Modelling urban development with geographical information systems and cellular automata

Urban development and migration from rural to urban areas are impacting prime agricultural land and natural landscapes, particularly in the less developed countries. These phenomena will persist and require serious study by those monitoring global environmental change. To address this need, various models have been devised to analyze urbanization and the physical, socioeconomic, and institutional factors impacting urban development.

The most promising and rapidly developing of these paradigms take advantage of new Geographical Information System (GIS) technology. Modelling Urban Development with Geographical Information Systems and Cellular Automata presents one such cutting-edge model that is more than just predictive. It describes how the model simulates the urbanization process, and it provides theoretical context to promote understanding. Starting with a practical overview of the modelling techniques used in urban development research, the author focuses on the cellular automata model and its greatest strength - the incorporation of fuzzy set and fuzzy logic approaches through which urban development can be viewed as a spatially and temporally continuous process.

Real-Life Application to Develop Future Planning Methods
The text describes a landmark study underway, in which the fuzzy constrained cellular automata model has been implemented in a GIS environment to simulate urban development in Sydney, Australia. Featuring a survey of associated research and a geographical database for the Sydney simulation, this book answers many general "what if" questions for urban planners and details a new approach that they can adapt to their own testing and evaluation needs. This modeling method will provide researchers and planners with the means to not just predict population trends, but to better prepare for their consequences.

