development of an optimal spatial decision-making system...
TRANSCRIPT
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Development of an optimal spatial decision-making system using approximate reasoning
Submitted by
David Thomas Bailey
BEng(Hons) QUT
A thesis submitted in partial fulfilment
of the requirements of the degree of
DOCTOR OF PHILOSOPHY
Research Centre for Built Environment and Engineering Research
Energy and Resource Management Research Program
Faculty of Built Environment and Engineering
Queensland University of Technology
Novemer 2005
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TTAABBLLEE OOFF CCOONNTTEENNTTSS
TABLE OF CONTENTS II
LIST OF FIGURES VI
LIST OF TABLES VIII
LIST OF ABBREVIATIONS IX
ABSTRACT X
STATEMENT OF AUTHORSHIP XI
ACKNOWLEDGEMENTS XII
1 INTRODUCTION AND METHODOLOGY 1 1.1 INTRODUCTION 1 1.2 THE PROBLEM 2
1.2.1 Context and fundamental elements of infrastructure site selection 2 1.2.2 Background 3 1.2.3 Use of Approximate Reasoning 5 1.2.4 Case Study 6
1.3 RESEARCH OUTLINE 7 1.3.1 Research Question 7 1.3.2 Aims 8 1.3.3 Objectives 8 1.3.4 Scope 8 1.3.5 Justification 9 1.3.6 Methodology 10
1.4 THESIS STRUCTURE 13 1.5 MULTI-DISCIPLINARY NATURE OF THE RESEARCH 14
2 PROBLEM DIAGNOSIS AND PRELIMINARY LITERATURE REVIEW 17
2.1 INTRODUCTION 17 2.2 PROBLEM DIAGNOSIS 18 2.3 INTRODUCTION TO LOCATION PROBLEMS 19
2.3.1 Problem classifications 20 2.4 DECISION SCIENCE TECHNIQUES 23
2.4.1 Map algebra 24 2.4.2 Multicriteria evaluation 25
2.4.2.1 Specifying and standardising evaluation criteria 28 2.4.2.2 Criterion weighting 33 2.4.2.3 Alternatives and the decision matrix 34 2.4.2.4 Aggregation via decision rules 36 2.4.2.5 Sensitivity analysis 39 2.4.2.6 Limitations of MCE 39
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2.4.3 Artificial intelligence and soft computing 40 2.4.3.1 Fuzzy logic 41 2.4.3.2 Neural networks 41 2.4.3.3 Genetic algorithms (GA’s) 42
2.5 TECHNOLOGY PLATFORMS 44 2.6 DISCUSSION 44 2.7 CONCLUSIONS 46
3 APPROXIMATE REASONING 47 3.1 INTRODUCTION 47 3.2 FUZZY LOGIC 47
3.2.1 Fuzzy sets 48 3.2.2 Fuzzy numbers 50
3.3 APPROXIMATE REASONING IN MULTICRITERIA DECISION-MAKING 51 3.3.1 Fuzzy MCE 52 3.3.2 Fuzzy inference systems 55 3.3.3 Pairwise comparison methods 57
3.4 USE OF APPROXIMATE REASONING IN LOCATION PROBLEMS 57 3.5 CONCLUSIONS 59
4 SPATIAL DECISION SUPPORT SYSTEMS 61 4.1 INTRODUCTION 61 4.2 OVERVIEW 62
4.2.1 Decision support and expert systems 62 4.2.2 Basic concepts of spatial decision support systems 64
4.3 COMPONENTS OF A SDSS 66 4.3.1 Geographical Information Systems 66 4.3.2 Dialog 68 4.3.3 Data 69
4.3.3.1 Spatial data representation 70 4.3.3.2 Raster data and cell size 71 4.3.3.3 Non-spatial data (Attribute Data) 72
4.3.4 Models 73 4.4 DEVELOPMENT AND IMPLEMENTATION 75 4.5 CONCLUSIONS 76
5 PROBLEM ANALYSIS AND CONCEPTUAL SYSTEM DESIGN 79 5.1 INTRODUCTION 79 5.2 CAUSES OF CURRENT LIMITATIONS ON SDSSS FOR SITE SELECTION 79
5.2.1 Multiple decision-makers 81 5.2.2 Uncertainty 82 5.2.3 Simplicity 84 5.2.4 Control 85
5.3 A CONCEPTUAL FRAMEWORK 85 5.3.1 Why use Approximate Reasoning? 85 5.3.2 Catering for multiple decision-makers 87 5.3.3 Handling Uncertainty 89 5.3.4 Creating simplicity 91 5.3.5 Giving decision-makers control 92
5.4 CONCLUSIONS 93
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6 ALGORITHM DESIGN 95 6.1 INTRODUCTION 95 6.2 ARAISS 95
6.2.1 Framework 96 6.2.2 Notation 98 6.2.3 Linguistic term sets 98
6.2.3.1 Semantic definition 101 6.2.3.2 Term generation 102 6.2.3.3 Uncertainty scaling 103
6.2.4 Dynamic weighting 105 6.2.5 Generating suitability values 106 6.2.6 Aggregation and output parameters 108
6.2.6.1 Utility 109 6.2.6.2 Safety 111 6.2.6.3 Consensus 112 6.2.6.4 Certainty 113
6.2.7 Adjusted aggregation and alternative exploration 114 6.3 ARAISS SIMULATION EXERCISES 114
6.3.1 Validating ARAISS 115 6.3.2 Example simulation 116
6.3.2.1 Data inputs 117 6.3.2.2 Results 118 6.3.2.3 Interpretation 119
6.4 CONCLUSIONS 119
7 INFRAPLANNER 121 7.1 INTRODUCTION 121 7.2 OVERVIEW OF THE PROTOTYPE SYSTEM 122
7.2.1 Target application 122 7.2.2 Target audience 123 7.2.3 Dialog design 123 7.2.4 Database 125 7.2.5 Model 126
7.3 DEVELOPMENT PROCESS 127 7.3.1 Planning 128 7.3.2 Research 129 7.3.3 Analysis and design 129 7.3.4 Construction 130 7.3.5 Implementation 131
7.4 THE INFRAPLANNER PROTOTYPE 131 7.4.1 Project tools 134 7.4.2 Creating maps 135
7.4.2.1 Suitability Maps 136 7.4.2.2 Decision Maps 140
7.4.3 Exploring maps 142 7.5 VALIDATING INFRAPLANNER 143 7.6 DISCUSSION 143
8 A CASE STUDY USING INFRAPLANNER 145 8.1 INTRODUCTION 145
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8.2 THE PROBLEM 146 8.3 PROCEDURE 148 8.4 RESULTS 163 8.5 DISCUSSION 171
9 CONCLUSIONS 175 9.1 INTRODUCTION 175 9.2 SUMMARY OF RESULTS 175
9.2.1 Answer to the Research Question 175 9.2.2 Achievement of the Research Aims 176 9.2.3 Achievement of the Research Objectives 176
9.3 RESEARCH OVERVIEW 177 9.3.1 Planning and research 178 9.3.2 Analysis and design 179 9.3.3 Construction 180 9.3.4 Implementation and feedback 181
9.4 VALIDATION 181 9.5 KEY FINDINGS 183 9.6 DIRECTIONS FOR FUTURE RESEARCH 184 9.7 CONCLUDING REMARKS 185
REFERENCES 187
A: PUBLICATIONS 199
B: THE BRISBANE AIRPORT ENVIRONMENT 203
C: MATLAB CODE 211
D: ARCOBJECTS VBA CODE 233
E: ANZIIS QUESTIONAIRE 345
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FIGURE 1.1: BRISBANE AIRPORT................................................................7
FIGURE 1.2: DEVELOPMENT PROCESS LOGIC MODEL.....................12
FIGURE 1.3: DISCIPLINES CONTAINED IN EACH CHAPTER ............15
FIGURE 2.1: CLASSIFICATION OF MULTICRITERIA DECISION PROBLEMS .......................................................................................................22
FIGURE 2.2: FRAMEWORK FOR MCE ......................................................27
FIGURE 2.3: DECISION MATRIX ................................................................36
FIGURE 3.1: FUZZY MEMBERSHIP FUNCTION FOR THE TERM ‘APPROXIMATELY THREE’ ........................................................................49
FIGURE 3.2: TRAPEZOIDAL FUZZY NUMBER TPZ(A,B,α,β) ..............50
FIGURE 3.3: FUZZY INFERENCE SYSTEM ..............................................56
FIGURE 3.4: THE VARIABLE SLOPE AS A FUZZY MEASURE............58
FIGURE 4.1: ESRI ARCGIS............................................................................68
FIGURE 5.1: SOURCES OF CURRENT LIMITATIONS ON SDSSS .......81
FIGURE 5.2: RELEVANCE MATRIX ...........................................................87
FIGURE 5.3: THE SUITABILITY TERM ‘OK’ AS TFN(0.3,0.5,0.7) ........89
FIGURE 5.4: FOOTPRINT OF UNCERTAINTY.........................................90
FIGURE 5.5: HOW A QUANTITATIVE UNCERTAINTY ASSESSMENT AFFECTS THE PRIMARY MF ......................................................................91
FIGURE 6.1: ARAISS FRAMEWORK ..........................................................97
FIGURE 6.2: TERM GENERATION ...........................................................103
FIGURE 6.3: FUZZY OUTPUTS FROM EQUATION 6.15 ......................118
FIGURE 7.1: THE INFRAPLANNER TOOLBAR .....................................131
FIGURE 7.2: HOW INFRAPLANNER TOOLS FIT INTO THE DECISION-MAKING FRAMEWORK ........................................................133
FIGURE 7.3: SETTING PROJECT INFORMATION................................134
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FIGURE 7.4: CREATING A NEW TERM SET ..........................................135
FIGURE 7.5: THE DISCRETE CRITERION MAP USER FORM...........136
FIGURE 7.6: CREATING A DISCRETE CRITERION MAP...................137
FIGURE 7.7: THE CONTINUOUS CRITERION MAP USER FORM ....138
FIGURE 7.8: CREATING A CONTINUOUS CRITERION MAP ............139
FIGURE 7.9: THE DECISION MAPS USER FORM .................................140
FIGURE 7.10: CREATING DECISION MAPS ...........................................141
FIGURE 7.11: MAP EXPLORATION..........................................................142
FIGURE 8.1: BRISBANE AIRPORT LAYOUT..........................................147
FIGURE 8.2: UNCONSTRAINED ALTERNATIVES................................150
FIGURE 8.3: CREATING A CONTINUOUS SUITABILITY MAP FOR COMMUNITY IMPACT ................................................................................152
FIGURE 8.4: CREATING A DISCRETE SUITABILITY MAP FOR ZONING ...........................................................................................................152
FIGURE 8.5: BAC TRAFFIC IMPACT SUITABILITY MAP ..................156
FIGURE 8.6: BAC COMMUNITY IMPACT SUITABILITY MAP .........157
FIGURE 8.7: TRAFFIC IMPACT SUITABILITY MAP FOR THE RESIDENTIAL COMMUNITY.....................................................................158
FIGURE 8.8: COMMUNITY IMPACT SUITABILITY MAP FOR THE RESIDENTIAL COMMUNITY.....................................................................159
FIGURE 8.9: PERFORMING AN AGGREGATION .................................162
FIGURE 8.10: UTILITY.................................................................................164
FIGURE 8.11: UNCERTAINTY ....................................................................165
FIGURE 8.12: RISK ........................................................................................166
FIGURE 8.13: CONFLICT.............................................................................167
FIGURE 8.14: ADJUSTED AGGREGATION.............................................169
FIGURE 8.15: SITES OF INTEREST...........................................................170
FIGURE 8.16: ALTERNATIVE EXPLORATION......................................171
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TABLE 1.1: DISCIPLINES INVOLVED IN THE RESEARCH..................15
TABLE 2.1: MAP ALGEBRA OPERATORS ................................................25
TABLE 2.2: TYPES OF ATTRIBUTES, A BRIEF DESCRIPTION, AND DESCRIBING AUTHORS................................................................................30
TABLE 4.1: DIMENSIONS OF A DSS ...........................................................64
TABLE 4.2: GIS SPATIAL ENTITIES IN VECTOR AND RASTER ........71
TABLE 4.3: COUPLING METHODS.............................................................73
TABLE 6.1: SEMANTIC DEFINITION OF PRIMARY TERMS.............102
TABLE 6.2: DECISION-MAKER RELEVANCE .......................................117
TABLE 6.3: DECISION-MAKER 1 INPUTS...............................................117
TABLE 6.4: DECISION-MAKER 2 INPUTS...............................................117
TABLE 6.5: DECISION-MAKER 3 INPUTS...............................................118
TABLE 6.6: FINAL OUTPUTS IN LINGUISTIC FORM..........................119
TABLE 8.1: LINGUISTIC TERMS...............................................................148
TABLE 8.2: CRITERIA DEFINITION.........................................................151
TABLE 8.3: CRITERION WEIGHTING .....................................................160
TABLE 8.4: DECISION-MAKER RELEVANCE VALUES ......................161
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LLIISSTT OOFF AABBBBRREEVVIIAATTIIOONNSS
AR Approximate Reasoning
ARAISS Approximate Reasoning Algorithm for Infrastructure Site
Selection
BAC Brisbane Airport Corporation
DSS Decision Support System
GA Genetic Algorithm
GIS Geographical Information System
GMCLP Group Multicriteria Location Problem
MCE Multicriteria Evaluation
MADM Multiattribute Decision Making
MODM Multiobjective Decision Making
OR Operations Research
OWA Ordered Weighted Averaging
QUT Queensland University of Technology
SDSS Spatial Decision Support System
VBA Visual Basic for Applications
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AABBSSTTRRAACCTT
There is a recognised need for the continued improvement of both the techniques
and technology for spatial decision support in infrastructure site selection. Many
authors have noted that current methodologies are inadequate for real-world site
selection decisions carried out by heterogeneous groups of decision-makers
under uncertainty. Nevertheless despite numerous limitations inherent in current
spatial problem solving methods, spatial decision support systems have been
proven to increase decision-maker effectiveness when used. However, due to the
real or perceived difficulty of using these systems few applications are actually in
use to support decision-makers in siting decisions. The most common difficulties
encountered involve standardising criterion ratings, and communicating results.
This research has focused on the use of Approximate Reasoning to improve the
techniques and technology of spatial decision support, and make them easier to
use and understand. The algorithm developed in this research (ARAISS) is based
on the use of natural language to describe problem variables such as suitability,
certainty, risk and consensus. The algorithm uses a method based on type II
fuzzy sets to represent problem variables. ARAISS was subsequently
incorporated into a new Spatial Decision Support System (InfraPlanner) and
validated by use in a real-world site selection problem at Australia’s Brisbane
Airport. Results indicate that Approximate Reasoning is a promising method for
spatial infrastructure planning decisions. Natural language inputs and outputs,
combined with an easily understandable multiple decision-maker framework
created an environment conducive to information sharing and consensus building
among parties. Future research should focus on the use of Genetic Algorithms
and other Artificial Intelligence techniques to broaden the scope of existing
work.
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SSTTAATTEEMMEENNTT OOFF AAUUTTHHOORRSSHHIIPP
Except where duly acknowledged in the text, this thesis contains neither:
a) Material which has been previously published under my name, or which has
been submitted as part of another degree or diploma.
b) Any other persons work, either published or unpublished.
This thesis is presented as an original contribution based on my doctoral research
at QUT, and has not been submitted elsewhere, under my name or that of any
other individual.
David Thomas Bailey
20 November 2005
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AACCKKNNOOWWLLEEDDGGEEMMEENNTTSS
My time at QUT has offered many opportunities to share and interact with
friends and colleagues on all levels. It should be no surprise that the work
contained in this thesis has grown from the foundation of help and support of
those around me.
Associate Professor Ashantha Goonetilleke, a most capable and tireless
individual with a passion for his role in academia and lively sense of humour, led
my supervisory team. What follows in these pages would not have materialised
without his guidance and faith in my ability.
Associate Professor Duncan Campbell joined me as an associate supervisor late
in my research but has made a contribution heavily out of proportion with the
length of time we have collaborated. His immediate enthusiasm, continuous
encouragement, and unquestionable academic prowess have been indispensable.
Dr John Hayes and Dr Mohamed Deriche made very early, central, and specific
contributions in guiding me with GIS and fuzzy logic respectively. Their input
seemed to both give direction and open up new possibilities at the same time, and
was highly appreciated.
Several other researchers and administrators at QUT have been fantastic sources
of support, advice, and also laughter. I certainly owe a sincere measure of
gratitude to Jenny, Shelley, Wael, Jack, Ramid, Lars, Steve and many others.
Finally I would like to confirm the axiom that writing a thesis places a large
stress on ones home life. To my partner Helga, whose love and support I could
not live without, my greatest thanks.
1Chapter 1 Introduction and Methodology
Chapter 1
IINNTTRROODDUUCCTTIIOONN AANNDD MMEETTHHOODDOOLLOOGGYY
1.1 Introduction
There is a recognised need for the continued improvement of both the techniques
and technology for spatial decision support in infrastructure site selection. In fact
while spatial decision support systems have been proven to increase decision-
maker effectiveness (Crossland, Wynne et al. 1995), few applications are
actually in use to support decision-makers in siting decisions (Maniezzo, Mendes
et al. 1998). This research focuses on the use of Approximate Reasoning to
improve the techniques and technology of spatial decision support. The processes
developed in this research were incorporated into a new Spatial Decision Support
System and validated by their use in a real world site selection problem at
Australia’s Brisbane Airport.
This thesis describes the development of a new Approximate Reasoning
Algorithm for Infrastructure Site Selection (ARAISS) and it’s implementation in
the ‘InfraPlanner’ prototype Spatial Decision Support System (SDSS). The
InfraPlanner SDSS allows a group of decision-makers to give natural language
assessments of multiple evaluation criteria, and receive natural language
feedback on site suitability. It is the product of an attempt to create a system to
cater for the challenging dynamics of infrastructure site selection whilst being
simple to use and easy to understand.
2Chapter 1 Introduction and Methodology
1.2 The Problem
Selecting strategically suitable locations for future infrastructure developments is
a fundamental activity performed by planners and managers of the environment
and infrastructure. These site selection decisions usually have a major impact on
the success of the development, however this is often the least scientific part of
the planning process. The site selection process is, by necessity, ill-structured,
requiring submissions from multiple stakeholders comprising government,
business and community groups, with disagreement amongst them being
commonplace. The source of disagreement is often the measurement or
weighting of a qualitative variable such as environmental or social impact, and
there may be no completely accurate way of determining which party is correct.
In such cases the stage is set for an acrimonious battle where emotion overrides
commonsense and the decision is made on political rather than practical grounds.
It is little wonder that solving these problems is described as a ‘surprisingly
difficult task’ (Carlsson and Fuller 1996).
In such a demanding environment it is essential for decision-makers to have
access to easily interpretable information, and tools for its analysis and
dissemination. The core problem of this research is the lack of software tools
available to decision-makers involved in infrastructure site selection decisions,
and the poor uptake of those tools where they do exist.
1.2.1 Context and fundamental elements of infrastructure site selection
The goal of an infrastructure site selection problem is to select an optimal site for
the successful deployment of an item of infrastructure. Examples include the
location of airports, industrial facilities and educational resources. Such decisions
are commonly made at a strategic level, as they are dominated by high-level
strategic concerns such as the triple bottom line of environmental, social and
economic impacts. It is the strategic aspects of infrastructure site selection that
have been the focus of this research, as this is where most conflict and
3Chapter 1 Introduction and Methodology
uncertainty arises. This research originates from recognition that existing
decision-making methods have limitations that make them either totally
unsuitable or inherently problematic in their implementation when faced with the
strategic issues involved in real world infrastructure site selection problems.
From a decision science perspective infrastructure site selection belongs to a
special class of decision problems referred to as ‘multi-criteria decision
problems’ in the decision science literature. Infrastructure site selection problems
posses four key characteristics that make them particularly challenging:
1. A large number of spatial alternatives
2. A heterogeneous group of decision-makers
3. Multiple evaluation criteria with an explicit spatial component
4. Uncertainty
These characteristics are not specific to infrastructure location problems alone, so
throughout this thesis the more generic term ‘Group Multicriteria Location
Problem’ (GMCLP) is used.
1.2.2 Background
Identification of the need for software-based spatial decision-making tools dates
back to the 1960s, when a new computer based spatial information processing
technology known as a Geographical Information System (GIS) was first
developed. GIS became widely commercially available in the early 1980s and
quickly became the platform of choice for spatial decision-making. The new term
Spatial Decision Support System (SDSS) was coined to signify the use of GIS in
a decision-making context, and countless specific techniques were developed and
implemented, however most were specific to a particular problem.
One promising, almost universally applicable technique, borrowed from the
domain of Operations Research, was Multicriteria Evaluation (MCE). MCE
methods serve to investigate a number of choice possibilities in the light of
multiple criteria and conflicting priorities, and form a natural framework for
4Chapter 1 Introduction and Methodology
further refinement to spatial decision-making techniques. The integration of
MCE and GIS seemed a natural fit, and Piotr Jankowski developed a practical
framework for MCE and GIS integration, in his often-cited 1995 paper
(Jankowski 1995).
MCE has often been used in GIS for Infrastructure site selection, however both
MCE and GIS continue to mature. Current theory and applications exhibit
several limitations that are particularly problematic when dealing with
GMCLP’s. The problems tend to stem from four main causes.
1. Decision-makers find the methods, and systems in which they are
implemented, difficult to use.
2. There is no robust method to accept inputs from a heterogeneous group.
3. There is generally no capacity for decision-makers to express a level of
uncertainty in their judgements.
4. Difficulty in understanding complex analytical methods leaves decision-
makers without a sense of control.
It may therefore be postulated that developing a SDSS for infrastructure site
selection should be driven by the need to produce a system that is easy to use,
has a robust method to accept inputs from multiple parties, is capable of dealing
with uncertainty and delivers a sense of control to users.
MCE has been shown to provide a reliable framework for such systems but to
further improve on their capabilities requires the integration of new techniques. It
is a fundamental hypothesis of this thesis that Approximate Reasoning (AR)
provides a practical means to augment existing methods, and more adequately
address the needs of decision-makers involved in infrastructure site selection. AR
methods allow the use of approximate ‘linguistic’ terms in an analysis, thereby
creating a more natural way for decision-makers to input preferences and receive
feedback.
Finally, one may enquire as to why multiple criteria or linguistic techniques are
needed when the predominant attitude is one of economic rationalism. According
5Chapter 1 Introduction and Methodology
to the economic rationalist perspective, private sector location problems reduce
to the single criteria problem of maximising profits (Beckman 1968). However
due to social pressure and legislation most major developments must now
consider at least three types of criteria - environmental, social and economic - to
satisfy the more enlightened paradigm of the “triple bottom line” (Pullar and
Pettit 2000). Public sector decision-making has always typically involved
consideration of multiple criteria. Even the simplest models consist of two, being
equity and efficiency (Morrill and Symons 1977). The use of MCE and AR
offers the ability to consider these, and other, disparate human concerns in a
constructive, quantitative way, and as such may play a part in producing better
outcomes to the challenging location problems of the twenty first century.
1.2.3 Use of Approximate Reasoning
Many decision-making methods use integers as a means to quantify decision-
maker assessments so they may be processed in an analytical model, however
real world decision-making is subject to uncertainties that make the use of
integers unrealistic. Uncertainty is a fundamental quality of infrastructure site
selection decisions, and much uncertainty is based upon the limited ability of
decision-makers to quantify qualitative evaluation criteria that are more easily
described by statements of natural language. For example stating that ‘a good
site for development will have a low environmental value’ may prove extremely
difficult to express in a quantitative way. In the previous sentence there are two
linguistic variables, one may be termed ‘site suitability’, and the other is
‘environmental value’ which must be ‘low’ for site suitability to be ‘good’. To
make constructive use of such input requires a method for dealing with these
linguistic variables in a numerical model.
Approximate Reasoning (AR) is a fuzzy logic based technique that can be used
to augment the MCE process by utilising fuzzy set methods to characterise and
operate upon imprecise inputs. In this approach linguistic inputs are quantified as
fuzzy numbers and manipulated with specialised fuzzy computation techniques.
Utilising AR and linguistic variables enables users to overcome some difficulties
6Chapter 1 Introduction and Methodology
encountered with regular MCE analysis. Fuzzy numbers provide a convenient
way to represent uncertainty, and procedures for criteria standardisation can
benefit from a universal linguistic suitability scale. Linguistic variables also
provide an easy means for decision-makers to input their opinions and value
judgements without needing to understand complex mathematical formulas,
thereby increasing the usability of spatial decision support tools in the real world.
Fuzzy methods have been implemented for spatial decisions in a limited way, via
inference systems or symbolic approaches. However there has been an absence
of fuzzy MCE techniques. This may be due to the fact that these methods tend to
require unfeasible amounts of processing to evaluate large numbers of
alternatives with a fuzzy algorithm. This thesis postulates that the rapid increase
in processing power over the last decade combined with the simplification of AR
methods allows the successful integration of fuzzy MCE into a SDSS.
1.2.4 Case Study
This research would have been impractical without access to a real world
example of the complexity encountered in infrastructure planning, and the future
development of the Brisbane Airport site provided an ideal case study. During
the next twenty years, improvements to deal with a forecasted trebling in
passenger movements will be phased in on the 2700 ha Brisbane Airport site,
which is situated on the Eastern Australian coastline just outside Brisbane and
adjoining Moreton Bay. There are many environmental, social, economic and
operational issues involved when locating future airport facilities, and it is this
rich decision-making environment that makes the project a valid example. A
comprehensive description of the Brisbane Airport site shown in Figure 1.1 can
be found in Appendix B.
7Chapter 1 Introduction and Methodology
Figure 1.1: Brisbane Airport
1.3 Research Outline
This research could be broadly classified as a combination of theory-focused
research consisting of the theoretical development of a new Approximate
Reasoning Algorithm for Infrastructure Site Selection (ARAISS), and design-
focused research consisting of the practical application of the theory in a new
SDSS (InfraPlanner). The research question, aims, objectives, scope, justification
and methodology are defined in the following subsections.
1.3.1 Research Question
If it were necessary to define the one question that has guided this research it
would be the following:
“Can Approximate Reasoning be integrated into a GIS based SDSS to mitigate
current difficulties with SDSSs utilised for Infrastructure Site Selection?”
8Chapter 1 Introduction and Methodology
It was hypothesised that the answer to this question was yes.
1.3.2 Aims
The aim of this research has been to create new knowledge at the intersection of
several disciplines surrounding the problem of infrastructure site selection, and
ultimately to produce a viable method of aiding decision-makers through the
complexities of site selection decisions. The confluence of Physical Planning,
Decision Science, Fuzzy Logic, Soft Computing, Decision Support and Expert
Systems, Geographical Information Systems, and Software Design, has opened
up many possibilities for new developments in site selection. This research has
aimed to capitalise on new developments in these areas by creating a practical
means to integrate them in a useful way.
1.3.3 Objectives
In keeping with the division of theory focus and design focus, two specific
research objectives were defined:
1. Develop a practical infrastructure site selection algorithm based on an
Approximate Reasoning ‘linguistic’ approach.
2. Develop a new spatial decision support system based on the algorithm
developed in objective 1.
The first objective resulted in the creation of ARAISS and the second in the
creation of the InfraPlanner system.
1.3.4 Scope
There were several factors that limited available resources and imposed
boundaries on the scope of this research. One of the main considerations was
time, which imposes a need for a clear focus and limits work considered
secondary to the research objectives. Moreover, as the research objectives
9Chapter 1 Introduction and Methodology
required a multidisciplinary approach, there was a consequent limitation on the
scope of work conducted in each area.
When considering the disciplinary scope of this thesis, the primary guiding
factors were the research objectives to design and implement a strategic decision-
making model. It therefore follows that the main contribution of this thesis lies in
the confluence of decision science and decision support systems. All other
disciplines served to provide specific tools and context to this main area of
research into what are the most promising ways to aid human beings make better
decisions. This thesis provides one context specific example, focused on
infrastructure site selection using an Approximate Reasoning model in a system
built inside a Geographical Information System.
Data security also posed a practical limitation on the scope of work undertaken,
particularly in terms of the validation problem used. It proved difficult to obtain
spatial data on areas outside the Brisbane Airport grounds whilst satisfying the
data access and security concerns of both the Brisbane Airport Corporation
(BAC) and Brisbane City Council. Consequently, the practical example outlined
in Chapter 8 utilised only data from within the Airport grounds, as was kindly
provided by BAC.
1.3.5 Justification
The problem at the centre of this research is of great significance. Infrastructure
planning decisions have a major effect on society, with most major projects
having significant environmental, social and economic impacts. The case study
used for this research offers a prime example of the impact site selection
decisions have on society. Brisbane International Airport is a major driving
engine for the economy of South East Queensland, and the planned development
of Airport site will involve a capital expenditure of around 1 Billion AUD. The
airport also provides a notable direct source of employment in the Brisbane area.
Important environmental considerations both inside the Airport boundary and
within its sphere of influence are many, as the Airport contains areas of saltwater
mangroves, and is located next to environmentally sensitive Moreton Bay. The
10Chapter 1 Introduction and Methodology
site also possesses three creeks, a river and a floodway, creating a clear need for
environmentally conscious development decisions. Social responsibilities,
including protection of cultural heritage sites and minimising impacts on
surrounding suburbs are also a major factor. Yet despite the enormous
importance of the site selection decisions involved in Airport planning, there is
no Spatial Decision Support System Currently in use. This is situation is typical
of many large-scale projects (Maniezzo, Mendes et al. 1998).
The basis for investigating the use of Approximate reasoning in site selection
also has a solid foundation in fact. Approximate reasoning works by quantifying
the uncertain, human elements of a problem. This is of immense practical value
as it is human intuition which is frequently the basis for decision-making (Turban
1995). Combining Approximate reasoning with GIS is now a realistic
proposition as GIS has grown to become a mature technology (Sui and
Goodchild 2001), with newfound flexibility and data processing power.
1.3.6 Methodology
As the theoretical outputs of the research were to be implemented into a practical
design process, it was decided to structure activities from a design perspective
and follow a Decision Support System development methodology. At a basic
level the method used in this research consisted of four stages:
1. Planning and Research:
• Needs assessment, problem diagnosis & definition of system
objectives.
• Review of relevant literature and gather other information.
2. Analysis & Design:
• Conceptual design of the InfraPlanner system.
• Development of the decision-making algorithm.
3. Construction:
• Coding and debugging of the InfraPlanner prototype.
4. Implementation and Feedback:
11Chapter 1 Introduction and Methodology
• Peer review of the model via publication and focus group.
• Testing and evaluation of InfraPlanner in a real world validation
problem.
• Critical assessment of the prototype and suggestions for future
improvements and research directions
Each step in the process required specific resources to conduct the activities
necessary to produce the desired outputs, and a Logic Model was employed as
the structure to organise the elements of the process. Logic modelling is a
resource management tool used to document the underlying reasons behind a
program of activities. In a logic model the program is divided into six elements.
1. Resources are the raw materials available
2. Activities make use of the available resources
3. Outputs are the tangible results of an activity
4. Customers are those who receive the outputs
5. Outcomes (Short, medium, or long term) are the reason for undertaking the
activities
6. External influences are those influences that are beyond the scope and
control of the program
The six elements of the process are grouped in the columns of a Logic Model and
arrows are used to signify connections between them. The logic model detailing
the sequence of events involved in the InfraPlanner development process is
shown in Figure 1.2, and further described in Chapter 7.
12 Chapter 1 Introduction and Methodology
Users / planners /
experts Statement of user needs
Developer User needs documented
Literature Research:
Critical review
State of the art report
Developmer/ other
researchers
Technology consultant
Analysis & Design:
Conceptual design
Decision-making model
& Design specification
Ideas for improvement documented
Planning: Interviews / focus groups
Software developer
Technology selection
Software developer
Construction: Coding
‘Infraplanner’ prototype
BAC users
Implementation: Prototype used in
validation problem
Ongoing system Adaptation and
better site selection decisions
GIS technology
External Influences: BAC staff turnover, technological development, advancements in operations research, political environment.
Resources Activities Outputs Customers Short term outcomes Long term outcomes
Figure 1.2: Development Process Logic Model
13Chapter 1 Introduction and methodology
1.4 Thesis structure
This thesis is structured to follow the development process outlined in Section
1.3 from start to finish, and as such can be broken into four major parts. Thesis
chapters are structured in the following way.
Chapter 1 Introduction and methodology
Part 1 Planning and Research
Chapter 2 Problem diagnosis and preliminary literature review
Chapter 3 Approximate reasoning
Chapter 4 Spatial decision support systems
Part 2 Analysis and Design
Chapter 5 Problem analysis and conceptual system design
Chapter 6 Algorithm design
Part 3 Construction
Chapter 7 Creating InfraPlanner
Part 4 Implementation and Feedback
Chapter 8 A case study at Brisbane Airport using InfraPlanner
Chapter 9 Conclusions
The Planning and Research section comprises three chapters. Chapter 2 details
the process of identifying the primary objectives of the Spatial Decision Support
System (SDSS), and the preliminary phase of the literature review. This first
review covered existing techniques and technology used in spatial decision-
making. Chapter 3 extends the review to provide a detailed introduction to the
Approximate reasoning techniques used in the new site selection algorithm
developed in this research. Finally, Chapter 4 focuses on the technical aspects of
implementing decision-making techniques in a SDSS.
14Chapter 1 Introduction and methodology
The Analysis and Design section comprises two chapters. Chapter 5 analyses the
main limitations on current Spatial Decision Support Systems, and proposes a
conceptual framework for mitigating these limitations. Statements about desired
capabilities are made, and conceptual ideas to achieve these capabilities are
discussed. Chapter 6 details the design of a new Approximate Reasoning
Algorithm for Infrastructure Site Selection (ARAISS). The mathematical
processes in the algorithm are derived and discussed. The process of testing
ARAISS for validity outside GIS using MATLAB software is also illustrated.
The Construction section comprises one chapter. Chapter 7 describes the
practical implementation of the ARAISS algorithm in a Spatial Decision Support
System created by customising ArcView GIS software using Visual Basic for
Applications (VBA).
The Implementation and Feedback section comprises one chapter. Chapter 8
describes how InfraPlanner was implemented in an experiment using a three
decision-maker six criteria site selection problem at Brisbane Airport.
1.5 Multi-disciplinary nature of the research
It was clear from the outset that achieving the research objectives required a
multi-disciplinary approach, which tends to compound complexity. Rather than
focusing on one aspect of the problem, the objectives required work to be carried
out in three broad categories, where each category contained at least two separate
disciplines, as shown in Table 1.1.
15Chapter 1 Introduction and methodology
4
2
4
3
3
5,7,8,9 6
Table 1.1: Disciplines involved in the research
CCaatteeggoorryy DDiisscciipplliinneess IInnvvoollvveedd
Infrastructure location problems Physical planning
Decision science
Approximate Reasoning algorithm Fuzzy logic
Soft computing
Spatial Decision Support Systems Decision Support and Expert Systems
Geographical Information Systems
Software design
The category of disciplines inherent in each chapter varies throughout the thesis,
and is described in Figure 1.3. It may be seen from Figure 1.3 that the major
research effort was spent in the intersection of the three primary areas. Each of
the disciplines invloved contained significant sub-problems, which were outside
the scope of the research program. An example is the problem of how to best
represent a linguistic term as a fuzzy number, a significant issue in the area of
approximate reasoning. This problem is discussed in Chapters 3 and 5, but was
beyond the scope of this research.
Figure 1.3: Disciplines contained in each chapter
Infrastructure Location Problems
Approximate Reasoning
Model
Spatial Decision Support Systems
16Chapter 1 Introduction and methodology
This page intentionally blank
17Chapter 2 Problem Diagnosis and Preliminary Literature Review
Chapter 2
PPRROOBBLLEEMM DDIIAAGGNNOOSSIISS AANNDD
PPRREELLIIMMIINNAARRYY LLIITTEERRAATTUURREE RREEVVIIEEWW
2.1 Introduction
The first step in the development process was to generate a clear statement of the
problem and survey existing approaches to its solution. To maintain the focus on
a practical application, a focus group was assembled, consisting of planning and
infrastructure managers from industry, and academics from QUT. The meeting
led to a consensus on key statements about the aims, objectives, and scope of the
proposed Spatial Decision Support System (SDSS). Once the groundwork had
been laid, an extensive literature review, encompassing the techniques and
technology of spatial decision-making, was conducted. Planning and research
initially overlapped as feedback from the literature review provided the impetus
for a more detailed description of desired functionality.
This chapter provides key statements from problem diagnosis, and a preliminary
review of the decision science techniques and technology most commonly used
for location problems. A detailed discussion of Approximate Reasoning
techniques, and the technical aspects of SDSSs follow in chapters 3 and 4.
18Chapter 2 Problem Diagnosis and Preliminary Literature Review
2.2 Problem diagnosis
The initial meeting for problem diagnosis and needs assessment was based
around the discussion paper in Appendix C, and took contributions from
professionals involved in planning, infrastructure development and management,
and environmental management. Also present were QUT academics from the
fields of Geographical Information Systems, Software Design, Mathematics, and
Environmental Management. Key statements emerging from the meeting were
as follows:
• The system will support decision-makers in planning, infrastructure
development and management, and environmental management with site
selection decisions.
• The system should be able to accommodate qualitative variables such as
socio-economic and environmental impacts.
• The system should accommodate multiple criteria and multiple points of
view of the measurement and weighting of those criteria.
• Outputs from the system should be graphical where possible, preferably
in a mapping format.
• The modelling capabilities of the system should be transparent and easily
understandable.
• The system should aim to aid decision-makers, not replace them.
These basic statements of desired functionality were then used as the focus for a
state of the art literature review. The first stage of the review focused on the
existing techniques and technology involved in spatial decision-making.
Specifically, the review was conducted to answer the following questions:
1. What are the analytical techniques used in the solution of the type of
Infrastructure site selection problems encountered by decision-makers at
BAC, and planners in general?
2. What are the major limitations of these techniques?
19Chapter 2 Problem Diagnosis and Preliminary Literature Review
3. What technology platforms are used in the analysis of Infrastructure site
selection problems?
4. What are the most promising methods for advancing current techniques and
technologies?
The remainder of this chapter is drawn from the first stage of the review.
2.3 Introduction to location problems
Solving location problems is an everyday activity performed by individuals and
groups who use spatial information to make decisions about such things as where
to live, where to shop, and how to manage the environment and infrastructure
(Jankowski, Andrienko et al. 2001). The primary objective of these problems is
to identify the most desirable location for a facility or service (Maniezzo,
Mendes et al. 1998), such as locating a new airport, allocating law enforcement
resources, or buying a new home.
The choice between competing locations is made according to how well each
location satisfies a set of conditions. These conditions, commonly referred to as
evaluation criteria or simply criteria, will vary across space and are unique to
each location problem. They may encompass issues such as maximisation of
utility, minimisation of detrimental environmental and social impact, and ease of
accessibility (Nijkamp, Rietveld et al. 1990). The term ‘criteria’ is generic and is
used to convey the concepts of both objectives and attributes. The primary
objective may also be referred to as the goal, and is usually the top level of a
hierarchy of sub-objectives (Saaty and Kearns 1985). These sub-objectives are
operationalised by assigning measures to achieve them, called attributes
(Malczewski 1999). For example if the objective is to minimise environmental
damage when locating an industrial facility, an attribute chosen to represent this
objective may be the number of acres of bushland lost.
20Chapter 2 Problem Diagnosis and Preliminary Literature Review
When criteria are conflicting, it is inevitable that trade-offs will need to be made.
In order to optimise the trade-off process, it is essential to specify how relatively
important each criterion is. This is usually a subjective process whereby the
decision-maker assigns weights to each criterion according to his or her
preferences (Bogetoft and Pruzan 1997). However spatial decisions are often
made by groups of decision-makers, to satisfy the needs of multiple stakeholders.
Such situations are described as group decision-making, and in a group
environment where decision-makers are autonomous and heterogeneous it is
inevitable that conflicts will occur (Chu-Carrol and Carberry 2000). These
conflicts generally arise because of the diverse values of the groups or
individuals involved, which lead to different weighting of criteria, but conflicts
may also arise from the definition of criteria, or the decision-making process
(Bogetoft and Pruzan 1997).
A site selection decision is essentially a choice between alternative sites. Each
alternative will have a set of outcomes (consequences) in relation to the various
evaluation criteria, however the set of outcomes is seldom completely
deterministic, and some level of uncertainty usually enters the decision-making
process (Spradlin 1997). Sources of uncertainty are generally two fold. Firstly
there may be some uncertainty about the validity of the information upon which
the decision is to be based, such as the reliability of an expert opinion (Keeney
and Raiffa 1976). Secondly there may exist some unpredictability about future
events and the state of the future environment in which the decision outcome
dwells, such as the weather or economic outlook. Types of uncertainty are also
twofold, the first being stochastic, as described by a probability distribution of
the alternate states of attributes and outcomes, and the second is fuzziness
(imprecision in data), as described by fuzzy set theory (Bellman and Zadeh
1970).
2.3.1 Problem classifications
This research was driven by infrastructure site selection problems, which are
referred to here by the more generic term Group Multi-criteria Location
Problems (GMCLP’s). GMCLP’s are complex real world decision problems with
21Chapter 2 Problem Diagnosis and Preliminary Literature Review
the objective of finding an optimal site for a facility or service from multiple
alternatives, using multiple evaluation criteria and the opinions of multiple
stakeholders.
GMCLP’s belong to a general class of decision-making problems referred to as
multicriteria decision problems. Classification of these problems is summarised
in Figure 2.1. It is widely accepted that multicriteria decision problems can be
broken into two categories. Multiattribute decision-making (MADM) problems
involve a finite or relatively small number of discrete alternatives, whereas
multiobjective decision-making (MODM) problems have a relatively large or
infinite number of feasible alternatives (Jankowski 1995). MADM and MODM
have also been referred to as discrete and continuous decision problems (Hwang
and Yoon 1981), as MADM implies a discrete number of pre-specified
alternatives, whereas in MODM the alternatives are generated during the solution
process. It is important to note that if there exists a direct correspondence
between objectives and attributes, a MODM problem becomes a MADM
problem, as the objectives may be completely defined by a limited number of
attributes in this scenario.
Group decision-making, where more than one set of goals or preferences is
considered, is then distinguished from individual decision-making, where the
objectives are agreed. This distinction is made on the grounds of conflicting
objectives rather than number of decision-makers. The level of uncertainty
provides a third division between deterministic problems, where all relevant
information is known, and probabilistic or fuzzy problems, where there is some
uncertainty. In real world decision problems uncertainty is commonplace, and
deterministic problems are rare.
22Chapter 2 Problem Diagnosis and Preliminary Literature Review
Figure 2.1: Classification of Multicriteria Decision Problems
Modified from: (Malczewski 1999)
A Rigorous definition of GMCLP’s is suggested here and defines them as:
‘the selection of an optimal location from a large number of spatial alternatives
by a heterogeneous group of decision-makers using multiple evaluation criteria
under uncertainty.’
