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Agent-Based Modelling and Geographical Information Systems: A Practical Primer [Kietas viršelis]

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  • Formatas: Hardback, 408 pages, aukštis x plotis: 242x170 mm, weight: 930 g
  • Serija: Spatial Analytics and GIS
  • Išleidimo metai: 28-Dec-2018
  • Leidėjas: Sage Publications Ltd
  • ISBN-10: 1473958644
  • ISBN-13: 9781473958647
Kitos knygos pagal šią temą:
Agent-Based Modelling and Geographical Information Systems: A Practical PrimerDidesnis paveikslėlis
  • Formatas: Hardback, 408 pages, aukštis x plotis: 242x170 mm, weight: 930 g
  • Serija: Spatial Analytics and GIS
  • Išleidimo metai: 28-Dec-2018
  • Leidėjas: Sage Publications Ltd
  • ISBN-10: 1473958644
  • ISBN-13: 9781473958647
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781473958647.html
Raktažodžiai:

This textbook explains how to design and build Agent Based Models and how to link them to Geographical Information Systems.



This is the era of Big Data and computational social science. It is an era that requires tools which can do more than visualise data but also model the complex relation between data and human action and interaction. Agent-Based Models (ABM) - computational models which simulate human action and interaction – do just that.

 

This textbook explains how to design and build ABM and how to link the models to Geographical Information Systems. It guides you from the basics through to constructing more complex models which work with data and human behaviour in a spatial context. All of the fundamental concepts are explained and related to practical examples to facilitate learning (with models developed in NetLogo with all code examples available on the accompanying website).  You will be able to use these models to develop your own applications and link, where appropriate, to Geographical Information Systems.

 
All of the key ideas and methods are explained in detail:

  • geographical modelling;
  • an introduction to ABM;
  • the fundamentals of Geographical Information Science;
  • why ABM and GIS;
  • using QGIS;
  • designing and building an ABM;
  • calibration and validation;
  • modelling human behaviour;
  • visualisation and 3D ABM;
  • using Big Geosocial Data, GIS and ABM.

An applied primer, that provides fundamental knowledge and practical skills, it will provide you with the skills to build and run your own models, and to begin your own research projects.

 

Recenzijos

A great introduction for all those interested in learning about agent-based simulation where physical space is an important factor. Importantly this integrates GIS and other common geographic approaches with simulation approaches. Both beginners and more advanced researchers will find a lot of useful information here. -- Bruce Edmonds A highly original textbook linking complex systems and agent-based modeling with GIS using theoretical and methodological perspectives, software implementations, and real-world applications. A much-needed book for students at all levels to learn about geosimulation and modelling with geographic automata. -- Suzana Dragicevic This textbook is a must-have for everybody who wants to learn or know about agent-based models integrated with geographic information systems. It combines in-depth introductions to basic concepts with hands-on advice on technical detail and covers all relevant aspects.  -- Volker Grimm I fully expect to recommend individual chapters to students when they come to me with detailed questions, when they need a refresher on some of the concepts, or when they show specific knowledge gaps that need to be filled in. -- Julia Kasmire * SAGE Journal: Environment and Planning B: Urban Analytics and City Science *

