new developments in the use of spatial technology in archaeology
TRANSCRIPT
New Developments in the Use of Spatial Technologyin Archaeology
Mark D. McCoy Æ Thegn N. Ladefoged
Published online: 27 March 2009
� Springer Science+Business Media, LLC 2009
Abstract Spatial technology is integral to how archaeologists collect, store,
analyze, and represent information in digital data sets. Recent advances have
improved our ability to look for and identify archaeological remains and have
increased the size and complexity of our data sets. In this review we outline trends
in visualization, data management, archaeological prospecting, modeling, and
spatial analysis, as well as key advances in hardware and software. Due to devel-
opments in education, information technology, and landscape archaeology, the
implementation of spatial technology has begun to move beyond superficial
applications and is no longer limited to environmental deterministic approaches. In
the future, spatial technology will increasingly change archaeology in ways that will
enable us to become better practitioners, scholars, and stewards.
Keywords Geographic information systems � Laser mapping �Remote sensing � Geophysical survey
Introduction
A common thread that links classic methodological innovations in field archaeol-
ogy, such as the pioneering use of aerial photography by Gordon Willey or grid-
based excavations by Sir Mortimer Wheeler, is how these methods have enhanced
our ability to find and record the locations of archaeological remains at a level of
precision necessary to interpret them. Today we use a wide variety of ‘‘spatial
M. D. McCoy (&)
Department of Anthropology, San Jose State University, One Washington Square,
San Jose, CA 95192-0113, USA
e-mail: [email protected]
T. N. Ladefoged
Department of Anthropology, University of Auckland, Auckland, New Zealand
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DOI 10.1007/s10814-009-9030-1
technologies’’ to continue to enhance our ability to collect, store, analyze, and
represent locational and attribute information in digital data sets (Lock 2000;
Wheatley 2000; Wheatley and Gillings 2002). These include geographic informa-
tion systems (GIS), global positioning (GPS), laser mapping, remote sensing, and
geophysical survey.
Since the last major review of GIS in archaeology, the inclusion of ST in research
has become the norm (Kvamme 1999). Thus we feel it is timely for us to take a look
back at how these technologies have been applied and what influence they have had
on how we investigate and interpret archaeological data. We begin by reviewing
applications of spatial technology for visualizing data and representing sites,
managing data, archaeological prospecting, modeling, and spatial analysis. Next, we
give a brief overview of technical advances in spatial technology in terms of
common hardware and software. This aspect of our review is explicitly aimed at
readers who are unfamiliar with spatial technology.
Finally, we consider the impact of spatial technology on archaeological method
and theory. Some of the most dramatic methodological shifts that have come with the
development and integration of spatial technology in archaeology have centered on
our ability to look for and identify archaeological remains and increase the size and
complexity of our data sets. The latter trend has inspired a suite of research on how
we as a discipline address the issue of scale (Bailey 2007; Holdaway and Wandsnider
2006; Lock and Molyneaux 2006; Wandsnider 2004). In terms of archaeological
theory, over the past decade we have seen GIS and other spatial technology outgrow
fears of a return to environmental determinism, and we now find fewer examples of
the unreflexive use of these tools without regard to theory. These changes have been
facilitated by greater integration of spatial technology in education, growth of
information technology, and developments in landscape archaeology.
Applications of spatial technology in archaeology
The application of spatial technology in archaeology can be classified into three
categories: visualization, data management, and spatial analysis (see also Ebert
[2004] who addresses these topics in terms of GIS). We begin by distinguishing
advances in visualizing data to conduct exploratory spatial data analysis (ESDA)
and visualizing archaeological data to represent features and artifacts. We
specifically include ESDA in our discussion of visualization since these two topics
are more closely related than ESDA and archaeological spatial analysis. Next, we
consider the management of data sets in the information age. Finally, we outline
how spatial technology has been applied to the problems of archaeological
prospecting, modeling to predict the location of archaeological material remains,
and spatial analysis of archaeological data.
Data visualization and representative visualization
A map empowers ‘‘the human eye and brain to perform intuitive spatial analysis’’
through reading and interpreting it (Goodchild and Janelle 2004, p. 6). Thus when
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we use the term visualization, we are often referring to two different kinds of
activities—data visualization and representative visualization—that use similar
methods but have distinctly different goals (see Richards 1998). The goal of data
visualization is the discovery of new information, patterns, or relationships among
variables through exploratory analyses of spatial representations of data (see
Beardah and Baxter 1996; Regnauld et al. 2002). With the advent of GIS, such
representations can be produced in two or three dimensions, visually compared with
other data by overlaying layers, and produced in unlimited combinations and scales.
Thus GIS has given archaeologists the opportunity to perform a kind of exploratory
data analysis (EDA; see Clark 1982), called exploratory spatial data analysis
(ESDA), in an unprecedented fashion (Goodchild and Janelle 2004). ESDA, in its
simplest form, uses the graphic and summary statistic functions of GIS software to
manipulate data to look for general trends (see Johnston et al. [2001] for a
description within the ESRI ArcGIS environment). This is particularly important,
for example, in remote sensing and geophysical survey data processing since
distinguishing anthropogenic from natural patterns, like face recognition, is still
often best done by the experienced human brain than automated pattern-recognition
software (see Lemmens et al. [1993] for an example using air photos). Relationships
or patterns that are identified should, however, be verified using confirmatory data
analysis (CDA) via spatial statistics (Clark 1982).
Representative visualization is the production of either a direct representation of
archaeological evidence—such as maps of sites—or the reconstructions of past
places or objects. These representations must take into account the viewer’s
perspective and prior knowledge and what data to include and how best to simplify
and interpolate evidence. In the creation of static maps, we have relatively well-
established cartographic standards that provide a template to make these represen-
tations readable to other professionals or appealing to a public audience. However,
the virtual world offers interactive or immersive representations of reality with a
whole host of new choices in terms of viewer’s perspective and what and how to
represent archaeological phenomena (Challis and Howard 2006; Dawson et al.
2007; Gillings 2005; Winterbottom and Long 2006). Indeed, we are likely on the
verge of an era of better, more accessible, and more creative virtual reproductions as
the internet evolves toward Web 2.0 applications, resources, and services, such as
Google Earth. This technology opens up avenues of representations that might
otherwise have been limited to desktop applications.
There have been qualitative and quantitative changes in how archaeologists
conduct visualization. New computer hardware, image-processing software, and
data from a range of sources have facilitated the display of archaeological
landscapes as a continuous palimpsest of artifacts, deposits, and architecture. As
researchers, this makes us better equipped to tease out a more complete record of
past activities through access to larger data sets and a variety of data models that can
be used to code contextual information (Cooper and Qiu 2006). As interpreters of
the past to the public, these representations hold potential for changing people’s
preconceived notions that ‘‘sites’’ are discrete entities and the only significant parts
of the archaeological record. Moreover, as we work toward a synthesis of contextual
information and spatial technology, we advance social science education (LeGates
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2005) and create the opportunity to show elements of the past that might otherwise
fall outside the Western gaze (Bender 1999b).
