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ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001 
happen if a network of retail outlets is changed, and determining 
optimal business strategies in a region. 
The incorporation of decision-making models within GIS to 
develop a SDSS is a powerful approach for solving spatial 
business problems. Business decisions take place in a specific 
context, hence generic SDSS are only of limited value. The 
analytical power required can only be obtained from a 
customised SDSS developed using a set of software modules 
and analytical tools that are pertinent in the context of a specific 
business application, here the closure of branch banks. 
2.1 The need and benefit of Integration 
The need for integration in this research is driven by the need to 
choose branches for closure, considering research to increase 
knowledge can improve the act of making choices and produce 
good decisions. Thus the ultimate goal of integration is not only 
to develop a better research tool, in the form of more powerful 
models and analysis software, but also to aid bank branch 
planning and the decision making process. 
Parks (1993) argued that integration is not a new idea but rather 
the further co-adaptation of existing tools and methods. The 
integration might cross-fertilize and mutually reinforce each 
other to develop new ways in which they coula be designed to 
serve additional users-including those who must participate in 
common decision-making process. Three primary reasons for 
integration suitable to begin such discussion are given as 
follows: 
Firstly, 80% of data in the banking industry are spatial 
referenced (King, 1993), spatial representation is critical to 
complex multiple location choice problems. Decision support in 
branch banking location problems requires facilities for the input, 
management and output (display) of spatial-referenced data, 
facilities that are currently available in existing GIS packages. 
But GIS currently lack the predictive and related analytic 
capabilities necessary to examine complex problems. The GUI 
and spatial operators in GIS have not been developed for 
multiple location problems and GIS have not generally been 
amenable to decision making tasks without considerable effort to 
customise them. 
Secondly, the decision making process can benefit from the use 
of multi-criteria decision making (MCDM) techniques, which 
provide both a sound methodology and platform for decision 
analysis and an operational framework for actual decision 
making (Roy, 1996). MCDM techniques can be used to rank 
branches in terms of whether they are more or less preferable 
for closure according to a variety of social, economic, and 
demographic criteria. This approach facilitates the decision 
making process by making it more explicit, rational, and efficient 
(Hobbs et a!., 1992). Decision making tools typically lack 
sufficiently flexible GIS-like spatial analytic components and are 
often inaccessible to potential users less expert than their 
makers. What GIS could offer to decision making is a flexible 
environment with a powerful spatial analysis tools, such as 
buffer and overlay, and the strong visualisation capability. 
Thirdly, GIS and MCDM technology can both be made more 
robust by their linkage. The effort to combine the strengths of 
these tools will be mutually beneficial. So writing separate 
pieces of software for location problems is not a good strategy, 
and the optimal option is to take advantage of these facilities 
through integration (Goodchild etai, 1992). 
2.2 Integration methods 
Many ways of integrating external software packages with GIS 
have been used in previous research, the architectures of which 
relate to the degree of ‘closeness’ involved (see Goodchild et al., 
1992; Fedra, 1993; Goodchild et al., 1993). The generally 
accepted classification identifies three levels of coupling or 
integration: loose coupling, close coupling, and full integration. 
Following the arguments by Jankowski (1995), the loose 
coupling architecture is used in this research for linking together 
the two software packages. The SDSS proposed in this study is 
designed around the integration of the Criterium DecisionPlus 
3.0.3 (CDP) software, a commercial decision making support 
package from InfoHarvest, with ArcView GIS 3.2 using Dynamic 
Data Exchange (DDE) in the Microsoft Windows environment. 
Dynamic Data Exchange enables the continuous and automatic 
exchange of data between ArcView and CDP by means of a bi 
directional data transfer with ArcView as the database and 
visualisation engine and CDP as the engine for data analysis 
(Fig. 1). 
Transfer 
Fig.1 GIS-MCDM System Integration 
3. METHODOLOGY 
Branch closure decision making usually involves many decision 
makers, most of whom are likely to have a limited technical 
competence and hence little understanding of the complexities 
of decision-making models or the way they work. There are 
advantages therefore if the formal models used can be quickly 
and easily understood, at least intuitively, by those involved in 
the decision-making process and it is best if the application of 
the models is not time consuming. 
A number of different models have been proposed to structure 
and solve multi-criteria decision problems and computational 
methods developed for their application. In this research, the 
analysis is based on the Simple Multi-Attribute Rating Technique 
(SMART) model, which is convincible to decision makers 
through its rich applications (Corner and Kirkwood 1991). 
3.1. The SMART model 
SMART was put forward by Edward’s’ in 1971 (Edwards, 1971), 
and it is closely related to the multi-attribute utility approach that 
had been developed by Keeney (1969). The basic equation in 
SMART, which is the formula for a weighted average is: 
V t - YjWj u ij (Eq-1) 
j 
subject to Yj W j = 1 
j 
where U, is the aggregate utility for the /th alternative 
Wj is the normalized importance weight of the /th 
attribute of value 
u,j is the normalized value of the /th alternative on the 
/th attribute 
In SMART, the lowest level criteria are called attributes. The 
numerical values (called ratings) assigned to these attributes are 
derived from value functions. The structure used to model the