AcritiqueofGISinArchaeology

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A critique of G.I.S. in archaeology. From visual seduction to spatial analysis
Article · January 1996
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Originally published as: J.A. BARCELO, M. PALLARES, 1996 A critique of GIS in
Archaeology. From Visual seduction to Spatial Analysis Archeologia e Calcolatori No. 6,
1996
A CRITIQUE OF G.I.S. IN ARCHAEOLOGY.
From visual seduction to spatial analysis.
Juan A. Barceló and Maria Pallarés
Universitat autonoma de barcelona
Area de Prehistoria. Facultat de Lletres
08193 Bellaterra (Spain)
Abstract
The purpose of this paper is to critically explore the role of
Geographical Information Systems in the archaeological research.
Currently some archaeologists seem largely captivated by new
computing technologies believing that the sofisticacion of
powerful software outputs will lend respectability by itself. In
our opinion GIS is merely a set of techniques to visualize and
manage large amounts of georeferenced data. There thus must be
other tools to move from visualization to explanation, which
fall within the domain of Spatial Analysis
The ultimate aim of this paper is to show how we can integrate
these
already
existing
tools
(geostatistics,
intra-site
statistical
tests,
digital
image
processing,
artificial
intelligence, etc.) in a GIS framework, in order to move from
beautiful images to hard analysis. We finally criticise the lack
of theoretical background in archaeological uses of G.I.S.
technology arguing that G.I.S is only software and may benefit
our research only in the frame of a sound theory and well
defined archaeological problems.
1.- SPATIAL ANALYSIS IN ARCHAEOLOGY AND GIS SOFTWARE
Most archaeological information is spatial in nature, because it
deals with the placement of archaeological finds, contiguity and
neighbourhood relationships between archaeological entities.
Since archaeologists realized the potencial benefits of studying
relationships between behaviour and the spatial distribution of
material culture, new ways of representing, visualize and
analyze archaeological findings have been proposed. Mapping, or
the map-based approach has represented the earliest phase of
spatial analysis in archaeology insofar as it has been for
centuries an extremely efficient storage medium for condensing a
large amount of spatial data and associated attribute variables
into a single sheet of information. In this respect, until
earlier 70's archaeological spatial analysis had relied on
descriptive informal methods based upon map inspection and
almost intuitive impressionistic interpretation. This visual
analysis benefited from the dissolution of composite maps into
overlay plans showing selected features and categories, to
examine their degree of correspondence and to make some
subjective judgments about the strengths of the relationships
between them. Although in the initial and exploratory stages of
many types of archaeological spatial analysis such methods are
still valuable, the human eye-brain system is not always a very
precise
instrument
to
assess
the
strengths
of
spatial
relationships.
This fact lead to the adoption of formal quantitative methods
borrowed from other disciplines such as plant ecology and
geography in order to get more objectivity in data recording and
analysis. Nonetheless, it is beyond of the scope of this paper
to detail the development of quantitative spatial analysis in
archaeology since much research time and effort has been spent
on this subject (see among others KINTIGH & AMMERMAN, 1982,
COGWILL, KINTIGH 1991; BLANKHOLM, 1991, READ 1989). This
quantitative movement got off on the wrong foot because it made
too often the straightforward assumption that archaeological
problems were easy to solve and that statistics and archaeology
shared the same
problem solving logic (AMMERMAN, 1992). This
thoughts yielded a mechanical non-reflective aplication of
mathematical techniques to solve bad defined archaeological
problems, without considering the analogy between mathematical
models and real problems. For more criticisms of quantitative
spatial analysis approach see, among others (ALDENDERFER 1987;
G.LOCK 1992; AMMERMAN 1992; BARCELO ET ALII 1994).
In such an atmosphere of disappointment about the performance of
formal techniques as they have been applied over the last two
decades one may understand the rapid growth and expansion of
Geographical Information Systems during the 80s. Currently, GIS
software has been proposed as the best solution for nearly all
archaeological problems, because it has the ability to store not
only the locational and attribute data for each archaeological
entity but also the spatial relationships between them. These
relationships can be very complex and diverse, including
topological, metrical and social attributes (HERNANDEZ 1995).
Therefore, GIS techniques are being used to organize different
spatial features in different map layers, producing new layers
to
represent
the
results
of
specific
combinations
of
geographical information.
Nevertheless,
despite
widespread
recognition
that
spatial
analysis is central to the purpose of GIS, most applications to
date have only shown their power to input, storage and
manipulate spatial data, in order to elaborate computer mapping.
Their lack of analytic capacities and modelling has been largely
considered as a major shortcoming in the research literature
(BIRKIN ET AL. 1987; GOODCHILD 1987; BATTY, 1988; FRENCH AND
WIGGINS, 1990; CLARKE 1990; OPENSHAW, 1990, FISHER & NIJKAMP,
1992; FOTHERINGHAM & ROGERSON, 1993; ANSELIN & GETIS 1992). In
fact many of the current archaeological GIS projects are only a
database containing a discrete representation of archaeological
data in a static two-dimensional space, with a functionality
largely limited to primitive geometrical operations to compute
simple relationships between points in the space, and to simple
query and summary descriptions (GOODCHILD et al. 1992). Such
systems are basically concerned with describing the Earth
surface rather than analyzing it. Or if you prefer, traditional
19th. century geography reinvented and clothed in 20th century
digital technology (OPENSHAW 1987).
In our view, the main problem is that archaeologists explore the
relationships among large spatial data sets following some
inductive approaches which consist in simple spatial operations
such
as
multilayer
spatial
overlay,
geometric
distance,
morphological calculations, topological operators or spatial
comparision, to produce a large volume of maps which condense
the information considered necessary to describe spatial
patterns. It seems as if the last objective was to insert the
maximum quantity of information into a map. This indiscriminate
map production is related with a lack of prior theory or
hypotheses about the kind of problems archaeologists want to
solve or about the expected relationships between spatial data,
believing that mapping equals spatial analysis. Certainly, GIS
has reduced archaeological research and problem solving to the
making of pretty but meaningless pictures applying costly
resources to answering irrelevant or poorly posed questions.
In the same way most archaeological GIS systems lack a coherent
body of theory and organizing principles by which real-world
spatial entities can be represented. Data structure for
archaeological entities is often defined to optimize computer
storage and retrieval operations to fit the prevalent GIS data
model, without consideration for accurate representation of
socio-historical reality (GAFFNEY, 1995).
