Geosimulation

# 2004 John Wiley & Sons, Ltd ISBN: 0-470-84349-7Geosimulation: Automata-based Modeling of Urban Phenomena. I. Benenson and P. M. To r r e n s

GeosimulationAutomata-based Modeling of Urban Phenomena

Itzhak BenensonTel Aviv University, Israel

andPaul M. Torrens

University of Utah, USA

Copyright # 2004 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester,West Sussex PO19 8SQ, EnglandTelephone (+44) 1243 779777

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Library of Congress Cataloguing-in-Publication Data

Benenson, Itzhak.Geosimulation : automata-based modeling of urban phenomena / Itzhak Benenson, Paul M. Torrens.

p. cm.Includes bibliographical references (p.).ISBN 0-470-84349-7 (cloth : alk. paper)1. Urban geography – Simulation methods. 2. Urban geography – Computer simulation.

I. Torrens, Paul M. II. TitleGF125.B46 2004307.76001013–dc22 2004004938

British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

ISBN 0-470-84349-7Typeset in 10/12pt Times by Thomson Press (India) Limited, New DelhiPrinted and bound in Great Britain by Antony Rowe Ltd, Chippenham, WiltshireThis book is printed on acid-free paper responsibly manufactured from sustainable forestryin which at least two trees are planted for each one used for paper production.

For my parents, Maya and Evsey, with love — Itzhak

Bert and Juicy, this is for you, for all thetimes 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. . . . . . . . . . . . 11.2 Defining Urban Geosimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Geosimulation Reflects the Object Nature of Urban Systems . . 21.2.2 Characteristics of the Geosimulation Model . . . . . . . . . . . . . . 3

1.2.2.1 Management of Spatial Entities . . . . . . . . . . . . . . . . . 31.2.2.2 Management of Spatial Relationships . . . . . . . . . . . . . 31.2.2.3 Management of Time . . . . . . . . . . . . . . . . . . . . . . . . 31.2.2.4 Direct Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Automata as a Basis for Geosimulation . . . . . . . . . . . . . . . . . . . . . . 41.3.1 Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3.2 Multiagent Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.3 Automata Systems as a Basis for Urban Simulation. . . . . . . . . 8

1.3.3.1 Decentralization . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.3.3.2 Specifying Necessary and Only Necessary Details . . . 91.3.3.3 Diversity of Characteristics and Behavior . . . . . . . . 101.3.3.4 Form and Function Come Together. . . . . . . . . . . . . 101.3.3.5 Simplicity and Intuition. . . . . . . . . . . . . . . . . . . . . 10

1.3.4 Geosimulation versus Microsimulation and Artificial Life. . . . 111.4 High-resolution GIS as a Driving Force of Geosimulation . . . . . . . . 12

1.4.1 GI Science, Spatial Analysis, and GIS . . . . . . . . . . . . . . . . . 121.4.2 Remote Sensing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.4.3 Infrastructure GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.4.4 GIS of Population Census. . . . . . . . . . . . . . . . . . . . . . . . . . 131.4.5 Generating Synthetic Data . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.5 The Origins of Support for Geosimulation . . . . . . . . . . . . . . . . . . . 161.5.1 Developments in Mathematics. . . . . . . . . . . . . . . . . . . . . . . 171.5.2 Developments in Computer Science . . . . . . . . . . . . . . . . . . . 17

1.6 Geosimulation of Complex Adaptive Systems . . . . . . . . . . . . . . . . . 181.7 Book Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2 Formalizing Geosimulation with Geographic Automata

Systems (GAS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.1 Cellular Automata and Multiagent Systems—Unite!. . . . . . . . . . . . . 212.1.1 The Limitations of CA and MAS for Urban Applications. . . . 212.1.2 The Need for Truly Geographic Representations in

Automata Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2 Geographic Automata Systems (GAS) . . . . . . . . . . . . . . . . . . . . . . 252.2.1 Definitions of Geographic Automata Systems . . . . . . . . . . . . 25

2.2.1.1 Geographic Automata Types . . . . . . . . . . . . . . . . . 262.2.1.2 Geographic Automata States and State

Transition Rules . . . . . . . . . . . . . . . . . . . . . . . . . . 272.2.1.3 Geographic Automata Spatial Referencing and

Migration Rules . . . . . . . . . . . . . . . . . . . . . . . . . . 282.2.1.4 Geographic Automata Neighbors and

Neighborhood Rules . . . . . . . . . . . . . . . . . . . . . . . 302.2.2 GAS as an Extension of Geographic Information Systems . . . 31

2.2.2.1 GAS as an Extension of the Vector Model . . . . . . . 312.2.2.2 GAS and Raster Models . . . . . . . . . . . . . . . . . . . . 31

2.3 GAS as a Tool for Modeling Complex Adaptive Systems . . . . . . . . . 322.4 From GAS to Software Environments for Urban Modeling . . . . . . . . 32

2.4.1 Object-Oriented Programming as a ComputationalParadigm for GAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.4.2 From an Object-Based Paradigm for GeosimulationSoftware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.4.3 GAS Simulation Environments as Temporally EnabledOODBMS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.4.4 Temporal Dimension of GAS . . . . . . . . . . . . . . . . . . . . . . . 342.5 Object-Based Environment for Urban Simulation (OBEUS)—A