However, intrinsically appealing maps and visual representations of archaeolog-
ical phenomena can hide complexities and reinforce expectations (Hodder 1999). An
example of this is the creation of ‘‘empty sites’’ that include beautifully rendered
architecture but no representations of people or artifacts (e.g., El-Hakim et al. 2002).
We recognize that there are good reasons to recreate, or represent, architecture or
spaces as devoid of humans for research purposes. However, to leave these same
spaces empty of people in representations for the public runs the risk of
dehumanizing the past (see Pickels 1995). Problematically, creating ‘‘digital people’’
requires a great deal of interpolation from diverse sets of data such as burials, art, and
artifact analyses. Another, more subtle problem with current virtual representations
is that they are almost always static ‘‘read-only’’ reconstructions. We believe this is a
lost opportunity to engage people in the act of discovery and to show the past as a
place with active actors rather than ‘‘faceless blobs’’ (Tringham 1991).
Finally, there is the problem that representative visualization is often detached
from main research or management goals. Visualization is at times dismissed or
unduly marginalized as pseudoscientific activities in exploration of data or showy
reconstructions with little to add to our body of knowledge or aid in preservation.
Growing interest in digital archaeology is beginning to change these practices and
show the considerable value of these techniques (Evans and Daly 2006); and we are
seeing greater concern with how best to use Web 2.0 advances to facilitate public
outreach.
Data management: digital data and the internet
Spatial technology has had an immense impact on our ability to create, manage, and
analyze data sets and share our findings with other professionals and the public.
Geographic information systems have gradually become the platform that archae-
ologists use to store geographically and numerically large sets of information on the
artifact, feature, and site levels using its read-write capability. These changes have
been especially important in cultural resource management and influence how
archaeologists approach the study of the historic and prehistoric past (Berry 2003;
Dyson-Bruce 2003; Ford 2007; Gregory and Ell 2006; Limp 2001; Ryavec 2001;
Stichelbaut 2006). Universities, museums, and government agencies have joined
forces at national and local levels to support databases with locational information.
For example, the Archaeology Data Service (ADS) and the Arizona Cultural
Resource Inventory (AZSITE) provide information on previous surveys and site
locations. In addition, they have helped clarify and prevent future conflicts in
existing data, provide a protected repository for archaeological knowledge, and
promote nondestructive analyses based on collated data from gray literature
(Richards 2002). Each agency has protocols in place to limit access to the location
of sensitive sites. Unfortunately, access to images, such as geophysical survey data,
remains limited in distribution, but there are ongoing attempts to bring these data
together, e.g., the North American Database of Archaeological Geophysics
(NADAG).
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A major problem with these larger and more accessible data sets is the
specification of explicit and standardized recording systems for context and
classification. No one disputes that in archaeology locational data must account for
context. However, how one codes context into a digital database often requires
making difficult decisions based on incomplete or uneven information. For example,
a GPS data dictionary allows a user to preprogram formal and free-form database
fields that will be attached to vector data (points, lines, polygons). This requires one
to explicitly define terms used—a methodological issue that has been addressed
through regional standards for defining sites and standardized descriptions of terms
in formal thesauruses such as the Scottish Thesaurus of Monument Types (Bains
and Brophy 2006). However, most regions do not have formal, explicitly published
classifications or thesauruses.
Decisions regarding coding context must in turn be incorporated in file
‘‘metadata,’’ i.e., data about data, that describes data fields and terms and gives
important information about how the file was georeferenced (Wise and Miller
1997). Table 1 is a list of the kinds of information one might include when creating
a metadata file for spatial data. Overall, to create databases that can be integrated,
one must both explicitly define terms and fields used in data recording and provide
metadata for spatial data sets.
As spatial technology evolves, it brings with it larger challenges in terms of
working cumulatively as a discipline. Despite our advances in creating, managing,
and publishing spatial data, archaeology has a long way to go in terms of our need
for cyberinfrastructure, as defined as an ‘‘archaeological information infrastructure
that will allow us to archive, access, integrate, and mine disparate data sets’’
(Kintigh 2006, p. 567). Snow et al. (2006, p. 958) point to several types of data that
are ‘‘impossible to access simultaneously because of the highly individualized
nature of traditional archeological field and laboratory research,’’ including (1) data
Table 1 Minimal metadata information for spatial data sets
Summary
Brief description of data set. This should include the reason(s) it was created and contact information for
the author.
Date Created
Ideally, this should be day/month/year. One could include version numbers for evolving data sets.
Data File Type
Vector (shapefile; point, line, polygon) or Raster (.tiff, .jpg; pixel dimensions, cell size).
Spatial Reference System
Georeferenced, nonreferenced, datum, coordinate system (NAD 83; UTM); One could also include
precision based on machine min/max specs or readings taken at known benchmarks. Any post
processing should be noted.
Definitions of Fields
Each variable recorded in a data field should be explicitly defined and units given (e.g., meters above sea
level).
Sources
Sources of published and/or unpublished data included in the data set.
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sets compiled by researchers independently of larger institutional databases, (2)
limited circulation ‘‘gray literature,’’ and (3) maps and other spatial data in reports.
Implementation of GIS to address the problem of data access has brought to the fore
the problems of data quality, institutional support in terms of personnel and time,
and the need for greater planning (see Wheatley and Gillings 2002). In the United
States, GIS users note ‘‘gaps’’ in data sets due to land ownership, or management,
and different recording systems from state to state. Overall, we are behind
disciplines like geography that have begun to solve the cumulative data problem
using ‘‘hub’’ websites (Geosciences Network, GEON).
Prospecting, modeling, and spatial analysis
Applications of spatial technology are as diverse today as when Kvamme (1999)
wrote his landmark review of GIS in archaeology. Nonetheless, we have seen a
florescence in three trends: prospecting for features and deposits, modeling with the
goal of finding archaeological remains, and spatial analysis to learn more about past
behavior. Below we give an overview of trends in these applications. We refer the
reader to our subsequent description of hardware and software for fuller descriptions
of the spatial technology employed.
Archaeological prospecting
Archaeological prospection is ‘‘all those methods by which past human activity can
be located and characterized’’ (David 2006, p. 1). The act of prospecting for sites is
inherently spatial and in many ways represents the leading edge in the application of
spatial technology in archaeology. In practical terms, these can be divided into
ground- and sea-based geophysical survey, aircraft-based laser mapping and
imaging, satellite imaging, and those methods that employ some mix of these. We
continue to develop how we dispatch these technologies through professional user
groups (such as the International Society for Archaeological Prospection),
specialized journals (like Archaeological Prospection, 1994 to present), and books
and articles illustrating applications and principles of good practice (Bickler and
Low 2007; David 2001; Kvamme 2003, 2006b; Limp 2006; Schmidt 2002, 2003).
