Using a GIS as a DSS Generator

by Peter Keenan,
Senior Lecturer, University College Dublin

An earlier version of this article appeared as Keenan, P. (1996) "Using a GIS as a DSS Generator", in Perspectives on DSS, edited by J. Darzentas, J. S. Darzentas and T. Spyrou, University of the Aegean, Greece, pp 33-40. The article was updated in April 1997 and minor changes were made in December 2004.


This article discusses the use of a Geographic Information System (GIS) as a Decision Support System (DSS) generator to create Spatial Decision Support Systems (SDSS). Many important areas of DSS application, such as routing and marketing, make use of spatial information. Development of Spatial DSS will allow effective support to be provided for decisions which make use of spatial data.


Decision Support Systems (DSS) is a well-established area of information systems (IS) application. Academic research in the DSS field dates from the work of Gorry and Scott-Morton (1971). While there are many definitions of a DSS, there is general agreement that these systems focus on specific decisions and on supporting rather than replacing the user's decision making processes. Definitions of DSS also emphasise the need to support semi-structured and unstructured decisions. Also, there is a general consensus in the definitions of DSS that interface, database and model components are usually required to fully support decisions.

In the period since DSS came to prominence there has been considerable growth in the importance of geographic information systems (GIS). This growth in GIS reflects the decreased cost of the required technology and the increasing availability of appropriate spatial data. Recent improvements in mainstream computer technologies facilitate this spread of the use of spatial data. These include inexpensive gigabyte sized hard disks, large high resolution colour monitors, graphics accelerators and CD-ROM storage. This explosion in the use of computer technology can also be seen in other areas, where a virtuous circle of declining hardware costs leads to larger software sales and therefore reduced software costs.

This trend of greater use of spatial information has led to some mapping functionality being included in software available on a mass market basis, for example the inclusion of simple mapping facilities in the Lotus 1-2-3 Release 5 spreadsheet. A similar mapping facility was introduced in Microsoft Excel version 7. While additional map display functionality is likely to be introduced in the future in spreadsheet and other popular software, this is unlikely to provide the full range of spatial query operations associated with a full fledged GIS. However this mass market use of mapping and GIS products creates a further extension of the demand for spatial data, increasing amounts of which are becoming available. Decision makers who make use of basic mapping products, such as those provided with spreadsheets, are likely to become aware of the need for more sophisticated software. Furthermore spreadsheet users come from fields that are not traditional users of GIS technology. If even a small proportion of users of mainstream business applications were attracted to using geographic data, it would represent a large increase in the market of GIS software and data.

Using GIS for Decision Support

Within the GIS field there is increasing interest in the use of GIS software to provide decision support. This is reflected in a GIS conference titled "DSS 2000" and in the increasing appearance of papers referring to spatial decision support systems (SDSS) at GIS conferences. While an increasing number of GIS based applications are described as being DSS, these descriptions suffer from a lack of agreement on what exactly a DSS actually constitutes. As Maguire (1991) points out, some authors have argued that a GIS is a DSS. However the description of these GIS applications as being DSS is not based on reference to the DSS literature. Many GIS based systems are described as being DSS on the basis that the GIS assisted in the collection or organisation of data used by the decision maker. This may be a reflection of the trend identified by Keen (1986) for the use of any computer system, by people who make decisions, to be defined as a DSS. However these differences of definition also reflect the differing needs of decision makers who use spatial information. For many of the current SDSS applications, the main information requirement of the decision makers is for relatively structured spatial information. This group may indeed find that standard GIS software provides for their decision making needs.

Many areas of DSS application are concerned with geographic data, including one influential early example of a DSS, the GADS system (Grace, 1976). A more recent important prototype DSS, Tolomeo (Angehrn and Lüthi, 1990) uses a geographical context for the development of visual interactive techniques. However there has been limited impact by mainstream GIS techniques on DSS research. This situation is beginning to change. Some, but by no means all, recent DSS textbooks are including GIS as a component of management support systems (Mallach, 1994; Turban, 1995). While these texts stress the usefulness of geographically related information, they do not provide a complete picture of the relationship of GIS to other management support systems. GIS related research is beginning to make an appearance at conferences associated with DSS, for example GIS based sessions organised at the Hawaii International Conference on System Sciences and the annual conference of the Association for Information Systems. There is increasing evidence of interest in GIS at Operations Research conferences, where applications integrating GIS and Operations Research techniques are discussed. Academic journals associated with the DSS field are beginning to publish GIS related papers. For example the paper by Crossland, Wynne and Perkins (1995) presented empirical evidence of the usefulness of a spatial approach to decision making.