MMUULLTTIICCRRIITTEERRIIAA DDEECCIISSIIOONN
PPRROOBBLLEEMMSS
Multiattribute decision problems
Individual
Certain Uncertain
Probabilistic Fuzzy
GGMMCCLLPP’’ss
Group
Certain Uncertain
Probabilistic Fuzzy
Multiobjective decision problems
Individual Group
Certain Uncertain
Probabilistic Fuzzy
Certain Uncertain
Probabilistic Fuzzy
23Chapter 2 Problem Diagnosis and Preliminary Literature Review
They contain the following four key attributes:
1. A large number of spatial alternatives:
The alternatives under consideration are numerous enough to make manual
analysis impractical i.e. the problem is non-trivial
2. A heterogeneous group of decision-makers:
Multiple parties are involved in the decision process and there is no guaranteed
consensus among them
3. Multiple evaluation criteria with an explicit spatial component
The decision is based on multiple, conflicting criteria that vary across space
4. Uncertainty
The relationship between the available raw data and site suitability is subject to
some kind of uncertainty
The GMCLP discussed in Chapter 8 offers a practical example of the type of
location problem defined above. It involves locating a new industrial facility
somewhere on the 2700 ha Brisbane Airport site. Stakeholders include the
Brisbane Airport Corporation, The Commonwealth Government and Community
representatives. Decision-makers wish to satisfy six evaluation criteria, which
include issues such as environmental value and community impact, that are hard
to quantify and subject to disagreements among parties, as well as uncertainty in
measurement.
2.4 Decision science techniques
Decision science, also referred to as decision analysis, operations research,
systems engineering and management science, has a long history. Put simply it is
the application of scientific method to everyday decision-making. Decision
science seeks to apply logical reasoning to decision problems in a structured
way, thereby making the decision process explicit and repeatable. It also offers a
means to look inside a particular decision and make explicit how and why it was
24Chapter 2 Problem Diagnosis and Preliminary Literature Review
made. Decision science has found many applications in engineering, the military
and business management. Although there is evidence that formal decision-
making methods in military strategy date back thousands of years, the field of
decision science is commonly assumed to have originated during World War II,
when scientific methods were applied to strategy in antisubmarine warfare by
T.C. Koopmans.
There are a multitude of formal decision-making methods. However those that
have been applied to location problems are relatively few and fall into three
broad categories.
1. Map algebra methods
Map algebra includes standard spatial functions and simple overlay methods
that screen out sites based on Boolean operators or simple arithmetic.
2. Multicriteria evaluation methods
Multicriteria evaluation methods offer the ability to rate criterion outcomes on a graduated scale and choose the relative importance or weight of each criterion.
3. Artificial intelligence methods (soft computing or geocomputation)
These methods include neural networks, fuzzy systems and evolutionary algorithms. They are usually complex in nature and offer great potential for complex spatial problems.
The following Sections provide an overview of these three groups of methods,
particularly the widely applied family of multicriteria evaluation techniques. A
more detailed review of the Approximate Reasoning methods is provided in
Chapter 3.
2.4.1 Map algebra
Map algebra, or overlay analysis, is the most basic level of spatial analysis. It
involves the use of simple arithmetic, Boolean and relational operators to
combine input maps. Table 2.1 provides a sample of map algebra operations.
25Chapter 2 Problem Diagnosis and Preliminary Literature Review
Table 2.1: Map algebra operators
TTyyppee OOppeerraattiioonn
Arithmetic Subtraction, Addition, Multiplication, Division
Boolean And, Or, Not
Relational Less than, Greater than, Equal to
The basic concept of overlay analysis using map algebra in planning problems
with environmental criteria was first documented in the profoundly influential
work ‘Design with Nature’ (McHarg 1969). Map algebra is the basis of all more
complex methods, and provides a powerful tool for site selection (Tomlin 1990),
whilst being easy to use and understand. However several authors have pointed
out that the use of Map algebra alone tends to oversimplify analysis (Hopkins
1977; Hobbs 1980; Pereira and Duckstein 1993), and more powerful tools are
needed for complex decision environments. The next level of complexity in
analysis is provided by multicriteria evaluation methods.
2.4.2 Multicriteria evaluation
The term Multicriteria Evaluation (MCE), which emerged in the 1970s, is used to
represent a variety of methods for solving multicriteria decision problems.
Multicriteria evaluation has widely and consistently been recognised as
appropriate to deal with spatial decisions eg. (Pereira and Duckstein 1993;
Eastman, Jin et al. 1995; Jankowski 1995; Laaribi, Chevallier et al. 1996;
Malczewski 1999). It is suitable for discrete multiattribute decision making
(MADM) situations such as spatial allocation, and can handle quantitative and/or
qualitative data (Pettit and Pullar 1999). MCE methods serve to investigate a
number of choice possibilities in the light of multiple criteria and conflicting
priorities (Voogd 1983), and have been described as weighing independent
criteria in terms of judged relative importance or value (Smith 1980).
Applications of MCE in site selection are many and have included such diverse
problems as urban waste management (Haastrup, Maniezzo et al. 1998) power
26Chapter 2 Problem Diagnosis and Preliminary Literature Review
plant siting (Hobbs 1980) ecosystem management (Ji 1996; Prato 1999)
allocation of educational resources (Kwak and Changwon 1998) and sustainable
development (Nijkamp and Giaoutzi, 1993; Sharifi, et al., 2002). MCE is
considered by many to be an immature, non-comprehensive sub-discipline of
Operations Research (Bogetoft and Pruzan 1997). Should current patterns
continue, MCE will continue to evolve and be refined over time.
While individual MCE processes may vary, they generally fit within the same
overall framework, as shown in Figure 2.2. There are two major approaches to
organizing the sequence, and they find their division at the stage of identifying
alternatives and criteria. A value-focused approach seeks to identify criteria
(values) first, and an alternative focused approach will first identify alternatives
(Keeney 1992). Keeney (1992) goes on to argue that the value focused method is
superior as values are more fundamental than alternatives.
There are five major steps involved before reaching the final recommendation
stage, which can broadly be defined as:
1. Specifying and standardising the evaluation criteria
2. Weighting the evaluation criteria
3. Identifying feasible alternatives, and bringing them together with criteria in a
decision matrix
4. Performing an aggregation
5. Performing a sensitivity analysis
27Chapter 2 Problem Diagnosis and Preliminary Literature Review
Figure 2.2: Framework for MCE
modified from (Malczewski 1999)
CHOICE
DESIGN
INTELLIGENCE
Problem Definition
Criterion Weights
Constraints
Decision Matrix
Alternatives
Decision-makers Preferences
Aggregation via Decision
Rules
Sensitivity Analysis
Recommendation
Standardised Evaluation
Criteria
28Chapter 2 Problem Diagnosis and Preliminary Literature Review
In a more general sense decision-making processes consist of three phases,
intelligence, design and choice (Simon 1960). The MCE framework in Figure 2.2
is shown in context with these three phases of the decision-making process. It
can be seen that the intelligence phase comprises the systematic collection of
data and information about the problem, the design phase involves processing the
information, and choice entails the selection of a solution based on the processed
outputs. The five major steps within the three phases are explained in detail
within the following sections.
2.4.2.1 Specifying and standardising evaluation criteria
There is no single technique for specifying evaluation criteria in all cases, and
several approaches may be used in parallel. The advice of experts such as
ecologists, social scientists, and economists, is usually used to define, measure
and rate complex criteria (Pullar and Pettit 2000), whilst the popular Delphi
technique may be used to formalise the input of opinions from these experts and
formulate relevant factors (Pettit and Pullar 1999). The Delphi method involves
anonymous inputs made by a group of heterogeneous experts who are given
feedback between rounds (Author 1999). However this does not eliminate the
danger of people’s personal feelings taking priority over facts, and an
examination of relevant literature and/or a rigorous analytical simulation should
also be used (Keeney and Raiffa 1976).
The criteria for a given location problem are operationalised by attributes which
are seldom homogeneous. Attributes may fall into several, sometimes
overlapping, categories, and are often described by the varying scales upon
which they are measured.
At a basic level the scales are generally considered to be one of four types:
1. A Nominal scale is a list of names (labels) used to identify the state of an
attribute. Using this scale it is possible to see if two attributes are equal or
not.
29Chapter 2 Problem Diagnosis and Preliminary Literature Review
2. An Ordinal scale provides a relative ordering. Using this scale it is possible
to compare attributes using the less than and greater than operators.
3. An Interval scale is a continuous scale using equal intervals from an
arbitrary zero point. Using this scale it is possible to perform additions,
subtractions and scaling by a constant.
4. A Ratio scale is a continuous scale using equal intervals from an absolute
zero point. This scale permits all the previous operations plus multiplication
and division.
Table 2.2 provides further classifications for attributes based on the opinions of
various researchers. A given attribute may be a member of several of these
groups.
30Chapter 2 Problem Diagnosis and Preliminary Literature Review
Table 2.2: Types of attributes, a brief description, and describing authors.
TTyyppee DDeessccrriippttiioonn RReesseeaarrcchheerr
Factor Measured on a continuous scale (Eastman, Jin et al. 1995)
Constraint Boolean (0 or 1) serving to
limit the alternatives under
consideration
(Eastman, Jin et al. 1995)
Proxy Indirectly related to the
objective
(Keeney and Raiffa 1976)
Direct Directly related to the objective (Keeney and Raiffa 1976)
Benefit Maximisation is desirable (Hwang and Yoon 1981)
Cost Minimisation is desirable (Hwang and Yoon 1981)
Qualitative Measured on a qualitative
nominal or ordinal scale
(Voogd 1983)
Quantitative Measured on a quantitative
interval or ratio scale
(Voogd 1983)
Natural Measured on an established &
commonly used scale requiring
no subjective input
(Keeney and Raiffa 1976)
Constructed Measured on a scale
constructed by subjective input
(Keeney and Raiffa 1976)
Deterministic Certain (Malczewski 1999)
Probabilistic Described by probability theory (Malczewski 1999)
Linguistic A natural language term
semantically defined by a fuzzy
membership function
(Zadeh 1976)
31Chapter 2 Problem Diagnosis and Preliminary Literature Review
A consequence of having to use incommensurate attributes (i.e. attributes having
different scales of measurement) is the necessity to perform transformations to
derive a standard scale upon which they may be commensurately compared. The
type of transformations used will depend upon the level and type of uncertainty
in the raw data as well as the qualitative or quantitative nature of the data (Voogd
1983).
Linear scale transformation methods are suitable for quantitative, deterministic
data. The most common linear transformations are the maximum score and score
range procedures (Voogd 1983) given below.
Maximum Score Transformation:
(benefit criterion) (2.1)
(cost criterion) (2.2)
Where: ijx' is the transformed score of the ith alternative wrt the jth attribute
ijx is the raw score of the ith alternative wrt the jth attribute
maxjx is the maximum raw score for the jth attribute
The maximum score transformation has the advantage of being proportional,
with the most desirable score always equal to unity. However interpretation of
the least desirable transformed score is difficult (Malczewski 1999).
Score Range Transformation:
(benefit criterion) (2.3) minmax
min
'jj
jijij
b
xxxx
x−
−=
max'j
ijij
b
xx
x =
max1'j
ijij
c
xx
x −=
32Chapter 2 Problem Diagnosis and Preliminary Literature Review
(cost criterion) (2.4)
The score range transformation has the advantage of giving a complete range of
values from 0 (the worst score) to 1 (the best), but has the disadvantage of not
being proportional.
Another method for obtaining a standardised scale is through value or utility
functions. A value function is a utility function used in a deterministic decision
situation (Keeney and Raiffa 1976). It yields a standardised scale by
transforming the raw score of the attribute in question to a value between 0 and 1
via subjective value judgements (Hepner 1984). One of the most widely used
procedures for generating a value function is the midvalue method, which is
carried out in the following steps (Bodily 1985).
1. Determine the range over which the attribute is to be assessed, and assign the
values 0 and 1 to the end points.
2. Find the midvalue point (the point having a value (utility) half way between
the end points) from decision-maker input and assign the value of 0.5 to that
point.
3. Find the midvalue points either side of 0.5 to yield the points with a value of
0.25 and 0.75.
4. Repeat step 3 to obtain as many points as needed.
5. Draw the value curve through the previously obtained points and fit an
analytical expression to the curve.
The term utility function describes both deterministic value functions, and
functions obtained using a probabilistic approach (Keeney and Raiffa 1976).
Among the techniques applied in the latter situation, the indifference technique,
otherwise described as the 50-50-lottery method (Bodily 1985), is analogous to
the midvalue method. The major steps involved in the two procedures are the
minmax
max
'jj
ijjij
c
xxxx
x−
−=
33Chapter 2 Problem Diagnosis and Preliminary Literature Review
same. However the indifference technique requires the decision-maker to obtain
midvalues by assessing an outcome that has the same utility as a 50-50 gambling
of two other outcomes with established utility values, as described in equation
2.5.
N.B. In equations 2.5 and 2.8 the term ‘p’ is used to denote two different
variables. The usage has been kept consistent with the referenced work in each
case and the reader should treat each case independently and not assume that a
given meaning applies in other sections of the text.
(2.5)
Where: )( jj xu is the utility function of the jth attribute
)( +jj xu is the utility of the best outcome (= 1)
)( −jj xu is the utility of the worst outcome (= 0)
p is probability
Construction of the curve is accomplished by varying p in increments until an
adequate number of points have been established.
2.4.2.2 Criterion weighting
The evaluation criteria specified for a given MCLP often vary in terms of the
relative importance decision-makers place on them. This creates the problem of
quantifying the disparity of importance amongst evaluation criteria, which may
be solved by a number of different procedures. This section describes three
procedures for weighting criteria in location problems: ranking, rating, and
pairwise comparison. The choice of which procedure to use will generally
depend upon the competing requirements of accuracy and ease of use.
The simplest method of weighting criteria is to arrange them in rank order using
units on an ordinal scale, according to the decision-makers preferences. Weights
are then assigned according to the following formula (Stillwell, Seaver et al.
1981):
pxupxupxu jjjjjj =−+== −+ )()1())((?)(
34Chapter 2 Problem Diagnosis and Preliminary Literature Review
(2.6)
Where: wj = The weight of the jth criterion
Rj = The rank of the jth criterion
n = The number of criteria
The ranking approach is attractive in practice due to its simplicity (Voogd 1983).
Rating offers a slightly more comprehensive approach by either allocating a pre-
determined number of points across the evaluation criteria, or assigning a value
to the criteria of maximum importance and rating each subsequent criteria in
relation to it (Stillwell, Seaver et al. 1981). Weights are then normalised to a
suitable interval scale.
One of the better techniques for developing criteria weights is the pairwise
comparison method developed by Saaty (1980) within the context of his
Analytical Hierarchy Process (AHP) (Saaty 1980; Eastman, Jin et al. 1995). In
AHP pairwise comparisons of criterion importance are recorded in a square
reciprocal matrix. The AHP offers particularly fine resolution but can be time
consuming.
2.4.2.3 Alternatives and the decision matrix
After an analysis of constraints, the set of feasible decision alternatives may be
specified. Constraints in this context are Boolean criteria that serve to reduce the
number of available alternatives under consideration. This process may be either
compensatory or noncompensatory, where a compensatory analysis allows
weaknesses on one criterion to be traded off against strengths on another, and
noncompensatory techniques enable the elimination of an alternative based
( )1
1
1+−∑
+−=
∑=
k
n
k
jj
rn
rnw
35Chapter 2 Problem Diagnosis and Preliminary Literature Review
solely on the poor performance of a single criterion (Jankowski 1995). Feasible
alternatives are those that survive this elimination process.
The set of feasible alternatives may then be combined with the remaining criteria
attributes and weights in a decision matrix, which is a construct used to visualise
the computational heart of the multicriteria evaluation process. It enables the
comparison of decision alternatives in relation to their set of criteria outcomes as
shown in Figure 2.3.
36Chapter 2 Problem Diagnosis and Preliminary Literature Review
Figure 2.3: Decision Matrix
modified from: (Malczewski 1999)
Generation of the decision matrix provides the necessary inputs for an
aggregation to be performed. Aggregation provides the means by which
alternatives may be quantitatively compared, and is implemented according to
the decision rules.
2.4.2.4 Aggregation via decision rules
Decision rules are procedures that allow for ordering alternatives (Starr and
Zeleney 1977). They typically combine criteria into a single composite index,
and a statement of how alternatives are to be compared using that index
(Eastman, Jin et al. 1995). This section describes three common aggregation
AAttttrriibbuuttee11 AAttttrriibbuuttee22 AAttttrriibbuuttee33 ……………….... AAttttrriibbuutteenn
Alternative1 Outcome
1,1
Outcome
1,2
Outcome
1,3
……….
.
Outcome
1,n
Alternative2 Outcome
2,1
Outcome
2,2
Outcome
2,3
……….
.
Outcome
2,n
………. ………. ………. ………. ………. ………. Alternativem Outcome
m,1
Outcome
m,2
Outcome
m,3
……….
.
Outcome
m,n
Preferences Weight1 Weight2 Weight3 ……….. Weightn
State of decision
environment
GGOOAALL
Objective 1 Objective 2
Sub-objective Sub-objective Sub-objective
37Chapter 2 Problem Diagnosis and Preliminary Literature Review
procedures: Linear weighted combination, compromise programming and
ordered weighted averaging.
The most prevalent procedure in spatial MCE is linear weighted combination
(Eastman, Jin et al. 1995). The underlying mathematical theory of the linear
weighting process is presented in the following equation, which brings together
alternative outcomes and the criterion weights in a linear equation.
(2.7)
Where:
S = outcome score for alternative j
w = weight of criterion i (on a 0 to 1 scale)
x = score of criterion i for alternative j (on an arbitrary but common
evaluation scale)
c = constraints on alternative j (0 or 1)
⊗ = Multiplication
(Eastman, Jin et al. 1995)
Compromise programming is based on the displaced ideal concept, which
considers that there is an ideal solution and attempts to minimise the distance
from it (Zeleny 1982). Locating the solution closest to the ideal solution is
accomplished using weighted Lp norms or metrics as shown in equation 2.8.
(2.8)
subject to
X∈x , q,1,2,...... kfor 0 =≥kw
where
Lp is the distance metric
wk is the weight of the kth objective function
jiij cxwS ∏⊗= ∑ )(
( ) ( )( ) ( )
pp
kk
kkk
pkp xfxf
xfxfwL
1
min⎥⎥⎦
⎤
⎢⎢⎣
⎡⎥⎦
⎤⎢⎣
⎡−−
=−+
+∑
38Chapter 2 Problem Diagnosis and Preliminary Literature Review
fk+(x) is the ideal solution of the kth objective function
fk-(x) is the nadir or anti-ideal value of the kth objective function
p is a power parameter ranging from 1 to infinity
Ordered Weighted Averaging (OWA) is a variation from linear weighted
combination. Whereas a linear weighted combination is a compensatory
technique, meaning that bad outcomes on one criterion can be compensated for
by better outcomes on another, OWA offers the ability to control the level of
compensation. The strict OWA operator weights criteria on the basis of their
rank suitability order rather than being weighted on their inherent qualities
(Yager 1988). Thus, if at a certain location three criteria (X,Y,Z) are ranked in
terms of suitability ZYX (highest to lowest) and it has been decided to apply
weights of 0.5, 0.3, 0.2, then the weighted combination would look like equation
2.9.
0.5Z + 0.3Y + 0.2X (2.9)
A hybrid approach based on the OWA operation also includes the original
criterion weights with order weights. This approach provides flexibility in the
degree of trade-off applied at a given location. In linear weighted combination
criteria weights determine how factors trade-off relative to one another. However
the level of trade-off is not adjustable. Extension via OWA offers the ability to
adjust the level of trade-off and risk aversion. Risk aversion is measured on a
scale between a totally AND aggregation where the minimum suitability term
has total dominance and a weight of 1 (representing total risk aversion) and a
totally OR aggregation where the maximum term has total dominance and a
weight of 1 (representing a risk-taking attitude). The level of trade-off
approaches zero as order weights approach either end of an ANDORness scale.
The terms ANDness ORness and TRADEOFF are quantified using the following
equations (Jiang and Eastman 2000).
ANDness = (1/(n-1))Σ((n-i)Worder i) (2.10)
ORness = 1 - ANDness (2.11)
39Chapter 2 Problem Diagnosis and Preliminary Literature Review
TRADEOFF = 1
)/11
2
iorder (−
−−
∑n
nn W (2.12)
Where
n is the total number of factors
i is the order of factors
Worder i is the order weight for the factor of the ith order
N.B. It should be noted that these AND / OR operations are dissimilar to that
employed in Approximate Reasoning. The fuzzy sets used in Approximate
Reasoning are subject to AND / OR operations similar to those used in
traditional set theory.
2.4.2.5 Sensitivity analysis
Once alternatives have been analysed and rated, a sensitivity analysis provides a
means to test the robustness of favourable solutions in the light of uncertainty.
This process involves examining how a change of one criterion score will affect
the final result (Voogd 1983). The two most important elements to consider in
sensitivity analysis are criterion weights and criterion (attribute) values, with the
objective being to find the set of nondominated solutions. Nondominated
solutions are described as those which are feasible and no other feasible solution
exists that improves the performance on a single criterion outcome without
worsening another (Malczewski, Pazner et al. 1997).
2.4.2.6 Limitations of MCE
There are limitations within current MCE theory and its application to spatial
problems as MCE is still a maturing field. The main limitations of MCE based
spatial decision support may be listed as follows:
• The process of standardising criteria continues to provide a major challenge
(Eastman, Jin et al. 1995; Jiang and Eastman 2000)
40Chapter 2 Problem Diagnosis and Preliminary Literature Review
• Deriving criterion weights particularly when multiple stakeholders are
involved is difficult, as any given group will usually exhibit some variance of
opinion (Herrera, Herrera-Viedma et al. 1996)
• A robust methodology for accepting inputs from multiple stakeholders is
required (Jankowski 1995; Malczewski 1996)
• A method for dealing with uncertainty in spatial datasets is required (Beard
1994; Hunter and Goodchild 1995; Kyriakidis and Goodchild 1999)
• The final step of deciding which particular solution provides the absolute best
location for the facility in question when several solutions have a similar
rating has proved difficult (Eastman, Jin et al. 1995)
• Different MCE decision rules generate different outputs and there is no
accurate way of choosing the best method (Carver 1991; Heywood, Oliver et
al. 1995).
• The size and shape of the proposed site is not explicitly included in an
analysis (Brookes 1997)
• The use of MCE in computer-based decision support systems is limited by
the fact that highly capable analytical systems are often used as simple
visualisation tools, primarily due to difficulties in use and understanding of
the systems by strategic decision-makers (Klosterman 2000)
2.4.3 Artificial intelligence and soft computing
Artificial intelligence (AI) is an attempt to reproduce aspects of human
intelligence via a computer algorithm. The most promising AI techniques for
spatial analysis are contained within the area of soft computing, and are useful
for handling the many types of uncertainty and ambiguity inherent in complex
systems. Real world decision-making is subject to uncertainty in the sense that
the goals, constraints and consequences of possible actions are not always
precisely known (Bellman and Zadeh 1970). It is this uncertainty that is the
foundation of many current difficulties with the analysis of spatial problems.
Three soft computing techniques that are becoming more common in spatial
analysis are fuzzy logic, artificial neural networks and genetic algorithms. This
section discusses these three methods.
41Chapter 2 Problem Diagnosis and Preliminary Literature Review
2.4.3.1 Fuzzy logic
A common flaw in spatial decision-making is the unreasonable level of
resolution implied when screening attribute values, whereby an artificial cut-off
value is specified. For example, if it is specified an acceptable site must be within
5km of a river, why is a site 4.99km away acceptable and a site 5.01km away
unacceptable (Malczewski 2002)? There is usually ambiguity and imprecision in
defining such cut-off values, which may be represented using fuzzy set theory.
In classical set theory a set is a collection of definite and distinct objects. Objects
contained within the set are its members and any member of the collection of
objects (universe of discourse) from which the set is drawn (e.g. the set of real
numbers) is either a member or non-member of the set. Fuzzy set theory applies
a degree of membership instead of considering an object as strictly in or out.
Limitations in fuzzy logic approaches to site selection are mainly based on the
uncertainty involved in choosing membership functions to represent the
linguistic terms. As yet there appears to be no definite method to do this, and as
the meaning of words tends to vary with the person using them, it may be
unfeasible to develop a completely reliable way to solve the problem. A detailed
introduction to the specifics of fuzzy logic as they apply to decision-making is
provided in Chapter 3.
2.4.3.2 Neural networks
Neural networks are a simulation of the way a human brain functions at a cellular
level. A collection of processing units acting as neurons, are connected by a
weighted network representing the role of synapses. When presented with a set
of inputs and a set of outputs during a training phase, the network stores the
relationships between inputs and outputs in the structure of connections. In this
way the network can ‘learn’ to perform new tasks.
42Chapter 2 Problem Diagnosis and Preliminary Literature Review
Neural networks have been found to be an effective tool for spatial decision-
making (Sui 1993; Zhou and Civko 1996) and have the advantage of being
independent of the problem domain, thereby allowing a user to focus on the
problem rather than a set of rules for its solution. They have been applied to
spatial problems such as land valuation (Almond, Jenkins et al. 1997) and urban
development (Feng and Xu 1999), as well as traditional multicriteria problems
(Zhou and Civko 1996). A major drawback of using neural networks in site
selection problems is that the way a network adapts its structure to solve
problems is largely hidden from the user. Such a ‘black box’ approach to
decision-making is unlikely to be fully embraced by decision-makers and interest
groups (O'Sullivan and Unwin 2003), as there is a sense of loss of control.
2.4.3.3 Genetic algorithms (GA’s)
Genetic algorithms (GAs) were devised by John Holland in the 1960s, and
developed with the aid of his students and colleagues throughout the 60s and 70s.
In his 1975 book, Holland presented the GA as an abstraction of biological
evolution, providing a theoretical framework for natural and artificial adaptive
systems (Holland 1975). GA’s have two primary advantages. Firstly they are
efficient in complex search spaces, and are capable of avoiding the problem of
finding only local optima. Secondly they are independent of the problem domain,
providing a method capable of application to a multitude of complex real world
problems.
While there is no rigorous definition of a GA, most GA’s have four elements in
common (Mitchell 1999).
1. A population of chromosomes: A chromosome represents a point on the
search space, i.e. a candidate solution. Chromosomes are typically coded as
bit strings, e.g. 111001, whereby each locus (gene) in the chromosome has
two possible alleles: 0 and 1. (Chromosomes consisting of strings made up of
non-binary alphabets with more possible alleles are also allowable).
43Chapter 2 Problem Diagnosis and Preliminary Literature Review
2. Selection via fitness: A fitness function is used to assess how well each
chromosome solves the problem at hand by assigning it a score.
Chromosomes with high scores are more likely to have the chance to breed.
3. Breeding via crossover: The crossover operation randomly chooses a locus
and exchanges the subsequences before and after that locus between two
chromosomes to create two offspring. For example crossing over 000111 and
111000 after the third locus creates 000000 and 111111.
4. Mutation: The mutation operation randomly flips bits in a chromosome.
The probability of this occurring is usually very small, often in the vicinity of
0.001.
A simple GA starts with a randomly generated population, selects individuals for
crossover using an increasing function of fitness, replaces the existing population
with the offspring created from crossovers, randomly mutates some genes in
some individuals, then iterates the process from the point of selection. The run
lasts for a specified number of generations or until a stopping criterion (e.g. a
specified level of fitness) is satisfied.
Although the exact mechanisms underpinning GA’s have not been fully defined,
it is generally accepted that GA’s work by retaining the building blocks of good
solutions. These building blocks are referred to as schema (Holland 1975). A
schema is a partial template for a solution, for example in a six bit chromosome
one schema may be *10***, which represent the set of all bit strings with a one
at the second locus and a zero at the third.
GA’s have been applied to such spatial problems as motorway routing (Pereira
1996) defining the size and shape of selected sites (Brookes 1997), and multi-
objective site selection problems such as transmission tower siting (Krzanowski
and Raper 1999). While the fundamental process is relatively simple in concept
there are some quite challenging aspects to implementing a GA, most
predominantly in terms of problem representation. From a decision-maker point
of view they are may also be perceived as a ‘black box’ decision aid.
44Chapter 2 Problem Diagnosis and Preliminary Literature Review
2.5 Technology platforms
The practical implementation of a decision science method requires a suitable
technology platform. This is provided by the mature technology of Geographical
Information Systems (GIS). A GIS provides a customisable software
environment with a set of tools for spatial data storage, manipulation and display.
Use of decision-making models in GIS results in a hybrid system commonly
referred to as a Spatial Decision Support System (SDSS). SDSS and GIS are
inextricably linked whereby a SDSS offers support in relation to a particular
spatial problem and GIS provides the highly evolved technical toolbox with
which to implement the SDSS (Crossland, Wynne et al. 1995).
Due to recent advances in computer hardware and software, and the growth of
information generation and distribution technologies such as remote sensing and
the Internet, SDSSs have arrived in both concept and application (Brail 2000).
There is much published literature on SDSSs and new applications will continue
to emerge as technology improves, and becomes more widely accepted.
Published applications cover a wide range of spatial problems from land use
planning to vehicle routing. A detailed description of GIS in spatial decision-
making is provided in Chapter 4.
2.6 Discussion
GMCLP’s are a demanding type of multicriteria decision problem that are
commonplace in infrastructure planning and environmental management, as well
as other more routine activities such as real estate investment. A particularly
challenging aspect of GMCLP’s is the inherent uncertainty involved. The
uncertainty most prominent in these problems is not easily classified by a
probability distribution, and is derived from questions such as; ‘whose opinion is
most important?’; ‘which criteria are most important?’; ‘how reliable are our
predictions of future scenarios?’; ‘how can we best classify qualitative
attributes?’; and more generally ‘what is the exact relationship between raw data
45Chapter 2 Problem Diagnosis and Preliminary Literature Review
and site suitability?’. Having no precise answer to these questions makes it
impossible to consistently and precisely identify the best alternative(s). This
leads to an inability to accurately measure the quality of an algorithmically
derived solution. How can we ever be certain that an Airport has been placed in
the best position, or that an industrial facility has a negligible effect on
surrounding ecosystems, or that we have made the best choice when buying a
new home? In reality the utility of a selected site may not be known with any real
precision until some years after the decision has been made, and even then the
performance metrics used are subject to uncertainty and disagreement. It is little
wonder that solving such problems is described as a ‘surprisingly difficult task’
(Carlsson and Fuller 1996). However the significant impacts and expense
inherent in many site selection decisions, particularly those involving large-scale
infrastructure, demands that the methods employed in their solution be the best
available.
Decision Science has provided several formal methods for multicriteria decision
problems that are applicable to GMCLP’s. These range from simple Boolean
operations to advanced artificial intelligence and soft computing techniques. The
most widely applied methods come from the field of multicriteria evaluation, but
several shortcomings have been noted. Most important of these are the inability
to deal with uncertainty, inability to deal with a group environment, and the
perception by decision-makers that the methods are not user friendly. The more
advanced AI and soft computing techniques offer an ability to overcome some of
the shortcomings of MCE, but it is necessary to deploy them in a user friendly
way that avoids the ‘black box’ scenario. Until this is accomplished, the potential
of advanced techniques will only be tapped in specialised highly technical
applications.
An important characteristic of the many decision science techniques available is
their heterogeneous nature. The problem of choosing the best method for a
particular problem has largely been overlooked in current literature on spatial
decision-making, despite the fact that applying different techniques to the same
problem often results in different answers. Different methods tend to seek
different characteristics in a solution. For example some decision situations
46Chapter 2 Problem Diagnosis and Preliminary Literature Review
require an answer with minimal risk of a bad outcome on any criteria, and non-
compensatory methods or an OWA approach best serves this objective. Other
problems may be best solved using a compensatory method or, if there is a group
environment, by looking for the best level of consensus between parties. As yet
there appears to be no hybrid method, whereby decision-makers directly choose
their desired set of characteristics, and the algorithm adjusts outcomes to fulfil
this requirement.
2.7 Conclusions
The initial review found that MCE is currently the dominant analytical technique
for the solution of multicriteria location problems. However several
shortcomings were noted. Most important of these are the inability to deal with
uncertainty, inability to deal with a group environment, and the perception by
decision-makers that current methods are not user friendly. The universally
accepted technology platform for the analysis of location problems was found to
be a GIS, coupled or fully integrated with decision-making models. Advanced
artificial intelligence and soft computing techniques offer an ability to overcome
some of the shortcomings of MCE, but it is necessary to deploy them in a user
friendly way in order to avoid the perception of a ‘black box’ scenario.
47Chapter 3 Approximate Reasoning
Chapter 3
AAPPPPRROOXXIIMMAATTEE RREEAASSOONNIINNGG
3.1 Introduction
Approximate Reasoning (AR) is a fuzzy logic based technique that can be useful
in decision-making. AR utilises fuzzy set methods to characterise and operate
upon imprecise inputs. In approximate reasoning linguistic inputs are quantified
as fuzzy numbers and manipulated with specialised fuzzy computation
techniques.
Utilising AR and linguistic variables enables users to overcome some difficulties
encountered with MCE analysis. Fuzzy numbers provide a convenient way to
represent linguistic uncertainty, and procedures for criteria standardisation can
benefit from a universal linguistic suitability scale.
This Chapter provides an introduction to the fundamentals of AR and it’s use in a
decision-making context. Existing AR methods for site selection are then
highlighted, and conclusions drawn.
3.2 Fuzzy logic
Approximate reasoning is based on the concepts of fuzzy logic introduced by
Lotfi A Zadeh, a professor of Electrical Engineering at the University of
California at Berkely (Zadeh 1965). The key insight of fuzzy logic is that an
emphasis on precise and detailed modelling leads to models that are hard to
understand due to their complexity. This led to the principle of incompatibility
48Chapter 3 Approximate Reasoning
(Zadeh 1973), which challenges the ability of conventional analysis techniques to
deal with complex systems:
‘Stated informally the essence of this principle is that as the complexity of a
system increases, our ability to make precise and yet significant statements about
its behaviour diminishes until a threshold is reached beyond which precision and
significance (or relevance) become mutually exclusive characteristics.’
The fundamental numerical structure of fuzzy logic was derived by reflecting
upon the fact that the cognitive skills possessed by human beings were able to
grasp the essential nature and characteristics of systems that proved too complex
to model successfully. Humans seemed to do this by using vague or fuzzy
linguistic expressions to describe the states and relationships inherent in the
system, and this led to the development of fuzzy logic methods to quantify these
approximate relationships. A simple way of characterising fuzzy logic is
therefore to say that it is logic of approximate reasoning (Zadeh 1975), and the
use of fuzzy logic is referred to by a number of terms including fuzzy systems,
fuzzy computation, and Approximate Reasoning. The specific unit used to
accomplish fuzzy logic operations is a fuzzy set.
3.2.1 Fuzzy sets
Fuzzy sets are an extension of classical set theory, based on the concept that
linguistic descriptions often have vague rather than sharp boundaries. All
concepts of classical set theory have their counterpart in fuzzy set theory,
however there are some concepts unique to fuzzy sets. Fuzzy sets reject the
requirement of classical sets that each object be either a member or non-member
of any given set. Thus, if X is a collection of objects denoted generically by x, a
fuzzy set A in X is a set of ordered pairs:
A = {(x, μA(x))|x∈X} (3.1)
49Chapter 3 Approximate Reasoning
Where: μA(x) is the membership function of x in A
The membership function, μA(x), maps x from the universe of discourse
consisting of all possible values of x, to the membership space [0,1] The
membership function may assume any value from 0 to 1 inclusive. If μA(x)
assumes only the values 0 and 1, A is crisp set, not a fuzzy set. Many different
types of membership functions are possible. Figure 3.1 shows a typical
membership function for a fuzzy set representing the linguistic expression
approximately three, where the universe of discourse is the set of all real
numbers.
0
1
0 1 2 3 4 5 6
Figure 3.1: Fuzzy membership function for the term ‘approximately three’
The fuzzy membership function shown in Figure 3 describes the possibility that
each real number fulfils the description ‘approximately three’. It is important to
note that the concept of possibility employed in fuzzy logic is conceptually
different from the more commonly recognised concept of probability, although
both assume values between zero and one (Ruspini and Mamdani 1998). The
relationship between these two areas has been the subject of much debate,
however an understanding of the complex issues raised by comparing fuzzy logic
to probability is not a pre-requisite for using and understanding fuzzy systems,
and consequently is not explored any further here.
μA(x)
x
50Chapter 3 Approximate Reasoning
3.2.2 Fuzzy numbers
An extension of the concept of a fuzzy set is that of a fuzzy number. A fuzzy
number M is a convex normalised fuzzy set. It is piecewise continuous and has a
peak value of 1, which occurs at least once. There is an infinite set of fuzzy
numbers.
There are several types of membership functions suitable to represent fuzzy
numbers. These range from gaussian and sigmoidal functions to linear parameter
based representations such as triangular or trapezoidal fuzzy numbers. A
standard trapezoidal fuzzy number T can be represented completely by a
quadruplet Tpz(a,b,α,β) where the interval [a,b] is the core, α and β are the left
and right bandwidths respectively, and [a-α, b+β] is the support, as shown in
Figure 3.2.
Support
μ
xa - α βb +a b
Figure 3.2: Trapezoidal Fuzzy Number Tpz(a,b,α,β)
(Bonissone 1982)
51Chapter 3 Approximate Reasoning
Several authors consider that linear trapezoidal fuzzy numbers are suitable to
capture the vagueness of linguistic assessments, since it may be impossible and
unnecessary to use more complex representations e.g. (Tong and Bonissone
1980; Bonissone and Decker 1986; Delgado, Verdegay et al. 1992). This
representation also has the advantage of being able to represent crisp numbers or
sets (α = β = 0), as well as triangular fuzzy numbers (a = b). The support may
also be used as a measure of how vague or uncertain the term is in relation to the
base variable.
3.3 Approximate reasoning in multicriteria decision-making
Approximate reasoning has been introduced to decision-making largely because
of the ability of fuzzy sets to capture the vagueness of linguistic terms in
statements of natural language, that are often used by decision-makers. As
human reasoning is approximate rather than precise in nature, problems where
human knowledge is required as an input are described as being approximate
reasoning problems. In general terms an approximate reasoning problem is one
where information is inadequate to make precise, categorical statements about a
system (Ruspini and Mamdani 1998). The solution of such a problem takes the
form of a set of possibilities. An example of approximate reasoning is (Ruspini
and Mamdani 1998):
A1: Most engineers are clever
A2: Bill is an engineer
A3: It is very likely that Bill is clever
Where the fuzzy terms most and very likely could be represented by using fuzzy
numbers. Use of fuzzy numbers in this way forms the basis for the approximate
reasoning approach to decision analysis, which has also been termed the
‘linguistic approach’ (Zadeh 1976).
52Chapter 3 Approximate Reasoning
The linguistic approach to decision analysis relies on a systematic use of words
to characterize the values of variables, probabilities, relations, and truth-values of
assertions. The central concept is that of a linguistic variable whose values are
words or sentences, which serve as names of fuzzy subsets of a universe of
discourse. The linguistic approach represents a blend of quantitative and
qualitative analysis by using numbers to make the meaning of words more
precise.
A linguistic variable as defined by Zadeh (1976) is characterised by a quintuple
(X, T(X), U, G, M) where:-
X is the name of the variable. (e.g. Age)
T(X) is the term set which gives x it’s linguistic values. (e.g. Young, Not
Young,…Old, etc)
U is the universe of discourse. (e.g 0-150)
G is a syntactic rule which generates the terms in T(X).
M is a semantic rule which associates with each term, x, in T(X) its meaning,
M(X). The meaning is defined by a membership function μx which associates
each member of U with a degree of compatibility in X, within the interval [0,1].
It is possible to distinguish three main types of approximate reasoning methods
for multicriteria decision-making from the literature:
1. Fuzzy MCE
2. Fuzzy inference systems
3. Pairwise comparison methods
The three methods are described in the following sections.
3.3.1 Fuzzy MCE
Fuzzy MCE is an extension of the multiattribute decision-making process
described in Section 2.4.2. MCE in its simplest form reduces to the linear
53Chapter 3 Approximate Reasoning
weighted combination of an alternative’s criteria outcomes with the weighting
coefficient of each criterion as shown in Equation 2.7. Fuzzy approaches
generally process fuzzy data then dissolve fuzziness at the end of the procedure
to produce a set or sets of crisp data (Perny and Roy 1992).
The general case is well represented by the Baas and Kwakernaak (1977)
approach, where both an alternatives criterion outcome and criterion weighting
criteria are treated as fuzzy quantities. In order to determine the fuzzy evaluation
of alternative Ai based on fuzzy ratings and weights we first consider the
normalised version of the linear weighted combination function g mapping 2n
into (where is the set of real numbers and 2n represents 2n tuples of the set in n-dimensional Euclidian space) (Baas and Kwakernaak 1977).
Ignoring constraints:
(3.2)
where: z = (w1, w2, …., wn, r1, r2, …., rn)
Multiplication is carried out according to the minimum rule, so on the product
space 2n we define a membership function μzi, given by:
(3.3)
Where: )( jwj wμ is the membership function of criteria weights
)( kRik rμ is the membership function of criteria outcomes
Λ is the minimum operator
Through the mapping g: 2n → the fuzzy set z = ( 2n, μzi) induces a fuzzy set _R i = ( μR), with membership function:
∑∑
=
jj
jjj
w
rwzg
)()(
⎥⎦⎤
⎢⎣⎡ΛΛ⎥⎦
⎤⎢⎣⎡Λ=
==)()(
11 kRik
n
kjwj
n
jzi rw μμμ
54Chapter 3 Approximate Reasoning
)()( sup_
_
)(:
_zr zi
rzgziR
μμ=
= , ∈_r (3.4)
To simplify calculation Bonissone (1982) devised a parameter-based approach
using trapezoidal fuzzy numbers that simplifies computation and understanding.
The algebraic operations required in a weighted combination are performed as
shown in equations 3.2 and 3.3 (Bonissone 1982).
Addition:
(3.5)
Multiplication:
( ) ( )∏=
⊗=2
12222211111 ,,,,,,
ii baTpzbaTpzTpz βαβα
= ( )2112212112212121 ,,, ββββαααα ++−+ bbaabbaaTpz (3.6)
Where ⊗ is an approximated algebraic operator.
This approach enables rapid computation of weighted combinations of fuzzy
quantities, and gives a fuzzy set as its final output, and the fuzzy output is then
ranked or linguistically rated. There is a vast amount of literature on the ranking
of fuzzy numbers, and this may be partly due to the fact that no method seems to
give a satisfactory solution to every situation, and many methods produce
different rankings for the same problem (Fodor, Perny et al. 1998). Ribeiro
(1996) asserts the simple measure of taking the highest support as the best is a
sufficient ranking method for fuzzy multiple attribute decision making (Ribeiro
1996), and contends that using a crisp scale for criteria weights creates a simpler
set of outputs to rank and is more efficient in terms of computation without loss
of meaning. Bonissone (1982) suggested linguistic approximation as a useful
rating method. His procedure associates a label with a membership distribution
( ) ( )2222211111
2
1
,,,,,, βαβα baTpzbaTpzTpzi
i ⊕=∑=
( )21212121 ,,, ββαα ++++= bbaaTpz
55Chapter 3 Approximate Reasoning
on the basis of semantic similarity, via feature extraction and pattern recognition
techniques.
