Geosimulation: Automata-Based Modeling of Urban Phenomena

Geosimulation Automata BasedModelingOfUrbanPhenomena TV pdf Geosimulation # 2004 John Wiley & Sons, Ltd ISBN 0 470 84349 7 Geosimulation Automata based Modeling of Urban Phenomena I Benenson and P M T[.]

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 Copyright # 2004 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 Email (for orders and customer service enquiries): cs-books@wiley.co.uk Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to permreq@wiley.co.uk, or faxed to (+44) 1243 770620 This publication is designed to provide accurate and authoritative information in regard to the subject matter covered It is sold on the understanding that the Publisher is not engaged in rendering professional services If professional advice or other expert assistance is required, the services of a competent professional should be sought Other Wiley Editorial Offices John Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USA Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USA Wiley-VCH Verlag GmbH, Boschstr 12, D-69469 Weinheim, Germany John Wiley & Sons Australia Ltd, 33 Park Road, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons Canada Ltd, 22 Worcester Road, Etobicoke, Ontario, Canada M9W 1L1 Wiley also publishes its books in a variety of electronic formats Some of the content that appears in print may not be available in electronic books 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) Urban geography – Simulation methods Urban geography – Computer simulation I Torrens, Paul M II Title GF125.B46 2004 2004004938 307.760 010 13–dc22 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 0-470-84349-7 Typeset in 10/12pt Times by Thomson Press (India) Limited, New Delhi Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire This book is printed on acid-free paper responsibly manufactured from sustainable forestry in 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 the times you have rescued me and for making the good times so much better — Paul Contents Preface xiii Acknowledgements xvii Introduction to Urban Geosimulation Formalizing Geosimulation with Geographic Automata Systems (GAS) 21 1.1 A New Wave of Urban Geographic Models is Coming 1.2 Defining Urban Geosimulation 1.2.1 Geosimulation Reflects the Object Nature of Urban Systems 1.2.2 Characteristics of the Geosimulation Model 1.2.2.1 Management of Spatial Entities 1.2.2.2 Management of Spatial Relationships 1.2.2.3 Management of Time 1.2.2.4 Direct Modeling 1.3 Automata as a Basis for Geosimulation 1.3.1 Cellular Automata 1.3.2 Multiagent Systems 1.3.3 Automata Systems as a Basis for Urban Simulation 1.3.3.1 Decentralization 1.3.3.2 Specifying Necessary and Only Necessary Details 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 2.1 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) 2.2.1 Definitions of Geographic Automata Systems 2.2.1.1 Geographic Automata Types 2.2.1.2 Geographic Automata States and State Transition Rules 2.2.1.3 Geographic Automata Spatial Referencing and Migration Rules 2.2.1.4 Geographic Automata Neighbors and Neighborhood Rules 2.2.2 GAS as an Extension of Geographic Information Systems 2.2.2.1 GAS as an Extension of the Vector Model 2.2.2.2 GAS and Raster Models 2.3 GAS as a Tool for Modeling Complex Adaptive Systems 2.4 From GAS to Software Environments for Urban Modeling 2.4.1 Object-Oriented Programming as a Computational Paradigm for GAS 2.4.2 From an Object-Based Paradigm for Geosimulation Software 2.4.3 GAS Simulation Environments as Temporally Enabled OODBMS 2.4.4 Temporal Dimension of GAS 2.5 Object-Based Environment for Urban Simulation (OBEUS)—A Minimal Implementation of GAS 2.5.1 Abstract Classes of OBEUS 2.5.2 Management of Time 2.5.3 Management