Geosimulation: Automata-Based Modeling of Urban Phenomena

303 Pages·2004·3.256 MB·English

by  Itzhak Benenson, Paul M. Torrens(auth.)

Geosimulation
Geosimulation: Automata-based Modeling of Urban Phenomena. I. Benenson and P. M. To r r e n s
# 2004 John Wiley & Sons, Ltd ISBN: 0-470-84349-7
Geosimulation
Automata-based Modeling of Urban Phenomena
Itzhak Benenson
Tel Aviv University, Israel
and
Paul M. Torrens
University of Utah, USA
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Benenson,Itzhak.
Geosimulation:automata-basedmodelingofurbanphenomena/ItzhakBenenson,PaulM.Torrens.
p. cm.
Includesbibliographicalreferences(p.).
ISBN0-470-84349-7(cloth:alk.paper)
1.Urbangeography–Simulationmethods. 2.Urbangeography–Computersimulation.
I.Torrens,PaulM. II.Title
GF125.B46 2004
307.76001013–dc22 2004004938
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AcataloguerecordforthisbookisavailablefromtheBritishLibrary
ISBN0-470-84349-7
Typesetin10/12ptTimesbyThomsonPress(India)Limited,NewDelhi
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For my parents, Maya and Evsey, with love — Itzhak
Bert and Juicy, this is for you, for all the
times you have rescued me and for making
the good times so much better — Paul
Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
1 Introduction to Urban Geosimulation. . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 A New Wave of Urban Geographic Models is Coming. . . . . . . . . . . . 1
1.2 Defining Urban Geosimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Geosimulation Reflects the Object Nature of Urban Systems . . 2
1.2.2 Characteristics of the Geosimulation Model . . . . . . . . . . . . . . 3
1.2.2.1 Management of Spatial Entities . . . . . . . . . . . . . . . . . 3
1.2.2.2 Management of Spatial Relationships . . . . . . . . . . . . . 3
1.2.2.3 Management of Time . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2.4 Direct Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Automata as a Basis for Geosimulation . . . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Cellular Automata. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3.2 Multiagent Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.3 Automata Systems as a Basis for Urban Simulation. . . . . . . . . 8
1.3.3.1 Decentralization. . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.3.2 Specifying Necessary and Only Necessary Details . . . 9
1.3.3.3 Diversity of Characteristics and Behavior . . . . . . . . 10
1.3.3.4 Form and Function Come Together. . . . . . . . . . . . . 10
1.3.3.5 Simplicity and Intuition. . . . . . . . . . . . . . . . . . . . . 10
1.3.4 Geosimulation versus Microsimulation and Artificial Life. . . . 11
1.4 High-resolution GIS as a Driving Force of Geosimulation . . . . . . . . 12
1.4.1 GI Science, Spatial Analysis, and GIS . . . . . . . . . . . . . . . . . 12
1.4.2 Remote Sensing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.4.3 Infrastructure GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.4 GIS of Population Census. . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.5 Generating Synthetic Data . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.5 The Origins of Support for Geosimulation . . . . . . . . . . . . . . . . . . . 16
1.5.1 Developments in Mathematics. . . . . . . . . . . . . . . . . . . . . . . 17
1.5.2 Developments in Computer Science. . . . . . . . . . . . . . . . . . . 17
1.6 Geosimulation of Complex Adaptive Systems. . . . . . . . . . . . . . . . . 18
1.7 Book Layout. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2 Formalizing Geosimulation with Geographic Automata
Systems (GAS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1 Cellular Automata and Multiagent Systems—Unite!. . . . . . . . . . . . . 21
2.1.1 The Limitations of CA and MAS for Urban Applications. . . . 21
2.1.2 The Need for Truly Geographic Representations in
Automata Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
viii Contents
2.2 Geographic Automata Systems (GAS) . . . . . . . . . . . . . . . . . . . . . . 25
2.2.1 Definitions of Geographic Automata Systems . . . . . . . . . . . . 25
2.2.1.1 Geographic Automata Types . . . . . . . . . . . . . . . . . 26
2.2.1.2 Geographic Automata States and State
Transition Rules. . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.2.1.3 Geographic Automata Spatial Referencing and