There are several exhaustive works on the history of geophysical survey and remote
sensing; we focus primarily on identifying recent trends in prospecting research
(Conyers 2004; Conyers and Goodman 1997; Johnson 2006).
Geophysical survey works through a ‘‘reliance on basic physical laws, a
recognition of fundamentally different signatures between human and natural
features, and generally without reference to anthropological analogs or models’’
(italics in original, Kvamme 2006c). In archaeological applications of geophysical
survey, we have seen greater and greater attention to specific survey methodology
(Papadopoulos et al. 2006a, b; Weaver 2006), a focus on dealing with unique
challenges such as detecting features in complex landscapes (Conyers 2006; Gibson
and George 2006), describing impacts to features (Herbich and Peeters 2006),
detailing the relationship between the properties of natural and cultural sediments
(Bates 2005; Dalan 2006; Dalan and Bevan 2005; Miki et al. 2006), and the value of
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using multiple methods (Challis and Howard 2006; Kvamme 2006a; Kvamme et al.
2006b).
Gibson and George’s (2006) recent work at the site of an early sixth century A.D.
Irish monastic settlement is an excellent example of how multiple methods can be
applied in a well-planned geophysical survey aimed at detecting buried architecture.
The authors began by identifying two survey areas, both immediately adjacent to a
local church, that were further subdivided into 30 9 30-m blocks. These blocks were
then subjected to resistance and magnetic measurements at 1-m and 0.25-m
resolution, respectively. The resulting spatial data set was typical of these types of
geophysical surveys, including a total of 55,800 resistivity and 223,200 magnetic
point readings spread over a total of 62 blocks. Point data were then interpolated to
create grayscale raster maps that were in turn georeferenced and displayed in GIS
software and used to identify several classes of buried features—a 300-m-long natural
paleochannel, a network of smaller artificial drainages, linear agricultural field
boundaries, and, most importantly, zones tentatively identified as locations of human
activity or architecture. An example of the latter was then subjected to more intensive
electric and ground-penetrating radar survey that produced cross-section slices at
depths up to several meters below the modern ground surface. These revealed intact
walls confirming the presence of a building. Overall, the team reached their goal by
working from an extensive to an intensive survey strategy while interpreting patterns
discovered in terms of natural and cultural site formation processes.
In remote sensing we also have seen applications of existing technologies to the
problem of finding, identifying, and mapping archaeological features. Naturally,
there has been continued use of aerial photography, including the use of remotely
controlled airborne cameras (Argote-Espino and Chavez 2005; Bewley 2003; Bitelli
et al. 2004; Hailey 2005). But most research has taken advantage of satellite
imagery like Landsat, SPOT, IKONOS, Quickbird, CORONA (declassified 2002),
and ASTER (advanced spaceborne thermal emission and reflection radiometer)
(Altaweel 2005; Challis 2007; Challis et al. 2002; De Laet et al. 2007; Lasaponara
and Masini 2006, 2007; Masini and Lasaponara 2006, 2007; Tan et al. 2005;
Winterbottom and Dawson 2005). We also have seen increased use of satellite
imaging radar (SIR), synthetic aperture radar (SAR), airborne imaging radar
(AIRSAR) (Evans et al. 2007; Holcomb 2001; Shimoji 1995), and aircraft-mounted
scanners that use light detection and ranging (lidar) to create digital elevation
models (DEM) of the earth’s surface (Carey et al. 2006; Challis 2006; Crutchley
2006). In the UK, high-resolution lidar (1–2 m) has proved especially useful,
allowing prospecting on par and exceeding that possible with air photography
(Bewley et al. 2005). For example, in a recent report on efforts by the Aerial Survey
Team at English Heritage, Crutchley (2006, p. 252) has described how off-the-shelf
lidar data (2-m resolution) was used to create grayscale 3D elevation models for the
purpose of archaeological prospecting. In this case, a series of 2 9 2-km square
blocks based on the UK Ordnance Survey grid were checked against modern and
historic maps to narrow the list of anomalies identified to those that likely
represented archaeological phenomena. The resulting map shows the precise
location of hundreds or possibly thousands of individual features (Crutchley 2006,
Fig. 1).
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Advances in GIS and remote sensing have promoted the creation of digital maps
that now regularly include thousands of individual features spaced over large study
areas (Ladefoged and Graves 2000, 2008; Ladefoged et al. 2003; McCoy 2005,
2006). For example, sites have been discovered over a portion of the Maya region
by Saturno et al. (2007) by using satellite (Landsat Thematic Mapper, IKONOS, and
QuickBird) and airborne radar data (STAR-3i and AIRSAR) to detect vegetation
signatures later checked through field survey. This technology also has helped
complete the remote survey of the Angkor landscape in Cambodia that now covers
3,000 km2 and reveals ‘‘a cumulative settlement palimpsest, with an organic and
polynuclear form arising from social and environmental processes operating over
more than half a millennium’’ (Evans et al. 2007, p. 14279).
As the examples above demonstrate, image interpretation and ground truthing are
at the core of any prospecting. As with ESDA, we rely on sound human judgment
for this rather than automated pattern recognition. For example, the interpretation of
images is often done by hand, and ideally the results are checked via pedestrian
survey, more intensive remote-sensing methods, or excavations to link the patterns
found to specific types of archaeological phenomena, features, and deposits.
However, some never get around to ground truthing for a range of reasons (see Miki
et al. [2006] for a rare example of an experimentally derived link between
geophysical patterns and features). In the future, we expect to see improvements in
our ability to identify archaeological phenomena through setting explicit standards
(Bickler and Low 2007), increasing integration of geophysical and GIS data sets
(Gillings and Goodrick 1996; Neubauer 2004), and more regular feedback from
excavators to geophysical teams (Johnson 2006, p. 12).
The increased use of geophysical and remote-sensed data in archaeology has not
been even around the world. European archaeologists, especially in the UK,
represent the extreme in incorporating ‘‘geophys’’ in research, CRM, and in the
popular television program ‘‘Time Team.’’ The gap with North American
practitioners is so great it has caused Johnson (2006, p. 3) to pose the question,
‘‘Why is it that the random person on the street in London is likely to be able to
discuss the relative merits of using a magnetometer rather than GPR, while many
North American archaeologists are not sure what these instruments do in the first
place?’’ Indeed, we are somewhat at a loss to explain this trend, and it seems
unlikely any single factor is to blame.
Site predictive modeling (archaeological locational modeling)
Site predictive modeling, or archaeological locational modeling (ALM), to
determine the probability of undiscovered sites in a given area has been a major
focus of GIS applications in archaeology over the past decade (Mehrer and Wescott
2006; Wescott and Brandon 2000). Since the goals of site predictive modeling are
aligned with prospecting, we distinguish this type of modeling from spatial analysis,
or behavioral modeling, which is focused on determining how people experienced
and interacted with their natural and social environments through the evaluation of
things like settlement patterns, sight, movement, and even sound (Mlekuz 2004).