of Relationships 2.5.4 Implementing System Theory Demands 2.5.5 Miscellaneous, but Important, Details 2.6 Verifying GAS Models 2.6.1 Establishing Initial and Boundary Conditions 2.6.2 Establishing the Parameters of a Geosimulation Model 2.6.3 Testing the Sensitivity of Geosimulation Models 2.7 Universality of GAS 35 35 37 38 39 40 40 41 42 44 44 System Theory, Geography, and Urban Modeling 47 3.1 The Basic Notions of System Theory 3.1.1 The Basics of System Dynamics 3.1.1.1 Differential and Difference Equations as Standard Tools for Presenting System Dynamics 3.1.1.2 General Solutions of Linear Differential or Difference Equations 3.1.1.3 Equilibrium Solutions of Nonlinear Systems, and Their Stability 3.1.1.4 Fast and Slow Processes and Variables 3.1.1.5 The Logistic Equation—The Simplest Nonlinear Dynamic System 3.1.1.6 Spatial Processes and Diffusion Equations 25 25 26 27 28 30 31 31 31 32 32 32 33 34 34 47 48 48 49 51 52 53 54 Contents ix 3.1.2 When a System Becomes a ‘‘Complex’’ System 3.1.2.1 How Nonlinearity Works 3.1.2.2 How Opennes Works The 1960s, Geography Meets System Theory 3.2.1 Location Theory: Studies of the Equilibrium City 3.2.2 Pittsburgh as an Equilibrium Metropolis 3.2.3 The Moment Before Dynamic Modeling 3.2.4 Models of Innovation Diffusion—The Forerunner of Geosimulation Stocks and Flows Urban Modeling 3.3.1 Forrester’s Model of Urban Dynamics 3.3.1.1 Computer Simulation as a Tool for Studying Complex Systems 3.3.1.2 Forrester’s Results and the Critique They Attracted 3.3.2 Regional Models: the Mainstream of the 1960s and 1970s 3.3.2.1 Aggregated Models of Urban Phenomena 3.3.2.2 Stocks and Flows Integrated Regional Models Criticisms of Comprehensive Modeling 3.4.1 List of Sins 3.4.2 Keep it Simple! What Next? Geosimulation of Collective Dynamics! 3.5.1 Following Trends of General Systems Science 3.5.2 Revolution in Urban Data 3.5.3 From General System Theory to Geosimulation 57 58 62 73 73 74 77 79 79 81 82 83 87 87 88 88 88 89 90 Modeling Urban Land-use with Cellular Automata 91 3.2 3.3 3.4 3.5 4.1 Introduction 4.2 Cellular Automata as a Framework for Modeling Complex Spatial Systems 4.2.1 The Invention of CA 4.2.1.1 Formal Definition of CA 4.2.1.2 Cellular Automata as a Model of the Computer 4.2.1.3 Turing Machine 4.2.1.4 Neuron Networks 4.2.1.5 Self-reproducing Machines and Computational Universality 4.2.1.6 Feedbacks in Neuron Networks and Excitable Media 4.2.1.7 Markov Processes and Markov Fields 4.2.1.8 Early Investigations of CA 4.2.2 CA and Complex Systems Theory 4.2.2.1 The Game of Life—A Complex System Governed by Simple Rules 4.2.2.2 Patterns of CA Dynamics 4.2.3 Variations of Classic CA 4.2.3.1 Variations in Grid Geometry and Neighborhood Relationships 4.2.3.2 Synchronous and Asynchronous CA 77 79 79 91 93 93 93 95 95 96 97 97 98 99 100 100 101 105 105 105 x Contents 4.3 Urban Cellular Automata 4.3.1 Introduction 4.3.2 Raster but not Cellular Automata Models 4.3.3 The Beginning of Urban Cellular Automata 4.3.4 Constrained Cellular Automata 4.3.5 Fuzzy Urbanization 4.3.6 Urbanization Potential as a Self-existing Characteristic of a Cell 4.3.6.1 From Monocentric to Polycentric City Representations 4.3.6.2 Real-World Applications of Potential-Based Models 4.3.7 Urbanization as a Diffusion Process 4.3.7.1 Spatial Ecology of the Population of Urban Cells 4.3.7.2 Spread of Urban Spatial Patterns 4.3.8 From Fixed Cells to Varying Urban Entities 4.3.8.1 Infrastructure Objects as Self-existing Urban Entities 4.3.8.2 Changing Urban Partition 4.4 From Markov Models to Urban Cellular Automata 4.4.1 From Remotely Sensed Images to Markov Models of Land-use Change 4.4.2 The Link Between Markov and Cellular Automata Models 4.5 Integration of CA and Markov Approaches at a Regional Level 4.5.1 Flat Merging of Markov and CA Models 4.5.2 Hierarchy of Inter-regional Distribution and CA Allocation 4.6 Conclusions Modeling Urban Dynamics with Multiagent Systems 5.1 Introduction 5.2 MAS as a Tool for Modeling Complex Human-driven Systems 5.2.1 Agents as ‘‘Intellectual’’ Automata 5.2.2 Multiagent Systems as Collections of Bounded Agents 5.2.3 Why we Need Agents in Urban Models? 