International Journal of Geographical Information Systems,
1992, Vol. 6, No. 1, 31–4
Geographical Information Science*
Michael F. Goodchild
Abstract. Research papers at conferences such as the European Geographical Infor-
mation Systems (EGIS) and the International Symposia on Spatial Data Handling
address a set of intellectual and scientific questions which go well beyond the limited
technical capabilities of current technology in geographical information systems.
This paper reviews the topics which might be included in a science of geographical
information. Research on these fundamental issues is a better prospect for long-term
survival and acceptance in the academy than the development of technical capabil-
ities. This paper reviews the current state of research in a series of key areas and
speculates on why progress has been so uneven. The final section of the paper looks
to the future and to new areas of significant potential in this area of research.
The geographical information system (GIS) community has come a long way in the
past decade. Major research and training programmes have been established in a
number of countries, new applications have been found, new products have appeared
from an industry which continues to expand at a spectacular rate, dramatic improve-
ment continues in the capabilities of platforms, and new significant data sets have
become available. It is tempting to say that GIS research, and the meetings at which
this research is featured, are simply a part of this much larger enthusiasm and
excitement, but there ought to be more to it than that.
What, after all, is the purpose of all of this activity? Expressions such as ‘spatial
data handling’ may describe what we do, but give no sense of why we do it. This
was one of the themes behind Tomlinson’s keynote address at the First International
Symposium on Spatial Data Handling in Zürich in 1984 (Tomlinson 1984). The title
of the conference suggests that spatial data are somehow difficult to handle, but will
that always be so? It suggests a level of detachment from the data themselves, as if
the U.S. Geological Survey were to send out tapes labeled with the generic warning
‘handle with difficulty’. It is reminiscent of the name of the former Commission on
Geographical Data Sensing and Processing of the International Geographical Union.
A quick review of the titles of the papers at that or subsequent meetings should be
* Based on keynote addresses by the author at the Fourth International Symposium on Spatial Data
Handling, Zürich, July 1990 (Goodchild 1990), and EGIS 91, Brussels, April 1991 (Goodchild 1991).
enough to assure anyone that their authors are concerned with much more than the
mere handling and processing of data — from a U.S. perspective, that the community
is more than the United Parcel Service of GIS.
Geographical information systems are sometimes accused of being technology
driven, a technology in search of applications. That seems to be more true of some
periods of the 25-year history of GIS than of others. For example, it is difficult to
suggest that Tomlinson and the developers of the Canadian Geographical Information
System (CGIS) (Tomlinson et al. 1976) were driven by the appallingly primitive
hardware capabilities of 1965. On the other hand the prospect of a menu driven, full
colour, pull-down menu raster GIS in the 386-based personal computer on one’s
desk has clearly sold many systems in the past few years. Technological development
comes in distinct bursts, and so does the technology drive behind GIS. It may be
the motivation behind the desire to handle spatial data, but it fails to explain many
of the diverse research efforts being reported at meetings and in the literature.
There have also been phases when applications have driven GIS. CGIS itself
was an application in search of a technology, and the drive was sufficiently strong
to lead to the prototype of the first map scanner, and to numerous other technological
developments (Tomlinson et al. 1976). McHarg had worked out the principles of
the map overlay technique (McHarg 1969) long before Berry and others automated
them in MAP and its derivatives (Berry 1987); school bus routing software has been
around much longer than the problem’s implementation in a standard GIS. But again,
much of the subject matter of GIS research lies well beyond any reasonably fore-
There have also been phases when applications have driven GIS. CGIS itself
was the widespread distribution of Landsat and SPOT imagery, and the availability
of digital elevation models and street files in many countries have certainly led to
applications well beyond those used to justify the data’s compilation. TIGER, for
example, appears to be spawning its own industry of updaters, repackagers and
application developers, although it exists in principle only to serve the needs of the
1990 U.S. Census.
However, although the driving seat of GIS is undoubtedly crowded, I would like
to deal in this paper largely with the fourth driver located apparently irrelevantly in
the back seat, the ‘S’ word. It seems to me that there is a pressing need to recognize
and develop the role of science in GIS. This is meant in two senses. The first has
to do with the extent to which GIS as a field contains a legitimate set of scientific
questions, the extent to which these can be expressed and the extent to which they
are generic, rather than specific to particular fields of application or particular
contexts. To what extent is the GIS research community driven by intellectual
curiosity about the nature of GIS technology and the questions that it raises? And
if GIS can be motivated by science, then what are its subfields, what are its questions,
and what is its agenda? The second sense has to do with the role of GIS as a toolbox
in science generally — with GIS for science rather than the science of GIS. What
do we need to do to ensure that GIS, and spatial data handling technology, play their
legitimate role in supporting those sciences for which geography is a significant key,
or a significant source of insight, explanation and understanding?
To do this we must first establish that spatial, or rather, geographical data are
unique, and that their problems cannot therefore be subsumed under some larger
field. We must also establish that there are problems which are generic to all
geographical data, or at least establish that it is possible to distinguish those that are
from those that are not. For example, the accuracy of attributes on a choropleth map
of crime statistics would seem to be very little informed by knowledge of attribute
accuracy for geographical data generally, but to require instead a level of under-
standing of the specific problems of crime statistics. However, the accuracy of
population estimates for an arbitrarily defined polygon may well be known from,
or at least informed by, the general properties of the modified areal unit problem
2 What is unique about spatial data?
In many facilities management systems, the role of the GIS is to provide an alter-
native key to data, a method of access based on geographical location. In essence,
a spatial database has dual keys, allowing records to be accessed either by attributes
or by locations. However, dual keys are not unusual. The spatial key is distinct, as
it allows operations to be defined which are not included in standard query languages.