List of Figures
xii
List of Tables
xxi
Preface xxii
List of Acronyms
xxiv
About the Authors xxv
Foreword xxvi
1 Agent-Based Modelling and Geographical Information Systems
1(13)
1.1 Introduction
1(1)
1.2 Complexity and Geographical Systems
2(3)
1.3 Models
5(2)
1.4 Data
7(1)
1.5 Individuals
7(2)
1.6 Agent-Based Modelling and Geographical Information Systems
9(1)
1.7 Outline of the Book
10(2)
1.8 Annotated Bibliography
12(2)
2 Introduction to Agent-Based Modelling
14(21)
2.1 Introduction
14(6)
2.1.1 What Is an Agent?
15(3)
2.1.2 Agent Rules
18(1)
2.1.3 An Agent's World
19(1)
2.2 Advantages of Agent-Based Modelling
20(2)
2.3 Limitations of Agent-Based Modelling
22(1)
2.4 A Gallery of Applications
23(8)
2.4.1 Segregation
24(2)
2.4.2 SugarScape
26(2)
2.4.3 Transportation Modelling
28(2)
2.4.4 Decision-Making
30(1)
2.5 Discussion
31(2)
2.6 Annotated Bibliography
33(2)
3 Designing and Developing an Agent-Based Model
35(27)
3.1 Introduction
35(2)
3.2 Overview
37(5)
3.2.1 Purpose and Process
38(1)
3.2.2 Data Collection and Evaluation Plan
39(1)
3.2.3 Development and Software
40(1)
3.2.4 Visualisation
40(1)
3.2.5 Biases, Uncertainty and Assumptions
41(1)
3.3 World
42(4)
3.3.1 External Systems
42(1)
3.3.2 Space
43(2)
3.3.3 Time
45(1)
3.3.4 Populations
45(1)
3.3.5 Physical Rules
46(1)
3.4 Interactions
46(3)
3.4.1 Physical Interactions
46(2)
3.4.2 Communication
48(1)
3.4.3 Resource Exchange
48(1)
3.5 Agents
49(3)
3.5.1 Characteristics
49(1)
3.5.2 Decisions
50(1)
3.5.3 Actions
51(1)
3.6 Building a Segregation Model
52(6)
3.6.1 Segregation Model: Overview
52(2)
3.6.2 Segregation Model: World
54(2)
3.6.3 Segregation Model: Interactions
56(1)
3.6.4 Segregation Model: Agents
56(2)
3.7 Other Frameworks and Approaches
58(1)
3.7.1 Pattern-Orientated Modelling
58(1)
3.7.2 Overview, Design Concepts and Details (ODD) Protocol
58(1)
3.8 Discussion
59(1)
3.9 Annotated Bibliography
60(2)
4 Building Agent-Based Models with NetLogo
62(33)
4.1 Introduction
62(1)
4.2 NetLogo Basics
63(10)
4.2.1 The NetLogo Program
65(3)
4.2.2 Contexts: Observer, Turtle, Patch (and Link)
68(2)
4.2.3 The ask Command
70(3)
4.3 First NetLogo Model
73(10)
4.3.1 Creating the World
74(1)
4.3.2 Buttons and Procedures
75(2)
4.3.3 Sliders and Variables
77(1)
4.3.4 Creating Turtles and Patches
78(2)
4.3.5 Making the Model go
80(3)
4.4 Advanced NetLogo Model
83(10)
4.4.1 Advanced Variables
84(3)
4.4.2 Grass Grows
87(2)
4.4.3 Giving Birth
89(2)
4.4.4 Creating a Graph
91(2)
4.5 Annotated Bibliography
93(2)
5 Fundamentals of Geographical Information Systems
95(30)
5.1 Introduction
95(1)
5.2 A (Very Brief) History of GIS
96(2)
5.3 Representing the World
98(4)
5.3.1 Raster Data
99(2)
5.3.2 Vector Data
101(1)
5.4 Time in GIS
102(1)
5.5 GIS Software
103(2)
5.6 Sources of Geographical Data
105(5)
5.6.1 Volunteered and Ambient Geographical Information
106(1)
5.6.2 Social Media Data
106(2)
5.6.3 Remotely Sensed Data
108(2)
5.7 Preparing GIS Data Using QGIS
110(7)
5.7.1 Performing Spatial Operations