5.3 Interpreting Agency 5.4 Urban Agents, Urban Agency, and Multiagent Cities 5.4.1 Urban Agents as Entities in Space and Time 5.4.2 Cities and Multiagent System Geography 5.5 Agent Behavior in Urban Environments 5.5.1 Location and Migration Behavior 5.5.2 Utility Functions and Choice Heuristics 5.5.3 Rational Decision-making and Bounded Rationality 5.5.4 Formalization of Bounded Rationality 5.5.5 What we Know About Behavior of Urban Agents—The Example of Households 106 106 107 113 116 121 122 123 126 131 132 133 137 137 138 140 142 144 146 147 150 150 153 153 154 154 154 155 155 158 158 160 160 161 162 163 165 170 Contents xi 5.5.5.1 Factors that Influence Household Preferences 5.5.5.2 Householder Choice Behavior 5.5.5.3 Stress-resistance Hypotheses of Household Residential Behavior 5.5.5.4 From Householder Choice to Residential Dynamics 5.5.5.5 New Data Sources for Agent-Based Residential Models 5.6 General Models of Agents’ Collectives in Urban Interpretation 5.6.1 Diffusion-limited Aggregation of Developers’ Efforts 5.6.2 Percolation of the Developers’ Efforts 5.6.3 Intermittency of Local Development 5.6.4 Spatiodemographic Processes and Diffusion of Innovation 5.7 Abstract MAS Models of Urban Phenomena 5.7.1 Adaptive Fixed Agents as Voters or Adopters of Innovation 5.7.2 Locally Migrating Social Agents 5.7.2.1 Schelling Social Agents 5.7.2.2 Random Walkers and Externalization of Agents’ Influence 5.7.3 Agents That Utilize the Entire Urban Space 5.7.3.1 Residential Segregation in the City 5.7.3.2 Adapting Householder Agents 5.7.3.3 Patterns of Firms 5.7.4 Agents That Never Stop 5.7.4.1 Pedestrians on Pavements 5.7.4.2 Depopulating Rooms 5.7.4.3 Cars on Roads 5.7.5 Multi-type MAS—Firms and Customers 5.8 Real-world Agent-based Simulations of Urban Phenomena 5.8.1 Developers and Their Work in the City 5.8.2 Pedestrians Take a Walk 5.8.3 Cars in Urban Traffic 5.8.4 Citizens Vote for Land-use Change 5.8.5 In Search of an Apartment in the City 5.9 MAS Models as Planning and Assessment Tools 5.10 Conclusions 193 195 195 199 205 205 208 213 216 220 224 224 227 230 233 237 244 248 Finale: Epistemology of Geosimulation 251 6.1 Universal Questions 6.1.1 Social Phenomena are Repeatable 6.1.2 We are Interested in Urban Changes During Time Intervals Derived from Those of a Human Lifespan 6.1.3 Urban Systems are Unique because They are Driven by Social Forces 6.1.4 The Uniqueness of Urban Systems is not Necessarily Exhibited 6.1.5 Why we Hope to Understand Urban Systems? 170 172 172 173 175 176 177 178 180 182 184 184 190 190 251 252 252 253 253 253 xii Contents 6.1.6 6.2 Tight-coupling between the Urban Theory and Urban Data 6.1.7 Automata versus State Equations The Future of Geosimulation 6.2.1 The Applied Power of Geosimulation 6.2.2 The Theoretical Focus of Geosimulation 6.2.3 From Modeling of Urban Phenomena to Models of a City: Integration Based on a Hierarchy of Models 6.2.4 From Stand-alone Models to Sharing Code and Geosimulation Language 254 255 255 255 256 256 257 Bibliography 259 Index 283 Preface Are we witnessing a revolution in urban geography? The answer to that question is almost 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 evolution of world cities and massive Megalopoli Factual data has not always been available to settle 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 we have, we always thirst for more; it is a hallmark of life in an Information Age But data aside, the general tone of discussion and views on urban geography appear to be coming full circle All too often, the general impression is that of discussing the same old issues, although they are often marketed in new forms This rebranding is undoubtedly important, but we are not so much interested in shifting units, marketing products, as we are in uncovering knowledge Put briefly, there is a strong sense that all the good theoretical stuff has been said before What might free us from ever-wandering around the same Moăbius 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 become available to us, directly to our desktops and our libraries These data cover a bewildering array of urban phenomena—information on urban infrastructure and populations at all levels of spatial and temporal resolutions have been generated and accumulated We have not utilized most of them yet This is not because they are inaccessible; we simply shy away, for the most part, from getting stuck into these huge reservoirs of remotely sensed data and census databases Modern statistical and GIS environments enable combination of qualitative and quantitative methods, often freely, 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 the phenomenon at hand, and then generate forecasts or what-if scenarios By these means we might thin out all-embracing descriptions, perhaps even give birth to novel ideas How has that worked out for us? Has it been successful thus far? We must admit that most urban models 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 such exercises; examining closely, we see that phenomenological ‘‘regression’’ between potential 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 xiv Preface city becomes the product of researcher’s intuition—more late-night SimCity than science We probably have as many models of cities as we have cities; a sure sign of discrepancy between urban theory and urban dynamics One can pessimistically assert that, with all due respect, we have been kidding ourselves; what we thought of as theory is not, in a natural science sense at least The theory simply does not explan our observed 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 and modeling tools, in the form in which they were employed until recently, on the other This discrepancy has roots in over-simplified representation of elementary components 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 city and its dynamics Cities exhibit properties that resemble chemical reactions, but that does not change the fact that molecules in a chemical reaction not make decisions Humans are not dumb particles Yet, most urban models strip the city of its intelligence 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 traditional cybernetic black-box approach and obtain the model, which is by definition convenient 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 dictate structure and rules that not fit at all to a system driven by individual and autonomous 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 a pipeline Sometimes the decision-making processes are so complicated that we prefer avoiding formalization The models should point us where to stop on the way from molecules 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 human self We assume therefore, that there is no need to directly account for real complexity of urban inanimate and animate objects when formalizing urban phenomena Instead, we could succeed with avatars, which exhibit simple human-like or human-driven activities We believe that an intuitive separation of ‘‘simple’’ and ‘‘complex’’ can be sufficient Inherent physical constraints, spatial ones, first and foremost, act in the city so strongly that the consequences of really human feelings and reasons, such as