LEADER 05526nam 2200721 a 450 001 9910811539603321 005 20240401194625.0 010 $a9781118527078 010 $a1118527070 010 $a9781118527085 010 $a1118527089 010 $a9781119970804 010 $a1119970806 010 $a9781118555071 010 $a1118555074 035 $a(CKB)2550000001111796 035 $a(EBL)1434092 035 $a(OCoLC)859161211 035 $a(MiAaPQ)EBC1434092 035 $a(DLC) 2013007573 035 $a(Au-PeEL)EBL1434092 035 $a(CaPaEBR)ebr10748659 035 $a(CaONFJC)MIL511688 035 $a(PPN)242295541 035 $a(Perlego)1001252 035 $a(EXLCZ)992550000001111796 100 $a20130211d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aSpatial simulation $eexploring pattern and process /$fDavid O'Sullivan and George L.W. Perry 205 $a1st ed. 210 $aChichester, West Sussex, U.K. $cJohn Wiley & Sons Inc.$d2013 215 $a1 online resource (342 p.) 300 $aDescription based upon print version of record. 311 08$a9781119970798 311 08$a1119970792 311 08$a9781299804371 311 08$a1299804373 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Foreword; Preface; Acknowledgements; Introduction; About the Companion Website; Chapter 1 Spatial Simulation Models: What? Why? How?; 1.1 What are simulation models?; 1.1.1 Conceptual models; 1.1.2 Physical models; 1.1.3 Mathematical models; 1.1.4 Empirical models; 1.1.5 Simulation models; 1.2 How do we use simulation models?; 1.2.1 Using models for prediction; 1.2.2 Models as guides to data collection; 1.2.3 Models as `tools to think with'; 1.3 Why do we use simulation models?; 1.3.1 When experimental science is difficult (or impossible) 327 $a1.3.2 Complexity and nonlinear dynamics1.4 Why dynamic and spatial models?; 1.4.1 The strengths and weaknesses of highly general models; 1.4.2 From abstract to more realistic models: controlling the cost; Chapter 2 Pattern, Process and Scale; 2.1 Thinking about spatiotemporal patterns and processes; 2.1.1 What is a pattern?; 2.1.2 What is a process?; 2.1.3 Scale; 2.2 Using models to explore spatial patterns and processes; 2.2.1 Reciprocal links between pattern and process: a spatial model of forest structure; 2.2.2 Characterising patterns: first- and second-order structure 327 $a2.2.3 Using null models to evaluate patterns2.2.4 Density-based (first-order) null models; 2.2.5 Interaction-based (second-order) null models; 2.2.6 Inferring process from (spatio-temporal) pattern; 2.2.7 Making the virtual forest more realistic; 2.3 Conclusions; Chapter 3 Aggregation and Segregation; 3.1 Background and motivating examples; 3.1.1 Basics of (discrete spatial) model structure; 3.2 Local averaging; 3.2.1 Local averaging with noise; 3.3 Totalistic automata; 3.3.1 Majority rules; 3.3.2 Twisted majority annealing; 3.3.3 Life-like rules 327 $a3.4 A more general framework: interacting particle systems3.4.1 The contact process; 3.4.2 Multiple contact processes; 3.4.3 Cyclic relationships between states: rock-scissors-paper; 3.4.4 Voter models; 3.4.5 Voter models with noise mutation; 3.5 Schelling models; 3.6 Spatial partitioning; 3.6.1 Iterative subdivision; 3.6.2 Voronoi tessellations; 3.7 Applying these ideas: more complicated models; 3.7.1 Pattern formation on animals' coats: reaction-diffusion models; 3.7.2 More complicated processes: spatial evolutionary game theory; 3.7.3 More realistic models: cellular urban models 327 $aChapter 4 Random Walks and Mobile Entities4.1 Background and motivating examples; 4.2 The random walk; 4.2.1 Simple random walks; 4.2.2 Random walks with variable step lengths; 4.2.3 Correlated walks; 4.2.4 Bias and drift in random walks; 4.2.5 L ?evy flights: walks with non-finite step length variance; 4.3 Walking for a reason: foraging and search; 4.3.1 Using clues: localised search; 4.3.2 The effect of the distribution of resources; 4.3.3 Foraging and random walks revisited; 4.4 Moving entities and landscape interaction; 4.5 Flocking: entity-entity interaction; 4.6 Applying the framework 327 $a4.6.1 Animal foraging 330 $aA ground-up approach to explaining dynamic spatial modelling for an interdisciplinary audience. Across broad areas of the environmental and social sciences, simulation models are an important way to study systems inaccessible to scientific experimental and observational methods, and also an essential complement to those more conventional approaches. The contemporary research literature is teeming with abstract simulation models whose presentation is mathematically demanding and requires a high level of knowledge of quantitative and computational methods and approaches. Furth 606 $aSpatial data infrastructures$xMathematical models 606 $aSpatial analysis (Statistics) 615 0$aSpatial data infrastructures$xMathematical models. 615 0$aSpatial analysis (Statistics) 676 $a003 700 $aO'Sullivan$b David$f1966-$01638505 701 $aPerry$b George L. W$01658528 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910811539603321 996 $aSpatial simulation$94012579 997 $aUNINA