3.3.2 Fuzzy inference systems
Fuzzy inference systems are the most commonly used form of approximate
reasoning. The key element of a fuzzy inference system is a set of linguistic rules
derived from expert knowledge of the problem, which form the basis for the
inference engine to assess alternatives. Consequently, fuzzy inference systems
are also commonly referred to as fuzzy rule-based systems, and fuzzy expert
systems. The rules usually take the form of an if-then statement such as ‘if the
slope is steep then the site suitability is poor’. Where the linguistic terms ‘steep’
and ‘poor’ are defined by fuzzy sets on the universe of discourse consisting of all
possible slope and suitability values respectively.
In a standard Mamdani type inference system (Mamdani and Assilian 1975)
there are four steps involved in the process, as shown in Figure 3.3, which
illustrates an inference system for site selection with two criteria.
56Chapter 3 Approximate Reasoning
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10 12
Slope (%)
Steep
0.7
Figure 3.3: Fuzzy inference system
The inference process starts with obtaining crisp values for the required inputs.
The next step is to take the inputs and determine the degree to which they belong
to each of the appropriate fuzzy sets via membership functions. This value is then
used to create a new output fuzzy set for each rule via implication, and these sets
are then aggregated into a single fuzzy set. The last step is to derive a crisp value
from the fuzzy set, which is commonly the centroid of the set. Different
inference systems often use different methods for implication and aggregation.
Building an inference system for a particular task is made easier by the fact that
the tools for their construction are available in off the shelf software packages
such as MATLAB. The major effort involved is in tailoring the if-then rules to
produce consistently accurate outputs.
IInnppuutt 11:: SSllooppee
IInnppuutt 22:: EElleevvaattiioonn
Rule 1: If slope is steep then
suitability is poor
Rule 2: If elevation is high then
suitability is good
Σ OOuuttppuutt:: SSuuiittaabbiilliittyy
Step 1:
Obtain inputs as
crisp numbers
Step 2:
Evaluate If Then
rules using fuzzy
reasoning
Step 3:
Aggregate outputs
Step 4:
De-fuzzify to
obtain a crisp
answer
57Chapter 3 Approximate Reasoning
3.3.3 Pairwise comparison methods
Pairwise comparison methods are based on the aggregation of fuzzy binary
preference relations. The preference analysis is carried out on the Cartesian
product A x A, where A = {a1,….,an}, the set of feasible alternatives. Preferences
are assessed for each criterion, and the resulting binary relations are aggregated
to obtain the best overall alternative (Perny and Roy 1992). Pairwise comparison
involves much computational effort when dealing with large numbers of
alternatives.
3.4 Use of approximate reasoning in location problems
Fuzzy sets have most prominently been applied to spatial decision-making as a
substitute for a utility function when standardising criteria. Use of fuzzy sets in
this way has been instrumental in designing many decision-making tools
(Stefanakis, Vazirgiannis et al. 1996; Jiang and Eastman 2000). Jiang and
Eastman (2000) argue that all standardised criteria belong to a general class
termed fuzzy measures. This approach provides a function for mapping attribute
values to a [0,1] utility scale as shown in Figure 3.4, producing a fuzzy set on the
universe of discourse derived from all possible attribute values. While fuzzy
measures may be used to generate inputs to multicriteria evaluation procedures,
the use of fuzzy sets in this way does not technically constitute an approximate
reasoning system. Also, the utility value given for each attribute value is crisp
not fuzzy, perhaps leading to a false sense of precision. There is also no rule
specifying that this function be continuous or convex, so not all such fuzzy sets
are fuzzy numbers.
58Chapter 3 Approximate Reasoning
00.10.20.30.40.50.60.70.80.9
1
0 2 4 6 8 10
Slope (%)
Util
ity
Figure 3.4: The variable slope as a fuzzy measure
Although fuzzy logic has been shown to have particular advantages in site
selection due to the continuous nature of many spatial variables (Hall, Wang et
al. 1992; Burrough and McDonnell 1998; Fisher 2000; Jiang and Eastman 2000),
there has been relatively little literature on fuzzy site selection methods in GIS.
Existing methods are based on either inference systems or fuzzy MCE, as
pairwise comparisons are unfeasible with the large numbers of alternatives
usually inherent in site selection.
Zeng (2001) uses fuzzy sets to codify expert knowledge and fuzzy selection
criteria such as distance from schools and other facilities in REGIS, a Real Estate
Geographical Information System. The fuzzy inference system used in REGIS
requires the specification of multiple If-Then statements such as ‘IF the SLOPE
is good OR excellent AND the aspect is good AND proximity to a school is good
THEN the location is excellent’ (Zeng and Zhou 2001). While inference systems
have been well proven in many applications, the need for such a comprehensive
set of statements is a burden when preparing for each new problem, making
REGIS a problem specific system.
A simple fuzzy MCE approach is to process linguistic labels directly using a
symbolic approach (Malczewski 2002). This method is based on the premise that
the set of linguistic terms is an ordered structure uniformly distributed on a scale.
Symbolic MAX and MIN operators are used, which simply choose the lowest or
59Chapter 3 Approximate Reasoning
highest ordered term in a comparison, rather than operate on membership
functions. The screening procedure first takes the MAX of the criterion outcome
and criterion weight and then takes the MIN of each criterion’s score from this
operation for each alternative. The final step is a binary screening, in which an
alternative scoring below a given level is assigned zero and all others are
assigned unity. This approach exhibits a loss of information, as no membership
functions are used, which is compensated for by the advantage of allowing
decision-makers to use linguistic terms instead of defining artificial cut-off
points.
Fuzzy MCE using membership functions has generally been used in site
selection outside GIS, where there are a small number of feasible alternatives to
consider. Liang and Wang (1991) use parameter based fuzzy numbers and
arithmetic operations in their method of facility site selection, however this
method has not been used in a Spatial Decision Support System. It has been
noted, however, that the method is suitable for application in a computerised
system (Liang and Wang 1991).
3.5 Conclusions
Most fuzzy methods used in spatial problems process crisp values obtained from
fuzzy membership functions and not the functions themselves, thereby losing the
information value of a fuzzy quantity. A fuzzy number possesses both a mean
value and a spread (support) that may be used to indicate the uncertainty of an
answer. However there is currently no robust way for decision-makers to input
their level of confidence in applying a particular linguistic label. The inflexibility
of an inference system once the rules are generated, and the processing power
required for pairwise comparison fuzzy methods, seem to indicate that a fuzzy
MCE approach to facility site selection is most appropriate.
60Chapter 3 Approximate Reasoning
This page intentionally blank
Chapter 4 Spatial Decision Support Systems 61
Chapter 4
SSPPAATTIIAALL DDEECCIISSIIOONN SSUUPPPPOORRTT SSYYSSTTEEMMSS
4.1 Introduction
Spatial Decision Support Systems (SDSSs) are a kind of Decision Support
System (DSS) concerned with spatial problems such as site selection. SDSSs
offer support to decision-makers by transforming data into information via a
model. The technology platform upon which to operate is provided by the mature
technology of Geographical Information Systems (GIS). SDSS and GIS are
inextricably linked whereby a SDSS offers support in relation to a particular
spatial problem and GIS provides the highly evolved technical toolbox with
which to implement the SDSS.
This chapter provides an overview of SDSS fundamentals, the ways in which
SDSSs are used and developed, and a discussion of the main components of a
SDSS, before assessing the limitations of current systems. When discussing the
various components of a SDSS, the Dialog, Data, and Models (DDM) paradigm
is used as a means of classification. DDM specifies three main areas for
consideration; the dialog that provides an interface with the user, the data upon
which the system is based, and the models that transforms data into usable
information.
Chapter 4 Spatial Decision Support Systems 62
4.2 Overview
4.2.1 Decision support and expert systems
Although the term Decision Support System (DSS) has no universally accepted
definition, and is often relegated to the status of a buzzword as it has a low
communication value. There is a vast literature on DSSs and due to the sheer
magnitude of published work this research has focused on the components of
DSSs relevant to the problem at hand. Specific systems have been researched in a
discipline specific way, and all systems referenced in detail are spatial in nature
and the techniques that form their foundation are essential to the exposition of
the current research. A complete survey of the thousands of systems detailed in
the literature is beyond the scope of this review.
The basic concepts involved in DSS’ are simple, and were published in the early
1970s when such systems were defined as ‘interactive computer-based systems,
which help decision-makers utilize data and models to solve unstructured
problems’ (Scott-Morton 1971). In an unstructured problem human intuition is
frequently the basis for decision-making (Turban 1995), whereas structured
problems are solved using a set of strictly quantifiable procedures. Thus DSS’ do
not actually make decisions, but transform raw data into usable information
(Malczewski 1999). They enable an evaluation of technical aspects of choices,
providing an enhancement to a human decision-makers’ intuitive process, instead
of replacing it. In general a DSS does this by either optimising factors or
satisfying constraints as a complement to the limitations of human memory (Lu,
Yu et al. 2001). A more contemporary definition of a DSS would include other
attributes such as flexibility, adaptability, and a user-friendly interface, but the
fundamental purpose of a DSS is still to transform data into information via a
model.
Most DSS’, like most decision problems, will be unique, and instead of framing
another definition or attempting taxonomy, a comprehensive description of an
Chapter 4 Spatial Decision Support Systems 63
individual system may be obtained by examining its dimensions as measured on
appropriate scales. This approach is illustrated in Table 4.1.
The dimensions shown in Table 4.1 reflect possible qualities of a decision-
making situation. If a decision situation exhibits several of these dimensions then
it is a suitable candidate for a DSS (Pullar and Pettit 2000). Thus, spatial
decisions benefiting from a SDSS are usually not trivial, as they involve multiple
facets and require the agreement of several parties whose interests do not always
converge (Laaribi, Chevallier et al. 1996). Due to the cost of constructing a
SDSS, suitable decision situations will also have a high cost of failure
(Jankowski, Andrienko et al. 2001).
Another type of support system often confused with a DSS is an Expert System
(ES). Expert systems are based on applied artificial intelligence, and attempt to
reproduce or exceed the level of performance of a human expert in a specialised
problem area. The basic premise of an expert system is to transfer expertise from
the expert to a computer system. Expert systems tend to have a narrow focus, and
they are generally too inflexible for the multi-disciplinary nature of site selection
problems (Turban 1995).
Chapter 4 Spatial Decision Support Systems 64
Table 4.1: Dimensions of a DSS
modified from: (Pullar and Pettit 2000)
DDiimmeennssiioonn DDeessccrriippttiioonn SSccaalleess Data The nature of the data, is it
objective or subjective? • Physical • Statistical • Subjective
Type The type of data eg. Monetary value, habitat value.
• Quantitative • Indicative • Qualitative
Spatial Spatial data plus influences on neighbouring sites and patterns of distribution.
• Static • Neighbouring • Distributed
Analysis Type of analysis. • Query • Formulation • Simulation
Temporal Change based on predictions from the past or dynamic modelling.
• Static • Predictive • Dynamic
Model Classes of models. • Descriptive • Empirical • Deterministic
Reliability Techniques to asses sensitivity and risk.
• Certainty • Uncertainty • Risk
Objectives A single ultimate objective may be multi-faceted or there may be multiple competing objectives.
• Single • Multi-faceted • Multiple
Congruency Compatibility of objectives. • Complementary • Reconcilable • Conflicting
Involvement Level of collaboration and negotiation required in the decision-making process.
• Single • Consultative • Diverse group
4.2.2 Basic concepts of spatial decision support systems
Spatial Decision Support Systems (SDSSs) are a type of DSS focused on spatial
problems such as site selection. SDSSs require a technology platform upon
which to operate, and this is provided by the mature technology of Geographical
Information Systems (GIS). Due to recent advances in computer hardware and
Chapter 4 Spatial Decision Support Systems 65
GIS software, and the growth of information generation and distribution
technologies such as remote sensing and the Internet, SDSSs have arrived in both
concept and application (Brail 2000). There is much published literature on
SDSS applications, with new applications continually emerging as technology
improves and becomes more widely accepted. A small sample of published
applications includes:
• Urban land use planning (Bell, Dean et al. 2000; Quattrochi, Luvall et al.
2000)
• Rural land use planning (Matthews, Sibbald et al. 1999)
• City planning (Shiffer 1994)
• Transport infrastructure planning (Affum 1997)
• Resource management (Ghermay, Rochon et al. 2000)
• Hazardous waste siting (Pullar and Pettit 2000)
• Ecosystems management (Ji 1996; Bellamy and Lowes 1999)
• Environmental impact assessment (Klungboonkrong and Taylor 1998;
Colorni, Laniado et al. 1999)
• Air pollution monitoring (Flassak, Witt et al. 1995)
• Water pollution monitoring (Leon, Lam et al. 2000)
• Vehicle routing (Keenan 1998)
• Spatial allocation of educational resources (Malczewski and Jackson
2000)
• Telecommunications transmission tower siting (Krzanowski and Raper
1999)
SDSSs have been proven effective in practice as well as in theory. Laboratory
experiments conducted to investigate the effects of using a SDSS on decision-
maker performance found significant differences between solutions to a site
selection task developed by SDSS users and those developed by non-users. SDSS
users experienced shorter solution times and fewer errors (Crossland, Wynne et
al. 1995). However, despite the large number of published applications, there are
still many limitations to current systems. One of the major hurdles yet to be
crossed by SDSS designers is improving ease of use. It is common for highly
Chapter 4 Spatial Decision Support Systems 66
capable SDSSs to be used as simple visualisation tools, primarily due to
difficulties in use and understanding of the systems by strategic decision-makers
(Klosterman 2000). Consequently, one of the main objectives in SDSS design
should be to increase willingness to use a SDSS as many studies reveal that
millions of dollars have been wasted on unused SDSSs (Lu, Yu et al. 2001).
In summary it can be stated that a SDSS is a software system reliant on GIS
technology, built to help solve a non-trivial spatial problem. A SDSS does not
solve problems by itself, but will transform raw data into usable information. The
analytical capabilities of a SDSS should ideally be made accessible to users via a
simple intuitive interface, and effort should be made to increase the perceived
benefit of using the system, or there is a real risk that it will end up as an
expensive data visualisation tool. When properly designed and implemented
SDSSs have been proven to be an effective aid to spatial decision-making.
4.3 Components of a SDSS
When discussing the various components of a SDSS, the Dialog, Data, and
Models (DDM) paradigm is useful as a means of classification (Sprague and
Watson 1993). According to this approach the components of any DSS can be
broken down into three parts where the dialog is the interface between the user
and the system, data is the database that supports the system, and the models
provide the intelligence necessary for analysis. In a SDSS all components may
reside within a GIS.
4.3.1 Geographical Information Systems
As a SDSS is constructed to assist decision-makers with spatial decision
problems, it must be spatially explicit. To perform the operations involved in
spatial data storage, display and manipulation, a SDSS draws upon GIS
technology. SDSS and GIS are inextricably linked whereby a SDSS offers
Chapter 4 Spatial Decision Support Systems 67
support in relation to a particular spatial problem and GIS provides the highly
evolved technical toolbox with which to implement the SDSS (Crossland,
Wynne et al. 1995).
In the broadest possible sense a GIS is a set of software tools that allow for the
processing of spatial data into information, generally information tied explicitly
to, and used to make decisions about, some portion of the earth (Demers 2000).
GIS provides an ideal technology platform for development of Spatial Decision
Support Systems, as the purpose of most GIS is ultimately of a decision support
nature (Kraak 1999). There is no precise, absolutely agreed definition of a GIS.
However a GIS will generally consist of the following four subsystems.
1. A data input subsystem that collects and pre-processes spatial data from
various sources. This subsystem is also largely responsible for the
transformation of different types of spatial data.
2. A data storage and retrieval subsystem that organises the spatial data in a
manner that allows retrieval, updating, and editing.
3. A data manipulation and analysis subsystem that performs tasks on the data,
aggregates and disaggregates, estimates parameters and constraints, and
performs modelling functions.
4. A reporting subsystem that displays all or part of the database in tabular,
graphic, or map form.
Figure 4.1 shows the ESRI ArcGIS interface. Map layers are listed on the left
and a map screen illustrating the spatial characteristics of the variable are
displayed in the main window. The non-spatial data or ‘attributes’ of each spatial
entity are listed in a separate table, as shown at the bottom of the screen for
zoning regions.
Chapter 4 Spatial Decision Support Systems 68
Figure 4.1: ESRI ArcGIS
GIS has now matured to the point that Sui and Goodchild (2001) argue that the
rapid development of GIS technology renders traditionally instrumental views of
GIS inadequate to capture the essence of the technology. GIS may now be
thought of as a new media, communicating geographical information in digital
form by conveying aspects of the real world to a wide variety of non-technical
users, and thereby playing a part in their everyday lives (Sui and Goodchild
2001). In fact one of the fundamental challenges currently faced by GIS
developers is to find new applications that do justice to the available technology
(Hunter 1997).
4.3.2 Dialog
The most important components of any SDSS are those that involve interaction
with the user (Pereira and Duckstein 1993). These interface components are
referred to here as the dialog, and due to the situation specific nature of a SDSS,
the standard interface of most commercially available GIS lacks the necessary
Chapter 4 Spatial Decision Support Systems 69
tools for an effective dialog (Malczewski 1999). This leaves the SDSS developer
with the task of providing a suitable dialog to access the specific modelling
capabilities offered by the system.
The purpose of the dialog is to provide a simple and effective means for users to
enter and extract information, and this task is not limited to the creation of
colourful mapping outputs. It has been observed that the quality of decisions is
dependent on the quality and amount of relevant information available
throughout the entire decision making process (Shiffer 1992), and that achieving
consensus in a group environment requires that complete information is available
to all parties (Sharifi, Toorn et al. 2002). Creating a dialog that can be used in a
consistent manner by multiple parties throughout the decision process involves
two core requirements; simplicity and flexibility (Sprague and Watson 1993).
Dialogs employing tabular information display have been widely used in multi-
criteria DSS’ (see Voogd 1983; Massam 1988) but the large number of
alternatives involved in site selection decisions seems best communicated
through graphic outputs (Tomlin 1990). Visualisation is a critical aspect of a
multicriteria location analysis, and should be considered an integral part of
decision support approaches. SDSSs should allow the decision-maker to analyse
outcomes in an interactive, exploratory way to satisfy their individual priorities
(Malczewski, Pazner et al. 1997; Klosterman 2000). It may therefore be stated in
summary that a successful SDSS dialog requires the combination of simplicity
and flexibility with interactive visualisation techniques.
4.3.3 Data
The basis of any SDSS is its data. In fact the outputs from all SDSS operations
are reliant on the comprehensiveness and accuracy of the base data set. It is
therefore prudent to address certain fundamental issues before embarking on a
data collection exercise. These issues include (ESRI 2001):
• What is the intended use of the data?
Chapter 4 Spatial Decision Support Systems 70
• What are the specific geographic features needed?
• What attributes of (i.e. information about) these features are needed?
• What is the geographic extent of the area of interest?
• How current should the data be?
• Will periodic updates be needed?
• Is the data compatible with the software and hardware available to
end-users?
• What are the data licensing requirements?
After working through the fundamental issues, more technical considerations
should be addressed, and uncertainty is generally considered to play the decisive
role in the technical fitness of a dataset. The bottom line here is that the data used
should be of a high enough quality to be useful in the decision making process.
DeBruin and Bregt (2001) suggest the value of a given data set is proportional to
the reduction in uncertainty it brings to a chance node in a decision tree (DeBruin
and Bregt 2001). Their method of data evaluation requires a loss function
representing the cost of an incorrect judgement about the target outcome, and
probabilistic accuracy measures for the spatial data. Many authors have noted
that there is a need for these accuracy measures to be incorporated in metadata
accompanying spatial datasets. They should specify such things as estimates of
error variance, confidence intervals or probability distributions (Beard 1994;
Hunter and Goodchild 1995; Kyriakidis and Goodchild 1999). However these
measures are not always available, and in the absence of such information, an
uncertainty assessment may be carried out by comparing samples of the datasets
in consideration with reference data, although obtaining samples of the dataset
before purchase may also prove difficult.
4.3.3.1 Spatial data representation
SDSS data will be either spatial or non-spatial in nature, where spatial data
includes all data with an explicit spatial component, such as the location of a
house, or an ownership boundary. GIS offers three basic ways of representing
Chapter 4 Spatial Decision Support Systems 71
spatial data, either as points, lines or polygons. The data structure underlying
these entities can be either raster or vector. The raster system uses a grid of
equally sized cells, each with a corresponding attribute value. Vector structures
utilise points of zero area described by coordinate pairs. The points may be
joined to form a line or polygon, as shown in Table 4.2. Vector structures
generally offer greater positional accuracy whereas the raster format is more
suited to analysis operations such as MCE (Eastman, Jin et al. 1995). Most
modern GIS packages are capable of conversion between the two.
Table 4.2: GIS Spatial Entities in Vector and Raster
VVeeccttoorr RRaasstteerr
Point
Line
Polygon
4.3.3.2 Raster data and cell size
When using the raster format for modelling, a decision must be made about the
size of the raster cells. It is an intuitive and well-documented fact that the
accuracy of raster maps will decrease as cell size increases (Congalton 1997).
Wehde (1982) found that as cell size increased beyond twice the size of the
minimum mapping unit, mapping error would reach 100 percent for minimum
sized polygons. However as cell size becomes smaller other problems, such as
increase in data storage requirements, and a decrease in processing speed become
apparent. Over-estimation of the perimeter of polygons is another consequence
of increased resolution (Theobald 2000). Accuracy of the original data should be
Chapter 4 Spatial Decision Support Systems 72
the prime consideration when selecting cell size (Goonetilleke and Jenkins
1995).
4.3.3.3 Non-spatial data (Attribute Data)
Non-spatial data is usually data directly related to the spatial entity, such as
whether a house is one or two level, who owns the land, or the lot number.
Nonspatial data may be structured in several ways, ranging from a simple record
structure to more complex relational approaches. The flat-file, or record based
data model is the simplest data structure available, and consists of a spreadsheet
or rectangular database consisting of a row for each geographical entity, broken
into a fixed set of attribute fields for non spatial entries. These simple record
based models have largely been superseded by the relational model, which is a
set of tabular relations, where rows or ‘tuples’ for each entity have associated
attribute fields whose values are drawn from their specific domains. Each table in
a relational database contains at least one column similar to a column in other
tables, which acts as the relational join. Relational databases support traditional
set operations such as union, intersection and difference, as well as other specific
relational operations. Interaction with the database is usually performed via a
specialised language such as the Structured Query language (SQL), which has
become a de facto standard (Worboys 1999). A further development is that of
object based GIS data models, of which the fundamental concept is that of
encapsulation, which places a wrapper around an identifiable collection of data
(referred to as instance variables) and the code that operates upon it. The state of
an object is then determined by the value of the instance variables in its wrapper.
An example of a geographical object may be a country, defined by its border as a
polygon. The instance variables in this case may include: Name, Population,
Capital, etc.
While advanced data structures are invaluable in complex information storage,
querying and display, the flat file format is usually adequate for storage of inputs
to most multi-criteria decision-making processes. The values being stored and
processed in multicriteria evaluation tend to be numeric suitability scores that are
Chapter 4 Spatial Decision Support Systems 73
adequately represented by the flat file approach, and are easily accessed for the
necessary arithmetic operations.
4.3.4 Models
A model is a simplified abstraction of reality, used to simulate real actions. In a
SDSS models act as the intelligence of the system, providing a means to examine
the outcomes of potential courses of action, and implement decision analysis. In
decision analysis one distinguishes three features of the situation, the decision to
be made, events which can affect the result, and the result itself. Logical and
mathematical models are constructed to represent the relations between these
three features of the decision situation.
Although individual models will vary, multicriteria decision analysis in a SDSS
will generally fit within a well-established framework. Multi-criteria spatial
decision-making utilises the ability of GIS to represent a geographical area of
interest in multiple dimensions. Each dimension is represented as a map layer,
detailing the spatial variation of some variable (see figure 4.1). These map layers
are then processed to provide information on how that particular variable
(criterion) effects the overall decision. Each of the processed map layers is
referred to as a ‘suitability map’, and the process of standardising criteria to
create suitability maps is one of the major modelling challenges (Eastman, Jin et
al. 1995).
Most spatial multicriteria decision-making processes utilise raster data, whereby
every pixel represents an alternative in the site selection process, and
implementation of numerical techniques is straightforward. Suitability maps are
combined with decision-maker preferences according to the rules of the decision
model on a cell-by-cell basis, and an aggregated output generated. The resulting
processed map may be thought of as a locational decision matrix, with each
locational alternative displayed according to its calculated suitability score, as
illustrated in Figure 4.2. Note that in this example a single solution is shown
however multiple solutions may be found.
Chapter 4 Spatial Decision Support Systems 74
Suitability Maps
Criteria Standardisation
Weights Aggregation
C1 Wx
Σ
C2 Wy
C3 Wz
Locational Decision Matrix
Figure 4.2: Multi-criteria decision-making in GIS
Figure 4.2 shows a relatively simple MCE process, entirely implemented within
GIS, however some decision-making and data pre-processing models cannot be
implemented directly within the GIS environment. They require mechanisms to
link them with the facilities offered by the GIS. A well-referenced methodology,
based on an extensive body of literature, has been developed to describe the
different ways in which model linkages, or ‘coupling’ may be achieved. An
extensive review was conducted by Lilburne (1996), who noted that integration
has generally been classified in terms of data transfer and consistency in the
interface as summarised in Table 4.2.
Chapter 4 Spatial Decision Support Systems 73
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Chapter 4 Spatial Decision Support Systems 74
A more succinct way of classifying coupling methods is offered by Scholten
(1997), who states that the problem of linking new spatial analytical functions to
a standard GIS can be facilitated using three major approaches.
1. Full integration of spatial analytic procedures within the GIS
2. Close coupling between statistical spatial data analysis software and GIS
3. Loose coupling where an independent spatial data analysis module relies on
a GIS for its input data, and for functions such as such as graphic display, via
the import and export of data in a common format.
The idea of coupling external models to a GIS is steadily losing favour, as it is
now commonly proposed that the ideal SDSS would be a fully integrated system
(Klosterman 2000). Due to recent improvements GIS software is now
sufficiently robust to develop discipline specific applications in a fully integrated
way (Pettit and Pullar 1999), and this paves the way for a widely recognised
trend within GIS applications to progress from an operational support system to a
fully integrated strategic decision support system (Scholten and LoCascio 1997).
New decision support applications may now be fully developed within the GIS
environment without the need for external coupling by utilising inbuilt
customisation tools. An example of such tools is ArcObjects, provided with the
ESRI ArcGIS software packages (ESRI 2001). ArcObjects provides an
infrastructure for application customisation and incorporates software
components that expose the full range of functionality in ArcGIS to developers.
ArcObjects enables elements such as complete maps (the map object) to be
simply customised and integrated with other elements of a model in a seamless
fashion. These new customisable GIS packages may pave the way for fully
integrated systems to become the norm.
Chapter 4 Spatial Decision Support Systems 75
4.4 Development and implementation
The construction of a DSS, spatial or otherwise, is a complicated process, and
there is no single best approach. A complex DSS requires a group of people to
build and maintain it, therefore suitable decision problems should also have a
high ‘decision equity’ (i.e. the cost of making a poor decision is high)
(Jankowski, Andrienko et al. 2001). Additionally they should be non trivial
(above common sense reasoning), perhaps involving multiple facets and
requiring the agreement of several parties whose interests do not always
converge (Laaribi, Chevallier et al. 1996). Turban (1995) recommends an 8-step
development process, consisting of:
1. Planning: Needs assessment, problem diagnosis & definition of objectives
2. Research: Review relevant information
3. Analysis: Develop a conceptual design, define resources & normative models
4. Design: Develop specifications for the four components, Interface, GIS
Database, Knowledge base (relational database), & model base.
5. Construction: Technical implementation of the design
6. Implementation: Testing, evaluation, demonstration, orientation, &
deployment
7. Maintenance: Ongoing support & documentation
8. Adaptation : Continually repeat the process to improve the system
When planning for a SDSS early and continuous involvement with management
and end users is advisable, as proper fit in an institutional context is essential for
successful DSS use. It is also prudent to address the following institutional issues
in the planning stage (Brown 1998):
• Should any kind of decision aiding procedure be used and if so what kind?
• Is the DSS’ role to enhance or justify decisions?
• Where in the organisation should the DSS be located?
• Who should be involved and how?
Chapter 4 Spatial Decision Support Systems 76
• Should use of the DSS be required / optional / reviewable?
• Should it lead to a preferred external action or predict option consequences?
• Should the preferred action be mandated / for information only / disclosed
publicly?
Once the decision to implement a SDSS has been made and institutional issues
have been addressed, a development strategy must be chosen and implemented.
A fundamental assumption in the development of many software systems is that
there is complete foreknowledge of requirements. This is often an unrealistic
assumption for SDSS design, where the problems analysed by system will be
constantly changing, and users will find new ways to utilise the SDSS. An
iterative development process, which involves close co-operation with the end
users of the system, is usually the most successful strategy. The process may be
termed evolutionary prototyping and is carried out in four steps (Turban 1995).
1. Selection of a core sub-problem.
2. Development of a functional system for that sub-problem using the eight
steps above.
3. Evaluation of the system, its structure and outcomes.
4. Implementation of refinements, modifications and expansion.
A prototyping strategy like the one above allows the system to grow with users
requirements, and provides the necessary flexibility to incorporate new
functionality.
4.5 Conclusions
SDSSs are a type of DSS that integrate GIS technology with decision-making
models to aid in the solution of spatial problems. The ideal SDSS would be both
flexible and user friendly, be fully integrated within a GIS software package,
provide real-time graphical interactivity and cater for group decision-making.
Chapter 4 Spatial Decision Support Systems 77
Decision-makers and DSS designers generally experience difficulty in predicting
exactly how a SDSS will be utilised, which makes the development of a SDSS a
challenging task. Early and continuous involvement of end users is advisable, as
is designating exactly how the system will fit in an institutional context, although
this may also change over time. Evolutionary strategies whereby a prototype
system is developed to focus on a sub-problem, and tailored from experience to
be more fully functional is an intuitively sound approach and one that is
consistently recommended in the literature. This approach tends towards creating
a more generic package that will function as a living system and continue to
evolve over time.
As the foundation of a SDSS GIS technology is a vital ingredient in the
development process. GIS is now a mature enough technology to develop fully
integrated systems using inbuilt customisation tools, as many off the shelf GIS
packages now provide access to spatial and analytical functions at a fine enough
level of granularity to be useful to SDSS developers. This trend may have
adverse consequences, as spatial models should ideally be based on hard science
and remain independent of particular software packages. It is also important that
SDSS design should not be driven by technology, but by problems and users,
however the ease with which new models can now be implemented by GIS
customisation, and the professional look of the result, make the fully integrated
system hard to resist.
The major hurdle facing developers is how to make systems that are simple and
easy to use. There is a general tendency towards ‘shallow use’ of SDSSs by real
world planners and decision-makers, which is largely the result of real or
perceived difficulty in using such systems. The need for simplicity should also
extend to the decision-makers understanding of how the system works.
Experience has shown that even if a system is user friendly, decision-makers are
unlikely to accept outputs that are generated by a ‘black-box’ that is beyond their
understanding and control. There are also few applications that fully cater for the
group environments that are the norm in infrastructure site selection. There is
also a real void of systems capable of accepting uncertainty assessments directly
from decision-makers. Allowing decision-makers to specify their uncertainty
Chapter 4 Spatial Decision Support Systems 78
levels when making judgements may lead to more honest assessments and
ultimately to better decisions.
79Chapter 5 Problem analysis and conceptual system design
Chapter 5
PPRROOBBLLEEMM AANNAALLYYSSIISS AANNDD
CCOONNCCEEPPTTUUAALL SSYYSSTTEEMM DDEESSIIGGNN
5.1 Introduction
The planning and research phase produced a set of objectives for system design,
and reviewed current literature to identify the obstacles and opportunities
relevant to achieving those objectives. Analysis and Conceptual System Design
entailed a critical analysis of this information to draw out the primary reasons for
limitations on current approaches to site selection. New approaches to mitigating
these limitations were then proposed at a conceptual level. The outputs from this
process were a set of desired system characteristics and the underlying concepts
that were proposed to deliver those characteristics.
5.2 Causes of current limitations on SDSSs for site selection
Literature has shown that current approaches to site selection employed in
Spatial Decision Support Systems (SDSSs) have exhibited practical limitations,
and it is postulated here that these limitations may be classified into two groups:
1. Limitations on effectiveness: Limitations on effectiveness stem from the
analytical inability of a SDSS to accurately model the problem.
80Chapter 5 Problem analysis and conceptual system design
2. Limitations on use: Limitations on use stem from real or perceived
difficulties in using or understanding the SDSS that prevent users from
utilising its full potential.
Both classes of limitation are capable of undermining the ability of a SDSS to aid
the decision process, and tend to have a circular relationship with each other.
Thus if a system is ineffective at modelling the characteristics of the decision
situation it becomes difficult to use, and if a system is difficult to use it is less
likely that decision-makers will employ it to model the situation effectively.
Looking further into the problem reveals that each of the two classes of
limitations exhibits a set of root causes. The main causes of the limitations on
current SDSSs identified in this research are illustrated in Figure 5.1.
81Chapter 5 Problem analysis and conceptual system design
Figure 5.1: Sources of current limitations on SDSSs
It follows that four primary capabilities are desirable in any new SDSS. Firstly a
simple and robust method to accept inputs from multiple decision-makers,
secondly an ability to handle uncertainty, thirdly an overall sense of simplicity in
ease of use and understanding, and lastly a sense of control. These four areas are
discussed more fully in the remainder of Section 5.2, and conceptual ideas to
mitigate these limitations are introduced in Section 5.3.
5.2.1 Multiple decision-makers
Most large-scale site selection decisions are made with the interests of multiple
stakeholder groups in mind, and groups may differ in their opinions in several
ways. It is common for decision-makers to differ in their weighting of the various
criteria, however they may also assess the same criterion differently or disagree
on an aspect of the decision-making process itself. A decision may be broken
down into a hierarchy of objectives and attributes (Saaty 1980), and exactly
which objective each attribute serves may also be a source of conflict. A method
is needed whereby each decision-makers opinion on weighting of criteria,
criterion assessment and uncertainty is considered.
LLiimmiittaattiioonnss oonn SSppaattiiaall DDeecciissiioonn SSuuppppoorrtt
SSyysstteemmss
Limitations on effectiveness
Limitations on use
Inability to deal with a
heterogeneous group
Inability to deal with
uncertainty
Lack of
simplicity
Lack of control
82Chapter 5 Problem analysis and conceptual system design
The difficulty in solving group problems lies in how to fully accept and combine
differing assessments from each member of the group. Deriving an aggregated
criterion weighting alone is inadequate if criterion assessments vary, and
assessing each criterion is complicated by conflicting opinions about which
attributes best represent which objective. As aggregation procedures tend to
produce a single measure of suitability, there is also a need to keep conflicts
visible after an aggregation, so they may be fully explored.
5.2.2 Uncertainty
Uncertainty in spatial decision-making has traditionally been considered in terms
of the physical processes and variables that form the basis of imprecise datasets
upon which decisions are made. Keeney and Raiffa (1976) define two basic
sources of uncertainty, the first being uncertainty about the source data used to
make a decision, and the second is uncertainty about future events which may
effect the decision outcome. These two sources of uncertainty can be further
classified into two types of uncertainty, being either probabilistic or fuzzy
(Malczewski 1999). Probabilistic uncertainty is described by a probability
distribution and fuzziness by fuzzy set theory. However these distinctions may
mean little to a strategic decision-maker making subjective value judgements,
who has little or no training in mathematical analysis.
Uncertainty in site selection is often due to uncertainty in a value judgement that
may be hard to directly associate with a physical process. In an unstructured
problem such as site selection, human intuition is frequently the basis for
decision-making (Turban 1995). The uncertainty inherent in intuitive value
judgements is quantified in the minds of decision-makers, and may bear no
measurable relationship to the stochastic uncertainty in source data or future
events, although it may be partially or wholly based on these factors. This highly
elusive and difficult to represent type of uncertainty plays a major role in human
reasoning. It is proposed here that this type of uncertainty be defined by the term
‘decision-maker uncertainty’ as the intuitive reasoning of the decision-maker is
the physical process most responsible for it. Decision-maker uncertainty arises
when the decision-makers themselves provide the only measure of the
83Chapter 5 Problem analysis and conceptual system design
relationship between source data and suitability by making statements such as ‘A
location less than fifty metres from the main road would be good’. It is extremely
common in decision problems with qualitative variables, and has been largely
overlooked in the literature on site selection.
The distinction between decision-maker uncertainty and data uncertainty is
important as data uncertainty such as known inaccuracies in distance
measurement or temporal fluctuations in demand has often been represented
successfully through the use of stochastic modelling techniques (Murray 2003).
Decision-maker uncertainty is somewhat different in nature. In the mind of a
decision-maker if a suitability assessment is uncertain they might simply lower
their assessment to compensate. If asked for an estimate of their confidence in
the assessment they will most probably answer with a linguistic term such as
‘very certain’ or ‘uncertain’. Underneath this perceived level of uncertainty there
is also the inherent vagueness of the assessment itself. The simplest way to
provide a suitability assessment is via a set of linguistic terms such as ‘good’,
‘bad’, ‘very bad’, etc, but inherent in these terms is an element of linguistic
uncertainty.
One may therefore postulate that when dealing with subjective linguistic
suitability assessments from decision-makers the overall level of decision-maker
uncertainty comprises two elements. Firstly the uncertainty quantified by the
decision-maker, for which the term ‘quantitative uncertainty’ is suggested.
Secondly there is the imprecision of the suitability term used by the decision-
maker, which is defined as ‘linguistic uncertainty’ in the literature eg. (Zadeh
1975; Herrera and Herrera-Viedma 2000). In keeping with the requirement of
simplicity it is therefore necessary to devise a method to directly incorporate
these two elements into an analysis. The problem is how to turn an assessment
such as ‘I am certain that location A is good with respect to Criterion 1’ into a
mathematical quantity that can be manipulated by an analytical model, whilst
retaining its information value.
84Chapter 5 Problem analysis and conceptual system design
5.2.3 Simplicity
The need for simplicity in Spatial Decision Support Systems is paramount, as has
been noted by many authors eg. (Crossland, Wynne et al. 1995; Brail 2000; Lu,
Yu et al. 2001). There are two kinds of simplicity to consider here. Firstly the
semantics of the analytical method used should be simple enough for users to
easily interact with the system. Secondly the mathematics of the technique
should be simple enough to be implemented in an algorithm capable of analysing
millions of discrete alternatives in a realistic timeframe. These two requirements
are often conflicting, as simplifying user interaction generally requires more
effort behind the scenes.
Simplicity in use and interaction has often been noted as a requirement in
Decision Support Systems (Turban 1995), and one of the main objectives in DSS
design should be to increase willingness to use DSS as many studies reveal that
millions of dollars have been wasted on unused DSS’ (Lu, Yu et al. 2001). In
fact while spatial decision support systems have been proven to increase
decision-maker effectiveness (Crossland, Wynne et al. 1995), few applications
are actually in use to support decision-makers in siting decisions (Maniezzo,
Mendes et al. 1998), and highly capable analytical systems are often used as
simple visualisation tools, primarily due to difficulties in use and understanding
of the systems by strategic decision-makers (Klosterman 2000).
Most GIS have very limited inbuilt capabilities for the simple integration of
decision-maker preferences with spatial data, and the use of MCE within GIS
provides a platform for this integration (Malczewski 1999). However there are
many stages in the MCE process that are complex or cumbersome to implement.
Among these are criteria rating and standardisation, selection of an appropriate
aggregation procedure, and differentiating amongst alternatives with a similar
overall rating.
85Chapter 5 Problem analysis and conceptual system design
5.2.4 Control
A key requirement of any decision support methodology is to deliver a sense of
control of the decision-making process to the decision-makers themselves.
Methods that operate as a ‘black box’, where users have little understanding or
control over outputs are unlikely to be fully embraced by decision-makers and
interest groups (O'Sullivan and Unwin 2003). However delivering a sense of
control to decision-makers requires that they be able to ‘look inside’ the
analytical processes employed and choose among the various analysis options
available. The choices made at this level are crucial to the overall results
obtained, as it is a well-noted fact that different decision-making methods often
produce different results. The most dominant influence on outputs is exerted by
the choice of aggregation procedure (Carver 1991; Heywood, Oliver et al. 1995).
5.3 A conceptual framework
The conceptual framework presented in this section is a blueprint for a new
Spatial Decision Support System for infrastructure site selection, at a conceptual
level. It proposes key methods and concepts for the new system, whilst leaving
the details of algorithm design to Chapter 6, and construction of the actual
system to Chapter 7.
5.3.1 Why use Approximate Reasoning?
It is a core hypothesis of this research that AR is a suitable basis for an
infrastructure site selection algorithm. AR techniques offer two immediate
advantages over crisp (non-fuzzy) methods. Firstly they enable uncertainty to be
factored into an analysis, as is discussed in more detail in Section 5.3.3. Secondly
they simplify that analysis as is described in Section 5.3.4. The use of AR in
decision-making is also backed up by a vast amount of literature and practical
experience, as was touched on in Chapter 3. But perhaps the most potent
86Chapter 5 Problem analysis and conceptual system design
argument for AR can be made at a more conceptual level. The fundamental
advantage of AR in decision-making is that it is a bridging technology that
enables human beings to more effectively interact with an analytical model.
The advent of powerful modelling capabilities, made possible by the digital
computer, has brought about an enormous increase in our ability to precisely
simulate complex real world systems. Engineers and scientists value this
precision in data, however the human mind has a finite ability to resolve detail
and store information. It uses words as labels for imprecise bundles, also termed
fuzzy granules, as a means to cope with complex problems. This mismatch of
precision between human and computer produces a decrease in our ability to
make precise and significant statements about models, as they grow more
complex.
In general there may be distinguished three distinct entities related to modelling
the physical world:
1. The physical process to be modelled.
2. The abstract (usually mathematical) representation of that process, termed the
model.
3. Human understanding of both the physical process and the mathematical
model, which makes construction and application of the model possible.
AR enhances the bridge between mathematical models and the associated
physical reality by facilitating a better human understanding of the modelling
process. Fuzzy methods are capable of capturing the vagueness of linguistic
terms in statements of natural language. This in turn provides greater capability
to model systems through human commonsense (approximate) reasoning and
creates a more useful aid to decision-making (Klir and Yuan 1999).
87Chapter 5 Problem analysis and conceptual system design
5.3.2 Catering for multiple decision-makers
It is proposed here that to adequately deal with the inevitable conflicts that will
arise from a heterogeneous group of decision-makers requires three elements.
1. A criterion weighting approach that applies to attributes and not objectives.
2. A method to accept differences of opinion in both criteria weighting and
rating.
3. The ability to identify conflicts in the post aggregation data exploration phase
of the decision-making process.
The first step in overcoming the three hurdles facing a heterogeneous group is to
ask decision-makers to assess and weight attributes directly. In this way the
attributes can be weighted and assessed with respect to the objective foremost in
the mind of each decision-maker, thereby bypassing the conflict that may arise in
trying to reach a consensus on which attributes best represent each objective.
To allow the processing of differences of opinion in weighting and rating, each
alternatives criterion outcomes and criterion weights are weighted and combined
using a Relevance Matrix (RM). The format for a RM is shown in Figure 5.2.
R = ⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
JKJ
K
RR
RR
..........
..........
1
111
M
Figure 5.2: Relevance matrix
Deriving the relevance matrix is ideally achieved via consensus, and should be
based on the competency of a decision-maker to make assessments relating to
each criterion. However it may also be derived via a non-weighted averaging of
each decision-maker’s assessments of the competencies of others in the group.
Each criterions relevance values are normalised so that a criterion does not gain
extra importance based on solely relevance values. The values defined in the
The relevance matrix describes the relevance of the kth decision-makers opinion with respect to attribute j. (values are scaled after input so each criterions relevance values sum to 1)
88Chapter 5 Problem analysis and conceptual system design
relevance matrix are then used in a double weighted MCE aggregation of fuzzy
suitability scores.
Double weighting has been used previously to add extra weight to lower criterion
outcomes in hybrid Ordered Weighted Averaging techniques (Jiang and Eastman
2000). It is proposed that a criterion assessment from each decision-maker is
weighted according to the decision-makers preference and relevance as shown in
Equation 5.1.
∑∑= =
××=J
j
K
kjkjkijki WR
1 1
OS | i = 1…I (5.1)
N.B. Fuzzy quantities are shown in bold type
Where:
iS is the suitability of alternative i.
ijkO is the criteria outcome for alternative i with relation to criterion j and
decision-maker k, including quantitative and linguistic uncertainty.
jkR is the relevance of decision-maker k’s opinion with respect to criterion j.
jkW is the weight assigned to criterion j by decision-maker k
The aggregation output should be a fuzzy number representative of each
alternative’s overall compensatory suitability and uncertainty.
The task now remains to extract conflicts in the post aggregation data exploration
phase. To accomplish this it is proposed to extract an extra parameter
representative of conflict. The complete set of parameters proposed is discussed
fully in Section 5.3.5.
89Chapter 5 Problem analysis and conceptual system design
5.3.3 Handling Uncertainty
Although the use of fuzzy numbers to model linguistic uncertainty is common,
there is no universal method to derive the fuzzy numbers, or adjust the fuzzy
number to include the extra dimension of the quantitative uncertainty level
placed on the linguistic assessment. The problem of matching a fuzzy number
with a linguistic label dates back to the genesis of the linguistic approach, and is
beyond the scope of this thesis. However the ability to adjust a fuzzy number in
line with a decision-maker uncertainty assessment is a simpler task.
Incorporating decision-maker uncertainty into an analysis in this way offers real
advantages, as this type of uncertainty assessment can always be obtained
regardless of the level of knowledge about source data.
Linguistic uncertainty has long been represented using fuzzy set theory. Usually
the fuzzy set is reduced to a parametric form such as a triangular or trapezoidal
fuzzy number. Figure 5.3 shows a triangular fuzzy number (TFN) which
represents a linguistic term in three parameters (a,b,c).
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
a c
b
Suitability
μ 'OK'
Figure 5.3: The suitability term ‘OK’ as TFN(0.3,0.5,0.7)
90Chapter 5 Problem analysis and conceptual system design
A method is now needed to encapsulate the level of quantitative uncertainty
expressed when the term is used in the form ‘I am very certain that this
alternative is OK with respect to criterion 3’. This may be accomplished using
the relatively new concept of a type-2 fuzzy set and its footprint of uncertainty
(FOU). A type-1 fuzzy set has a crisp membership function where each point on
the universe of discourse (x-axis) has a crisp membership value μ on the y-axis.
A type-2 fuzzy set possesses a secondary membership function (2MF) drawn
along a third axis that describes the relationship between the universe of
discourse and the primary membership function (Mendell and John 2002). The
2MF exists within a footprint of uncertainty (FOU). An example of a FOU is
shown in Figure 5.4.
0
0.2
0.4
0.6
0.8
1
Suitability
μ
Primary Term FOU
Figure 5.4: Footprint of uncertainty
In this case the FOU of the suitability term is defined by moving vertices a and c
of the primary TFN outwards to the boundary of [0,1]. This provides a means to
superimpose quantitative uncertainty on the primary TFN by varying the
expected value of the 2MF with the quantitative uncertainty assessment. In this
case primary vertices a and c are reallocated to a different point for different
quantitative uncertainty assessments as shown in Figure 5.5.
91Chapter 5 Problem analysis and conceptual system design
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Suitability
μ
Totally certain
Certain
Moderately certain
Uncertain
Totally uncertain
'OK'
Figure 5.5: How a quantitative uncertainty assessment affects the primary
MF
It is proposed that by using TFN’s and the type-2 concepts described above it is
possible to represent linguistic suitability and uncertainty assessments as a
simple, robust mathematical quantity, capable of being manipulated by an
analytical model whilst retaining the original information value. The scaled TFN
will provide the basic unit to be manipulated in the algorithm. All that remains is
to devise a mathematical method of deriving the new a and c values from the
original primary MF and a linguistic uncertainty assessment. The specifics are
left for Chapter 6.
5.3.4 Creating simplicity
Simplicity in use and interaction is largely a product of choosing an AR
technique. Decision-makers benefit from a universal linguistic suitability scale
that greatly simplifies criteria standardisation. AR enables the use of words as the
basis for interaction with the system, both in terms of input and feedback.
Another way to increase ease of use is to produce a fully integrated system,
utilising a standard GIS interface. GIS is a mature technology and the methods
standard GIS packages employ to view and interact with spatial information are
the result of an ongoing process of refinement dating back to the 1960s. Instead
92Chapter 5 Problem analysis and conceptual system design
of trying to reinvent the wheel and create an entirely new interface, it is more
efficient to create a set of tools to enhance existing GIS functionality where
necessary, whilst retaining the highly effective aspects of the standard interface.
This may be achieved by incorporating tools into a toolbar that integrates
seamlessly with existing functionality.
The mathematical simplicity required to enable the analysis of large numbers of
alternatives in real-time is provided by adhering to three constraints:
1. Utilising parameter-based fuzzy numbers, thereby avoiding the extra burden
of more complex membership functions.
2. Manipulation of the TFN’s by arithmetic operations, easily performed by GIS
software.
3. Use of a scoring function to de-fuzzify outputs, thereby avoiding the use of
pairwise comparisons to rank alternatives, as the number of calculations
required to do this becomes unwieldy with large numbers of alternatives.
Details of the fuzzy algorithm are given in Chapter 6.
5.3.5 Giving decision-makers control
It is proposed here that in order to successfully deliver control to decision-makers
an easily understandable method to choose between different aggregation
procedures is required. This may be achieved by generating four descriptive
parameters for each alternative that are indicative of the qualities sought by
differing aggregation procedures, and independent of the problem domain.
Decision-makers then decide which of these parameters are most important to the
problem at hand. The parameters are as follows:
1. Utility: Utility is a measure of an alternatives fulfilment of all evaluation
criteria in a compensatory way. It is calculated via a weighted summation of
all criterion outcomes. A good solution requires good utility.
2. Certainty: Certainty is a measure of how predictable the outcome for a
particular alternative is. A good solution is one with a high level of certainty.
93Chapter 5 Problem analysis and conceptual system design
3. Safety: Safety is a measure proportional to the lowest criterion outcomes.
Alternatives with poor outcomes on some criteria may rate well in terms of
utility but will be unsafe or ‘risky’. A good solution is a safe solution.
4. Consensus: Consensus requires that all parties agree on the various aspects
of an alternative. Alternatives that are rated similarly on all criteria by all
decision-makers in the group exhibit a high level of consensus. A good
solution requires consensus.
None of the four parameters are sufficient to guarantee a good solution in
isolation. However by weighting and combining them decision-makers take
control of the process, and find solutions that best satisfy the dynamics of each
problem. Moreover by breaking down each solution into easily understandable
quantities, the mystery of what happens during analysis is lessened in the eyes of
users.
The four parameters should constitute an integral part of the interactive data
exploration and visualisation phase of the decision-making process. Using a
graphical point and click interface, decision-makers should be able to explore
each alternative site by receiving linguistic feed back on the four parameters,
plus an overall aggregated rating derived from combining them.
5.4 Conclusions
It was proposed that limitations on current SDSSs are derived from an inability
to deal with multiple conflicting parties, an inability to handle uncertainty, a lack
of simplicity in use and interaction and not delivering enough control to decision-
makers. This chapter has provided a conceptual blueprint for algorithm design
and system construction by outlining the desired characteristics of the system. It
was found that the system should possess the following characteristics:
• The ability to accept inputs from a heterogeneous group of decision-
makers, independently weighting and rating multiple attributes.
94Chapter 5 Problem analysis and conceptual system design
• An approximate reasoning algorithm based on a fuzzy MCE aggregation
of parameter-based fuzzy numbers that encapsulate linguistic suitability
and uncertainty assessments.
• The algorithm should utilise arithmetic operators for aggregation and a
scoring function for de-fuzzification to minimise calculation time and
enable real-time interactivity.
• The system should be fully integrated into existing GIS software.
• Linguistic outputs should be a set of descriptive parameters that give
decision-makers the ability to choose the characteristics of a solution that
are most appropriate to their specific problem, thereby enabling them to
gain control over the properties maximised during aggregation.
Chapter 6 Algorithm design 95
Chapter 6
AALLGGOORRIITTHHMM DDEESSIIGGNN
6.1 Introduction
The previous chapter has highlighted several limitations on current approaches to
site selection, and provided a conceptual blueprint for mitigating those
limitations. Algorithm design consisted of the formal implementation of those
conceptual ideas that specifically relate to the decision-making model. The
implementation takes the form of a new Approximate Reasoning Algorithm for
Infrastructure Site Selection (ARAISS). While it is not the contention of this
research that it is possible to develop a perfect analytical model for the solution
of all Infrastructure Site Selection Problems, ARAISS implements several
concepts that offer an improvement over current methodologies.
The core capabilities of ARAISS are its use of approximate reasoning to handle
uncertainty, its multiple decision-maker capability, its simplicity, and the way it
hands over control to decision-makers. ARAISS is described in detail in Section
6.2, and the results from MATLAB testing of the algorithm are given in Section
6.3. Conclusions are then drawn.
6.2 ARAISS
One of the principal outcomes of this research is the ARAISS algorithm
described in this section. ARAISS is a new and unique approach to infrastructure
site selection, which is loosely based on fuzzy multiattribute utility theory
(Ribeiro 1996). ARAISS is specifically targeted to an audience of strategic
Chapter 6 Algorithm design 96
decision-makers locating a new facility. It was designed to accommodate
qualitative and quantitative variables, and offers a means to perform an initial
analysis based on the issues of foremost importance in the minds of stakeholders.
As such it is a generic Spatial Decision Support algorithm suitable for the first
stage of a site selection process. A more comprehensive follow up assessment
incorporating a more detailed analysis is envisaged as a means to further validate
recommendations made from the ARAISS process.
6.2.1 Framework
Figure 6.1 shows the general framework for ARAISS. It is a two-phase
procedure where the final location is sought via an iterative process of reducing
alternatives. In Phase 1, decision-makers first define the problem, and then a
constraint analysis is performed to exclude totally unfeasible alternatives. A set
of linguistic suitability terms to be used when rating the various criteria is then
defined. Each decision-maker then contributes their preferences for criterion
weighting and rating, and this information is combined with decision-maker
relevance values in an aggregation. The aggregation derives output parameters
for the Utility, Certainty, Safety and Consensus of each alternative.
Phase 2 involves exploration and reduction of alternatives. Decision-maker
preferences for minimum acceptable parameter values, and parameter weights
are sought. They provide a means to rate and rank alternatives in terms of their
overall suitability, and thereby reduce the number of alternatives under
consideration by consensus. The desired outcome of this process is the selection
of a site or sites, which conform to the strategic needs of all decision-makers.
Once the strategic analysis has been performed, it may be necessary to analyse
tactical and operational issues using a more specific modelling procedure, and to
consider micro-placement issues such as footprint and orientation before making
a comprehensive decision. This last non-strategic phase is beyond the scope of
this thesis.
Chapter 6 Algorithm design 97
Figure 6.1: ARAISS Framework
Define problem
Define decision-maker relevance values
Perform compensatory aggregation function
Identify feasible alternative sites for analysis
Choose Decision-makers
Identify Utility, Certainty, Safety and Consensus
Final site selection
Define and rate criteria (factors)
Start
Define linguistic terms
Tactical and operational assessment
Micro-placement
Identify constraints
Explore and reduce alternatives
Iterate
Phase 2
Perform adjusted aggregation
Define criterion weights
Review inputs
Phase 1
Chapter 6 Algorithm design 98
6.2.2 Notation
Notation and terminology for site selection analysis has been inconsistent in the
literature. For example compare the notation of Malczewski (1995) to that of
Eastman (1995). To avoid confusion the following notation is used consistently
throughout this thesis:
A = {A1,A2,……AI} The set of I feasible alternatives
C = {C1,C2,……CJ} The set of J criteria (factors)
D = {D1,D2,……DK} The set of K decision-makers
W = ⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
JKJ
K
WW
WW
..........
..........
1
111
M
R = ⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
JKJ
K
RR
RR
..........
..........
1
111
M
Ok =
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
JIkkJ
Ikk
OO
OO
..........
..........
1
111
M
The overall suitability of alternative i (Si) is some function of each decision-
makers preferences for criterion outcomes and weights, combined with their
relevance to each criterion.
6.2.3 Linguistic term sets
The natural language approach to decision analysis relies on a systematic use of
words to characterize the values of variables, probabilities, relations, and truth-
values of assertions. The central concept is that of a linguistic variable whose
values are words or sentences, which serve as the names of fuzzy subsets of a
universe of discourse. The linguistic approach represents a blend of quantitative
The matrix of criterion outcomes for alternative i and criterion j, based on decision-maker k’s suitability and uncertainty assessments.
The matrix of relevance values specifying the relevance of the kth decision-makers opinion with respect to criterion j. (values are scaled after input so each criterions relevance values sum to 1)
The matrix of criterion weights specifying the kth decision-makers opinion of the weighting of criterion j.
Chapter 6 Algorithm design 99
and qualitative analysis by using numbers to make the meaning of words more
precise (Zadeh 1976).
A linguistic variable is generally characterised by the quintuple (X, T(X), D, Y,
M) where:-
X is the name of the variable. (e.g. Age)
T(X) is the term set which gives x it’s linguistic values. (e.g. Young, Not
Young,…Old, etc)
D is the universe of discourse. (e.g 0-150)
Y is a syntactic rule which generates the terms in T(X).
M is a semantic rule which associates with each term, x, in T(X) its
meaning, M(X). The meaning is defined by a membership function μ(x)
that associates each member of D with a degree of compatibility in x,
within the interval [0,1].
ARAISS uses four term sets:
T(S) site suitability terms
T(W) terms for weighting of criteria and decision-maker relevance
T(U) terms describing the level of uncertainty
T(G) terms for generating new suitability terms in T(S)
Generation of linguistic term sets involves two primary considerations. The first
is the selection of a grammar, i.e. the cardinality of the term set and syntactic
labelling as defined by a syntactic rule. The second is how to define a semantic
for each term, which in this case will take the form of a triangular fuzzy number
(TFN) or crisp number, via a semantic rule.
On the issue of grammar the first consideration is cardinality i.e. the number of
terms in the set. The term set should be small enough to be manageable and not
impose unnecessary precision, yet comprehensively cover adequate
discrimination of assessments. Typical values of cardinality are odd numbers,
Chapter 6 Algorithm design 100
usually close to 7, with the middle term centred on a utility of 0.5 (Herrera and
Herrera-Viedma 2000). The labels themselves can be generated using either a
context-free grammar consisting of pre-existing primary terms, which are
expanded upon using the syntactic rule G, or by means of an ordered structure.
However both approaches imply limitations on flexibility and do not allow
decision-makers to interactively generate new terms. For example, when
considering the importance of a criterion, where does one place the word
‘significant’ within a set such as {…., unimportant, moderately important,
important, …..}.
ARAISS uses a hybrid method, whereby a set of five primary suitability terms
P(S) based on an ordered structure, may be enhanced by the addition of a
maximum of four new user specified terms N(S). The term generation term set
T(G) enables users to interactively generate additional suitability terms via a
linguistic comparison to existing terms. All term sets other than suitability are
composed only of primary terms, and are not subject to additions. Primary terms
are as follows:
Primary suitability terms: P(S) = {s0 = totally unsuitable, s1 = bad, s2 =
indifferent, s3 = good, s4 = perfect}
Primary weighting terms: P(W) = T(W) = {w0 = irrelevant, w1 = unimportant,
w2 = moderately important, w3 = important, w4 = critical}
Primary uncertainty terms: P(U) = T(U) = {u0 = very certain, u1 = certain, u2 =
moderately certain, u3 = uncertain, u4 = very uncertain}
Generation terms: P(G) = T(G) = {g0 = zero, g1 = very small, g2 = small, g3 =
medium, g4 = large, g5 = very large}
The advantages of approaching the issue of term set generation in this way are
threefold. Firstly the cardinality of the suitability term set is limited to
manageable values between five and nine, secondly it provides a solid foundation
structure that broadly covers all possible values, and lastly it enables decision-
Chapter 6 Algorithm design 101
makers to include terms with a specific contextual meaning. A consequence is
that semantic definition is a little more complex, although this is transparent to
the user.
6.2.3.1 Semantic definition
There are a number of options for semantic definition of a linguistic term.
Approaches used range from assuming a symmetrical distribution of terms on the
given universe of discourse and allocating each term a subdomain within it
(Yager 1995), to more complex approaches whereby the term set can be non-
symmetrical and subdomains are further characterized by fuzzy membership
functions (Herrera, Herrera-Viedma et al. 1996).
ARAISS utilizes two types of semantic definitions. Suitability terms are
characterized here as triangular fuzzy numbers, whereas all other terms are
allocated crisp values. The advantage sought in combining fuzzy and non-fuzzy
semantic values is to preserve the extra information conveyed by a fuzzy
number, without falling victim to the problems of multiplication and division of
two TFN’s. The multiplication and division operators proposed for use on TFN’s
are only approximations (Bonissone 1982), leading to, among others, the
problem of an unwarranted increase in the support. Proposed semantic definition
of primary terms is described in Table 6.1 where:
T(S) are triangular fuzzy numbers on [0,1]
T(W) are crisp numbers on [0,1]
T(U) are integers 0..N-1 where N is the number of uniformly distributed,
ordinal uncertainty terms
T(G) are crisp numbers on [0,1]
Chapter 6 Algorithm design 102
Table 6.1: Semantic definition of primary terms
Suitability (as a TFN)
Weighting Uncertainty Term generation
Totally unsuitable
(0,0,0)
Irrelevant
.1
Very Certain
0
Zero**
Very Small 0 .1
Bad (0,.2,.4) Unimportant .3 Certain 1 Small .3 Indifferent (.3,.5,.7) Moderately
Important .5 Moderately
Certain 2
Moderate .5
Good (.6,.8,1) Important .7 Uncertain 3 Large .7 Perfect (1,1,1) Critical 1* Very
Uncertain 4 Very Large .9
* This is the static value & may also be dynamic - see section 6.2.4
** The term g0 = zero allows re-labeling of any term.
Two operations are performed on the linguistic suitability terms prior to
aggregation. The first is the generation of new suitability terms to enable
decision-makers to utilize context specific words and increase the resolution of
the set. Secondly, uncertainty scaling of suitability terms provides a means to
represent quantitative uncertainty separate from the linguistic uncertainty of the
suitability term.
6.2.3.2 Term generation
Term generation is necessary to give decision-makers the use of context specific
words, and to increase the resolution of the term set. It is facilitated by a hedging
procedure that enables the addition of up to four new suitability terms to a set of
around five primary terms, whilst still preserving the ordinal quality of the set.
The first step in this process is choosing the term that will immediately precede
the new term in utility. The semantic value of the new term will take the form of
a TFN, with its centre of gravity situated between this term and the next term
above. Equation 6.1 defines the breakpoints of the new term:
(6.1)
Where:
gxxxx )(' −+− −+=
Chapter 6 Algorithm design 103
'x is the value of the new breakpoint
−x is the value of the breakpoint in the lower term
+x is the value of the corresponding breakpoint in the higher term
g is the term generation term
Figure 6.2 illustrates the new suitability term ‘Acceptable’ which has been
generated as a ‘large’ increase upon ‘Indifferent’.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Suitability
μ
Totally UnsuitableBadIndifferentGoodPerfectAcceptable
Figure 6.2: Term generation
6.2.3.3 Uncertainty scaling
It was proposed in Chapter 5 that there are two types of uncertainty inherent in
decision-maker suitability assessments, linguistic and quantitative. In ARAISS
linguistic uncertainty is represented by the fuzziness of the primary suitability
term (TFN(a,b,c)), whereas quantitative uncertainty is represented using the
concept of a type-2 fuzzy set and its footprint of uncertainty (FOU). The FOU
has also been used to model other types of uncertainties such as ambiguity,
nonspecificity or strife (Mendell and John 2002). The FOU of the suitability term
is defined here by moving vertices a and c of the primary TFN outwards to the
boundary of [0,1] as shown in Figure 5.4. Primary vertices a and c are
Chapter 6 Algorithm design 104
reallocated according to the uncertainty assessment as shown in Figure 5.5, and
these points are defined as follows:
(6.2)
(6.3)
(6.4)
Where:
Supp is the width of the support of the new primary membership
n is the term number chosen by the decision-maker from a set of N-1
uniformly distributed uncertainty terms
The scaled term now envelops both suitability and quantitative uncertainty
information in a type-1 fuzzy number, enabling the use of relatively simple type-
1 processing procedures whilst increasing information value.
acnN
acSupp −+⎟⎠⎞
⎜⎝⎛
−+−
=1
1
⎪⎪⎪
⎩
⎪⎪⎪
⎨
⎧
−
<−−
<
=
Otherwise 2
2 1 if 1
2b if 0
Suppb
SuppbSupp
Supp
a
⎪⎪⎪
⎩
⎪⎪⎪
⎨
⎧
+
<−
<
=
Otherwise 2
2 1 if 1
2 if
Suppb
Suppb
SuppbSupp
c
Chapter 6 Algorithm design 105
6.2.4 Dynamic weighting
The representation of importance assessments via linguistic terms with static
semantic definitions has inherent limitations. These stem from the fact that some
classifications of importance are dependant on other values of importance in the
set. For example, a decision-maker may wish a criterion to be weighted so
heavily as to outweigh all other criteria. This is defined here as critical
importance, and it’s semantic definition requires a dynamic weighting approach.
When using critical importance it is necessary to generate the semantic values of
weights as a function of the other weighting terms in the set, as shown in
Equation 6.5. Critical importance is not equivalent to evaluating an alternative on
the basis of that criterion alone, as alternatives with similar ratings for the critical
criterion are further classified according to other criterion outcomes. ARAISS
utilises the concept of critical importance in criterion weighting (Wjk j=1..J),
decision-maker relevance values (Rjk k=1..K), and in weighting the output
parameters defined in Section 6.2.6. Critical importance can only be
implemented once in any set of weighting factors.
(6.5)
Where:
cW is the critical weighting coefficient
nW is the static weight of factor n (the static weight of critical is 1)
N is the number of weights in the set
The final step is to normalize all weights in the set by dividing by Wc.
∑=
=N
nnc WNW
1
2
Chapter 6 Algorithm design 106
6.2.5 Generating suitability values
Site selection procedures in GIS require the creation of a set of suitability maps,
which were introduced in Chapter 4. A suitability map is a representation of how
a given criterion varies over space. For the purposes of algorithm design, two
classes of suitability maps must be considered: discrete and continuous. Discrete
maps are those based on categorical variables such as land use or zoning. Such
variables are relatively simple for decision-makers to directly translate into
linguistic suitability values. Continuous variables such as slope, proximity or
elevation may also be discretely categorized using cut-off values, however this
approach loses valuable detail and there is usually ambiguity and imprecision in
defining such cut-off values (Malczewski 2002). A better approach is to
represent continuous criteria by using a utility function (Jiang and Eastman
2000).
However whilst utility functions may be used to generate inputs to fuzzy data
processing procedures such as fuzzy inference systems, the utility value given for
each attribute value is crisp, not fuzzy. Although crisp numbers may be
successfully processed by ARAISS it is more realistic to represent utility values
as a fuzzy number, similar to those defined in the suitability term set. However
there is now a dichotomy between the discrete number of terms and the
continuous variation of the attribute value.
Both the continuous nature of the variable and the fuzziness of its utility value
are preserved in ARAISS, using a fuzzification method. Decision-makers
classify points on the domain of the source variable according to their suitability
and uncertainty, and values that lie at a point x, in between the classified points,
are given a fuzzy rating as follows:
(6.6)
( )( )lh
llhx xx
xxSuppSuppSupp−
−−=
Chapter 6 Algorithm design 107
(6.7)
(6.8)
(6.9)
Where:
Suppx is the width of the support of the suitability TFN at point x
Supph is the width of the support of TFN (ah,bh,ch) at the next highest rated point
Suppl is the width of the support of TFN (al,bl,cl) at next lowest rated point
xh is the next highest rated point
xl is the next lowest rated point
N.B. the term ‘Rating’ is used here to signify a numeric evaluation of an
alternative that is not dependant on the value of other alternatives. When
alternatives are evaluated directly against one another the term ‘Ranking’ is
used.
( )( )lh
llh
xxxxbbb
−−−
=
⎪⎪⎪
⎩
⎪⎪⎪
⎨
⎧
−
<−−
<
=
Otherwise 2
21 if 1
2 if
x
xx
x
Suppb
SuppbSupp
Suppbo
a
⎪⎪⎪
⎩
⎪⎪⎪
⎨
⎧
+
<−
<
=
Otherwise 2
21 if 1
2 if
x
x
xx
Suppb
Suppb
SuppbSupp
c
Chapter 6 Algorithm design 108
6.2.6 Aggregation and output parameters
An aggregation procedure brings together all the decision variables to produce an
overall evaluation of each alternative. Many possible aggregation methods were
discussed in Chapter 2, and the choice of aggregation procedure for ARAISS was
made with three requirements in mind:
1. The procedure should yield syntactic and semantic definitions of utility,
certainty, safety and consensus.
2. The procedure should have a high resolution, making it easier to isolate the
best alternative(s). i.e. the procedure should serve to limit the number of
alternatives with the same rating.
3. The procedure should not be too computationally intensive, enabling the user
to interact with the system in real time.
The first aggregation in ARAISS is based on fuzzy multiattribute decision-
making theory as described by (Ribeiro 1996). In order to process linguistic
variables, procedures for performing arithmetic operations on the trapezoidal
fuzzy numbers are needed. A comprehensive set of operations was developed by
Bonissone (1982) and is used in ARAISS. The fundamental operations used are
addition, subtraction, multiplication and division as shown below.
N.B Notation is in the bandwidth format as shown in Chapter 3.
( ) ( )2222211111 ,,,,,,: βαβα baTpzbaTpzAddition +
(6.10)
( ) ( )2222211111 ,,,,,,: βαβα baTpzbaTpznSubtractio −
(6.11)
( ) ( )2222211111 ,,,,,,: βαβα baTpzbaTpztionMultiplica ×
(6.12)
( )21212121 ,,, ββαα ++++= bbaaTpz
( )21212121 ,,, αββα −−−−= abbaTpz
( )2112212112212121 ,,, ββββαααα ++−+= bbaabbaaTpz
Chapter 6 Algorithm design 109
( )βα ,,,1:baTpz
Inverse
(6.13)
2
1:TpzTpzDivision
(6.14)
6.2.6.1 Utility
A measure for utility is derived using a double weighted fuzzy combination, as
shown Equation 6.15. Each alternative is given a compensatory outcome based
on suitability assessments from the set of decision-makers. The assessments are
weighted by criterion weights and the relevance of each decision-makers
opinion.
∑∑= =
××=J
j
K
kjkjkijki WROS
1 1| i = 1…I (6.15)
Where:
iS is the compensatory suitability of alternative i (as a TFN)
ijkO is the criteria outcome for alternative i with relation to criterion j and
decision-maker k, including quantitative uncertainty (as a TFN).
jkR is the relevance of decision-maker k’s opinion with respect to criterion j.
jkW is the weight assigned to criterion j by decision-maker k
⎟⎟⎠
⎞⎜⎜⎝
⎛+
−−
−=
)(,
)(,1,1
ββ
αα
bbaabaTpz
21
1Tpz
Tpz ×=
Chapter 6 Algorithm design 110
The output of Equation 6.15 is a fuzzy number representative of each
alternative’s overall compensatory suitability and uncertainty. To enable the
derivation of a linguistic rating for each alternative it is first necessary to carry
out a simple score range normalisation using Equation 6.16.
(6.16)
Normalisation of TFN’s is accomplished in ARAISS by using crisp numbers for
xmax and xmin, with xmin set to 0 and xmax set to cmax, (the third breakpoint of the
highest possible score using the maximum suitability term and the user defined
weighting and relevance parameters in an aggregation).
The next step is to rank and rate the normalised outputs. There is a vast amount
of literature on the ranking of fuzzy numbers (Fodor, Perny et al. 1998). Some
ranking procedures such as dominance relation require a series of pairwise
comparisons that can create a substantial computational burden, particularly if a
large number of alternatives exist. The simplest and most computationally
efficient ranking methods are scoring functions that assign a crisp value to each
set independently. ARAISS ranks fuzzy numbers using a scoring function that
measures a TFN’s centre of gravity along the x-axis. The score is calculated
using Equation 6.17.
Rs(i) = Rs(N(Si)) = Rs(TFN(a,b,c)) =
(6.17)
Where:
Rs(i) is the utility score for alternative i
minmax
min))((xx
xxxTFN−
−=Ν
22
2)(
2)(
211
22
bcab
abbc
b−+−
⎟⎟⎠
⎞⎜⎜⎝
⎛ −−−⎟⎠⎞
⎜⎝⎛ −
+
Chapter 6 Algorithm design 111
Rating a fuzzy number via a linguistic approximation is essentially a pattern
recognition problem, solved by extracting a set of features for comparison. As
the suitability term set is ordinal, a single feature can be used to ascertain the
position of the closest term. ARAISS uses the score from Equation 6.17, as
shown in Equation 6.18.
If
(6.18)
Where:
sl is the linguistic suitability term approximation
operator
ns is the nth term in a set of N-1 suitability terms
iS is the overall suitability of alternative i as a TFN
6.2.6.2 Safety
Safety is gained by making a decision that satisfies all criteria according to some
minimum standard. Safety is assured by eliminating risk, which is apparent in
alternatives with at least one poor criterion outcome. A very bad score on even a
minimally weighted criterion may, in reality, affect the overall rating of an
alternative much more than it’s weight suggests. Ordered weighted averaging has
been proposed as a countermeasure to this situation (Yager 1988), and may be
incorporated into a weighted aggregation procedure to provide control over the
level of compensation (Jiang and Eastman 2000). However to utilise this
approach decision-makers need to specify a precise value for the level of
compensation, which may not always be possible, and the aim here is to avoid
the need for non-linguistic input. ARAISS generates a linguistic assessment of
nis sS =)(l
( ) ( ))()()()(1
0 isns
N
nisns SRsRSRsR −Λ=−−
=
Chapter 6 Algorithm design 112
the risk inherent in each alternative using the risk score gained from Equation
6.19 in Equation 6.20:
(6.19)
If
(6.20)
Where:
Rr(i) is the risk score for alternative i
MinO is the minimum outcome required to eliminate risk (specified
linguistically by decision-makers)
rl is the linguistic risk term approximation operator
nr is the nth element of a set of N-1 risk terms (we use the term generation
term set here)
∧ is the minimum operator
6.2.6.3 Consensus
Consensus is reached when all parties are in agreement on criterion weights and
outcomes. Consensus is achieved by eliminating conflict, which occurs when an
alternative is rated poorly and weighted highly on a criterion by one decision-
maker, and is rated well, or weighted poorly on the same criterion by another
decision-maker. Risk is a necessary but not sufficient condition for conflict, so
the analysis is limited to those alternatives with a risk measure greater than zero.
Conflict is assessed using Equation 6.21, and a linguistic assessment of the level
nrr riR =))((l
( )
( )
( )⎪⎪⎪
⎩
⎪⎪⎪
⎨
⎧
⎟⎠⎞
⎜⎝⎛ ∧∧−
≥⎟⎠⎞
⎜⎝⎛ ∧∧
===
==
Otherwise
For 0
)(11
11
Mins
ijk
K
k
J
jsMins
Minsijk
K
k
J
js
r
OR
OROR
OROR
iR
( ) ( ))()(1
0iRriRr rn
N
nrn −∧=−−
=
Chapter 6 Algorithm design 113
of conflict is obtained in an identical way to that of Risk. Again the term
generation terms are used.
(6.21)
Where:
Rc(i) is the conflict score for alternative i
∨ is the maximum operator
6.2.6.4 Certainty
Certainty is the level of confidence placed in an outcome. Certainty is achieved
by eliminating uncertainty, and the uncertainty score Ru(i) is the width of the
support of the aggregated output. Uncertainty is rated linguistically by Equation
6.22.
nu ui =)(l
If
(6.22)
Where:
ul (i) is the linguistic uncertainty approximation for alternative I
U((ls(Si),un) is the TFN of the linguistic suitability term approximation for Si,
scaled for uncertainty using un
nu is the nth term in a set of N-1 uncertainty terms
2
)()()(
111⎟⎠⎞
⎜⎝⎛ −∧−−∨∨
==== jkijks
K
kjkijks
K
k
J
j
c
wORwORiR
( )( ) ( )
( )( ) ( )inis
N
n
inis
SSuppuSlUSupp
SSuppuSlUSupp
−∧
=−−
=),(
),(1
0
Chapter 6 Algorithm design 114
6.2.7 Adjusted aggregation and alternative exploration
Decision-makers can now decide which parameters are most important as they
explore and reduce the set of feasible alternatives in an interactive, iterative
process. Alternatives are reduced by selecting minimum standards for each of the
four parameters or creating an overall adjusted suitability value via Equation
6.23. The adjusted suitability score is then used to generate an adjusted linguistic
suitability rating using Equation 3.19. Weighting of the four parameters is via
consensus, or a non-weighted averaging of each decision-maker’s preferences,
which enables a variety of non-compensatory outcomes to be generated.
(6.23)
Where:
A(i) is the adjusted suitability value of alternative i
ws is the weighting of the suitability score
wu is the weighting of the uncertainty score
wr is the weighting of the risk score
wc is the weighting of the conflict score
6.3 ARAISS simulation exercises
Before comprehensive testing implementation in a SDSS it was necessary to test
and debug the algorithm in a way that would allow obvious flaws in the
methodology to be found. The MATLAB mathematical analysis software was
used to trial the algorithm in a number of simulated site selection problems. The
problems were based on a relatively small number of alternatives, which allowed
criterion ratings to be manually specified without spatial functions. The author
simulated all input data with the aim of creating a rich enough decision-making
environment for all the features of the algorithm to be tested. An example of the
crus
ccrruuss
wwwwwiRwiRwiRwiRiA
+++−+−+−+
=))(1())(1())(1()()(
Chapter 6 Algorithm design 115
procedure used is given in Section 6.3.2, and the full MATLAB code is provided
in Appendix D.
6.3.1 Validating ARAISS
A fundamental problem in designing an algorithm to solve infrastructure site
selection problems is that there is often no perfect solution to find, and it is not
always possible to derive the best compromise from initial assessments. Using a
pre-determined optimization algorithm is standard procedure in many areas of
problem solving, and works particularly well when the exact utility of a solution
can be precisely measured and used as feedback to improve performance.
However the exact utility of a solution in site selection is seldom known.
Multiple, conflicting criteria, and the added human element of conflicting
opinions of measurement and importance create an ill-structured problem that is
often dynamic, in that assessments may change as the solution space is
examined. It is also relevant to note that problem-solving strategies vary from
person to person, making the group situation a particularly dynamic environment.
In such a climate the traditional model of testing a new algorithm against known
others using standard test data and set benchmarks becomes obsolete.
In the case of ARAISS a second major hurdle is the absence of a standard dataset
with which to generate results and compare those results to known solutions.
Datasets used in other published work on multi-criteria site selection either lacks
multiple decision-maker inputs, or uncertainty data. In fact due to the unique
approach of ARAISS, which requires decision-makers to weight output
parameters not generated by other methods, comparative testing is challenging
from the outset.
It is proposed in this research that the best method of assessment for a site
selection algorithm is to use real world examples, where the algorithm can be
utilized in an actual decision-making situation, and the decision-makers
themselves can assess its performance and usefulness. This approach was taken
with ARAISS after its implementation in a GIS based SDSS, and is detailed in
Chapter 8.
Chapter 6 Algorithm design 116
6.3.2 Example simulation
Several MATLAB simulations were conducted to test the common sense validity
of the algorithm, and the following example is typical of the process used. The
problems were all based on three decision-makers rating five alternatives with
respect to three criteria. The term set used was shown in Table 6.1, and Figure
6.2.
Abbreviations used in the example problem are as follows:
Suitability
Totally Unsuitable (TU), Bad (B), Indifferent (In), Good (G), Perfect (P).
Uncertainty
Very Certain (VC), Certain (C), Moderately Certain (MC), Uncertain (U), Very
Uncertain (VU).
Weights
Irrelevant (Ir), Unimportant (U), Moderately Important (MI), Important (Im),
Critical (C).
Term Generation and Feedback
Zero (Z), Very Small (VS), Small (S), Moderate (M), Large (L), Very Large
(VL).
Other Abbreviations
Decision-maker-n (DMn), Criterion-n (Cn), Alternative-n (An) (n= 1,2,3,…).
Chapter 6 Algorithm design 117
6.3.2.1 Data inputs
Decision-maker relevance values for the example problem are given in Table 6.2,
and decision-maker preferences for weighting and rating are provided in Tables
6.3-5.
Table 6.2: Decision-maker relevance
C1 C2 C3 DM1 U C U DM2 Im Im Im DM3 Im Im MI
Table 6.3: Decision-maker 1 inputs
Alternative C1 C2 C3 A1 In, C G, VC In, MC A2 In, VU G, U In, VU A3 P, C P, C B, MC A4 P, C B, VC P, MC A5 TU, MC B, MC B, MC
Wgts MI C Im
Table 6.4: Decision-maker 2 inputs
Alternative C1 C2 C3 A1 G, VC In, C G, MC A2 G, U In, U In, MC A3 P, MC P, C B, MC A4 P, VU P, U P, C A5 B, MC TU, MC B, MC
Wgts I I I
Chapter 6 Algorithm design 118
Table 6.5: Decision-maker 3 inputs
Alternative C1 C2 C3 A1 G, VC G, VC In, C A2 In, VU In, VU G, VU A3 P, C P, VC TU, MC A4 P, VC G, C P, C A5 TU, MC TU, MC B, MC
Wgts C Ir MI
6.3.2.2 Results
Inputs were aggregated according to the ARAISS decision rules given in Section
6.2.6. Figure 6.3 shows outputs from the fuzzy weighted combination of
Equation 6.15 after normalisation. Table 6.6 shows output parameter values and
the results of an adjusted aggregation in linguistic form.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
μ
Suitability
A1A2A3A4A5
Figure 6.3: Fuzzy outputs from Equation 6.15
Chapter 6 Algorithm design 119
Table 6.6: Final outputs in linguistic form
Alternative Utility Uncertainty Risk Conflict Overall Rank
A1 G MC Z M G 2 A2 I VU Z Z G 1 A3 G VC VL M I 3 A4 G VC M L I 4 A5 B MC VL M B 5
Wgts MI VU U I
6.3.2.3 Interpretation
Results from the example problem show that the algorithm performs as expected.
Alternative 1 scores well in terms of Utility Uncertainty and Risk but exhibits a
‘Moderate’ level of conflict, which brings it’s overall rank back to second, as
consensus is considered ‘Important’ in a solution. Alternative 2 scores well on all
counts but uncertainty, but as uncertainty is considered ‘very unimportant’ in this
example Alternative 2 is ranked as the best option. Alternative 3 suffers from a
‘Very Large’ risk level, Alternative 4 from conflict, and Alternative 5 is rates
poorly on all counts.
6.4 Conclusions
This chapter has described the ARAISS site selection algorithm and
demonstrated its use in an example problem. The algorithm works by accepting
linguistic inputs from a set of decision-makers, which are brought together in a
fuzzy double-weighted aggregation. Four parameters are then extracted for each
alternative, indicating levels of Utility, Safety, Consensus, and Certainty.
Weighting and combining the four parameters enables decision-makers to decide
which aspects of the solution are most important to their specific problem, and
thereby delivers a real means of control over algorithm performance.
Chapter 6 Algorithm design 120
In a mathematical sense ARAISS is a hybrid algorithm, combining elements of
fuzzy set theory and multicriteria evaluation in a new way. ARAISS can generate
compensatory and non-compensatory solutions, in a multi-decision-maker
framework, but remains computationally efficient enough to use in problems
with large numbers of alternatives.
The algorithm performed as expected in example problems, delivering sound
results in a five alternative, three decision-maker problem with simulated inputs.
Potential solutions were successfully differentiated on the basis of utility,
certainty, risk, and conflict, and the ultimate solution was chosen based on a
weighted summation of these parameters.
Chapter 7 InfraPlanner 121
Chapter 7
IINNFFRRAAPPLLAANNNNEERR
7.1 Introduction
InfraPlanner is a prototype Spatial Decision Support System (SDSS) designed to
aid decision-makers with Group Multicriteria Location Problems. The system
was designed primarily to implement a spatial decision-making model based on
the ARAISS algorithm described in Chapter 6, however the choice of model is
only one aspect of a SDSS. The construction of a SDSS is a complex task
involving ongoing consultation with end users and other experts, and there are
many possible alternatives for interface design and model integration, as well as
data format and handling issues. Facing these problems has previously been
identified as an important area for ongoing research (Goodchild, Haining et al.
1992), and will continue to provide challenges for some time, to those adapting
new methods to a GIS environment (Scholten and LoCascio 1997). Pressing
spatial problems cannot wait for new technologies to arrive, and developers
should adapt technologies available now, to suit their particular situation (Lam
and Swayne 2001).
The InfraPlanner prototype SDSS is a ‘Fully Integrated’ system that consists of a
set of tools integrated into ArcGIS software. ArcGIS is an umbrella term for a
rage of GIS applications furnished by the Environmental Systems Research
Institute (ESRI). The primary application used in development of InfraPlanner
was the ArcMap module, which is a vector and raster GIS package. ArcMap is
provided in a subset of ArcGIS products collectively referred to as ArcView. The
system was created by developing new tools in ArcMap using Visual Basic for
Applications (VBA) customisation.
InfraPlanner is a ‘Knowledge Based’ system, in that it provides both access to
data, and facilities for manipulating that data via a set of rules. It was designed to
Chapter 7 InfraPlanner 122
allow the input and analysis of data and expert knowledge during a group
multicriteria site selection process, and it is possible to utilise the system in the
following ways within that context:
• Data storage and visualisation via the standard GIS database and interface
• Information sharing and consultation via generation of linguistic suitability
maps
• As a scenario analysis tool for use by planners and decision-makers in
isolation via a fuzzy multicriteria evaluation analysis
• Group site selection analysis via a group fuzzy multicriteria evaluation
analysis
This Chapter describes InfraPlanner in its entirety. Firstly a general system
overview is provided, the development process followed is then described, and
finally the use and functionality of the system are illustrated.