GIS techniques are beginning to have an impact on DSS applications. The survey by Eom, Lee and Kim (1993) identified marketing and routing as important areas of DSS application. These fields are also recognised as areas of GIS application (Maguire, 1991). In the area of routing Bodin, who Eom, Lee and Kim (1993) identified as an important author in routing DSS, has argued for incorporation of GIS in routing (Bodin and Levy, 1994). Keenan (1995) proposed a classification of routing problems with respect to their spatial content and the usefulness of a SDSS. The importance of demographic data, which is widely available in a suitable format for use in GIS software, has lead to the developments of a number of GIS products aimed at marketing applications, for example the GIS products from Tactician Corp.

Within the DSS literature there are many definitions of DSS (Mallach 1994, pages 5-7). Many widely accepted definitions of DSS identify the need for a combination of database, interface and model components directed at a specific problem. In terms of these definitions a GIS would not be regarded as a DSS as it lacks support for the use of problem specific models. However the view of GIS as a DSS is not entirely without support in the existing definitions of DSS. Alter (1980) proposed an influential framework for DSS which includes data driven DSSs that do not have a substantial model component. Standard GIS software could be regarded as an analysis information system in Alter's framework, the critical component of such a system being the database component. Common to all definitions of DSS is a sense that these systems must support a particular type of decision. This characteristic distinguishes DSS from general purpose management information systems (MIS). While GIS applications may contain the information relevant to a decision, they are usually general purpose systems, not focused on a particular decision. For those types of decision where the standard features of a GIS provide the information essential to the decision maker, a GIS may indeed be a DSS. However, for the full range of problem areas where GIS techniques can make an important contribution, particular problem related models are needed to fully support decisions. For these areas at least, a standard GIS cannot be said to be a DSS because such a system lacks the support that the use of customised models can provide. For this wide range of second order uses of spatial data, additional processing or integration with non-spatial models is required to fully support the decision maker. This will extend the present use of a GIS as a DSS, to a situation where a GIS will be used to build a DSS.

Spatial Decision Making

SDSS can therefore be seen as an important subset of DSS, whose potential for rapid growth has been facilitated by technical developments. The availability of appropriate inexpensive technology for manipulating spatial data enables SDSS applications to be created. The benefits of using GIS based systems for decision making are increasingly recognised. In a review of GIS, Muller (1993) identified SDSSs as a growth area in the application of GIS technology. However the value of SDSS is not determined by its innovative use of technology. Instead the contribution of these applications will be determined by how well they support the need for a spatial component in decision making.

I suggest that three categories of decision maker may find that SDSS can make a contribution to their decisions. The first group is in the traditional areas of application of GIS, in disciplines such as geology, forestry, and land planning. In these fields GIS was initially used as a means of speeding up the processing of spatial data, for the completion of activities that contribute directly to productivity. In this context the automated production of maps, in these disciplines, has a role similar to that of data processing in business. In these subject areas there will be growth of decision making applications in much the same way as data processing applications evolved into DSS in traditional business applications. An example of this type of application is the DSS for the assessment of geological risk by Mejia-Navarro and Garcia (1995).