Minimal Implementation of GAS. . . . . . . . . . . . . . . . . . . . . . . . . . 352.5.1 Abstract Classes of OBEUS . . . . . . . . . . . . . . . . . . . . . . . . 352.5.2 Management of Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372.5.3 Management of Relationships . . . . . . . . . . . . . . . . . . . . . . . 382.5.4 Implementing System Theory Demands . . . . . . . . . . . . . . . . 392.5.5 Miscellaneous, but Important, Details. . . . . . . . . . . . . . . . . . 40

2.6 Verifying GAS Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402.6.1 Establishing Initial and Boundary Conditions . . . . . . . . . . . . 412.6.2 Establishing the Parameters of a Geosimulation Model . . . . . 422.6.3 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 . . . . . . . . . . . . . . . . . . . . . . . 473.1.1 The Basics of System Dynamics . . . . . . . . . . . . . . . . . . . . . 48

3.1.1.1 Differential and Difference Equations as StandardTools for Presenting System Dynamics . . . . . . . . . . 48

3.1.1.2 General Solutions of Linear Differential or DifferenceEquations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.1.1.3 Equilibrium Solutions of Nonlinear Systems,and Their Stability . . . . . . . . . . . . . . . . . . . . . . . . 51

3.1.1.4 Fast and Slow Processes and Variables . . . . . . . . . . 523.1.1.5 The Logistic Equation—The Simplest Nonlinear

Dynamic System . . . . . . . . . . . . . . . . . . . . . . . . . 533.1.1.6 Spatial Processes and Diffusion Equations . . . . . . . . 54

viii Contents

3.1.2 When a System Becomes a ‘‘Complex’’ System . . . . . . . . . . 573.1.2.1 How Nonlinearity Works . . . . . . . . . . . . . . . . . . . . 583.1.2.2 How Opennes Works . . . . . . . . . . . . . . . . . . . . . . 62

3.2 The 1960s, Geography Meets System Theory . . . . . . . . . . . . . . . . . 733.2.1 Location Theory: Studies of the Equilibrium City . . . . . . . . . 733.2.2 Pittsburgh as an Equilibrium Metropolis . . . . . . . . . . . . . . . . 743.2.3 The Moment Before Dynamic Modeling . . . . . . . . . . . . . . . 773.2.4 Models of Innovation Diffusion—The Forerunner

of Geosimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773.3 Stocks and Flows Urban Modeling. . . . . . . . . . . . . . . . . . . . . . . . . 79

3.3.1 Forrester’s Model of Urban Dynamics . . . . . . . . . . . . . . . . . 793.3.1.1 Computer Simulation as a Tool for Studying

Complex Systems . . . . . . . . . . . . . . . . . . . . . . . . . 793.3.1.2 Forrester’s Results and the Critique They Attracted . 79

3.3.2 Regional Models: the Mainstream of the 1960s and 1970s . . . 813.3.2.1 Aggregated Models of Urban Phenomena . . . . . . . . 823.3.2.2 Stocks and Flows Integrated Regional Models . . . . . 83

3.4 Criticisms of Comprehensive Modeling . . . . . . . . . . . . . . . . . . . . . 873.4.1 List of Sins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873.4.2 Keep it Simple! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

3.5 What Next? Geosimulation of Collective Dynamics! . . . . . . . . . . . . 883.5.1 Following Trends of General Systems Science . . . . . . . . . . . 883.5.2 Revolution in Urban Data . . . . . . . . . . . . . . . . . . . . . . . . . . 893.5.3 From General System Theory to Geosimulation . . . . . . . . . . 90

4 Modeling Urban Land-use with Cellular Automata . . . . . . . . . . . . . . . 91

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.2 Cellular Automata as a Framework for Modeling Complex

Spatial Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 934.2.1 The Invention of CA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

4.2.1.1 Formal Definition of CA . . . . . . . . . . . . . . . . . . . . 934.2.1.2 Cellular Automata as a Model of the Computer . . . . 954.2.1.3 Turing Machine . . . . . . . . . . . . . . . . . . . . . . . . . . 954.2.1.4 Neuron Networks . . . . . . . . . . . . . . . . . . . . . . . . . 964.2.1.5 Self-reproducing Machines and Computational

Universality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974.2.1.6 Feedbacks in Neuron Networks and Excitable Media 974.2.1.7 Markov Processes and Markov Fields . . . . . . . . . . . 984.2.1.8 Early Investigations of CA. . . . . . . . . . . . . . . . . . . 99

4.2.2 CA and Complex Systems Theory . . . . . . . . . . . . . . . . . . . 1004.2.2.1 The Game of Life—A Complex System Governed

by Simple Rules . . . . . . . . . . . . . . . . . . . . . . . . . 1004.2.2.2 Patterns of CA Dynamics . . . . . . . . . . . . . . . . . . 101

4.2.3 Variations of Classic CA . . . . . . . . . . . . . . . . . . . . . . . . . 1054.2.3.1 Variations in Grid Geometry and Neighborhood

Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054.2.3.2 Synchronous and Asynchronous CA . . . . . . . . . . . 105

Contents ix

4.3 Urban Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064.3.2 Raster but not Cellular Automata Models . . . . . . . . . . . . . . 1074.3.3 The Beginning of Urban Cellular Automata . . . . . . . . . . . . 1134.3.4 Constrained Cellular Automata . . . . . . . . . . . . . . . . . . . . . 1164.3.5 Fuzzy Urbanization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1214.3.6 Urbanization Potential as a Self-existing Characteristic

of a Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224.3.6.1 From Monocentric to Polycentric City

Representations . . . . . . . . . . . . . . . . . . . . . . . . . 1234.3.6.2 Real-World Applications of Potential-Based

Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1264.3.7 Urbanization as a Diffusion Process. . . . . . . . . . . . . . . . . . 131

4.3.7.1 Spatial Ecology of the Population of Urban Cells. . 1324.3.7.2 Spread of Urban Spatial Patterns . . . . . . . . . . . . . 133

4.3.8 From Fixed Cells to Varying Urban Entities . . . . . . . . . . . . 1374.3.8.1 Infrastructure Objects as Self-existing Urban

Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1374.3.8.2 Changing Urban Partition . . . . . . . . . . . . . . . . . . 138

4.4 From Markov Models to Urban Cellular Automata . . . . . . . . . . . . 1404.4.1 From Remotely Sensed Images to Markov Models

of Land-use Change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1424.4.2 The Link Between Markov and Cellular Automata Models. . 144

4.5 Integration of CA and Markov Approaches at aRegional Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1464.5.1 Flat Merging of Markov and CA Models . . . . . . . . . . . . . . 1474.5.2 Hierarchy of Inter-regional Distribution and

CA Allocation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1504.6 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

5 Modeling Urban Dynamics with Multiagent Systems. . . . . . . . . . . . . 153

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1535.2 MAS as a Tool for Modeling Complex Human-driven Systems . . . . 154

5.2.1 Agents as ‘‘Intellectual’’ Automata . . . . . . . . . . . . . . . . . . 1545.2.2 Multiagent Systems as Collections of Bounded Agents . . . . 1545.2.3 Why do we Need Agents in Urban Models? . . . . . . . . . . . . 155

5.3 Interpreting Agency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1555.4 Urban Agents, Urban Agency, and Multiagent Cities . . . . . . . . . . . 158

5.4.1 Urban Agents as Entities in Space and Time. . . . . . . . . . . . 1585.4.2 Cities and Multiagent System Geography . . . . . . . . . . . . . . 160

5.5 Agent Behavior in Urban Environments . . . . . . . . . . . . . . . . . . . . 1605.5.1 Location and Migration Behavior . . . . . . . . . . . . . . . . . . . 1615.5.2 Utility Functions and Choice Heuristics . . . . . . . . . . . . . . . 1625.5.3 Rational Decision-making and Bounded Rationality. . . . . . . 1635.5.4 Formalization of Bounded Rationality . . . . . . . . . . . . . . . . 1655.5.5 What we do Know About Behavior of Urban

Agents—The Example of Households . . . . . . . . . . . . . . . . 170

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5.5.5.1 Factors that Influence Household Preferences. . . . . 1705.5.5.2 Householder Choice Behavior . . . . . . . . . . . . . . . 1725.5.5.3 Stress-resistance Hypotheses of Household

Residential Behavior . . . . . . . . . . . . . . . . . . . . . . 1725.5.5.4 From Householder Choice to Residential

Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1735.5.5.5 New Data Sources for Agent-Based

Residential Models . . . . . . . . . . . . . . . . . . . . . . . 1755.6 General Models of Agents’ Collectives in Urban Interpretation . . . . 176

5.6.1 Diffusion-limited Aggregation of Developers’ Efforts. . . . . . 1775.6.2 Percolation of the Developers’ Efforts . . . . . . . . . . . . . . . . 1785.6.3 Intermittency of Local Development . . . . . . . . . . . . . . . . . 1805.6.4 Spatiodemographic Processes and Diffusion of Innovation . . 182

5.7 Abstract MAS Models of Urban Phenomena . . . . . . . . . . . . . . . . . 1845.7.1 Adaptive Fixed Agents as Voters or Adopters of Innovation . 1845.7.2 Locally Migrating Social Agents . . . . . . . . . . . . . . . . . . . . 190

5.7.2.1 Schelling Social Agents. . . . . . . . . . . . . . . . . . . . 1905.7.2.2 Random Walkers and Externalization of Agents’

Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1935.7.3 Agents That Utilize the Entire Urban Space . . . . . . . . . . . . 195

5.7.3.1 Residential Segregation in the City . . . . . . . . . . . . 1955.7.3.2 Adapting Householder Agents . . . . . . . . . . . . . . . 1995.7.3.3 Patterns of Firms . . . . . . . . . . . . . . . . . . . . . . . . 205

5.7.4 Agents That Never Stop . . . . . . . . . . . . . . . . . . . . . . . . . . 2055.7.4.1 Pedestrians on Pavements . . . . . . . . . . . . . . . . . . 2085.7.4.2 Depopulating Rooms. . . . . . . . . . . . . . . . . . . . . . 2135.7.4.3 Cars on Roads . . . . . . . . . . . . . . . . . . . . . . . . . . 216