For example, it is possible to retrieve all point records lying within an arbitrary,
user-defined polygon, an operation which is not defined in standard query languages
such as SQL. In essence, the spatial key is multidimensional, but again multidimen-
sional keys are known from other areas, and analogues of point in polygon retrieval
can be defined for non-spatial dimensions.
What distinguishes spatial data is the fact that the spatial key is based on two
continuous dimensions. It is possible to visit any location (x, y) in the real, geo-
graphical world, defined in principle with unlimited precision, and return a value
for a variable, for example, topographic elevation z. Terrain is thus characterized by
an infinite number of tuples <x, y, z>. In network applications z is defined only for
locations on the network, but the number of tuples is still infinite if variation is
continuous along this one-dimensional structure of links and nodes. Time series also
have continuous keys, but are rarely conceived, measured or represented as contin-
uous, and there appears to be little commonality of interest in the problems of
temporal data handling. By contrast, there is ample evidence of commonality in the
spatial data handling disciplines.
Many of our data models, particularly polygon networks and triangulated irreg-
ular networks (TINs), reflect an underlying view of space as continuous and the
need to accommodate the user who wishes to determine z at some arbitrary and
precise (x, y). One implication of this is that there exists a multiplicity of possible
conceptual data models for spatial data, and that the choice between them for a
given phenomenon is one of the more fundamental issues of spatial data handling.
Another distinctive feature of spatial data is what Anselin (1989) refers to as
spatial dependence, the propensity for nearby locations to influence each other and
to possess similar attributes. Without spatial dependence, there would be no reason-
able prospect of creating even approximate views of continuous spatial variation
within a discrete, finite machine. It is not uncommon for tuples which have similar
values of a key to have similar values of other attributes, but the structure of spatial
dependence is unusual, relying as it does on both dimensions of the (x, y) key, with
similarity determined by a metric.
Finally, geographical data are distributed over the curved surface of the earth, a
fact which is often forgotten in the limited study areas of many GIS projects. We
have worried for centuries about how to portray the earth’s surface on a flat sheet
of paper, and have developed an extensive technology of map projections. However,
as a result we have few methods for analyzing data on the sphere or spheroid, and
know little about how to model processes on its curved surface. Moreover, we tend
to have treated GIS displays as if they were virtual sheets of paper, and insisted on
viewing geographical data as if they were projected to a flat surface, instead of
exploiting the potential of electronic display to create views of the globe itself. We
need to develop the appropriate techniques for working with the globe, and making
use of solid modeling rather than conventional two-dimensional graphics, if we are
to understand geographical processes at the global scale and contribute effectively
to global science. We must rescue the orthographic projection from its present
3 The content of geographical information science
Having established that geographical information has unique properties and prob-
lems, we can now review the set of generic questions which might make up a
geographical information science. This can be done in a largely linear fashion, from
data collection to analysis, although some themes tend to cut across this simple
arrangement. However, it seems appropriate to begin this review with a disclaimer.
What I present in this paper is in many ways my own view, and I would expect it
to be challenged. I think my own biases will become clear in what follows. Because
of the field’s diversity and dynamism it is difficult, if not impossible, for any one
individual to attempt a general overview. What follows is therefore almost inevitably
incomplete and uneven.
Research is often identified as either pure or applied — driven by basic and
innocent human curiosity or by the practical everyday needs of human society. Many
GIS are a response to human needs for information management and analysis, and
in that sense one might expect GIS research to be more applied than pure. However,
one view of pure research is that it is research that has not yet found application;
pure research is a long-term investment just as applied research is a short-term
investment. From an academic perspective, pure research is often associated with
higher prestige, but applied research with greater funding. I have tried to cover the
full range from pure to applied, feeling that both are important to GIS. At the same
time ‘basic research’ is the primary purpose of the U.S. National Center for Geo-
graphical Information and Analysis, and the center is very fortunate in being funded
to do research the applications of which may lie years or even decades into the future.
During the design phase of the CGIS in the 1960s, it became clear that the only
practical way to input the large number of maps needed would be by some form of
scanning device (Tomlinson et al. 1976). At that time no scanner for map-sized
documents existed, and it was necessary to invent one. A prototype drum scanner
was built by IBM Canada and successfully tested, at what by modern standards
would be regarded as vast expense. Other parts of the CGIS design team were busy
inventing other, equally fundamental and now familiar solutions to technical GIS
problems, such as the Morton order.
In the almost three decades of development of GIS that are now behind us,
similar ‘how to do it’ research has produced a large number of algorithms, data
structures, spatial indexing schemes and other technological solutions. Some of these
are unique to GIS, but many have been reinvented in several related disciplines. The
Morton order, for example, occurs in the literature of several spatial data handling
fields under different names (Samet 1989), and descriptions of algorithms for finding
Thiessen polygons are spread over a wide range of journals. At the same time there
is a growing sense in GIS research that our emphasis has changed, as more and
more of the underlying technical problems of GIS are solved. Attention has moved
from primitive algorithms and data structures to the much more complex problems
of database design, and the issues surrounding the use of GIS technology in real
applications. The following sections identify some of these key issues.