Table 1 : Computerised Support for Decision Making (adapted from Turban, 1995 page 19)
Phase Description Traditional Tools Spatial Tools
Early compute, "crunch numbers", summarise, organise early computer programs, management science models computerised cartography
Intermediate find, organise and display decision relevant information database management systems, MIS workstation GIS
Current perform decision relevant computations on decision relevant information; organise and display the results. Query based and user friendly approach. "What if" analysis financial models, spreadsheets, trend exploration, operations research models, decision support systems. spatial decision support systems

The greater complexity of spatial information processing and its greater demands on information technology have lead to the ten to fifteen year time lag identified by Densham (1991). As information technology costs decline, inexpensive personal computers can now cope with the demands imposed by the manipulation of spatial data. The rapid increase in the 1980's in the use of database managers, led by Dbase II, is being emulated by the increase in the use of spatial database tools at present. In the context of decision support we are now seeing the movement towards the widespread use of PC based GIS systems that reflects the move towards PC based DSS in the 1980's.

The second group of decision makers, for whom SDSS can make an important contribution, is in fields such as routing or location analysis. Although the spatial component of such decisions is clear, DSS design has in the past been driven predominantly by the management science models used. In the future these models will be incorporated into GIS based SDSS, providing superior interface and database components to work with the models. This synthesis of management science and GIS techniques will provide more effective decision making. Keenan(1995) has argued that the use of GIS techniques can extend the range of decision support for vehicle routing problems, allowing consideration of path constraints that have not been comprehensively modelled in the past. Routesmart (Bodin, Fagan, Levy, and Rappoport , 1992) is a good example of the use of GIS for a routing application.

The third group of decision makers who will find SDSS important include those where the importance of both spatial data and modelling is somewhat neglected at present. In disciplines such as marketing, additional possibilities for analysis are provided by the availability of increasing amounts of spatially correlated information, for example demographic data. Furthermore the geographic convenience of product supply relative to customers' locations is an important tool of market driven competition. The availability of user friendly SDSS to manipulate this type of data will lead to additional decision possibilities being examined which are difficult to evaluate without the use of such technology (Grimshaw 1994).

Building a SDSS

Because of the variety of decision making situations where spatial information is of importance, clearly SDSS will be an increasingly important subset of DSS in the future. It is useful to examine the relationship of GIS software to such systems. Densham (1991) discusses the development of DSS in the context of the framework proposed by Sprague (1980). In Sprague's framework a DSS may be built from tools, individual software components that can be combined to form a DSS. These would include programming languages, programming libraries and small specialised applications. At a higher level in Sprague's framework are DSS generators, from which a specific DSS can be quickly built. Generators may be built from lower level tools. Sprague envisioned that different specific DSS applications would require different combinations of the generator and tools. Sprague used GADS (Grace 1976), which can be regarded as a form of GIS, as an example of a DSS generator.

In building DSS, specific generators have been designed for certain classes of problem. In other situations general purpose software such as spreadsheets or DBMS packages have been regarded as generators. In modern DBMS and spreadsheet software, the use of macro and programming languages facilitates the creation of specific applications. Various generators have strengths and weaknesses in terms of their provision of the key components of a DSS; an interface, a database, and models (Table 2). In the case of a spreadsheet, modelling is the basic function of the software; various interface features such as graphs are provided, but the database organisation is simplistic. DBMS software, such as Access or Paradox, has good database support, provision for interface design through the use of forms, report and charts, but almost no modelling support. In this case the modelling support has to be added to the specific DSS built from such a system.

Figure 1 : Technology Levels of DSS (Sprague, 1980)

Diagram of Sprague's framework

The decision regarding the appropriate mix of DSS tools and the use of a generator is an important component of the process of building a DSS. However there is a very real sense in which the types of DSS considered for a given class of problem are a function of the available DSS generators for that class of problem. In practice a small DSS project could be built, using an off-the-shelf spreadsheet or DBMS package, in less time than it would take to fully evaluate the full range of alternative methods of constructing the DSS. Therefore the DSS solutions actually constructed are strongly influenced by the perceived availability of suitable generators. Consequently the effective application of DSS technology can benefit from additional generator software becoming available. Awareness of the potentia of the use of GIS based systems as DSS generators will lead to problems, currently being solved in other ways, being approached by using a SDSS.