This tendence can explain why many recent applications of GIS,
particularly in regional settlement location studies, reflect an
envionmentally
deterministic
approach
to
archaeological
explanation (as have been pointed out by GAFFNEY & VAN LEUSEN
1995, VERHAGEN & MCGLADE 1995, LOCK, 1995). Archaeologists are
mainly working with environmental variables that are amenable to
cartography describing the topography, lithology and hidrology
of an area and that are relatively simple to map, forgetting the
importance of social distances in the analysis. Indeed,
archaeologists seem largely concerned with locational patterning
of regions and they use GIS to produce models that focus on
relationships between regional site distribution and mappable
attributes of the environment, considering social action as
dependent upon the environment and economy.
Archaeological data are inherently difficult to handle and
current
archaeological
spatial
descriptions
suffer
from
different kinds of endemic errors that prevent the use of
analytical tools (spatial statistics) to understand dependence
relationships in the archaeological record. Despite these
problems, most of archaeologists believe that GIS software may
provide an easy solution to their problems, without considering
that GIS is not even an archaeological method, but a purely
technique. It can be useful if we used it in a specific way and
in the frame of a comprehensive reasoned theoretical base. On
the contrary, its results will be misleading if we use it
blindly, thinking that techniques are enough to explain
historical phenomena.
The reseach challenge lies in building on these principles a new
theory of spatial relationships and using a new set of spatial
analytical tools.
2.- THE NEED OF A SOCIO-SPATIAL THEORY
2.1. THE CONCEPT OF SOCIAL SPACE
Even nowadays most archaeologist still do not recognize that
space is a very general concept used in many different contexts
to denote different things. There are abstract mathematical
spaces (structures made up of arbitrary elements according to a
set of axioms), psychological spaces, economical spaces,
physical spaces or the "real" space in which human activity
evolves, etc. (HERNANDEZ, 1994).
The word “Space” seems to denote a "set" of entities to which
may be attached associated attributes or properties, togeher
with a relation or relationships, defined on that set (GATRELL
1983, 1991, SMITH ET AL. 1987, PEUQUET 1988). Space is then the
network
of
all
underlying
spatial
relationships
between
different entities. From this point of view a straight line
between two entities is one of these possible relations, but
this is only one way to conceptualize distance. There are other
ways to look at spatial relationships, for instance through
topological relationships: two entities or objects are far apart
from each other; they are close together, but do not touch; they
touch, or they overlap. A spatial variable is, consequently, any
quantitative or qualitative property of data varying spatially
(topologically or according a specific distance measure), and
which contribute to explain the dependency relationships between
the locations of those entities (CRESSIE 1991). Given thise
features, we can define social space as any network of spatial
relationships linking any set of social units.
2.2. SOCIAL ACTIVITY AREAS
These social units are not always sets of people (families,
local groups, villages, tribes, political territories, etc.),
but
any set of social actions (productive, reproductive) that
have been performed in a single location. We may call these
units Social Activity Areas. Given the fact that there are many
different possible associations between social actions performed
at a single location, there are many different ways to describe
those social units, and thus many levels of representations. In
this respect some isolated finds may define a low-level social
activity area, whereas the spatial relationships among those
low-level units may contribute to the definition of higher-level
units. Here, low
or high level refers not to any importance
hierarchical importance order, but to the degree of complexity
and representation language needed.
Some examples of social activity areas may be:
• an empty area, for instance, a ceremonial area after ritual
cleaning.
• an isolated grindstone
• a hearth
• a garbage pit or dumping area
• a storage pit
• any activity area wihin the settlement (butchering area, tool
manufacturing area, food preparing area, metallurgical or
artesanal activity area, etc.)
• a house
• a village (a set of houses, storage and garbage pits, activity
areas, etc.)
• a grave (the set of rituals and ceremonies performed in that
location, not the people buried there)
• a burial area (a set of graves and ritual structures)
• a territory (for instance all its village components and the
set of social interaction networks among them)
Social activity areas are not geometrical figures but spheres of
social interaction without any fixed boundaries; they are
characteristically ‘fuzzy’ (GOODCHILD 1987, BURROUGH 1990,
CASTLEFORD 1992, USERY 1993). Moreover, their features and
topological characteristics change/vary according to time (KAHN
& GORRY, 1977, ALLEN, 1984; ZHU ET AL. 1987, LANGRAN, 1989),
because the units of social space are not only multidimensional,
but dynamic. Consequently, not any single partition method of
geographical space results in a model of the social space in
use.
Archaeologists used to mechanically define social activity areas
from de discovered artifact concentrations. However, there is not
a direct correspondence between the observed properties of
archaeological contexts and social activities because of the
enormous variety of transformational processes with different
dimensions and temporal rythms that can have acted upon the
archaeological record. Consequently, in order to divide physical
space in spatial areas where some social actions were performed,
we have to discover temporally dynamic, multidimensional and
fuzzy sets of spatial associations among archaeological finds.
It had also been wrongly assumed that spatial association implied
"tool-kits" and specific activity areas in space. However we are
not looking only for tools but for any association of artifacts,
elements or features useful to diagnose a social activity. In the
same way, as it was shown by early etnoarchaeological studies
(YELLEN, 1977) activity areas needed not be spatially dispersed
but there are different alternative models which embraces:
• Dispersed or segregated activity areas that assumes that
different objects and features are partitioned into spatially
distinct units or loci, each corresponding to a single
activity or group or related activities. (SPETH,1976,)
• Agglomerated or multifuncional activity areas, characterized
by the overlapping of different social activities.
Given the effects due to time, we have to distinguish between
more or less stable (fixed) areas and dynamic (temporally
modified) ones. This temporal distance may be present in a single
continuos episodic occupation but it is stronger as a product of
different processes of reoccupation and reuse. Insofar as the
archaeological record is the product of repeated depositional
events over different periods of time, we must also take into
account this change through time.