7.2 Overview of the prototype system
The core attributes of the prototype system fall into five categories as shown
below. These five aspects of the system are described in the following five
Sections.
1. Target application
2. Target audience
3. Dialog design
4. Database
5. Model
7.2.1 Target application
The term ‘target application’ describes the problem or types of problems the
system was designed for. Definition of a target application provides focus for the
development process, and makes designing evaluation problems possible.
Chapter 7 InfraPlanner 123
The InfraPlanner system was intended as a generic site selection tool capable of
providing support for Group Multicriteria Location Problems (GMCLP’s) of a
strategic nature. These site selection problems are characterised by multiple
decision-makers, multiple criteria, a large number of spatial alternatives and
uncertainty. The target application of the prototype system can be stated more
specifically by the following sentence:
‘To provide a tool to aid in the solution of strategic GMCLP’s within the 2700 ha
Brisbane Airport site’
7.2.2 Target audience
Experience has shown that a SDSS needs to be targeted to a specific audience to
be successful, as the system will eventually become redundant if there is no
potential or actual user in mind (Lam and Swayne 2001). Targeting specific users
was also useful for identifying the most important individuals for ongoing
consultation.
The InfraPlanner system is targeted at strategic decision-makers from either
technical or non-technical backgrounds. The prototype system, focused on
Brisbane Airport, has a target audience consisting of:
Those working at Brisbane Airport in Management, Planning, Infrastructure
Development, and Environmental management.
7.2.3 Dialog design
The dialog, or interface, provides users with the means to interact with the
system, and needs to be as user-friendly as possible. Experience has shown that a
user-friendly interface should possess both simplicity and flexibility.
Chapter 7 InfraPlanner 124
Simplicity has been consistently cited as a desirable quality in a SDSS interface,
and there are several basic qualities a simple user interface should possess,
including (Sparkman 1999):
• Consistent, clearly understood screen layout
• Readable font size
• Shallow menu hierarchy
• A plan for programmatically guiding the user
• Limited information display at one time
• Status updates
• Online help
Specific strategies beyond the basic rules of good software design are seldom
given in research literature. Two further strategies employed in the development
of InfraPlanner to enhance the ease and simplicity of user interaction were:
1. Linguistic interaction
As discussed in Chapter 3, it is natural and intuitive for humans to interact using
words. InfraPlanner was designed to be a fully linguistic system where inputs
and feedback are given in natural language. The interfaces were specifically
designed to accommodate vague linguistic statements such as ‘I am moderately
certain that 100 metres from the waterway is good’ and thereby avoid asking
decision-makers to undertake complex mathematical procedures.
2. Heavy use of the existing GIS interface
GIS is a mature technology and the methods standard GIS packages employ to
view and interact with spatial information are the result of an ongoing process of
refinement dating back to the 1960s. Instead of expending resources producing
an entirely new interface, InfraPlanner was conceived as a set of tools that
enhance existing GIS functionality where necessary, whilst retaining the highly
effective aspects of the standard interface. This was achieved by incorporating
Chapter 7 InfraPlanner 125
InfraPlanner tools into a toolbar that integrates seamlessly with existing
functionality.
Flexibility was also a requirement of the user interface. This implies that the
system should be suitable for the full range of site selection decisions possible,
with respect to the target audience. InfraPlanner incorporates two strategies to
increase flexibility:
1. Provision of a comprehensive generic database covering a range of spatial
information within the primary area of interest
2. Use of a generic multicriteria evaluation model
7.2.4 Database
The foundation of any SDSS is its data, and a comprehensive database was
compiled for the prototype system. The geographical area of interest was defined
as all areas within the Airport boundary plus significant sites bordering on the
Airport. The accumulated data provided the basis for defining criteria falling into
three main categories:
1. Environmental Impact
2. Cultural impact
3. Operational issues (general airport planning rules)
Whilst it is unrealistic to plan for all possible data needs, the following themes
provided a sound basis for many site selection problems.
• Flora
• Fauna
• Habitat value
• Topography
• Land use and zoning
• Cultural heritage sites
Chapter 7 InfraPlanner 126
• Contaminated sites
• Airport facilities including all buildings, roads, taxiways and runways
• Environmentally sensitive areas such as waterways
• Nearby residential communities
The real value of data is made apparent when it is combined with expert
knowledge and processed into useful information. This is accomplished by a
decision-making model.
7.2.5 Model
The Approximate Reasoning Algorithm for Infrastructure Site Selection
(ARAISS) described in Chapter 6 provides the ability to combine raw data
themes in accordance with decision-maker knowledge. It is a generic multi-
criteria group decision-making model capable of application to a wide variety of
strategic site selection decisions. The model is accessed via a set of user forms
designed to aid the various stages of the decision-making process.
The model accepts two types of inputs:
1. Data: in the form of pre-processed raster layers indicative of the spatial
variation of a raw attribute value.
2. Knowledge: in the form of linguistic assessments from decision-makers.
The model outputs raster layers of the same resolution as input layers, attributed
and colour coded to match terms in the linguistic term set used in decision-maker
input. Two basic types of raster layers may be created.
1. Criterion Maps: Criterion Maps are the InfraPlanner equivalent of a
suitability map, with the added element of uncertainty. They are a raster map
identifying the spatial variation of the suitability and uncertainty of one
attribute, according to one decision-maker. Criterion Maps are created from a
data layer indicative of the spatial variation of the raw value of an attribute
Chapter 7 InfraPlanner 127
(eg. a zoning map) by associating the raw attribute value with the suitability
and uncertainty values provided by the decision-maker. Criterion Maps can
be either discrete (based on a categorical variable such as land ownership) or
continuous (based on a variable that covers a continuous range of values such
as proximity from a feature).
2. Decision maps: Decision Maps are created by combining criterion maps with
decision-maker preferences for criterion weights, decision-maker relevance,
and output parameter weights. Decision maps describe the spatial variation of
aggregated site parameters: Suitability, Uncertainty, Risk, and Conflict. The
output of an adjusted aggregation (whereby Suitability, Uncertainty, Risk,
and Conflict are weighted and combined) is also a type of decision map.
The specifics of how InfraPlanner accomplishes this are given in Section 7.4.
7.3 Development process
Development of the InfraPlanner system followed a simplified evolutionary
prototyping structure, conducted in close consultation with end users. The
process consisted of a logical sequence of activities, which was documented via a
logic model.
Logic modelling is a resource management tool used to document the underlying
reasons and goals behind a program of activities. In a logic model the program is
divided into six elements.
1. Resources are the raw materials available
2. Activities make use of the available resources
3. Outputs are the tangible results of an activity
4. Customers are those who receive the outputs
5. Outcomes (Short, medium, or long term) are the reason for undertaking the
activities
6. External influences are those influences that are beyond the scope and control
of the program
Chapter 7 InfraPlanner 128
The logic model detailing the sequence of events involved in the InfraPlanner
development process was shown in Figure 1.2. The main activities in the model
are described in Sections 7.3.1 – 7.3.5.
7.3.1 Planning
The planning phase of system development was conducted in conjunction with
end users from BAC. A needs assessment and problem diagnosis was conducted
to define the goals of the system, and to determine the types of decisions the
system would provide assistance with. Goals defined in the planning phase
included:
• The system should support decision-makers with facilities placement
decisions within the Brisbane Airport grounds
• The system should be able to accommodate qualitative variables such as
socio-economic and environmental impacts
• The system should accommodate multiple criteria and points of view
• Outputs from the system should be graphical where possible, preferably in a
mapping format
• The modelling capabilities of the system should be transparent and easily
understandable
These basic statements of desired functionality were then used as the focus for a
state of the art literature review.
Chapter 7 InfraPlanner 129
7.3.2 Research
The research phase consisted of a state of the art review of published literature on
the techniques and technology involved in spatial decision-making. Specifically,
the review was conducted to answer the following research questions:
1. What are the analytical techniques used in the solution of multicriteria
location problems?
2. What are the major limitations of these techniques?
3. What technology platforms are used in the analysis of spatial problems and
what are their major characteristics?
4. What are the most promising methods for advancing current techniques and
technologies?
The review clearly showed that Multi-criteria evaluation (MCE) is the most
suitable analytical technique for the solution of multicriteria location problems.
However several shortcomings were noted. Most important of these are the
inability to deal with uncertainty, inability to deal with a group environment, and
the perception by decision-makers that current methods are not user friendly. The
universally accepted technology platform for the analysis of location problems
was found to be a GIS, coupled or fully integrated with decision-making models.
Advanced Artificial Intelligence and soft computing techniques offered an ability
to overcome some of the shortcomings of MCE, but it was necessary to deploy
them in a user friendly way in order to avoid the perception of a ‘black box’
scenario.
7.3.3 Analysis and design
The primary objective of the design phase was to produce a clear conceptual
system design specification based on outputs from the planning and research
phases, and to provide input from technology experts. This phase produced two
major outputs.
Chapter 7 InfraPlanner 130
1. A new fuzzy model for the type of location problems encountered by
decision-makers at Brisbane Airport and strategic decision-makers in
general. The model is described in Chapter 2.
2. A design specification for the prototype system to implement the new
model. The key objectives contained in the design specification are
summarised in Section 7.2, and the working system is fully described in
Section 7.4.
7.3.4 Construction
Construction covered the technical implementation of the design. In the case of
InfraPlanner construction consisted of integrating the new fuzzy decision-making
model into the selected GIS package. Technology selection was a vital aspect of
the design process as the capabilities of the chosen GIS package have a
significant impact on functionality, compatibility and development time. The
three key aspects considered when choosing among the many commercially
available systems were:
1. Level of raster analysis functionality
2. Customisation capabilities
3. File format compatibility
ArcGIS was eventually chosen, as it possessed comprehensive raster analysis
functionality, offered an inbuilt Visual Basic for Applications (VBA)
customisation environment, and was compatible with the existing MicroStation
CAD software employed by Brisbane Airport.
ArcGIS customisation is based around the manipulation of a set of
programmatically controllable software objects collectively referred to as
ArcObjects. ArcObjects offered access to the objects that make up ArcGIS
software at a high enough level of granularity to be a flexible and effective
development tool. Construction mainly focused on development of the set of user
forms described in Section 7.4. The forms offer an intuitive visual way to
Chapter 7 InfraPlanner 131
implement the algorithm described in Chapter 6. Source code is provided in the
Appendix.
7.3.5 Implementation
Implementation consisted of testing and evaluation of the system. InfraPlanner
was tested and evaluated using a real world site selection problem faced by the
Brisbane Airport. Brisbane Airport planners, regulators, and external consultants
were involved in the validation problem, which involved the location of a new
recycling facility on the Airport site. The validation problem is fully described in
Chapter 8.
7.4 The InfraPlanner prototype
InfraPlanner consists of a set of tools designed to implement ARAISS in a GIS
environment. The tools are accessed in ArcMap via the InfraPlanner Toolbar
shown in Figure 7.1.
Figure 7.1: The InfraPlanner toolbar
Tools available from the InfraPlanner Toolbar are structured as follows:
Project Tools:
Select project: A user form to select a stored decision project
Create new project: A user form to create a new decision project
View project Information: A user form listing current project options
Create Maps:
Chapter 7 InfraPlanner 132
Criterion map: User forms to create discrete or continuous
suitability maps from a raster map
Decision maps: A user form to bring suitability maps together in an
aggregation and create output parameter maps
Format Map:
Tools to format existing raster maps with numeric attribute values into a
suitability, utility, risk, uncertainty or conflict map. The transformation is purely
visual not analytical, and is not discussed further here.
Explore Maps:
A point and click tool to be used to interactively explore selected locations and
all their outcomes, or to perform an adjusted aggregation.
InfraPlanner tools are used to follow the general framework of ARAISS, as
shown in Figure 7.2. Specific tools are described in the following Sections.
Chapter 7 InfraPlanner 133
Figure 7.2: How InfraPlanner tools fit into the decision-making framework
Define problem
Define decision-maker relevance values
Perform compensatory aggregation function
Identify feasible alternative sites for analysis
Choose Decision-makers
Identify Utility, Certainty, Safety and Consensus
Final site selection
Define and rate criteria (factors)
Start
Define linguistic terms
Tactical and operational assessment
Micro-placement
Identify constraints
Explore and reduce alternatives
Iterate
Perform adjusted aggregation
Define criterion weights
Review inputs
Chapter 7 InfraPlanner 134
7.4.1 Project tools
The project tools are used to specify and view the type of decision, decision-
makers involved, criteria, and the linguistic term set used for input and feedback.
Setting the project information is shown in Figure 7.3.
Figure 7.3: Setting project information
Chapter 7 InfraPlanner 135
Inherent in the input of project information is choosing a linguistic suitability
term set to be used for decision-maker input and feedback. InfraPlanner creates
new term sets by using Equation 6.1 to add new terms to a set of primary
suitability terms as shown in Figure 7.4. The user first chooses the primary term
set to build on and then uses the ‘Create Term Set’ user form to follow the
process described in Section 6.2.3.
Figure 7.4: Creating a new term set
7.4.2 Creating maps
InfraPlanner provides the ability to create several types of maps used in the
decision-making process. In most cases some pre-processing is required to
provide the system with suitably classified raster input maps. Pre-processing is
performed using standard Map Algebra techniques, such as those described in
Chapter 7 InfraPlanner 136
Chapter 2. Pre-processing usually consists of converting a vector map to raster
format, or performing a proximity function to create a raster map indicative of
distance from some feature. Boolean constraint maps are also created using
standard map algebra techniques.
7.4.2.1 Suitability Maps
Suitability maps are either based on discrete (categorical) variables such as
regional zoning, or a variable that takes a continuous range of values, such as
elevation. Discrete suitability maps are created using the form shown in Figure
7.5. Users specify a source theme containing the pre-processed baseline data for
the suitability map and classify the categories it contains using linguistic
suitability and uncertainty assessments.
Figure 7.5: The discrete criterion map user form
Users interact with the discrete criterion map user form as shown in Figure 7.6.
Chapter 7 InfraPlanner 137
Figure 7.6: Creating a discrete criterion map.
Name the new Criterion Map to be created
Load the Discrete Criterion Map form from the InfraPlanner Toolbar
Input a description of the map
Select a source theme from the pre-screened list of discrete source maps
Choose the attribute field within the map to linguistically classify.
Rate each attribute category in terms of suitability and uncertainty
Click the ‘Create’ button to create the new map
The new criterion map is created and displayed
Chapter 7 InfraPlanner 138
Continuous criterion maps are created using the form shown in Figure 7.7. Users
specify points along the domain of the source variable and rate them with
linguistic suitability and uncertainty values. Points whose values lie between the
rated points are classified according to equations 6.6 – 6.9, which is essentially a
linear extrapolation of the centre point, and support of the TFN.
Figure 7.7: The continuous criterion map user form
Users interact with the continuous criterion map user form as shown in Figure
7.8.
Chapter 7 InfraPlanner 139
Figure 7.8: Creating a continuous criterion map
Name the new Criterion Map to be created
Load the Continuous Criterion Map form from the InfraPlanner
Toolbar
Input a description of the map
Select a source theme from the pre-screened list of continuous source maps
Rate a minimum of 3 points in terms of suitability and uncertainty
Click the ‘Create’ button to create the new map
The new criterion map is created and displayed
Chapter 7 InfraPlanner 140
7.4.2.2 Decision Maps
The term ‘Decision Maps’ is a generic term used within InfraPlanner to describe
the four aggregated parameter maps (Suitability, Uncertainty, Risk and Conflict).
Using the ‘Create Decision Maps’ user form, users associate a previously created
criterion map with each criterion and decision-maker in the chosen decision
project. They also input the decision-maker relevance and criterion weighting for
each criterion in the decision project, with respect to each decision-maker. The
output parameter maps are created by an aggregation of the criterion maps, using
equations 6.15 – 6.22. The ‘Create Decision Maps’ user form is shown in figure
7.9, and user interaction is illustrated in Figure 7.10.
Figure 7.9: The decision maps user form
Chapter 7 InfraPlanner 141
Figure 7.10: Creating decision maps
Name the new Decision Maps to be created.
Load the Decision Map user form from the InfraPlanner
Toolbar
Choose a constraint map to limit the area under consideration
Enter the Decision-maker relevance value for the displayed decision-maker
Click the ‘Add‘ button when the inputs are correct to cycle to the next set of inputs and repeat the previous step.
Click the ‘Create’ button when all inputs have been entered to create the new maps
The new parameter maps are created and
displayed
Enter the weight of the displayed criterion according to the displayed decision-maker.
Choose the previously created criterion map that represents the displayed criterion according to the displayed decision-maker.
Chapter 7 InfraPlanner 142
7.4.3 Exploring maps
Map exploration is facilitated using a point and click tool that allows users to
examine any feasible alternative site in all its dimensions. An interactive report is
displayed which provides information on the four output parameters plus
individual criterion outcomes and provides an opportunity to set the weighting
parameters for an adjusted aggregation. Map exploration is shown in Figure 7.11.
Figure 7.11: Map exploration
Chapter 7 InfraPlanner 143
7.5 Validating InfraPlanner
Validation of the working prototype was conducted in three ways. The first and
most valuable means of validation was the case study presented in Chapter 8.
Results showed that users found InfraPlanner simple to use and understand, and
selected sites that they deemed acceptable. Secondly a peer reviewed paper was
presented at ANZIIS 2003 as shown in Appendix A. Lastly a focus group was
created at ANZIIS 2003, consisting of five researchers and academics from the
fields of AI and soft computing. A combination of discussion paper and
questionnaire was created for the group, and is reproduced in Appendix E. The
focus group was given the discussion paper after the presentation of the
conference paper, and asked for their feedback on the algorithm and the
methodology used to create it. The group exercise quickly took the form of a
vigorous discussion, in which feedback was positive, with all present agreeing
that both the development process and the model derived from it was valid. Some
researchers noted that the use of a software design flowchart would be a good
way to represent the model, as they found the logic model used difficult to
follow. Other specific comments included:
• Documentation of the model should be in a commonly accepted, and easily
understandable format
• Users need to be able to understand the impacts of preferences given during
the data input phase
• Use of fuzzy numbers to represent words is valid, but needs more work in
terms a rigorous and repeatable way of defining the membership function
• Users should understand how the model works
• The concept of weighting inputs based on relevance of opinion was valid but
a rigorous way of generating the relevance matrix was needed
7.6 Discussion
InfraPlanner is a working prototype of a generic SDSS for GMCLP’s of a
strategic nature. The system demonstrates that approximate reasoning techniques
are suitable for use in SDSSs, although designing and building the InfraPlanner
Chapter 7 InfraPlanner 144
Spatial Decision Support System proved to be extremely challenging.
Constructing a DSS is generally considered to be a complex, time consuming
task, requiring a group of skilled individuals, and this was proven in practice.
There are many small issues that are not generic enough to be mentioned in
publications on SDSSs but nonetheless proved problematic. Among these were
choosing a GIS package from the myriad of options available, and dealing with
the organisational changes that occurred during the development process.
Chapter 8 A case study using InfraPlanner 145
Chapter 8
AA CCAASSEE SSTTUUDDYY UUSSIINNGG IINNFFRRAAPPLLAANNNNEERR
8.1 Introduction
Validation of any proposed algorithm requires a practical implementation to test
assumptions made during the design process. In many cases there exists a set or
sets of standard real world data and solutions upon which to compare the
accuracy of a given algorithm. In the case of site selection decisions under the
types of uncertainty discussed in Chapter 5, there appears to be no standard
dataset that incorporates all the variables used as inputs to the InfraPlanner
algorithm. Specifically there is no dataset that includes subjective uncertainty
assessments from multiple decision-makers, and preferences for decision-maker
relevance, or the priorities placed on the four output parameters; Utility,
Certainty, Consensus, and Safety. To overcome this data shortage problem an
experiment was conducted using a real world site selection decision at Brisbane
Airport, where the desired inputs and outputs could be generated and commented
upon by decision-makers themselves.
Inputs were generated for three stakeholder groups using actual decision-makers
or representatives chosen by the experimenter for their knowledge of the
situation. The problem used was real, and the objective was to choose the best
location for a recycling facility on the 2700 ha Brisbane Airport site. This chapter
details the problem and all inputs, the process used to implement InfraPlanner in
deriving solutions, the results generated, and a discussion of their relevance.
Chapter 8 A case study using InfraPlanner 146
8.2 The problem
The problem worked through here concerns the location of a new recycling
facility on the Brisbane Airport grounds. The Airport occupies 2700ha of land,
located 13km North East of the Queensland State Capital, Brisbane, and
adjoining Moreton Bay. The site is flat and low lying, occupying part of the
original Brisbane river delta, which has undergone extensive changes since the
1830s, with most of the original network of tidal waterways being replaced with
constructed drains. Much of the vegetation on the site has been planted in the last
15 years, and was chosen to reduce the attraction of birds. There are, however,
some environmentally sensitive areas to consider when locating new
developments, as well as issues associated with airport facilities, Government
legislation and the effects of airport operations on local communities. Figure 8.1
shows the general layout of the Brisbane Airport site.
The facility to be located inputs masonry from demolished buildings and, via
crushing and grinding, turns out various grades of landfill material. The main
impacts of such an operation on its immediate vicinity are noise and dust
emissions. There are three separate groups with an interest in the outcome. The
Brisbane Airport Corporation (BAC), as represented by their Infrastructure
Planning Manager. The Commonwealth Government, as represented by an
independent contractor responsible for ensuring regulatory compliance, and a
local residential community adjoining the Airport, whose inputs were provided
by an Airport representative with knowledge of their concerns. The groups differ
considerably in their priorities and suitability assessments, creating a rich
decision-making environment.
Chapter 8 A case study using InfraPlanner 147
Figure 8.1: Brisbane Airport Layout
Chapter 8 A case study using InfraPlanner 148
8.3 Procedure
The experiment was structured to follow the decision-making framework shown
in Figure 6.1, with the first steps involving problem definition and definition of a
linguistic term set. The linguistic terms used are shown in Table 8.1.
Table 8.1: Linguistic terms
Suitability (as a TFN) Weighting Uncertainty Term generation
Totally unsuitable
(0,0,0) Irrelevant 0
Very Certain 0 Zero 0
Bad (0,.2,.4) Unimportant .3 Certain 1 Very Small .1 Indifferent (.3,.5,.7) Moderately
Important .5 Moderately
Certain 2 Small .3
Good (.6,.8,1) Important .7 Uncertain 3 Medium
.5
Perfect (1,1,1) Very Important
.9 Very Uncertain
4 Large .7
Probably Good*
(.51,.71,.91) Critical 1* Very Large .9
• The suitability term ‘probably good’ was included as a ‘large’ increase on ‘indifferent’ at the request of decision-makers..
With the problem defined as ‘selecting the best site for the recycling facility’, the
next step was to identify the constraints (Boolean criteria) that would limit the
sites under consideration. During an initial consultation a set of five constraints
was derived:
1. Airport Boundary: The site must lie within the existing airport
boundary to avoid the cost of land acquisition.
2. Existing Buildings: Sites already occupied should be excluded to avoid
the loss of existing facilities.
3. Road access: The site must be within 200m of selected access
roads to avoid the cost of building new access.
4. Zoning: The site must lie in a zone designated ‘General
Industry’ or ‘Light Industry’ as defined by the
BAC 1998 Master Plan to comply with
Government planning requirements.
Chapter 8 A case study using InfraPlanner 149
5. Conservation: The site must not occupy an area of high
conservation value, thereby preserving the
sensitive areas on the Airport grounds.
The map of unconstrained alternatives (available sites) was derived using map
algebra techniques, and is shown in Figure 8.2.
Chapter 8 A case study using InfraPlanner 150
Figure 8.2: Unconstrained Alternatives
Chapter 8 A case study using InfraPlanner 151
The next step in the process involved the definition and linguistic assessment of
criteria that vary on a suitability scale from ‘Totally Unsuitable’ to ‘Perfect’.
Decision-makers directly defined six criteria as important to the site selection
process. They are described in Table 8.2:
Table 8.2: Criteria definition
Criterion
Name
Type Description
Environmental value
Continuous As all areas of high conservation value are excluded, this criterion defines on a continuous scale how the distance from sensitive areas affects suitability
Zoning Discrete The facility may be placed in either a ‘General Industry’ or ‘Light Industry’ zone, and this criterion describes how that decision affects suitability.
Tenant Amenity Continuous Defines how the distance from sensitive tenants affects suitability
Community Impact
Continuous Defines how distance from the closest residential community affects suitability
Landfill Discrete It would be desirable to locate the facility close to areas which are more in need of the fill material generated by the facility.
Traffic Impact Discrete To regulate traffic flow and trucking noise, the use of some access roads is more desirable than others.
These criteria are represented as a set of suitability maps, created using
InfraPlanner interfaces to convert linguistic inputs from each decision-maker to a
spatially explicit format, as shown in Figures 8.3 and 8.4.
Chapter 8 A case study using InfraPlanner 152
Figure 8.3: Creating a continuous suitability map for community impact
Figure 8.4: Creating a discrete suitability map for zoning
Chapter 8 A case study using InfraPlanner 153
Each decision-maker will generate a map for each criterion for which their
opinion is deemed to be sufficiently relevant to include, and which they feel to be
relevant to the decision environment. Thus a decision-maker may opt out of
generating a map on the grounds of lack of expertise or if they feel that a
particular criterion should have no effect upon overall suitability. The inputs
required to generate the maps take the form of sentences, from which the relevant
information is input to the suitability map generation interface. Inputs were as
follows:
BAC Inputs:
Environmental value is ‘important’: It is ‘moderately certain’ that sites
of moderate conservation value are ‘good’ whilst it is ‘very certain’ that
all others are ‘perfect’.
Zoning is ‘very important’: It is ‘very certain’ that general industry zones
are ‘perfect’ whilst it is ‘moderately certain’ that light industry zones are
‘good’.
Tenant Amenity is ‘important’: It is ‘very certain’ that sites less than
50m from sensitive tenants are ‘totally unsuitable’. It is ‘moderately
certain’ that sites 100m from sensitive tenants are ‘good’. It is ‘certain’
that sites 500m from sensitive tenants are ‘perfect’.
Community Impact is ‘important’: It is ‘very certain’ that sites less than
500m from Pinkenba are ‘totally unsuitable’. It is ‘uncertain’ that sites
1000m from Pinkenba are ‘good’. It is ‘very uncertain’ that sites 2000m
from Pinkenba are ‘perfect’, and ‘certain’ that sites 4000m from Pinkenba
are ‘perfect’.
Landfill is ‘moderately important’: It is ‘very certain’ that sites on
Lomandra Dr are ‘perfect’. It is ‘moderately certain’ that sites on Randle
Rd, Sugarmill Rd and Viola Pl are ‘good’. It is ‘moderately certain’ that
sites on Airport Dr are ‘indifferent’.
Chapter 8 A case study using InfraPlanner 154
Traffic impact is ‘important’: It is ‘very certain’ that sites on Airport
Drive are ‘bad’. It is ‘moderately certain’ that sites on Lomandra Drive
and Viola Pl are ‘good’. It is ‘certain’ that sites on Randle Road and
Sugarmill Rd are ‘perfect’.
Community Inputs:
Environmental value is ‘very important’: It is ‘moderately certain’ that
sites of moderate conservation value are ‘probably good’ whilst it is
‘certain’ that all others are ‘perfect’.
Zoning is ‘irrelevant’:
Tenant Amenity is ‘irrelevant’:
Community Impact is ‘critical’: It is ‘very certain’ that sites less than
2000m from Pinkenba are ‘totally unsuitable’. It is ‘uncertain’ that sites
3000m from Pinkenba are ‘good’. It is ‘certain’ that sites 4000m from
Pinkenba are ‘perfect’.
Landfill is ‘irrelevant’:
Traffic impact is ‘important’: It is ‘very certain’ that sites on Airport
Drive are ‘perfect’. It is ‘certain’ that sites on Lomandra Drive and Viola
Pl are ‘bad’. It is ‘very certain’ that sites on Randle Road and Sugarmill
Rd are ‘bad’.
Government Inputs:
Environmental value is ‘important’: It is ‘moderately certain’ that sites
of moderate conservation value are ‘good’ whilst it is ‘very certain’ that
all others are ‘perfect’.
Chapter 8 A case study using InfraPlanner 155
Zoning is ‘very important’: It is ‘very certain’ that general industry zones
are ‘perfect’ whilst it is ‘moderately certain’ that light industry zones are
‘good’.
Tenant Amenity is ‘important’: It is ‘very certain’ that sites less than
50m from sensitive tenants are ‘totally unsuitable’. It is ‘moderately
certain’ that sites 100m from sensitive tenants are ‘good’. It is ‘certain’
that sites 500m from sensitive tenants are ‘perfect’.
Community Impact is ‘important’: It is ‘very certain’ that sites less than
500m from Pinkenba are ‘totally unsuitable’. It is ‘uncertain’ that sites
1000m from Pinkenba are ‘good’. It is ‘very uncertain’ that sites 2000m
from Pinkenba are ‘perfect’, and ‘certain’ that sites 4000m from Pinkenba
are ‘perfect’.
Landfill is ‘moderately important’: It is ‘very certain’ that sites on
Lomandra Dr are ‘perfect’. It is ‘moderately certain’ that sites on Randle
Rd, Sugarmill Rd and Viola Pl are ‘good’. It is ‘moderately certain’ that
sites on Airport Dr are ‘indifferent’.
Traffic impact is ‘important’: It is ‘very certain’ that sites on Airport
Drive are ‘bad’. It is ‘moderately certain’ that sites on Lomandra Drive
and Viola Pl are ‘good’. It is ‘certain’ that sites on Randle Road and
Sugarmill Rd are ‘perfect’.
As illustrative examples, the suitability maps created by BAC and the residential
community representative for Traffic Impact and Community Impact are shown
in Figures 8.5, 8.6, 8.7 and 8.8.
Chapter 8 A case study using InfraPlanner 156
Figure 8.5: BAC Traffic Impact Suitability Map
Chapter 8 A case study using InfraPlanner 157
Figure 8.6: BAC Community Impact Suitability Map
Chapter 8 A case study using InfraPlanner 158
Figure 8.7: Traffic Impact Suitability Map for the Residential Community
Chapter 8 A case study using InfraPlanner 159
Figure 8.8: Community Impact Suitability Map for the Residential
Community
Chapter 8 A case study using InfraPlanner 160
It was then necessary to perform an aggregation using the criterion weightings,
relevance weights, and suitability maps previously created. Inputs are
summarised in Tables 8.3 and 8.4. The interface used is shown in Figure 8.9.
Table 8.3: Criterion weighting
Criterion Weights
BAC Pinkenba Commonwealth
Environmental
Important
Very
Important Important
Zoning Very Important Irrelevant Very Important
Tenant amenity Important Irrelevant Important
Pinkenba Important Critical Important
Landfill Moderately
Important Irrelevant
Moderately
Important
Traffic
Important Important
Moderately
Important
Chapter 8 A case study using InfraPlanner 161
Table 8.4: Decision-maker Relevance values
Criterion DM Relevance
BAC Pinkenba Commonwealth
Environmental Important
Moderately
Important Important
Zoning
Very
Important Irrelevant Very Important
Tenant amenity Important Irrelevant Important
Pinkenba Important
Very
Important Important
Landfill Important Irrelevant
Moderately
Important
Traffic Important Important
Moderately
Important
Chapter 8 A case study using InfraPlanner 162
Figure 8.9: Performing an aggregation
Chapter 8 A case study using InfraPlanner 163
8.4 Results
After all data was input the InfraPlanner aggregation interface was used to create
the following four maps:
1. A compensatory double-weighted aggregation (Utility)
2. Conflict assessment (Consensus)
3. Risk assessment (Safety)
4. Uncertainty assessment (Certainty)
The four parameter maps are shown in Figures 8.10 – 8.13.
Chapter 8 A case study using InfraPlanner 164
Figure 8.10: Utility
Chapter 8 A case study using InfraPlanner 165
Figure 8.11: Uncertainty
Chapter 8 A case study using InfraPlanner 166
Figure 8.12: Risk
Chapter 8 A case study using InfraPlanner 167
Figure 8.13: Conflict
Chapter 8 A case study using InfraPlanner 168
An adjusted aggregation based on decision-maker preferences for the importance
of minimizing conflicts risks and uncertainty, or maximizing compensatory
suitability was then performed to enable an adjusted overall suitability estimate
to be derived. To perform an adjusted aggregation it is necessary to weight the
four output parameters, and the following weightings were used to derive the
map shown in Figure 8.14:
Utility is ‘Very Important’
Risk is ‘Very Important’
Conflict is ‘Important’
Uncertainty is ‘Unimportant’
Using the output maps and some further analysis it was possible to identify the
sites of interest (in this case based on individual cells) as shown in Figure 8.15.
Examining the sites exposes the main difficulties behind the site selection task.
Decision-makers disagreed on the best site for the facility and there was also a
difference between the site with the best utility and the site with the best safety.
The sites identified as having the best consensus and certainty, were not viable
solutions in this case as decision-makers agreed with certainty that these sites
were unsuitable. This illustrated that the parameters are not suitable for use in
isolation and must be combined effectively to generate valid solutions. The
adjusted aggregation leaned towards the site with a good combination of all
factors.
Using the interactive exploration interface it was then possible to examine each
alternative comprehensively, and narrow down possible solutions by setting
minimum thresholds for any parameter. The interface offers the ability to view
the decision area as a regular map or use any of the derived raster maps. Clicking
on a particular location produces a natural language analysis in real time as
shown in Figure 8.16.
Chapter 8 A case study using InfraPlanner 169
Figure 8.14: Adjusted aggregation
Chapter 8 A case study using InfraPlanner 170
Figure 8.15: Sites of interest
Chapter 8 A case study using InfraPlanner 171
Figure 8.16: Alternative exploration
Unfortunately no location was completely satisfactory to all, and the primary
benefit gained from the system was the clear identification of the source of
conflict, which has become the subject of negotiation between parties.
8.5 Discussion
The nature of the site selection problem presented here is typical of many real
world situations. A fundamental problem in designing systems to solve such
problems is that there is often no universally accepted solution to find, and it is
not always possible to derive the best compromise from initial assessments. Most
GIS based decision-making methods assume that crisp numerical suitability
assessments can be processed according to a pre-determined algorithm to derive
a solution. However the complex nature of many site selection decisions make
such assumptions unrealistic. It was noted during the selection process that
decision-makers were reluctant to place their faith in a derived solution without
fully understanding how that solution was obtained. This creates a significant
Chapter 8 A case study using InfraPlanner 172
hurdle for system designers whose aim is to replicate, and by default replace, the
decision-making process.
Using a pre-determined optimisation algorithm is standard procedure in many
areas of problem solving, and works particularly well when the exact utility of a
solution can be precisely measured and used as feedback to improve
performance. However the exact utility of a solution in site selection is seldom
known. Multiple, conflicting criteria, and the added human element of
conflicting opinions of measurement and importance create an ill-structured
problem that is often dynamic, in that assessments may change as the solution
space is examined. It is also relevant to note that problem-solving strategies vary
from person to person, making the group situation a particularly dynamic
environment.
InfraPlanner was designed as an intelligent spatial decision support system to
provide decision-makers with relevant, understandable processed information,
whilst leaving them in control of the decision-making process. To this end it was
noted that decision-makers expressed satisfaction with outputs, as they enabled
the group to find the core elements behind their conflicting assessments. In a real
world situation, where political issues can dominate operational concerns, it is
often most beneficial to identify these core areas as they may be traded off for
concessions outside the sphere of the site selection task.
Giving decision-makers the ability to generate a variety of solutions that
maximized aggregated suitability or minimized risk, conflict and uncertainty
provided an easily understandable way for decision-makers to take more control
of the analysis, rather than accepting imposed heuristics. Moreover, whilst the
system makes computationally deriving a solution from input data possible, its
major strength was the high information value of outputs. The experiment
confirmed that a focus on a meaningful, interactive exploration of alternative
outcomes, as opposed to attempting to derive a solution from initial inputs, is a
valid way to support decision-makers in their task. Further specific feedback was
limited due to data privacy issues.
Chapter 8 A case study using InfraPlanner 173
There are some important limitations of the current ‘InfraPlanner’ system:
Firstly, the method used is limited to analysing problems with a single objective,
which makes it unsuitable for situations where multiple facilities are to be
located simultaneously or multiple land uses considered. Secondly, the use of
single cells as alternatives does not accurately represent the true size and spatial
configuration of a proposed development, which has been surprisingly seldom
noted (Brookes 1997). Lastly, utilizing linguistic terms for data input may
unnecessarily limit the accuracy of results in those cases where hard quantitative
data is available.
Another difficulty noted in the group situation was in the definition of criteria.
As an example, some decision-makers noted overlap in their perception of
community impact versus environmental impact. Some authors have described
multicriteria decisions, particularly those with multiple objectives, in terms of a
hierarchical structure, whereby some criteria encompass others, eg (Saaty 1980).
In a group situation this provides another area for disagreement and/or
misunderstanding.
The experience gained from the example at Brisbane Airport proved the validity
of an approximate reasoning approach to group site selection problems under
uncertainty. The InfraPlanner system enabled decision-makers to express their
assessments linguistically and receive meaningful linguistic feedback, whilst
taking more control of the process than other methods allow, and satisfaction
with outputs was expressed. The results also indicated a definite benefit from
utilizing a multi-decision-maker framework, as consensus was unattainable. An
emphasis on providing meaningful processed information, rather than offering a
heuristically derived solution was also found to be beneficial.
Chapter 8 A case study using InfraPlanner 174
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175Chapter 9 Conclusions
Chapter 9
CCOONNCCLLUUSSIIOONNSS
9.1 Introduction
This research has focused on the use of Approximate Reasoning to improve the
techniques and technology of spatial decision support in Infrastructure Site
Selection. A new Approximate Reasoning Algorithm for Infrastructure Site
Selection (ARAISS) was developed and implemented in a new Spatial Decision
Support System (InfraPlanner). The algorithm was then tested and validated in a
real world site selection problem at Australia’s Brisbane Airport.
This concluding chapter presents a final overview of the research presented in
this thesis. The activities conducted during the research program are summarised,
and the conclusions drawn are highlighted. Directions for future research are
suggested before finishing with a set of concluding remarks.
9.2 Summary of Results
The project was a combination of theory-focused research consisting of the
theoretical development of a new fuzzy site selection algorithm (ARAISS) and
design-focused research consisting of the practical application of the theory in a
new SDSS (InfraPlanner). Results are summarised in the following sections.
9.2.1 Answer to the Research Question
As stated in Chapter 1, the fundamental question behind this research was:
176Chapter 9 Conclusions
“Can Approximate Reasoning (AR) be integrated into a GIS based SDSS to
mitigate current difficulties with SDSSs utilised for Infrastructure Site
Selection?”
Results from hypothetical trial problems and a real world case study have clearly
shown that it is both possible and beneficial to integrate approximate reasoning
into a GIS based SDSS. As detailed in the following sections, ARAISS
performed well in both simulated and actual problems, in providing valid
solutions and supplementary information in a format that was easy for decision-
makers to use and understand.
9.2.2 Achievement of the Research Aims
The aim of this research was to create new knowledge at the intersection of
Physical Planning, Decision Science, Soft Computing, Decision Support and
Expert Systems, Geographical Information Systems, and Software Design.
This research has contributed to knowledge by showing that the integration of
AR and SDSSs is possible and putting forth one practical way to achieve it. A
new AR algorithm for site selection in GIS was devised, tested and implemented
in a real world case study. Both the algorithm and the outcomes of the case study
were separately peer reviewed and published at international conferences as
shown in Appendix A.
9.2.3 Achievement of the Research Objectives
Two specific research objectives were defined for this research:
1. Develop a practical infrastructure site selection algorithm based on an
Approximate Reasoning ‘linguistic’ approach.
2. Develop a new spatial decision support system based on the algorithm
developed in objective 1.
177Chapter 9 Conclusions
Objective 1 resulted in the creation of an Approximate Reasoning Algorithm for
Infrastructure Site Selection (ARAISS). ARAISS implements several concepts
that offer an improvement over current methodologies. The core capabilities of
ARAISS are its use of approximate reasoning to handle uncertainty, its multiple
decision-maker capability, its simplicity, and the way it hands over control to
decision-makers.
Objective 2 resulted in the creation of the InfraPlanner Spatial Decision Support
System. InfraPlanner is a prototype Spatial Decision Support System (SDSS)
designed to aid decision-makers with Group Multicriteria Location Problems. It
was created in ArcView GIS and is based on ARAISS.
9.3 Research Overview
The research was conducted in four phases as illustrated below:
1. Planning and Research:
• Needs assessment, problem diagnosis & definition of system
objectives.
• Review relevant literature and gather other information.
2. Analysis & Design:
• Conceptual design of the InfraPlanner system.
• Development of the decision-making algorithm.
3. Construction:
• Coding and debugging of the InfraPlanner prototype.
4. Implementation and Feedback:
• Testing and evaluation of InfraPlanner in a real world validation
problem.
• Critical assessment of the prototype and suggestions for future
improvements to the system.
178Chapter 9 Conclusions
The activities conducted and conclusions drawn from each of the phases are
summarised in the following sections.
9.3.1 Planning and research
Planning and research consisted of a set of increasingly targeted critical literature
reviews. The initial review found that Multicriteria Evaluation (MCE) was
currently the dominant analytical technique used in the solution of infrastructure
site selection problems. Several shortcomings were noted with current MCE
techniques, and most important of these were the inability to deal with
uncertainty, inability to deal with a group environment, and the perception by
decision-makers that current methods are not user friendly.
The universally accepted technology platform for the analysis of location
problems was found to be a Geographical Information System (GIS), coupled or
fully integrated with decision-making models. Advanced artificial intelligence
(AI) and soft computing techniques offered an ability to overcome some of the
shortcomings of MCE, but it was necessary to deploy them in a user friendly way
in order to avoid the perception of a ‘black box’ system. A ‘black box’ system
occurs when users have little or no understanding or control of an analysis
beyond the input of data and knowledge, and it was found that systems based on
current advanced AI techniques often fall within this category.
Approximate reasoning methods based on the use of fuzzy sets, were then
investigated. It was found that most fuzzy methods used in spatial problems
process crisp values obtained from simplifying fuzzy membership functions, and
not the functions themselves, thereby losing the information value of a fuzzy
quantity. A fuzzy number possesses both a mean value and a spread (support)
that may be used to indicate the uncertainty of an answer, however it was found
that there was currently no robust way for decision-makers to input their level of
confidence in applying a particular linguistic label.
The inflexibility of a fuzzy inference system once the rules were generated, and
the extra processing power required for fuzzy pairwise comparison methods left
179Chapter 9 Conclusions
fuzzy MCE as the most appropriate approach to facility site selection. A method
was needed to incorporate approximate reasoning in an MCE analysis suitable
for site selection problems.
The planning and research phase concluded with a review of current software
systems used to solve site selection problems. The umbrella term used for these
systems is Spatial Decision Support Systems (SDSSs). SDSSs are a type of
Decision Support System (DSS) that integrates GIS technology with decision-
making models to aid in the solution of spatial problems. It was found that the
ideal SDSS would be both flexible and user friendly, be fully integrated within a
single GIS software package, provide real-time graphical interactivity and cater
for group decision-making.
