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E-raamat: Spatial Simulation: Exploring Pattern and Process

(University of Auckland), (University of Auckland, Auckland, New Zealand)
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  • Ilmumisaeg: 05-Aug-2013
  • Kirjastus: Wiley-Blackwell
  • Keel: eng
  • ISBN-13: 9781118555071
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 05-Aug-2013
  • Kirjastus: Wiley-Blackwell
  • Keel: eng
  • ISBN-13: 9781118555071

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A 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.  Furthermore, simulation models designed to represent specific systems and phenomena are often complicated, and, as a result, difficult to reconstruct from their descriptions in the literature.  This book aims to provide a practical and accessible account of dynamic spatial modelling, while also equipping readers with a sound conceptual foundation in the subject, and a useful introduction to the wide-ranging literature.

Spatial Simulation: Exploring Pattern and Process is organised around the idea that a small number of spatial processes underlie the wide variety of dynamic spatial models. Its central focus on three building-blocks of dynamic spatial models forces of attraction and segregation, individual mobile entities, and processes of spread guides the reader to an understanding of the basis of many of the complicated models found in the research literature. The three building block models are presented in their simplest form and are progressively elaborated and related to real world process that can be represented using them.  Introductory chapters cover essential background topics, particularly the relationships between pattern, process and spatiotemporal scale.  Additional chapters consider how time and space can be represented in more complicated models, and methods for the analysis and evaluation of models. Finally, the three building block models are woven together in a more elaborate example to show how a complicated model can be assembled from relatively simple components.

To aid understanding, more than 50 specific models described in the book are available online at patternandprocess.org for exploration in the freely available Netlogo platform.  This book encourages readers to develop intuition for the abstract types of model that are likely to be appropriate for application in any specific context.  Spatial Simulation: Exploring Pattern and Process will be of interest to undergraduate and graduate students taking courses in environmental, social, ecological and geographical disciplines.  Researchers and professionals who require a non-specialist introduction will also find this book an invaluable guide to dynamic spatial simulation.

Arvustused

The book by OSullivan 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)