3.1 Data collection and measurement
If spatial reality is continuous and subject to complex structures of spatial depen-
dence, then how should it be compiled and measured? More generally, how do
people perceive the real world of geographical variation, structure it and learn about
it? Although many of these questions are part of the research agendas of remote
sensing, photogrammetry, geodesy and cognitive psychology, the lines of demarca-
tion are far from distinct. Should GIS or remote sensing concern the problems of
transferring information from one technology to the other, and more importantly
making good sense of it? Is it GIS or remote sensing if ancillary geographical
information is used to improve the accuracy of classification or if an image is used
to update a GIS layer? Ultimately it matters little to which of the many pigeonholes
we assign each topic. There are undoubtedly substantial scientific questions here,
which require a depth of understanding of the nature of spatial variation, and one
person’s remote sensing may well be another’s geographical information science.
The process of discretization, with its implied generalization, abstraction and
approximation, takes place as data are collected, interpreted or compiled, and choices
are made at this stage that affect the ultimate uses of the data. When those uses
change, as they have been doing with the widespread use of GIS, it may be necessary
or beneficial to rethink the process of data collection. For example, with digital
management and delivery of census data, is it still appropriate to conduct a census
on a decennial basis? Is the traditional approach to geological field mapping the
most appropriate if the eventual objective is a digital three-dimensional representa-
tion of the subsurface? How will topographic mapping change now that it is cost-
effective to survey new features using the Global Positioning System? Geographical
data collection is often the domain of specialists in well established disciplines, so
it may be many years before these kinds of questions are investigated or answered.
To date the introduction of GIS seems to have had very little effect on the process
of data collection.
3.2 Data capture
Enormous strides have been made in the technology for capturing digital geograph-
ical data in the past decade, and the systems now on the market are capable of a
high level of intelligence in interpreting scanned map documents. The problem
remains the poor quality of the documents, and the ambiguities that are caused by
aspects of map design. As a result, manual digitizing remains a widely used approach,
despite its high cost, tedium, and failure to show significant improvements in effi-
ciency. Two trends may change this situation substantially in the next few years.
One is the increasing avoidance of the map document as a step in the data compilation
and input process. Surveying and photogrammetry are moving away from compila-
tion using paper maps, and the more interpretive fields such as land use, vegetation
or soil mapping are likely to follow suit. The digital total station is likely to be
followed by the digital plane table and perhaps even the digital field geology note-
book. The other is the long recognized possibility that comparatively minor changes
in a map’s design can make it vastly easier to scan and interpret (Shiryaev 1987).
3.3 Spatial statistics
As spatial data are always an approximation or generalization of reality, they are
full of uncertainty and inaccuracy. A change of data model or scale can introduce a
loss of information, as can digitizing or scanning. Processing in a finite machine
also inserts its own form of uncertainty, although this is often insignificant in relation
to the errors inherent in the data themselves. Many human geographical constructs
are implicitly uncertain, including spatial objects (‘Indian Ocean’, ‘Europe’) and
their relationships (‘in’, ‘across’). Whether we think of uncertainty in set theoretical
terms through notions of fuzziness or in statistical terms through the calculus of
probabilities, the study of spatial data uncertainty, its measurement and modeling,
and the analysis of its propagation through the processes of spatial data handling
are undoubtedly part of geographical information science. How should one compile
an accurate representation of geographical variation for input to a database? How
should one represent the uncertainty or inaccuracy present in a digital representation?
How can uncertainty be propagated from database to GIS products?
Geographical data bring their own special set of problems to spatial statistics.
Whereas in medical imaging the problem may be to determine the true location of
objects from ‘dirty’ pictures (Besag 1986), in geographical images there is often no
clear concept of truth, as objects are often the products of interpretation or gener-
alization. We need much better methods of measuring and describing uncertainty,
particularly in the complex spatial objects common in GIS. We need better methods
for dealing with the world as a set of overlapping continua, instead of forcing the
world into the mould of rigidly bounded objects. Most of the answers to these
questions will have to come from spatial statistics, but geographical information
specialists must provide the motivation and the examples, and define the overall
objectives and constraints.
Although all geographical data are uncertain to some degree, all of the current
generation of GIS follow the common practice in cartography and represent geo-
graphical objects as if their positions and attributes were perfectly known; data
quality may or may not be addressed in a separate statement. The consequences of
uncertainty for GIS products are never estimated. Recent research has followed
several different and productive lines in attempting to address the problem of data
quality. One is to match precision to accuracy. In a locational sense, this means
using limited precision in data representation and processing, most often through
the use of a raster whose size is determined by data accuracy. Various forms of
quadtree structure have also bee used to fit locational precision to known levels of
accuracy. There have been several recent papers on finite resolution processing in
GIS (e.g. Franklin 1984, Dutton 1989) and finite resolution geometry is an active
research area in mathematics.
Another productive approach has been to incorporate techniques from geosta-
tistics, notably kriging, as the statistical basis of these techniques makes uncertainty
explicit. We now have several useful models of digitizing error, and its consequences
for estimated measures such as area (e.g., Chrisman and Yandell 1988, Keefer et al.