Like all applications of spatial technology, these have been used in both CRM and
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academia; however, the vast majority of site predictive work has been aimed at
managing archaeological phenomena across large, often poorly surveyed, sections
of North America. Some have suggested that the more complete knowledge of
archaeological site distributions in Europe explains why predictive modeling has not
held wide appeal in the region (Richards 1998, p. 337; see also these examples of
European modeling, Fry et al. 2004; Legg and Taylor 2006).
The advent of GIS has caused a ‘‘second age’’ in modeling to predict the
locations of archaeological sites (Kvamme 2006c). In a typical digital model,
existing locational data on archaeological sites is linked to one or more
environmental variable, which in turn are used to assess the likelihood of similar
sites in unsurveyed areas. Confidence in these predictions usually rests on how well
the model ‘‘finds’’ known sites, sometimes a sample of the same sites that were used
to create the model in the first place. This information is then used to make
decisions about how best to mitigate the impacts of development.
It is understandable why site predictive modeling has become big business in the
United States as public and private land managers are faced with increasingly
smaller budgets and shorter project times. In more theoretical terms, part of the
appeal of site predictive modeling lies with processual archaeology’s promotion of
cultural materialism and causal linkages between changes in human behavior and
the natural environment. In practical terms, a predictive model can be used to
produce a quantitative evaluation of the likelihood of sites in a given area. It also
provides cultural resource mangers a tool to make their rationale as to why certain
areas are deemed sensitive transparent and accessible to other land managers (see
Gunasekera 2004).
Predictive modeling has generated internal debate between modelers as well as
external critiques. For example, J. Ebert (2000, p. 130) points to ‘‘seven big mistakes’’
that have ensnared second-wave modelers, including issues of data quality,
appropriateness of methods, time, and the use of inductive and deductive logic.
While space does not allow for an extended review of the evolution of modeling (see
D. Ebert 2004; Kvamme 2006c), it is important to note that fundamental criticisms
have been leveled at predictive modeling in terms of environmental determinism,
questionable assumptions, and the need for better thought out models of human
behavior and site formation processes (Wheatley and Gillings 2002). Wheatley (2004,
p. 1) has gone as far as to characterize predictive modeling as ‘‘over-generalising,
deterministic and de-humanised’’ and ‘‘now essentially detached from contemporary
theoretical archaeological concerns.’’ On the other hand, Kvamme (2006c, p. 5) had
defended predictive modeling as playing a role in the larger scientific goal of
‘‘recognizing order in the chaos of the natural world by formulating generalizations or
rules (laws, principles) of increasing specificity.’’ Together, these represent contrast-
ing theoretical viewpoints on the importance to ‘‘space’’ and ‘‘place’’ in
archaeological research, a topic we return to below.
Spatial analysis
Spatial analysis continues to be a central part of archaeology, with GIS providing a
powerful tool in regional studies of migration and settlement patterns (Elliott 2005;
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Galaty 2005; Jennings and Craig 2003; Jones 2006; Peterson and Drennan 2005),
ancient agriculture (Bevan and Conolly 2002; Friedman et al. 2003; Ladefoged and
Graves 2000, 2008; McCoy 2005, 2006), and warfare (Field 2005). Specialized
studies have used spatial calculation functions for artifact and intrasite analysis,
creating viewsheds, cost surfaces, and simulations.
We have seen slow but steady improvement in how archaeologists deal with
spatial data at the level of artifacts. Recent research has examined the preservation,
taphonomy, and attributes of artifacts (Abe et al. 2002; Bird et al. 2007; Byerly et al.
2005; Chapman and Cheetham 2002; Cooper and Qiu 2006; D’Andrea et al. 2002;
Marean et al. 2001; Matthiesen et al. 2004; Nigro et al. 2003). In addition, as a
consequence of advances in spatial technology, there is greater theoretical concern
for intrasite spatial patterning on par with the concern for site-level patterns in the
spatial archaeology of the 1960s and 1970s (Wheatley and Gillings 2002, p. 236).
For example, Jones and Munson (2005) have used geophysical methods to
investigate intrasite patterning of hearths and other features; Holdaway et al. (2005)
have used GIS and laser technology to record and analyze tens of thousands of
artifacts and features in an early historic Maori village in New Zealand.
One of the most commonplace behavioral modeling applications using spatial
technology is the analysis of how people in the past incorporated intervisibility, or
viewshed, in their decision making with regard to the location and form of features
(Ayala and Fitzjohn 2002; Jones 2006; Lake 2007; Lake and Woodman 2003;
Llobera 2007; Ogburn 2006; Williams and Nash 2006) or visibility within a site
(Clark 2007; Dawson et al. 2007). Early simplistic applications of the GIS viewshed
function—a method of calculating the total area visible from a point within a raster
model of regional topography (DEM) relative to the viewer’s local elevation and
height—have been to some degree replaced by more realistic estimations of the
limitations of human vision through the use of Higuchi and fuzzy viewsheds
(Wheatley and Gillings 2000; see also Fisher 1991, 1992, 1994, 1995, 1996, 1998).
Each of these digital models gives decaying value to increasingly distant locations
within a viewshed. Viewsheds also can account for the size of the target object and
the cumulative visibility of certain locations by applying these functions to different
sites within a study area. However, as more individual locations are included in a
model, it becomes more difficult to reasonably account for local conditions that
would have impacted different lines-of-sight in the past. This is sometimes called
the ‘‘tree problem,’’ but it is also valid when considering factors such as climate and
visibility at night.
In recent applications of viewshed analysis, we have seen an explicit concern for
context and agency. Llobera’s (2003, p. 31) detailed review of viewshed analysis
suggests the refinement of the technique to include a number of visualscapes,
including consideration of spatial configurations that can ‘‘vary the scope, scale and
intent of the visual analysis.’’ Williams and Nash (2006) investigated the viewsheds
of sites in the Andean mountains to understand how these were intricate components
of ancient religious beliefs (see also Llobera 2001). In contrast, Briault (2007, p.
122) used viewshed analysis to suggest that ‘‘…visibility and landscape location
were perhaps less important to peak sanctuary cult than traditionally supposed.’’
Giles (2007) used viewshed analysis to suggest that people’s perceptions and
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experiences of material culture were dynamic and did not progress in a linear
manner from one period to the next. Clark (2007) and Dawson et al. (2007) applied
viewshed analysis within architectural structures, religious and residential, respec-
tively, and concluded that buildings were intentionally constructed to restrict and
channel vision and experience.
Wheatley and Gillings (2000, p. 1) have warned us there is a ‘‘very real risk of
[viewshed] becoming an unquestioned, and deeply uncritical, orthodoxy within
archaeological practice.’’ Today, we do see a healthy amount of skepticism
regarding the application of the viewshed function (e.g., Fitzjohn 2007; Frieman and
Gillings 2007; Wheatley 2004). Nonetheless, we should see more specific
discussion of what level of field checking is necessary to validate a digital model,
a better grounding in topics where sight is a central variable (such as archaeoas-
tronomy, warfare, hunting, or ritual performance), and explicit linkages between
digital models and larger social structures and individual agency based on the
simple notion that we must account for the eye of the beholder.