Foreword xiii
Preface xv
Acknowledgements xix
Introduction xxi
About the Companion Website xxv
1 Spatial Simulation Models: What? Why? How?
1(28)
1.1 What are simulation models?
2(10)
1.1.1 Conceptual models
4(3)
1.1.2 Physical models
7(1)
1.1.3 Mathematical models
7(1)
1.1.4 Empirical models
8(1)
1.1.5 Simulation models
9(3)
1.2 How do we use simulation models?
12(3)
1.2.1 Using models for prediction
13(1)
1.2.2 Models as guides to data collection
13(1)
1.2.3 Models as `tools to think with'
14(1)
1.3 Why do we use simulation models?
15(8)
1.3.1 When experimental science is difficult (or impossible)
16(2)
1.3.2 Complexity and nonlinear dynamics
18(5)
1.4 Why dynamic and spatial models?
23(6)
1.4.1 The strengths and weaknesses of highly general models
23(4)
1.4.2 From abstract to more realistic models: controlling the cost
27(2)
2 Pattern, Process and Scale
29(28)
2.1 Thinking about spatiotemporal patterns and processes
30(8)
2.1.1 What is a pattern?
30(1)
2.1.2 What is a process?
31(1)
2.1.3 Scale
32(6)
2.2 Using models to explore spatial patterns and processes
38(18)
2.2.1 Reciprocal links between pattern and process: a spatial model of forest structure
39(1)
2.2.2 Characterising patterns: first- and second-order structure
40(3)
2.2.3 Using null models to evaluate patterns
43(3)
2.2.4 Density-based (first-order) null models
46(2)
2.2.5 Interaction-based (second-order) null models
48(1)
2.2.6 Inferring process from (spatio-temporal) pattern
49(4)
2.2.7 Making the virtual forest more realistic
53(3)
2.3 Conclusions
56(1)
3 Aggregation and Segregation
57(40)
3.1 Background and motivating examples
58(2)
3.1.1 Basics of (discrete spatial) model structure
59(1)
3.2 Local averaging
60(4)
3.2.1 Local averaging with noise
63(1)
3.3 Totalistic automata
64(6)
3.3.1 Majority rules
65(3)
3.3.2 Twisted majority annealing
68(1)
3.3.3 Life-like rules
69(1)
3.4 A more general framework: interacting particle systems
70(13)
3.4.1 The contact process
71(2)
3.4.2 Multiple contact processes
73(3)
3.4.3 Cyclic relationships between states: rock-scissors-paper
76(2)
3.4.4 Voter models
78(2)
3.4.5 Voter models with noise mutation
80(3)
3.5 Scheming models
83(3)
3.6 Spatial partitioning
86(2)
3.6.1 Iterative subdivision
86(1)
3.6.2 Voronoi tessellations
87(1)
3.7 Applying these ideas: more complicated models
88(9)
3.7.1 Pattern formation on animals' coats: reaction-diffusion models
89(2)
3.7.2 More complicated processes: spatial evolutionary game theory
91(2)
3.7.3 More realistic models: cellular urban models
93(4)
4 Random Walks and Mobile Entities
97(36)
4.1 Background and motivating examples
97(2)
4.2 The random walk
99(12)
4.2.1 Simple random walks
99(3)
4.2.2 Random walks with variable step lengths
102(1)
4.2.3 Correlated walks
103(5)
4.2.4 Bias and drift in random walks
108(1)
4.2.5 Levy flights: walks with non-finite step length variance
109(2)
4.3 Walking for a reason: foraging and search
111(8)
4.3.1 Using clues: localised search
115(1)
4.3.2 The effect of the distribution of resources
116(3)
4.3.3 Foraging and random walks revisited
119(1)
4.4 Moving entities and landscape interaction
119(2)
4.5 Flocking: entity-entity interaction
121(4)
4.6 Applying the framework
125(8)
4.6.1 Animal foraging
126(2)
4.6.2 Human `hunter-gatherers'
128(1)
4.6.3 The development of home ranges and path networks
129(1)
4.6.4 Constrained environments: pedestrians and evacuations
129(2)
4.6.5 Concluding remarks
131(2)
5 Percolation and Growth: Spread in Heterogeneous Spaces
133(36)
5.1 Motivating examples
133(4)
5.2 Percolation models
137(11)
5.2.1 What is percolation?
137(1)
5.2.2 Ordinary percolation
138(4)
5.2.3 The lost ant
142(3)
5.2.4 Invasion percolation
145(3)
5.3 Growth (or aggregation) models
148(10)
5.3.1 Eden growth processes: theme and variations
149(6)
5.3.2 Diffusion-limited aggregation
155(3)
5.4 Applying the framework
158(10)
5.4.1 Landscape pattern: neutral models and percolation approaches
158(4)
5.4.2 Fire spread: Per Bak's `forest fire model' and derivatives
162(4)
5.4.3 Gullying and erosion dynamics: IP + Eden growth + DLA
166(2)
5.5 Summary
168(1)
6 Representing Time and Space
169(24)
6.1 Representing time
170(5)
6.1.1 Synchronous and asynchronous update
170(2)
6.1.2 Different process rates
172(1)
6.1.3 Discrete time steps or event-driven time
173(1)
6.1.4 Continuous time
174(1)
6.2 Basics of spatial representation
175(4)
6.2.1 Grid or lattice representations
175(2)
6.2.2 Vector-based representation: points, lines, polygons and tessellations
177(2)
6.3 Spatial relationships: distance, neighbourhoods and networks
179(6)
6.3.1 Distance in grids and tessellations
179(2)
6.3.2 Neighbourhoods: local spatial relationships
181(2)
6.3.3 Networks of relationships
183(2)
6.4 Coordinate space: finite, infinite and wrapped
185(3)
6.4.1 Finite model space
185(1)
6.4.2 Infinitely extensible model space
186(1)
6.4.3 Toroidal model space
187(1)
6.5 Complicated spatial structure without spatial data structures
188(2)
6.6 Temporal and spatial representations can make a difference
190(3)
7 Model Uncertainty and Evaluation
193(36)
7.1 Introducing uncertainty
193(1)
7.2 Coping with uncertainty
194(4)
7.2.1 Representing uncertainty in data and processes
195(3)
7.3 Assessing and quantifying model-related uncertainty
198(13)
7.3.1 Error analysis
200(1)
7.3.2 Sensitivity analysis
200(2)
7.3.3 Uncertainty analysis
202(2)
7.3.4 Analysis of model structural uncertainty
204(2)
7.3.5 Difficulties for spatial data and models
206(1)
7.3.6 Sensitivity and uncertainty analysis for a simple spatial model
207(4)
7.4 Confronting model predictions with observed data
211(5)
7.4.1 Visualisation and difference measures
212(2)
7.4.2 Formal statistical tests
214(2)
7.5 Frameworks for selecting between competing models
216(6)
7.5.1 Occam's razor
216(1)
7.5.2 Likelihood
217(3)
7.5.3 Multi-model inference
220(2)
7.6 Pattern-oriented modelling
222(4)
7.6.1 POM case-study: understanding the drivers of treeline physiognomy
224(2)
7.7 More to models than prediction
226(3)
8 Weaving It All Together
229(36)
8.1 Motivating example: island resource exploitation by hunter-gatherers
230(1)
8.2 Model description
231(13)
8.2.1 Overview
232(4)
8.2.2 Design concepts
236(2)
8.2.3 Details
238(6)
8.3 Model development and refinement
244(3)
8.3.1 The model development process
244(2)
8.3.2 Model refinement
246(1)
8.4 Model evaluation
247(15)
8.4.1 Baseline dynamics
247(7)
8.4.2 Sensitivity analysis
254(4)
8.4.3 Uncertainty analysis
258(4)
8.5 Conclusions
262(3)
9 In Conclusion
265(6)
9.1 On the usefulness of building-block models
265(1)
9.2 On pattern and process
266(2)
9.3 On the need for careful analysis
268(3)
References 271(28)
Index 299
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.