The literature consistently noted that the major hurdle facing developers was how
to make systems that are simple and easy to use. There was found to be a general
tendency towards ‘shallow use’ of SDSSs by real world planners and decision-
makers, which was largely the result of real or perceived difficulty in using such
systems. There was also found to be a void of systems capable of accepting
uncertainty assessments directly from decision-makers.
9.3.2 Analysis and design
It was noted during the analysis stage that limitations on current SDSSs are
derived from an inability to deal with multiple conflicting parties, an inability to
handle uncertainty, a lack of simplicity in use and interaction and not delivering
enough control to decision-makers. A conceptual blueprint for the design of a
new algorithm and its implementation in a SDSS was created, and it was
proposed that the new system should possess the following characteristics:
• The ability to accept inputs from a heterogeneous group of decision-makers,
independently weighting and rating multiple attributes.
180Chapter 9 Conclusions
• An approximate reasoning algorithm based on a fuzzy MCE aggregation of
parameter-based fuzzy numbers that encapsulate linguistic suitability and
uncertainty assessments.
• The algorithm should utilise arithmetic operators for aggregation and a
scoring function for de-fuzzification to minimise calculation time and enable
real-time interactivity.
• The system should be fully integrated into existing GIS software.
• Linguistic outputs should be a set of descriptive parameters that give
decision-makers the ability to choose the characteristics of a solution that are
most appropriate to their specific problem, thereby enabling them to gain
control over the properties maximised during aggregation.
The ARAISS site selection algorithm was designed to achieve the goals outlined
during conceptual design. The algorithm works by extracting four parameters
inherent in each alternative that indicate levels of Utility, Safety, Consensus, and
Certainty. Weighting of the four parameters enables decision-makers to decide
which aspects of the solution are most important to their specific problem, and
thereby delivers a real means of control over algorithm performance. The
algorithm performed as expected in example problems, delivering sound results
in a five alternative, three decision-maker problem with simulated inputs.
9.3.3 Construction
InfraPlanner was created as a working prototype of a generic SDSS for site
selection problems of a strategic nature. The system demonstrated that
approximate reasoning techniques are suitable for use in SDSSs, although
designing and building the InfraPlanner Spatial Decision Support System proved
to be extremely challenging. Constructing a DSS is generally considered to be a
complex, time consuming task, requiring a group of skilled individuals, and this
was proven in practice. There are many small issues that are not generic enough
to be mentioned in publications on SDSSs but nonetheless proved problematic.
Among these were choosing a GIS package from the myriad of options available,
181Chapter 9 Conclusions
and dealing with the organisational changes that occurred during the
development process.
9.3.4 Implementation and feedback
An experiment was conducted using a real world site selection decision at
Brisbane Airport, where the desired inputs and outputs could be generated and
commented upon by actual decision-makers. Inputs were generated for three
stakeholder groups using actual decision-makers or representatives chosen by the
experimenter for their knowledge of the situation. The problem used was real,
and the objective was to choose the best location for a recycling facility on the
2700 ha Brisbane Airport site.
The experiment confirmed that a focus on a meaningful, interactive exploration
of alternative outcomes, as opposed to attempting to derive a solution from initial
inputs, is a valid way to support decision-makers in their task. The results
generated by the system were found to be sound, and corresponded well with the
real sites preferred by the decision-making group. Decision-makers found the
method easy to use and the outputs were perceived as helpful.
It is important to note that ARAISS was designed to analyse qualitative site
selection problems with a single objective, and may need to be augmented to
cater for at least three other common types of site selection problems. Firstly in
situations where multiple facilities are to be located simultaneously or multiple
land uses considered. Secondly where the use of single cells as alternatives does
not effectively represent the spatial configuration of a proposed development.
Thirdly where using linguistic terms for data input does not offer the best means
of information input, for example where hard quantitative data is available.
9.4 Validation
A fundamental problem in designing an algorithm to solve infrastructure site
selection problems is that there is often no perfect solution to find, and it is not
182Chapter 9 Conclusions
always possible to derive the best compromise from initial assessments. Using a
pre-determined optimization algorithm is standard procedure in many areas of
problem solving, and works particularly well when the exact utility of a solution
can be precisely measured and used as feedback to improve performance.
However the exact utility of a solution in site selection is seldom known.
Multiple, conflicting criteria, and the added human element of conflicting
opinions of measurement and importance create an ill-structured problem that is
often dynamic, in that assessments may change as the solution space is
examined. It is also relevant to note that problem-solving strategies vary from
person to person, making the group situation particularly dynamic. In such a
climate the traditional model of testing a new algorithm against others using
standard test data and set benchmarks becomes obsolete.
In the case of ARAISS a second major hurdle is the absence of a standard dataset
with which to generate results and compare those results to known solutions.
Datasets used in other published work on multi-criteria site selection either lacks
multiple decision-maker inputs, or uncertainty data. In fact due to the unique
approach of ARAISS, which requires decision-makers to weight output
parameters not generated by other methods, validation is challenging from the
outset.
In the absence of an existing dataset with all the necessary inputs and outputs to
test ARAISS and InfraPlanner, there were three practical means of validation
applicable to this research:
1. Using custom made sample datasets based on hypothetical problems to
evaluate the success of the algorithm.
2. Using a real problem to gain feedback on the suitability of results
produced by ARAISS, and the benefits gained by using InfraPlanner.
3. Peer review of the ARAISS model and the process used to develop it.
Use of the first method was described Section 6.3, which gives the results of
simulation exercises conducted using MATLAB. Several MATLAB simulations
were conducted to test the common sense validity of the algorithm. The problems
183Chapter 9 Conclusions
were all based on three decision-makers rating five alternatives with respect to
three criteria. Results showed that ARAISS performed as expected, producing
commonsense results and successfully extracting the four output parameters of
Utility, Certainty, Risk and Conflict.
Chapter 8 describes the use of Infra Planner in a real site selection problem at
Australia’s Brisbane Airport. The problem was based on six criteria with inputs
coming from three separate decision-maker groups. Once again sound results
were produced, with the algorithm selecting the same site as had been previously
earmarked. Decision-makers found it easy to provide their preferences
linguistically, and the output information provided by InfraPlanner was found to
be useful and easily interpretable.
Peer review was facilitated first and foremost by publication of the ARAISS
algorithm and its implementation in InfraPlanner as shown in Appendix A, and
secondly via a focus group conducted at the ANZIIS 2003 conference. Feedback
was positive, with all present agreeing that both the development process and the
model derived from it was valid. Some researchers noted that the use of a
software design flowchart would also be a good way to represent the model, as
they found the logic model used difficult to follow.
9.5 Key Findings
The following summary points are presented here as the key findings of this
research.
1. The key limitations of existing MCE techniques used for site selection were
found to be:
a) An inability to handle uncertainty.
b) An inability to handle a group decision-making environment.
c) Real or perceived difficulty of use, and a limited sense of control.
184Chapter 9 Conclusions
2. It was found that approximate reasoning could be used to mitigate these
difficulties in the following ways:
a) Uncertainty in spatial decision-making may be modelled using fuzzy
numbers to quantify linguistic suitability assessments. The fuzzy numbers
may be scaled for varying levels of uncertainty using the concept of type-
2 fuzzy sets.
b) Inputs from a heterogeneous group may be brought together by the use of
a relevance matrix, which is a device to weight a decision-makers ability
to judge a particular criterion.
c) The use of linguistic inputs and outputs, coupled with an emphasis on
providing useful information rather than direct solutions was found to
provide a simple way to interact with a SDSS, that delivered a greater
sense of control. In particular the identification of the overall utility,
uncertainty, risk, and conflict inherent in each solution provides greater
information value than a single numerical score.
9.6 Directions for future research
Expanding on the working prototype opens up several possibilities, and further
work is recommended to expand ARAISS and InfraPlanner to be capable of
handling multiple facility problems, and explicitly include the size and spatial
configuration of the required land parcels. Potential also exists to include existing
philosophies that have proven effective in this type of problem such as factor
analysis, approaches based on the triple bottom line, the use of key performance
indicators, and data envelopment analysis.
Genetic algorithms also offer a promising method to explore feasible alternatives
without resorting to the massive number of calculations required to fully examine
the solution space of such problems. Research on other artificial intelligence
techniques such as neural networks should produce benefits in complex spatial
decisions.
185Chapter 9 Conclusions
At a practical level, this new functionality may be added to InfraPlanner in three
basic ways:
1. By enhancement of the suitability map generation capability to include
extra parameters
2. By enhancement of the aggregation capability to process extra
information
3. By enhancement of the interactive feedback capability to display and
optimise based on the extra data required in the approaches outlined
above
There also exist several fundamental difficulties with multi-criteria decision-
making not addressed in this thesis, that offer promising direction for future
work. These include:
• Selection of criteria and criterion overlap
• Development of more accurate means of semantically representing decision-
maker preferences
• Methods for generating consensus in a group environment
• Methods for choosing a suitable decision-maker group
• Methods to quickly process raw data into the format necessary for use in a
SDSS
9.7 Concluding remarks
This research has produced a new fuzzy algorithm for the selection of sites for
large-scale infrastructure, and implemented it in a new Spatial Decision Support
System. The algorithm performed well in both hypothetical and real site selection
problems, however hard empirical validation is difficult to perform when there is
no completely accurate way to rate solutions to complex site selection tasks.
186Chapter 9 Conclusions
The construction of the algorithm, based on type-2 fuzzy set concepts, proved
practical, and produced results consistent with those chosen by real world
planners and decision-makers. Calculation times were sufficiently short to enable
seamless integration into a real-time GIS based analysis, and the format of inputs
and outputs proved simple and easy for users to understand.
Further validation of the methods developed in this research is recommended, as
are the integration of artificial intelligence techniques and other decision-making
philosophies. The confluence of Physical Planning, Decision Science, Fuzzy
Logic, Soft Computing, Decision Support and Expert Systems, Geographical
Information Systems, and Software Design should prove to be fertile ground for
innovation for many years to come.
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Appendix A Publications 199
Appendix A
PPUUBBLLIICCAATTIIOONNSS
Appendix A Publications 200
Appendix A Publications 201
The following peer reviewed original publications were the direct result of the
research presented in this thesis. They are reprinted here in Appendix A.
1. Bailey, D., A. Goonetilleke, and M. Deriche. A decision support system for site selection of large-scale infrastructure facilities using natural language. in Operations Research into the 21st Century. 2003. Noosa, Australia: The Australian Society for Operations Research (ASOR).
2. Bailey, D., A. Goonetilleke, and D. Campbell. Information analysis and dissemination for site selection decisions using a fuzzy algorithm in GIS. in Information and Knowledge Sharing. 2003. Scottsdale, Arizona: ACTA Press.
3. Bailey, D., D. Campbell, and A. Goonetilleke. An experiment with approximate reasoning using 'InfraPlanner'. in ANZIIS 2003. 2003. Sydney: Queensland University of Technology.
Appendix A Publications 202
Appendix B The Brisbane Airport Environment 203
Appendix B
TTHHEE BBRRIISSBBAANNEE AAIIRRPPOORRTT
EENNVVIIRROONNMMEENNTT
Appendix B The Brisbane Airport Environment 204
Appendix B The Brisbane Airport Environment 205
Brisbane Airport occupies a site of 2700 ha, located 13km north east of the
Brisbane CBD, adjoining Moreton bay. The flat and low lying site occupies part
of the original Brisbane river delta, which has undergone extensive changes since
the 1830s, with most of the original network of tidal waterways being replaced
with constructed drains. Much of the vegetation on the site has been planted in
the last 15 years, and was chosen to reduce the attraction of birds.
The major infrastructure currently on the Airport site as shown in Figure 8.1
consists of:
• 3560m long main runway (01/19) and associated taxiways
• 1760m long cross runway (14/32) and associated taxiways
• Domestic terminal building and apron
• International terminal building and apron
• General aviation buildings and apron
• Old international terminal building and apron
• Several Maintenance and support facilities
• Two major freight facilities plus several smaller facilities
• Three major catering facilities
• Private and rental vehicle parking areas
• Administration offices and control facilities
• Refuelling facilities and depot
• Lighting to runways, taxiways, aprons roads and car parks
Ten habitats have been classified on the site, as shown in the table below:
HHaabbiittaatt DDeessccrriippttiioonn SSttaattuuttoorryy
CCoonnssiiddeerraattiioonnss
CCoonnsseerrvvaattiioonn
VVaalluuee
Casuarina Plantation
Monoculture of casuarina glauca, originally providing a relatively poor habitat, but well-established areas infested with weeds and may become attractive.
Low
Appendix B The Brisbane Airport Environment 206
Open Grassland
Closely mown grass surrounding the airports core facilities, which provides a poor habitat.
Low
Remnant Mangrove Communities (general)
Avicennia marina, the grey mangrove, is the dominant species. These are native mangrove communities, which are in good condition, provide a complex and diverse habitat, and contribute to the productivity of adjoining fisheries.
A,E,I,C,W,R,J,Q,F,N
High
Serpentine creek mouth mangrove community
A closed scrub community of Ceriops taga,l which is uncommon within the bay. It is contiguous with the luggage point to jubilee creek mangroves and provides a significant habitat for mammals, reptiles, amphibians and avifauna.
A,E,I,C,W,R,J,Q,F,N
Very high
Channel Mangrove Communities
Numerous drainage channels have been colonised by, or planted with grey or river mangroves.
A,E,I,W,R,J,Q,F,N
Moderate
Remnant Saltmarsh Communities
These communities fringe areas of remnant mangrove, and also occur in freshwater wetland sedges adjacent to Kedron Brook Floodway. The saltcouch Sporobolous virginicus) dominates, although patches of samphire are common.
A,E,I,W,R,J,Q,F,N
High
Freshwater Wetlands and Sedge Communities
These communities are recently colonised in poorly drained and inadequately filled former sandmining areas. The dominant species is Phragmites australis, although much diversity is supported by wetland areas in general.
A,E,I,W,R,J,Q,F,N
High
Coastal Dunes and Foreshore
May form important habitats for migratory (and other) birds.
A,E,I,C,W,R,J,Q,F,N
Moderate
Remnant and Engineered Creeks and Channels
It is likely that Serpentine and Jackson creeks support significant communities of flathead, whiting and bream. Engineered creeks and channels are likely to serve as significant nursery grounds for many species that subsequently migrate to the open waters of Moreton Bay, as well as invertebrates.
A,E,I,W,R,J,Q,F,N
Moderate
Remnant Bushland
A small (5ha) isolated bushland site at Pinkenba
A,I,Q Low
Abbreviations for statutory considerations:
Appendix B The Brisbane Airport Environment 207
A - Airports act 1996 and the Airports (Environment Protection) Regulations E - Endangered Species Protection Act 1992 I - Environmental Protection (Impact Proposals) Act 1974 C - Coastal Protection and Management Act 1995 W - Wetlands Policy of the Commonwealth of Australia 1997 R - Ramsar Convention J - JAMBA & CAMBA treaties Q - QLD Environmental Protection Act 1994 F - Fisheries Act 1994 N - Nature Conservation Act 1994 The airport is also generally bounded to the east, north and west by ecologically
significant habitats. At least thirteen rare, endangered or vulnerable fauna species
may be associated with the sites tidal or swampy areas as shown in the table
below.
Scientific Name Common Name Conservation Status Siting Commonwealth State
Birds
Esacus neglectus beach thick knee - V P Anas castanea chestnut teal - R O Numenius Madagascariensis eastern curlew - R O Sterna albifrons little tern E V O Ephippiorhynchus asiaticus jabiru - R P Rostratula benghalensis painted snipe - R P Dryolimnas pectoralis Lewin’s rail - R P Insects
Acrodipsas illidgei Illidge’s blue butterfly - E P
Marine Reptiles
Caretta caretta loggerhead turtle E E M Chelonia mydas green turtle V V M
Marine Mammals
Dugong dugon dugong - V M Sousa chinensis ndo-Pacific humpback - R M Dolphin
Terrestrial Mammals
Xeromys myoides alse water rat V R P Commonwealth: Commonwealth Endangered Species Protection Act 1992
Appendix B The Brisbane Airport Environment 208
State: Queensland Nature Conservation Act 1992 and Nature Conservation (wildlife) Regulations 1994
E Endangered: in danger of extinction, and survival is unlikely if threats continue V Vulnerable: likely to become Endangered in the near future if threats continue R Rare: not considered Endangered or Vulnerable and may be abundant in restricted areas O Observed on site P Possibly on site M Marine animal probably occurring near site
The foreshore, intertidal and freshwater wetlands of the airport site may also
support a number of bird species protected by international treaty. These birds
are generally associated with the Moreton Bay area, which is arguably the most
important feeding ground for migratory waders along the east Australian coast
(Driscoll 1992).
The most significant communities on, and adjacent to the site are all associated
with wetland habitats: both intertidal (mangrove and saltmarsh) and freshwater.
Each of these habitats could be detrimentally affected by a range of activities
including:
Reclamation
Changes to the drainage patterns and hydrology of the site
Alteration to tidal inundation patterns of the site and flushing of the waterways
Increase in sediment loads
Dredging and maintenance of the channels
Discharge of contaminated water or fuel/chemical spillage
Increase in the nutrient levels of the water
Disturbance of acid sulphate soils and the consequent acidification of the water
Feral animals
Control of mosquito and biting midges
Proliferation of exotic weeds
Increases in noise and activity levels
In general, the effects include the following:
Increases in noise and activity could detrimentally affect bird life
Appendix B The Brisbane Airport Environment 209
Fragmentation and development could lead to the further introduction and
proliferation of exotic weeds and feral animals
Decreases in water quality are likely to affect populations of turtle, dugong and
dolphins in the area
RUST PPK, in their 1996 review also states that air pollution may pose
significant environmental concerns, in the areas immediately surrounding the
airport. This is not addressed in the AES, as the Airports (Environmental
Protection) Regulations do not apply to pollution generated by an aircraft. It is
dealt with under Commonwealth legislation namely the Air Services Act 1995
and Air Navigation (Aircraft Engine Emissions) Regulations. The affects of
aircraft noise on the environment may also be considerable.
The area upon which the Brisbane Airport is situated is claimed as the traditional
country of the Turrbal corporation, which has indicated that there were special
places within the vicinity of the airport site, including an unrecorded bora ring
destroyed during runway construction.
Since European settlement, a range of land-uses and events occurred on the area
now known as Brisbane Airport, most of which have left little trace. The only
physical items listed on the register of the National estate lie outside the airport
boundary, however significant archaeological sites connected with the convict
era and the WWII history of Brisbane, may exist within the site.
Appendix B The Brisbane Airport Environment 210
211Appendix C MATLAB Code
Appendix C
MMAATTLLAABB CCOODDEE
212Appendix C MATLAB Code
213Appendix C MATLAB Code
The following MATLAB code was used to perform a simulation of the ARAISS
algorithm on a three decision-maker, three criteria, five alternative problem. Code for the
main functions only has been included. Full code is available upon request.
Tripleanalyse.M (Main Loop)
% performs a fuzzy analysis and normalisation on the 3
datasets & termset & relevance mtx in the workspace
decode;
aggregateandnormalise;
rankoutputs;
showmatches;
plotoutputs;
clear;
Decode.M
% decodes the 3 coded decision matrices (dm1 2 & 3) using
the suitability & uncertainty termsets in the workspace
saves the resulting 3d fuzzy matrices
% NEED TO ADJUST THE WAY WEIGHTS ARE INPUT AS SOME ARE NOW
>= 1
load termset termset;
load dm1 dm1;
load dm2 dm2;
load dm3 dm3;
load rm rm;
[rows,cols] = size(dm1);
%weightsum is for use in critical weight
dm1weightsum = 0.0;
dm2weightsum = 0.0;
214Appendix C MATLAB Code
dm3weightsum = 0.0;
for i = 1:cols
dm1weightsum = dm1weightsum + dm1(rows, i);
dm2weightsum = dm2weightsum + dm2(rows, i);
dm3weightsum = dm3weightsum + dm3(rows, i);
end
fuzzydm1 = zeros(rows,cols,4);
for i = 1:rows
for j = 1:cols
for k = 1:4
if i < rows
fuzzydm1(i,j,k) = termset(dm1(i,j),k);
elseif dm1(i,j) < 1
fuzzydm1(i,j,k) = dm1(i,j);
else
fuzzydm1(i,j,k) = 2 * cols *
(dm1weightsum) + .1; %critical
dm1critscore = fuzzydm1(i,j,k)
end
end
end
end
fuzzydm2 = zeros(rows,cols,4);
for i = 1:rows
for j = 1:cols
215Appendix C MATLAB Code
for k = 1:4
if i < rows
fuzzydm2(i,j,k) = termset(dm2(i,j),k);
elseif dm2(i,j) < 1
fuzzydm2(i,j,k) = dm2(i,j);
else
fuzzydm2(i,j,k) = 2 * cols *
(dm2weightsum) + .1; %critical
dm2critscore = fuzzydm2(i,j,k)
end
end
end
end
fuzzydm3 = zeros(rows,cols,4);
for i = 1:rows
for j = 1:cols
for k = 1:4
if i < rows
fuzzydm3(i,j,k) = termset(dm3(i,j),k);
elseif dm3(i,j) < 1
fuzzydm3(i,j,k) = dm3(i,j);
else
fuzzydm3(i,j,k) = 2 * cols *
(dm3weightsum) + .1; %critical
dm3critscore = fuzzydm3(i,j,k)
end
216Appendix C MATLAB Code
end
end
end
[rows,cols] = size(rm);
fuzzyrm = zeros(rows,cols,4);
%normalise the crit rm values
rmcrit = zeros(1,cols);
rmsum = zeros(1,cols);
for i = 1:rows
for j = 1:cols
if rm(i,j) == 1
rmcrit(j) = 1
rmsum(j) = rm(1,j) + rm(2,j) + rm(3,j);
end
end
end
for j = 1:cols
if rmcrit(j) == 1
for i = 1:rows
if rm(i,j) < 1
rm(i,j) = rm(i,j) / (2 * rows * (rmsum(j))
+ .1)
end
end
end
end
217Appendix C MATLAB Code
for i = 1:rows
for j = 1:cols
for k = 1:4
fuzzyrm(i,j,k) = rm(i,j);
end
end
end
% now scale the fuzzy dm matrices for uncertainty
load dm1uncert dm1uncert;
load dm2uncert dm2uncert;
load dm3uncert dm3uncert;
for i = 1:rows-1 % no need to scale weights
for j = 1:cols
fuzzydm1(i,j,:) =
uncertscale(fuzzydm1(i,j,:),dm1uncert(i,j));
fuzzydm2(i,j,:) =
uncertscale(fuzzydm2(i,j,:),dm2uncert(i,j));
fuzzydm3(i,j,:) =
uncertscale(fuzzydm3(i,j,:),dm3uncert(i,j));
end
end
% Normalise weights with critical component - uncertainty
not a factor in weight
dm1weightsum = 0.0;
dm2weightsum = 0.0;
218Appendix C MATLAB Code
dm3weightsum = 0.0;
[rows,cols] = size(dm1);
dm1crit = 0.0;
dm2crit = 0.0;
dm3crit = 0.0;
for i = 1:cols
if dm1(rows, i) == 1
dm1crit = 1
end
end
for i = 1:cols
if dm2(rows, i) == 1
dm2crit = 1
end
end
for i = 1:cols
if dm3(rows, i) == 1
dm3crit = 1
dm3(rows, i)
end
end
if dm1crit == 1
for i = 1:cols
219Appendix C MATLAB Code
for j = 1:4
fuzzydm1(rows, i, j) = fuzzydm1(rows, i, j)/
dm1critscore;
end
end
end
if dm2crit == 1
for i = 1:cols
for j = 1:4
fuzzydm2(rows, i, j) = fuzzydm2(rows, i, j)/
dm2critscore;
end
end
end
if dm3crit == 1
for i = 1:cols
for j = 1:4
fuzzydm3(rows, i, j) = fuzzydm2(rows, i, j)/
dm3critscore;
end
end
end
save fuzzyrm fuzzyrm;
save fuzzydm1 fuzzydm1;
save fuzzydm2 fuzzydm2;
220Appendix C MATLAB Code
save fuzzydm3 fuzzydm3;
Aggregateandnormalise.M
function f = aggregateandnormalise()
% aggregates & normalises the 3 decisionmatrices stored in
the workspace to obtain alternative ratings (last row of
dm1
% 2 & 3 is weights)
getmax;
load maxscore maxscore;
markerpoint = maxscore(2);
load fuzzydm1 fuzzydm1;
load fuzzydm2 fuzzydm2;
load fuzzydm3 fuzzydm3;
load fuzzyrm fuzzyrm;
[rows,cols,dims] = size(fuzzydm1);
aggdm1 = zeros(rows-1,dims);
for i = 1:rows-1
rating = zeros(1,dims);
for j = 1:cols
for k = 1:dims
221Appendix C MATLAB Code
outcome(k) = fuzzydm1(i,j,k);
weight(k) = fuzzydm1(rows,j,k);
relevance(k) = fuzzyrm(1,j,k);
end
rating =
trapadd(rating,trapmult(outcome,trapmult(weight,relevance))
);
end
aggdm1(i,:) = rating;
end
aggdm2 = zeros(rows-1,dims);
for i = 1:rows-1
rating = zeros(1,dims);
for j = 1:cols
for k = 1:dims
outcome(k) = fuzzydm2(i,j,k);
weight(k) = fuzzydm2(rows,j,k);
relevance(k) = fuzzyrm(2,j,k);
end
rating =
trapadd(rating,trapmult(outcome,trapmult(weight,relevance))
);
end
222Appendix C MATLAB Code
aggdm2(i,:) = rating;
end
aggdm3 = zeros(rows-1,dims);
for i = 1:rows-1
rating = zeros(1,dims);
for j = 1:cols
for k = 1:dims
outcome(k) = fuzzydm3(i,j,k);
weight(k) = fuzzydm3(rows,j,k);
relevance(k) = fuzzyrm(3,j,k);
end
rating =
trapadd(rating,trapmult(outcome,trapmult(weight,relevance))
);
end
aggdm3(i,:) = rating;
end
for i = 1:rows-1
finaloutcome =
trapadd(aggdm1(i,:),trapadd(aggdm2(i,:),aggdm3(i,:)));
223Appendix C MATLAB Code
outputmatrix(i,:) =
trapnormalise(finaloutcome,markerpoint);
end
save outputmatrix outputmatrix;
Rankoutputs.M
function f = rankoutputs()
% ranks the outputmatrix from best to worst result using a
weighted addition of breakpoints
load outputmatrix outputmatrix;
[rows,cols] = size(outputmatrix);
x = zeros(1,rows);
for i = 1:rows
%TFN = outputmatrix(i,:);
x(i) = 0.1 * outputmatrix(i,1) + .4 *
outputmatrix(i,2) + .4 * outputmatrix(i,3) + 0.1 *
outputmatrix(i,4);
end
f = zeros(1,rows);
for i = 1:rows
best = 0;
for j = 1:rows
224Appendix C MATLAB Code
if x(j) > best
best = x(j);
f(i) = j;
y = j;
end
end
x(y) = 0;
end
rankorder = f
Showmatches.M
% matches the TFN's in the outputmatrix to the closest
terms in the suitability, uncertainty, risk & conflict
termsets
load outputmatrix outputmatrix;
[rows,cols] = size(outputmatrix);
matches = zeros(rows,6);
for i = 1:rows
matches(i,:) = lingapprox(i);
end
MatchOrder = ['Suitability ', 'Uncertainty ', 'Risk ',
'Conflict ', 'Overall']
Matches
225Appendix C MATLAB Code
Plotoutputs.M
% plot the alternative aggregated outcomes
load outputmatrix outputmatrix;
[rows,cols] = size(outputmatrix);
y = [0 1 1 0];
scalevector = [0,1,0,1];
for i = 1:rows
switch i
case{1}, linecolor = 'r';
case{2}, linecolor = 'g';
case{3}, linecolor = 'b';
case{4}, linecolor = 'c';
case{5}, linecolor = 'm';
case{6}, linecolor = 'k';
otherwise, linecolor = 'y';
end
set(gca,'NextPlot','add');
plot(outputmatrix(i,:),y,linecolor);
labels(i,1) = int2str(i);
end
axis(scalevector);
legend(labels);
% getmaxscore;
% getminscore;
% load maxscore maxscore;
226Appendix C MATLAB Code
% load minscore minscore;
% plot(trapnormalise(maxscore,maxscore(2)),y,linecolor);
% plot(trapnormalise(minscore,maxscore(2)),y,linecolor);
Lingapprox.M
function f = lingapprox(a)
% chooses a term from the term set that is the closest to
the TFN by comparing center of gravity score
% returns the index of the term and the index of
uncertainty measure
% suitability
load outputmatrix outputmatrix
tfn = outputmatrix(a,:);
load termset termset;
[rows,cols] = size(termset);
displacementset = zeros(1,rows);
for i = 1:rows
displacementset(i) = abs(score(termset(i,:)) - score(tfn));
end
match = 1;
227Appendix C MATLAB Code
min = displacementset(1);
for i = 1:rows
if displacementset(i) < min
min = displacementset(i);
match = i;
end
end
suitabilityterm = match;
suitabilityvalue = score(tfn);
%uncertainty
load uncertaintyset uncertaintyset
[rows,cols] = size(uncertaintyset);
displacementset = zeros(1,rows);
for i = 1:rows
uncertterm =
uncertscale(termset(suitabilityterm,:),uncertaintyset(i));
uncerttermsupport = uncertterm(4) - uncertterm(1);
tfnsupport = tfn(4) - tfn(1);
displacementset(i) = abs(uncerttermsupport - tfnsupport);
end
match = 1;
min = displacementset(1);
for i = 1:rows
228Appendix C MATLAB Code
if displacementset(i) < min
min = displacementset(i);
match = i;
end
end
uncertaintyterm = match ;
uncertaintyvalue = uncertaintyset(match);
% Risk
load termgenset termgenset;
[rows,cols] = size(termgenset);
displacementset = zeros(1,rows);
for i = 1:rows
displacementset(i) = abs(termgenset(i) - riskscore(a));
end
match = 1;
min = displacementset(1);
for i = 1:rows
if displacementset(i) < min
min = displacementset(i);
match = i;
end
end
riskterm = match;
229Appendix C MATLAB Code
riskvalue = riskscore(a);
% conflict
load termgenset termgenset;
[rows,cols] = size(termgenset);
displacementset = zeros(1,rows);
for i = 1:rows
displacementset(i) = abs(termgenset(i) - conflictscore(a));
end
match = 1;
min = displacementset(1);
for i = 1:rows
if displacementset(i) < min
min = displacementset(i);
match = i;
end
end
conflictterm = match;
conflictvalue = conflictscore(a);
load parameterweights parameterweights;
for i = 1:4
if parameterweights(i) == 1 % critical
230Appendix C MATLAB Code
parameterweights(i) = 8 * (parameterweights(1) +
parameterweights(2) + parameterweights(3) +
parameterweights(4)) + .1;
end
end
load termset termset;
[rows,cols] = size(termset);
overallscore = parameterweights(1)* suitabilityvalue +
parameterweights(2) * (1-uncertaintyvalue) +
parameterweights(3) * (1-riskvalue) + parameterweights(4) *
(1-conflictvalue);
normaloverallscore = overallscore/(parameterweights(1) +
parameterweights(2) + parameterweights(3) +
parameterweights(4));
for i = 1:rows
displacementset(i) = abs(score(termset(i,:)) -
normaloverallscore);
end
match = 1;
min = displacementset(1);
for i = 1:rows
if displacementset(i) < min
min = displacementset(i);
match = i;
231Appendix C MATLAB Code
end
end
overallsuitabilityterm = match;
f = [suitabilityterm uncertaintyterm riskterm conflictterm
overallsuitabilityterm normaloverallscore];
232Appendix C MATLAB Code
233Appendix D ArcObjects VBA Code
Appendix D
AARRCCOOBBJJEECCTTSS VVBBAA CCOODDEE
234Appendix D ArcObjects VBA Code
235Appendix D ArcObjects VBA Code
The following code controlled the functionality of the InfraPlanner interfaces
described in Chapter 7. Full code is available upon request.
CREATE DISCRETE CRITERION MAP
Option Explicit
Private m_pMxDoc As IMxDocument
Private m_pMaps As IMaps
Private m_pMap As IMap
Private m_pLayer As ILayer
Private m_pEnumLayers As IEnumLayer
Private strCriteriaName As String
Private strCriteriaDesc As String
Private m_pDecisionMap As IMap
Private m_pMapFrame As IMapFrame
Private m_pPageLayout As IPageLayout
Private m_pActiveView As IActiveView
Private m_bClearFlag As Boolean
Private m_iNumCategories As Integer
Private m_iCategoryNumber As Integer
Private m_pTable As ITable
Private m_lRatingArray() As Long 'The new rating
Private m_dValueArray() As Double 'Raw value of the
category
Private Sub btnAddRating_Click()
If cboRating.ListIndex < 0 Then
236Appendix D ArcObjects VBA Code
MsgBox "Select a rating"
Exit Sub
End If
lboCategories.Selected(m_iCategoryNumber) = False
Dim pFields As IFields
Set pFields = m_pTable.Fields
Dim pRow As IRow
If m_iCategoryNumber <= m_iNumCategories - 1 Then
lboRating.AddItem cboRating.Text
Set pRow = m_pTable.GetRow(m_iCategoryNumber)
m_lRatingArray(m_iCategoryNumber) =
DecodeRating(cboRating.ListIndex + 1)
m_dValueArray(m_iCategoryNumber) =
pRow.Value(pFields.FindField("Value"))
'uncomment these lines to check the values going
in
'MsgBox "Rating: " & m_iCategoryNumber & ", " &
m_lRatingArray(m_iCategoryNumber) & vbLf + _
'"Value: " & m_dValueArray(m_iCategoryNumber)
m_iCategoryNumber = m_iCategoryNumber + 1
Else: MsgBox "Box Full"
End If
237Appendix D ArcObjects VBA Code
If m_iCategoryNumber <= m_iNumCategories - 1 Then
lboCategories.Selected(m_iCategoryNumber) =
True
lblCategory.Caption = lboCategories.Value
End If
End Sub
Private Sub btnCreate_Click()
If Not m_iCategoryNumber = m_iNumCategories Then
MsgBox "Need to rate all categories"
Exit Sub
End If
Create
Unload Me
End Sub
Private Sub btnExit_Click()
Unload Me
End Sub
Private Sub cboField_Change()
lboCategories.Clear
238Appendix D ArcObjects VBA Code
lboRating.Clear
If Not m_bClearFlag Then
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pRasterBandCollection As IRasterBandCollection
Set pRasterBandCollection = pRLayer.Raster
Dim pRasterBand As IRasterBand
Set pRasterBand = pRasterBandCollection.Item(0)
Set m_pTable = pRasterBand
Dim pQueryFilter As IQueryFilter
Set pQueryFilter = New QueryFilter
pQueryFilter.SubFields = cboField.Text
'pQueryFilter.WhereClause = "STATE_NAME =
'California'"
m_iNumCategories = m_pTable.RowCount(pQueryFilter)
If m_iNumCategories <= 20 Then
Dim pFields As IFields
Set pFields = m_pTable.Fields
Dim pRow As IRow
Dim i As Integer
For i = 0 To m_pTable.RowCount(pQueryFilter) -
1
239Appendix D ArcObjects VBA Code
Set pRow = m_pTable.GetRow(i)
lboCategories.AddItem
pRow.Value(pFields.FindField(cboField.Text))
Next i
ReDim m_lRatingArray(m_iNumCategories) As Long
ReDim m_dValueArray(m_iNumCategories) As
Double
Else: MsgBox "More than 20 categories, this layer
may be unsuitable"
End If
m_iCategoryNumber = 0
lboCategories.Selected(m_iCategoryNumber) = True
lblCategory.ZOrder (0)
lblCategory.Caption = lboCategories.Value
lblCategory.Caption = lboCategories.Value
End If
End Sub
Private Sub cboRating_Change()
240Appendix D ArcObjects VBA Code
End Sub
Private Sub cboSourceTheme_Change()
MapControl1.ClearLayers
m_bClearFlag = True
cboField.Clear
m_bClearFlag = False
lboCategories.Clear
'need to clear the chart too
Dim i As Integer
For i = 0 To m_pMap.LayerCount - 1
Set m_pLayer = m_pMap.Layer(i)
If m_pLayer.Name = cboSourceTheme.Text Then
Exit For
End If
Next i
MapControl1.AddLayer m_pLayer
' Rotate the map to -62.9 deg
Dim pRotateOperation As IRotateOperation
241Appendix D ArcObjects VBA Code
Set pRotateOperation = New RotateOperation
pRotateOperation.ActiveView = MapControl1.ActiveView
pRotateOperation.Rotation = -62.9
pRotateOperation.Do
'get the field names
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pRasterBandCollection As IRasterBandCollection
Set pRasterBandCollection = pRLayer.Raster
Dim pRasterBand As IRasterBand
Set pRasterBand = pRasterBandCollection.Item(0)
Dim pTable As ITable
Set pTable = pRasterBand
Dim pFields As IFields
Set pFields = pTable.Fields
For i = 0 To pFields.FieldCount - 1
cboField.AddItem pFields.Field(i).Name
Next i
End Sub
Private Sub Frame1_Click()
242Appendix D ArcObjects VBA Code
End Sub
Private Sub Label10_Click()
End Sub
Private Sub lblCategory_Click()
End Sub
Private Sub UserForm_Click()
End Sub
Private Function DecodeRating(ByVal Rating) As Long
If (Not ((Rating = 1) Or (Rating = 2) Or (Rating = 3)
Or (Rating = 4) Or (Rating = 5) Or (Rating = 6) Or
(Rating = 7))) Then
MsgBox "Invalid rating: " & Rating
Exit Function
End If
Select Case Rating
Case 1
DecodeRating = 0
Case 2
DecodeRating = 1
Case 3
DecodeRating = 3
243Appendix D ArcObjects VBA Code
Case 4
DecodeRating = 5
Case 5
DecodeRating = 7
Case 6
DecodeRating = 9
Case 7
DecodeRating = 10
End Select
End Function
Private Sub UserForm_Initialize()
Set m_pMxDoc = ThisDocument
Set m_pMaps = m_pMxDoc.Maps
Set m_pMap = m_pMaps.Item(0)
lblCategory.Visible = True
'Get the rasterlayers
Dim i As Integer
i = 0
Do Until m_pMap.Name = "Source Rasters"
244Appendix D ArcObjects VBA Code
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
'set up the source theme combo box
Dim j As Integer
For j = 0 To m_pMap.LayerCount - 1
Set m_pLayer = m_pMap.Layer(j)
If TypeOf m_pLayer Is IRasterLayer Then
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pRasterBandCollection As IRasterBandCollection
Set pRasterBandCollection = pRLayer.Raster
Dim pRasterBand As IRasterBand
Set pRasterBand = pRasterBandCollection.Item(0)
Dim pRasterProps As IRasterProps
Set pRasterProps = pRasterBand
If pRasterProps.IsInteger = True Then
cboSourceTheme.AddItem m_pMap.Layer(j).Name
m_pMap.Layer(j).Visible = True
End If
End If
245Appendix D ArcObjects VBA Code
Next j
AddTerms
End Sub
Private Sub AddTerms()
cboRating.AddItem "Totally Unsuitable"
cboRating.AddItem "Very Bad"
cboRating.AddItem "Bad"
cboRating.AddItem "Indifferent"
cboRating.AddItem "Good"
cboRating.AddItem "Very Good"
cboRating.AddItem "Perfect"
End Sub
Private Sub CreateDiscreteCriteria()
'This sub uses a raster model
' can use AlgbOp for single operation
' Get raster from layer
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pInRaster As IRaster
Set pInRaster = pRLayer.Raster
246Appendix D ArcObjects VBA Code
' Create a RasterModel object
Dim pRModel As IRasterModel
Set pRModel = New RasterModel
' Create spatial analysis environment
Dim pEnv As IRasterAnalysisEnvironment
Set pEnv = pRModel
' Set output workspace
Dim pWS As IWorkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pWS = pWSF.OpenFromFile("c:\temp", 0)
Set pEnv.OutWorkspace = pWS
' & vbLf + _ is used to seperate equations
' N.B. get an error if use + vbLf + _ as shown in
samples
' N.B. can't reuse created rasters, once created
they are set & trying to
' change them creates an error
' N.B leave a space before closing quotes
'just copy the raster & then edit it's table to
re-classify
pRModel.Script = "[CriteriaMap] = [input1] / 10 "
247Appendix D ArcObjects VBA Code
' "[seg2] = ([input2] = " &
m_dValueArray(2) & ") * " & m_lRatingArray(2) & vbLf +
_
' "[seg3] = ([input3] = " &
m_dValueArray(3) & ") * " & m_lRatingArray(3) & vbLf +
_
' "[seg4] = ([input4] = " &
m_dValueArray(4) & ") * " & m_lRatingArray(4) & vbLf +
_
' "[seg5] = ([input5] = " &
m_dValueArray(5) & ") * " & m_lRatingArray(5) & vbLf +
_
' "[seg6] = ([input6] = " &
m_dValueArray(6) & ") * " & m_lRatingArray(6) & vbLf +
_
' "[seg7] = ([input7] = " &
m_dValueArray(7) & ") * " & m_lRatingArray(7) & vbLf +
_
' "[CriteriaMap] = [Seg1] +
[Seg2] + [seg3] + [seg4] + [seg5] + [seg6] + [seg7]"
'
' Bind to raster
pRModel.BindRaster pInRaster, "input1"
' Run the model
pRModel.Execute
' Get outputs
Dim pRaster1 As IRaster
Set pRaster1 = pRModel.BoundRaster("CriteriaMap")
248Appendix D ArcObjects VBA Code
' Unbind raster
pRModel.UnbindSymbol "input1"
' attempt to do the reclass via the table
' Dim pRasterBandCollection As
IRasterBandCollection
' Set pRasterBandCollection = pRaster1
'
' Dim pRasterBand As IRasterBand
' Set pRasterBand = pRasterBandCollection.Item(0)
'
' Set m_pTable = pRasterBand.AttributeTable
'
'
' ' Get field index
' Dim FieldIndex As Integer
' FieldIndex = m_pTable.FindField("Value")
'
' Dim pQueryFilter As IQueryFilter
' Set pQueryFilter = New QueryFilter
' pQueryFilter.SubFields = "Value"
'
' Dim pCursor As IRasterCursor
' Set pCursor = m_pTable.Update(pQueryFilter, True)
'
' Dim pRow As IRow
' Dim i As Integer
'
' For i = 0 To m_pTable.RowCount(pQueryFilter) - 1
'
' Set pRow = pCursor.Next
'
249Appendix D ArcObjects VBA Code
'
' 'Set pRow = m_pTable.GetRow(i)
'
' MsgBox "Old value " & i & " = " &
pRow.Value(FieldIndex)
'
' pRow.Value(FieldIndex) = m_lRatingArray(i)
'
' MsgBox "New value " & i & " = " &
pRow.Value(FieldIndex) & vbLf + _
' "Array value: " & m_lRatingArray(i)
'
'
'
'
'
' Next i
'
'
' Add the results into Map
i = 0
Do Until m_pMap.Name = "Criteria Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
Dim pRLayer1 As IRasterLayer
Set pRLayer1 = New RasterLayer
250Appendix D ArcObjects VBA Code
pRLayer1.CreateFromRaster pRaster1
Set m_pLayer = pRLayer
m_pMap.AddLayer pRLayer1
pRLayer1.Name = TxtName.Text
m_pLayer.Name = pRLayer.Name
Set m_pMxDoc.ActiveView = m_pMap
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
End Sub
Private Sub Create()
' Get the input raster from the first layer in ArcMap
Dim pRasterLy As IRasterLayer
Dim pLy As ILayer
Set pLy = m_pLayer
If Not TypeOf pLy Is IRasterLayer Then Exit Sub
Set pRasterLy = pLy
Dim pGeoDs As IGeoDataset
Set pGeoDs = pRasterLy.Raster
' Create a raster descriptor and select the field
used for reclassify
Dim pRD As IRasterDescriptor
Set pRD = New RasterDescriptor
pRD.Create pGeoDs, New QueryFilter, "Value"
Set pGeoDs = pRD
' Create a Spatial operator
Dim pReclassOp As IReclassOp
251Appendix D ArcObjects VBA Code
Set pReclassOp = New RasterReclassOp
' Set output workspace
Dim pEnv As IRasterAnalysisEnvironment
Set pEnv = pReclassOp
Dim pWS As IWorkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pWS =
pWSF.OpenFromFile("c:\temp\temporaryrasters", 0)
Set pEnv.OutWorkspace = pWS
' Set the Remap
Dim pRemap As IRemap
Dim pNRemap As INumberRemap
Set pNRemap = New NumberRemap
Dim i As Integer
For i = 0 To m_iNumCategories - 1
pNRemap.MapValue m_dValueArray(i),
m_lRatingArray(i)
Next i
Set pRemap = pNRemap
' Perform Spatial operation
Dim pOutRaster As IRaster
Set pOutRaster = pReclassOp.ReclassByRemap(pGeoDs,
pRemap, False)
252Appendix D ArcObjects VBA Code
'This sub uses a raster model
' can use AlgbOp for single operation
' Create a RasterModel object
Dim pRModel As IRasterModel
Set pRModel = New RasterModel
Set pEnv = pRModel
' & vbLf + _ is used to seperate equations
' N.B. get an error if use + vbLf + _ as shown in
samples
' N.B. can't reuse created rasters, once created
they are set & trying to
' change them creates an error
' N.B leave a space before closing quotes
'remap limited to integers & this creates a
double value
pRModel.Script = "[CriteriaMap] = [input1] * 0.1
"
' Bind to raster
pRModel.BindRaster pOutRaster, "input1"
' Run the model
253Appendix D ArcObjects VBA Code
pRModel.Execute
' Get outputs
Dim pRaster1 As IRaster
Set pRaster1 = pRModel.BoundRaster("CriteriaMap")
' Unbind raster
pRModel.UnbindSymbol "input1"
' Add it into ArcMap
Set pRasterLy = New RasterLayer
pRasterLy.CreateFromRaster pRaster1
i = 0
Do Until m_pMap.Name = "Criteria Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
Set m_pLayer = pRasterLy
m_pMap.AddLayer pRasterLy
pRasterLy.Name = TxtName.Text
m_pLayer.Name = pRasterLy.Name
Set m_pMxDoc.ActiveView = m_pMap
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
SetUpLayer
254Appendix D ArcObjects VBA Code
End Sub
Sub SetUpLayer()
'Classifies the layer in m_pLayer linguistically
' Get raster input from layer
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pRaster As IRaster
Set pRaster = pRLayer.Raster
' Create classfy renderer and QI RasterRenderer
interface
Dim pClassRen As IRasterClassifyColorRampRenderer
Set pClassRen = New
RasterClassifyColorRampRenderer
Dim pRasRen As IRasterRenderer
Set pRasRen = pClassRen
Dim pProps As IRasterClassifyUIProperties
Set pProps = pClassRen
'pProps.ShowClassGaps = True
' Set raster for the render and update
Set pRasRen.Raster = pRaster
pClassRen.ClassCount = 5
255Appendix D ArcObjects VBA Code
pRasRen.Update
'Make the start & end colors
Dim StartColor As IColor
Set StartColor = New RgbColor
StartColor.RGB = RGB(255, 0, 0)
Dim EndColor As IColor
Set EndColor = New RgbColor
EndColor.RGB = RGB(0, 255, 0)
' Create a color ramp to use
Dim pRamp As IAlgorithmicColorRamp
Set pRamp = New AlgorithmicColorRamp
pRamp.Size = 5
pRamp.FromColor = StartColor
pRamp.ToColor = EndColor
pRamp.CreateRamp True
' Create symbol for the classes
Dim pFSymbol As IFillSymbol
Set pFSymbol = New SimpleFillSymbol
' loop through the classes and apply the color and
label
pClassRen.ClassCount = 5
Dim i As Integer
For i = 0 To pClassRen.ClassCount - 1
pFSymbol.Color = pRamp.Color(i)
pClassRen.Symbol(i) = pFSymbol
256Appendix D ArcObjects VBA Code
Select Case i
Case 0
pClassRen.Label(i) = "Totally Unsuitable"
Case 1
pClassRen.Label(i) = "Bad"
Case 2
pClassRen.Label(i) = "Indifferent"
Case 3
pClassRen.Label(i) = "Good"
Case 4
pClassRen.Label(i) = "Perfect"
End Select
Next i
' attempt at setting breaks N.B. there may be a
problem
' if the raster does not contain values that
extend to these breaks!