Although it has been less common, there have been recent developments in
behavioral modeling centered on the movement of people through a landscape
(Hernandez 2006; Howey 2007; Whitley 2004). We have begun to see spatial
technology used to study global-scale migrations (Anderson and Gillam 2000; Field
and Lahr 2006) as well as regional-scale interaction (Hare 2004; Kantner 2004).
Still others have centered on achieving a phenomenological understanding of ritual
spaces associated with the anthropology of bodily movement (Llobera 2000).
However, in terms of tracking trade and migration, what is remarkable to us is the
near absence of spatial technology in applications of artifact sourcing through
chemical characterization studies (but see Scott [2007]; Kantner [2008] for a recent
review of sourcing and regional archaeology). Equally puzzling is the relative rarity
with which GIS is used in building better culture histories through syntheses of large
data sets with chronological and locational data (see Colledge et al. 2004; Hill et al.
2004; McCoy 2007; Russell 2004). One would think the utility of GIS for qualifying
and quantifying patterns and storing locational data sets of chronometric and
chemical characterizations of artifacts and natural sources would make it a key
technology for both applications, but that simply has not been the case.
While other social sciences regularly employ simulation—for examples see the
Journal of Artificial Societies and Social Simulation—archaeologists remain
conservative and rarely use agent-based modeling. This has begun to change in
recent years, and we do find combinations of simulation and spatial technology in
visualizing changes in demography, migration, and settlement patterns (Anderson
and Gillam 2000; Bandy 2004; Jones 2006; Ladefoged et al. 2008; Peterson and
Drennan 2005). Kohler and van der Leeuw’s (2007) recent volume represents the
cutting edge of the combination of spatial technology and agent-based modeling. In
particular, articles by Kohler et al. (2007), Kirch et al. (2007), and Wilkinson et al.
(2007) summarize the results of long-term projects that integrate large amounts of
spatial data to investigate the ecodynamics of the US Southwest, Hawai‘i, and
Mesopotamia, respectively. The chapter by Kohler et al. (2007) is exceptional in the
quantity of data and the sophistication with which it is incorporated into agent-based
models.
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Spatial technology: Hardware, software, and mobile GIS
Many of the spatial technology tools employed in the applications described above
are as ubiquitous in archaeology as the Marshalltown trowel. Nonetheless, we feel a
brief technical overview is necessary to lay out the range of tools currently used for
the benefit of those unfamiliar with them. To do this, we use minimum technical
jargon and point readers toward resources that give advice on best practices. We
begin by outlining advances in hardware, including GPS and geophysical survey
equipment as well as satellite imagery and 3D laser mapping. With the exception of
laser scanning, which has been used to create digital models of artifacts, this
hardware is most often used in archaeological survey. Next, we describe current
trends in GIS, computer-aided drawing, and other software. Finally, we describe
‘‘Mobile GIS,’’ a combination of hardware, software, and spatial data that is
becoming commonplace in archaeological fieldwork.
Hardware
Global positioning
Today global positioning systems are installed in virtually every type of vehicle and
mobile electronic device on the planet. The application of GPS in archaeological
survey, previously considered high-tech (Ladefoged et al. 1998), is now the norm
(Breman 2003; Craig and Aldenderfer 2003; Gravili and Ialuna 2006; Johnson and
Wilson 2003; Ladefoged et al. 2003). With this technological proliferation, the
general public has become familiar with the basics of GPS, and the market for GPS
products has promoted a shift in the available functionality. For example, modern
basic hiking or driving GPS has the interactivity and technical abilities that not long
ago would have been available only in more expensive mapping or survey-grade
GPS. A major breakthrough in GPS technology occurred on May 1, 2000, with the
end of ‘‘selective availability’’ (SA), an intentional error introduced to GPS signals
by the US federal government. Prior to this, to achieve reasonable degrees of
accuracy and precision one needed to use base-station readings taken over the same
time period and location as rover GPS readings to differentially correct data in
postprocessing. With the removal of selective availability and without differential
correction, current GPS units with multiple receiver channels average point readings
to obtain unprocessed accuracies in the 2–10 m range.
It is still necessary to correct raw GPS signals to maximize the accuracy of
readings. More often this is done in real-time as opposed to in postprocessing
computing. In 2003, the first global satellite augmentation system (GSAS), also
known as a satellite-based augmentation system (SBAS), provided real-time
differential correction to North American GPS users through the wide area
augmentation system (WAAS). Developed by the US Federal Aviation Adminis-
tration and Department of Transportation to promote aviation safety, WAAS is a
system of about 25 ground reference stations coordinated through master stations on
the Atlantic and Pacific coasts that beam data to GPS units that have built-in WAAS
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capability. The result is more accurate and precise readings with no data
postprocessing or added cost beyond the purchase of the WAAS-enabled unit.
GPS can receive the wide-reaching WAAS signals worldwide. However, they
will not increase the precision of readings outside North America. To address this,
there are currently two other global satellite augmentation systems under
construction. In Europe, the European geostationary navigation overlay service
(EGNOS) came online in 2008. In Asia, the Japanese began launching satellites for
the multifunctional satellite augmentation system (MSAS) in 2005. Therefore,
outside North America and in cases when high degrees of accuracy and precision
are critical, postprocessing to differentially correct GPS rover files—either using
online base station data or a dedicated stationary mapping or survey-grade GPS
unit—is still recommended to achieve the greatest degree of precision possible
within the specifications of a given GPS receiver.
Geophysical survey
Over the past decade, terrestrial and marine geophysical survey has been increasingly
used in archaeology, especially so in Europe but also in North America and around
the world. There are several good texts on the topic (Conyers 2004; Gaffney and
Gater 2003; Johnson 2006; Kvamme 2003; Wiseman and El-Baz 2007), review
articles (Neubauer 2001), and best-practice articles, especially in the journal
Archaeological Prospecting (see ‘‘Recent literature’’ section below). Although there
are a range of methods, archaeologists have tended to focus on ground-penetrating
radar, proton magnetometry, and electronic resistivity/electromagnetic conductivity.
Each technique produces a continuous surface or cross-section interpolation image
based on an active source—radar waves and electricity in terrestrial environments,
sonar waves in marine environments—or a passive source (i.e., magnetism). The
greater the contrast between cultural and natural deposits in terms of density and
composition, the better the chance of detection (e.g., Dalan 2006; Dalan and Banerjee
1996; Dalan and Bevan 2002). Other than metal artifacts, no geophysical survey
technique is likely to detect artifacts in the absence of architectural or thermal
features. Ground truthing through excavation is necessary to definitively match
patterns detected to archaeological phenomenon. Training in use of field equipment
and image interpretation is beginning to be explicitly taught in archaeological field
schools, and professional archaeologists today are savvier than ever as to how to use
the physical properties of natural and cultural deposits to detect buried features.
