably to take m axim um advantage o f the sun. T h e same m ight be said for field systems. There m ight also be resource im plications; the proxim ity o f most sites to a w ater source, for exam ple. Similarly, R o m a n period cem eteries m ight norm ally be associated w ith contem porary roads. T h e generalisations o f trends in the archaeological data in relation to environm ental and cultural inform ation are widespread. O n the basis o f such trends, it is possible to begin to m odel w here certain sites are m ore or less likely to be.

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Rudston long barrow

, Kilham long barrow



'

Denby long barrow

^

1/

i

g

ifl 0 __ 1

0.5

1

2 K ilom etres

1_ _ _ _ _ _ _ _ _ _ i_ _ _ _ _ _ _ _ _ _ 1_ _ _ _ _ _ _ _ _ _ l _ i _ _ _ _

34 C um ulative view shed m odel o f the R u d ston landscape, East Yorkshire, show ing the areas (in lighter shades) w h ich w ou ld be most frequently seen w hilst m ovin g along cursus A . It may be noted that the R u d sto n and D en b y long barrow s maintain a v iew on the w estern h o rizo n du rin g this route, perhaps indicating a reason for the ‘d o g -leg ’ in the plan o f the cursus

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B efore continuing w ith exam ples, there has been m uch debate on the efficacy o f using such generalisations, particularly w hen considering cultural activity in the past. T h e environm ental determ inism debate (cf. Gaffney and van Leusen 1995) has been prevalent in relation to G IS-based predictive m odelling. To w hat extent are the activities o f people in the past determ ined by their environm ent? It can be postulated that the interpretations o f activities during different periods have been m ore or less environm entally determ ined than during others. To quote Bradley: in the literature as a whole, successful farmers have social relations with one another, while hunter-gatherers have ecological relations with hazelnuts (Bradley 1 9 8 4 , 1 1 ).

M uch has been w ritten about the predictive m odelling o f archaeological site locations using G IS. T h e approach has proven popular in areas o f the U nited States and continental Europe, although it has been less well used w ithin the U K . To take an exam ple o f this type o f w ork, areas o f m iddle-late Bronze A ge agriculture in an upland environm ent m ay be considered (colour plate S). O bserved examples have dem onstrated a num ber o f consistent themes. We can say that in each o f the observed cases, there has been a correlation betw een field systems and the reuse o f early Bronze A ge barrows, and that this link has been firstly visual and secondly proxim al.Thirdly, in each case, field systems have been found on south-facing slopes, bein g advantageous in terms o f climate. Fourthly, the field systems have been on gentle gradients w here soil depletion is likely to be less pronounced. In terms o f the G IS, models may be erected o f each o f these situations. Barrow visibility can be understood in terms o f a view shed m odel, and proxim ity through a distance buffer. South-facin g slopes m ay be calculated using an aspect function, and a slope m odel w ill provide inform ation regarding gradient. I f models are generated for each o f these scenarios the results will consist o f four different G IS rasters. 1. Viewshed. This is a binary m odel w hereby cells in the raster are ascribed a 1 for visible areas and a 0 for invisible areas from the barrow. 2. Distance. This m odel outputs cells relating to distance from the barrow. 3. Aspect. T h e resulting aspect m odel provides cell values in degrees from 0-360 reflecting the compass direction that any slopes are facing. Flat areas at given a value o f - 1 . 4. Slope. Slope can be calculated in terms o f degrees, givin g cell values between o and 90, or per cent, givin g values o f betw een o and 100.

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In their raw form , these m odels provide qualitative inform ation for assessing site locations, such as perhaps dem onstrating that most kn ow n settlements o f a given period m ight be located on south-facing slopes. H ow ever, the different values o f each o f the resulting models (e.g. slope m easured betw een o and 90 degrees, and aspect m easuring betw een 0 and 360 degrees) mean that m ultiple surface analyses are less m eaningful. Instead, each m odel needs to be calibrated to enable the correct w eigh tin g o f each to the study. As it stands in this exam ple, there is the assumption, on the basis o f the observed phenom ena, that each o f the four parameters is o f equal im portance. Thus, i f each o f the m odels is reclassified to reflect equal w eigh tin g on the basis o f the inform ation that is required, it becom es possible to begin a m ore quantitative analysis. V iew sh ed already provides binary values o f appropriate areas (visible, or 1) and inappropriate areas (not visible, or o). U sin g this as a basis, it is possible to reclassify the other themes on the same basis, w ith a value o f 1 for suitable areas and o for unsuitable areas. Aspect may be considered as suitable w h en areas are either flat (cell value - 1) , or broadly fact south (90-270 degrees), and so the m odel may be recalibrated w ith these areas becom in g 1 and all other areas becom in g 0. Similarly, slope m ay be reclassified. Arbitrarily, a value can be chosen for slope beyond w h ich it becom es too steep tor agriculture. This m ay be obtained from the observed archaeological remains in the field. For exam ple, it m ight be decided that 0-5 degrees may be classified as suitable and given a value o f i, w hereby greater slopes may be unsuitable and given a value ot o. W ithout considering distance for the tim e being, these three binary models m ay be considered together. B y adding the three models together, the resulting calculation w ill return a raster w ith cell values ranging from 0-3, w here the higher the num ber, the higher the potential for finding Bron ze A ge field remains, w ithin the parameters being m odelled. In terms o f distance, it is possible either to assess this qualitatively from the resulting m odel, or else provide a level o f ‘fuzziness’ . In reclassifying the distance m odel, there are a num ber o f options.W e can either generate a binary m odel based upon observed data for the m axim um distance from a barrow that field systems have been recorded, or else it can be assumed that the closer to the barrow the better. In the latter case, the values for the reclassification o f the distance m odel m ight range linearly between 1 for the closest areas and o for areas further away, providing fractions for cells in betw een. T h e advantage o f this type o f m odel is that it is less prescriptive and allows for the m odelling o f trends rather than absolutes. B y adding the resulting reclassified distance m odel to the other three models, 4 becom es the most suitable area, but w ith a m ore gradual distribution o f less suitable areas. Equally, fuzzy reclassifications can be used for the other parameters such as slope and aspect, w here the most suitable areas are provided w ith the highest values, but w here there is a graduation betw een this and the unsuitable areas. This type

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o f m odelling enables us to begin considering the unknow n data w h ich are all appropriate for archaeology. In addition to approaching site location analysis from a fuzzy m odelling perspective, it is also som etim es appropriate to add w eighting to different models. For exam ple, it m ight be considered that visibility is m ore im portant than the other factors in setting out w here field systems w ould be, and so its values for suitable areas could be 2 rather than 1, doubling its w eighting in the resulting m odel. Ultim ately, these values may be tweaked to provide a best fit to observed data and from that it becom es possible to extrapolate the m odel to w id er areas. H owever, it should be rem em bered that G IS only provides a m odel and that this m odel is only as good as the input data and the ways in w hich those data are m anaged. Thus such a G IS m odel should be considered as a guide to fieldw ork, and also som ething that needs to be tested, including the testing o f areas that have not been predicted in order to lim it criticisms o f determ inism . O therw ise, the results may be tested in other ways w ithin the G IS. B y exclu din g parts o f the archaeological data at the outset, models can be made using the data that are left. O n ce a best fit m odel has been generated, it can be tested against the data that w ere excluded. D u e to the nature o f such m odels, and also to the themes o f determ inism , the use o f predictive m odelling has been the subject o f m uch debate. M u ch o f this has been w ithin its role in cultural resource m anagem ent (C R M ) , and w ill be considered in chapter 10.