5.7.5 Multi-type MAS—Firms and Customers. . . . . . . . . . . . . . . 2205.8 Real-world Agent-based Simulations of Urban Phenomena . . . . . . . 224

5.8.1 Developers and Their Work in the City . . . . . . . . . . . . 2245.8.2 Pedestrians Take a Walk . . . . . . . . . . . . . . . . . . . . . . . 2275.8.3 Cars in Urban Traffic . . . . . . . . . . . . . . . . . . . . . . . . . 2305.8.4 Citizens Vote for Land-use Change . . . . . . . . . . . . . . . 2335.8.5 In Search of an Apartment in the City . . . . . . . . . . . . . 237

5.9 MAS Models as Planning and Assessment Tools . . . . . . . . . . . . 2445.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

6 Finale: Epistemology of Geosimulation . . . . . . . . . . . . . . . . . . . . . . 251

6.1 Universal Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2516.1.1 Social Phenomena are Repeatable . . . . . . . . . . . . . . . . 2526.1.2 We are Interested in Urban Changes During Time

Intervals Derived from Those of a Human Lifespan . . . . 2526.1.3 Urban Systems are Unique because They are

Driven by Social Forces . . . . . . . . . . . . . . . . . . . . . . . 2536.1.4 The Uniqueness of Urban Systems is not

Necessarily Exhibited . . . . . . . . . . . . . . . . . . . . . . . . . 2536.1.5 Why do we Hope to Understand Urban Systems? . . . . . 253

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6.1.6 Tight-coupling between the Urban Theory andUrban Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

6.1.7 Automata versus State Equations . . . . . . . . . . . . . . . . . 2556.2 The Future of Geosimulation. . . . . . . . . . . . . . . . . . . . . . . . . . 255

6.2.1 The Applied Power of Geosimulation. . . . . . . . . . . . . . 2556.2.2 The Theoretical Focus of Geosimulation. . . . . . . . . . . . 2566.2.3 From Modeling of Urban Phenomena to Models of

a City: Integration Based on a Hierarchy of Models. . . . 2566.2.4 From Stand-alone Models to Sharing Code and

Geosimulation Language. . . . . . . . . . . . . . . . . . . . . . . 257

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283

xii Contents

Preface

Are we witnessing a revolution in urban geography? The answer to that question isalmost certainly that, yes, we are.That is a bold statement to make. But, let’s consider the evidence.During the last four decades, a volume of research on topics of urban geography has

been conducted—everything from the geography of urban graveyards to the evolutionof world cities and massive Megalopoli. Factual data has not always been available tosettle the arguments that discussion has generated either in print or in conversation,but data are, and always will be, in short supply. Regardless of how much data wehave, we always thirst for more; it is a hallmark of life in an Information Age. Butdata aside, the general tone of discussion and views on urban geography appear to becoming full circle. All too often, the general impression is that of discussing the sameold issues, although they are often marketed in new forms. This rebranding isundoubtedly important, but we are not so much interested in shifting units, marketingproducts, as we are in uncovering knowledge. Put briefly, there is a strong sense thatall the good theoretical stuff has been said before.What might free us from ever-wandering around the same Mobius strip; more data,

better data?Not so long ago, the data excuse was a pretty good one. It is not any more. Since the

last decade of the twentieth century, an enormous volume of data has becomeavailable to us, directly to our desktops and our libraries. These data cover abewildering array of urban phenomena—information on urban infrastructure andpopulations at all levels of spatial and temporal resolutions have been generated andaccumulated. We have not utilized most of them yet. This is not because they areinaccessible; we simply shy away, for the most part, from getting stuck into thesehuge reservoirs of remotely sensed data and census databases. Modern statistical andGIS environments enable combination of qualitative and quantitative methods, oftenfreely, and we are no longer critically constrained in terms of computing power.So, what then; data analysis?The common sense view works something like this: let us take a theory, fit

the appropriate data, develop and evaluate a clear and lucid understanding of thephenomenon at hand, and then generate forecasts or what-if scenarios. By thesemeans we might thin out all-embracing descriptions, perhaps even give birth to novelideas. How has that worked out for us? Has it been successful thus far?We must admit that most urban models do not work well enough when we deal with

real cities. In some cases, the theory turns out to be ‘‘too general’’ to be of use in suchexercises; examining closely, we see that phenomenological ‘‘regression’’ betweenpotential factors and observed consequences, but not the theory, is applied. In other,not less frequent, situations, application of theory demands so much ‘‘tuning’’ that the

city becomes the product of researcher’s intuition—more late-night SimCity thanscience. We probably have as many models of cities as we have cities; a sure sign ofdiscrepancy between urban theory and urban dynamics. One can pessimistically assertthat, with all due respect, we have been kidding ourselves; what we thought of astheory is not, in a natural science sense at least. The theory simply does not explan ourobserved facts. We have another view.We say that the lack of success in urban modeling is an interim problem, caused by

noncorrespondence between the theory of urban geography on the one hand andmodeling tools, in the form in which they were employed until recently, on the other.This discrepancy has roots in over-simplified representation of elementarycomponents of the urban systems considered.The city is an artificial creature and urban geography is first and foremost human

geography. Human behavior and, especially, the decisions of humans, drive the cityand its dynamics. Cities exhibit properties that resemble chemical reactions, but thatdoes not change the fact that molecules in a chemical reaction do not make decisions.Humans are not dumb particles. Yet, most urban models strip the city of itsintelligence.Under this view, that an individual in the urban collective—and its decision—

occupies nothing more than 1/n-th of the aggregate, one can apply a traditionalcybernetic black-box approach and obtain the model, which is by definitionconvenient for mathematical analysis.The problem is that the individuality and autonomy of urban objects are lost in this

case. This is not a superficial problem—most of these black-box views dictatestructure and rules that do not fit at all to a system driven by individual andautonomous decision-makers: the city.The models, not the geographic theory, are thus backward and must be reworked.Decision-making, as well as the other forms of individual behaviors of urban

objects or entities, has numerous faces. Sometimes we can ignore them altogether,and we may be forgiven for assuming that drivers in a city behave as water in apipeline. Sometimes the decision-making processes are so complicated that we preferavoiding formalization. The models should point us where to stop on the way frommolecules to decision-makers.In this book, we treat the city as a creature, the complexity of which is above the

complexity of physical and chemical systems, but below the complexity of a humanself. We assume therefore, that there is no need to directly account for real complexityof urban inanimate and animate objects when formalizing urban phenomena. Instead,we could succeed with avatars, which exhibit simple human-like or human-drivenactivities. We believe that an intuitive separation of ‘‘simple’’ and ‘‘complex’’ can besufficient. Inherent physical constraints, spatial ones, first and foremost, act in the cityso strongly that the consequences of really human feelings and reasons, such as loveand hate, can be less important, at today’s level of understanding, at least for the outerobserver.We, thus, believe that an object-based and ‘‘loosely human’’ approach can explain

much in cities. Several advances support this vision of urban system dynamics. Andhere we turn to the evidence for our bold opening statement, arguing for a revolutionin urban geography.

xiv Preface

� General system theory, which started 60 years ago with black-box cybernetic models, isdeveloping toward understanding system dynamics as collective phenomena; itsmodern principles are applicable to multiple interacting decision-making objects, inthe same way that those principles apply to collectives of simpler physical, chemical, orecological particles.

� Formal frameworks are standing by in the sidelines—cellular automata and theirextension, multiagent systems, are evidently powerful for modeling and simulatingcollectives of interacting individual urban autonomous objects.

� High-resolution infrastructure and population GIS and remote sensing databasesprovide data at the resolution of the urban objects we are interested in, whether theyare objects or aggregations of objects.

� Modern programming technology is based on object-oriented paradigms and a numberof computer environments for simulating and investigating the dynamics of thecollectives of autonomous objects already exist.

All this accompanies a recent boom in urban modeling, with models that tend to actat the resolution of real-world urban objects, and increasingly, describe the behaviorof those objects in terms not far-removed from our views. This is an exciting time tobe working in this field. All the ingredients for a crystallization of these ideas arethere—a burgeoning paradigm shift for urban geography, urban analysis, and urbanmodeling. We call it urban geosimulation.

Preface xv

Acknowledgements

Many people justly deserve some expression of my appreciation. I wish to thank myfriends and colleagues: Juval Portugali, Itzhak Omer, and Erez Hatna. We haveworked together for many years, and various parts of the book reflect our commonwork and discussions. Special thanks go to Erez Hatna who, besides developing theYaffo model, created several of the figures found throughout the text. I also want tothank Shai Aronovich and Saar Noam, with whom we developed the first version ofOBEUS. Lena, Kobi, Pola and Manya Benenson provided crucial support, especiallyManya, who built the clay city and located it in the artificial environment of ourbackyard. The city appears on the book’s cover, merged with the image of Tel-Avivthat was so kindly provided by the municipality’s GIS Department. Lastly, I want toexpress my gratitude to my colleagues from the Environmental Simulation Laboratoryand the Department of Geography and Human Environment, Tel-Aviv University, fortheir moral support throughout the writing of this book.

Itzhak BenensonTel-Aviv, April 2004.

Thanks to Carolina Tobon, Muki Haklay, Martin Dodge, Naru Shiode, and DarylLloyd for being such fantastic friends and colleagues and for fueling the creativeprocess with cranberry juice, trips to Bartok and Sak, Refreshers, discount Japanesefood, and Caffe Nero americanos. Boards of Canada, Linkin Park, Mogwai, andDashboard Confessional provided the best soundtrack at crunch-time. Thanks, alsoand in particular, to the Benenson family.

Paul M. TorrensSalt Lake City, April, 2004.

Permission to reproduce the following illustrations is gratefully acknowledged:

Figure 1.7 CODATA Society.Figure 2.1 CRC Press LLC.Figure 2.2 Reprinted from Computers, Environment and Urban Systems,

Benenson, I., S. Aronovich, and S. Noam, ‘‘Let’s TalkObjects: Generic Methodology for Urban High-ResolutionSimulation’’. Copyright 2004, with permission from Elsevier.

Figure 2.3 CRC Press LLC.Figure 2.7 Reprinted from Computers, Environment and Urban Systems,

24(6), Benenson, I., S. Aronovich, and S. Noam, ‘‘Let’s TalkObjects: Generic Methodology for Urban High-ResolutionSimulation’’, pp. 559–581. Copyright 2000, with permissionfrom Elsevier.

Figure 2.8 Reprinted from Computers, Environment and Urban Systems,24(6), Benenson, I., S. Aronovich, and S. Noam, ‘‘Let’s TalkObjects: Generic Methodology for Urban High-ResolutionSimulation’’, pp. 559–581. Copyright 2000, with permissionfrom Elsevier.

Figure 2.9 Reprinted from Computers, Environment and Urban Systems,24(6), Benenson, I., S. Aronovich, and S. Noam, ‘‘Let’s TalkObjects: Generic Methodology for Urban High-ResolutionSimulation’’, pp. 559–581. Copyright 2000, with permissionfrom Elsevier.

Figure 3.1 Andow, D.A., P.M. Kareiva, S.A. Levin, and A. Okubo, Spreadof invading organisms, Landscape Ecology, 4(2/3), 1990, pp.177–188, reproduced with permission from Kluwer AcademicPublishers.

Figure 3.9 Reproduced by permission of The RAND Corporation.Figure 3.10 Reproduced by permission of The RAND Corporation.Figure 3.11 Professor J. W. Forrester.Figure 4.5 Steinitz, C. and P. Rogers, A System Analysis Model of

Urbanization and Change: An Experiment in InterdisciplinaryEducation, 1970, reproduced by permission of The MIT Press.

Figure 4.6 Steinitz, C. and P. Rogers, A System Analysis Model ofUrbanization and Change: An Experiment in InterdisciplinaryEducation, 1970, reproduced by permission of The MIT Press.

Figure 4.7 University of North Carolina.Figure 4.8 Steinitz, C. and P. Rogers, A System Analysis Model of

Urbanization and Change: An Experiment in InterdisciplinaryEducation, 1970, reproduced by permission of The MIT Press.

Figure 4.9 Tobler, W., Cellular Geography. Philosophy in Geography. S.Gale and G. Olison 1979, pp. 379–386, with kind permissionof Kluwer Academic Publishers.

Figure 4.10 Clark University.Figure 4.13 European Commission Joint Research Center.Figure 4.14 Reproduced by permission of Pion Limited, London.Figure 4.15 Reproduced by permission of Ferdinando Semboloni.Figure 4.16 Reproduced by permission of Pion Limited, London.Figure 4.17 Reproduced by permission of Pion Limited, London.Figure 4.18 Reproduced by permission of Pion Limited, London.Figure 4.19 Reproduced by permission of Pion Limited, London.Figure 4.20 Reprinted from Computers, Environment and Urban Systems, 24,

Bell, M., C. Dean, and M. Blake, ‘‘Forecasting the pattern ofurban growth with PUP: a web-based model interfaced withGIS and 3D animation’’. Copyright 2004, with permissionfrom Elsevier.

Figure 4.21 Reproduced by permission of Jeannette Candau.Figure 4.22 Reproduced by permission of Jeannette Candau.Figure 4.23 Reproduced by permission of Jeannette Candau.Figure 4.24 Reproduced by permission of Jeannette Candau.

xviii Acknowledgements

Figure 4.25 Reproduced by permission of Pion Limited, London.Figure 4.26 Reproduced by permission of Pion Limited, London.Figure 4.27 Reproduced by permission of Pion Limited, London.Figure 4.28 European Commission Joint Research Center.Figure 5.5 Reproduced by permission of Oxford University Press, Inc.Figure 5.6 OUP.Figure 5.7a Reprinted figure 1 with permission from Mandelbrot, B.B.,

B. Kol and A. Aharony, Physical Review Letters, 88(5)1–4, 2002. Copyright 2002 by the American Physical Society.

Figure 5.7b Reproduced by permission of World Scientific PublishingCompany.

Figure 5.8 Reprinted with permission from ‘‘Modeling urban growthpatterns with correlated percolation’’ by H.A. Makse, et al,Phys. Rev. E, 1998, 58(6):7054–7062.

Figure 5.9 Reprinted with permission from ‘‘Modeling urban growthpatterns with correlated percolation’’ by H.A. Makse, et al,Phys. Rev. E, 1998, 58(6):7054–7062.

Figure 5.10 World Scientific Publishing Company.Figure 5.11 Reproduced by permission of The Royal Society.Figure 5.12 Reproduced by permission of The Royal Society.Figure 5.13 Reprinted from Physica A, 303, Schweitzer, F., J. Zimmermann

and H. Muhlenbein, ‘‘Coordination of decisions in a spatialagent model’’, pp. 189–216. Copyright 2000, with permissionfrom Elsevier.