pClassRen.Break(0) = 0
pClassRen.Break(1) = 0.09
pClassRen.Break(2) = 0.31
pClassRen.Break(3) = 0.69
pClassRen.Break(4) = 0.91
pClassRen.Break(5) = 1
257Appendix D ArcObjects VBA Code
' Update the renderer and plug into layer
pRasRen.Update
Set pRLayer.Renderer = pClassRen
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
End Sub
CONTINUOUS CRITERION
Option Explicit
Private m_pMxDoc As IMxDocument
Private m_pMaps As IMaps
Private m_pMap As IMap
Private m_pLayer As ILayer
Private m_pEnumLayers As IEnumLayer
Private strCriteriaName As String
Private strCriteriaDesc As String
Private m_pDecisionMap As IMap
Private m_pMapFrame As IMapFrame
Private m_pPageLayout As IPageLayout
Private m_pActiveView As IActiveView
Private m_dMaxDistance As Double
Private m_dMinDistance As Double
' Rated points
Private m_dDistance1 As Double
Private m_dDistance2 As Double
258Appendix D ArcObjects VBA Code
Private m_dDistance3 As Double
Private m_dDistance4 As Double
Private m_dDistance5 As Double
Private m_iRating1 As Integer
Private m_iRating2 As Integer
Private m_iRating3 As Integer
Private m_iRating4 As Integer
Private m_iRating5 As Integer
Private m_iPointCount As Integer
Private Function DecodeRating(ByVal Rating) As Double
If (Not ((Rating = 1) Or (Rating = 2) Or (Rating = 3)
Or (Rating = 4) Or (Rating = 5) Or (Rating = 6) Or
(Rating = 7))) Then
MsgBox "Invalid rating: " & Rating
Exit Function
End If
Select Case Rating
Case 1
DecodeRating = 0
Case 2
DecodeRating = 0.1
Case 3
DecodeRating = 0.3
Case 4
DecodeRating = 0.5
259Appendix D ArcObjects VBA Code
Case 5
DecodeRating = 0.7
Case 6
DecodeRating = 0.9
Case 7
DecodeRating = 1
End Select
End Function
Private Sub CreateProximityCriteria()
'Get the number of line segments & gradients & y-
intercepts
' only works for 3 points as a test
If Not (m_iPointCount = 3) Then
MsgBox "Must be 3 points"
Exit Sub
End If
Dim m1 As Double
Dim m2 As Double
Dim yint1 As Double
Dim yint2 As Double
Dim y1 As Double
260Appendix D ArcObjects VBA Code
Dim y2 As Double
Dim x1 As Double
Dim x2 As Double
' segment 1
y1 = DecodeRating(m_iRating1)
y2 = DecodeRating(m_iRating2)
x1 = m_dDistance1
x2 = m_dDistance2
If Not ((x2 - x1) = 0) Then
m1 = (y2 - y1) / (x2 - x1)
yint1 = y1 - ((y2 - y1) / (x2 - x1)) * x1
Else: MsgBox "Divide by zero error"
Exit Sub
End If
' segment 2
y1 = DecodeRating(m_iRating2)
y2 = DecodeRating(m_iRating3)
x1 = m_dDistance2
x2 = m_dDistance3
If Not ((x2 - x1) = 0) Then
m2 = (y2 - y1) / (x2 - x1)
yint2 = y1 - ((y2 - y1) / (x2 - x1)) * x1
Else: MsgBox "Divide by zero error: Operation
terminated"
Exit Sub
End If
'This sub uses a raster model
261Appendix D ArcObjects VBA Code
' can use AlgbOp for single operation
' Get raster from layer
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pInRaster As IRaster
Set pInRaster = pRLayer.Raster
' Create a RasterModel object
Dim pRModel As IRasterModel
Set pRModel = New RasterModel
' Create spatial analysis environment
Dim pEnv As IRasterAnalysisEnvironment
Set pEnv = pRModel
' Set output workspace
Dim pWS As IWorkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pWS =
pWSF.OpenFromFile("c:\temp\temporaryrasters", 0)
Set pEnv.OutWorkspace = pWS
' Set model, & vbLf + _ is used to seperate
equations
' N.B. get an error if use + vbLf + _ as shown in
samples
' N.B. can't reuse created rasters, once created
they are set & trying to
' change them creates an error
' N.B leave a space before closing quotes
262Appendix D ArcObjects VBA Code
pRModel.Script = "[Bool1] = [input1] <= " &
m_dDistance2 & vbLf + _
"[Bool2] = [input1] > " &
m_dDistance2 & vbLf + _
"[Seg1] = (([input1] * " & m1 &
") + " & yint1 & ") * [Bool1]" & vbLf + _
"[Seg2] = (([input1] * " & m2 &
") + " & yint2 & ") * [Bool2]" & vbLf + _
"[Complete] = [Seg1] + [Seg2] "
& vbLf + _
"[Under1] = [Complete] <= 1 " &
vbLf + _
"[Over0] = [Complete] > 0 " &
vbLf + _
"[Over1] = [Complete] > 1 " &
vbLf + _
"[Over0Under1] = [Under1] *
[Over0] " & vbLf + _
"[CriteriaMap] = ([Complete] *
[Over0Under1]) + [Over1] "
' to avoid the crap below try sizing the segments
using x intercepts
' then let the end segments = 0 or 1 if necessary
263Appendix D ArcObjects VBA Code
' & vbLf + _
' "[CriteriaMap] = [Complete]*
([Complete] > 0.0) "
' & vbLf =
' "[Under1] = [Complete1] <= 1 "
& vbLf + _
' "[Over1] = [Complete1] > 1 " &
vbLf + _
' "[CriteriaMap] = [Complete1] *
[Under1] "
' & vbLf + _
' "[CriteriaMap] = [Over1] * 1.0
"
' & vbLf + _
' "[CriteriaMap] = [Complete2] +
[1Map] "
'doesn't work yet
' Bind to raster
pRModel.BindRaster pInRaster, "input1"
' Run the model
pRModel.Execute
' Get outputs
Dim pRaster1 As IRaster
Set pRaster1 = pRModel.BoundRaster("CriteriaMap")
' Unbind raster
pRModel.UnbindSymbol "input1"
264Appendix D ArcObjects VBA Code
' Add the results into Map
Dim i As Integer
i = 0
Do Until m_pMap.Name = "Criteria Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
Dim pRLayer1 As IRasterLayer
Set pRLayer1 = New RasterLayer
pRLayer1.CreateFromRaster pRaster1
m_pMap.AddLayer pRLayer1
pRLayer1.Name = TxtName.Text
Set m_pMxDoc.ActiveView = m_pMap
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
Set m_pLayer = pRLayer1
SetUpLayer
End Sub
Private Sub btnExit_Click()
Unload Me
End Sub
Private Sub cboRating_Change()
265Appendix D ArcObjects VBA Code
End Sub
Private Sub cboSourceTheme_Change()
MapControl1.ClearLayers
'need to clear the chart too
Dim i As Integer
For i = 0 To m_pMap.LayerCount - 1
Set m_pLayer = m_pMap.Layer(i)
If m_pLayer.Name = cboSourceTheme.Text Then
Exit For
End If
Next i
MapControl1.AddLayer m_pLayer
' Rotate the map to -62.9 deg
Dim pRotateOperation As IRotateOperation
Set pRotateOperation = New RotateOperation
pRotateOperation.ActiveView = MapControl1.ActiveView
pRotateOperation.Rotation = -62.9
pRotateOperation.Do
'get the max & min values for the label
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
266Appendix D ArcObjects VBA Code
Dim pRasterBandCollection As IRasterBandCollection
Set pRasterBandCollection = pRLayer.Raster
Dim pRasterBand As IRasterBand
Set pRasterBand = pRasterBandCollection.Item(0)
Dim pRasterStats As IRasterStatistics
Set pRasterStats = pRasterBand.Statistics
m_dMaxDistance = pRasterStats.Maximum
m_dMinDistance = pRasterStats.Minimum
lblMaxDistance.Caption = m_dMaxDistance
lblMinDistance.Caption = m_dMinDistance
' Set the distance cbo box up
cboDistance.Clear
Dim iBoxNumber As Integer
iBoxNumber = 0
cboDistance.AddItem iBoxNumber
For i = 1 To 20
iBoxNumber = iBoxNumber + m_dMaxDistance / 20
cboDistance.AddItem iBoxNumber
Next i
iBoxNumber = m_dMaxDistance + 1
cboDistance.AddItem iBoxNumber
lboRatedPoints.Clear
m_iPointCount = 0
267Appendix D ArcObjects VBA Code
End Sub
Private Sub ComboBox2_Change()
End Sub
Private Sub btnCreate_Click()
CreateProximityCriteria
Unload Me
End Sub
Private Sub btnAddPoint_Click()
'Add a rated point to the list
'check if there is a distance & rating
If ((Not IsNumeric(cboDistance.Text)) Or
(cboRating.ListIndex = -1)) Then
MsgBox "Must have a valid distance and rating
selected"
Exit Sub
End If
'Get the point number & update variables
'Need to change this to an array
268Appendix D ArcObjects VBA Code
Select Case m_iPointCount
Case 0
m_dDistance1 = cboDistance.Text
m_iRating1 = cboRating.ListIndex + 1
Case 1
If (cboDistance.Text <= m_dDistance1) Then
MsgBox "Enter points from lowest distance to highest"
Exit Sub
End If
m_dDistance2 = cboDistance.Text
m_iRating2 = cboRating.ListIndex + 1
Case 2
If (cboDistance.Text <= m_dDistance2) Then
MsgBox "Enter points from lowest distance to highest"
Exit Sub
End If
m_dDistance3 = cboDistance.Text
m_iRating3 = cboRating.ListIndex + 1
Case 3
If (cboDistance.Text <= m_dDistance3) Then
MsgBox "Enter points from lowest distance to highest"
Exit Sub
End If
269Appendix D ArcObjects VBA Code
m_dDistance4 = cboDistance.Text
m_iRating4 = cboRating.ListIndex + 1
Case 4
If (cboDistance.Text <= m_dDistance4) Then
MsgBox "Enter points from lowest distance to highest"
Exit Sub
End If
m_dDistance5 = cboDistance.Text
m_iRating5 = cboRating.ListIndex + 1
Case Else
MsgBox "Can't add any more points"
Exit Sub
End Select
lboRatedPoints.AddItem m_iPointCount + 1 & ": " &
cboDistance.Text & " " & cboRating.Text
' Update the chart
m_iPointCount = m_iPointCount + 1
UpdateChart
End Sub
Private Sub UpdateChart()
Dim i As Integer
Dim XValue, YValue As Double
270Appendix D ArcObjects VBA Code
With chtUtility
.chartType = VtChChartType2dXY
.ShowLegend = False
With .Plot.Axis(VtChAxisIdY).AxisTitle
.VtFont.Size = 12
.Visible = True
.Text = "Utility"
End With
With .Plot.Axis(VtChAxisIdX).AxisTitle
.VtFont.Size = 12
.Visible = True
.Text = "Raw Value"
End With
.Title.VtFont.Size = 12
.Title = "Rated Points"
.Plot.Axis(VtChAxisIdY).AxisScale.Type =
VtChScaleTypeLinear
.Plot.Axis(VtChAxisIdX).AxisScale.Type =
VtChScaleTypeLinear
'Tip from KB article Q194221:
.Plot.UniformAxis = False
'.Footnote.Text = ""
End With
'PenColor = True 'Draw in color
'ShowMarker = True 'Show plot points
'PenColor = False 'Black and White
271Appendix D ArcObjects VBA Code
'ShowMarker = False 'Don't show plot points
'Create a new array of plot points for this Series
'We will redim the first subscript differently, to
show that each
'series can have a different # of plot points:
ReDim ChartPoints(1 To m_iPointCount, 1 To 2)
'Update the array data:
' doesn't work - deletes previous points
For i = 1 To m_iPointCount
' this would work better using an array!
Select Case i
Case 1
XValue = m_dDistance1
YValue = DecodeRating(m_iRating1)
Case 2
XValue = m_dDistance2
YValue = DecodeRating(m_iRating2)
Case 3
XValue = m_dDistance3
YValue = DecodeRating(m_iRating3)
Case 4
XValue = m_dDistance4
272Appendix D ArcObjects VBA Code
YValue = DecodeRating(m_iRating4)
Case 5
XValue = m_dDistance5
YValue = DecodeRating(m_iRating5)
End Select
ChartPoints(i, 1) = XValue
ChartPoints(i, 2) = YValue
Next i
'We need to increase the ColumnCount. For X-Y Scatter
graphs, we
'need 2 columns for each series.
chtUtility.ColumnCount = 2
With chtUtility
With .Plot
.Wall.Brush.Style = VtBrushStyleSolid
'Normally, you might want the Wall background
of the Chart
'to be in color, if you're using Color pens,
and to be white
'if using B&W pens, but, since we're drawing
both a color
273Appendix D ArcObjects VBA Code
'series *and* a B&W series on *one* chart,
we'll just make
'the wall color, for now. If you want White,
uncomment the
'line found about 10 lines down:
'.Wall.Brush.FillColor.Set 255, 255, 225
'You can set the individual Pen colors
here, or just use
'the defaults.
'Else 'Based on an article in the VB KB:
'Uncomment the next line if you want the
wall color to
'be white:
'.Wall.Brush.FillColor.Set 255, 255, 255
'Set the different patterns for Black and
White plotting.
'You need to set the Pen for only the 'X'
column:
End With
.columnLabelCount = 2
'If the current series has more plot points that
the previous
'one, we need to change .RowCount accordingly:
.RowCount = UBound(ChartPoints, 1)
'Both of the next 2 lines seem to do the same
thing:
.Plot.SeriesCollection(1).SeriesMarker.Show = True
274Appendix D ArcObjects VBA Code
.Plot.SeriesCollection.Item(1).SeriesMarker.Show =
False
'Create the plot points for this series from the
ChartPoints array:
For i = 1 To UBound(ChartPoints, 1)
.DataGrid.SetData i, 1, ChartPoints(i, 1),
False
.DataGrid.SetData i, 2, ChartPoints(i, 2),
False
Next i
' 'Remove null points from *this* series, if it has
*fewer*
' 'points than the prior ones. If you don't remove
null points,
' 'then the graph will add 0,0 points, erroneously.
See MS
' 'Knowledge Base article Q177685 for more info:
' For lRow2 = lRow To OldRowCount&
' .DataGrid.SetData lRow2, CurSeries * 2 - 1,
0, True
' .DataGrid.SetData lRow2, CurSeries * 2, 0,
True
' Next
'
' 'Remove null points from *prior* series, if this
series
' 'has *more* points than the prior ones:
' If CurSeries > 1 Then
' For lRow = OldRowCount& + 1 To .rowCount
' For lRow2 = 1 To CurSeries - 1
' .DataGrid.SetData lRow, lRow2 * 2 -
1, 0, True
' .DataGrid.SetData lRow, lRow2 * 2, 0,
True
275Appendix D ArcObjects VBA Code
' Next
' Next
' End If
.Column = 1
.ColumnLabel = "Series " & Str(1)
.Refresh
End With
End Sub
Private Sub CommandButton2_Click()
End Sub
Private Sub Graph1_HotHit(HitSet As Integer, HitPoint
As Integer)
End Sub
Private Sub CommandButton4_Click()
UpdateChart
End Sub
Private Sub chtUtility_OLEStartDrag(Data As
MSChart20Lib.DataObject, AllowedEffects As Long)
276Appendix D ArcObjects VBA Code
End Sub
Private Sub Label1_Click()
End Sub
Private Sub MapControl1_OnMouseDown(ByVal button As
Long, ByVal shift As Long, ByVal x As Long, ByVal y As
Long, ByVal mapX As Double, ByVal mapY As Double)
End Sub
Private Sub TextBox1_Change()
End Sub
Private Sub UserForm_Click()
End Sub
Private Sub UserForm_Initialize()
Set m_pMxDoc = ThisDocument
Set m_pMaps = m_pMxDoc.Maps
Set m_pMap = m_pMaps.Item(0)
m_iPointCount = 0
'Get the rasterlayers
Dim i As Integer
i = 0
277Appendix D ArcObjects VBA Code
Do Until m_pMap.Name = "Source Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
Dim j As Integer
For j = 0 To m_pMap.LayerCount - 1
Set m_pLayer = m_pMap.Layer(j)
If TypeOf m_pLayer Is IRasterLayer Then
cboSourceTheme.AddItem m_pMap.Layer(j).Name
m_pMap.Layer(j).Visible = True
End If
Next j
AddTerms
End Sub
Private Sub UserForm_Terminate()
End Sub
Private Sub UserForm_Zoom(Percent As Integer)
End Sub
Private Sub AddTerms()
278Appendix D ArcObjects VBA Code
cboRating.AddItem "Totally Unsuitable"
cboRating.AddItem "Very Bad"
cboRating.AddItem "Bad"
cboRating.AddItem "Indifferent"
cboRating.AddItem "Good"
cboRating.AddItem "Very Good"
cboRating.AddItem "Perfect"
cboUncertainty.AddItem "Totally Uncertain"
cboUncertainty.AddItem "Uncertain"
cboUncertainty.AddItem "Moderately Certain"
cboUncertainty.AddItem "Certain"
cboUncertainty.AddItem "Totally Certain"
End Sub
Sub SetUpLayer()
'Classifies the layer in m_pLayer linguistically
' Get raster input from layer
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pRaster As IRaster
Set pRaster = pRLayer.Raster
' Create classfy renderer and QI RasterRenderer
interface
Dim pClassRen As IRasterClassifyColorRampRenderer
Set pClassRen = New
RasterClassifyColorRampRenderer
Dim pRasRen As IRasterRenderer
Set pRasRen = pClassRen
Dim pProps As IRasterClassifyUIProperties
Set pProps = pClassRen
279Appendix D ArcObjects VBA Code
'pProps.ShowClassGaps = True
' Set raster for the render and update
Set pRasRen.Raster = pRaster
pClassRen.ClassCount = 5
pRasRen.Update
'Make the start & end colors
Dim StartColor As IColor
Set StartColor = New RgbColor
StartColor.RGB = RGB(255, 0, 0)
Dim EndColor As IColor
Set EndColor = New RgbColor
EndColor.RGB = RGB(0, 255, 0)
' Create a color ramp to use
Dim pRamp As IAlgorithmicColorRamp
Set pRamp = New AlgorithmicColorRamp
pRamp.Size = 5
pRamp.FromColor = StartColor
pRamp.ToColor = EndColor
pRamp.CreateRamp True
' Create symbol for the classes
Dim pFSymbol As IFillSymbol
280Appendix D ArcObjects VBA Code
Set pFSymbol = New SimpleFillSymbol
' loop through the classes and apply the color and
label
pClassRen.ClassCount = 5
Dim i As Integer
For i = 0 To pClassRen.ClassCount - 1
pFSymbol.Color = pRamp.Color(i)
pClassRen.Symbol(i) = pFSymbol
Select Case i
Case 0
pClassRen.Label(i) = "Totally Unsuitable"
Case 1
pClassRen.Label(i) = "Bad"
Case 2
pClassRen.Label(i) = "Indifferent"
Case 3
pClassRen.Label(i) = "Good"
Case 4
pClassRen.Label(i) = "Perfect"
End Select
Next i
' attempt at setting breaks N.B. there may be a
problem
' if the raster does not contain values that
extend to these breaks!
281Appendix D ArcObjects VBA Code
pClassRen.Break(0) = 0
pClassRen.Break(1) = 0.09
pClassRen.Break(2) = 0.31
pClassRen.Break(3) = 0.69
pClassRen.Break(4) = 0.91
pClassRen.Break(5) = 1
' Update the renderer and plug into layer
pRasRen.Update
Set pRLayer.Renderer = pClassRen
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
End Sub
AGGREGATION
Option Explicit
Private m_pMxDoc As IMxDocument
Private m_pMaps As IMaps
Private m_pMap As IMap
Private m_pLayer As ILayer
Private m_pEnumLayers As IEnumLayer
Private strCriteriaName As String
282Appendix D ArcObjects VBA Code
Private strCriteriaDesc As String
Private m_pDecisionMap As IMap
Private m_pMapFrame As IMapFrame
Private m_pPageLayout As IPageLayout
Private m_pActiveView As IActiveView
Private m_iDMNumber As Integer ' the current DM
Private m_iCriterionNumber As Integer ' the current
criterion
Private m_iNumCriteria As Integer
Private m_dWeightArray() As Double
Private m_sCriteriaArray() As String
Private m_bInputCompleteFlag As Boolean
Private Sub btnAddCriteria_Click()
If (cboWeight.ListIndex < 0) Or (cboMap.ListIndex < 0)
Or (cboRelevance.ListIndex < 0) Then
MsgBox "Select relevance, weight and map"
Exit Sub
End If
If m_bInputCompleteFlag Then
MsgBox "Data input complete. Click create to make
a decision map"
Exit Sub
283Appendix D ArcObjects VBA Code
End If
If (m_iCriterionNumber =
ProjectOptions.g_iNumCriteria) And (m_iDMNumber =
ProjectOptions.g_iNumDMs) Then
m_bInputCompleteFlag = True
End If
lboCriteria.AddItem "It is " & cboRelevance.Text & "
that " & lblDM.Caption & " rates " &
lblCriterion.Caption & " as " & cboWeight.Text & "
Using Map: " & cboMap.Text
' now put variables in arrays for maps & weights etc
ProjectOptions.g_dWeightArray(m_iDMNumber,
m_iCriterionNumber) = DecodeRating(cboWeight.ListIndex
+ 1)
ProjectOptions.g_sMapArray(m_iDMNumber,
m_iCriterionNumber) = cboMap.Text
ProjectOptions.g_dRelevanceArray(m_iDMNumber,
m_iCriterionNumber) =
DecodeRating(cboRelevance.ListIndex + 1)
'if there is no map the weight must be zero
If ProjectOptions.g_sMapArray(m_iDMNumber,
m_iCriterionNumber) = "NONE" Then
ProjectOptions.g_dWeightArray(m_iDMNumber,
m_iCriterionNumber) = 0
End If
'now update criterion & dm number
284Appendix D ArcObjects VBA Code
If Not m_bInputCompleteFlag Then
If m_iCriterionNumber <
ProjectOptions.g_iNumCriteria Then
m_iCriterionNumber = m_iCriterionNumber + 1
lblCriterion.Caption =
ProjectOptions.g_strCriteriaArray(m_iCriterionNumber)
Exit Sub
End If
m_iCriterionNumber = 1
lblCriterion.Caption =
ProjectOptions.g_strCriteriaArray(m_iCriterionNumber)
m_iDMNumber = m_iDMNumber + 1
lblDM.Caption =
ProjectOptions.g_strDMArray(m_iDMNumber)
End If
End Sub
Private Sub btnCreate_Click()
If Not m_bInputCompleteFlag Then
MsgBox "Input not complete"
Exit Sub
End If
If cboConstraint.ListIndex < 0 Then
ProjectOptions.g_sConstraintMap = "NONE"
285Appendix D ArcObjects VBA Code
Else
ProjectOptions.g_sConstraintMap =
cboConstraint.Text
End If
SaveProjectData
CreateDecisionMap
CreateRiskMap
CreateConflictMap
'Dim Response
'
'Response = MsgBox("Do you want a risk map?", vbYesNo)
'
'If Response = vbYes Then
' CreateRiskMap
'End If
'
Unload Me
End Sub
Private Sub btnExit_Click()
Unload Me
End Sub
Private Sub cboCriteria_Change()
End Sub
286Appendix D ArcObjects VBA Code
Private Sub btnTest_Click()
'SaveProjectData
End Sub
Private Sub cboConstraint_Change()
End Sub
Private Sub cboMap_Change()
MapControl1.ClearLayers
If GetLayer(cboMap.Text) = True Then
MapControl1.AddLayer m_pLayer
Else: MapControl1.ClearLayers
MapControl1.Refresh
Exit Sub
End If
' Rotate the map to -62.9 deg
Dim pRotateOperation As IRotateOperation
Set pRotateOperation = New RotateOperation
pRotateOperation.ActiveView = MapControl1.ActiveView
pRotateOperation.Rotation = -62.9
pRotateOperation.Do
End Sub
Private Sub CommandButton4_Click()
287Appendix D ArcObjects VBA Code
MsgBox ProjectOptions.g_strSelectedProject & vbLf &
ProjectOptions.g_strProjectName & vbLf &
ProjectOptions.g_strDMArray(2)
End Sub
Private Sub Label13_Click()
End Sub
Private Sub Label14_Click()
End Sub
Private Sub Label8_Click()
End Sub
Private Sub Label9_Click()
End Sub
Private Sub ListBox1_Click()
End Sub
Private Sub MapControl1_OnMouseDown(ByVal button As
Long, ByVal shift As Long, ByVal x As Long, ByVal y As
Long, ByVal mapX As Double, ByVal mapY As Double)
End Sub
Private Sub SrceThm_Click()
288Appendix D ArcObjects VBA Code
End Sub
Private Sub UserForm_Click()
End Sub
Private Sub UserForm_Initialize()
lblProjectName.Caption =
ProjectOptions.g_strProjectName
TxtMapName.Text = ProjectOptions.g_strProjectName
' dimension the weight & map arrays
ReDim
ProjectOptions.g_sMapArray(ProjectOptions.g_iNumDMs,
ProjectOptions.g_iNumCriteria)
ReDim
ProjectOptions.g_dWeightArray(ProjectOptions.g_iNumDMs
, ProjectOptions.g_iNumCriteria)
ReDim
ProjectOptions.g_dRelevanceArray(ProjectOptions.g_iNum
DMs, ProjectOptions.g_iNumCriteria)
m_bInputCompleteFlag = False
m_iDMNumber = 1
m_iCriterionNumber = 1
lblDM.Caption =
ProjectOptions.g_strDMArray(m_iDMNumber)
lblCriterion.Caption =
ProjectOptions.g_strCriteriaArray(m_iCriterionNumber)
289Appendix D ArcObjects VBA Code
Set m_pMxDoc = ThisDocument
Set m_pMaps = m_pMxDoc.Maps
Set m_pMap = m_pMaps.Item(0)
m_iNumCriteria = 0
'Get the criteria rasterlayers
If GetMap("Criteria Rasters") = False Then
MsgBox "Problem locating map"
Exit Sub
End If
'set up the map combo box
Dim j As Integer
For j = 0 To m_pMap.LayerCount - 1
Set m_pLayer = m_pMap.Layer(j)
If TypeOf m_pLayer Is IRasterLayer Then
cboMap.AddItem m_pMap.Layer(j).Name
cboConstraint.AddItem m_pMap.Layer(j).Name
m_pMap.Layer(j).Visible = True
End If
Next j
cboMap.AddItem "NONE"
cboConstraint.AddItem "NONE"
290Appendix D ArcObjects VBA Code
AddWeights
End Sub
Private Sub AddWeights()
cboWeight.AddItem "Irrelevant"
cboWeight.AddItem "Very Unimportant"
cboWeight.AddItem "Unimportant"
cboWeight.AddItem "Moderately Important"
cboWeight.AddItem "Important"
cboWeight.AddItem "Very Important"
cboWeight.AddItem "Critically Important"
cboRelevance.AddItem "Irrelevant"
cboRelevance.AddItem "Very Unimportant"
cboRelevance.AddItem "Unimportant"
cboRelevance.AddItem "Moderately Important"
cboRelevance.AddItem "Important"
cboRelevance.AddItem "Very Important"
cboRelevance.AddItem "Critically Important"
End Sub
Private Function DecodeRating(ByVal Rating) As Double
If (Not ((Rating = 1) Or (Rating = 2) Or (Rating = 3)
Or (Rating = 4) Or (Rating = 5) Or (Rating = 6) Or
(Rating = 7))) Then
MsgBox "Invalid weight: " & Rating
Exit Function
End If
Select Case Rating
291Appendix D ArcObjects VBA Code
Case 1
DecodeRating = 0
Case 2
DecodeRating = 0.1
Case 3
DecodeRating = 0.3
Case 4
DecodeRating = 0.5
Case 5
DecodeRating = 0.7
Case 6
DecodeRating = 0.9
Case 7
DecodeRating = 1
End Select
End Function
Private Sub SetUpLayer()
'Classifies the layer in m_pLayer linguistically
292Appendix D ArcObjects VBA Code
' Get raster input from layer
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pRaster As IRaster
Set pRaster = pRLayer.Raster
' Create classfy renderer and QI RasterRenderer
interface
Dim pClassRen As IRasterClassifyColorRampRenderer
Set pClassRen = New
RasterClassifyColorRampRenderer
Dim pRasRen As IRasterRenderer
Set pRasRen = pClassRen
Dim pProps As IRasterClassifyUIProperties
Set pProps = pClassRen
'pProps.ShowClassGaps = True
' Set raster for the render and update
Set pRasRen.Raster = pRaster
pClassRen.ClassCount = 5
pRasRen.Update
'Make the start & end colors
Dim StartColor As IColor
Set StartColor = New RgbColor
StartColor.RGB = RGB(255, 0, 0)
Dim EndColor As IColor
293Appendix D ArcObjects VBA Code
Set EndColor = New RgbColor
EndColor.RGB = RGB(0, 255, 0)
' Create a color ramp to use
Dim pRamp As IAlgorithmicColorRamp
Set pRamp = New AlgorithmicColorRamp
pRamp.Size = 5
pRamp.FromColor = StartColor
pRamp.ToColor = EndColor
pRamp.CreateRamp True
' Create symbol for the classes
Dim pFSymbol As IFillSymbol
Set pFSymbol = New SimpleFillSymbol
' loop through the classes and apply the color and
label
pClassRen.ClassCount = 5
Dim i As Integer
For i = 0 To pClassRen.ClassCount - 1
pFSymbol.Color = pRamp.Color(i)
pClassRen.Symbol(i) = pFSymbol
Select Case i
Case 0
pClassRen.Label(i) = "Totally Unsuitable"
Case 1
pClassRen.Label(i) = "Bad"
Case 2
pClassRen.Label(i) = "Indifferent"
294Appendix D ArcObjects VBA Code
Case 3
pClassRen.Label(i) = "Good"
Case 4
pClassRen.Label(i) = "Perfect"
End Select
Next i
' attempt at setting breaks N.B. there may be a
problem
' if the raster does not contain values that
extend to these breaks!