M O D E L L IN G RO U TEW AYS A nother aspect o f G IS functionality is the capability o f m odelling routeways o f ‘least-cost’ , frequently referred to as cost-path analysis. O n a flat, uniform landscape, the easiest or fastest route betw een two points w ill norm ally be a straight line. In a G IS, this flat surface may be represented as a raster, w hereby the ‘cost’ o f the route w ill be determ ined by adding the values o f all cells w hich the route passes through together. I f each cell has a value o f 1, then the total cost w ill be i + i + i + in . H ence, a longer distance w ill have a greater cost com pared with a shorter route. In m ore hilly terrain, slope m ight be considered to be costly in terms o f m ovem ent. A steep slope w ill clearly have a greater cost to m ovem ent than a flat area. In this case, the raster w ill have higher cell values for areas o f steeper slope com pared w ith those areas o f m ore gentle gradients. A n y route through the landscape may be considered as the total o f all cells passed through betw een two points. H ence the costs (in cell values) o f any route through the landscape betw een twx> points can be calculated. Furtherm ore, it is possible to

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use the G IS to generate the ‘least-cost route’ by identifying the pathway w h ich incurs the least cum ulative values in relation to the cost-surface m odel. C ost o f m ovin g through an archaeological landscape is a subjective concept and may apply to a num ber o f parameters. W hilst slope m ight be im portant, areas o f bog, rivers and other physical barriers m ay be ascribed values w ithin a raster relating to cost o f m ovem ent through them. Similarly, conceptual boundaries to m ovem ent m ight include foreign territories or sacred areas; ‘Topography is a fundam ental com ponent o f the m echanics o f m ovem ent and yet is by no means the com plete explanation o f it’ (Bell and L o ck 2000, 88). W hilst the G IS interpretation o f least-cost paths is algorithm ic, it is entirely dependent upon the user’s definition o f the cost-surface and w h ich factors they consider to be o f greatest im portance. In the traditional m odel, however, the principal factor used in the construction o f cost-surfaces has been slope. For exam ple, Gaffney and Stancic (1991) interpreted the general accessibility o f the landscape o f the island o f Hvar, Croatia, on the basis o f cost-path analysis, w ith cost being defined by slope. M ore com p lex approaches have incorporated considerations o f slope in relation to other factors. M ad ry and R ak o s (1996), for exam ple, exam ined routes betw een hillforts on the basis o f slope in conjunction w ith m aintaining highest possible elevation and m axim um v ie w o f hillforts in the area. H owever, regardless o f the influence o f other factors, some consideration o f the caveats o f slope-based cost-surface m odelling should be made. Principally, there are two themes to consider:

Slope angle and cost Traditional approaches to defining cost-surfaces based on slope have considered increase o f slope as having a constant relationship to increase in effort. T h e slope m odel defines a raster based upon an elevation raster, or D E M , w hereby cell values are calculated as the m axim um rate o f change betw een a cell and its eight neighbours, and can be expressed in degrees or percentage. I f the slope m odel provides raster cell values betw een i ° and 6o°, and is used as the cost-surface, then effort is assumed to be 60 times greater for the steepest slopes compared w ith the flatter areas. H ow ever, in practice things are m ore com plex. E ffort may be defined as a function o f the mass o f the person w alking, the gravitational pull on that person and the height ascended over a given horizontal distance. As the first two factors, mass and gravity, are constant, then the only variable in defining effort is change in elevation. E ffectively this means that any increase in the angle o f slope reflects a greater change in elevation betw een starting point and finishing p o in t.T h e traditional slope m odel only provides values relating to slope change and not effort, since it does not take elevation into account, but only angles o f slope. A w ay o f getting round this and generating an accurate m odel o f

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X5 M easuring increased effort w ith increased slope (in degrees) w hen m ovin g through a landscape using the algorithm defined by B ell and Lock (2000)

effort in the cost-surface has been proposed by Bell and L ock (2000) through the use o f trigon om etry It the resulting surface is to reflect effort, it is calculated for m ovem ent betw een each cell, providing a know n distance for each calculation, defined by the cell size o f the raster. T h e difference in effort betw een clim bing a gentle slope (A) com pared w ith a steep slope (B) over the same horizontal distance (each cell) can be expressed from the slope m odel angles as tanA : tauB. This provides understanding o f shift in elevation betw een the two cells (35). To take the exam ple o f the earlier slope m odel w ith values ranging betw een i ° and 6o°, using the tangent calibration, effort values defined in cells w ill be betw een 0 .0 17 4 5 -1.7 3 2 0 . H ence it becom es 99 times m ore effort to clim b a 6o° slope com pared w ith a slope o f ju st i° .

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Direction o f movement T h e second issue reladng to using slope as a cost-surface for pathway analysis lies w ith the direction o f m ovem ent. Fundam entally slope w ill provide im pedance against m ovem ent uphill, but w ill provide little resistance to som eone m oving dow nhill. Thus the direction in w hich som eone moves w ill influence the am ount o f effort that is required. In some cases, m ovem ent dow nhill w ill reduce the cost o f travel to the person m ovin g through the landscape. In terms o f m any routeways that function in both directions this w ill clearly be less o f an issue. H ow ever, w hen interpreting routes through a landscape w ith an elem ent o f directionality, further calibration is required. In these cases an anisotropic cost algorithm m ay be used (Bell and L o ck 2000, D e Silva and Pizziolo 2 0 0 1) .This algorithm incorporates the direction o f force (i.e. downslope) in conjunction w ith its m agnitude (slope). Effectively, an anisotropic algorithm w ill im ply force against som eone m ovin g uphill and force w ith som eone m ovin g downhill. W hilst this is appropriate for most applications, there is no consideration o f the im plications o f this over very rough terrain. B eyo n d certain thresholds in slope, effort m ovin g dow nhill w ill ultimately becom e increased as the slope no longer assists m ovem ent. In an archaeological sense, analysing routeways becom es im portant for a num ber o f reasons and this has been demonstrated in a variety o f cases. For exam ple, a consideration o f hillfort positions on the prehistoric R id g ew a y in O xfordshire prom pted researchers to interpret an early date for its origins (Bell and L o ck 2000). T h e results from cost-path analysis demonstrated that the prehistoric R id g e w a y deviated in places from its m odern position, in some cases passing though the centre o f hillforts. This im plied that a function o f the hillforts may have been to control m ovem ent along the R idgew ay. Hillforts were positioned to control m ovem ent along the route at the expense o f m aintaining long-distance visibility o f the landscape, calculated using view ed analysis. T h e im plications o f this w o rk w ere that the R id g e w a y predated the construction o f the hillforts along the route, w h ich appeared to serve a particular function. A simple exam ple o f the archaeological application o f cost-path analysis using G IS is through the study o f R o m a n roads in Bath (colour plate 9). In this area, a series o f excavations and observations have recorded sections o f R o m an -p e rio d roads w ithin the m odern city in areas that w ould have been outside o f the R o m a n -p e rio d urban limits. W hilst the positions o f the roads approaching Bath are quite w ell know n, w here these roads pass betw een the observed sections is not. Greater significance in terms o f the roads m ay be placed in relation to other R o m a n -p e rio d activities in the surrounding landscape, including villas and associated cem eteries. H ence, through a greater understanding o f the roads surrounding R o m a n Bath, it w ou ld be possible to interpret the w ider

no

Landscape Analysis

contem porary landscape. In this case it was possible to firstly assess the positions o f know n roads w ithin the landscape to consider to w hat extent their positions w ere determ ined by topographic variables. B y generating different ‘cost-surfaces’ based on variations in the w eighting o f different factors (including slope) it was possible to com pare the routes o f different ‘least-cost paths’ w ith the know n positions of the roads. O n the basis o f this w ork it was possible to extrapolate by using the best-fit cost-surface to determ ine the least-cost route through the areas w here the position o f the road is not know n. U sing the ends o f the know n (excavated) sections ot road as starting and finishing positions, it was possible to extrapolate a predicted routeway that best fitted the parameters defined by the raster. This effectively filled in the gaps in the R o m a n road pattern, with hypothetical road positions w h ich could be tested through future fieldw ork.

C O N C L U S IO N S In this chapter the ways in w h ich G IS can address the types o f questions that are com m on w ithin landscape analysis have been considered. T h is has involved a range o f functions including map overlay and vector-based m odelling. In addition, other ways o f m odelling archaeological situations on the basis o f observed data have been considered as w ell as m ethods o f extrapolating it for areas w here data are less available.This has been demonstrated in relation to view shed analysis, site location analysis and the defining o f routeways through cost-path analysis. It has also shown that, whilst single m ethods o f analysis can be used to address certain singular questions, the use o f m ultiple approaches together can generate more sophisticated m odels that can ultim ately focus fieldw ork for further investigation. In the next chapter, themes o f predictive m odelling are considered further, but in relation to the m odelling o f past physical landscapes, and particularly the reconstruction o f environm ental changes.

Ill

7

Landscape archaeology as reconstruction IN T R O D U C T IO N In the previous chapter the ways in w hich G IS can be used to strip away m ore recent layers o f human activity to reveal earlier ones, to analyse observable data, and to predict the positions o f archaeological features through the extrapolation o f identified trends and phenom ena w ere explored. A substantial elem ent o f understanding archaeological landscapes involves the interpretation o f w hat landscape features w ere in the area at a given time (colour plate 10). In addition to the archaeological landscape, it is im portant to consider w hat the natural environm ent m ight have been like. This m ight have impacts 011 the activities o f people, or m ight have been im pacted upon by people in the past. To quote M ark Gillings to:

attempt to examine the organisation of activity foci in the context of the surrounding landscape requires that the following fundamental assumption to hold true. The modern observable landscape form, and its related dynamics must be closely comparable to those in operation during antiquity (Gillings 1995, 67). H ence, a consideration o f the environm ent remains im portant. Furtherm ore, G IS provides the form at to put some flesh on the bones o f environm ental archaeology through the reconstruction o f environm ental factors, including presenting a forum for exam in in g and m odelling issues such as the rate o f change. H owever, there is som e room for caution here. T h e use o f G IS w ithin archaeology has been accused o f being environm entally determ inistic (see Gaffney and van Leusen 1995) due to the reliance on the interpretation o f the interaction betw een environm ental factors and cultural activity. W ithin the

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realms o f palaeoenvironm ental reconstruction, particularly w here it is linked w ith the interpretation o f cultural landscapes and peop le’s activities in the past, users o f G IS should arguably be sympathetic to this debate.

P A L A E O E N V IR O N M E N T A L DATA Landscapes change through time. O n the surface, elem ents o f vegetation change. T h e most significant events m ight be considered to be the early post-glacial p eriod w ith the establishment o f pine and birch forest in m any areas, or perhaps w oodland clearance. A t a deeper level, changes to sea level w ill alter how the lowlands and river valleys appear and react significantly, w ith changes such as alluviation, peat grow th (36) and alterations to the nature o f the channel itself; its w idth and speed o f flow. In som e areas changes in climate have resulted in massive landscape changes, such as the grow th o f blanket peat over areas ot upland m oorland. R aised bogs w ill have seen dramatic change w ith the developm ent o f w et fen to om brotrophic peat, killing other form s o f vegetation as conditions becom e w etter and m ore acidic. In som e cases environm ental change can be extrem ely rapid, particularly w ith relation to catastrophic events, whilst in other cases change is m ore gradual. T h e potential to m odel rate o f change is also w ithin the toolkit provided by G IS. T h e im portance o f integrating palaeoenvironm ental data into landscape studies has been dem onstrated on numerous occasions. In a paper produced in 1997 Bender, T ille y and H am ilton exam ined a broad range o f factors relating to a Bronze A ge landscape near Leskernick on B o d m in M oor, C o rn w all (Bender ct al. 1997). M an y considerations were made, and som e very new and innovative ideas and m ethods w ere presented. W ithin the investigation o f the site, the view s from each o f the ‘huts’ were exam ined through the construction o f a portable w ooden door fram e that could be positioned so that the view s from inside the dwellings during the Bronze A ge could be reconstructed. A num ber o f theories w ere generated from this exercise about significant places outside o f the settlement. H ow ever, recent research in the area had investigated the palaeoecology o f the area and specifically the types o f vegetation that w ould have been present at the tim e that the settlement was occu pied (Gearey et al. 2000a, 2 0 0 0 b ).T h e results from this research had dem onstrated that the landscape at that time was dom inated by dense hazel w oodland. T h is w ou ld most clearly have obscured and altered the v iew from the settlement, w h en com pared w ith that o f today and raised a num ber o f new questions. Fundamentally, i f those view s were so im portant du ring the Bronze A ge then they w ould have needed to be maintained. O therw ise the v iew theory w ould need to be discredited. A t one

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36 H atfield M oo rs, South Yorkshire. T h is wetland landscape conceals a later N eo lith ic landscape buried beneath peat follow in g extrem e environm ental change over the past sooo years

level, it is not possible to k n o w w here each tree w ould have been w ithin the landscape, but through considering the impact o f palaeoenvironm ental data it becom es possible to begin altering the types o f theories and interpretations that may be delivered (Chapm an and G earey 2000). Palaeoenvironm ental data com e in a num ber o f different form ats. These include the results from

traditional scientific

approaches, such as pollen,

plant macrofossils and insects, and also evidence from historical maps, textual descriptions and intuitive approaches. Scientific approaches, such as palynology, norm ally result in diagrams indicating changes in vegetation com m unities by depth. Som etim es these diagrams w ill have radiocarbon dates associated w ith particular layers. Pollen diagrams are com m only expressed as statistical represen­ tations of species as a percentage o f total land pollen. T h ere are num erous considerations to be made w hen interpreting pollen diagrams, including the relative quantity o f pollen produced by different species, its dispersal distance (Birks and Birks 1980) and differential decay in the soil (H avinga 1984). It is also im portant to consider the different types o f ecological conditions that different species require. H owever, for the purposes o f m odelling, the level o f detail should be considered in relation to the questions being asked o f the landscape and the

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spatial resolution at w h ich analyses are being made. O th er types o f palaeoenvironm ental data include the quantities and types o f m olluscs, insects and plant macrofossils w ithin a sedim entary unit w h ich can each provide different levels o f inform ation about different environm ents and at different spatial scales, although it is norm ally best to consider m ultiple sources o f data to provide an overview o f the landscape at a particular time. In addition, microfossils from diatoms can provide inform ation on salinity o f an environm ent at a particular time and testate am oebae can provide inform ation regarding surface wetness at a specific level. A consideration o f different form s o f data in relation to date and vertical position can also provide inform ation regarding sea level at a given period (e.g. Lon g et al. 1998). As a rule, the m ore layers o f palaeoenvironm ental data that are available, the better for understanding the past environm ent. O th er form s o f data include historical docum ents including maps and textual descriptions o f the environm ent. As m entioned in chapter 4, these sources can provide a range o f inform ation regarding how an environm ent appeared at a particular time, although these are arguably m ore useful for m ore recent periods and m ight be m ore com prehensive for som e geographical areas than others. Intuitive approaches m ight seem less relevant but do have som e applicability here, particularly in cases w here other data do not exist. For exam ple, in the case o f industrial archaeological sites, w oodland m ight be considered to be o f im portance as one o f m any resources needed to keep the m achinery running. We m ight kn ow that dense w oodland existed, but not its exact location. Som e indication w ill be available from a consideration o f w here w oodland remains today and an investigation o f these areas on the ground m ight give a better indication (e.g. M u ir 2000). H owever, it is norm ally likely that m uch of the w oodland w ill have since been cleared. In a hypothetical sense, it becom es possible to m odel areas m ore likely to have been w oodland; the steeper valley slopes perhaps and other areas w h ich m ight not have been used for other functions. W hatever the case, hypothetical m odelling o f these areas can be useful in beginning to interpret the landscape in a m ore holistic sense.