2.3. RELATIONSHIPS BETWEEN SOCIAL ACTIVITY AREAS
Social activity areas are not spatially undifferentiated
or
isolated. They are in a intrinsically better or worse location
for some purpose because of their position relative to some other
meaningful unit: Social groups build their own space because they
appropriate some biophysical spatial areas and are able to defend
them against the members of their own species (TRICOT &
RAFFESTEIN 1979). Consequently, space, or spatiallity is not a
property of
distinct areas existing outside and prior to
society, but it is socially constructed (LEFEBVRE, 1974;
SOJA,1980,1985; SAYER, 1985; PRED,1985; COUCELIS,1988, VERHAGEN &
MCGLADE 1995, BARCELÓ, 1995 PALLARÉS, 1993). Given that social
interaction is the formation process of social spaces, from an
archaeological point of view, “space” is interesting only when
viewed as something constituted, reproduced and changed by social
relations, and in turn constraining the unfolding of such
relations (COUCELIS, 1988). The fundamental premise of the socio-
spatial dialectic, alredy pointed out by Lefebvre is that social
and spatial relationships are dialectically inter-reactive,
interdependent; that social relations of production are both
space-forming and space-contingent (SOJA, 1980). In this respect,
as spatiality is simultaneously the medium and outcome of social
action and relationship, it is not only a product but also a
producer and reproducer of the relations of production and
reproduction (LEFEBVRE 1974, SOJA & HADJIMICHALIS, 1979). Human
behavior influence the organization of constructed environments
and constructed environtment influence the behaviour; each of
them can be modified by the other (ALTMAN, 1975; CANTER ET ALii
1975; RAPAPORT 1970; LAVIN 1981; MAXWELL 1983, SANDERS).
As a result, in order to study social spaces we should discover
the spatial properties of all relationships linking those areas
defined by the set of associated social actions performed there.
Social Space is not only a partition of geographical space into
“social” areas, but a network of distances between those units.
These
distances
are
built
upon
difference/similarity
relationships between social activity areas. For instance:
• distance produced by the spatial proximity between each area
• distance produced by the diversity on resources in each area
• distance produced by the
diversity of production activities
in each area
• distance produced by the differences in volume of production
in each area
• distance produced by the diversity of consumption activities
in each area
• distance produced by the differences in volume of consumption
in each area
• distance produced by the differences on quantity (density) of
social agents in each area
• distance produced by the differences on the nature of social
agents in each area
• distance produced by the differences on quantity of social
interactions (contact) in each area
• distance produced by the diversity of interactions (contact)
in each area
If we arrive to integrate in a GIS environment different layers
showing fuzzy social activity areas at different levels of
complexity and temporal modifications, and we extract similarity
relationships
between
any
kind
of
units
to
define
a
multidimensional non-euclidean distance metric, we will be in
the position to describe the structure of a social space.
However, current commercially GISs are still generally designed
around basic geometric models of spatial data, using a
representation subscribing to the map model of reality, instead
of an accurate representation of social reality. The basic
raster and vector models place primary importance on locations
of
geographic
phenomena,
sacrificing
the
rich
analysis
capabilities provided by structuring entities on the basis of
classification attribution and interrelationships (GOODCHILD
1987, USERY 1993). The principal limitation results from the
requirement to structure geographic entities as mathematical
points, lines, and areas for vector-based systems and as grid
cells for raster systems. Neither of these models exists in
historical/anthropological reality, since settlements are not
points, activity areas are not cercles, and grid-cell structures
are arbitrary partitions of space corresponding to no definable
geographic entity.
This innappropriate language of representation is a consequence
of the very fact that GIS systems lack a coherent body of theory
and organizing principles by which real-world archaeological
entities can be represented in a social space. A representation
based on “geography” and “social theory”, including the complex
interrelations of the social and physical characteristics of
locations, in which the content is emphasized over the
underlying geometric structure, has the potential to better
support archeological models and analytical procedures.
Geographers now widely recognize that the spatial context of
many human activities cannot fully be described in terms of the
distance
measures
associated
with
euclidean
space.
The
realization that human spatial behavior may be described and
analyzed more accurately through the use of alternative
topologies is an important discovery in human geography
(GORENFLO & GALE (1990). The same can be true in the case of
archaeological space. The proper way of analyzing social spaces
needs the construction of non-euclidean representations of
spaces based on the geometrical relationships among the above
mentioned variables.
New models must be developed to fully support spatial analysis
and to relate particular social processes to particular spatial
associations of objects, elements and relationships.
3.INTEGRATING SOCIAL SPACE THEORY AND SPATIAL ANALYSIS
TECHNIQUES IN GIS PROJECTS
Some basic
features of social spaces can be determined
automatically
through
a
combination
of
analytical
and
statistical processing, using set theory. For example, several
social activity areas are defined by the presence/absence of
specific archaeological finds. In those cases we can built a
production rule associating the presence of archaeological
material with their interpretation as a distinct social activity
area. An Expert System may be programmed to do this job,
provided
we
have
the
right
knowledge-base
with
ethnoarchaeological and experimental information. If we want to
map the resulting social areas, we should stablish links between
the GIS program and the Expert System, as if the Expert System
was an “intelligent” component in the database.
Nevertheless, understanding the complexity of spatial processes
and therefore how relational patterns are produced, controlled,
and reproduced is not an easy task. Most social space categories
are fuzzy, because it is not possible to specify a rule that
identifies all of its members and only its members.
For
example, in most cases food preparing activity areas and food
consumption activity areas overlap in space and we cannot
identify diagnostic categories or features that separate clearly
both activities. The solution comes from a multidimensional
approach that accepts the existence of social activity areas at
different resolution levels. The goal of the analysis is not to
reduce the underlying dimensionality, but to analyze how lowlevel areas are oganized in higher-level units, and how distance
relationships between low-level areas contribute to explain the
multidimensional structure of social space. This can be done
through the combination of geographic locations, attributes,
topology, classification attribution, interrelationships and
contextual information to the established categories or features
(USERY, 1993). Here, contextual information is not definable in
absolute terms such as environmental contextual data (soil
productivity, sol erosion, vegetation, etc) or the "backgroung
variable" as most actual GIS projects attempt. Rather, they are
defined (sensu Carr) relative to te phenomenon of interest and
the current model being applied to understand it. In this
respect, contextual data become more essential to the process of
identifying a phenomenon as its characteristics become more
ambiguous.
In the following sections we introduce some methods and
techniques
to
discover,
analyze
and
modelling
spatial
dependence, which can be integrated in a GIS platform to study
the
social
space
defined
by
the
human
group
we
are
investigating.
3.1. FROM ARTIFACT DISTRIBUTION TO SOCIAL ACTIVITY AREAS
Because activity areas have two relevant dimensions of variation
-spatial discreteness and content- the analytic problem is to
simultaneously identificate discret units in what seems a
continuous space defining the spatial structure, and then, to
detect the specific associations of elements, objects and
features that reflect social activities.