pClassRen.Break(0) = 0
pClassRen.Break(1) = 0.09
pClassRen.Break(2) = 0.31
pClassRen.Break(3) = 0.69
pClassRen.Break(4) = 0.91
pClassRen.Break(5) = 1
' Update the renderer and plug into layer
pRasRen.Update
Set pRLayer.Renderer = pClassRen
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
295Appendix D ArcObjects VBA Code
End Sub
Private Sub CreateDecisionMap()
'This sub uses a raster model
' can use AlgbOp for single operation
NormaliseArrays
'add all the coefficients & inverse
Dim i As Integer
Dim j As Integer
Dim bLastMap As Boolean
Dim bDecisionMapFound
Dim dCoefficientArray() As Double
Dim iLastCriterion As Integer
Dim dTotalWeight As Double ' total coefficient weights
to normalise final decision map
dTotalWeight = 0
'get the last map reference right in case the last map
is NONE
For i = 0 To ProjectOptions.g_iNumCriteria - 1
If
ProjectOptions.g_sMapArray(ProjectOptions.g_iNumDMs,
ProjectOptions.g_iNumCriteria - i) <> "NONE" Then
iLastCriterion = ProjectOptions.g_iNumCriteria
- i
Exit For
End If
Next i
ReDim dCoefficientArray(ProjectOptions.g_iNumDMs,
ProjectOptions.g_iNumCriteria) As Double
296Appendix D ArcObjects VBA Code
For i = 1 To ProjectOptions.g_iNumDMs
For j = 1 To ProjectOptions.g_iNumCriteria
dCoefficientArray(i, j) =
(ProjectOptions.g_dWeightArray(i, j) *
ProjectOptions.g_dRelevanceArray(i, j))
dTotalWeight = dTotalWeight +
dCoefficientArray(i, j)
Next j
Next i
' Main Loop
Dim iDM As Integer
Dim iCriterion As Integer
Dim pCriteriaMap As IRasterLayer
Dim pDecisionMap As IRasterLayer
If ProjectOptions.g_sConstraintMap <> "NONE" Then
Dim pConstraintMap As IRasterLayer
For i = 0 To m_pMap.LayerCount - 1
Set pConstraintMap = m_pMap.Layer(i)
If pConstraintMap.Name =
ProjectOptions.g_sConstraintMap Then
Exit For
End If
Next i
Dim pInRaster3 As IRaster
Set pInRaster3 = pConstraintMap.Raster
End If
For iDM = 1 To ProjectOptions.g_iNumDMs
For iCriterion = 1 To ProjectOptions.g_iNumCriteria
bDecisionMapFound = False
297Appendix D ArcObjects VBA Code
If (iCriterion = iLastCriterion) And (iDM =
ProjectOptions.g_iNumDMs) Then
bLastMap = True
End If
'get criteriamap
If ProjectOptions.g_sMapArray(iDM, iCriterion) <>
"NONE" Then
For i = 0 To m_pMap.LayerCount - 1
Set pCriteriaMap = m_pMap.Layer(i)
If pCriteriaMap.Name =
ProjectOptions.g_sMapArray(iDM, iCriterion) Then
Exit For
End If
Next i
Dim pInRaster1 As IRaster
Set pInRaster1 = pCriteriaMap.Raster
Else: GoTo Line1 'there is no map
End If
'Now get decisionmap
If (iDM > 1) Or (iCriterion > 1) Then
For i = 0 To m_pMap.LayerCount - 1
Set pDecisionMap = m_pMap.Layer(i)
If pDecisionMap.Name = "TempDecisionMap" Then
bDecisionMapFound = True
Exit For
End If
Next i
298Appendix D ArcObjects VBA Code
Dim pInRaster2 As IRaster
Set pInRaster2 = pDecisionMap.Raster
End If
' Create a RasterModel object
Dim pRModel As IRasterModel
Set pRModel = New RasterModel
' Create spatial analysis environment
Dim pEnv As IRasterAnalysisEnvironment
Set pEnv = pRModel
' Set output workspace
Dim pWS As IWorkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pWS =
pWSF.OpenFromFile("c:\temp\temporaryrasters", 0)
Set pEnv.OutWorkspace = pWS
' & vbLf + _ is used to seperate equations
' N.B. get an error if use + vbLf + _ as shown in
samples
' N.B. can't reuse created rasters, once created they
are set & trying to
' change them creates an error
' N.B leave a space before closing quotes
299Appendix D ArcObjects VBA Code
If (bLastMap = True) And
(ProjectOptions.g_sConstraintMap <> "NONE") Then
pRModel.Script = "[Map1] = [CriteriaMap] * " &
dCoefficientArray(iDM, iCriterion) & vbLf + _
"[Map2] = ([DecisionMap] + [map1]) /
" & dTotalWeight & vbLf + _
"[Output] = [Map2] *
[ConstraintMap]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
pRModel.BindRaster pInRaster2, "DecisionMap"
pRModel.BindRaster pInRaster3, "ConstraintMap"
ElseIf bLastMap = True Then
pRModel.Script = "[Map1] = [CriteriaMap] * " &
dCoefficientArray(iDM, iCriterion) & vbLf + _
"[Output] = ([DecisionMap] + [map1])
/ " & dTotalWeight
pRModel.BindRaster pInRaster1, "CriteriaMap"
pRModel.BindRaster pInRaster2, "DecisionMap"
ElseIf ((iDM > 1) Or (iCriterion > 1)) And
bDecisionMapFound = True Then
pRModel.Script = "[Map1] = [CriteriaMap] * " &
dCoefficientArray(iDM, iCriterion) & vbLf + _
"[Output] = [DecisionMap] + [map1]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
pRModel.BindRaster pInRaster2, "DecisionMap"
Else ' first map
300Appendix D ArcObjects VBA Code
pRModel.Script = "[Output] = [CriteriaMap] * " &
dCoefficientArray(iDM, iCriterion)
pRModel.BindRaster pInRaster1, "CriteriaMap"
End If
' Run the model
pRModel.Execute
' Get outputs
Dim pRaster1 As IRaster
Set pRaster1 = pRModel.BoundRaster("Output")
' Unbind raster & delete layer
pRModel.UnbindSymbol "CriteriaMap"
If ((iDM <> 1) Or (iCriterion <> 1)) And
bDecisionMapFound Then
pRModel.UnbindSymbol "DecisionMap"
Dim pLayer As ILayer
For i = 0 To m_pMap.LayerCount - 1
Set pLayer = m_pMap.Layer(i)
If pLayer.Name = "TempDecisionMap" Then
m_pMap.DeleteLayer pLayer
Exit For
End If
Next i
End If
301Appendix D ArcObjects VBA Code
' Add the results into Map
If bLastMap = True Then
If GetMap("Decision Rasters") = False Then
MsgBox "Problem locating map"
Exit Sub
End If
End If
Dim pRLayer1 As IRasterLayer
Set pRLayer1 = New RasterLayer
pRLayer1.CreateFromRaster pRaster1
Set m_pLayer = pRLayer1
m_pMap.AddLayer pRLayer1
If bLastMap = True Then
pRLayer1.Name = TxtMapName.Text
Else: pRLayer1.Name = "TempDecisionMap"
End If
m_pLayer.Name = pRLayer1.Name
Line1: ' for goto
Set m_pMxDoc.ActiveView = m_pMap
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
Next iCriterion
Next iDM
SetUpLayer
302Appendix D ArcObjects VBA Code
ProjectOptions.g_sDecisionMapName = TxtMapName.Text
End Sub
Private Sub NormaliseArrays()
' make each criterions relevance values sum to 1
Dim dCriterionTotal As Double
Dim c As Integer
Dim d As Integer
Dim DM As Integer
For c = 1 To ProjectOptions.g_iNumCriteria
dCriterionTotal = 0
For d = 1 To ProjectOptions.g_iNumDMs
dCriterionTotal = dCriterionTotal +
ProjectOptions.g_dRelevanceArray(d, c)
Next d
For DM = 1 To ProjectOptions.g_iNumDMs
ProjectOptions.g_dRelevanceArray(DM, c) =
ProjectOptions.g_dRelevanceArray(DM, c) /
dCriterionTotal
Next DM
Next c
End Sub
Private Sub CreateRiskMap()
'This sub uses a raster model
' can use AlgbOp for single operation
303Appendix D ArcObjects VBA Code
'NormaliseArrays ' no need if decision map has been
created
Dim bRiskMapFound As Boolean
Dim dMinScore As Double 'The minimum acceptable score
to avoid risk
dMinScore = 0.5
Dim bLastMap As Boolean
bLastMap = False
Dim iLastCriterion As Integer
Dim i As Integer
'get the last map reference right in case the last map
is NONE
For i = 0 To ProjectOptions.g_iNumCriteria - 1
If
ProjectOptions.g_sMapArray(ProjectOptions.g_iNumDMs,
ProjectOptions.g_iNumCriteria - i) <> "NONE" Then
iLastCriterion = ProjectOptions.g_iNumCriteria
- i
Exit For
End If
Next i
Do Until m_pMap.Name = "Criteria Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
If ProjectOptions.g_sConstraintMap <> "NONE" Then
Dim pConstraintMap As IRasterLayer
304Appendix D ArcObjects VBA Code
For i = 0 To m_pMap.LayerCount - 1
Set pConstraintMap = m_pMap.Layer(i)
If pConstraintMap.Name =
ProjectOptions.g_sConstraintMap Then
Exit For
End If
Next i
Dim pInRaster3 As IRaster
Set pInRaster3 = pConstraintMap.Raster
End If
' Main Loop
Dim iDM As Integer
Dim iCriterion As Integer
Dim pCriteriaMap As IRasterLayer
Dim pRiskMap As IRasterLayer
For iDM = 1 To ProjectOptions.g_iNumDMs
For iCriterion = 1 To ProjectOptions.g_iNumCriteria
If (iCriterion = iLastCriterion) And (iDM =
ProjectOptions.g_iNumDMs) Then
bLastMap = True
End If
'get criteriamap
If ProjectOptions.g_sMapArray(iDM, iCriterion) <>
"NONE" Then
For i = 0 To m_pMap.LayerCount - 1
Set pCriteriaMap = m_pMap.Layer(i)
If pCriteriaMap.Name =
ProjectOptions.g_sMapArray(iDM, iCriterion) Then
305Appendix D ArcObjects VBA Code
Exit For
End If
Next i
Dim pInRaster1 As IRaster
Set pInRaster1 = pCriteriaMap.Raster
Else ' no map
GoTo Line1
End If
'Now get risknmap
bRiskMapFound = False
If (iDM <> 1) Or (iCriterion <> 1) Then
For i = 0 To m_pMap.LayerCount - 1
Set pRiskMap = m_pMap.Layer(i)
If pRiskMap.Name = "TempRiskMap" Then
bRiskMapFound = True
Exit For
End If
Next i
Dim pInRaster2 As IRaster
Set pInRaster2 = pRiskMap.Raster
End If
' Create a RasterModel object
Dim pRModel As IRasterModel
Set pRModel = New RasterModel
306Appendix D ArcObjects VBA Code
' Create spatial analysis environment
Dim pEnv As IRasterAnalysisEnvironment
Set pEnv = pRModel
' Set output workspace
Dim pWS As IWorkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pWS =
pWSF.OpenFromFile("c:\temp\temporaryrasters", 0)
Set pEnv.OutWorkspace = pWS
' & vbLf + _ is used to seperate equations
' N.B. get an error if use + vbLf + _ as shown in
samples
' N.B. can't reuse created rasters, once created they
are set & trying to
' change them creates an error
' N.B leave a space before closing quotes
If (bLastMap = True) And
(ProjectOptions.g_sConstraintMap <> "NONE") Then
pRModel.Script = "[NoNewRisk] = [CriteriaMap] >=
[RiskMap]" & vbLf + _
"[NewRisk] = [CriteriaMap] <
[RiskMap] " & vbLf + _
"[PartialRiskMap] = [RiskMap] *
[NoNewRisk]" & vbLf + _
"[PartialCritMap] = [CriteriaMap] *
[NewRisk]" & vbLf + _
307Appendix D ArcObjects VBA Code
"[FinalMap] = [PartialRiskMap] +
[PartialCritMap]" & vbLf + _
"[ContainsRisk] = [FinalMap] < " &
dMinScore & vbLf + _
"[Map1] = ((" & dMinScore & " -
[FinalMap]) / " & dMinScore & ") * [ContainsRisk]" &
vbLf + _
"[Output] = [Map1] *
[ConstraintMap]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
pRModel.BindRaster pInRaster2, "RiskMap"
pRModel.BindRaster pInRaster3, "ConstraintMap"
ElseIf bLastMap = True Then
pRModel.Script = "[NoNewRisk] = [CriteriaMap] >=
[RiskMap]" & vbLf + _
"[NewRisk] = [CriteriaMap] <
[RiskMap] " & vbLf + _
"[PartialRiskMap] = [RiskMap] *
[NoNewRisk]" & vbLf + _
"[PartialCritMap] = [CriteriaMap] *
[NewRisk]" & vbLf + _
"[FinalMap] = [PartialRiskMap] +
[PartialCritMap]" & vbLf + _
"[ContainsRisk] = [FinalMap] < " &
dMinScore & vbLf + _
"[Output] = ((" & dMinScore & " -
[FinalMap]) / " & dMinScore & ") * [ContainsRisk]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
308Appendix D ArcObjects VBA Code
pRModel.BindRaster pInRaster2, "RiskMap"
ElseIf ((iDM > 1) Or (iCriterion > 1)) And
bRiskMapFound Then
pRModel.Script = "[NoNewRisk] = [CriteriaMap] >=
[RiskMap]" & vbLf + _
"[NewRisk] = [CriteriaMap] <
[RiskMap] " & vbLf + _
"[PartialRiskMap] = [RiskMap] *
[NoNewRisk]" & vbLf + _
"[PartialCritMap] = [CriteriaMap] *
[NewRisk]" & vbLf + _
"[Output] = [PartialRiskMap] +
[PartialCritMap]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
pRModel.BindRaster pInRaster2, "RiskMap"
Else 'the first map in the array
pRModel.Script = "[Output] = [CriteriaMap]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
End If
' Run the model
pRModel.Execute
' Get outputs
Dim pRaster1 As IRaster
Set pRaster1 = pRModel.BoundRaster("Output")
' Unbind raster & delete layer
309Appendix D ArcObjects VBA Code
pRModel.UnbindSymbol "CriteriaMap"
If ((iDM <> 1) Or (iCriterion <> 1)) And bRiskMapFound
Then
pRModel.UnbindSymbol "RiskMap"
Dim pLayer As ILayer
For i = 0 To m_pMap.LayerCount - 1
Set pLayer = m_pMap.Layer(i)
If pLayer.Name = "TempRiskMap" Then
m_pMap.DeleteLayer pLayer
Exit For
End If
Next i
End If
' Add the results into Map
If bLastMap = True Then
i = 0
Do Until m_pMap.Name = "Decision Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
End If
Dim pRLayer1 As IRasterLayer
Set pRLayer1 = New RasterLayer
pRLayer1.CreateFromRaster pRaster1
310Appendix D ArcObjects VBA Code
Set m_pLayer = pRLayer1
m_pMap.AddLayer pRLayer1
If bLastMap = True Then
pRLayer1.Name = TxtMapName.Text & "_Risk"
Else: pRLayer1.Name = "TempRiskMap"
End If
m_pLayer.Name = pRLayer1.Name
Line1:
Set m_pMxDoc.ActiveView = m_pMap
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
Next iCriterion
Next iDM
SetUpRiskOrConflictLayer
End Sub
Sub SetUpRiskOrConflictLayer()
'Classifies the layer in m_pLayer linguistically
for risk or conflict
' Get raster input from layer
Dim pRLayer As IRasterLayer
Set pRLayer = m_pLayer
Dim pRaster As IRaster
Set pRaster = pRLayer.Raster
311Appendix D ArcObjects VBA Code
' Create classfy renderer and QI RasterRenderer
interface
Dim pClassRen As IRasterClassifyColorRampRenderer
Set pClassRen = New
RasterClassifyColorRampRenderer
Dim pRasRen As IRasterRenderer
Set pRasRen = pClassRen
Dim pProps As IRasterClassifyUIProperties
Set pProps = pClassRen
'pProps.ShowClassGaps = True
' Set raster for the render and update
Set pRasRen.Raster = pRaster
pClassRen.ClassCount = 5
pRasRen.Update
'Make the start & end colors
Dim StartColor As IColor
Set StartColor = New RgbColor
StartColor.RGB = RGB(0, 255, 0)
Dim EndColor As IColor
Set EndColor = New RgbColor
EndColor.RGB = RGB(255, 0, 0)
' Create a color ramp to use
312Appendix D ArcObjects VBA Code
Dim pRamp As IAlgorithmicColorRamp
Set pRamp = New AlgorithmicColorRamp
pRamp.Size = 5
pRamp.FromColor = StartColor
pRamp.ToColor = EndColor
pRamp.CreateRamp True
' Create symbol for the classes
Dim pFSymbol As IFillSymbol
Set pFSymbol = New SimpleFillSymbol
' loop through the classes and apply the color and
label
pClassRen.ClassCount = 5
Dim i As Integer
For i = 0 To pClassRen.ClassCount - 1
pFSymbol.Color = pRamp.Color(i)
pClassRen.Symbol(i) = pFSymbol
Select Case i
Case 0
pClassRen.Label(i) = "Zero"
Case 1
pClassRen.Label(i) = "Small"
Case 2
pClassRen.Label(i) = "Medium"
Case 3
pClassRen.Label(i) = "Large"
Case 4
pClassRen.Label(i) = "Very Large"
End Select
313Appendix D ArcObjects VBA Code
Next i
' attempt at setting breaks N.B. there may be a
problem
' if the raster does not contain values that
extend to these breaks!
pClassRen.Break(0) = 0
pClassRen.Break(1) = 0.09
pClassRen.Break(2) = 0.31
pClassRen.Break(3) = 0.69
pClassRen.Break(4) = 0.91
pClassRen.Break(5) = 1
' Update the renderer and plug into layer
pRasRen.Update
Set pRLayer.Renderer = pClassRen
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
End Sub
Private Sub CreateMinCriterionMap(iCriterion)
314Appendix D ArcObjects VBA Code
' cycles thru the criterion maps from each DM &
Calculates the min Oijk
'This sub uses a raster model
' can use AlgbOp for single operation
'NormaliseArrays ' no need if decision map has been
created
Dim bMinMapFound As Boolean
Dim bLastMap As Boolean
bLastMap = False
Dim i As Integer
Do Until m_pMap.Name = "Criteria Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
Dim iDM As Integer
Dim pCriteriaMap As IRasterLayer
Dim pMinMap As IRasterLayer
' Main Loop
For iDM = 1 To ProjectOptions.g_iNumDMs
bMinMapFound = False
If (iDM = ProjectOptions.g_iNumDMs) Then
bLastMap = True
End If
315Appendix D ArcObjects VBA Code
'get criteriamap
If ProjectOptions.g_sMapArray(iDM, iCriterion) <>
"NONE" Then
For i = 0 To m_pMap.LayerCount - 1
Set pCriteriaMap = m_pMap.Layer(i)
If pCriteriaMap.Name =
ProjectOptions.g_sMapArray(iDM, iCriterion) Then
Exit For
End If
Next i
Dim pInRaster1 As IRaster
Set pInRaster1 = pCriteriaMap.Raster
ElseIf bLastMap = True Then ' there is no crit map &
the last temp map is it
If GetLayer("TempMinMap") = True Then
m_pLayer.Name = TxtMapName.Text & "_Min " &
ProjectOptions.g_strCriteriaArray(iCriterion)
GoTo Line1
End If
Else ' there is no crit map
GoTo Line1
End If
'Now get MinMap
If (iDM <> 1) Then
316Appendix D ArcObjects VBA Code
For i = 0 To m_pMap.LayerCount - 1
Set pMinMap = m_pMap.Layer(i)
If pMinMap.Name = "TempMinMap" Then
bMinMapFound = True
Exit For
End If
Next i
Dim pInRaster2 As IRaster
Set pInRaster2 = pMinMap.Raster
End If
' Create a RasterModel object
Dim pRModel As IRasterModel
Set pRModel = New RasterModel
' Create spatial analysis environment
Dim pEnv As IRasterAnalysisEnvironment
Set pEnv = pRModel
' Set output workspace
Dim pWS As IWorkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pWS =
pWSF.OpenFromFile("c:\temp\temporaryrasters", 0)
Set pEnv.OutWorkspace = pWS
' & vbLf + _ is used to seperate equations
317Appendix D ArcObjects VBA Code
' N.B. get an error if use + vbLf + _ as shown in
samples
' N.B. can't reuse created rasters, once created they
are set & trying to
' change them creates an error
' N.B leave a space before closing quotes
If (iDM > 1) And bMinMapFound Then
pRModel.Script = "[NoNewMin] = [CriteriaMap] >=
[MinMap]" & vbLf + _
"[NewMin] = [CriteriaMap] < [MinMap]
" & vbLf + _
"[PartialMinMap] = [MinMap] *
[NoNewMin]" & vbLf + _
"[PartialCritMap] = [CriteriaMap] *
[NewMin]" & vbLf + _
"[Output] = [PartialMinMap] +
[PartialCritMap]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
pRModel.BindRaster pInRaster2, "MinMap"
Else 'the first map in the array
pRModel.Script = "[Output] = [CriteriaMap]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
End If
' Run the model
pRModel.Execute
318Appendix D ArcObjects VBA Code
' Get outputs
Dim pRaster1 As IRaster
Set pRaster1 = pRModel.BoundRaster("Output")
' Unbind raster & delete layer
pRModel.UnbindSymbol "CriteriaMap"
If (iDM > 1) And bMinMapFound Then
pRModel.UnbindSymbol "MinMap"
Dim pLayer As ILayer
For i = 0 To m_pMap.LayerCount - 1
Set pLayer = m_pMap.Layer(i)
If pLayer.Name = "TempMinMap" Then
m_pMap.DeleteLayer pLayer
Exit For
End If
Next i
End If
' Add the results into Map
Dim pRLayer1 As IRasterLayer
Set pRLayer1 = New RasterLayer
pRLayer1.CreateFromRaster pRaster1
Set m_pLayer = pRLayer1
m_pMap.AddLayer pRLayer1
If bLastMap = True Then
319Appendix D ArcObjects VBA Code
pRLayer1.Name = TxtMapName.Text & "_Min " &
ProjectOptions.g_strCriteriaArray(iCriterion)
Else: pRLayer1.Name = "TempMinMap"
End If
m_pLayer.Name = pRLayer1.Name
Line1:
Set m_pMxDoc.ActiveView = m_pMap
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
Next iDM
SetUpLayer
End Sub
Private Sub CreateMaxCriterionMap(iCriterion)
' cycles thru the criterion maps from each DM &
Calculates the max Oijk
'This sub uses a raster model
' can use AlgbOp for single operation
'NormaliseArrays ' no need if decision map has been
created
Dim bMaxMapFound As Boolean
320Appendix D ArcObjects VBA Code
Dim bLastMap As Boolean
bLastMap = False
Dim i As Integer
Do Until m_pMap.Name = "Criteria Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
' Main Loop
Dim iDM As Integer
Dim pCriteriaMap As IRasterLayer
Dim pMaxMap As IRasterLayer
For iDM = 1 To ProjectOptions.g_iNumDMs
bMaxMapFound = False
If (iDM = ProjectOptions.g_iNumDMs) Then
bLastMap = True
End If
'get criteriamap
If ProjectOptions.g_sMapArray(iDM, iCriterion) <>
"NONE" Then
For i = 0 To m_pMap.LayerCount - 1
Set pCriteriaMap = m_pMap.Layer(i)
If pCriteriaMap.Name =
ProjectOptions.g_sMapArray(iDM, iCriterion) Then
Exit For
End If
321Appendix D ArcObjects VBA Code
Next i
Dim pInRaster1 As IRaster
Set pInRaster1 = pCriteriaMap.Raster
ElseIf bLastMap = True Then ' there is no crit map &
the last temp map is it
If GetLayer("TempMaxMap") = True Then
m_pLayer.Name = TxtMapName.Text & "_Max " &
ProjectOptions.g_strCriteriaArray(iCriterion)
GoTo Line1
End If
Else ' there is no crit map
GoTo Line1
End If
'Now get MaxMap
If (iDM > 1) Then
For i = 0 To m_pMap.LayerCount - 1
Set pMaxMap = m_pMap.Layer(i)
If pMaxMap.Name = "TempMaxMap" Then
bMaxMapFound = True
Exit For
End If
Next i
Dim pInRaster2 As IRaster
Set pInRaster2 = pMaxMap.Raster
End If
322Appendix D ArcObjects VBA Code
' Create a RasterModel object
Dim pRModel As IRasterModel
Set pRModel = New RasterModel
' Create spatial analysis environment
Dim pEnv As IRasterAnalysisEnvironment
Set pEnv = pRModel
' Set output workspace
Dim pWS As IWorkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pWS =
pWSF.OpenFromFile("c:\temp\temporaryrasters", 0)
Set pEnv.OutWorkspace = pWS
' & vbLf + _ is used to seperate equations
' N.B. get an error if use + vbLf + _ as shown in
samples
' N.B. can't reuse created rasters, once created they
are set & trying to
' change them creates an error
' N.B leave a space before closing quotes
If (iDM > 1) And bMaxMapFound Then
pRModel.Script = "[NoNewMax] = [CriteriaMap] <=
[MaxMap]" & vbLf + _
"[NewMax] = [CriteriaMap] > [MaxMap]
" & vbLf + _
323Appendix D ArcObjects VBA Code
"[PartialMaxMap] = [MaxMap] *
[NoNewMax]" & vbLf + _
"[PartialCritMap] = [CriteriaMap] *
[NewMax]" & vbLf + _
"[Output] = [PartialMaxMap] +
[PartialCritMap]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
pRModel.BindRaster pInRaster2, "MaxMap"
Else 'the first map in the array
pRModel.Script = "[Output] = [CriteriaMap]"
pRModel.BindRaster pInRaster1, "CriteriaMap"
End If
' Run the model
pRModel.Execute
' Get outputs
Dim pRaster1 As IRaster
Set pRaster1 = pRModel.BoundRaster("Output")
' Unbind raster & delete layer
pRModel.UnbindSymbol "CriteriaMap"
If (iDM > 1) And bMaxMapFound = True Then
pRModel.UnbindSymbol "MaxMap"
Dim pLayer As ILayer
324Appendix D ArcObjects VBA Code
For i = 0 To m_pMap.LayerCount - 1
Set pLayer = m_pMap.Layer(i)
If pLayer.Name = "TempMaxMap" Then
m_pMap.DeleteLayer pLayer
Exit For
End If
Next i
End If
' Add the results into Map
Dim pRLayer1 As IRasterLayer
Set pRLayer1 = New RasterLayer
pRLayer1.CreateFromRaster pRaster1
Set m_pLayer = pRLayer1
m_pMap.AddLayer pRLayer1
If bLastMap = True Then
pRLayer1.Name = TxtMapName.Text & "_Max " &
ProjectOptions.g_strCriteriaArray(iCriterion)
Else: pRLayer1.Name = "TempMaxMap"
End If
m_pLayer.Name = pRLayer1.Name
Line1:
Set m_pMxDoc.ActiveView = m_pMap
m_pMxDoc.ActiveView.Refresh
325Appendix D ArcObjects VBA Code
m_pMxDoc.UpdateContents
Next iDM
SetUpLayer
End Sub
Private Sub CreateConflictMap()
' Creates a map expressing conflict amongst DM's
' Just simple rating conflict for now
'This sub uses a raster model
' can use AlgbOp for single operation
'NormaliseArrays ' no need if decision map has been
created
Dim bLastMap As Boolean
bLastMap = False
Dim i As Integer
Do Until m_pMap.Name = "Criteria Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
Loop
' Main Loop
Dim iCriterion As Integer
326Appendix D ArcObjects VBA Code
Dim pMinCriteriaMap As IRasterLayer
Dim pMaxCriteriaMap As IRasterLayer
Dim pConflictMap As IRasterLayer
If ProjectOptions.g_sConstraintMap <> "NONE" Then
Dim pConstraintMap As IRasterLayer
For i = 0 To m_pMap.LayerCount - 1
Set pConstraintMap = m_pMap.Layer(i)
If pConstraintMap.Name =
ProjectOptions.g_sConstraintMap Then
Exit For
End If
Next i
Dim pInRaster4 As IRaster
Set pInRaster4 = pConstraintMap.Raster
End If
For iCriterion = 1 To ProjectOptions.g_iNumCriteria
'create the min & max maps for that criterion
CreateMinCriterionMap (iCriterion)
CreateMaxCriterionMap (iCriterion)
If (iCriterion = ProjectOptions.g_iNumCriteria) Then
bLastMap = True
End If
'get min & max criteria maps
For i = 0 To m_pMap.LayerCount - 1
Set pMinCriteriaMap = m_pMap.Layer(i)
327Appendix D ArcObjects VBA Code
If pMinCriteriaMap.Name = TxtMapName.Text &
"_Min " &
ProjectOptions.g_strCriteriaArray(iCriterion) Then
Exit For
End If
Next i
Dim pInRaster1 As IRaster
Set pInRaster1 = pMinCriteriaMap.Raster
For i = 0 To m_pMap.LayerCount - 1
Set pMaxCriteriaMap = m_pMap.Layer(i)
If pMaxCriteriaMap.Name = TxtMapName.Text &
"_Max " &
ProjectOptions.g_strCriteriaArray(iCriterion) Then
Exit For
End If
Next i
Dim pInRaster2 As IRaster
Set pInRaster2 = pMaxCriteriaMap.Raster
'Now get ConflictMap
If (iCriterion > 1) Then
For i = 0 To m_pMap.LayerCount - 1
Set pConflictMap = m_pMap.Layer(i)
If pConflictMap.Name = "TempConflictMap" Then
Exit For
End If
Next i
Dim pInRaster3 As IRaster
Set pInRaster3 = pConflictMap.Raster
End If
328Appendix D ArcObjects VBA Code
' Create a RasterModel object
Dim pRModel As IRasterModel
Set pRModel = New RasterModel
' Create spatial analysis environment
Dim pEnv As IRasterAnalysisEnvironment
Set pEnv = pRModel
' Set output workspace
Dim pWS As IWorkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pWS =
pWSF.OpenFromFile("c:\temp\temporaryrasters", 0)
Set pEnv.OutWorkspace = pWS
' & vbLf + _ is used to seperate equations
' N.B. get an error if use + vbLf + _ as shown in
samples
' N.B. can't reuse created rasters, once created they
are set & trying to
' change them creates an error
' N.B leave a space before closing quotes
If (iCriterion = ProjectOptions.g_iNumCriteria) And
(ProjectOptions.g_sConstraintMap <> "NONE") Then
329Appendix D ArcObjects VBA Code
pRModel.Script = "[CriterionConflict] = [MaxMap] -
[MinMap]" & vbLf + _
"[NoNewConflict] = [ConflictMap] >=
[CriterionConflict]" & vbLf + _
"[NewConflict] = [ConflictMap] <
[CriterionConflict]" & vbLf + _
"[PartialConflictMap] =
[ConflictMap] * [NoNewConflict]" & vbLf + _
"[PartialCritConfMap] =
[CriterionConflict] * [NewConflict]" & vbLf + _
"[Output] = ([PartialConflictMap] +
[PartialCritConfMap]) * [ConstraintMap]"
pRModel.BindRaster pInRaster1, "MinMap"
pRModel.BindRaster pInRaster2, "MaxMap"
pRModel.BindRaster pInRaster3, "ConflictMap"
pRModel.BindRaster pInRaster4, "ConstraintMap"
ElseIf (iCriterion > 1) Then
pRModel.Script = "[CriterionConflict] = [MaxMap] -
[MinMap]" & vbLf + _
"[NoNewConflict] = [ConflictMap] >=
[CriterionConflict]" & vbLf + _
"[NewConflict] = [ConflictMap] <
[CriterionConflict]" & vbLf + _
"[PartialConflictMap] =
[ConflictMap] * [NoNewConflict]" & vbLf + _
"[PartialCritConfMap] =
[CriterionConflict] * [NewConflict]" & vbLf + _
"[Output] = [PartialConflictMap] +
[PartialCritConfMap]"
pRModel.BindRaster pInRaster1, "MinMap"
330Appendix D ArcObjects VBA Code
pRModel.BindRaster pInRaster2, "MaxMap"
pRModel.BindRaster pInRaster3, "ConflictMap"
Else 'the first map in the array
pRModel.Script = "[Output] = [MaxMap] - [MinMap]"
pRModel.BindRaster pInRaster1, "MinMap"
pRModel.BindRaster pInRaster2, "MaxMap"
End If
' Run the model
pRModel.Execute
' Get outputs
Dim pRaster1 As IRaster
Set pRaster1 = pRModel.BoundRaster("Output")
' Unbind raster & delete layers
pRModel.UnbindSymbol "MaxMap"
pRModel.UnbindSymbol "MinMap"
Dim pLayer As ILayer
For i = 0 To m_pMap.LayerCount - 1
Set pLayer = m_pMap.Layer(i)
If pLayer.Name = TxtMapName.Text & "_Max " &
ProjectOptions.g_strCriteriaArray(iCriterion) Then
m_pMap.DeleteLayer pLayer
Exit For
End If
Next i
331Appendix D ArcObjects VBA Code
For i = 0 To m_pMap.LayerCount - 1
Set pLayer = m_pMap.Layer(i)
If pLayer.Name = TxtMapName.Text & "_Min " &
ProjectOptions.g_strCriteriaArray(iCriterion) Then
m_pMap.DeleteLayer pLayer
Exit For
End If
Next i
If (iCriterion > 1) Then
pRModel.UnbindSymbol "ConflictMap"
For i = 0 To m_pMap.LayerCount - 1
Set pLayer = m_pMap.Layer(i)
If pLayer.Name = "TempConflictMap" Then
m_pMap.DeleteLayer pLayer
Exit For
End If
Next i
End If
' Add the results into Map
If bLastMap = True Then
i = 0
Do Until m_pMap.Name = "Decision Rasters"
Set m_pMap = m_pMaps.Item(i)
i = i + 1
332Appendix D ArcObjects VBA Code
Loop
End If
Dim pRLayer1 As IRasterLayer
Set pRLayer1 = New RasterLayer
pRLayer1.CreateFromRaster pRaster1
Set m_pLayer = pRLayer1
m_pMap.AddLayer pRLayer1
If bLastMap = True Then
pRLayer1.Name = TxtMapName.Text & "_Conflict"
Else: pRLayer1.Name = "TempConflictMap"
End If
m_pLayer.Name = pRLayer1.Name
Set m_pMxDoc.ActiveView = m_pMap
m_pMxDoc.ActiveView.Refresh
m_pMxDoc.UpdateContents
Next iCriterion
SetUpRiskOrConflictLayer
End Sub
Private Function GetLayer(ByVal LayerName As String)
As Boolean
333Appendix D ArcObjects VBA Code
' This function puts the layer with LayerName into
m_pLayer
' and returns true if all went OK
' N.B. the correct map must be in m_pMap
Dim ReturnValue As Boolean
ReturnValue = False
Dim i As Integer
For i = 0 To m_pMap.LayerCount - 1
Set m_pLayer = m_pMap.Layer(i)
If m_pLayer.Name = LayerName Then
ReturnValue = True
Exit For
End If
Next i
GetLayer = ReturnValue
End Function
Private Function GetMap(ByVal MapName As String) As
Boolean
' sets m_pMap to the named map & RETURNS TRUE IF ALL
IS ok
Set m_pMap = m_pMaps.Item(0)
Dim ReturnValue As Boolean
ReturnValue = False
Dim i As Integer
334Appendix D ArcObjects VBA Code
For i = 0 To m_pMaps.Count - 1
Set m_pMap = m_pMaps.Item(i)
If m_pMap.Name = MapName Then
ReturnValue = True
Exit For
End If
Next i
GetMap = ReturnValue
End Function
Private Sub SaveProjectData()
'writes variables in the projectoptions module to
ProjectName_DATA
If ProjectOptions.g_strProjectName = "" Then
MsgBox "No project to save"
Exit Sub
End If
Dim sDataFileName As String
sDataFileName = "D:\ArcGIS\DecisionTools\ProjectData\"
& ProjectOptions.g_strProjectName & "_DATA"
Open sDataFileName For Random As #1
Put #1, 1, ProjectOptions.g_sDecisionMapName ' Write
record to file.
Put #1, 2, ProjectOptions.g_sConstraintMap
' Arrays
Dim DM As Integer
335Appendix D ArcObjects VBA Code
Dim Cr As Integer
Dim SaveCounter As Integer
SaveCounter = 3
For DM = 1 To ProjectOptions.g_iNumDMs
For Cr = 1 To ProjectOptions.g_iNumCriteria
Put #1, SaveCounter,
ProjectOptions.g_dRelevanceArray(DM, Cr)
SaveCounter = SaveCounter + 1
Next Cr
Next DM
For DM = 1 To ProjectOptions.g_iNumDMs
For Cr = 1 To ProjectOptions.g_iNumCriteria
Put #1, SaveCounter,
ProjectOptions.g_dWeightArray(DM, Cr)
SaveCounter = SaveCounter + 1
Next Cr
Next DM
For DM = 1 To ProjectOptions.g_iNumDMs
For Cr = 1 To ProjectOptions.g_iNumCriteria
Put #1, SaveCounter,
ProjectOptions.g_sMapArray(DM, Cr)
SaveCounter = SaveCounter + 1
Next Cr
Next DM
336Appendix D ArcObjects VBA Code
Close #1
End Sub
EXPLORATION
Option Explicit
Private m_pMxApp As IMxApplication
Private m_pMxDoc As IMxDocument
Private m_pMaps As IMaps
Private m_pMap As IMap
Private m_pIdentify As IIdentify
Private m_pIDArray As IArray
Private m_pRasterIdObj As IRasterIdentifyObj
Private m_pIdObj As IIdentifyObj
Private m_RatingArray As Variant
Private m_pLayer As ILayer
Private Sub btnExit_Click()
Unload Me
End Sub
Private Sub cboDM_Change()
LboCriteriaOutcomes.Clear
If Not GetMap("Criteria Rasters") Then
MsgBox "Problem locating Criteria Rasters"
Exit Sub
End If
337Appendix D ArcObjects VBA Code
Dim DM As Integer
DM = cboDM.ListIndex + 1
Dim i As Integer
For i = 1 To ProjectOptions.g_iNumCriteria
'Get the criteria map
If ProjectOptions.g_sMapArray(DM, i) = "NONE" Then
LboCriteriaOutcomes.AddItem
(ProjectOptions.g_strCriteriaArray(i) & ": No Map")
GoTo Line1
End If
If Not GetLayer(ProjectOptions.g_sMapArray(DM, i))
Then
LboCriteriaOutcomes.AddItem
(ProjectOptions.g_strCriteriaArray(i) & ": Map (" &
ProjectOptions.g_sMapArray(DM, i) & ") not found")
GoTo Line1
End If
Set m_pIdentify = m_pLayer
'Convert x and y to map units
Set m_pIDArray =
m_pIdentify.Identify(ThisDocument.p_pPoint)
'Get the FeatureIdentifyObject
If Not m_pIDArray Is Nothing Then
Set m_pRasterIdObj = m_pIDArray.Element(0)
Set m_pIdObj = m_pRasterIdObj
'm_pIdObj.Flash m_pMxApp.Display
338Appendix D ArcObjects VBA Code
'Report info from FeatureIdentifyObject
'MsgBox "Layer:" & m_pIdObj.Layer.Name & vbNewLine
& "Feature:" & m_pIdObj.Name
Else
MsgBox "Nothing to identify for " & m_pLayer.Name
End If
If IsNumeric(m_pIdObj.Name) Then
LboCriteriaOutcomes.AddItem
(ProjectOptions.g_strCriteriaArray(i) & ": " &
LingApprox(m_pIdObj.Name))
Else: LboCriteriaOutcomes.AddItem
(ProjectOptions.g_strCriteriaArray(i) & ": Location
not rated")
End If
Line1:
Next i
End Sub
Private Sub Frame2_Click()
End Sub
Private Sub Label1_Click()
End Sub
Private Sub Label28_Click()
End Sub
Private Sub Label29_Click()
339Appendix D ArcObjects VBA Code
End Sub
Private Sub TabStrip1_Change()
End Sub
Private Sub UserForm_Click()
End Sub
Private Sub UserForm_Initialize()
If ProjectOptions.g_strProjectName = "" Then
MsgBox "No project selected"
Exit Sub
End If
'need to change this so term sets can change
m_RatingArray = Array("Totally Unsuitable", "Bad",
"Indifferent", "Good", "Perfect")
Set m_pMxApp = Application
Set m_pMxDoc = Application.Document
Set m_pMaps = m_pMxDoc.Maps
Dim i As Integer
'set up the DM combo box
For i = 1 To ProjectOptions.g_iNumDMs
cboDM.AddItem ProjectOptions.g_strDMArray(i)
Next i
'get decision rasters & decision map
If Not GetMap("Decision Rasters") Then
340Appendix D ArcObjects VBA Code
MsgBox "Decision Rasters not found"
Exit Sub
End If
If Not GetLayer(ProjectOptions.g_sDecisionMapName)
Then
MsgBox "Decision Map for " &
ProjectOptions.g_strProjectName & " not found"
Exit Sub
End If
Set m_pIdentify = m_pLayer
'Convert x and y to map units
Set m_pIDArray =
m_pIdentify.Identify(ThisDocument.p_pPoint)
'Get the FeatureIdentifyObject
If Not m_pIDArray Is Nothing Then
Set m_pRasterIdObj = m_pIDArray.Element(0)
Set m_pIdObj = m_pRasterIdObj
If m_pIdObj.CanIdentify(m_pLayer) Then
m_pIdObj.Flash m_pMxApp.Display
'Report info from FeatureIdentifyObject
'MsgBox "Layer:" & m_pIdObj.Layer.Name &
vbNewLine & "Feature:" & m_pIdObj.Name
Else: MsgBox "Can't identify"
Exit Sub
End If
Else: MsgBox "Nothing to identify"
341Appendix D ArcObjects VBA Code
Exit Sub
End If
'check if the point is OK
If m_pIdObj.Name = "NoData" Or (Not
IsNumeric(m_pIdObj.Name)) Or m_pIdObj.Name > 1 Or
m_pIdObj.Name < 0 Then
MsgBox "Location not rated"
Exit Sub
End If
lblLocationx.Caption = "X: " & ThisDocument.p_pPoint.x
lblLocationy.Caption = "Y: " & ThisDocument.p_pPoint.y
lblProject = ProjectOptions.g_strProjectName
lblScore = m_pIdObj.Name 'the raw value
lblRating = LingApprox(m_pIdObj.Name)
If GetLayer(ProjectOptions.g_sDecisionMapName &
"_Conflict") Then
Set m_pIdentify = m_pLayer
Set m_pIDArray =
m_pIdentify.Identify(ThisDocument.p_pPoint)
Set m_pRasterIdObj = m_pIDArray.Element(0)
Set m_pIdObj = m_pRasterIdObj
lblConflict = m_pIdObj.Name
End If
If GetLayer(ProjectOptions.g_sDecisionMapName &
"_Risk") Then
Set m_pIdentify = m_pLayer
Set m_pIDArray =
m_pIdentify.Identify(ThisDocument.p_pPoint)
Set m_pRasterIdObj = m_pIDArray.Element(0)
Set m_pIdObj = m_pRasterIdObj
342Appendix D ArcObjects VBA Code
lblRisk = m_pIdObj.Name
End If
End Sub
Private Function GetMap(ByVal MapName As String) As
Boolean
' sets m_pMap to the named map & RETURNS TRUE IF ALL
IS ok
Set m_pMap = m_pMaps.Item(0)
Dim ReturnValue As Boolean
ReturnValue = False
Dim i As Integer
For i = 0 To m_pMaps.Count - 1
Set m_pMap = m_pMaps.Item(i)
If m_pMap.Name = MapName Then
ReturnValue = True
Exit For
End If
Next i
GetMap = ReturnValue
End Function
Private Function LingApprox(ByVal Score As Double) As
String
If IsNumeric(Score) And Score >= 0 And Score <= 1 Then
343Appendix D ArcObjects VBA Code
If Score < 0.1 Then
LingApprox = m_RatingArray(0)
Exit Function
ElseIf Score < 0.3 Then
LingApprox = m_RatingArray(1)
Exit Function
ElseIf Score < 0.7 Then
LingApprox = m_RatingArray(2)
Exit Function
ElseIf Score < 0.9 Then
LingApprox = m_RatingArray(3)
Exit Function
Else: LingApprox = m_RatingArray(4)
End If
Else: LingApprox = "Unscored or score out of range"
End If
End Function
Private Function GetLayer(ByVal LayerName As String)
As Boolean
' This function puts the layer with LayerName into
m_pLayer
' and returns true if all went OK
' N.B. the correct map must be in m_pMap
Dim ReturnValue As Boolean
ReturnValue = False
Dim i As Integer
For i = 0 To m_pMap.LayerCount - 1
344Appendix D ArcObjects VBA Code
Set m_pLayer = m_pMap.Layer(i)
If m_pLayer.Name = LayerName Then
ReturnValue = True
Exit For
End If
Next i
GetLayer = ReturnValue
End Function
345Appendix E ANZIIS Questionaire
Appendix E
AANNZZIIIISS QQUUEESSTTIIOONNAAIIRREE
346Appendix E ANZIIS Questionaire
347Appendix E ANZIIS Questionaire
Stating the problem
Selecting sites for infrastructure developments is a complex task, involving multiple stakeholders
with conflicting interests and poorly defined or uncertain evaluation criteria. A software system is
needed to store, analyze and visualize data and information relevant to the site selection task. The
use of Geographical Information Systems (GIS) in site selection has a long history, with most
approaches being based on a multiple criteria evaluation (MCE) framework. However most GIS-
based MCE methods have inherent difficulties and limitations, generally stemming from the
following causes.
• The methods tend to require crisp numeric value judgments, whereas some criteria may only be
defined using qualitative measures.
• There is generally an assumption of consensus among decision-makers, which does not exist in
reality.
• Assessments by decision-makers are input without defining the level of certainty the decision-
maker places on the assessment.
• The overall aggregated suitability of an alternative is derived solely from a weighted summation
of suitability assessments, without consideration of conflicts, risks or uncertainty inherent in the
alternative.
‘InfraPlanner’ is a SDSS, developed using a fuzzy algorithm to mitigate these difficulties. Broadly
speaking InfraPlanner is an intelligent information system based on approximate reasoning that
offers the following capabilities:
• Linguistic interaction: Linguistic interaction is provided using primary term sets semantically
defined by parameter-based fuzzy numbers, which may be enhanced via a hedging procedure to
add more terms. Both input and feedback is accomplished linguistically.
• Multiple decision-maker capability: The system accepts linguistic inputs from each party
involved in the decision-making process. Conflict between parties is assessed based on differing
suitability and weighting judgments and factored into overall site suitability.
• Uncertainty assessment: There are two types of uncertainty inherent in decision-maker
suitability assessments: linguistic and quantitative. Linguistic uncertainty is represented by the
fuzziness of the primary suitability term, whereas quantitative uncertainty is represented using
the concept of a type-2 fuzzy set and its footprint of uncertainty (FOU). Quantitative uncertainty
348Appendix E ANZIIS Questionaire
is the term used here to represent uncertainty in the source data and/or its relationship with site
suitability.
• User controllable aggregation: Users have the ability to choose an aggregation that minimizes
uncertainty, risk or conflict, or maximizes compensatory suitability. A variety of compensatory
and non-compensatory linguistically defined outcomes may be delivered.
To illustrate how InfraPlanner works and the process by which it was developed, two Logic Models
have been created. A Logic Model presents a model of how a program/process works to solve a
specified problem. The Development Logic Model illustrates how the problem of developing
InfraPlanner was approached. The InfraPlanner Algorithm Logic Model illustrates how
InfraPlanner aids the solution of site selection problems.
349Appendix E ANZIIS Questionaire
InfraPlanner Development Process Logic Model Resources Activities Outputs Customers Short term outcomes Long term outcomes
Users / planners / other
experts User needs
outlined Development
team User needs documented
Literature Critical review
State of the art report
Development team/ other researchers
Technology consultant Technology
review Technology requirements
Ideas for improvement documented
Interviews / focus groups
Development team
Technology selection
Development team
Software development
‘Infraplanner’ SDSS
BAC users / other planners /
researchers
SDSS developed & deployed
Better site selection decisions
GIS technology
External Influences: BAC staff turnover, technological development, advancements in operations research, political environment.
350Appendix E ANZIIS Questionaire
‘Infraplanner’ Algorithm Logic Model Resources Activities Outputs Customers Short term outcomes Long term outcomes
Raw spatial data
Single variable raster maps
DM’s / Users Raw data pre-processed
Single variable raster maps
Create suitability maps
Raster suitability maps
DM’s / Users / stakeholders
Spatial data now represents suitability
GIS conversion function
Output maps Explore & reduce
alternatives
Suitable sites DM’s / users / stakeholders
Reduced set of alternatives
Final site selected
External Influences: Local economy, political environment, community participation.
Linguistic DM prefernces
Suitability map interface
Raster suitability maps
Create parameter
maps
Suitability, uncertainty, risk & conflict maps
DM’s / Users / stakeholders
Spatial data processed & ready
for exploration
Linguistic DM prefernces
Aggregation interface
351Appendix E ANZIIS Questionaire
Questions
Development process Logic Model
1. Are the resources / activities shown in the logic model sufficient to achieve the
desired outcome? Y / N
If not, what resources / activities should be added to the development process?
……………………………………………………………………………………
……………………………………………………………………………………
………………………………………………………………
2. Is the sequence of activities logical? Y / N
If not, how could the sequence be changed?
……………………………………………………………………………………
……………………………………………………………………………………
………………………………………………………………
3. What other external influences should be considered?
……………………………………………………………………………………
……………………………………………………………………………………
………………………………………………………………
Algorithm Logic Model
4. Are the resources / activities shown in the logic model sufficient to achieve the
desired outcome? Y / N
If not, what resources / activities should be added to the solution algorithm?
……………………………………………………………………………………
……………………………….
……………………………………………………………………………………
………………………………..
5. Is the sequence of activities logical? Y / N
If not, how could the sequence be changed?
352Appendix E ANZIIS Questionaire
……………………………………………………………………………………
……………………………………………………………………………………
………………………………………………………………
6. What other external influences should be considered?
……………………………………………………………………………………
……………………………………………………………………………………
………………………………………………………………
General decision-making issues
7. Is a fuzzy set a valid way to quantify a word? Y / N
8. Should the opinion of some participants in the decision-making process be
weighted more heavily than others in some cases? Y / N
9. If so in what situation:
a) If they have greater expertise in rating a particular criterion
b) If they have a greater stake in the outcome
c) If they carry more responsibility for the outcome
9. Would you rather:
a) A computer algorithm to make a site selection decision for you
b) A computer algorithm to give you processed information based on your
preferences but still make the decision yourself
c) A combination of a and b
General comments: