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A ground-up approach to explaining dynamic spatialmodelling for an interdisciplinary audience. Across broad areas of the environmental and social sciences,simulation models are an important way to study systemsinaccessible to scientific experimental and observational methods,and also an essential complement to those more conventionalapproaches. The contemporary research literature is teemingwith abstract simulation models whose presentation ismathematically demanding and requires a high level of knowledge ofquantitative and computational methods and approaches. Furthermore, simulation models…mehr

Produktbeschreibung
A ground-up approach to explaining dynamic spatialmodelling for an interdisciplinary audience. Across broad areas of the environmental and social sciences,simulation models are an important way to study systemsinaccessible to scientific experimental and observational methods,and also an essential complement to those more conventionalapproaches. The contemporary research literature is teemingwith abstract simulation models whose presentation ismathematically demanding and requires a high level of knowledge ofquantitative and computational methods and approaches. Furthermore, simulation models designed to represent specificsystems and phenomena are often complicated, and, as a result,difficult to reconstruct from their descriptions in theliterature. This book aims to provide a practical andaccessible account of dynamic spatial modelling, while alsoequipping readers with a sound conceptual foundation in thesubject, and a useful introduction to the wide-rangingliterature. Spatial Simulation: Exploring Pattern and Process isorganised around the idea that a small number of spatial processesunderlie the wide variety of dynamic spatial models. Its centralfocus on three 'building-blocks' of dynamic spatialmodels - forces of attraction and segregation, individualmobile entities, and processes of spread - guides the readerto an understanding of the basis of many of the complicated modelsfound in the research literature. The three building block modelsare presented in their simplest form and are progressivelyelaborated and related to real world process that can berepresented using them. Introductory chapters cover essentialbackground topics, particularly the relationships between pattern,process and spatiotemporal scale. Additional chaptersconsider how time and space can be represented in more complicatedmodels, and methods for the analysis and evaluation of models.Finally, the three building block models are woven together in amore elaborate example to show how a complicated model can beassembled from relatively simple components. To aid understanding, more than 50 specific models described inthe book are available online at patternandprocess.org forexploration in the freely available Netlogo platform. Thisbook encourages readers to develop intuition for the abstract typesof model that are likely to be appropriate for application in anyspecific context. Spatial Simulation: Exploring Patternand Process will be of interest to undergraduate and graduatestudents taking courses in environmental, social, ecological andgeographical disciplines. Researchers and professionals whorequire a non-specialist introduction will also find this book aninvaluable guide to dynamic spatial simulation.