love and hate, can be less important, at today’s level of understanding, at least for the outer observer 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 And here we turn to the evidence for our bold opening statement, arguing for a revolution in urban geography Preface     xv General system theory, which started 60 years ago with black-box cybernetic models, is developing toward understanding system dynamics as collective phenomena; its modern principles are applicable to multiple interacting decision-making objects, in the same way that those principles apply to collectives of simpler physical, chemical, or ecological particles Formal frameworks are standing by in the sidelines—cellular automata and their extension, multiagent systems, are evidently powerful for modeling and simulating collectives of interacting individual urban autonomous objects High-resolution infrastructure and population GIS and remote sensing databases provide data at the resolution of the urban objects we are interested in, whether they are objects or aggregations of objects Modern programming technology is based on object-oriented paradigms and a number of computer environments for simulating and investigating the dynamics of the collectives of autonomous objects already exist All this accompanies a recent boom in urban modeling, with models that tend to act at the resolution of real-world urban objects, and increasingly, describe the behavior of those objects in terms not far-removed from our views This is an exciting time to be working in this field All the ingredients for a crystallization of these ideas are there—a burgeoning paradigm shift for urban geography, urban analysis, and urban modeling We call it urban geosimulation Acknowledgements Many people justly deserve some expression of my appreciation I wish to thank my friends and colleagues: Juval Portugali, Itzhak Omer, and Erez Hatna We have worked together for many years, and various parts of the book reflect our common work and discussions Special thanks go to Erez Hatna who, besides developing the Yaffo model, created several of the figures found throughout the text I also want to thank Shai Aronovich and Saar Noam, with whom we developed the first version of OBEUS Lena, Kobi, Pola and Manya Benenson provided crucial support, especially Manya, who built the clay city and located it in the artificial environment of our backyard The city appears on the book’s cover, merged with the image of Tel-Aviv that was so kindly provided by the municipality’s GIS Department Lastly, I want to express my gratitude to my colleagues from the Environmental Simulation Laboratory and the Department of Geography and Human Environment, Tel-Aviv University, for their moral support throughout the writing of this book Tel-Aviv, April 2004 Itzhak Benenson Thanks to Carolina Tobo´n, Muki Haklay, Martin Dodge, Naru Shiode, and Daryl Lloyd for being such fantastic friends and colleagues and for fueling the creative process with cranberry juice, trips to Barto´k and Sak, Refreshers, discount Japanese food, and Caffe´ Nero americanos Boards of Canada, Linkin Park, Mogwai, and Dashboard Confessional provided the best soundtrack at crunch-time Thanks, also and in particular, to the Benenson family Salt Lake City, April, 2004 Paul M Torrens Permission to reproduce the following illustrations is gratefully acknowledged: Figure 1.7 Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.7 CODATA Society CRC Press LLC Reprinted from Computers, Environment and Urban Systems, Benenson, I., S Aronovich, and S Noam, ‘‘Let’s Talk Objects: Generic Methodology for Urban High-Resolution Simulation’’ Copyright 2004, with permission from Elsevier CRC Press LLC Reprinted from Computers, Environment and Urban Systems, 24(6), Benenson, I., S