V E G E T A T IO N M O D E L L IN G M apping vegetation w ithin a G IS is most appropriately achieved through ascribing polygons to different vegetation types, w hether these relate to single tree types or, m ore usually, to vegetation com m unities. Previously, studies aim ed at reconstructing patterns o f vegetation have been approached from a num ber o f angles. T h e most obvious position for generating a G IS m odel o f vegetation, particularly in the absence o f palaeoenvironm ental data, is through the intuitive

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m odelling o f trees on steeper slopes o f a landscape. It may be considered that the steeper slopes w ill be the last areas to be cleared o f w oodland. In the absence o f a clear opportunity for arable cultivation, these areas o f the landscape could be profitably utilised for m anaged w oodland and perhaps w oodland grazing. W hilst it m ight not always be the case, in the absence o f other form s o f data, it is reasonable to assume that areas o f w oodland might persist on valley slopes for longer than elsewhere. H aving assumed this, it becom es possible to generate a m odel o f w hat the landscape m ight have looked like at a particular period, based on this single hypothesis. In terms o f G IS, norm ally only topography is considered and, in particular, the differences in slope. To perform this analysis, it is necessary to obtain a D E M o f the area o f study. This provides us w ith a raster m odel w here each cell’s attributes relate to elevation. Assum ing that the area o f study is topographically variable, w ith large variations in elevation, it becom es appropriate to investigate slope. W ithin most G IS packages there is a function w hereby it is possible to derive slope from the D E M (see chapter 5). This calculation assesses the difference in height betw een adjacent cells w ithin the D E M to generate a new raster w here each cell relates to slope angle or per cent. In the resulting raster, higher cell values w ill relate to greater slope. Thus, it becom es possible to bracket steeper slopes from flatter areas. T h e exact values for this m ight be subjectively selected and dependent upon the topography o f the area o f study. From this it becom es possible to either reassign values for the raster (steep slopes = 1, other areas = o) or else to use the appropriate grid or raster calculator to generate polygons o f those areas w here steep slopes are encountered. Either way, the output provides a visual representation o f w here the w ooded valleys are most likely to have been w ithin the landscape, and at the very least provides a hypothetical m odel that m ight be tested and adapted through additional research (colour plate 11). Taking things further, it is possible to m odel past vegetation patterns based on a m ixture o f input data, including slope, elevation, soil type, geolo gy and so forth. Spikins (2000) presented an interpretative vegetation map for the north o f England using a com bination o f input variables. It was considered that, w ithin a given context, plant com m unities w ithin a landscape could be predicted. Input variables w ere substrate type (with soil types categorised into six principal groups based on geology), altitudinal limits (in relation to climate, w ith data obtained from beetle analyses, and contours being used to exam ine lines o f m axim um tree growth at different times o f climate change follow ing the last Ice A ge), coastlines (interpreted for sea-level change and isostatic uplift, used w ith in the G IS as a series o f dated outlines, providing m ore o f an aesthetic function rather than being involved in the analysis) and tree spread for different species (using B irk s’ (1989) m odel o f tree spread in 500-year time slices follow ing the last glaciation).

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Each o f the input-data layers were generated as vector polygons. B y com bining the layers, m any smaller polygons were created, being the union o f each o f the input variables, constructed from elevation (calculated at 50m interval bands), soils and tree spread (changing for each time slice o f 500 years). A n algorithm was then w ritten to define the most likely dominant tree type w ithin each o f these smaller polygons through an assessment o f each tree species in relation to other parameters w ithin the m odel. T h e results were rendered using the dominant species attribute for each o f the polygons at each o f the time-slice periods. T h e study covered the w hole o f the north o f England, north o f the Wash, and so a low resolution was appropriate. A t a higher resolution, and incorporating a m ore com prehensive interpretation o f pollen data, a study o f an Iron A g e wetland landscape at Sutton C o m m o n , South Yorkshire, was made possible through the m odelling o f a single plant type that was abundant w ithin the contem poraneous palynological record (Gearey and Chapm an 2005). T h e plant was Alnus (alder), w h ich displays a num ber o f ecological preferences. A lder can grow in w et areas w here w ater levels do not exceed certain heights. Essentially they like their roots seasonally w et, but not too wet. W ithin the area o f study, a num ber o f additional sources o f data already existed from other research. T h ese included w ater table data, providing the m odern shape o f the w ater table and its seasonal fluctuations over the local area. Through a consideration o f the levels o f archaeological and palaeoenvironmental deposits it was possible to provide an approximation o f likely levels o f the water table across the site during the Iron Age. Through m odelling the water table in relation to the ground surface, it was possible to indicate w here the water was in relation to the ground surface - above it or below it, and to w hat extent. W ith the prevalence o f alder w ithin the pollen layers contem porary w ith the Iron A ge features it was considered that the reconstruction o f a single species was appropriate. U sing this m odel it was possible to classify areas o f the appropriate levels o f wetness and to reconstruct alder vegetation (37). Given the strict groundwater conditions in w hich alder w ill grow naturally, it was possible to reconstruct areas o f the D E M that were wet enough, but not too wet. Subtracting the D E M from the water table raster, cell values provide information o f distance o f the water surface from the ground surface, w ith positive values reflecting standing water and negative values reflecting groundwater. This was also achieved using a constrained random cell-generator w ith values o f between zero and eight, reflecting the range in height o f typical alder carr. In this case, areas o f standing water could also be m odelled.The results from the m odelling included areas o f carr within an area o f the archaeological enclosure and across the top o f a causeway leading to the site. It seemed unlikely that carr w ould have been grow ing on the areas that were being constructed at that time and so a level o f ‘gardening’ was carried out.

Landscape Archaeology as Reconstruction

_J7 Hillshaded D E M o f Sutton C o m m o n w ith reconstructed alder carr vegetation and standing water

A range o f other approaches are currently being developed to accurately m odel vegetation from palaeoenvironm ental data. For exam ple, one approach has been to generate models from m odern pollen assemblages w here the vegetation pattern may be measured (e.g. Fyfe 2006). Fundamentally, however, w ithin a G IS, the representation o f vegetation can be troublesome. For exam ple, the m odelling o f tree patterns has been undertaken as polygon blocks w hich inhibit the true nature o f vegetation in term s o f m ovem ent or visibility through it (Tschan et al. 2000).

M O D E L L IN G E N V IR O N M E N T A L C H A N G E W hilst vegetation is clearly im portant, depending on the time frame w hich is being considered, the m odelling o f geom orphological change can be extrem ely im portant. For exam ple, large landscape change, such as through river movements, flooding (e.g. Gillings 1995), erosion (e.g. Verhagen 1996), sedim ent m ovem ent (e.g. W ainw right and T h orn es 1991) or peat growth, can alter the nature o f the landscape considerably and thus influence the way in w h ich the archaeology

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o f palaeo-landscapes is interpreted. W ithin inland areas this is norm ally most dramatic in the case o f rivers, w ith the m asking, land-altering consequences o f alleviation, particularly as a consequence o f sea-level changes (see Lon g et al. 1998).W ithin the U p perT iszaV alley in north-east Hungary, a study attempted to address the issue o f fluvial activity, and particularly flooding, in relation to cultural activity (Gillings 1995). This study integrated a com bination o f topographic parameters (elevation) w ith hydrological functions w ithin the G IS and proxim ity to w ater sources (rivers) and borehole data to simulate a flood event in the past. It was recognised that elevation alone was insufficient to simulate the effects o f flooding and that proxim ity to the river was also im portant. Furtherm ore, the com plexities o f w ater ru n -o ff and m ovem ent over the surface o f a landscape w ill affect the im pact o f a flood event on a com m unity in the past. T h e results o f this study dem onstrated, for exam ple, that the cycles o f flooding w ithin the U pper Tisza Valley had a ‘ marked impact upon the settlem ent and exploitation o f the region du ring the M iddle N eo lith ic’ (Gillings 1995, 82). In this case it was concluded that the value o f G IS could ‘not be over-stressed’ in creating a more robust analysis o f the archaeological remains in the area (Ibid. 83). A second dramatic environm ental change w h ich can significantly alter an environm ent through time, and in contrast to alluviation and flooding, is the developm ent o f peat bogs w hich can mask earlier landscapes, both w ithin uplands and lowlands. H ow ever, the most dramatic o f such cases are w ithin lowland raised mires. Here, regional environm ental changes, such as increases in sea level, can result in an increase in the surface wetness o f an area, initially leading to shifts in local vegetation w h ich w ill inevitably have an influence on cultural activity. In the case o f raised mires, these developing wetlands are transformed, resulting in peat grow th as the processes o f organic break dow n o f dead vegetation is inhibited by the raised w ater table and consequently anoxic conditions (Coles and C oles 1996,Van de N o o rt et al. 20 0 1, C hapm an and C heetham 2002). W ith the dead vegetation not rotting, and the continued grow th o f additional species such as Sphagnum mosses, the developing m ire surface becom es divorced from the local hydrology and grows, thus becom ing ‘raised’ and hydrologically fed by precipitation alone. From a cultural archaeological perspective, the significance o f raised mires may be split into tw o principal themes. T h e first is defined by the exceptional preservation o f organic material, including b o g bodies