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  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 336
  • Erscheinungstermin: 5. August 2013
  • Englisch
  • ISBN-13: 9781118555071
  • Artikelnr.: 39477898
Autorenporträt
DAVID O'SULLIVAN, PhD, is Assistant Professor of Geography at The Pennsylvania State University in University Park, Pennsylvania. George L.W. Perry, University of Auckland, New Zealand.
Inhaltsangabe
Foreword xiii Preface xv Acknowledgements xix Introduction xxi About the Companion Website xxv 1 Spatial Simulation Models: What? Why? How? 1 1.1 What are simulation models? 2 1.1.1 Conceptual models 4 1.1.2 Physical models 7 1.1.3 Mathematical models 7 1.1.4 Empirical models 8 1.1.5 Simulation models 9 1.2 How do we use simulation models? 12 1.2.1 Using models for prediction 13 1.2.2 Models as guides to data collection 13 1.2.3 Models as 'tools to think with' 14 1.3 Why do we use simulation models? 15 1.3.1 When experimental science is difficult (or impossible) 16 1.3.2 Complexity and nonlinear dynamics 18 1.4 Why dynamic and spatial models? 23 1.4.1 The strengths and weaknesses of highly general models 23 1.4.2 From abstract to more realistic models: controlling the cost 27 2 Pattern, Process and Scale 29 2.1 Thinking about spatiotemporal patterns and processes 30 2.1.1 What is a pattern? 30 2.1.2 What is a process? 31 2.1.3 Scale 32 2.2 Using models to explore spatial patterns and processes 38 2.2.1 Reciprocal links between pattern and process: a spatial model of forest structure 39 2.2.2 Characterising patterns: first
and second
order structure 40 2.2.3 Using null models to evaluate patterns 43 2.2.4 Density
based (first
order) null models 46 2.2.5 Interaction
based (second
order) null models 48 2.2.6 Inferring process from (spatio
temporal) pattern 49 2.2.7 Making the virtual forest more realistic 53 2.3 Conclusions 56 3 Aggregation and Segregation 57 3.1 Background and motivating examples 58 3.1.1 Basics of (discrete spatial) model structure 59 3.2 Local averaging 60 3.2.1 Local averaging with noise 63 3.3 Totalistic automata 64 3.3.1 Majority rules 65 3.3.2 Twisted majority annealing 68 3.3.3 Life
like rules 69 3.4 A more general framework: interacting particle systems 70 3.4.1 The contact process 71 3.4.2 Multiple contact processes 73 3.4.3 Cyclic relationships between states: rock
scissors
paper 76 3.4.4 Voter models 78 3.4.5 Voter models with noise mutation 80 3.5 Schelling models 83 3.6 Spatial partitioning 86 3.6.1 Iterative subdivision 86 3.6.2 Voronoi tessellations 87 3.7 Applying these ideas: more complicated models 88 3.7.1 Pattern formation on animals' coats: reaction
diffusion models 89 3.7.2 More complicated processes: spatial evolutionary game theory 91 3.7.3 More realistic models: cellular urban models 93 4 Random Walks and Mobile Entities 97 4.1 Background and motivating examples 97 4.2 The random walk 99 4.2.1 Simple random walks 99 4.2.2 Random walks with variable step lengths 102 4.2.3 Correlated walks 103 4.2.4 Bias and drift in random walks 108 4.2.5 L¿evy flights: walks with non
finite step length variance 109 4.3 Walking for a reason: foraging and search 111 4.3.1 Using clues: localised search 115 4.3.2 The effect of the distribution of resources 116 4.3.3 Foraging and random walks revisited 119 4.4 Moving entities and landscape interaction 119 4.5 Flocking: entity
entity interaction 121 4.6 Applying the framework 125 4.6.1 Animal foraging 126 4.6.2 Human 'hunter
gatherers' 128 4.6.3 The development of home ranges and path networks 129 4.6.4 Constrained environments: pedestrians and evacuations 129 4.6.5 Concluding remarks 131 5 Percolation and Growth: Spread in Heterogeneous Spaces 133 5.1 Motivating examples 133 5.2 Percolation models 137 5.2.1 What is percolation? 137 5.2.2 Ordinary percolation 138 5.2.3 The lost ant 142 5.2.4 Invasion percolation 145 5.3 Growth (or aggregation) models 148 5.3.1 Eden growth processes: theme and variations 149 5.3.2 Diffusion
limited aggregation 155 5.4 Applying the framework 158 5.4.1 Landscape pattern: neutral models and percolation approaches 158 5.4.2 Fire spread: Per Bak's 'forest fire model' and derivatives 162 5.4.3 Gullying and erosion dynamics: IP + Eden growth + DLA 166 5.5 Summary 168 6 Representing Time and Space 169 6.1 Representing time 170 6.1.1 Synchronous and asynchronous update 170 6.1.2 Different process rates 172 6.1.3 Discrete time steps or event
driven time 173 6.1.4 Continuous time 174 6.2 Basics of spatial representation 175 6.2.1 Grid or lattice representations 175 6.2.2 Vector
based representation: points, lines, polygons and tessellations 177 6.3 Spatial relationships: distance, neighbourhoods and networks 179 6.3.1 Distance in grids and tessellations 179 6.3.2 Neighbourhoods: local spatial relationships 181 6.3.3 Networks of relationships 183 6.4 Coordinate space: finite, infinite and wrapped 185 6.4.1 Finite model space 185 6.4.2 Infinitely extensible model space 186 6.4.3 Toroidal model space 187 6.5 Complicated spatial structure without spatial data structures 188 6.6 Temporal and spatial representations can make a difference 190 7 Model Uncertainty and Evaluation 193 7.1 Introducing uncertainty 193 7.2 Coping with uncertainty 194 7.2.1 Representing uncertainty in data and processes 195 7.3 Assessing and quantifying model
related uncertainty 198 7.3.1 Error analysis 200 7.3.2 Sensitivity analysis 200 7.3.3 Uncertainty analysis 202 7.3.4 Analysis of model structural uncertainty 204 7.3.5 Difficulties for spatial data and models 206 7.3.6 Sensitivity and uncertainty analysis for a simple spatial model 207 7.4 Confronting model predictions with observed data 211 7.4.1 Visualisation and difference measures 212 7.4.2 Formal statistical tests 214 7.5 Frameworks for selecting between competing models 216 7.5.1 Occam's razor 216 7.5.2 Likelihood 217 7.5.3 Multi
model inference 220 7.6 Pattern
oriented modelling 222 7.6.1 POM case
study: understanding the drivers of treeline physiognomy 224 7.7 More to models than prediction 226 8 Weaving It All Together 229 8.1 Motivating example: island resource exploitation by hunter
gatherers 230 8.2 Model description 231 8.2.1 Overview 232 8.2.2 Design concepts 236 8.2.3 Details 238 8.3 Model development and refinement 244 8.3.1 The model development process 244 8.3.2 Model refinement 246 8.4 Model evaluation 247 8.4.1 Baseline dynamics 247 8.4.2 Sensitivity analysis 254 8.4.3 Uncertainty analysis 258 8.5 Conclusions 262 9 In Conclusion 265 9.1 On the usefulness of building
block models 265 9.2 On pattern and process 266 9.3 On the need for careful analysis 268 References 271 Index 299
Rezensionen
"O'Sullivan and Perry have written a book that defines the key principles and methods that underlie modeling and simulation, building on ways in which the elements that make up our systems move, diffuse, and grow. They succeed magnificently in producing a gentle, unassuming and convincing picture of how spatial models work and are validated. Essential reading for all engaged in this field." -- Michael Batty, University College London
"The book by O'Sullivan and Perry thoroughly introduces basic theoretical work and offers not only a rich source of inspiration but also readily accessible examples from various applications that can be adopted and adapted in order to get started." (Frontiers of Biogeography, 2 June 2014)

"In summary, the book brings a comprehensiveness and structure that will aid any researcher in the development of a spatial simulation model, no matter their experience. In moving from simple "building blocks" to sophisticated extensions of fundamental processes, the book brings a new maturity to the field of spatial simulation. As Volker Grimm correctly points out in the foreword - "This book was badly needed.." (Journal of Artificial Societies and Social Simulation, 1 March 2014)