Figure 5.14 Reproduced by permission of The Simsoc Consortium.Figure 5.15 Reproduced by permission of The Simsoc Consortium.Figure 5.18 Reproduced by permission of Taylor & Francis Ltd. http://

www.tandf.co.uk/journals.Figure 5.19 Reproduced by permission of Taylor & Francis Ltd. http://

www.tandf.co.uk/journals.Figure 5.20 Reprinted from Computers, Environment and Urban Systems, 22,

Benenson, I., ‘‘Multi-agent simulations of residentialdynamics in the city’’, pp. 25–42. Copyright 2003, withpermission from Elsevier.

Figure 5.21 Reprinted from Computers, Environment and Urban Systems, 22,Benenson, I., ‘‘Multi-agent simulations of residentialdynamics in the city’’, pp. 25–42. Copyright 2003, withpermission from Elsevier.

Figure 5.22 OUP.Figure 5.23 Reproduced by permission of Pion Limited, London.Figure 5.24 Reprinted from Computers, Environment and Urban Systems, 22,

Benenson, I., ‘‘Multi-agent simulations of residentialdynamics in the city’’, pp. 25–42. Copyright 2003, withpermission from Elsevier.

Figure 5.25 Reprinted from Computers, Environment and Urban Systems, 22,Benenson, I., ‘‘Multi-agent simulations of residential

Acknowledgements xix

dynamics in the city’’, pp. 25–42. Copyright 2003, withpermission from Elsevier.

Figure 5.26 Reprinted from Computers, Environment and Urban Systems, 22,Benenson, I., ‘‘Multi-agent simulations of residentialdynamics in the city’’, pp. 25–42. Copyright 2003, withpermission from Elsevier.

Figure 5.27 Reprinted from Computers, Environment and Urban Systems, 22,Benenson, I., ‘‘Multi-agent simulations of residentialdynamics in the city’’, pp. 25–42. Copyright 2003, withpermission from Elsevier.

Figure 5.28 Reprinted from Journal of Urban Economics, 45, Page, S.E.,‘‘On the Emergence of Cities’’, pp. 184–208. Copyright 1999,with permission from Elsevier.

Figure 5.29 Reprinted from Mathematics and Computers in Simulation, Vol27, Gipps and Marksjo, ‘‘A micro-simulation model forpedestrian flows’’, pp. 95–105, Copyright 1985, withpermission from Elsevier.

Figure 5.30 Reprinted from Mathematics and Computers inSimulation, Vol 27, Gipps and Marksjo, ‘‘Amicro-simulation model for pedestrian flows’’,pp. 95–105, Copyright 1985, with permission fromElsevier.

Figure 5.31 Reproduced by permission of D. Helbing.Figure 5.32 Reproduced by permission of D. Helbing.Figure 5.33 Reproduced by permission of D. Helbing.Figure 5.34 Reprinted from Physica A, 295, Burstedde, C., K. Klauck, A.

Schadschneider, and J. Zittartz, ‘‘Simulation of pedestriandynamics using a two-dimensional cellular automaton’’,pp. 507–525. Copyright 2000, with permission fromElsevier.

Figure 5.35 Reprinted with permission from Nature, 47(28):487–490,Helbing, D., I. Farkas, and T. Vicsek (2000), ‘‘Simulatingdynamical features of escape panic’’. Copyright 2000Macmillan Magazines Limited.

Figure 5.36 Reprinted with permission from Nature 47(28):487–490,Helbing, D., I. Farkas, and T. Vicsek (2000), ‘‘Simulatingdynamical features of escape panic’’. Copyright 2000Macmillan Magazines Limited.

Figure 5.37 Reprinted with permission from Nature 47(28):487–490,Helbing, D., I. Farkas, and T. Vicsek (2000), ‘‘Simulatingdynamical features of escape panic’’. Copyright 2000Macmillan Magazines Limited.

Figure 5.38 Reprinted from Parallel Computing, 27(5), Wahle, J., L. Neubert,J. Esser, and M. Schreckenberg, ‘‘A cellular automatontraffic flow model for online simulation of traffic’’,pp. 719–735. Copyright 2001, with permissionfrom Elsevier.

xx Acknowledgements

Figure 5.39 Reprinted from Transportation Research Part C, 10,Wahle, J., A.L.C. Bazzan, F. Klugl, and M. Schreckenberg,‘‘The impact of real-time information in a two-route scenariousing agnet-based simulation’’, pp. 399–417. Copyright 2002,with permission from Elsevier.

Figure 5.40a Reprinted from Parallel Computing, 27(5), Wahle, J., L. Neubert,J. Esser, and M. Schreckenberg, ‘‘A cellular automatontraffic flow model for online simulation of traffic’’,pp. 719–735. Copyright 2001, with permission from Elsevier.

Figure 5.40b Reprinted from Physica A, 2857, Schadscheider, A., ‘‘Statisticalphysics of traffic flow’’, pp. 101–120. Copyright 2000, withpermission from Elsevier.

Figure 5.41 Reprinted from Parallel Computing, 27(5), Wahle, J., L.Neubert, J. Esser, and M. Schreckenberg, ‘‘A cellularautomaton traffic flow model for online simulation oftraffic’’, pp. 719–735. Copyright 2001, with permission fromElsevier.