Nonetheless, the results of geophysical surveys still tend to be somewhat of a ‘‘black
box’’ outside the community of practitioners. However, this is changing as training in
geophysical methods is integrated into undergraduate and graduate curricula.
Remote sensing
Remotely sensed images of the earth’s surface have increased in quality,
availability, and utility within a GIS environment (Bolten et al. 2006; De Laet
et al. 2007; Montufo 1997). While we have seen improvements in newer generations
of Landsat and SPOT, commercial satellite imagery, including IKONOS and
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Quickbird (DigitalGlobe), has begun to push the coverage and quality of images to
the point where they overlap with mid- to high-altitude air photograph resolution.
Naturally, as in any remotely sensed imagery, resolution is only one of many
variables on which the quality and utility of an image can be judged. For example,
availability has been a major force driving the use of remotely sensed data. The
declassification of CORONA imagery taken in the 1960s and 1970s also has proven
useful to archaeology (Challis 2007). Would-be users can access high-quality
images instantly through virtual globe software such as Google Earth or NASA’s
World Wind. However, outside urban areas in the developed world, the quality and
coverage is not consistently high. More importantly, archaeological applications
using satellite or air photograph imagery often require individual image files that
can be imported into a GIS environment and overlain with other data. These images,
while less accessible, are oftentimes pre-georeferenced, saving users a great deal of
data processing. Nonetheless, increased processing power of personal computers
makes self-georeferencing feasible, given the necessary control points. The
availability of large-format map scanners as well as readily available georeferenced,
remotely sensed data has led to the retirement of most large digitizer tablets for
vector digitizing (see Wheatley and Gillings 2002, Fig. 1.3), as this can be done
more easily via on-screen digitization. We also should note that like air photos
(Richards 1980), satellite imagery continues to be of limited use in a marine context
when compared with techniques uniquely devised for marine environments such as
remote submersibles (e.g., Ballard et al. 2002; see articles in International Journalof Nautical Archaeology).
Laser mapping: range finders, 3D scanners, and lidar
The laser total station, introduced to archaeology in the late 1980s (Kvamme et al.
2006a; McPherron 2005; Rick 1996), has been joined by a family of tools that use
lasers to collect spatial data. These include range finders (Hayakawa et al. 2007),
terrestrial 3D scanners (Karaim 2002), and lidar (Carey et al. 2006; Challis 2006;
Crutchley 2006).
Laser range finders calculate distance instantaneously by measuring the time it
takes light reflected from a targeted surface to return to the machine. However, that
measurement can be linked with other survey equipment such as a digital compass,
clinometer, and/or GPS to collect coordinate and elevation data in landscapes where
setting up a total station is difficult. Hayakawa et al. (2007) have used just such a
hybrid device to map and create a digital elevation model of a Syrian Neolithic tell
site with complex topography and difficult terrain.
Total stations and laser ranger finders are precise tools that need to be manually
aimed at each data point location, although the newer models automatically track
target prisms. In contrast, 3D laser scanners are indiscriminate and systematically
sweep their field of view, sending out lasers in rapid succession and collecting
millions of data points. These millions of individual X, Y, and Z coordinate
measurements are referenced to the machine’s location and together are referred to
as ‘‘point clouds.’’ Each individual point is anonymous, simply representing the
location of the first solid surface that a particular laser hit. The only other
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information attached to these points is a reflectivity value of the surface scanned
(i.e., surfaces with colors or materials with greater or lower natural reflectivity have
different relative reflectivity values). However, because scans from one direction
represent only the surfaces visible from that viewpoint, it takes multiple scans from
different locations to create full digital models. These models in turn can be used to
make distance or volume measurements, or to render digital images of architecture
and artifacts, or to create castlike reproductions for teaching or display (Brizzi et al.
2006; Doneus and Neubauer 2005; Ioannides and Wehr 2002; Mulrooney et al.
2005; Zollikofer and Ponce de Leon 2005). Others have used scanners to do away
with traditional site excavation grids, recording the precise 3D provenience of
thousands of artifacts, features, and surfaces (Holdaway et al. 2005). However, the
high cost of proprietary software necessary to process 3D laser data has proven to be
a bottleneck to its adoption on a broad basis (Boehler et al. 2002).
Light detecting and ranging, or lidar, uses aircraft laser sweeps to create
remarkably detailed models of ground topography that can in turn be used for a
range of purposes such as survey and archaeological prospection (Bewley et al.
2005; Carey et al. 2006; Challis 2006; Challis and Howard 2006; Crow et al. 2007;
Crutchley 2006; Devereux et al. 2005; Harmon et al. 2006; Kvamme et al. 2006b;
Powlesland et al. 2006; Rowlands and Sarris 2007). Of the myriad of new laser
mapping available, lidar is perhaps one of the most useful for archaeology since
recording landscape topography is a regular part of surveying. However, the spatial
resolution of data is a limiting factor. If we return to Crutchley’s (2006) study
outlined above, the author notes that in one case a previously documented set of
Bronze Age barrows were not detectable even when researchers exaggerated the
elevation scale of their digital model to 20 times normal in a futile effort to portray
and pick these subtle features out from the surrounding landscape.
Software
Software to manage spatial data includes fully functional geographic information
systems (e.g., ESRI’s ArcGIS, MapInfo’s GIS products, open source GIS software
GRASS or Quantum GIS), image analysis programs (e.g., ERDAS IMAGINE),
spatial technology-specific programs (e.g.,Trimble’s Pathfinder GPS software),
computer-aided drawing suites (e.g., AutoCAD; Microstation; Adobe Illustrator), a
wide range of programs that can handle data point coordinates, vector files, and
raster images (e.g., Microsoft’s Access and Excel, Adobe Photoshop), and
sometimes special statistical software (e.g., the open-source program ‘‘R’’).
GIS programs have developed markedly over the past decade. Significant
advances in ESRI’s ArcGIS software 9.X versions include better raster file
georeferencing capabilities, more comprehensive extensions for statistical, surface,
and spatial analysis, and stand-alone parallel software for specialized analysis.
Following the lead of open-source developments, ArcPad represents the first
generation of commercial GIS software made for the Windows Mobile operating
system, giving users the freedom to bridge the gap between desktop and field
capability. There also has been a major change in the commercial software market
with the demand for ‘‘GIS-lite’’ programs for car navigation systems such as
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TomTom or for hiking GPS. Other companies, such as MemoryMap, offer stand-
alone programs and maps and may allow for recording way points and routes. These,
however, lack the relational database and analytical capabilities of a full-scale GIS.