One of the failures of initial research on spatial analysis was
the underlying assumption about the homogeneity of structuring
processes across space. It was traditionally assumed that
artifact concentration could directly “map” activity areas, but
an
expanding
array
of
actualistic,
ethnographic
and
archaeological studies have shown that spatial patterning of
artifacts/features
does
not
necesarly
reflect
activity
organization, since more than one single process can influence
archaeological spatial distributions. Indeed, it has largely
accepted that spatial arrangements are the product of multiple
spatially non-uniform process and recent work has focused on
ways
to
isolate
local
effects
and
to
dissect
spatial
arrangements into their component parts or spatially homogeneous
subsets, prior to using techniques that make global, homogeneity
assumptions. (f.i. WHALLON,1984, READ 1989,Carr etc etc)
Because artifact concentrations do not correspond, necessary
with social spaces, there is difficult to imagine that a single
statistical or whatever method, is able to translate a series of
coordinates into a geometrical representation of social space.
Some attention has been paid in the research literature to
demonstrate that a single-method approaches to spatial data
oversimplify
artifact
distributions
(f.ex.
WHALLON,
1984,
BLANKHOLM, 1991, KINTIGH, 1991; GREG et al.. 1991; PALLARËS
1993; BARCELÓ, 1995)
We defend the use of several methods in a complementary fashion.
Here we propose some of them.
3.1.1.- POINT PATTERN ANALYSIS
There
is
a
fertile
literature
concerned
with
different
techniques to identifying spatial patterning and thus isolate
discrete areas. This task has been specially performed by means
of different clustering methods, that try to partition objects
and features into groups based on observed similarities or
differences. First appoaches to spatial analysis applied methods
which assumed that an assemblage was the result of a single
spatially uniform process acting uniformly over a site or a
territory area and only provided indices of spatial patterning,
reducing
a
spatial
distribution
to
summary
statistics,
indicating a tendency towards clustering, regular or random
patterning. One of the most prominent exemples in this kind of
formal spatial analysis has been the classic application of the
nearest-neighbor as a global index of the patterning of a
distribution, whose limitations have been largelly summarized.
As these procedures did not characterized the nature of spatial
arrangements, Whallon (1984) advocated use of less reductive
methods, more consistent with anthropological models of behavior
and which make minimal assumptions about the data structure. A
similar strategy was later proposed by Kintigh & Ammerman (1982)
to respond to archaeological needs. The methods of pattern
recognition that at present seem to have a major performance are
Pure
locational
clustering,
Unconstrained
Clustering,
and
Presab, as has been demonstrated by their resolution power over
ethnoarchaeological cases and successively disturbed simulated
materials (see KINTIGH 1991, BLANKHOLM, 1991; GREGG ET ALii
1991). Although this tecniques have been specially applied to
intrasite spatial analysis they are equally valid for intersite
spatial analysis (RIDING & SAMPSON, 1990, BLANKHOLM 1991;
BARCELÓ 1995).
Pure locational clustering or k-means spatial analysis (KINTIGH
& AMMERMAN 1982, KINTIGH, 1991; BLANKHOLM, 1990, GREGG et alii
1991; PALLARÉS, 1993, BARCELÓ, 1995) performs a nonhierarchical
divisive clustering analysis which atempts to minimize the
intracluster
variances
while
maximizing
the
intercluster
istances. It does not provide an index to test spatial
clustering, but it focuses on artifact density and location
defining discrete clusters in the space with their component
points.
Another alternative is an assemblage composition approach that
tries to find definable differences in assemblage composition
directly disregarding the physical location of points/density.
This can be done by means of the Unconstrained clustering
(WHALLON, 1984), a multistep analytical approach that tries to
operate under as few constraints as possible concerning size,
shape, density, composition and internal organization or
structure. It groups areas of a site in terms of their
proportional
artifact
class
composition
(WHALLON,
1984;
KINTIGH,1991; BLANKHOLM, 1991; GREG et alii 1991, PALLARÉS,
1993), by using relative density vectors as the basis for a Ward
clustering analysis of locations.
These methods have been applied specially to continuos data, but
they may also be used with binary data as H.P.Blankholm proposes
developing his Presab method (Blankholm 1991, 1993). In this
case, instead of dealing with absolute or relative densities,
frequencies or nearest neighbour istances, Presab deals with the
presence or absence of data categories which are clustered by
means of the k-means algorithm. So, activity areas are defined
according to which categories of items that were used in them
and not by their frequencies or discard rates.
There are also other statistical methods that have been applied
to isolate discrete areas, for instance Correspondance Analysis,
(JOHNSON, 1984, DJINDJIAN 1988, BLANKHOLM 1991). Nevertheless,
it is beyond the scope of this paper to detail technical
questions
or
to
make
a
comparative
evaluation
of
the
shortcomings and resolution power of each method and technique.
We simple advocate the use of those methods which have been
developed to solve archaeological problems and operate under as
few constrainsts as possible. If we use them in a complementary
fashion,
working
both
with
continuous/binary
data,
and
cordinate/gridded data, thus they may be useful to isolate
discrete activity areas. Moreover, their complementarity with
other methods which we propose in the following sections and the
integration
of
contextual
information
thanks
to
the
classificatory power of GIS can overcome some of the classic
limitations of this statistical tests.
3.1.2.- IMAGE PROCESSING TECHNIQUES
Image processing techniques are powerful exploratory and
quantitative tools that have been greatly underutilized in
archaeology. There are tree main types of image processing:
detection of spatial structures, spatial modelization, and
regionalization (VOIRON CANICIO, 1993). Currently applications
in archaeology have focused mainly on field survey data, and
regional intersite analysis, in order to locate sites and
features, define physiographic regions, soil zones, etc.
Nevertheless, most of these techniques can also be applied with
any spatially dense data in two or three dimensions- raw count,
weigth, and quantity data- at the intrasite scale, to
investigate spatial organization and thus establish social
activity areas (see Hartmann 1988, Lang, 1992). The purpose of
image processing is not to see images, but to analyse
information contained in an image, searching for unknown
structure by removing the effects of noise or blurring, or to
find a relation between an input image and an archaeological
model.
Digital image processing techniques can be divided into two
major areas, spatial-domain methods which involve operations
directly on the pixel values, and frequency-domain methods which
make use of some transforms to change the image into a
representation of the frequency information of the data. (LANG,
1992)
In the first stage the image is digitized and pre-processed
using local convolution operators to improve the image data
suppressing unwilling distortions or enhancing some image
features important for further processing. Some of these methods
are
geometric
transformations,
brightness
interpolation,
smoothing, edge detectors, neighbourhood, etc.( SONKA, HLAVAC&
BOYLE +referencies)
The second stage of the analysis is Segmentation. It is the
process of dividing an image into regions or parts of uniform
appearance that have a strong correlation with objects or areas
of the real world contained in the image (SONKA, HLAVAC& BOYLE).