Aronovich, and S Noam, ‘‘Let’s Talk Objects: Generic Methodology for Urban High-Resolution Simulation’’, pp 559–581 Copyright 2000, with permission from Elsevier xviii Acknowledgements Figure 2.8 Figure 2.9 Figure 3.1 Figure 3.9 Figure 3.10 Figure 3.11 Figure 4.5 Figure 4.6 Figure 4.7 Figure 4.8 Figure 4.9 Figure 4.10 Figure 4.13 Figure 4.14 Figure 4.15 Figure 4.16 Figure 4.17 Figure 4.18 Figure 4.19 Figure 4.20 Figure 4.21 Figure 4.22 Figure 4.23 Figure 4.24 Reprinted from Computers, Environment and Urban Systems, 24(6), Benenson, I., S Aronovich, and S Noam, ‘‘Let’s Talk Objects: Generic Methodology for Urban High-Resolution Simulation’’, pp 559–581 Copyright 2000, with permission from Elsevier Reprinted from Computers, Environment and Urban Systems, 24(6), Benenson, I., S Aronovich, and S Noam, ‘‘Let’s Talk Objects: Generic Methodology for Urban High-Resolution Simulation’’, pp 559–581 Copyright 2000, with permission from Elsevier Andow, D.A., P.M Kareiva, S.A Levin, and A Okubo, Spread of invading organisms, Landscape Ecology, 4(2/3), 1990, pp 177–188, reproduced with permission from Kluwer Academic Publishers Reproduced by permission of The RAND Corporation Reproduced by permission of The RAND Corporation Professor J W Forrester Steinitz, C and P Rogers, A System Analysis Model of Urbanization and Change: An Experiment in Interdisciplinary Education, 1970, reproduced by permission of The MIT Press Steinitz, C and P Rogers, A System Analysis Model of Urbanization and Change: An Experiment in Interdisciplinary Education, 1970, reproduced by permission of The MIT Press University of North Carolina Steinitz, C and P Rogers, A System Analysis Model of Urbanization and Change: An Experiment in Interdisciplinary Education, 1970, reproduced by permission of The MIT Press Tobler, W., Cellular Geography Philosophy in Geography S Gale and G Olison 1979, pp 379–386, with kind permission of Kluwer Academic Publishers Clark University European Commission Joint Research Center Reproduced by permission of Pion Limited, London Reproduced by permission of Ferdinando Semboloni Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reprinted from Computers, Environment and Urban Systems, 24, Bell, M., C Dean, and M Blake, ‘‘Forecasting the pattern of urban growth with PUP: a web-based model interfaced with GIS and 3D animation’’ Copyright 2004, with permission from Elsevier Reproduced by permission of Jeannette Candau Reproduced by permission of Jeannette Candau Reproduced by permission of Jeannette Candau Reproduced by permission of Jeannette Candau Acknowledgements Figure 4.25 Figure 4.26 Figure 4.27 Figure 4.28 Figure 5.5 Figure 5.6 Figure 5.7a Figure 5.7b Figure 5.8 Figure 5.9 Figure 5.10 Figure 5.11 Figure 5.12 Figure 5.13 Figure 5.14 Figure 5.15 Figure 5.18 Figure 5.19 Figure 5.20 Figure 5.21 Figure 5.22 Figure 5.23 Figure 5.24 Figure 5.25 xix Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London European Commission Joint Research Center Reproduced by permission of Oxford University Press, Inc OUP Reprinted figure 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 Reproduced by permission of World Scientific Publishing Company Reprinted with permission from ‘‘Modeling urban growth patterns with correlated percolation’’ by H.A Makse, et al, Phys Rev E, 1998, 58(6):7054–7062 Reprinted with permission from ‘‘Modeling urban growth patterns with correlated percolation’’ by H.A Makse, et al, Phys Rev E, 1998, 58(6):7054–7062 World Scientific Publishing Company Reproduced by permission of The Royal Society Reproduced by permission of The Royal Society Reprinted from Physica A, 303, Schweitzer, F., J Zimmermann and H Muhlenbein, ‘‘Coordination of decisions in a spatial agent model’’, pp 189–216 Copyright 2000, with permission from