Migration Rules. . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.2.1.4 Geographic Automata Neighbors and
Neighborhood Rules. . . . . . . . . . . . . . . . . . . . . . . 30
2.2.2 GAS as an Extension of Geographic Information Systems . . . 31
2.2.2.1 GAS as an Extension of the Vector Model . . . . . . . 31
2.2.2.2 GAS and Raster Models . . . . . . . . . . . . . . . . . . . . 31
2.3 GAS as a Tool for Modeling Complex Adaptive Systems. . . . . . . . . 32
2.4 From GAS to Software Environments for Urban Modeling. . . . . . . . 32
2.4.1 Object-Oriented Programming as a Computational
Paradigm for GAS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.4.2 From an Object-Based Paradigm for Geosimulation
Software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.3 GAS Simulation Environments as Temporally Enabled
OODBMS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.4 Temporal Dimension of GAS . . . . . . . . . . . . . . . . . . . . . . . 34
2.5 Object-Based Environment for Urban Simulation (OBEUS)—A
Minimal Implementation of GAS. . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5.1 Abstract Classes of OBEUS . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5.2 Management of Time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5.3 Management of Relationships. . . . . . . . . . . . . . . . . . . . . . . 38
2.5.4 Implementing System Theory Demands . . . . . . . . . . . . . . . . 39
2.5.5 Miscellaneous, but Important, Details. . . . . . . . . . . . . . . . . . 40
2.6 Verifying GAS Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.6.1 Establishing Initial and Boundary Conditions . . . . . . . . . . . . 41
2.6.2 Establishing the Parameters of a Geosimulation Model . . . . . 42
2.6.3 Testing the Sensitivity of Geosimulation Models. . . . . . . . . . 44
2.7 Universality of GAS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3 System Theory, Geography, and Urban Modeling. . . . . . . . . . . . . . . . 47
3.1 The Basic Notions of System Theory. . . . . . . . . . . . . . . . . . . . . . . 47
3.1.1 The Basics of System Dynamics . . . . . . . . . . . . . . . . . . . . . 48
3.1.1.1 Differential and Difference Equations as Standard
Tools for Presenting System Dynamics . . . . . . . . . . 48
3.1.1.2 General Solutions of Linear Differential or Difference
Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.1.1.3 Equilibrium Solutions of Nonlinear Systems,
and Their Stability . . . . . . . . . . . . . . . . . . . . . . . . 51
3.1.1.4 Fast and Slow Processes and Variables . . . . . . . . . . 52
3.1.1.5 The Logistic Equation—The Simplest Nonlinear
Dynamic System . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1.1.6 Spatial Processes and Diffusion Equations. . . . . . . . 54
Contents ix
3.1.2 When a System Becomes a ‘‘Complex’’ System . . . . . . . . . . 57
3.1.2.1 How Nonlinearity Works. . . . . . . . . . . . . . . . . . . . 58
3.1.2.2 How Opennes Works . . . . . . . . . . . . . . . . . . . . . . 62
3.2 The 1960s, Geography Meets System Theory . . . . . . . . . . . . . . . . . 73
3.2.1 Location Theory: Studies of the Equilibrium City . . . . . . . . . 73
3.2.2 Pittsburgh as an Equilibrium Metropolis. . . . . . . . . . . . . . . . 74
3.2.3 The Moment Before Dynamic Modeling . . . . . . . . . . . . . . . 77
3.2.4 Models of Innovation Diffusion—The Forerunner
of Geosimulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.3 Stocks and Flows Urban Modeling. . . . . . . . . . . . . . . . . . . . . . . . . 79
3.3.1 Forrester’s Model of Urban Dynamics . . . . . . . . . . . . . . . . . 79
3.3.1.1 Computer Simulation as a Tool for Studying
Complex Systems. . . . . . . . . . . . . . . . . . . . . . . . . 79
3.3.1.2 Forrester’s Results and the Critique They Attracted . 79
3.3.2 Regional Models: the Mainstream of the 1960s and 1970s. . . 81
3.3.2.1 Aggregated Models of Urban Phenomena . . . . . . . . 82
3.3.2.2 Stocks and Flows Integrated Regional Models. . . . . 83
3.4 Criticisms of Comprehensive Modeling . . . . . . . . . . . . . . . . . . . . . 87
3.4.1 List of Sins. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.4.2 Keep it Simple!. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.5 What Next? Geosimulation of Collective Dynamics! . . . . . . . . . . . . 88
3.5.1 Following Trends of General Systems Science . . . . . . . . . . . 88