In archaeology this can be used to locate areas where
archaeologicl sites are likely to be found. Edge detection and
image enhancement are techniques used to identify specific
cultural features.
Morphological mathematical transformations may constitute the
third stage of the analysis. They are based on geometry and
shape and deals with binary morphological sets and with
numerical morphological functions (VOIRON CANICIO 1993). Their
function is to simplify images and preserve the main shape
characteristics of objects. Between the big number of possible
morphological transformations, the most common are dilation,
erosion, opening, closing, and skeleton. (SONKA, HLAVAC& BOYLE)
Continuos or discrete Image transforms are widely used in image
filtering,
image
data
compression
and
image
description
processes (SONKA, HLAVAC& BOYLE). There are many different image
transforms (f. instance, fourier transform, hadamard transform,
discrete image transforms, lowpass and higpass filters), but the
must common image enhancement problems include noise suppresion,
edge enhancement and structured noise removal.
The last processing step evaluates results of morphology using
different
mumerical
descriptors
or
a
syntactic
approach
classification. One approach has been to use usual methods of
multivariate analysis, however, these techniques do not really
exploit the spatial component of the data because they assume
that data vectors in neighbouring pixels are independent, but
clearly this is not so (CRESSIE, 1991). There are some spatial
dependencies
caused
by
the
contamination
resulting
from
oversampling and resampling, spatial continuity of the ground
clases and problems of scale that can result in biased estimates
of
the
covariance
matrix,
and
hence
in
an
increased
classification error rate (Cressie,1991).
Therefore, the accuracy with which a remotely sensed image may
be classified is dependent on the characteristics of the
training data and the nature of the classifier (Mather, 1987;
Schalkoff, 1992; Foody et alii, 1995). A wide range of
techniques which make no assumption about the statistical
distribution of the data may also be used such as non-parametric
classifiers, fuzzy classifiers, or neural network tecniques (see
among others HEPNER et al. 1990; SHORT, 1991; FOODY ET alii,
1995; BENEDIKTSSON et al. 1990; CHEN ET Alii, 1995).
3.1.4.- ARTIFICIAL INTELLIGENCE METHODS
In terms of spatial interaction modelling, a neural net approach
brings with it the promise of being able to offer a new view on
the nature of the spatial interaction functions that can be
deduced from instances of interaction data . We can use a
Kohonen self-organising map to discover a continuous topological
mapping of a function from one n-dimensional space to another mdimensional space by means of self-organisation based on data
examples. It has been shown to be capable of representing highly
complex and non-linear functions. This type of net are often
used as pattern detectors but in a spatial interaction context
the mapping to be learnt might be considered to involve a
spatial interaction field.(Openshaw 1993)
3.2. COMPARING
LEVELS
SOCIAL
ACTIVITY
AREAS
AT
DIFFERENT
COMPLEXITY
Once we have defined some discrete units by means of different
complementary methods the next stage of the analysis consist of
comparing these social activity areas at different complexity
levels. The objective is to integrate in a GIS environment
several layers with different information concerning location,
morfology, size, content and contextual information of every
discret unit in order to extract similarity relationships
between them that permit us to describe the structure of a
social space.
This process must be done by means of a formal GIS language with
a defined syntax and vocabulary specific to map analysis which
can define any model of spatial inte-relationships. In this
respect, it is needed a mapalgebra that defines not a simple
arithmetic combination of map layers but integrates some more
complex spatial operators.
Specially relevant for us are the posibilities to compute
mathematical and boolean operatons with points and clusters of
points. This paradigm is based on the formalized system for
expressing GIS functions developed by C.Dana Tomlin (Tomlin 1990,
Mills 1994). The representation language described by Mills (
MILLS, 1994), seems one of the most powerful modelling paradigm
because it works with different
operations that seem able to
induce any kind of associative principes, conections and
relationships betwen the variables of interest to describe social
spaces. This language, called MapAlgebra recognize that in all
geographic analysis, there are only four fundamental types of
spatial operators:
• local operations apply familiar mathematical functions (ADD,
SUBSTRACT, MULTIPLY, DIVIDE) to each point’s value (or values)
on one or more layers. A single example is the sum of two map
layers.
• focal operations compute a new value for each point as a
function of the existing values, distances, and/or directions
within
its
neighbourhood/containment
and
adjaceny/.
Neighbourhoods may be defined in terms of physical separation,
travel
cost,
or
intervisibility.
MapAlgebra
has
the
possibility of calculating “spreading” and “radiating” nonEuclidean funtions, which extend the concept of neighbourhood
to include neighbourhoods defined by time and other factors
(including those operating within other layers). For example,
we can define cluster of points defined by a ring-shaped
nighbourhood, with a diameter more than 1 km. but less than 5
km.
• zonal operations compute a new value for each location as a
function of the existing values from one layer that are
contained in zones of another layer. A simple example is
summing the individual residence units on one layer,
controlled by the site-catchements boundaries on another.
• incremental operations characterize each location as an
increment of one, two or three-dimensional geographic form.
The size and shape of these increments are inferred from the
value (or values) of each location relative to those of its
adjacent neighbours on one or more specified layers. A simple
example is a map showing the direction of drainage at each
location.
Some useful MapAlgebra expressions may be :
Density = ZonalSum of activity areas
within a settlement
In this case it is assumed that we started with one map layer
having a `1' for activity areas -or a count of activity areas
per cell- and another with each settlement.
Direction= IncrementalDrainage of Altitude
Runoff = FocalSum of 1 spreading through Direction
The first operation Incremental Drainage, results in a layer
showing the direction of drainage from each cell. The second
uses the focal operation modifiers “spreading thorugh” to sum
values along the connected drainage directions
3.3. GEOGRAPHICAL DISTANCE BETWEEN SOCIAL ACTIVITY AREAS
Our first task is to measure the degree of spatial variation in
each layer of social activity areas.
If the observed spatial variability between social areas has
not any known source (time, function, ethnic, cultural,
economic, social, etc.), then we shall not expect any spatial
association. Spatial heterogeneity occurs when there is a lack
of spatial uniformity in relationships between the variables
under study. When the variation is not wholly erratic, and
there is some regularity, we say that there is a certain degree
of spatial dependence between spatial units (OLIVER & WEBSTER
1990): ”everything is related to everything else, but near
things are more related than distant things”. This type of
dependence is often referred to as spatial autocorrelation
(CLIFF & ORD 1973) or network autocorrelation (DOREIAN 1982)
although the notion of autocorrelation is more limited than
that of dependence.