1988). Finally, there have been several successful efforts to model geographical data
sets as random fields, or derivatives of random fields, and to use this approach to
model uncertainty in GIS objects (e.g., Goodchild 1989). Between all of these
methods, we probably now have an adequate set of models of accuracy from which
to build an error-tracking GIS. However, spatial statistics is not an easy field, and
many of these techniques go well beyond elementary statistics in their conceptual
3.4 Data modeling and theories of spatial data
Data models are the logical frameworks which we use to represent geographical
variation in digital databases. As each must be an approximation, the choice between
alternative models constrains not only the functions available, but also the accuracy
of products. Of all the developments in GIS in the past decade, perhaps the most
exciting has been the proliferation of data models, and the growing literature on
their relative merits. The debate over raster and vector goes back to the earliest days,
but has now been joined by debates over objects, layers, the philosophy of object
orientation, hierarchical models of complex objects, and the entire range of possi-
bilities inherent in time dependence and three dimensions. Despite the interest, we
still do not have a complete and rigorous framework for geographical data modeling,
even in the static two-dimensional case, and without one it is difficult to see how
GIS can escape the constraints imposed by specific system implementations. How
much capability is being lost by forcing contemporary applications into the multi-
layer raster model used by many systems, or the point–line–area coverage model
used by many others? This is both a pure and an applied research problem. On the
one hand, we must develop a comprehensive framework for geographical data
modeling, with an associated terminology, to provide the basis for standards and an
ideal against which specific systems can be measured. On the other hand, an abstract
framework is of little value if it does not influence practice, through implementation
in the products of the vendors. Here the real issue is whether it is possible to enlarge
or ‘retrofit’ the data model underlying an existing product, or whether any attempt
to do so is doomed to cause inconsistency and incoherence.
These issues are precipitating lively discussion over the entire question of the
degree to which we view, analyse, represent and model the world as discrete or
continuous, as a collection of objects or a set of fields. Do we think in terms of
variables with defined values everywhere in space, or of an empty space littered
with possibly overlapping objects? In essence, these issues have brought the GIS
debate from the comparative obscurity of internal data structures to the much more
general issues of how we understand geographical variation. Everyday human expe-
rience sees a world of objects, but the science of natural processes deals more with
continuous variation (Frank and Mark 1991). Thus the object oriented debate threat-
ens to pit the New Agers against the embattled remnants of the Enlightenment, and
what could be more stimulating than that?
3.5 Data structures, algorithms and processes
Many of the results of basic research which have accumulated over the past 25 years
in this field of research concern internal representations of data, and the algorithms
which operate on them. The quadtree (Samet 1989), band sweep algorithms for
overlay (White 1977), analysis of computational complexity (Preparata and Shamos
1988) and the arc-node data structure (Peucker and Chrisman 1975) are all intellec-
tual breakthroughs of lasting significance. Many challenging problems remain, for
example in the design of efficient algorithms to minimize overposting and in other
areas of cartographic design, or in developing better methods for converting between
various terrain data models. Many systems now handle data through database man-
agement systems, and data structure issues have moved more and more into the
realm of computer science. We seem, however, to have reached a point where all of
the simpler, more generic problems have been solved, and where what remains is a
set of difficult, context-specific problems. It seems clear, for example, that further
advances in the conversion of terrain data models (for example, from contour to
TIN) will require a much better understanding of the nature of terrain (Mark 1979),
and will perhaps have to be specific to terrain type (e.g. fluvial versus glacial). There
will also continue to be a need for research on efficient methods of storage and
access to deal with the enormous volumes of data likely to become available in the
Geographical information systems have often been criticized for failing to give
adequate attention to principles of cartographic design (Buttenfield and Mackaness
1991), or for regarding the map as a simple store of information rather than a tool
for communication. If we think of the database as the truth, then a map is no more
than a store, as there is often a simple correspondence between objects in the database
and objects on the map. However, if the database is seen merely as an approximation
of the geographical truth, then the design of output displays is critical, as it can
affect the user’s view of the world. Such simple things as the choice of background
colour, or the contrast between adjacent polygons (McGranaghan 1991) can have a
The capabilities of electronic display go far beyond those of conventional car-
tography. We need research on the design of animated displays, three dimensional
display, the use of icons and metaphors in user interfaces, continuous gradation of
colour and tone, zoom and browse, multiple media including voice and pointing
devices, multiple windows which allow simultaneous access to spatial and temporal
series of multivariate data. We need to use the electronic medium to think far beyond
improvements to the design of choropleth maps. All of these are fundamental prob-
lems to a science of geographical information.
3.7 Analytical tools
A GIS is a tool for supporting a wide range of techniques of spatial analysis,
including processes to create new classes of spatial objects, to analyse the locations
and attributes of objects, and to model using multiple classes of objects and the
relationships between them. It includes primitive geometric operations such as cal-
culating the centroids of polygons, or building buffers around lines, as well as more
complex operations such as determining the shortest path through a network. The
functionality of leading products continues to grow, with no obvious end in sight.
Despite widespread recognition that analysis is central to the purpose of a GIS,
the lack of integration of GIS and spatial analysis, and the comparative simplicity
of the analytical functionality of many systems continues to be a major concern. In
the early days of the statistical package SAS, there was a very rapid increase in the
range of tests and techniques implemented in the system. Unfortunately, the same
has not been true of GIS, and remarkably little progress has been made in incorpo-
rating the range of known techniques of spatial analysis into current products.