3.5.2 Revolution in Urban Data. . . . . . . . . . . . . . . . . . . . . . . . . . 89
3.5.3 From General System Theory to Geosimulation . . . . . . . . . . 90
4 Modeling Urban Land-use with Cellular Automata. . . . . . . . . . . . . . . 91
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.2 Cellular Automata as a Framework for Modeling Complex
Spatial Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.2.1 The Invention of CA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.2.1.1 Formal Definition of CA. . . . . . . . . . . . . . . . . . . . 93
4.2.1.2 Cellular Automata as a Model of the Computer. . . . 95
4.2.1.3 Turing Machine . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.2.1.4 Neuron Networks. . . . . . . . . . . . . . . . . . . . . . . . . 96
4.2.1.5 Self-reproducing Machines and Computational
Universality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.2.1.6 Feedbacks in Neuron Networks and Excitable Media 97
4.2.1.7 Markov Processes and Markov Fields. . . . . . . . . . . 98
4.2.1.8 Early Investigations of CA. . . . . . . . . . . . . . . . . . . 99
4.2.2 CA and Complex Systems Theory. . . . . . . . . . . . . . . . . . . 100
4.2.2.1 The Game of Life—A Complex System Governed
by Simple Rules. . . . . . . . . . . . . . . . . . . . . . . . . 100
4.2.2.2 Patterns of CA Dynamics . . . . . . . . . . . . . . . . . . 101
4.2.3 Variations of Classic CA . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.2.3.1 Variations in Grid Geometry and Neighborhood
Relationships. . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.2.3.2 Synchronous and Asynchronous CA. . . . . . . . . . . 105
x Contents
4.3 Urban Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.2 Raster but not Cellular Automata Models. . . . . . . . . . . . . . 107
4.3.3 The Beginning of Urban Cellular Automata . . . . . . . . . . . . 113
4.3.4 Constrained Cellular Automata . . . . . . . . . . . . . . . . . . . . . 116
4.3.5 Fuzzy Urbanization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
4.3.6 Urbanization Potential as a Self-existing Characteristic
of a Cell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
4.3.6.1 From Monocentric to Polycentric City
Representations . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.3.6.2 Real-World Applications of Potential-Based
Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
4.3.7 Urbanization as a Diffusion Process. . . . . . . . . . . . . . . . . . 131
4.3.7.1 Spatial Ecology of the Population of Urban Cells. . 132
4.3.7.2 Spread of Urban Spatial Patterns . . . . . . . . . . . . . 133
4.3.8 From Fixed Cells to Varying Urban Entities. . . . . . . . . . . . 137
4.3.8.1 Infrastructure Objects as Self-existing Urban
Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
4.3.8.2 Changing Urban Partition . . . . . . . . . . . . . . . . . . 138
4.4 From Markov Models to Urban Cellular Automata . . . . . . . . . . . . 140
4.4.1 From Remotely Sensed Images to Markov Models
of Land-use Change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.4.2 The Link Between Markov and Cellular Automata Models. . 144
4.5 Integration of CA and Markov Approaches at a
Regional Level. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
4.5.1 Flat Merging of Markov and CA Models . . . . . . . . . . . . . . 147
4.5.2 Hierarchy of Inter-regional Distribution and
CA Allocation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
4.6 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
5 Modeling Urban Dynamics with Multiagent Systems. . . . . . . . . . . . . 153
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
5.2 MAS as a Tool for Modeling Complex Human-driven Systems. . . . 154
5.2.1 Agents as ‘‘Intellectual’’ Automata . . . . . . . . . . . . . . . . . . 154
5.2.2 Multiagent Systems as Collections of Bounded Agents . . . . 154
5.2.3 Why do we Need Agents in Urban Models?. . . . . . . . . . . . 155
5.3 Interpreting Agency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
5.4 Urban Agents, Urban Agency, and Multiagent Cities . . . . . . . . . . . 158
5.4.1 Urban Agents as Entities in Space and Time. . . . . . . . . . . . 158
5.4.2 Cities and Multiagent System Geography. . . . . . . . . . . . . . 160
5.5 Agent Behavior in Urban Environments . . . . . . . . . . . . . . . . . . . . 160