There is evidence that GIS software is becoming increasingly suitable for use as a generator for a SDSS. As GIS designers gain a greater awareness of decision making possibilities, their systems will be designed to facilitate interaction with models. GIS software provides a sophisticated interface for spatial information. Even limited functionality GIS software will provide the ability to zoom and to display or highlight different features. GIS provides database support that is designed to allow for the effective storage of spatial data. Furthermore GIS software provides a link between the interface and database to allow the user to easily query spatial data. However for the full range of potential uses of a spatial data in decision making, a GIS is not a complete DSS because of the almost complete absence of problem specific models or support for the organisation of such models.

A large number of models and modelling techniques have been developed to support decision makers. Many of these are of interest to potential users of spatial decision support systems. These models are drawn from well-established disciplines such as statistics or management science, and in most cases do not require the use of spatial data. However for SDSS techniques to be of interest, real world problems need only to have a spatial component in one aspect of the decision making. The models in the problem may operate on non-spatial (attribute) data in the SDSS. However the data set to be used for the modelling process may be identified by spatial operations. For example spatial analysis may determine the number of potential users of a new shop; this could provide data for use in a financial model. Conversely the outcome of a non-spatial model may identify spatial operations which need to be performed. For instance a vehicle routing algorithm may produce truck routes, spatial techniques could then identify the areas affected by noise resulting from the increased traffic.

One possible example of the use of modelling is in selecting a facility location. There might be a number of criteria for such a decision, some of these would be spatial in nature. For instance a school might need to be located near to the districts from which potential pupils would travel. A refuse disposal facility, on the other hand, might need to be located away from populated areas. GIS based spatial operations could be used to provide an index of suitability for sites for such a facility. The decision maker might have a variety of other factors to weigh up, however, and techniques such as multi-criteria decision making such as the analytical hierarchy process (AHP) might be used to reach a final decision. One example of the use of AHP for site location was in Carlsson and Walden (1995). This type of approach could be combined with the use of GIS techniques. Where a traditional model is used to rank the alternatives, spatial operations might be needed to identify the impact of the decision, for example to identify those affected by the location of a new facility.

Table 2 : DSS Generator Features
DSS Component Spreadsheets Database Managers GIS
Interface tables, forms, charts tables, forms, reports multi-layer maps, plots
Database independent cell entries linked database tables linked spatial and non-spatial databases
Database Tools rudimentary sort and selection comprehensive queries spatial query
Models built in mathematical functions, statistical and management science tools basic mathematical functions basic summarisation and network analysis models
Model Building Tools recorded or programmed macros macro and database query languages macro (script) languages, programming interfaces to other programming languages

A good example of a combination of spatial and non-spatial factors arises in vehicle routing, a well established area of DSS research. In this case GIS techniques could be used to evaluate the suitability of paths that vehicles should take. This might mean that elevation data would be used to identify sections of the road network with steep gradients. The results of such an analysis could then be used by traditional management science based vehicle routing algorithms. These types of applications illustrate the need for problem specific models in many areas of SDSS application (Keenan 1995). For instance a GIS could be used to model flooding along the banks of a river. The information derived from the GIS about the likelihood of flooding could provide the bulk of the information needed for some types of decision, such as land use planning. Planners could then restrict development of the low lying land which was liable to flood. However for emergency evacuation modelling, in an area already inhabited, the output from the GIS might become the input to further modelling based on the routing of emergency vehicles. For this application a GIS is not in itself a DSS unless support for the additional modelling can be provided. In this case the routing algorithms need to be able to interact effectively with the GIS. Another routing scenario that requires complex SDSS functionality is that of the routing of hazardous goods. In this category of problems the object of the routing process may be to avoid passing populated areas or to avoid areas where accidents might occur due to steep gradients or strong winds. {Graphic stripped}

Diagram of links between GIS and models

Figure 2: Building a SDSS by integrating models with GIS. Simpler and less effective integration of models only allows the modelling routines to access the non-spatial data. Full integration requires that model routines be able to make use of all the features of the GIS.