The analysis then pretends to examine if the characteristics in
one location have anything to do with characteristics in a
neighbour location through the definition of a general (some
times linear) model of the space. What we are looking is if
what happens in one location is related (depends on) with what
happens in the mean of neighbour locations. The result of the
analysis may be an answer to this question (yes, all neighbour
locations are similar, or not) or an statistical model
representing how differences between locations depend on their
mutual distances.
The question is not only to discover the degree of spatial
dependence, but to explain why the location of social activity
areas show that level of spatial homogeneity or heterogeneity,
and
if
that
level
of
geostatistical
association
is
significative in social behaviour terms. Moreover, spatial
dependence has to be analyzed between similar social activity
areas in the same layer, and between categorically different
social areas in different layers.
EJEMPLO
There are many different techniques to compute this “degree” of
spatial dependence:
G STATISTIC (GETIS & ORD,1992)
. INDEX DE CONCENTRACIÓ DE TRICOT (PERÒ DISCUTIR RELACIÓ
CONCENTRACIÓ/GRAU D'ESPECIALITZACIÓ DE L'ESPAI SOCIAL. NO ESTIC
D'ACORD).
. PEARSON'S C2GOODNESS-OF-FIT TEST (CRESSIE 1991)
. MORISITA INDEX
. MORA'S I
. KERNEL ESTIMATORS OF DENSITY FUNCTIONS (SILVERMAN 1986,
BAXTER).
. RIPLEY'S K FUNCTION.
Polinomial surfaces of various orders may be fitted to the maps
containing social activity areas
in an attempt to model the
spatial relationships among social units
or whatever
archaeological categories. In this case the goal is to obtain a
geometrical surface generalizing the observed distribution of
data to portray their overall patterns of location.
Existing methods of surface interpolation can be divided into:
trend surface
analysis, whereby polynomials or sometimes
trigonometric functions are fitted by least squares regression
on the spatial coordinates as predictors (Hodder and Orton
1976). This approach has several shortcomings: it loses detail
because of powerful smoothing; instability caused by outliers
or observational errors or when enough terms are included in
the function to retain local detail; and variation in one part
of the region affects the fit os the surface elsewere (Oliver
and Webster 1990).
other interpolation techniques such as low order polynomials,
spline functions, polyhedra, Delauney’s triangulation, and
weighted moving averages. Each of these methods has its own
disadvantage,
but
the
following
remarks
apply
to
all
traditional interpolators. First, spatial dependence in the
data is assumed implicitly; there is no provision to determine
whther the assumption holds. No account is taken of the form of
the spatial variation. None of the methods provides any
estimates of errors of estimators.
Kriging
(Schiepatti 1985, Oliver and Webster 1990, Cressie
1991, Voiron Canicio 1993, Davis et al. 1994) is essentially a
method of estimation by local weighted averaging, using weights
to ensure that there is no bias and minimize the estimation
variance. In other words, it refers to making inferences on
unobserved values of a random process considered the cause of
spatial distribution of observed points. Spatial continuity is
estimated
weighting the influences of data points by their
statistical distance instead of their geometric distance from
one another. Kriging can be expected to consistently replicate
data at the observation locations, but the resulting surface
has greater continuity than reality because of the smoothing
effect of the process (Davis et al. 1994). Kriging overcomes
many of the shortcomings of the traditional methods of
interpolation. The kriging weights are determined by the
spatial structure existing on data points (the variogram). It
is an optimal interpolator in the sense that the estimates are
unbiased and have known minimum variances. Since the estimation
variances can be determined and mapped like the estimates, and
assuming a particular distribution, we can calculate the
confidence we can place in the estimates. (Oliver and Webster
1990)
In Archaeology we can use this last method to analyze a series
of settlement locations in a region or the localisation of some
intrasite activity areas within the settlement. In contrast
with the geological method, we are not interested in
calculating the value of empty zones (locations without
archaeological remains) (Voiron Canicio 1986). The method
allows the discovery of spatial structures between recorded
locations, determining the existence of sub-spaces and their
limits. It is also a means of comparison of different spaces in
different periods of time: if sub-spaces have had allways the
same structures, boundaries and components. Kriging is then a
method to analyse the patterning existing in our spatial data,
discovering all differences and discontinuities and ordering
them by their relevance.
3.4. OTHER DISTANCE MEASURES BETWEEN SOCIAL ACTIVITY AREAS
Our task is to explain the discontinuities measured on the
spatial dependency model according with the differences in
social action documented for each area or location. This can be
done allocating each location in the archaeological or the
geographical layer to an area in the social space structure
layer. This allocation can be done by a neural network, whose
objective is to create a non-linear rule able to assign a
classificatory label to some clusters of data. (Chen et al.
1995, Foody et al., 1995). Neural Network generalization is far
better than classical statistical prediction methods because it
is possible to take into account data errors and different
measurement scales. They also make weaker assumptions about the
statistics of the input data than a parametric Bayes
classifier; and they are capable of forming highly nonlinear
decision boundaries in the feature space and therefore has the
potential of outperforming a parametric Bayes classifier when
feature statistics deviate significantly from the assumed
Gaussian satistics.
The result is not a new map, but an alternative to regression
models. The neural network computes a distribution-free
discriminant function for the discontinuities in the social
space layer, based on the distiguishing features of the
training set: all archaeological and biophysical evidences for
social actions performed in those locations. These kind of
representation allows also the assignation of weights of
importance to each input data variable if the relative quality
or relevance of these variables is known with respect to the
classes of interest. Different weighting factors may be
assigned for different classes. This option allows to include
detailed information about how individual features and classes
are related based on theoretical assumptions (degree of social
complexity, degree of human dependence to the ecosystem, etc.).
USAR FIG. 4, EN FOODY ET AL. p. 394.
One way of exploiting this classification is introducing
“abstract maps”. They contain for each object in a scene a data
structure with the same neighbourhood structure as the domain
required for the task at hand. We obtain as a result dependency
networks. The user can make queries to the map, asking, for
example for the existence of especific dependency path between
two nodes, for instance, between a hunter and
a gatherer
involving an arc labelled “exchange”.
TRANSPARENCIAS EN HERNANDEZ p. 76
5.6. Network Analysis and Fuzzy sets
Social spaces do not end with contextual classification. We
need to build a general model to understand how the space was
used by a human society. We can build this model from
relationships (distances and dependences) between classes
discovered in the last stage of our analysis.