There are many reasons for this. One obvious reason is the heavy emphasis in
the GIS marketplace on information management rather than analysis. The lucrative
markets for GIS technology have comparatively unsophisticated needs, emphasizing
simple queries and tabulations. Another is the relative obscurity of spatial analysis,
a set of techniques developed in a variety of disciplines, without any clear system
of codification or strong conceptual or theoretical framework. Even now it is difficult
to identify more than a handful of texts (e.g., Haining 1990, Upton and Fingleton
1985). Although one might expect that GIS could provide the basis for a system of
codification for spatial analysis, the poor level of current understanding of geograph-
ical data models is a major difficulty. Tomlin (1990) has made one of the few attempts
to add some sort of structure or framework to the proliferation of GIS functions,
which in the case of ARC/INFO is already around 103. We badly need a taxonomy
of spatial analysis, developed perhaps from an enumerated set of data models, but
going well beyond the primitive geometrical operations.
At this stage, integration of GIS and spatial analysis is proceeding slowly, in at
least three different modes. Some analytical capabilities are being added directly to
GIS, for example in the recent expansion of functionality in several modules for
network analysis. Some progress is being made in loosely coupled analysis, where
an independent analysis module relies on a GIS for its input data, and for such
functions as display. However, still missing is an effective form of tight coupling,
in which data could be passed between a GIS and a spatial analysis module without
loss of higher structures, such as topology, object identity, metadata, or various kinds
of relationships. At present this is impossible, to a large extent because of a lack of
standards for data models. Instead, coupling has to occur at a lower level, and higher
structures have to be rebuilt on an arbitrary basis.
Integration between GIS and spatial analysis might also take the form of a
language, whose primitive elements would represent the fundamental operations of
spatial analysis. The beginnings of such a language already exist in the macro
languages of many of the current generation of GIS, and in various attempts to
extend SQL to spatial operations. However, all of these are specific to, and heavily
dependent on limited data models, and there is remarkably little similarity between
them at this time. At Santa Barbara we have been attempting to define a common
language from an analysis of the languages used by a variety of current GIS, but a
more satisfactory solution would begin with the conceptual framework provided by
a comprehensive data model.
Another problem in integrating GIS and spatial analysis is that in the former
discretization of space is explicit, whereas in many forms of spatial analysis it is
often either implicit, or unspecified. Many forms of spatial analysis are written on
continuous fields, and fail to deal with the uncertainties introduced by the inevitable
process of discretization. For example, in GIS there can be no measure of slope that
is independent of discretization, and similarly the length of an area object’s boundary
is dependent on its digital representation. However, slope and length commonly
appear as unqualified parameters in spatial models. In this sense, the integration of
GIS and spatial analysis is a two-way process, in which the inadequacies of both GIS
and spatial analysis must be addressed.
Most of the current generation of GIS provide some sort of macro or script
facility, allowing the user to define products from complex sequences of operations,
but to invoke them with a single instruction. Although these often include the ability
to construct customized environments and interfaces, they do not as yet provide
tools which are specific to the needs of spatial analysis. One limited exception is
Prime/Wild’s ATB, a set of tools constructed on top of System/9 which allows the
user to work with complex analyses, visualize their sequences and manage interme-
diate results. Tools like this will be needed increasingly if GIS are to move into an
era of more sophisticated analysis and decision support, because it is not uncommon
for relatively simple GIS products to involve processing tens of layers through similar
numbers of primitive steps. We need to research methods for keeping track of data
lineage and error propagation, backtracking to recover intermediate results, and
preventing the user from combining operations in incorrect or meaningless ways
(Lanter 1990). We also need research on ways of incorporating this sort of analysis
into the GIS acquisition and planning process.
This emphasis on complex multistage analysis and the generation of products
from a multilayered database seems very different from research on knowledge based
systems, spatial reasoning and spatial query. One of the attractions of the GIS field
is its breadth of applications, and the correspondingly extreme variety of environ-
ments for the design of user interfaces. In data modeling, the important question is
not whether extended relational or object oriented models are better for geographical
data, but what types of geographical data are best modeled by each approach.
Similarly, the important research issue in the design of user interfaces is to determine
the optimal environment for each of the many types of GIS application. What is
best for a vehicle navigation system may be entirely different from what is best for
a forest resource manager with a deeply seated fear of keyboards and VDUs, either
colour or monochrome.
3.8 Institutional, managerial and ethical issues
Research is just beginning to appear on the issues involved in implementing and
managing GIS, especially in large institutions. This is difficult research, and general-
izations are not discovered easily. However, the success of several large projects in
the U.S.A., and the discussions surrounding several large acquisitions by federal
agencies, have created the opportunity for a number of useful case studies. Many more
are needed, particularly given the importance of such research for improving the
institutional environment in the future. We need a much better understanding of
the processes of adoption of GIS technology and its effects on organizations; of the
value of geographical information and the benefits of GIS; and of processes for utilizing
geographical information in decision making. Theoretical frameworks for addressing
many of these issues already exist in the relevant social science disciplines, and we
need to make much more effective use of them in tackling the specific issues of GIS.
Despite the problems involved in adopting any new technology, GIS have been
widely adopted in local government, utilities and resource management agencies.