Elsevier Reproduced by permission of The Simsoc Consortium Reproduced by permission of The Simsoc Consortium Reproduced by permission of Taylor & Francis Ltd http:// www.tandf.co.uk/journals Reproduced by permission of Taylor & Francis Ltd http:// www.tandf.co.uk/journals Reprinted from Computers, Environment and Urban Systems, 22, Benenson, I., ‘‘Multi-agent simulations of residential dynamics in the city’’, pp 25–42 Copyright 2003, with permission from Elsevier Reprinted from Computers, Environment and Urban Systems, 22, Benenson, I., ‘‘Multi-agent simulations of residential dynamics in the city’’, pp 25–42 Copyright 2003, with permission from Elsevier OUP Reproduced by permission of Pion Limited, London Reprinted from Computers, Environment and Urban Systems, 22, Benenson, I., ‘‘Multi-agent simulations of residential dynamics in the city’’, pp 25–42 Copyright 2003, with permission from Elsevier Reprinted from Computers, Environment and Urban Systems, 22, Benenson, I., ‘‘Multi-agent simulations of residential xx Acknowledgements Figure 5.26 Figure 5.27 Figure 5.28 Figure 5.29 Figure 5.30 Figure 5.31 Figure 5.32 Figure 5.33 Figure 5.34 Figure 5.35 Figure 5.36 Figure 5.37 Figure 5.38 dynamics in the city’’, pp 25–42 Copyright 2003, with permission from Elsevier Reprinted from Computers, Environment and Urban Systems, 22, Benenson, I., ‘‘Multi-agent simulations of residential dynamics in the city’’, pp 25–42 Copyright 2003, with permission from Elsevier Reprinted from Computers, Environment and Urban Systems, 22, Benenson, I., ‘‘Multi-agent simulations of residential dynamics in the city’’, pp 25–42 Copyright 2003, with permission from Elsevier Reprinted from Journal of Urban Economics, 45, Page, S.E., ‘‘On the Emergence of Cities’’, pp 184–208 Copyright 1999, with permission from Elsevier Reprinted from Mathematics and Computers in Simulation, Vol 27, Gipps and Marksjo, ‘‘A micro-simulation model for pedestrian flows’’, pp 95–105, Copyright 1985, with permission from Elsevier Reprinted from Mathematics and Computers in Simulation, Vol 27, Gipps and Marksjo, ‘‘A micro-simulation model for pedestrian flows’’, pp 95–105, Copyright 1985, with permission from Elsevier Reproduced by permission of D Helbing Reproduced by permission of D Helbing Reproduced by permission of D Helbing Reprinted from Physica A, 295, Burstedde, C., K Klauck, A Schadschneider, and J Zittartz, ‘‘Simulation of pedestrian dynamics using a two-dimensional cellular automaton’’, pp 507–525 Copyright 2000, with permission from Elsevier Reprinted with permission from Nature, 47(28):487–490, Helbing, D., I Farkas, and T Vicsek (2000), ‘‘Simulating dynamical features of escape panic’’ Copyright 2000 Macmillan Magazines Limited Reprinted with permission from Nature 47(28):487–490, Helbing, D., I Farkas, and T Vicsek (2000), ‘‘Simulating dynamical features of escape panic’’ Copyright 2000 Macmillan Magazines Limited Reprinted with permission from Nature 47(28):487–490, Helbing, D., I Farkas, and T Vicsek (2000), ‘‘Simulating dynamical features of escape panic’’ Copyright 2000 Macmillan Magazines Limited Reprinted from Parallel Computing, 27(5), Wahle, J., L Neubert, J Esser, and M Schreckenberg, ‘‘A cellular automaton traffic flow model for online simulation of traffic’’, pp 719–735 Copyright 2001, with permission from Elsevier Acknowledgements Figure 5.39 Figure 5.40a Figure 5.40b Figure 5.41 Figure 5.42 Figure 5.43 Figure 5.44 Figure 5.45 Figure 5.46 Figure 5.47 Figure 5.48 Figure 5.49 Figure 5.50 Figure 5.51 Figure 5.52 Figure 5.53 xxi 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 scenario using agnet-based simulation’’, pp 399–417 Copyright 2002, with permission from Elsevier Reprinted from Parallel Computing, 27(5), Wahle, J., L Neubert, J Esser, and M