A way of modelling the qualitativeness of “cognitive space” is
by using a relative representation of spatial knowledge based
on locative relations (over selected spatial dimensions) among
objects, and between objects and distinguished reference
structures. Consequently, a scene is represented as a net of
mutually constraining locative relations. Inference is done by
general constaint satisfaction mechanisms extended to take the
structure of the domain into account, and by specialized
procedures, which operate on data structure that analogically
reflect the structure of the relational domain on a higher
level of abstraction. The advantage of these data structures is
that, since they have th same structure as the relational
domain they represent, operations such as a change in point of
view or the composition of
efficiently (Hernández 1994).
relations
can
be
performed
(WANG, HALL SUBARYONO, 1990) define A FUZZY RELATIONAL DATA
MODEL TO IMPROVE THE REPRESENTATION OF INFORMATION IN GIS.
GEOGRAPHICAL
INFORMATION
THAT
IS
PROTRAYED
SPTIALLY
IN
CARTOGRAPHIC FORM IS CONVENTIONALLY REPRESENTED BY THEMATIC
MAPS. A THEMATIC MAP IS A SET OF SPATIAL ENTITIES, PONTS, LINES
AND AREAS, THAT ARE DESCRIBED BY THEIR NON-SPATIAL ATTRIBUTES
ABOUT A SINGLE THEME. SUCH A METHOD CANNOT PROPERLY REPRESENT
COMPLEX SITUATIONS SUCH AS A MIXTURE OF COVER AND INTERMEDIATE
CONDITIONS WHICH OCCUR QUITE OFTEN. IN A FUZZY REPRESENTATION,
CLASSES CAN BE DEFINED AS FUZZY SETS AND SPATIAL ENTITIES AS
SET ELEMENTS. EACH SPATIAL ENTITY IS ASSOCIATED WITH A GROUP OF
MEMBERSHIP GRADES TO INDICATE THE EXTENTS TO WHICH THE ENTITY
BELONGS TO CERTAIN CLASSES. (WANG, HALL SUBARYONO, 1990) (FER
UNA SINTESI DEL QUE PROPOSEN).
DESARROLLAR
LA
IDEA
DE
CLASIFICACION
CONTEXTUAL
Y
CATEGORIZACION (usery)
The inference engine extracts knowledge from the fuzzy
knowledge base and makes inferences via rules and facts. A
typical spatial inference involving fuzzy terms may be
expressed as follows (Leung 1993):
Rule: If distance to the main site center (X) is short (A)
then Interaction (Y) is high (B)
Fact: Distance to site Center (X) of polygon K is very short
(A1)
______________________________________________
Approximate conclusion: Interaction (Y) in polygon
is veru
high (B1)
Knowledge-based information systems will play an important role
in decision analysis in the future. Spatial Analysis tools
without “intelligence” have very little chances to meet
challenges commonly encountered in the highly complex and
imprecise social spaces configurations. Our hability to
efficiently and effectively integrate databases, knowledge
(dclarative and procedural), expertise, intuition, inference,
and graphics into an unified system is crucial.
A lattice
of locations evokes the idea of regularly spaced
points , linked to nearest neighbours, second nearestneighbours, and so on. Of all possible spatial structures that
can generate, a data set whose spatial locations are a regular
graph is the closest analogue to a time series observed at
equally spaced time points.
Lattice theory can be applied to digital immage processing,
analyzing spatial relationships among pixels: any bit-mapped
image is a lattice of regularly distributed pixels in a
bidimensional-space.
Assuming data can be thought of as a partial realization of a
random phenomenon (i.e., a stochastic process), we can imagine
that the spatial variable is a countable collection of spatial
sites at which data are observed. Mathematically speaking we
can translate these individual points into vertices which are
conected by edges to points with the same value on the spatial
variable. We call it the neighbourhood structure, because we
suppose that the closer the points, the closer the measures of
spatial space.
EJEMPLO: PRIGOGINE y el Mississipi.
A graph of neighbourhood structure can be represented using a
Fuzzy Cognitive Map. DESCRIBIR. Image data are represented
usually as gray tones. We can consider them as fuzzy sets.
Suppose 0 denotes de absence of findings and 1 denotes the
presence; values between 0 and 1 denotes the confidence we have
in the presence of an archaeological
item in that point.
DESARROLLAR FUZZY SETS.
The only difficulty is how to build neighbourhood relationships
based on observed data. This is a straigthforword statistical
problem, that can be solved using any of the classical
clustering methods or similarity indexes.
Statistical similarity is problamatic (Tversky, Osherson.... )
Using Neural Nets and Machine Learning to estimate asymetrical
similarity indexes.
What we can do with the neighbourhood structure once we have
built the lattice? It should be deteermined whether the
locations:
are regular or irregular
represent points or regions
are indices for continuous or discrete random variables.
In other words, a lattice of geometrically linked points can be
analyzed to discover and measure the degree of spatial
dependence in more easier way that using stochastic models and
plans interpolation.
TEORIA DE LAS SOCIAL NETWORKS. Racine (1979, Progogine, Harary.
FUZZY COGNITIVE MAPS AS A
coherence and stability in
social spaces.
DYNAMIC
SIMULATION.
Studing
the
Often the lattice can, in principle, be partitioned into
regions corresponding to different areas or spatial units.
Neighbourhood structure can consequently be described and
analyzed at different levels, depending on the degree of
segmentation and the meaning of each unit. emember Clarke
classification of archaeological units: we can investigate a
lattice of archaeological types at a micro-level or a lattice
of technocomplexes at a macro-level.
5.5. Virtual Reality
visualization is a way of explanation: it transforms the
symbolic into the geometric, enabling researchers to observe
their simulations and computations. Visualization in a GIS
environment then has to be a part in the process of scientific
discovery, helping archaeologists to detect regularities in the
patterns of spatial relationships among social contexts.
Visual processing in the brain is tied to cartographic
cognition which is a unique process as it involves the use of
the brain to recognize patterns and relationships in their
spatial context. This cannot be easily replicated by GIS
software with essentially linear analytical processess based on
data structures which are topological, sequential, or object
oriented. In many cases the data in such systems can only be
fully understood by displaying them in map form. It is in the
cognitive
field,
especially
in
the
emerging
area
of
visualization, where
modern
cartography have their closest
interaction (Taylor 1993)
A.M.MacEachren Visualization is foremost an act of cognition, a
human ability to develop mental representations that allow
geograhers to identify patterns and to create or impose order
p. 101. Geographic visualization will be defined here as the
use of concrete visual representations-whether on paper or
through computer displays or other media- to make spatial
contexts and problems visible, so as to engage the most
powerful
human
information-processing
abilities,
those
associated with vision
DiBiase (1990) suggests that visualization tools may play
different roles at different stages of scientific research.