In DSS applications the focus of the decision maker is on the decision being made. The output from the DSS is of interest only to the extent that it facilitates decision making. The DSS user wants to make use of the DSS to explore aspects of the decision. In order to do this it should not be necessary for the user to go through long sequences of commands, to enable data to move between different modules of the system. It is central to the design of DSS that the modelling routines can automatically extract the relevant data from the database component of the system. In a DSS the user should only need to intervene in the system to control the modelling process, not to conduct the basic operations needed for modelling. In a SDSS the models must be able to make use of the spatial database tools as appropriate. This requires that the SDSS be built with modelling tools that allow the model designer access to the database and interface components of the SDSS. In order to be used as a DSS generator, GIS software must allow easy automatic interchange of data between the GIS modules and modelling techniques operating on non-spatial elements of the data. This may entail a departure from traditional assumptions in GIS design of the user operating the system by direct manipulation of interface commands. If it is to be used as a SDSS generator then GIS software must make data available in a format that is appropriate for modelling techniques drawn from other disciplines. For example many types of model require that roads be modelled as a network, in the mathematical sense. GIS software may not represent roads in this way. Even where roads can be represented as networks in a GIS the network layer may not be fully integrated with the other spatial data in the problem. This lack of integration hinders comprehensive use of GIS as a SDSS generator for network based problems.

Current Spatial Decision Support Systems Technology

Within the commonly accepted definitions, therefore, the construction of a specific DSS from GIS software is possible by incorporating models that make use of the GIS database and interface. In this context PC based GIS products with limited functionality may prove more manageable for applications design than full workstation based GIS systems. While these desktop systems lack the power of a full GIS, they may be able to make effective use of data which has been prepared for a specific purpose using a full feature GIS. In order to be the basis of SDSS, however, such systems must offer spatial database handling with appropriate access tools. It is not sufficient to simply use a mapping tool which provides simple maps without spatial query features.

The use of GIS as a DSS generator can make use of new facilities for interaction between software, techniques such as dynamic data exchange (DDE), object linking (OLE), and open database connectivity (ODBC). These techniques will allow data pass from the GIS to modelling software which can provide facilities not found in the GIS itself. Present software development trends suggest an object oriented future, in which small specialised applications, or applets, will be available for use as part of a larger package. In the PC environment the development of such small applications will be facilitated by the use of development tools such as Microsoft Visual Basic and Borland Delphi. Currently PC users can make use of applets based on Microsoft's OCX standard. An extension of these trends is the availability of a version of Microsoft's applet technology, known as ActiveX, developed for use over the Internet. In the future as SDSS spreads to a broader user community, systems are increasingly likely to be PC based with a widely used operating system, for example Microsoft Windows NT. This trend will reflect the movement towards a diverse set of users of spatial data. The specialist needs of these users will only require access to a limited subset of GIS data. Increasi g connectivity will mean that these systems will not stand alone but can connect to more powerful spatial database and processing facilities by employing client server technology and even using the Internet.

The use of this type of technology offers two possibilities. GIS software may be used for the main interface and database facilities, using applets for additional modelling or interface requirements. Therefore marketing or logistics models might be incorporated in an OCX to be used by PC based GIS software. Alternatively the main application might be developed in another programming language and OCX type applets used to provide some element of GIS functionality. A number of GIS related tools of this sort exist, for example MapObjects from ESRI, the market leaders in GIS software. However these tools have to be assessed to establish the extent to which they provide true GIS operations or whether they are simply mapping tools.

The developments in GIS software since 1990 may allow the use of off-the-shelf software as the basis for an SDSS. An example of this type of software is the ArcView package from ESRI. As its name suggests, this software is primarily designed as to allow the user to view and query spatial data. ArcView is available for the Windows, Macintosh and UNIX environments. It is intended that the full ARC/INFO package will be required for some GIS operations. ArcView has its own macro language: Avenue, which can interact with SQL database servers, and the ability to use platform specific links with other software. An optional network analysis package is available for Arcview allowing its use for a variety of applications which need this functionality, for example transportation modelling. Together with its ability to support spatial queries, these characteristics make ArcView a potential generator for many types of SDSS software.

Another widely used mapping product is MapInfo. The mapping software originally used in Microsoft Excel 97 was derived from MapInfo technology. The association with Microsoft may lead to software aimed at a broad spectrum of users, and not just those traditionally associated with DSS. The MapInfo package provides the Mapbasic language, which is likely to be developed to become increasingly similar to other programming tools, such as Microsoft Visual Basic.