(e.g. G lobb

1998,

Turner and Scaife 1995), w ithin th em .T h e second has m ore significance to the landscape archaeologist. T h e tremendous changes to such a landscape provide some exceptional challenges to interpretation. T h e changes themselves, often from dryland to w etland through to raised mire, present both physical changes and changes in the w ay that a landscape m ay be exploited. T h rough the growth

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o f the mire surface, the original land surface becom es im possible to read as a landscape from any single period. Shifts in both space (three-dim ensional) and time make the interpretation o f archaeological sites, and the prediction o f the locations o f other possible sites in these landscapes very difficult. This situation is extrem ely frustrating given the exceptional preservation potential that they hold and the threat o f on go in g com m ercial peat cutting that persists on many o f these sites. T h e palaeoenvironm ental com plexity o f these landscapes means that they cannot be readily interpreted on the ground. H ence, G IS-based approaches to landscape reconstruction and visualisation becom e extrem ely relevant.The raised mires o f T h o rn e and Hatfield M oo rs in South Yorkshire are the two largest low land peat bogs in the U K , m easuring 5.5 x 5.5k m and 4.6 x 4.8km respectively. A G IS-based approach to interpreting the environm ental developm ent o f these landscapes has been developed to provide a basis for interpreting and predicting archaeological site locations w ithin them (Chapm an and G earey 2003). H ere the process was based upon the creation o f a pre-peat land surface (38), based on a variety o f data sources including borehole data and geophysical survey (Ground Penetrating R ad a r (G P R )). The reconstruction o f the pre-peat landscape presents an early dryland landscape that could be interpreted as any other. Hence, the possible locations o f archaeological sites w ithin such a landscape can thus be predicted in relation to the regional picture (in this case provided by Van de N o o rt and Ellis 1997). T h rough the G IS-based integration o f the results from stratigraphic and palaeoenvironm ental

Sub -surface DEM s fro m borehole grids -

O rd n ance Su rv e y 1855 mapping draped over crude basal topographic model

Sub-surface D EM s fro m borehole g rid s

38 C o m b in in g data from a variety o f sources to build up an understanding o f past landscapes - H atfield M oors, South Yorkshire (4.5 x 4. 5km lo o k in g from the south)

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analyses (e.g. pollen, testate amoebae and coleopteran, particularly from previous studies), in conjunction w ith various dating methods, the growth o f the mire at different periods can be modelled in relation to the initial pre-peat surface providing time-slice mapping for different periods. From this position, through the creation o f a four-dimensional model o f the development o f the mire, it becomes possible to begin applying more standard landscape archaeology approaches to the interpretation o f the landscape at different periods.

M O D E L L IN G T H E SEA O n e o f the m ore dramatic changes to a landscape can be due to changes in sea level. This can have a direct im pact, such as the floodin g o f the N o rth Sea basin during the early H olocen e, or an indirect im pact such as isostatic changes. For areas w here shifts in sea level have been significant it is impossible to begin to interpret landscapes w ith out a high level o f reconstruction, w hether this is conceptually, physically or digitally. As the G IS provides the opportunity to apply algorithm s to a landscape surface, it is possible to begin retrospective m odelling of landscapes based upon w hat is understood from the archaeological and palaeoenvironm ental records. A n example o f w here such a reconstruction is required before a landscape may be interpreted is the site o f Green Island, near Poole in Dorset (colour plate 12). H ere an intense level o f Iron A g e-p e rio d industry and trade was being conducted on w hat is currently an island w ithin Poole H arbour. A subm erged routeway is know n to have accessed the island, indicating that the w ater level had considerably risen in the interm ediate time period. This is reflected by the palaeoenvironm ental data w hich provides a ‘sea-level cu rve’ for the region; effectively a tim e/elevation graph. In this case, the archaeology w ithin the current landscape made less sense and so it was advantageous to m odel how the landscape m ay have appeared during the Iron Age. T h e O rdnance Survey data provide elevations for land, but does not m odel the subm erged landscape. T h us this data source could only be used for part o f the m odelling process. B athym etric data is com m only available from organisations such as the Adm iralty w h o produce charts for shipping. B y com bining bathym etric data w ith the O rdnance Survey Landform Profile data, it is possible to generate m odels com bining the two. O n e approach to this is to create a m odel based on the O rdnance Survey data and to create a separate m odel based on the bathym etric data, whilst ensuring that areas outside o f the bathym etry are provided w ith a value o f o rather than ‘ N o D a ta ’ . This ensures that calculations can be m ade betw een the two resulting m odels. B y adding the bathym etry (w hich should be in negative values i f calibrated to absolute heights)

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to the O rdnance Survey data derived surface, the resulting m odel should provide subsurface detail. O th er form s o f data may be used to clean up elem ents o f the resulting m odel as required. O n ce this D E M has been created it becom es possible to recreate contem porary sea level for any given period by reading its height from the sea-level curve and reconstructing that as a polygon or raster over the top o f it. Thus the im pact o f varying sea level may be exam ined in relation to the archaeology. In the case o f Green Island, the resulting m odel made it possible to visualise the Iron A ge environm ent and thus m ake m ore sense ot how the site related to the surrounding landscape. H ow ever, it should also be considered that, whilst such a visualisation is clearly useful, it is also lim ited as it makes no consideration o f river m igration and m ore subtle geom orphological change. These can be added to the D E M as required, depending on the nature o f the input data that are available. Similarly, the inundation o f the N orth Sea basin during the early H olocene has resulted in a large area o f a prehistoric landscape being effectively unreachable archaeologically. W hilst geophysical approaches are being used to investigate this landscape (e.g. Fitch et al. 2005) the question o f social impact o f inundation may be addressed using a proxy landscape. In this case it has been suggested that a suitable analogy for the subm erged landscape is that o f Holderness, East Yorkshire (Coles 1998). Here it is possible to begin addressing the impact o f sea-level rise. U sing a D E M o f the Holderness landscape in conjunction w ith flint scatters obtained from fieldwalking (Van de N o o rt and Ellis 1995) it is possible to begin investigating the rate o f landscape change in relation to the rate o f sea-level rise ( 19). W hilst high-resolution data relating to sea-level change is hard to determ ine, a generalised m odel for the p eriod from 7500-4000 cal B P (later M esolithic to Bronze Age) has been provided by L on g et al. (19 9 8 ).T h ey have calculated that during this time sea level in the H u m ber region changed from approxim ately -9m O D to 0111 O D , a mean rise o f c.3.9m m per year. T h is figure reduced gradually to r.im m per year after 4000 cal BP. W hile the earlier figure relates to M esolithic sea-level rise it also provides a linear basis from w hich to calculate inundation in relation to the p roxy D E M o f H olderness and thus its potential effect on contem porary populations (40). In other words, it becom es possible to address w hether the timescales for change w ould have been sufficiently short for it to have been recognised by contem porary populations. B y calculating 10-year blocks (i.e. 39m m steps), the increase in surface area o f w ater can be m odelled, in addition to assessing changes in access across the landscape. T h e results from this study (Chapm an and Lillie 2004) dem onstrated that only localised change w ould have been discerned for the first 100 years o f m odelling but that, follow in g this period, rapid inundation is show n in the m odel, suddenly coverin g large areas o f the

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39 Above: T h e pro xy D o ggerlan d ' landscape, using a section o f H olderness, East Yorkshire 40 Opposite: H ypothetical inundation rates o f the proxy D o ggerlan d landscape follow in g the know n sea-level curve

