LEADER 05330nam 2200625 a 450 001 9910139020703321 005 20211024105537.0 010 $a1-118-52707-0 010 $a1-118-52708-9 010 $a1-119-97080-6 010 $a1-118-55507-4 035 $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(EXLCZ)992550000001111796 100 $a20130211d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aSpatial simulation$b[electronic resource] $eexploring pattern and process /$fDavid O'Sullivan and George L.W. Perry 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 $a1-119-97079-2 311 $a1-299-80437-3 320 $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-$0874020 701 $aPerry$b George L. W$0941055 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139020703321 996 $aSpatial simulation$92122211 997 $aUNINA LEADER 01545nam 22003973 450 001 9911054597903321 005 20260115080333.0 010 $a981-9557-61-5 035 $a(CKB)44940075800041 035 $a(MiAaPQ)EBC32480957 035 $a(Au-PeEL)EBL32480957 035 $a(EXLCZ)9944940075800041 100 $a20260115d2026 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition and Computer Vision $e8th Chinese Conference, PRCV 2025, Shanghai, China, October 15-18, 2025, Proceedings, Part XII 205 $a1st ed. 210 1$aSingapore :$cSpringer,$d2026. 210 4$dİ2026. 215 $a1 online resource (938 pages) 225 1 $aLecture Notes in Computer Science Series ;$vv.16283 311 08$a981-9557-60-7 330 $aThis 18-volume set constitutes the refereed proceedings of the 8th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2025, held in Shanghai, China, during October 15-18, 2025.The 692 full papers included in this book, comprising 66 oral presentations and 626 posters, were carefully reviewed and selected from 2370 submissions. 410 0$aLecture Notes in Computer Science Series 700 $aKittler$b Josef$013183 701 $aKittler$01748831 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911054597903321 996 $aPattern Recognition and Computer Vision$94519857 997 $aUNINA