5.5.1 Location and Migration Behavior . . . . . . . . . . . . . . . . . . . 161
5.5.2 Utility Functions and Choice Heuristics . . . . . . . . . . . . . . . 162
5.5.3 Rational Decision-making and Bounded Rationality. . . . . . . 163
5.5.4 Formalization of Bounded Rationality . . . . . . . . . . . . . . . . 165
5.5.5 What we do Know About Behavior of Urban
Agents—The Example of Households . . . . . . . . . . . . . . . . 170
Contents xi
5.5.5.1 Factors that Influence Household Preferences. . . . . 170
5.5.5.2 Householder Choice Behavior . . . . . . . . . . . . . . . 172
5.5.5.3 Stress-resistance Hypotheses of Household
Residential Behavior. . . . . . . . . . . . . . . . . . . . . . 172
5.5.5.4 From Householder Choice to Residential
Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
5.5.5.5 New Data Sources for Agent-Based
Residential Models. . . . . . . . . . . . . . . . . . . . . . . 175
5.6 General Models of Agents’ Collectives in Urban Interpretation. . . . 176
5.6.1 Diffusion-limited Aggregation of Developers’ Efforts. . . . . . 177
5.6.2 Percolation of the Developers’ Efforts . . . . . . . . . . . . . . . . 178
5.6.3 Intermittency of Local Development . . . . . . . . . . . . . . . . . 180
5.6.4 Spatiodemographic Processes and Diffusion of Innovation . . 182
5.7 Abstract MAS Models of Urban Phenomena. . . . . . . . . . . . . . . . . 184
5.7.1 Adaptive Fixed Agents as Voters or Adopters of Innovation . 184
5.7.2 Locally Migrating Social Agents. . . . . . . . . . . . . . . . . . . . 190
5.7.2.1 Schelling Social Agents. . . . . . . . . . . . . . . . . . . . 190
5.7.2.2 Random Walkers and Externalization of Agents’
Influence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
5.7.3 Agents That Utilize the Entire Urban Space . . . . . . . . . . . . 195
5.7.3.1 Residential Segregation in the City. . . . . . . . . . . . 195
5.7.3.2 Adapting Householder Agents . . . . . . . . . . . . . . . 199
5.7.3.3 Patterns of Firms . . . . . . . . . . . . . . . . . . . . . . . . 205
5.7.4 Agents That Never Stop. . . . . . . . . . . . . . . . . . . . . . . . . . 205
5.7.4.1 Pedestrians on Pavements . . . . . . . . . . . . . . . . . . 208
5.7.4.2 Depopulating Rooms. . . . . . . . . . . . . . . . . . . . . . 213
5.7.4.3 Cars on Roads . . . . . . . . . . . . . . . . . . . . . . . . . . 216
5.7.5 Multi-type MAS—Firms and Customers. . . . . . . . . . . . . . . 220
5.8 Real-world Agent-based Simulations of Urban Phenomena. . . . . . . 224
5.8.1 Developers and Their Work in the City . . . . . . . . . . . . 224
5.8.2 Pedestrians Take a Walk. . . . . . . . . . . . . . . . . . . . . . . 227
5.8.3 Cars in Urban Traffic. . . . . . . . . . . . . . . . . . . . . . . . . 230
5.8.4 Citizens Vote for Land-use Change . . . . . . . . . . . . . . . 233
5.8.5 In Search of an Apartment in the City . . . . . . . . . . . . . 237
5.9 MAS Models as Planning and Assessment Tools. . . . . . . . . . . . 244
5.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
6 Finale: Epistemology of Geosimulation. . . . . . . . . . . . . . . . . . . . . . 251
6.1 Universal Questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
6.1.1 Social Phenomena are Repeatable . . . . . . . . . . . . . . . . 252
6.1.2 We are Interested in Urban Changes During Time
Intervals Derived from Those of a Human Lifespan. . . . 252
6.1.3 Urban Systems are Unique because They are
Driven by Social Forces . . . . . . . . . . . . . . . . . . . . . . . 253
6.1.4 The Uniqueness of Urban Systems is not
Necessarily Exhibited. . . . . . . . . . . . . . . . . . . . . . . . . 253
6.1.5 Why do we Hope to Understand Urban Systems?. . . . . 253
xii Contents
6.1.6 Tight-coupling between the Urban Theory and
Urban Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
6.1.7 Automata versus State Equations. . . . . . . . . . . . . . . . . 255
6.2 The Future of Geosimulation. . . . . . . . . . . . . . . . . . . . . . . . . . 255
6.2.1 The Applied Power of Geosimulation. . . . . . . . . . . . . . 255
6.2.2 The Theoretical Focus of Geosimulation. . . . . . . . . . . . 256
6.2.3 From Modeling of Urban Phenomena to Models of
a City: Integration Based on a Hierarchy of Models. . . . 256
6.2.4 From Stand-alone Models to Sharing Code and
Geosimulation Language. . . . . . . . . . . . . . . . . . . . . . . 257
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283