Landscape Archaeology as Reconstruction

Time in years

Time in years

Landscape Archaeology and G I S

landscape. Furtherm ore, at certain times there w ere significantly greater levels o f inundation w hereby dramatic flooding took place over very short time periods. This is perhaps m ore significant insofar that, i f the m odel is correct, this flooding w ou ld have occurred during the later M esolithic p eriod at w h ich time the rate o f sea level had significantly reduced. Potentially this w ould mean that perception o f flood in g m ight have been greater at a time w h en the actual rate o f sea-level rise had reduced. Furtherm ore, the nature o f the inundation w ould have suddenly obstructed routes through the landscape w h ich m ight have had a greater conceptual effect on populations than the loss o f dry landmass. A third exam ple o f relative sea-level rise in relation to archaeology has been a previous study o f Finland (N unez et al. 1995). Finland and its neighbours were , affected quite dramatically by isostatic rebound follow in g the end o f the last (Devensian) glaciation. In Finland, whilst isostatic rebound has dim inished since deglaciation, the country is still rising by rates o f 9m m per year in the north­ west and by 311m l per year in the south-east. In contrast, during the eighth m illennium B C , rates w ere approxim ately 120m m per year and 3011m l per year in these same regions. As a result o f this, the land area o f Finland has increased over time w ith m ore rising from the Baltic Sea. To add further com plication to the environm ental history o f the country, sea-level rise throughout the post­ glacial p eriod has been variable, at times both slower and faster than the rate o f isostatic rebound, w ith sea-level transgressions continuing until about 6000 B C . T h e overall affects o f this have been variable (though m ostly increased) land area, a dramatically shifting shoreline, w ith additional effects on ecological zones, and thus resources, further inland. This type o f dramatic environmental change means that the interpretation o f cultural activity through time can be extrem ely problematic i f attempts to model them are not made. Thus the developm ent o f G IS w ithin archaeology in some regions has been crucial. However, in terms o f G IS it does present certain difficulties not least due to the tilting aspect o f the landscape w hich varies in different areas through time. To address this N unez et al. (1995) wrote an algorithm by w hich to simulate the topography as a D E M for any given period. Studying the area around Lake Saimaa, they calculated that ancient elevation values for a given cell w ithin the D E M w ould be equal to the present elevation o f that cell, minus the interpolated value for land uplift for that cell in metres per century, multiplied by time expressed in years. From the basis o f this reconstruction, the distribution o f archaeological sites o f different periods could be placed w ithin their simulated contem porary landscapes, thus providing additional data for interpretation. In a similar study, the dynam ic coastlines o f southern N o rw ay w ere m odelled in order to understand the landscape archaeology o f M esolithic activity w ithin

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the region (Boaz and U leb erg 2000). T h e results from this w ork highlighted how, through environm ental reconstruction, it was possible to begin to interpret cultural changes based upon the contem porary landscape perception and the im portance o f natural places in such interpretations. T h rou gh reconstruction, the distributions o f artefactual m aterial could be analysed m ore appropriately and engaged w ith in a virtual way.

TH E L IM IT S OF M O D E L L IN G T h ere are a num ber o f lim itations to the G IS m odelling o f environm ental conditions that should be highlighted at this juncture. Perhaps most importantly, the m odel is only as good as the data that it relies on. In m any cases the G IS m erely provides a visualisation o f w hat those data m ight actually mean. W hilst this is a useful tool for understanding potentially com p lex datasets, G IS models should be treated as m odels, or as hypotheses that may be tested on the ground. G iven this most fundam ental issue, a num ber o f other limitations should be considered w hen m odelling environm ental factors using G IS , such as the question o f resolution. W hat is the m ost appropriate resolution for any study o f landscape? This may only be addressed by the response, w hat is the m odelling intended to achieve? This question lies at the heart o f all landscape archaeological studies and w ill depend on the nature o f the questions being asked o f the data. For exam ple, addressing broad patterns o f vegetation m ight be fine at a m uch low er resolution and over a m uch larger area than a study investigating the im pact o f vegetation on visibility patterns w here the role o f the individual is im portant. O ther issues regarding m odelling limitations are m ore ephem eral, though arguably no less im portant. F or exam ple, how is it best to represent trees? W ithin three-dim ensional representation packages, such as E S R I A rcScene, trees may be defined as individual objects but they may also be represented in bulk as polygons (e.g. Tschan 2000), and the decision betw een the two m ight be determ ined by the resolution o f the study (cf H iguchi 1983). It w ill also be dependent 011 the nature o f the questions being asked. For exam ple there becom es a real problem in m odelling the visual im pact o f trees quantitatively by either m ethod. T h e last issue involves the role o f G IS. I f the m odelling o f environm ental factors is likely to be incorrect on the basis o f data extrapolation, then arguably the process is lim ited to visualisation. W hilst this visualisation is based upon collected data, interpolation levels are likely to be very high. A t this juncture G IS may be considered as an analytical tool as well as a representational tool. In many ways the boundaries betw een graphics and analysis are b ecom in g diminished.

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TH E IM P L IC A T IO N S FO R O T H ER S T U D IE S -

LA N D SC A PE S AS

H E U R IS T IC S G iven the limits to m odelling, w hat is the role o f G IS w ithin environm ental m odelling? T h e strength o f any m odel lies in its ability to generate possible outcom es rather than realities.W hile it is not possible to k n o w the exact location o f every tree in the past (with the possible exception o f subm erged forests), this should not prevent us from trying. T h e advantage that G IS has is an ability to generate n ew data on the basis o f older data. It is possible to begin to exam ine w hat a theory looks like in practice. A palynologist m ight m ention that alder carr present in the pollen diagram is likely to occupy the lo w -ly in g valleys. N o w it is possible to m odel w here that is likely to be; to provide an illustration o f a theory. W hilst the illustration m ight not be correct, it provides som ething ‘ on paper’ that can be discussed and altered. As rem arked by Gillings and G o o d rick (1996), G IS really should increasingly be considered as a place to think, w here the models being generated are thought o f as heuristic m odels. In other words, perhaps the role o f G IS in cases o f such m odelling is to provide scenarios from w h ich it becom es possible to ask n ew questions and to learn from our ow n responses.

C O N C L U S IO N S In norm al circumstances it is impossible to predict the specifics o f landscape change. Fundamentally, w e cannot kn ow exactly w here individual trees were, or w hat their fo rm m ight have been. H ow ever, G IS m odelling o f environm ental factors enables us to consider w hat different types o f data m ean in the landscape context. W hilst the m odelling is likely to have various levels o f accuracy, even w ithin a single m odel, it provides the opportunity to explore different pasts.This chapter has considered the m odelling o f vegetation from topographic data and from pollen data, and has addressed themes o f physical landscape change and also shifts in sea level, and the impacts these changes m ight have had. Furtherm ore, it has considered som e o f the limitations o f m odelling approaches to archaeological landscapes. In the follow in g chapter, the ways in w h ich G IS may be used to address themes w ith in theoretical approaches to landscape archaeology are addressed.