Identifies four stages: exploration, confirmation, synthesis
and presentation.
Scientific visualization is applied to use of computer graphics
and image processing technology in data-intensive scientific
applications p. 8. Muehrcke. 906. The more visualization
options we have, the better we understand the nature of
individual visual expressions. p. 9
Computer vision is the construction of explicit, meaningful
descriptions of physical objects from images so that they can
be modeled in digital form (Ballard i Brown 1982) Peuquet 891,
p. 378
GIS may be used as a means of presenting a series of
hypothetical scenarios which are relevant to our understanding
of
human/environmental
relationships.
This
can
be
done
principally through the construction of a ‘territorial’ model
designed to articulate a set of semi-autonomous activity
spheres which are said to be implicated in the rproduction and
organization of a specific archaeological locus, or settlement
(Verhagen et al. 1995).
Lang visualització científica que pot ser definida, segons un
article del 87 de la National Science Foundation from the
Association for Computing Machinery:
Visualization is a method of computing. It transforms the
symbolic into the geometric, enabling researchers to observe
their simulations and computations. Visualization offers a
method for seeing the unseen. It enriches the process of
scientific discovery and fosters profound and unexpected
insights....
Visualization embrances both image understanding and image
syntesis.
That
is,
visualization
is
a
tool
both
for
interpreting image data fed into a computer, and for generating
images from complex multi-dimensional data sets. It studies
those mechanisms in humans and computers wich allow them to
perceive, use and communicate visual information.
An estimated 50 percent of the brain's neurons are associated
with vision. Visualization in scientific computing aims to put
that neurological machinery to work. (MCCormick et al. 1987).
La visualització científica està emergint com la disciplina per
presentar dades abstractes en formes més interpretables pel
sistema visual humà: tridimensionals, imatges en color que
canvien al llarg del temps
Experimentation with virtual archaeological formations may lead
to new insights into data recording and analysis. The key
concept here is virtual, an allusion to a model, a replica, the
notion that something can act as a surrogate or replacement for
an original. In other words, it refers to a description of an
archaeological formation or to a simulated archeological
formation. (A simulated data set will normally be shaped by the
criteria used for recording an actual formation). (Reilly
1992). The challenge is no longer only to model
terrain,
structures o distributions with simple geometry, but to model
those amorphous humps, bumps and hollows, typically found in
the course of fieldwok.
Data visualization refers to those techniques which allow
visual interpretation of data through the representation,
modeling and display of solids, surfaces, properties and
animations, involving the use of graphics, image processing,
computer vision and user interfaces. It is a manifestation of
“seeing the unseeable”. Data visualization is a means whereby
much more multi-dimensional data can be brought within the
range of human experience and cognition. It should be stressed
that the graphical aspect of solid modelling systems is not
necessarily their prime function. These systems also embody
large volumes of structured three-dimensional information which
can be exploited to establish links to many different kinds of
database. (Reilly 1992). Realism in solid modelling is achieved
not only by modeling natural lighting effects, but by trying to
embody some element of the social cntext and function of the
subject
in
the
visualization
and
thereby
bring
the
visualization to life.
The object of this kind of graphical application is to provide
a description of the archaeological data in a way which is
optimized to communicate the maximum of information, with as
little spurious addition as possible.
3D VISUALIZATION: the aim might simply to illustrate, but often
there may be some more purposeful intention -to investigate or
to simulate thoughts towards an archaeological interpretation.
Ad a tool for scientific analysis, the visualizing process
resulting from solid modelling can sometimes rveal rlationships
within an archaeological ‘reconstruction’ more clearly than
other current methods of display. However, when archaeological
data is the source, soid modelling will usually entail a high
proportion of dubjective judjment, since so many controlling
paameters
are
not
completely
known
(colour,
texture,
dimensions, etc.)(Fletcher and Spicer 1992).
The object of representation of a surface is the communication
of information in the most efficient manner possible. Fletcher
and Spicer shows how to build terrain surface from topographic
information, and how the solid modelling of that surface
provides information about social space, or more specifically,
the constraints of topographic space on social space. The
criterion is simply to show the surface of the ground in as
naturalistic a way as possible, so as to utilize fully the
human abilities to observation and perception, and to couple it
with the experience and intuition of an arcaheological
training. It uses georeferenced data (topographic points in a
cartesian space x,y,z) and contextual information about the
aspect of surface (colour, texture, light, visibility, etc.).
(Fletcher and Spicer 1992).
Reilly comments an example of solid modelling applied to intrasite analysis: “In order to make it possible to see whether
material in the features was similar to and perhaps, therefore,
connected with material in the local parts of the overlying
layers, the digitised plans of the cut features (i.e. pits,
post-holes) were extruded to form prisms. These prisms were
then intersected with a solid model of the overlying layers
which, indicentally, were composed of box-shaped contexts
intended to lend a degree of precission in spatial provenancing
within these deposits. Colour-codes were employed to signify
different properties (e.g. average sherd size). The application
of a clipping plane down the sides or o the top of the model
could then be used to expose internal details and any visual
correlations between properties of the cut features and local
box-contents could be assesed and, if interesting, investigated
further (Reilly 1992, Reilly and Shennan 1989).
Grafland is another project: Rather than reduce the record to a
series of abstract single context plans and sections, each
context is defined as a three-dimensional solid which can be
examined from any elevation or sectional view. The ‘naturalism’
of the solid-modelled contexts is illusionary, of course, for
archaeological excavators could never experience these entities
in the manner that the solid modelling program allows, because
the archaeologist never actually sees a whole or complete
formation through excavation. It is by integrating each
separate perceptions of the formation that a clearer idea of
its totality may be reached. Recorded primary data is a
metaphor for elements of the material continuum that is is raw
archaeological material. It is this metaphorical data that
forms the basis of archaeological discussion. By enhancing and
enriching the quality of this metaphorical data w hope to
stimulate more and new archaeological discussions. Contexts
modelled as solid geometries are susceptible to a much wider
range of transformations and interactions than the older
methods of representation allowed. This increased freedom to
explore multi-dimensional data sets opens the way for further
insights into the nature of three-dimensional deposits and thei
recording.
With GIS is possible to introduce the actual physical
characteristics of a region into computer simulation. We no
longer need to make such simplifying assumptions as a
‘featureless and level two-dimensional plane. The actual
landform, soils, vegetation, hydrology, and other features of a
region can be incorporated for the simulation background,
allowing greater realism and undoubtedly better insights
(Kvamme 1995).
COMENTAR VISTAPRO Y KPT BRYCE.
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