In fact, the introduction of GIS has had a major effect on the management of
geographical information in society. At the same time there is increasing concern
over the power of GIS for surveillance and invasion of privacy. The research com-
munity has a responsibility to monitor and study the more substantive aspects of the
GIS phenomenon, including its significance to society as a whole. What will GIS
mean to the balance of power in society? Will it be a technology available only to
the empowered, or will it somehow serve to even the distribution of power? Thus
far there have been remarkably few studies of the ethics of GIS.
4 Tests of commonality
The preceding sections have looked at various candidate areas for inclusion in a
geographical information science. In each case there are clearly challenging scientific
questions to be posed and researched. There is no reason to believe that the list is
complete, or that there are not additional and substantive questions in other related
areas. In each case the spatial context appears to be distinctive, although clearly it
is more so in some than others. For example, we might debate whether the spatial
context was distinctive in the area of decision theory, but the issue seems clear-cut
for data modelling.
In the NCGIA research plan (NCGIA 1989), we argued that the absence of
solutions to issues such as these constituted impediments to the effective applications
of GIS technology. Other discussions of the GIS research agenda have come to
similar conclusions, although with different emphases (Craig 1989, Maguire 1990,
Masser 1990). Many are old issues, recognized long before the advent of GIS in
fields such as cartography, geodesy and geography. Some may not be unique to GIS.
For example, it is not immediately obvious that GIS technology diffuses in a fun-
damentally different fashion, or shows fundamentally different patterns of adoption
from other technologies. Is the measurement of GIS benefits a unique problem, or
an example of the more general problem of measuring the benefits of information
technology? Of course these questions are in themselves research issues.
At the same time it is very important to identify those areas where GIS have
created new and unique issues that are not common to other fields. In the early days
of GIS, it was possible to argue that the technology was filling an existing gap, and
making possible tasks that had been previously identified, but that were not easy to
carry out manually. The use of GIS or suitability analysis, by overlaying layers
(Tomlin 1990), mirrors the manual technique popularized by McHarg, although
admittedly adding some interesting new capabilities. CGIS was justified on the
grounds that the computer was a cost-effective alternative to hand measurement of
overlaid areas. But GIS make it possible to do things with data that the data’s
gatherers may never have envisioned. GIS technology is producing radical changes
in the way geographical data are collected, handled and analysed, and it will be
many years before the impact of existing technology is felt, let alone the impacts
of future developments.
Here are some of the issues that seem unique to GIS: how to model time-
dependent geographical data; how to capture, store and process three-dimensional
geographical data; how to model data for geographical distributions draped over
surfaces embedded in three dimensions; how to explore such data, for example, what
exploratory metaphors are useful; and how to evaluate the geographical perspective
on information and processes relative to more conventional perspectives?
These are important issues for GIS, and the GIS community needs a strong
commitment to research if it is going to make significant progress on them. As issues
that arise within the context of GIS, they are not of major concern in other disciplines.
However, at the same time the GIS community can benefit enormously from inter-
disciplinary research. Statisticians can make a very valuable contribution to solving
the error problem in GIS, and research in cognitive psychology may be helpful in
designing the cognitive aspects of user interfaces in GIS.
This argument leads naturally to a proposed definition of GIS research: research
on the generic issues that surround the use of GIS technology, impede its successful
implementation, or emerge from an understanding of its potential capabilities. Is
this ‘research about GIS’ or ‘research with GIS’? In a sense it is both, because these
are issues that are both fundamental to the technology of GIS, and also issues that
must be solved before the technology can be successfully applied. If the problems
of doing research with GIS are generic, then they are best tackled as part of the GIS
research agenda. However, problems that are specific to the application of GIS in a
particular field clearly need to be addressed in the context of that field, and with the
benefit of its expertise. Accuracy issues provide a useful example. There are aspects
of the accuracy problem that span a wide range of types of geographical data, and
need to be solved using generic models of uncertainty, analogous to the role played
by the Gaussian distribution in the theory of measurement error. However as noted
earlier, an analysis of crime data using a GIS will also raise problems of accuracy
that are specific to that particular application, and need an understanding of the
processes operating in criminology and in the collection of crime data if they are to
be understood fully.
However, mere existence of scientific questions is far from an adequate basis
for a science. Is there a commonality of interest here? Can these subfields find
sufficient basis for interaction that they will develop the lasting accoutrements of a
science, such as journals, societies, books and philosophers? Will researchers in
these subfields behave as a group of scholars? Is there a valid analogy between the
systems and science of geographical information on the one hand (tools supporting
researchers) and statistical packages and statistics on the other? Statistics is a highly
formalized discipline, but more technologically oriented groups can be found in such
areas as exploratory data analysis, statistical visualization and applied statistics.
Certainly the relationship between science and tools is stormy at times, but never-
theless vital to the success of both. The ongoing debate over the value of statistical
software in teaching statistics has interesting implications for the same issue in GIS.
It may be useful to look briefly at the arguments for a commonality of interest
in geographical information science, first in principle and then in practice. The field
is small — rhetoric about growth in the industry aside, no one would suggest that
the field of GIS is a major discipline. It is distinct, with its own reasonably unique
set of questions. And it is certainly challenging and innately appealing. On the
negative side, it is multidisciplinary, competing with longstanding cleavages and
rivalries. It lacks a core discipline, unlike the statistical analogy, where there has
been a steady growth in the number and size of academic departments for the past
few decades. One of the claimants to the core, geography, has traditionally been a
non-technical field, and in some areas of social geography there is a strong and
fundamental antipathy to technological approaches.
