DECISION SUPPORT, GIS, AND URBAN PLANNING
by
Michael Batty & Paul J. Densham
January 1996
Centre for Advanced Spatial Analysis
University College London, 1-19
Torrington Place
London WC1E 6BT, UK
ABSTRACT
Computers have been applied in urban planning almost since
their inception, but only recently with the development of graphics, distributed
processing, and network communications has software emerged which can now be
used routinely and effectively. At the basis of these developments are
geographic information systems (GIS) but gradually, these are being adapted to
the kind of decision and management functions that lie at the heart of the
planning process. In this brief note, we describe current developments showing
what is now possible in the development of spatial decision support systems
(SDSS), and planning support systems (PSS), and we then speculate on future
developments in decentralised decision-making which will dominate the field in
the next decade.
1. Computers in Urban Planning and Management
Computing devices have
been used in public planning for 100 years. Hermann Hollerith invented the
punched card machine at the turn of the century for the US Population Census,
and this eventually led to the formation of the world's largest computer
company, IBM. Once the digital computer was developed half century later,
applications in public planning and management became widespread. By the mid
1950s, population and transportation data were being processed by computers and
these were quickly followed by various simulation modelling efforts. By the late
1960s, urban data management systems were being widely implemented by public
agencies for a variety of routine and less routine management and strategic
planning functions. This experience has been well documented (Edralin, 1986) but
in the last 10 years, applications of computers in planning have changed
dramatically (Batty, 1995). The top-down approach based on remote, large-scale,
database computing has been replaced by a much more personal computing style in
which graphical display of urban data now provides the focus.
This bottom-up style is largely a consequence of changes in computing
technology. Once the microprocessor was invented, the path to miniaturisation
and personalisation was set and as the cost of memory fell dramatically, more
and more applications involved graphical computing. Geographic information
systems (GIS) are an obvious application but the way computers are being
accessed and results displayed is now largely graphic - witness the widespread
dissemination of recent Windows-based software - and this has led to a sea
change in the way computers are being applied in planning. There has also been a
change in types of application over the last 20 years. There is now much more
emphasis on data than on modelling, on routine applications for management
rather than the more grandiose applications to strategic planning which
dominated the 1950s and 1960s. This is reflected as much in the way planning is
now perceived in its current role in advanced (post) industrial societies, as in
the way the technology has changed.
These changes can even be detected in the development of GIS. 20 years ago,
the early beginnings of GIS were as an adjunct to strategic planning,
particularly in landscape and resources management. Software vendors such as
ESRI and ERDAS began this way while companies such as Intergraph came directly
from computer-aided design (CAD). In the last 10 years, the emphasis has shifted
to graphic display, the representation of spatial data, and its manipulation in
quite straightforward ways. In terms of planning and problem-solving processes,
to date there has been very little emphasis on formal analysis, simulation and
modelling and hardly any at all on design and decision-making aids. However this
picture is changing and new functions are being slowly added. In the next 10
years however, the use of computers in planning will clearly be affected by
developments in computer use in general - across networks based on decentralised
interaction between users - and it is likely that we will see a much greater
emphasis on informal decision-making using computers interactively. We will
return to these issues by way of conclusion.
2. Planning and Spatial Decision Support
Planning and management are
based on a generic problem solving process which begins with problem definition
and description, involves various forms of analysis which might include
simulation and modelling, moves to prediction and thence to prescription or
design which often involves the evaluation of alternative solutions to the
problem. Decision characterises every stage of this process while the process of
implementation of the chosen plan or policy involves this sequence once again.
The process takes place across many scales and is clearly `iterative' or
`cyclic' in form. Processes may be nested within one another while the extent to
which different professionals, managers and other decision-making interests are
involved through the various stages, depends upon the nature of specific
applications and their context. In practice, the process is often partial and
much diluted from this more formal characterisation. The typical process
illustrated in Figure 1, however, remains a basis for action.
