contoh jurnal internasional

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Jan 01 2011

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.

1 Introduction

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-

seeable application.

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

(Openshaw 1977).

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 . 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

obscurity.

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

sophistication.

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

coming decade.

3.6 Display

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

significant effect.

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.

Acknowledgments

The National Center for Geographic Information and Analysis is supported by the

National Science Foundation through grant SES 88-10917.

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