The incorporation in many GIS products of macro languages, such as Avenue in ArcView and Mapbasic in Mapinfo, facilitates the construction of a DSS. In other cases GIS software allows the use of external procedures. Such linkages may not be entirely integrated, but nevertheless they allow the useful combination of GIS software and models contained in external programs. An example of such software is found in Jankowski (1995), who discusses the integration of GIS software and multiple choice decision making (MCDM) techniques in a DSS. Routesmart (Bodin and Levy, 1994) provides vehicle routing functionality within the TransCad and Arcview environments.

Table 3 : TransCad : An Example of a GIS as a DSS Generator

Generator Functionality TransCad example
Interface interface customisation alteration of standard menus

DDE connection to other software

Database task relevant features road networks, route layers, data matrices
Models ability to build models Caliper Script macro language, with DDE links to modelling procedures in other languages

TransCad ( is a PC based GIS designed specifically for managing transportation data and is designed to facilitate the use of transportation models. As such it is an excellent example of a potential DSS generator as it provides a number of features which specifically support transportation modelling. These include provision for a road network layer, with the ability to store relevant network characteristics such as turn penalties. The concept of a route layer is supported, allowing multiple routes to use each road. Relevant data may be stored in matrix form, for example a matrix of distances between point features. TransCad allows extensive tailoring of the interface around the standard GIS components using the GDK supplied (Geographic Information System Developers Kit). Applications developed using this toolbox can communicate with external software using the widely DDE, OLE and ODBC standards. The developer therefore has the option of adding additional modelling functionality using TransCad as the main interface or of adding some of the GIS functionality of TransCad to another application. This combination of problem relevant features and customisation tools facilitates development of a specific DSS (Table 3). SDSS applications SDSS applications can be built using a combination of these features, for instance a customised interface together with the use of macros. An example of the steps required for the development of a hazardous waste application is shown above (Table 4).

Table 4 : Example of a hazardous waste routing SDSS using TransCad
Objective Actions Required SDSS technique
identify locations to be avoided set selection criteria e.g. all schools, hospitals decision maker intervention using spatial user interface
identify feasible roads exclude roads in buffer zone around points to be avoided standard TransCad operation called within macro
build road network generate network suitable for use by mathematical procedures standard TransCad operation called within macro
establish routing parameters enter speed, capacity etc. user intervention using form interface
route vehicles apply routing algorithm macro call of external procedure with communication via DDE
identify areas at risk from ccident involving vehicle generate buffer zone around routes standard TransCad operation called within macro


Given the advances in computer technology in general and GIS techniques in particular, I suggest that SDSS will be an important component of DSS applications in future. This trend will be driven by the relevance of spatial information as a component of the information needed for a wide range of decisions. This class of DSS will make an important contribution, not because of its use of the latest technology, but because it will allow decision makers to incorporate a spatial dimension in their decision making. This spatial dimension, which is not fully accounted for in traditional DSS designs, is an important feature of many areas of DSS application. These potential areas of application including fields, such as routing or marketing, which have been important fields of DSS application in the past. For this larger class of decision makers who might use SDSS, spatial data will be used with other types of information required originating in specialised models, often of a non-spatial nature. For this broad range of applications, GIS technology alone can only make a partial contribution to decision support. Comprehensive decision support will require the effective integration of GIS and non GIS techniques. This can be achieved by building systems using a GIS as a DSS generator. The building of SDSS application from GIS has been facilitated by recent technical developments, both within GIS software, and in programming tools generally. The challenge for SDSS builders is to achieve an appropriate synthesis of modelling techniques and interface with database approaches, drawn from the GIS and specialised domains, to provide effective decision support for these areas.


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About the Author

Dr. Peter Keenan, Dept of Management Information Systems, Quinn School of Business, University College Dublin, Belfield, Dublin 4, Ireland, email:,


Keenan, P., "Using a GIS as a DSS Generator", DSSResources.COM, 12/17/2004.

Peter Keenan provided permission to archive and feature this article at DSSResources.COM on October 28, 2004. This article was posted at DSSResources.COM on December 17, 2004.