Figure 1: The Planning Process as a Sequence of
Computable Methods Enabling Decision Support
Click here for
a full-size .GIF image (12 KB)
Figure 1 shows how GIS and relate modelling technologies fit within this
process. Indeed, this is the kind of structure that Harris (1989, 1991) refers
to as a planning support system (PSS) which links a variety of computer-based
software supporting decisions at different stages of the planning process
(Batty, 1995). As we have implied, this kind of process is rarely executed in a
comprehensive fashion and usually, only a few elements of it exist, often to the
exclusion of others. For example, many involved in public planning have access
to GIS but few are involved in linking GIS to modelling and forecasting while
the development of formal design methods using GIS is in its infancy (Manheim,
1986). In fact, a major research program involves the use and adaptation of GIS
through embedding and linking various types of predictive and prescriptive
models in formal terms. Strategies for such linking range from weak to strong
coupling (Batty, 1994). Models can be linked to GIS simply through the import
and export of data - weak coupling - while much stronger coupling exists where
models are embedded within GIS or GIS functions within models. We will
demonstrate, albeit rather briefly, examples of such coupling in the next
section where we show how urban spatial interaction (predictive) models and
location-allocation (prescriptive or optimising) models can be linked to GIS.
3. Spatial Technologies for Design and Decision Support
Before we are
able to demonstrate these ideas, we must briefly review the development of
spatial technologies of which GIS are central. Spatial technologies involve any
kind of software which is essentially descriptive of data with an explicit
spatial or geographical dimension. Mapping software is not explicitly spatial
for often data is not georeferenced in a form that can be manipulated, and thus
not strictly part of those technologies we define as being spatial. The
conventional definition of spatial technologies are those which have integrated
and explicit functions for storing, manipulating and displaying spatial data
where the spatial dimension is the key to each of these functions.
Geographic information systems are the main spatial technologies to date
although increasingly other systems dealing with spatial data are acquiring
spatial database and display capabilities. For example, the remote sensing
package Imagine from ERDAS can be used as a GIS while GIS packages such
as ARC/INFO and Intergraph's MGE provide very clear and
well-defined links to other mapping, CAD, and remote sensing software. Even the
growing desktop GIS packages such as MapInfo and ArcView 2 contain
formal links and macro languages which enable their functionality to be extended
directly through new programming or indirectly through links to other software.
There are many examples of extended functionalities which link GIS to analysis
and modelling and we will illustrate two varieties here. We have developed some
of the commonly used spatial interaction-location models based on simulating
transport flows from home to work within established GIS frameworks such as
ARC/INFO. In Figure 2, we show a typical screen from an application of
GIS to urban population density modelling which we have developed for the
Buffalo region in Western New York. We have defined a modelling process
consisting of data analysis, model calibration, and prediction as a set of
relations embedded within a GIS. This system uses ARC/INFO as the display
medium but also uses the software as the organising frame for the sequence of
analysis and modelling operations which are accessed as links to the outside
world through system macros.
Figure
2: Embedding the Modelling Process within the Proprietary GIS
ARC/INFO
Click here for
a full-size GIF image (49 KB)
The advantages of using GIS to structure simulation modelling is in the way
this software is neutral to its sources of data. Once generic data analysis
functions are set up, these can be applied to observed data, model results,
forecasts, designs, whatever. Data functions thus dominate the system. In short,
although the GIS acts as the framework, most of its relational functions are
never actually used, yet the structure of its software forms the essential
organisation of the application. In the ArcPlot frame which is shown, the
popup-pulldown menuing window at the top shows the sequence of modelling
operations, each element of which is accessed through the Arc Macro
Language link to other program modules, while the display of results of
these operations is the GIS itself. In the screen which is shown, the model has
already been calibrated and thematic maps of observed data, model predictions
and residuals are shown. There are many other graphics features such as 3-d
surfaces, scatter plots, and dynamically-linked or `hot' windows in the system,
all accessible through a hierarchy of menu items (Batty and Xie, 1994).