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i M od ellin g find-density from fieldw alking data

Air-Photograph

IFSAR ORI

LiDAR Elevation

Daedalus ATM (4,3,2)

LiDAR Intensity

Daedalus ATM NDVI

2 C o m p a rin g different types o f rem ote sensing data from the confluence o f the R iv e rs Trent and Soar in N ottingham shire

3 Opposite above: R ep resen tin g topography using grey shading for different heights 4 Opposite middle: T h e same area as colour plate 3 represented by hillshading 5 Opposite below: Slope m odelling identifying variations in gradient derived from the D E M show n in colour plate 3



Flat (-1)

C I I I

1 I I I

I

1 South (157.5-202.5) Southwest (202.5-247.5)

WM WM

North (0-22.5) Northeast (22.5-67.5) East (67.5-112.5) Southeast (1 12.5-157.5)

■ I West (247.5-292.5) Northwest (292.5-337.5) North (337.5-360)

6 km

8 U sin g predictive m odelling to identify areas o f likely B ronze A ge agriculture based on the hypothetical rules o f requiring south-facing slopes, relatively flat gradient and a v iew o f an earlier barrow w h ich remained in use during the middle B ronze A ge at Carsington, D erbyshire (note that the reservoir w o u ld not have existed in this period)

6 Opposite above: Aspect m odelling identifying w hich direction slopes face derived from the D E M show n in colour plate } 7 Opposite beloui: D E M o f the Sutton C o m m o n landscape show ing ‘islands’ w ithin the wetland, the palaeochannel and other local landscape features

g U sin g cost-path analysis to identify the likely position o f a R o m a n road (in green) betw een know n sections o f the road (in red) surrounding R o m a n Bath (10 x io k m view ed from the south-east)

10 Above: M od ellin g geo lo gy along the R iv e r R h in e near U trecht, H olland, to provide context for the positioning o f R o m a n -p e rio d w atchtow ers.T h e m odel shows that routeways through the landscape w ere restricted to the dryland (in green) and the river itself since the alluvial wetlands (in cream) and peatlands (in brown) w ou ld have been less accessible. T h e watchtowers were therefore located to control m ovem ent along this narrow route (5 x 4km lo o k in g north-east)

11 Opposite above: M od ellin g o f likely tree cover surrounding the Iron A ge hillfort near Gear, C o rn w all, based on slope, assum ing that the steeper slopes w ou ld have been less easy to clear o f vegetation (the hillfort enclosure measures 350 x 300m , view ed from the west) 12 Opposite below: M od ellin g sea-level change using multiple data sources to explain the Iron A ge landscape o f G reen Island in Poole H arbour, Dorset. T h e tw o images show the present landscape and the reconstructed Iron A ge landscape (16 x 16km view ed from the south)

l j Above: V ie w from the southern end o f the later N eo lith ic trackw ay on H atfield M oo rs. T h e first im age shows the trackw ay as it is, narrow ing along its length. T h e second im age shows how different the site w ould have looked i f it m aintained a constant w idth. It seems likely that a factor in its architecture was to provide a more impressive v ie w from this position in addition to controlling m ovem ent and access to the platform

13 Opposite above: Least-cost path from the shore to the N apo leo n ic Sh orncliffe R e d o u b t based on slope. T his route closely passes num erous defensive structures and thus w ou ld have been an unlikely approach for invaders. T h is is an extrem e exam ple o f conceptual and cultural constraints to calculating cost o f m ovin g through a landscape 14 Opposite below: T h e local landscape context o f the later N eo lith ic trackw ay and platform 011 H atfield M oors, South Yorkshire. T h e first im age shows the excavated features and the second image provides a simple reconstruction o f the features (10 0 x loom lo o k in g north)

16 M axim u m alder coverage and m inim um alder coverage (cleared) m odels on Sutton C o m m o n , South Yorkshire

17 C o m p arin g the visual impact o f reconstructed vegetation at Sutton C o m m o n using view shed analysis o f both the m inim um and m axim um vegetation models

1 8 Filling gaps in fieldw alking data using density m odelling to give an overall trend

lg C o m p arin g the results from the water table m onitorin g at Sutton C o m m o n with the preservation condition o f excavated w ooden remains

20 C ran nog in L och M igdale, Highlands. T h e site was underw ater and surveyed using G P S from a boat. T h e resulting m odel shows the subm erged landscape including the approach to the site from the loch edge (10 0 x 50m view ed from the south)

21 Applecross B roch, H ighlands — ridge m odelled from G P S data and overlain by an extruded polygon representing the structure based on excavated evidence to provide a simple visualisation (12 0 x 40m view ed from the north)

22 Integrating historical maps w ith topography - early seventeenth-century map o f Esher, Surrey, with extruded buildings representing W ayneflete T ow er (total area 1,3 x ik m view ed from the south-west)

23 M od ellin g vegetation based on a 1725 map o f Chesham Bois, H ertfordshire. C o m b in in g topography, historical m apping, reconstructed trees and a reconstructed m anor building (2.2 x 1 ,6km view ed from the east)

24 T h e R o m a n -p e rio d landscape o f D inn in gton , So m erset.T h e texture was derived from a variety o f G IS analyses w ith the results being ‘painted’ in graphics software and draped over the D E M to provide a clearer visualisation o f the landscape (4.5 x 3.8km view ed from the north-east)

23 Presenting results from G IS analysis using visualisation software - N o rth b oro u g h causewayed enclosure near Peterborough (overall diam eter o f the enclosure is 250m , view ed from the west)

8

Landscape archaeology and theory IN T R O D U C T IO N From a firm ly epistem ological perspective, it becom es increasingly difficult to place archaeological G IS w ithin any single paradigm. Arguably, G IS software provides a positivist tool that can m odel various input data to provide quantitative results. H owever, G IS also provides a qualitative graphical interface. B lu rrin g things further, G IS can provide a simulation o f an environm ent at a given p eriod through the stripping back o f later features such as buildings and the reconstruction o f environm ental features. T h e simulation o f past landscapes can enable them to be experien ced in a virtual way. Such potential provided by G IS can feed into a num ber o f theoretical themes used w ithin landscape archaeology. T h e central themes w ithin landscape theory are underlain by the definition o f landscape (cf. O lw ig 1993). T ille y (1996) sum m arised the relationships betw een archaeology and landscape in four ways (also referred to in chapter 1): 1. as ‘a set o f relationships betw een nam ed locales’ (p. 16 1) 2. to be ‘ experienced and kn ow n through the m ovem ent o f the hum an body in space and through tim e’ (p. 162) 3. as ‘a prim ary m edium o f socialisation’ (p. 162) 4. creating ‘self-identity’ by controlling know ledge and thereby influencing pow er structures (p. 162) From this the key principle m ay be considered to be that o f experience, and thus studies o f archaeological landscapes have been based upon an attempt to replicate the experien ce o f ‘B e in g -in -th e-w o rld ’ w hile tryin g to reconstruct the dialectic o f the existential ‘B e in g ’ (Tilley 1994, 12 ) .To date, the prim ary m ethod

Landscape Archaeology and G IS

o f m easuring experience has been through the analysis o f visibility patterns. For exam ple,Thom as (1993) investigated the visual im pact o f m onum ents, particularly around Avebury, suggesting themes o f inclusion and exclusion (similar to T ille y ’s fourth point, m entioned above). D evereux (1991) also analysed the spatial relationships betw een m onum ents and topography at A vebury by investigating their visual relationships. Sim ilarly,Tilley (1994) investigated three archaeological landscapes through a photographic essay and by recording patterns o f intervis­ ibility betw een m onum ents. W ith such dom inance in the use o f visibility patterns underlying so many theoretical approaches to landscape archaeological theory, G IS is well suited for explorin g and presenting these theoretical approaches in a quantitative way. H owever, there are num erous other ways in w h ich G IS may be used to address questions relating to theoretical approaches to landscape archaeology. As m entioned previously, G IS is becom in g m ore o f a ‘place to think’ (Gillings and G o o d rick 1996) than m erely a quantitative tool. This chapter explores some o f the approaches that have been used to address these themes, in addition to suggesting a num ber o f n ew ones.