Figure 5.42 Reprinted from Parallel Computing, 27(5), Wahle, J., L. Neubert,J. Esser, and M. Schreckenberg, ‘‘A cellular automatontraffic flow model for online simulation of traffic’’,pp. 719–735. Copyright 2001, with permissionfrom Elsevier.

Figure 5.43 Reprinted from Transportation Research Part C, 10, Hidas, P.,‘‘Modelling lane changing and merging in microscopic trafficsimulation’’, pp. 351–371. Copyright 2002, with permissionfrom Elsevier.

Figure 5.44 Reprinted from Transportation Research Part C, 10, Hidas, P.,‘‘Modelling lane changing and merging in microscopic trafficsimulation’’, pp. 351–371. Copyright 2002, with permissionfrom Elsevier.

Figure 5.45 Reprinted from Physica A, 287, Nagel, K., M. Shubik, M.Paczuski, and P. Bak, ‘‘Spatial competition and priceformation’’, pp. 546–562. Copyright 2000, with permissionfrom Elsevier.

Figure 5.46 Reproduced by permission of Pion Limited, London.Figure 5.47 Reproduced by permission of Pion Limited, London.Figure 5.48 Reproduced by permission of Pion Limited, London.Figure 5.49 Reproduced by permission of Pion Limited, London.Figure 5.50 Reproduced by permission of Pion Limited, London.Figure 5.51 Reproduced by permission of Pion Limited, London.Figure 5.52 Reproduced by permission of Pion Limited, LondonFigure 5.53 Reprinted from Parallel Computing, 27(5), Wahle, J., L.

Neubert, J. Esser, and M. Schreckenberg, ‘‘A cellularautomaton traffic flow model for online simulation of traffic’’,pp. 719–735. Copyright 2001, with permissionfrom Elsevier.

Acknowledgements xxi

Figure 5.54a Reprinted from Computer Physics Communications, 147, Wang,R. and H.J. Ruskin, ‘‘Modeling traffic flow at a single-laneurban roundabout’’, pp. 570–576. Copyright 2002, withpermission from Elsevier.

Figure 5.54b Reprinted from Parallel Computing, 27(5), Wahle, J., L.Neubert, J. Esser, and M. Schreckenberg, ‘‘A cellularautomaton traffic flow model for online simulation of traffic’’,pp. 719–735. Copyright 2001, with permissionfrom Elsevier.

Figure 5.55 Reprinted from Journal of Transport Geography, 3(2), Fox, M.‘‘Transport planning and the human activity approach’’, pp.105–116. Copyright 1995, with permission of Elsevier.

Figure 5.56 Reprinted from Parallel Computing, 27(5), Wahle, J., L.Neubert, J. Esser, and M. Schreckenberg, ‘‘A cellularautomaton traffic flow model for online simulation of traffic’’,pp. 719–735. Copyright 2001, with permission from Elsevier.

Figure 5.57 Nova Science Publishers.Figure 5.58 Reprinted from Landscape and Urban Planning, 56(1–2),

Ligtenberg, A., A.K. Breft, and R. Van Lammeren, ‘‘Multi-actor-based land use modelling: Spatial planning usingagents’’, pp. 21–33. Copyright 2001, with permission fromElsevier.

Figure 5.59 Reprinted from Landscape and Urban Planning, 56(1–2),Ligtenberg, A., A.K. Breft, and R. Van Lammeren, ‘‘Multi-actor-based land use modelling: Spatial planning usingagents’’, pp. 21–33. Copyright 2001, with permission fromElsevier.

Figure 5.60 Reprinted from Landscape and Urban Planning, 56(1–2),Ligtenberg, A., A.K. Breft, and R. Van Lammeren, ‘‘Multi-actor-based land use modelling: Spatial planning usingagents’’, pp. 21–33. Copyright 2001, with permission fromElsevier.

Figure 5.61 Reprinted from Landscape and Urban Planning, 56(1–2),Ligtenberg, A., A.K. Breft, and R. Van Lammeren, ‘‘Multi-actor-based land use modelling: Spatial planning usingagents’’, pp. 21–33. Copyright 2001, with permission fromElsevier.

Figure 5.62 Reproduced by permission of Pion Limited, London.Figure 5.63 Reproduced by permission of Pion Limited, London.Figure 5.64 OUP.Figure 5.65 OUP.Figure 5.66 OUP.Figure 5.67 Reproduced by permission of Pion Limited, London.Figure 5.69 Reprinted from Computers, Environment and Urban Systems,

28(1/2), Semboloni, F., J. Assfalg, S. Armeni, R. Gianassi, andF. Marsoni, ‘‘CityDev, an interactive multi-agents urban model

xxii Acknowledgements

on the web’’, pp. 45–64. Copyright 2003, with permissionfrom Elsevier.

Figure 5.70 Reprinted from Agricultural Economics, 25 (2–3), Berger, T.‘‘Agent-based spatial models applied to agriculture: asimulation tool for technology diffusion, resource use changesand policy analysis’’, pp. 245–260. Copyright 2001, withpermission from Elsevier.

Cover image(map)

Reproduced by permission of Tel-Aviv Municipality.

Cover image(clay city)

Reproduced by permission of Miriam Benenson.

Acknowledgements xxiii