A very different approach but one which leads to the same kind of application
involves developing purpose-built GIS type functions within a specific modelling
package. In short, rather than embedding less elaborate models within a
comprehensive GIS, it is possible to embed a limited range of GIS functions
within a more elaborate modelling framework. In Figure 3, we show a typical
example of a modelling process which has been developed within a purpose-built
visual environment, the model in question being based on simulating the
interaction between homes and workplaces through the journey-to-work for a crude
representation of the city of Melbourne, Australia (Batty, 1994). The modelling
process - beginning with data exploration/analysis, model calibration, and then
prediction/forecasting - is used to structure the visual layout with the main
portion of the screen given over to maps of data and results which pertain to
these three stages in the process. Moreover, these maps are drawn as bar charts
to indicate population and employment at different locations. Interzonal and
intrazonal flows can be visualised directly whereas these types of flow map are
difficult to customise and display quickly.
The problems of course lie with the absence of interactive functions such as
zoom and the way these are controlled with pointer devices such as the mouse.
Although we have built a rudimentary zoom (aggregation) facility into this
particular program, this is quite limited and were we to really attempt to
replicate the efficiency of proprietary systems, we would in effect be
replicating GIS. Nevertheless, the interactive modelling process implied by
Figure 3 comprises less than 3000 FORTRAN statements and as the user is
so close to both the graphics and the model, changes to the visualisation can be
made at will. Nevertheless, the system has to be so closely tailored to the city
in question that even changing the problem's size - from 8 to say 80 zones -
causes major changes in the visualisation which necessitates reprogramming.
Using proprietary GIS as in the Buffalo application avoids this.
Figure
3: Embedding Simple GIS Functions within a Visual Modelling
Environment
Click here for
a full-size GIF image (43 KB)
These two examples extend GIS to embrace predictive modelling but to
progress the planning process, GIS must be used for prescriptive or optimising
modelling which involves the design of solutions to formally structured
problems. This is perhaps a more focussed form of decision or planning support
although the use of GIS in any of the stages of the planning process shown in
Figure 1 involves the generic concept of decision support. Densham (1991) has
developed a suite of programs called LADSS (Location-Allocation Decision
Support System) which link heuristic optimisation techniques for matching the
supply of various facilities such as schools, shopping centres, or hospitals to
the demands for these same facilities by the affected population. Various
objective functions can be optimised but typically these involve functions which
minimise distance, travel time or travel costs between the demand and supply
points. Developing such models within GIS provides very powerful visualisation
facilities for display and manipulation, giving immediate intuitive evaluation
capabilities which a wide range of non-technical users and decision makers can
relate to. In Figure 4, we show one such screen from the output of LADSS
where the application is to school catchment districts in Iowa. Here the various
flow lines show the optimal allocation of schools to area offices, and within
such a visual environment for modelling, many different varieties of solution
based on widely different assumptions can be explored. In short, this kind of
visual environment provides the basis for informed learning about the problems
in question as well as providing the obvious outputs as locational solutions.
Figure
4: Allocation of School Districts to Area Education Authority Offices in
Iowa to minimise Travel Distance on the Primary Road System
Click here for
a full-size GIF image (35 KB)
There are many other types of visualisation capability which GIS provides
but we are not able to present those which pertain to data itself due to lack of
space. Animating data is possible both in temporal terms but also through ways
of exploring, hence understanding urban patterns. All we can do here is to refer
readers to the literature but increasingly such applications show ways in which
GIS and spatial technologies in general are coming to embrace all varieties of
analysis from near-data to near-design kinds of model (Batty and Howes, 1996;
Densham, 1996).