A major advance in the use of GIS software is the capability to present interactive
maps online through platforms like ArcIMS (ESRI). For example, the Royal
Commission on the Ancient and Historical Monuments of Scotland (RCAHMS)
gives registered users online access to PASTMAP, an interactive map with
background layers (i.e., political boundaries, streets) to guide users; the option to
make text-based geographic queries to narrow searches; and different data layers
representing scheduled ancient monuments, listed buildings, the record of national
monuments in Scotland (with symbols linked to precision at 10 km, 1 km, 100 m,
10 m, and 1 m scales), Scottish sites and monuments records, and gardens and
designed landscapes. Zooming in and clicking a point generates a report with tabs
representing results for each of the different data sets. Important metadata also are
returned, including the date the layer was last updated, contact information, brief
descriptions of how the data set was created, and disclaimers for using the data.
Open-source alternatives to commercial software are more viable than ever and
gaining in popularity. GRASS GIS, which has enjoyed an archaeological following
for many years, and Quantum GIS are attracting followers as they become usable on
Mac and Windows operating systems. We also are seeing the continued evolution of
spatial technology software in archaeology such as the TimeMap project, which now
offers software developed by Johnson (2002; Johnson and Wilson 2003) and his
colleagues. Others have shown the potential to incorporate software written for
handling spatial data in other fields to answer archaeological questions and for using
the 3D functionality of GIS to manage archaeological data (Fyfe 2006; Katsianis
et al. 2008). Overall, while we should not underestimate the importance of these
alternatives becoming viable beyond the early adopter community, we should note
that the vast majority of GIS users in archaeology today rely on commercial software.
Mobile GIS
As a by-product of better computer processors, smaller data storage units, new
software, and increased availability of seamless base map data, we have seen the
evolution of what is called Mobile GIS. Mobile GIS includes a wide variety of
hardware-software combinations from driving navigations systems to handheld GPS
with PDA capability on par with desktops to photomapping artifact scatters with
digital cameras and tablet PCs (Craig et al. 2006; Wagtendonk and De Jeu 2007).
One of the main advantages of these systems is that they provide real-time spatial
information processing and are often relatively easily customizable to the task at
hand. Taken even further, greater digital connectivity gives field projects the chance
to move to near paperless or complete paperless data recording (Schneiderman-Fox
and Pappalardo 1996; Zeidler 1997).
The increasing use of Mobile GIS brings to the fore the ongoing migration to an
increasing reliance on digital technology. There are three basic ways spatial data are
stored in archaeology: real static, white paper maps; digital data; and data sets that
exist in both formats either by going from real-to-digital or digital-to-real. Field
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mapping for survey and excavations at least begins as white paper maps—although
the near-paper capability of tablet PCs do have potential to replace even those
maps—and a subset of these are published in reports and articles while digital data
often remain unpublished. These different pathways of creating, storing, and
publishing spatial data create a host of issues for long-term archiving.
Overall, Mobile GIS, like all the spatial technologies described here, must be
weighed against traditional methods in terms of its costs and benefits. One particular
cost is the learning curve necessary to become proficient in these technologies.
Giving volunteers, students, or other laborers training in the use of spatial
technologies can be rewarding and contributes to making our discipline more
digitally integrated. However, this training takes away from time spent on other
training and may unintentionally promote the loss of basic skills, like the correct use
of a compass.
Conclusions: Impacts of spatial technology on archaeology
Developments in spatial technology have impacted core archaeological methods and
theory, including how we find and record archaeological remains, manage data, and
investigate the historical relationship between our species and the world around us.
Below we review the progress and continued challenges archaeology faces as we
integrate, and become reliant on, spatial technology.
The art and science of archaeological prospecting
Advances in spatial technology and the application of pre-existing technology to
archaeological problems have helped us become better at locating archaeological
remains via predictive modeling and remote sensing using data from satellites,
airborne instrumentation, and a variety of ground-based geophysical equipment.
The methodological impacts of these technologies can be seen in academic research
and cultural resource management. Results include not only the discovery and
protection of new sites and the more complete survey of known sites, but also the
promotion of an ethic on nondestructive, or minimally destructive, field
archaeology.
These spatial technologies have limitations when it comes to discovering
different types of remains and deposits. Some limitations are inherent in the
technique, such as the tendency for geophysical surveys to give clearer signal-to-
noise readings for shallow deposits that have high contrast with the background
natural sediment or size limits on what can be identified using data from airborne
sources. Others vary by region, as in how ground vegetation cover affects the utility
of satellite and air photo imagery. Fortunately, we are well prepared for qualifying
and quantifying these types of limitations in that we regularly ‘‘work from the
known to reveal the unknown’’ through ground truthing our evaluations of the
archaeological record. Thus while spatial technology may provide the tools for
archaeological prospecting, our past and future successes in using these tools are
tied directly to existing standards of evidence and methodological rigor.
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Recording and analyzing archaeological data
The fuller integration of spatial technology within archaeology has brought to the
fore new conceptions of archaeological data. This research often deals explicitly
with scale and complexity in the archaeological record (Bailey 2007; Lock and
Molyneaux 2006). In this vein, Holdaway and Wandsnider (2006) and Wandsnider
(2004) point to the palimpsest nature of archaeological deposits as complex,
forming ‘‘through multiple, temporally disparate additive and subtractive events,’’
and they stress the need to account for cultural and natural transformational
processes in deciphering remains. It is only with the ability to collect and analyze
large quantities of data that we are able to shift from a more traditional site-centric
settlement pattern approach to a fuller understanding of dynamic landscapes and
their archaeological interpretation (Wandsnider and Dooley 2004).
Spatial technology has enabled this shift by increasing the relative extent of spatial
data in terms of their geographic size, coverage, and number of records. This has in
part been fueled by a growing use of the siteless-survey approach to record features
and deposits as phenomena that reflect continuous use of an area rather than as
markers of discrete, bounded sites. This shift has been especially important in
protecting and understanding archaeological evidence that would otherwise be
considered too small, insignificant, and not worthy of being recorded as a site.
Nonetheless, the mere existence of spatial technology like GPS and GIS has not
extinguished the use of the site concept. Indeed, the site concept is often the
cornerstone for databases since, for all its shortcomings in terms of precision, there is
the need for continuity and ease of record keeping. Naturally, the resulting databases
that organize archaeological remains according to site-centric terms represent a
compromise between ease of record management and analytical capability.
Thanks to the fruits of the information age, we can now easily create complex
spatial databases based on different data models. For example, representing sites,
features, artifacts, or some combination of these as discrete vector shapes (i.e.,
points, lines, and polygons) or continuous raster surfaces is now a regular part of
archaeological practice. The latter requires one to interpolate between known data
points to estimate the values of locations with no data. There are of course different
methods to accomplish this, and we are seeing a greater explicit concern among
archaeologists as to how we interpolate our spatial data and more explicit awareness
of the importance of choosing data models appropriate for the archaeological
evidence or analysis at hand (Hageman and Bennett 2000).