Schreckenberg, ‘‘A cellular automaton traffic flow model for online simulation of traffic’’, pp 719–735 Copyright 2001, with permission from Elsevier Reprinted from Physica A, 2857, Schadscheider, A., ‘‘Statistical physics of traffic flow’’, pp 101–120 Copyright 2000, with permission from Elsevier Reprinted from Parallel Computing, 27(5), Wahle, J., L Neubert, J Esser, and M Schreckenberg, ‘‘A cellular automaton traffic flow model for online simulation of traffic’’, pp 719–735 Copyright 2001, with permission from Elsevier Reprinted from Parallel Computing, 27(5), Wahle, J., L Neubert, J Esser, and M Schreckenberg, ‘‘A cellular automaton traffic flow model for online simulation of traffic’’, pp 719–735 Copyright 2001, with permission from Elsevier Reprinted from Transportation Research Part C, 10, Hidas, P., ‘‘Modelling lane changing and merging in microscopic traffic simulation’’, pp 351–371 Copyright 2002, with permission from Elsevier Reprinted from Transportation Research Part C, 10, Hidas, P., ‘‘Modelling lane changing and merging in microscopic traffic simulation’’, pp 351–371 Copyright 2002, with permission from Elsevier Reprinted from Physica A, 287, Nagel, K., M Shubik, M Paczuski, and P Bak, ‘‘Spatial competition and price formation’’, pp 546–562 Copyright 2000, with permission from Elsevier Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London Reprinted from Parallel Computing, 27(5), Wahle, J., L Neubert, J Esser, and M Schreckenberg, ‘‘A cellular automaton traffic flow model for online simulation of traffic’’, pp 719–735 Copyright 2001, with permission from Elsevier xxii Acknowledgements Figure 5.54a Figure 5.54b Figure 5.55 Figure 5.56 Figure 5.57 Figure 5.58 Figure 5.59 Figure 5.60 Figure 5.61 Figure 5.62 Figure 5.63 Figure 5.64 Figure 5.65 Figure 5.66 Figure 5.67 Figure 5.69 Reprinted from Computer Physics Communications, 147, Wang, R and H.J Ruskin, ‘‘Modeling traffic flow at a single-lane urban roundabout’’, pp 570–576 Copyright 2002, with permission from Elsevier Reprinted from Parallel Computing, 27(5), Wahle, J., L Neubert, J Esser, and M Schreckenberg, ‘‘A cellular automaton traffic flow model for online simulation of traffic’’, pp 719–735 Copyright 2001, with permission from Elsevier 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 Reprinted from Parallel Computing, 27(5), Wahle, J., L Neubert, J Esser, and M Schreckenberg, ‘‘A cellular automaton traffic flow model for online simulation of traffic’’, pp 719–735 Copyright 2001, with permission from Elsevier Nova Science Publishers Reprinted from Landscape and Urban Planning, 56(1–2), Ligtenberg, A., A.K Breft, and R Van Lammeren, ‘‘Multiactor-based land use modelling: Spatial planning using agents’’, pp 21–33 Copyright 2001, with permission from Elsevier Reprinted from Landscape and Urban Planning, 56(1–2), Ligtenberg, A., A.K Breft, and R Van Lammeren, ‘‘Multiactor-based land use modelling: Spatial planning using agents’’, pp 21–33 Copyright 2001, with permission from Elsevier Reprinted from Landscape and Urban Planning, 56(1–2), Ligtenberg, A., A.K Breft, and R Van Lammeren, ‘‘Multiactor-based land use modelling: Spatial planning using agents’’, pp 21–33 Copyright 2001, with permission from Elsevier Reprinted from Landscape and Urban Planning, 56(1–2), Ligtenberg, A., A.K Breft, and R Van Lammeren, ‘‘Multiactor-based land use modelling: Spatial planning using agents’’, pp 21–33 Copyright 2001, with permission from Elsevier Reproduced by permission of Pion Limited, London Reproduced by permission of Pion Limited, London OUP OUP OUP Reproduced by permission of Pion Limited, London Reprinted from Computers, Environment and Urban Systems, 28(1/2), Semboloni, F., J Assfalg, S Armeni, R Gianassi, and F Marsoni, ‘‘CityDev, an interactive multi-agents urban model