Preface	p. xi
The Author	p. xiii
Chapter 1 Introduction to Urban Development Modelling	p. 1
1.1 Models and Modelling	p. 2
1.1.1 The Need for Models	p. 2
1.1.2 Characteristics of Models	p. 3
1.1.3 Types of Models	p. 4
1.1.4 Procedures of Model Building	p. 6
1.1.5 The Pitfalls	p. 7
1.2 Theoretical Approaches of Urban Development Modelling	p. 7
1.2.1 Urban Ecological Approach	p. 9
1.2.2 Social Physical Approach	p. 10
1.2.3 Neoclassical Approach	p. 11
1.2.4 Behavioural Approach	p. 13
1.2.5 Systems Approach	p. 14
1.3 Contemporary Practices of Urban Development Modelling	p. 16
1.3.1 Cities as Self-Organising Systems	p. 16
1.3.2 Fuzzy Set and Fuzzy Logic	p. 19
1.3.3 GIS and Urban Modelling	p. 19
1.4 Problems and Prospects	p. 20
1.4.1 Theoretical Problems	p. 20
1.4.2 Technical Problems	p. 22
1.4.3 Future Prospects	p. 22
1.5 Conclusion	p. 23
Chapter 2 Cellular Automata and Its Application in Urban Modelling	p. 25
2.1 Cellular Automata Modelling	p. 25
2.1.1 Cellular Automata Modelling: A Game	p. 25
2.1.2 A Simple Cellular Automata Model	p. 27
2.1.2.1 Five Basic Elements of a Cellular Automaton	p. 28
2.1.2.2 Mathematical Representation of a Cellular Automaton	p. 29
2.1.3 The Complex Features of Cellular Automata	p. 29
2.2 Cellular Automata in Urban Modelling	p. 30
2.2.1 An Urban Cellular Automata	p. 30
2.2.2 Advantages of Cellular Automata for Urban Modelling	p. 33
2.2.2.1 Simplicity in Model Construction	p. 34
2.2.2.2 Modelling Spatial Dynamics to Support "What If" Experiments	p. 34
2.2.2.3 A "Natural Affinity" with Raster GIS	p. 35
2.2.3 Early Applications of Cellular Automata in Urban Modelling	p. 35
2.3 Contemporary Cellular Automata-Based Urban Modelling Practices	p. 38
2.3.1 Space Tessellation: From Regular to Irregular Spatial Units	p. 38
2.3.1.1 Regular Cells of Small or Large Resolution	p. 38
2.3.1.2 Using Irregular Spatial Units	p. 40
2.3.2 From Binary and Multiple to Continuous Cell States	p. 41
2.3.3 Neighbourhood Definitions	p. 41
2.3.3.1 "Action-at-a-Distance" Neighbourhood	p. 41
2.3.3.2 Neighbourhood Size	p. 42
2.3.3.3 Neighbourhood Type	p. 43
2.3.3.4 Irregular Neighbourhood	p. 44
2.3.3.5 Sensitivity Analysis	p. 44
2.3.4 Variation in Transition Rules	p. 45
2.3.4.1 Constrained Cellular Automata	p. 45
2.3.4.2 The SLEUTH Model	p. 46
2.3.4.3 Fuzzy Constrained Cellular Automata Models	p. 47
2.3.4.4 Transition Rules Derived from Other Models	p. 48
2.3.4.5 Artificial Neural Network (ANN)-Based Cellular Automata Models	p. 49
2.3.4.6 Stochastic Cellular Automata Model	p. 50
2.3.5 Modelling Time	p. 51
2.4 Conclusion	p. 51
Chapter 3 Developing a Fuzzy Constrained Cellular Automata Model of Urban Development	p. 53
3.1 Urban Development and Fuzzy Sets	p. 53
3.1.1 Fuzzy Representation of Geographical Boundaries	p. 54
3.1.2 Fuzzy Set Theory	p. 55
3.1.2.1 Definition of Fuzzy Set	p. 55
3.1.2.2 Membership Function	p. 56
3.1.2.3 Fuzzy Operation	p. 58
3.1.3 Urban Development as a Fuzzy Process	p. 59
3.1.3.1 Defining Urban Areas	p. 59
3.1.3.2 Fuzzy Set Approach in Defining Urban Areas	p. 60
3.2 Fuzzy Logic Control in Cellular Automata-Based Urban Modelling	p. 62
3.2.1 Linguistic Variables and Fuzzy Logic	p. 63
3.2.1.1 Linguistic Variables	p. 63
3.2.1.2 Basic Logic Terms and Reasoning	p. 64
3.2.1.3 Fuzzy Logic	p. 66
3.2.2 Fuzzy Logic Control	p. 67
3.2.3 Fuzzy Logic Control in Cellular Automata-Based Urban Modelling	p. 69
3.3 Developing Fuzzy Constrained Cellular Automata for Urban Modelling	p. 70
3.3.1 The Temporal Process of Urban Development	p. 70
3.3.2 The Speed of Urban Development as a Fuzzy Set	p. 73
3.3.3 The Fuzzy Transition Rules and Inferencing	p. 76
3.3.3.1 Primary Transition Rules	p. 76
3.3.3.2 Rule Firing Threshold	p. 77
3.3.3.3 Secondary Transition Rules	p. 79
3.3.3.4 The Defuzzification Process	p. 82
3.3.4 The Defuzzification Process	p. 83
3.4 Conclusion	p. 83
Chapter 4 Sydney: Urban Development and Visualisation	p. 85
4.1 Sydney's Urban Development and Planning	p. 85
4.1.1 Historical Threads of Development	p. 88
4.1.2 Urban Development and Planning	p. 90
4.1.2.1 County of Cumberland Planning Scheme (1948)	p. 90
4.1.2.2 Sydney Region Outline Plan (1968)	p. 93
4.1.2.3 Sydney into its Third Century (1988)	p. 95
4.1.2.4 Cities for the 21st Century (1995)	p. 97
4.1.2.5 City of Cities (2005)	p. 97
4.1.3 Issues Relating to Sydney's Urban Development	p. 100
4.2 Data Collection and Processing	p. 100
4.2.1 Topographic Data	p. 100
4.2.2 Transportation Network	p. 101
4.2.3 Physical Urban Areas	p. 102
4.2.4 Land Excluded from Urban Development	p. 102
4.2.5 Urban Planning Schemes	p. 103
4.3 Defining Sydney's Urban Areas with Fuzzy Set Theory	p. 104
4.3.1 Urban Area Criteria for Statistical Purposes	p. 104
4.3.2 Defining a Fuzzy Boundary of Sydney's Urban Areas	p. 105
4.3.3 Visualising Sydney's Urban Development in Space and Time	p. 107
4.4 Conclusion	p. 110
Chapter 5 Modelling the Urban Development of Sydney: Model Specification, Calibration and Implementation	p. 111
5.1 Model Specification	p. 111
5.1.1 Cell Size and State	p. 111
5.1.2 Neighbourhood Configuration	p. 112
5.1.3 Transition Rules	p. 113
5.1.3.1 Urban Natural Growth Controlled by Primary Transition Rules	p. 113
5.1.3.2 Constrained Development by Secondary Rules	p. 114
5.1.3.3 Flexibility in Rule Implementation	p. 119
5.1.4 The Temporal Dimension	p. 120
5.2 Model Calibration	p. 120
5.2.1 Model Calibration Principles	p. 120
5.2.2 Simulation Accuracy Assessment	p. 122
5.2.2.1 The Error Matrix Approach	p. 122
5.2.2.2 A Modified Error Matrix Approach	p. 124
5.2.2.3 Kappa Coefficient Analysis	p. 126
5.3 Model Implementation in GIS	p. 128
5.3.1 Cellular Automata Modelling and GIS	p. 128
5.3.2 The ArcGIS Approach	p. 129
5.3.3 Graphic User Interface Design	p. 130
5.3.4 Model Calibration	p. 131
5.4 Conclusion	p. 132
Chapter 6 Modelling the Urban Development of Sydney: Results and Discussion	p. 133
6.1 A Summary of Results from the Model	p. 133
6.1.1 The Simulation and Calibration Sequence of the Model	p. 133
6.1.2 Overall Results under All Transition Rules	p. 134
6.2 The Impact of Individual Factors on Sydney's Urban Development	p. 138
6.2.1 Unconstrained Urban Growth	p. 139
6.2.2 Topographically Constrained Development	p. 142
6.2.3 Transportation-Supported Development	p. 142
6.2.4 Urban Planning Policies and Schemes	p. 144
6.2.5 Other Transition Rules	p. 145
6.3 The Impact of Neighbourhood Scale on the Model's Results	p. 146
6.3.1 Results from the Model under Different Neighbourhood Scales	p. 146
6.3.2 Simulation Accuracies of the Model over Time	p. 149
6.3.3 Neighbourhood Scale and Model Calibration	p. 151
6.4 Perspective Views on Sydney's Development to the year 2031	p. 151
6.4.1 Factors Affecting Sydney's Future Development	p. 151
6.4.1.1 Improvement in Transportation Infrastructure	p. 152
6.4.1.2 The Impact of the 2005 Metropolitan Strategic Plan	p. 153
6.4.2 Perspective Views of Urban Development under Different Planning Control Factors	p. 153
6.5 Conclusion	p. 157
Chapter 7 Future Research Directions	p. 159
7.1 Local and Global Transition Rules	p. 160
7.2 Applications of Fuzzy Set and Fuzzy Logic	p. 160
7.3 Urban Consolidation and Anti-urbanisation Processes	p. 161
7.4 The Spatial Area Unit and Its Interaction with the Neighbourhood Scale	p. 162
7.5 Reapplicability of the Model	p. 162
References	p. 163
Index	p. 177

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