While the internet can and will drive the cumulative size and complexity of data sets
upward by providing a platform for collaboration and archiving, presently we do not
have the standards and quality controls necessary to move forward. There is, however,
growing concern for data quality and standardization in the larger discipline-wide call
for cyberinfrastructure (Kintigh 2006). In addition, as we have noted, it is especially
important to provide metadata describing the methods used to create spatial data sets
for organizing, archiving, and sharing information via the internet. We are already
beginning to see organizations rise to the challenge, such as the Wyoming State
Historic Preservation Office which now provides registered users with online access to
two spatial databases—one an archive of completed and quality-checked projects
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(polygons of survey areas, site locations represented by points), the other a record of
recent or ongoing projects that have not yet moved to the permanent archive. Given
that archaeological data can be unwieldy and largely unstandardized, this is a sensible,
pragmatic solution to the backlog of information in the gray literature.
Interpreting the archaeological record
Over the years we have seen two main concerns regarding how new spatial
technology may be biasing or influencing interpretations of the archaeological
record. The first concern centers on environmental determinism and has been
addressed in several edited volumes (Aldenderfer and Maschner 1996; Allen et al.
1990; Lock 2000, 2003; Lock and Stancic 1995). While a myriad of views are
represented in these works, the essential issue is whether GIS inherently biases our
interpretations toward the environment as a causal factor in shaping human history at
the expense of historically contingent social structures and human agency. This is a
core question in understanding how our species uses, creates, and inhabits the world,
and one that divided the burgeoning field of landscape archaeology into two regional
theoretical camps with North Americans and Europeans focused on space and place,
respectively (Bender 1999a). Conceptually, the term landscape has come to be used
interchangeably to refer to the study of space—the central problem of modernity
involving rationality, objects, and practice—and the study of place, or the nature of
local experience, grounded in humanist, phenomenological, or existentialist theory.
However, rather than limit how we study the past, we have seen developments in
spatial technology run the gamut between theoretical extremes. Archaeologists have
found pragmatic uses of spatial technology for research that highlights the importance
of documenting the dynamic relationship between the environment and human history
(Kirch 2007; Kirch et al. 2007; Kohler et al. 2007), as well as research that points to the
significance of agency and historical contingency (see chapters in Meskell and Preucel
2004). Of course, not all spatial technologies have been equally attractive to all
landscape archaeologists. Those interested in meaning, place, and experience, have
inherently found new developments in representative visualization useful since these
tools are predisposed to handling contextual and qualitative data. In some ways the
applications we have found for spatial technology have mirrored larger discipline-
wide theoretical trends rather than the other way around (Conolly and Lake 2006, pp.
6–7). Putting aside for a moment specific internal and external critiques of predictive
modeling, Kvamme (2006c) is right to place these practices squarely within a
processual view rather than one completely disconnected from theory.
The further development of landscape archaeology is crucial if we are to continue
applying spatial technological tools and data sets with theoretical sophistication.
Specifically, it will take greater attention to both space and place to make necessary
methodological links between social theory and material remains and promote a view
of space informed by anthropological knowledge. We believe Wheatley (2004) also
is right to point out the dehumanized view predictive modelers have traditionally
taken as a fundamental weakness that must be addressed. Overall, the dreary
outcome of theoretical complacency is in the potential to have our greater capacity
for collecting and processing data outpace our ability to interpret and learn from it.
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This brings us to the second main concern: the unsophisticated application of the
spatial technology without regard to theory. Indeed, for many years the relationship
between spatial technology and archaeology has been likened to the ‘‘law of the
hammer’’ (Moore and Keene 1983) in that the appeal of the technology has caused
excessive, gratuitous application, or pounding, without regard to purpose, appro-
priateness, or theory (Drennan 2001, p. 668). However, we believe that
archaeologists are now well on their way to learning how to judiciously use GIS
and other spatial technology, thanks to several trends.
First, the availability of excellent textbooks aimed specifically at learning the
basic principles of applying GIS and spatial technology to archaeology has been a
key factor in promoting good practices (Conolly and Lake 2006; Wheatley and
Gillings 2002). While many students still get primary, or additional, training in
geography departments, our capability to train undergraduate and graduate students
in archaeological applications represents a significant step forward. We believe this
is especially important for coming generations of archaeologists who must be more
than uncritical consumers of spatial technology. We also are encouraged by the
incorporation of a fuller suite of spatial technology in archaeological curricula,
including remote sensing and geophysical survey.
Second, as archaeologists have become savvier when it comes to information
technology, we have seen a greater willingness to adopt spatial technology to solve
problems in research and management. One good example of this greater technical
awareness is in the rise of digital archaeology centered on ‘‘utilizing computers
based on an understanding of the strengths and limits of computers and information
technology as a whole’’ (Daly and Evans 2006, p. 3). In this vein, GIS really does
have the potential to change our field on the same scale as the invention of
radiocarbon dating, but in this case linking data collected across the globe in space
rather than time. Looking on the horizon, we see one trend that in particular may
help achieve this type of data integration, i.e., the evolution of the internet with
applications, resources, and services, referred to as Web 2.0, that promote increasing
functionality of websites and connectivity of people and data.
Third, we are seeing signs that archaeologists have begun to focus on meaningful
results of GIS analysis thanks to the growth of landscape archaeology. One clear
example of this is the improvement we have seen in the use of viewshed and cost-
surface functions. These have not been methodological advances per se but rather
reflect a scaling to human perception and movement in time and space. This is, put
simply, a clear victory for using a tool to advance anthropological knowledge rather
than the blind use of technology for technology’s sake. In the future, we believe it is
spatial technology’s capability to record contextual relationships between objects,
features, and other archaeological phenomena that holds the potential to further
reconstruct the social structures through which places gained value or were
socialized (e.g., Bradley 2000).
In sum, we have every reason to believe that spatial technology will continue to
change archaeology in ways that will foster our growth and maturity as a discipline
and help make us better practitioners, scholars, and stewards. Developments in past
decades have already radically altered the way archaeologists approach their data,
and it is now commonplace for office and field computers to interface with mass
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quantities of GPS, 3D scanner, theodolite, remotely sensed, and other sources of
spatial data. The evolution of Web 2.0 sites, such as Google Earth, have surpassed
many of the capabilities of commercial GIS programs implemented just a few years
ago. In another decade or two, however, the wonders of these new technologies will
pale in relation to what will be available. It is thus all the more important that we
continue to explore, debate, and discuss how these tools can help achieve our larger
goals.
Acknowledgments We thank our students and colleagues who have discussed the joys and pains of
spatial technology with us over the years. In particular, Michael Graves, Simon Holdaway, Lisa Holm,
Stephanie Jolivette, Patrick Kirch, Mara Mulrooney, Chris Stevenson, and Steve Shackley have
contributed to our use and understanding of spatial technology. The journal’s editors and anonymous
reviewers provided many useful comments and suggestions. Special thanks to K. Ann Horsburgh for
suggesting we produce this review.
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