In practice, commonality of interest is evident in the proliferation of GIS meet-
ings, and we are beginning to see a supply of books and journals. However, the
scientific track at GIS meetings is often small. People who attend GIS meetings
need a constant supply of novelty, whether in scientific research or vendor products,
and will soon desert if the supply dries up.
5 Options for the future
Looking back over nearly three decades of GIS research, it is clear that the greatest
progress has been made on the best defined and easiest problems, where solutions
lay in advances in the technology itself. Rapid progress was made on algorithms
and data structures in the 1970s and 1980s, but many of the difficult problems of
data modeling, error modeling, integration of spatial analysis and institutional and
managerial issues remain. Some of these may be unsolvable: for example, there may
simply be no generalities to be discovered in the process of adoption of GIS by
government agencies, however easy it may be to pose the research question.
Other issues have already been solved in a pure research sense, but implemen-
tation remains a major question of applied research. In accuracy, for example, a
substantial set of techniques has been defined, but the problem of moving them into
actual application remains. The academic research environment is set up to pursue
significant areas of research, but is generally poor at providing the means of imple-
mentation. For that we need a software industry that is tightly coupled to the research
community, but able to find the resources to motivate development. More impor-
tantly, we need an education system that responds rapidly to new research and is
able to build new concepts quickly into its programmes. Unfortunately, the higher
education sector is too often characterized by conservatism, and it may take many
years for new ideas to work themselves into the curriculum.
Research in GIS is like geographical data — the more closely one looks, the
more interesting issues appear. GIS research has only begun to tackle the important
issues in the research agenda. We are in an enviable position, working in a field with
such strong motivation and such a strong underlying industry, and with such an
interesting set of problems spanning so many disciplines and fields. I hope I have
shown in this paper that the handling of spatial information with GIS technology
presents a range of intellectual and scientific challenges of much greater breadth
than the phrase ‘spatial data handling’ implies — in effect, a geographical informa-
tion science. The term ‘geographical’ seems essential — much of what GIS research
is about concerns the geographical world and our relationships with it, and the term
is much richer than ‘spatial’. The change in meaning of the ‘S’ word — from systems
to science — seems to be going well, as evidenced by the success of the spatial data
handling series of conferences, the move of the AutoCarto series to fully refereed
papers, the new texts, subscriptions to the International Journal of Geographical
Information Systems, and submissions of GIS papers to such established journals as
Geographical Analysis, Computers and Geosciences, Computer Vision, Graphics and
Image Processing and publications of the Regional Science Association and the IEEE.
I hope I have also shown that a strong scientific programme serves not only
itself, but also the needs of industry and GIS users. GIS needs a strong scientific
and intellectual component if it is to be any more than a commercial phenomenon,
a short-lived flash in the technological pan. It is too easy to see current GIS as a
hardware and software technology in search of applications, and to see the field of
GIS as defined by the functional limits of its major vendor products. We need to
move from system to science, to establish GIS as the intersection between a group
of disciplines with common interests, supported by a toolbox of technology, and in
turn supporting the technology through its basic research. As currently perceived,
GIS sometimes seem about as close to a science as FORTRAN is to algebra.
In recent years we have seen a growing cleavage in GIS between two traditions,
that of spatial information on the one hand and that of spatial analysis on the other.
The spatial information tradition stresses large inventory databases, and gives geog-
raphy the role of an access mechanism. The spatial analysis tradition stresses rich
functionality and a range of data models, and gives geography a fundamental role
in analysis and modelling. The two traditions share common data structures and
algorithms, and rely on the same sources of data and hardware. However, this is not
enough to convince the academy of the existence of a scientific field. To claim this
we need to take a broader view, and to include data modelling accuracy, cognition,
reasoning, human–computer interfaces (HCI) and visualization, and to show how
these are integral parts of both traditions.
Without such arguments, the GIS field will fragment, and the GIS storm will
blow itself out. Associations as fundamentally disjoint as the Association of Amer-
ican Geographers and AM/FM will find it impossible to justify joint sponsorship of
conferences. Vendors will specialize in data input workstations, spatial analysis
workstations or facility management systems, with little potential for interaction or
integration. This would be tragic.
How can we ensure a lasting future for both geographical information systems
and science? Disciplines are like tribes, with their own totems, symbols and mem-
bership rules, languages and social networks. The GIS tribe is currently very cohe-
sive; it is well funded, the field is exciting and much useful research is being done.
However, in the longer term the field has not done well at behaving as a science,
and the academy is still doubtful about whether it needs to be taken seriously. Science
is hard and places heavy obligations on its practitioners. We have been too busy,
and technology has been moving too quickly. Too much of our literature is in
conference proceedings, which bring fast exposure but only to limited audiences,
and lack sufficient quality control. Few people have had the time to write the
textbooks or to identify the intellectual core, or to publish the good examples.
I believe we ensure the future of GIS by thinking about science rather than
systems, and by identifying the key scientific questions of the field and realizing
their intellectual breadth. Geographical information systems are a tool for geograph-
ical information science, which will in turn lead to their eventual improvement. We
need to speak to the academy, both directly and through key articles and texts, on
the philosophy, methodology and foundations of the field, and by placing GIS papers
in strong journals. All three communities — users, vendors and researchers — have
vital and symbiotic roles to play, and we will serve all three best by playing ours
in the fullest possible sense.
The National Center for Geographic Information and Analysis is supported by the
National Science Foundation through grant SES 88-10917.
Anselin, L., 1989, What is special about spatial data? Alternative perspectives on spatial data
analysis. Technical Report 89-4 (Santa Barbara, CA: National Center for Geographic
Information and Analysis).
Berry, J. K., 1987, Fundamental operations in computer-assisted map analysis. International
Journal of Geographical Information Systems, 1, 119–136.
Besag, J., 1986, On the statistical analysis of dirty pictures. Journal of the Royal Statistical
Society, B48, 259–302.
Buttenfield, B. P., and Mackaness, W. A., 1991, Visualiztion. In Geographical Information
Systems: Principle and Applications edited by D. J. Maguire, M. F. Goodchild and
D. W. Rhind (London: Longman).
Chrisman, N. R., and Yandell, B. 1988, A model for the variance in area. Surveying and
Mapping, 48, 241–246.
Craig, W. J., 1989, URISA’s research agenda and the NCGIA. Journal of the Urban and
Regional Information Systems Association, 1, 7–16.
Dutton, G., 1989, Modeling locational uncertainty via hierarchical tessellation. In Accuracy
of Spatial Databases, edited by M. F. Goodchild and S. Gopal (London: Taylor &
Francis), pp. 125–140.
Frank, A. U., and Mark, D. M., 1991, Language issues for GIS. In Geographical Information
Systems: Principles and Applications, edited by D. J. Maguire, M. F. Goodchild and
D. W. Rhind (London: Longman).
Franklin, W. R., 1984, Cartographic errors symptomatic of underlying algebra problems.
Proceedings, International Symposium on Spatial Data Handling, Zurich, pp.
Goodchild, M. F., 1989, Modeling error in objects and fields. InAccuracy of Spatial Databases,
edited by M. F. Goodchild and S. Gopal (London: Taylor & Francis), pp. 107–114.
Goodchild, M. F., 1990, keynote address: spatial information science. Proceedings, Fourth
International Symposium on Spatial Data Handling, Zurich, 1, 13–14.
Goodchild, M. F., 1991, Keynote address: progress on the GIS research agenda. Proceedings,
EGIS 91, Brussels, pp. 342–350.
Haining, R., 1990, Spatial Data Analysis in the Social and Environmental Sciences (Cam-
bridge: Cambridge University Press).
Keefer, B. J., Smith, J. L., and Gregoire, T. G., 1988, Simulating manual digitizing error with
statistical models. Proceedings GIS/LIS 88 (Falls Church, VA: American Society of
Photogrammetry and Remote Sensing/American Congress on Surveying and Map-
ping), pp. 475–483.
Lanter, D. P., 1990, Lineage in GIS: the problem and a solution. Technical Paper 90-6 (Santa
Barbara, CA: National Center for Geographic Information and Analysis).
Maguire, D. J., 1990, A research plan for GIS in the 1990s. The Association for Geographic
Information Yearbook 1990, (London, Taylor & Francis), pp. 267–277.
Mark, D. M., 1979, Phenomenon-based structuring and digital terrain modeling. GeoProcess-
ing, 1, 27–36.
Masser, I., 1990, The Regional Research Laboratory initiative: an update. The Association
for Geographic Information Yearbook 1990 (London: Taylor & Francis), pp. 259–263.
McGranaghan, M., 1991, Modeling simultaneous contrast on choropleth maps. Technical
Papers, 1991, ACSM/ASPRS/Auto-Carto 10 Annual Convention, Baltimore, MD,
March 25–29, 1991, Vol. 2, pp. 231–240.
McHarg, I. L., 1969, Design with Nature (New York: Doubleday).
NCGIA (National Center for Geographic Information and Analysis), 1989, The research plan
of the National Center for Geographic Information and Analysis) International Jour-
nal of Geographical Information Systems, 3, 117–136.
Openshaw, S., 1977, A geographical solution to scale and aggregation problems in region-
building, partitioning and spatial modelling. Transactions of the Institute of British
Geographers, 2 (NS), 459–472.
Peucker, T. K., and Chrisman, N. R., 1975, Cartographic data structures. American Cartog-
rapher, 2, 55–69.
Preparata, F. P., and Shamos, M. I., 1988, Computational Geometry: An Introduction (New
Samet, H., 1989, The Design and Analysis of Spatial Data Structures (Reading, MA: Addison-
Shiryaev, E. E., 1987, Computers and the Representation of Geographical Data (New York:
Tomlin, C. D., 1990, Geographic Information Systems and Cartographic Modeling (Engle-
wood Cliffs, New Jersey: Prentice Hall).
Tomlinson, R. F., 1984, Keynote address: geographical information systems — a new frontier.
Proceedings, International Symposium on Spatial Data Handling, Zurich, 1, 2–3.
Tomlinson, R. F., Calkins, H. W., and Marble, D. F., 1976, Computer Handling of Geograph-
ical Data (Paris: UNESCO).
Upton, G. J., and Fingleton, B., 1985, Spatial Data Analysis by Example (2 volumes) (New
White, D., 1977, A new method of polygon overlay. Proceedings, Advanced Study Symposium
on Topological Data Structures for Geographic Information Systems, Harvard.