4. Digital Environments for Decision Support
Our last foray into
developments in computing and GIS likely to affect urban planning in the next
decade involves the ways integration between spatial representation, modelling
and optimisation-design will be implemented in the coming years. Increasingly
these will take place in a digital environment which itself will be integrated
through networking. As an example of this, much of the data in the examples
which we have shown in Figures 2 to 4 has been retrieved and manipulated over
networks and over different platforms. But this has usually been by individuals
or small groups and as yet the more general user has not been involved
interactively with such applications. The next decade will see the development
of whole groups of non-technical users in planning being directly involved in
the use of this kind of information technology across distributed networks. The
current growth of the Internet and the World Wide Web is clear evidence of the
potential for this kind of integration and more and more data and applications
software are available in this medium. For example, at University College
London, we are building an interface to information about London which we refer
to as `Virtual London'. As a pilot to this project, we have an online version of
University College which essentially enables the user to visually wander around
the college in 3-d and pick up information about buildings, rooms, and people in
them. If you want to examine this and play with it yourself, it is available in
the public domain. If you click on our home page whose address is
http://www.geog.ucl.ac.uk/casa/ you can then download the relevant software and
run the application on a Mac or PC. This is the kind of application which will
become commonplace during the next decade.
Computing environments for collaborative, distributed spatial decision making
have been explored by Densham and Armstrong (1994) and a version of this on
which systems such as LADSS might run, is shown in Figure 5.
Heterogeneous processing environments such as this can support both individual
and group use of spatial decision support systems (SDSS). Each computer
architecture in this scheme provides a particular mix of processing
capabilities. Some architectures provide very high performance for a particular
subset of processing tasks while others trade performance, and glamour, for
flexibility. Similarly, the processing requirements of different kinds of
software vary greatly and dictate their suitability for different computing
platforms. SDSSs tend to have large computational burdens and diverse ranges of
processing requirements that make it difficult for any single computer
architecture to accommodate them effectively. Consequently, SDSS designers are
turning to suites of computers with heterogeneous processing characteristics to
support their systems (Densham and Armstrong, 1994). The processing requirements
of individual elements of a SDSS are analysed and the best available host
architecture is identified. Each user task is shipped to the appropriate
computers for processing across very high bandwidth communications channels.
Both individual users and groups working together to solve complex spatial
problems can be supported in this way.
Figure
5: The Use of Heterogeneous Processing Environments to Support Individual
and Group Use of Spatial Decision Support Systems
Click here for
a full-size GIF image (45 KB)
One of the benefits of this approach is that highly visual and interactive
modelling and analysis environments can be implemented (Densham, 1996). An
intrinsic element of such environments are new types of graphical display, such
as that in Figure 4, designed explicitly to meet users' needs at various stages
during problem-solving, that can be built by exploiting the computing resources
available in heterogeneous computing environments (Densham and Armstrong, 1993).
What is now required is much more considered analysis of the ergonomics and
problem-solving capabilities of such technologies, and the next decade is also
likely to see much greater emphasis on the ways in which technologies can be
used to improve processes of human interaction, hence processes and methods of
problem-solving.
5. The Future of GIS
GIS and related spatial technologies are suddenly
expanding to embrace many traditionally separate functions. At the same time,
many other types of software are beginning to add GIS-like functionality:
spreadsheets and their improved graphics capabilities in handling 2-d maps and
3-d visualisations are a case in point. In one sense, software is breaking up on
the desktop into basic modules which can be hooked together in diverse ways
while other software is becoming increasingly generic in that all manner of
textual, numerical, and graphical functions are being included under the same
rubric. GIS itself is changing as more functions are embodied in hardware and as
the vendors increasingly begin to specialise in applications, in data, and in
specialist niche markets based on computer services. In this paper we have
sketched the contemporary scene with respect to urban planning, where the future
is likely to be dominated by smaller, finer scale applications at the level of
urban design, the increasing development of 3-d GIS capability, and the use of
urban remote sensing for data capture and generation. Many of these applications
and extensions are underway at different centres around the world, as part of
the European Science Foundation's GISDATA program, as part of the US National
Science Foundation's National Center for Geographic Information and Analysis
(NCGIA) program, and at centres like our own here at the University of London.
The integration of diverse software and methods will have a major impact on what
we are able to plan for and how we might develop effective planning for complex
urban environments in the next decade.
References
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Created 15/1/96
Last modified by Paul Densham on 11/8/2000 (or
8/11/2000 depending on your accent)
pdensham@geog.ucl.ac.uk