SPACE A N D P LA C E In his book, Space and place - the perspectives o f experience, Tuan (1977) provided one o f the bases from w hich experiential landscape archaeology has progressed (e.g. T ille y 1994). This w o rk identified a dualistic approach to understanding our lived-in environm ent. ‘ Place is security, space is freedom ’ (Tuan 1977, 3). Put simplistically, landscapes consist o f a series o f places that are culturally constructed through the activities, stories or m em ories associated w ith them. T h e areas betw een places may be referred to as ‘space’ .T h is duality provides us w ith an opportunity to divide landscapes o f different periods betw een places, perhaps occupied by buildings or m onum ents, and the spaces betw een them. In terms o f com puting and G IS, the division o f cultural landscapes into places and spaces, w hilst overly simplistic in a prescriptive way, is extrem ely tangible and useful. O n the ground, the definition o f a significant ‘place’ at a given period may be explored by em bodyin g that locale and exp erien cin g the landscape, albeit in its m odern form . W ithin the com puter, this approach can again be used, but w ith the advantage o f explorin g different possible pasts, through the regression o f the m odern landscape, and the reconstruction o f site distributions and perhaps environm ental conditions. A place that m ay be considered as an archaeologically significant position, perhaps due to the situation o f a burial m onum ent, can be exam ined for its

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visual im pact, both from this place to its surrounding space, and inversely from the w ider landscape to the particular site. W hat other m onum ents fall w ithin the visible areas, w hich ones fall outside and w h ich follow its horizon? This technique has also been explored through digital landscape reconstruction. Fisher et al. (1997), for exam ple, analysed the positioning o f Bronze A ge cairns, dem onstrating a visual relationship betw een their positioning and corresponding view to/from the sea. In contrast, Lake et al. (1998) presented an approach to assess the hypothesis or observation that M esolithic flint w orkin g sites were located in places that w ere significant due to the view s they com m anded, perhaps due to the potential for observing the m ovem ent o f game. In order to test this, they created an autom ated approach w h ich assessed the visibility from each cell w ithin the study area D E M by calculating the num ber o f visible cells w ithin its view shed. B y autom ating this approach, they were able to obtain values for every cell w ithin the m odel, w hich could then be com pared w ith the num ber o f visible cells from the M esolithic sites. H ence, this enabled them to ask the question o f w hether the locations o f flint w orkin g sites w ere m ore significantly superior in terms o f view shed than other possible areas w ithin the landscape. W hilst considering the in fluencing factors, such as contem porary vegetation, the authors w ere able to quantitatively exam ine this hypothesis.

R O U T ES OF M O V E M E N T A N D N A R RA TIV ES OF V IS IB IL IT Y In relation to archaeology, T illey (1994) expanded the dualistic concept o f space and place to include pathways w here m ovem ent m ight occur betw een diflerent places and through space. U sin g the exam ple o f the Dorset cursu s,T illey argued that different elements o f the landscape as well as diflerent m onum ents from earlier periods were experien ced at different points along the route as part o f the architectural design o f the m onum ent. Furtherm ore, he used this approach to argue that the cursus enabled m ovem ent along it in a particular direction and hinted at cursus function based upon this. G IS provides the potential to explore these qualitative themes in a quantitative environm ent. T h rough the reconstruction o f a landscape as a D E M , m ore recent features and trees, w h ich on site w ould obscure the experience, can be eliminated. Furtherm ore, as discussed in the previous chapter, it is possible to reconstruct m any o f these elements o f the landscape too. H ence the G IS provides the tool for experien cing the landscape o f a given p eriod w ithout the clutter o f features from later periods. It is possible to exam ine the visual properties o f m onum ents w ithin a G IS, such as the R u d ston cursus com plex in East Yorkshire (D ym on d 1966, R ile y

Landscape Archaeology and G I S

1988, Stoertz 1997). Here, a group o f four cursus (cf. Stoertz 1997) cluster around a bend in the Great W old Valley on the Yorkshire W olds (41). Tw o particularly interesting features may be considered in relation to this landscape. T h e first relates to the cluster itself, bein g the largest concentration o f cursus m onum ents know n, providing the possibility o f com paring the results o f one cursus study w ith another w ithin the com plex. Secondly, the shape o f cursus A is unique insofar that it form s a ciog-leg bend w here cursuses are norm ally relatively straight (Loveday 1985, see chapter 6). A n exam ination o f the routes o f each o f the cursuses, founded upon G IS-based visibility (Chapm an 2005), revealed themes o f interpretation and chronology (42). O f the cursuses only one stratigraphic link has previously been made; betw een cursus C and cursus D, show ing D to be later. Stylistically, cursus A was considered to be the earliest o f the com p lex (Loveday 1985). U sing this loose fram ew ork, the visibility from positions at regular intervals along each o f the cursuses was assessed in G IS. From this narratives w ere w ritten for both directions o f m ovem ent, for exam ple north to south, and then south to north, linking back to the approach used by T illey (1994, see above). T h e results from this study identified that each o f the cursuses appeared to display different priorities in relation to their architecture and visibility patterns. T h e potentially earliest cursus, cursus A , displayed a close visual link to long barrows on the western horizon w hich were maintained in that position throughout the route. This link to the earlier period also explained the dog-leg plan o f the m onum ent w hich appears to have been designed to maintain this visual pattern (Chapm an 2003). This cursus also displayed themes o f ‘visual surprise’ upon approaching the end o f the m onum ent, though this only w orked w hen follow ing in one direction o f travel and not in the other. T ille y used such an observation to explain directionality o f the D orset cursus, and it appears that the same could be said for R u d sto n cursus A. cursuses B and C provided very similar visual responses to cursus A , but w ithout the constant link to earlier monuments. R ath er they m aintained a link to cursus A, reinforcing the interpretation that this was the earliest o f the group and thus emphasising Loveday’s m orphologically-based interpretation (1985).T h e key similarities were in the visual surprise elements at the end o f the route, norm ally characterised by keeping the cursus terminus out o f view until the very last few steps o f the route w hereupon it com es into view. W hilst the R udston group have all been levelled by ploughing, visual surprise was demonstrated at the Dorset cursus by an enlarged, and thus m ore dramatic, terminus at this end. R u d ston cursus D, in contrast, did not display any o f the traits o f the earlier cursuses, but rather form ed an enclosure w hich linked the cursus group, and associated monum ents at its southern end, w ith a visual link to a separate group o f monum ents to the north.

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Cursus D

Willy Howe

Cursus C Rudston long barrow

Denby long barrow long barrow

Gypsey Race

jH c u r s u s B Southside Mount

Cursus A

41 Location map ot the later N eo lith ic m onum ents form ing the R u d sto n com p lex 011 the Yorkshire Wolds, East Yorkshire

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42 Changes in the view sh ed w h en w alkin g along cursus A w ith in the R u d sto n landscape

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T h e visual approach, follow in g fro n iT illey ’s theoretical approach to interpreting the D orset cursus, thus dem onstrated apparently different functions between the m onum ents in addition to reinforcing the chronology o f the landscape. T h e interpretations o f function w ere also reinforced by the local landscape setting. T h e vast difference in visual response betw een cursuses A, B and C on the one hand and cursus D on the other was reflected by their relationships to the G ypsey R a c e stream running through the Great W old Valley Cursuses A, B and C all run perpendicular to the stream, w ith cursues A and C actually cutting across it, whereas cursus D is aligned w ith the stream and doesn’t cross it (Chapm an 2005). H ence it was possible to explore elements o f essentially phenom enological approaches to landscape archaeology, albeit focusing on visibility, w ith confidence.

C U M U L A T IV E V IE W S H E D A N A LY SIS A m ethod used to address theoretical themes o f landscape archaeology w ithin G IS is cumulative view shed analysis, first used by W heatley (1995).T h e significance o f a view shed from a given point has been interpreted to suggest that the location o f a site is significant due to the view shed that it com m ands (e.g. Thom as 1993, T illey 1994). Llobera (1996) applied these theoretical concepts to archaeological G IS studies, suggesting that G IS was not a lim ited, norm ative and reductionist tool, but rather had the potential to act as a heuristic tool; exploratory rather than dualistic. M ultiple view sheds were generated from the lines o f the linear ditches 011 the W essex chalklands to see i f a similar area could be seen from either side. It was indicated by the analysis that the view sheds w ere singlydirectional, supporting the v iew that they w ere territory markers. A further level o f cum ulative view shed analysis was then achieved through the creation o f a gradient view. This used a cum ulative view shed form ed by the com bination o f view s o f different distances (set at 50m intervals). For exam ple, areas closer to the observer w ould be seen w ithin each view shed, whereas areas further away w ould be seen few er tim es.T he conclusions from this research suggested that the function o f the linear ditches were to m ark territories, but that these were not in the enclosed, in w ard-lookin g sense but rather as markers. G IS has also been used as a tool to test hypotheses relating to visual significance o f the type suggested by Thom as (1993) and T illey (1994). T h e traditional (nonG IS) approach has been criticised by suggesting that the significance o f a location based on visual factors can only qualified i f it com m ands a significantly better view than other places w ithin that landscape. In other words, a statement that a site was positioned in a certain location because it enabled a greater view

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