ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
decision problem in SMART is called a value tree, or objective 
hierarchy, which is a true tree structure allowing one subcriteria 
to be connected to only one higher level criterion. 
For a model with multiple layers of criteria, lowest criteria weight 
(model weight, accumulated weights) is the product of 
normalized weights along each path from the Goal block to that 
criteria, summed over all such paths. The decision score of each 
alternative is the sum, over all lowest criteria (attribute) of each 
normalized rating multiplied by the model weight. 
Gardiner and Edwards (1975) suggested that SMART "is 
oriented not towards mathematical sophistication or intimacy of 
relation between underlying formal structures and practical 
procedures that implement them but rather towards easy 
communication and use in environment in which time is short 
and decision makers are multiple and busy". SMART is a 
powerful and flexible decision making tool. Because of its 
simplicity of both responses required of the decision maker and 
the transparent manner in which these responses are analysed, 
the method is likely to yield an enhanced understanding of the 
problem and be acceptable to the decision maker who is 
distrustful of a 'black box’ approach. SMART has been widely 
applied, and is the focus of many publications on Multi-Criteria 
Decision Making. 
Edwards (1977a and 1977b) identified the main stages in the 
SMART analysis as: 
1. Identify the decision maker (or decision makers). 
2. Identify the alternative courses of action. 
3. Identify the attributes which are relevant to the decision 
problem. 
4. For each attribute, assign values to measure the 
performance of the alternatives on that attribute. 
5. Determine a weight for that attribute that reflects how 
important the attribute is to the decision maker. 
6. For each alternative take a weighted average of the values 
assigned to that alternative to give a measure of how well 
an alternative performs over all the attributes. 
7. Make a provisional decision. 
8. Perform sensitivity analysis to see how robust the decision 
is to changes in the weights. 
3.2 Normalization when using SMART 
Attribute normalization converts the different scales of attribute 
to a common internal scale. In SMART this is done 
mathematically by means of a value function which transforms 
attribute values into ratings on a consistent internal scale with 
lower limit 0, and upper limit 1. The simplest choice of a value 
function is a linear one, and in most cases this is sufficient. The 
literature on Utility theory discusses the different ways of 
choosing a value function (Edwards, 1977a). Through using the 
value function the ratings of alternatives are not relative, so that 
varying the number of alternatives will not in itself change the 
decision scores. This allows new candidates to be added to the 
model or closed branches to be taken out of the model. 
In SMART, the normalization weight process results in the 
effective importance of a subcriteria by using Direct Rating (DR) 
methods, the user-given weight divides the sum of the weights 
at the same level against an upper level criteria. The DR 
method used in this process forces the numerical values, or 
priorities to range between 0 to 1. 
3.3 Criteria for determining branch bank viability 
There is now a considerable literature dealing with different 
aspects of the changes taking place in retail banking. Of 
relevance for this research are the studies focussing on the 
criteria and performance indicators related to making decisions 
about the closure of individual branches and the reshaping of 
branch bank networks. Selection of the non-financial criteria 
(Table 1) used in developing the SDSS in this study is based on 
a review of this literature and the findings of previous successful 
research (see for example, ABA, 1965; Morrall, 1996; Avkiran, 
1997). 
Table 1 The Principle Social and Demographic Criteria 
Determining Branch Profitability 
Banking population (age 15 & over) 
_ M 
re (u 
m — 
" S3 
re re 
Population (over 15) growth rate 
Competitor's branches 
*£ C 
§ 5 
Average annual family income 
£ S 
SZ «= 
Average age 
3 ö 
re o> 
Employed people 
O Q. 
</> 
Total (small) business No. 
Catchment area 
Small business in 200m buffer zone 
u 
Competitor's branch No. in 200m buffer 
0 
1 ® 
if) -O 
Working population in 200m buffer 
Same bank’s branches in 500m buffer 
O fc 
At shopping centre 
8 > 
Shopping centers Within 500m buffer 
Present of a free car park 
Proximity to public transport (within 1km) 
The criteria are divided into two higher level categories termed 
Catchment Specific and Location Specific criteria. Some of the 
individual criteria are taken from published sources while others 
have been generated using GIS. 
3.4 Criteria collection and management using GIS 
In this research, GIS analytical functions facilitate spatial 
manipulation and generate data based on spatial analysis. The 
database management functions are used for managing data 
derived from different sources and formats, and integrating data 
based on spatial relations. The results produced by ArcView can 
be presented in a standard attribute table (the decision table), 
the rows in which contain the set of branch alternatives, the 
columns the set of decision criteria, and the table elements the 
criteria scores. The decision table can be exported directly to 
CDP through DDE and a program written in AVENUE. The multi 
criteria decision-making analysis is performed in CDP, a 
weighted decision score is then calculated for each candidate 
branch bank, and these are exported to ArcView for display. 
3.5 Determining criteria weights 
The important components of an MCDM analysis are those that 
involve interaction with the decision makers, in order to develop 
a value function to obtain a set of weights for the evaluation 
criteria. Research (Nijkamp et al., 1990) has shown that decision 
makers prefer not to give their preference until the result of the 
analysis are presented to them, and generally speaking, it is too 
demanding for a decision maker to give a precise indication of 
his preferences. This point has been elaborated by Malczewski 
(1996), who further argued that it is difficult for decision makers 
to provide the information required by the weighting methods, 
and their preference judgements are often nor reliable enough to 
yield precise criteria weights. 
Nijkamp (1990) has demonstrated that it is sufficient to use 
hypothetical qualitative priority statements, linked with a 
particular policy scenario. Malczewski (1996) suggested that this 
problem can be overcome by using a series of sensitivity tests,