111(2)
5.7.2 Manipulating Vector Table Data
113(4)
5.8 Visualising Results
117(5)
5.8.1 Map Types
117(2)
5.8.2 Map Elements
119(1)
5.8.3 Styling Trends
120(1)
5.8.4 Interactivity
121(1)
5.9 Discussion
122(2)
5.10 Annotated Bibliography
124(1)
6 Integrating Agent-Based Models and GIS
125(47)
6.1 Introduction
125(2)
6.2 Coupling and Embedding GIS and Agent-Based Models
127(2)
6.3 Tools for Constructing and Developing Agent-Based Models
129(9)
6.3.1 Swarm
132(1)
6.3.2 MASON
132(1)
6.3.3 Repast
132(3)
6.3.4 NetLogo
135(2)
6.3.5 GAMA
137(1)
6.4 Integrating GIS Data into Agent-Based Models
138(27)
6.4.1 Using Raster Data in NetLogo
139(10)
6.4.2 Using Vector Data in NetLogo
149(16)
6.5 Exporting Data
165(4)
6.6 Discussion
169(1)
6.7 Annotated Bibliography
170(2)
7 Modelling Human Behaviour
172(22)
7.1 Introduction
172(2)
7.2 The Challenge of Simulating Human Behaviour
174(2)
7.3 Behavioural Frameworks
176(4)
7.3.1 Types of Behavioural Frameworks
177(3)
7.3.2 Challenges
180(1)
7.4 Mathematical Approaches
180(3)
7.4.1 Probabilistic Models
181(1)
7.4.2 Threshold Models
182(1)
7.5 Conceptual Cognitive Models
183(4)
7.5.1 Beliefs-Desires-Intentions Model
183(1)
7.5.2 Fast and Frugal Model
184(2)
7.5.3 Physical Conditions, Emotional State, Cognitive Capability and Social Status Model
186(1)
7.6 Case Study: Simulating Consumer Behaviour Using Probabilistic Rules
187(2)
7.7 Case Study: Simulating Behaviour in Riots Using a Cognitive Model
189(1)
7.8 Discussion
190(2)
7.9 Annotated Bibliography
192(2)
8 Networks
194(32)
8.1 Introduction
195(1)
8.2 Basic Network Properties
196(7)
8.2.1 Defining Graphs Mathematically
196(1)
8.2.2 Building Graphs in NetLogo
197(1)
8.2.3 Adjacency Matrices and Node Degree
198(1)
8.2.4 Traversing Graphs
199(1)
8.2.5 Graph Density
200(1)
8.2.6 Calculating Node Importance
201(2)
8.3 Social Networks
203(5)
8.3.1 Random Networks
204(2)
8.3.2 Small-World Networks
206(1)
8.3.3 Scale-Free Networks
207(1)
8.4 Transport Networks: Agents Navigating a Road Network
208(9)
8.5 Linking Geographical and Social Networks
217(6)
8.6 Discussion
223(1)
8.7 Annotated Bibliography
224(2)
9 Spatial Statistics
226(18)
9.1 Introduction
227(1)
9.2 Hypothetical Data
228(2)
9.3 Goodness of Fit with Global Statistics
230(3)
9.4 Visual Comparisons
233(2)
9.5 Description of the Properties of Point Data
235(2)
9.6 Local Indicators of Spatial Association (LISA)
237(2)
9.6.1 Dual KDE
237(1)
9.6.2 GI*
237(2)
9.7 Multi-scale Error Analysis
239(2)
9.8 Discussion
241(1)
9.9 Annotated Bibliography
242(2)
10 Evaluating Our Models: Verification, Calibration, Validation
244(21)
10.1 Evaluating Models: An Overview
244(1)
10.2 Verification
245(6)
10.2.1 Code Testing
246(1)
10.2.2 Simplifying Environments
247(3)
10.2.3 Expected Outcome Alignment
250(1)
10.2.4 Docking
250(1)
10.3 Calibration
251(10)
10.3.1 Qualitative Calibration
253(1)
10.3.2 Quantitative Calibration
254(5)
10.3.3 Quantitative Calibration Example-.WalkThisWay
259(2)
10.4 Validation
261(1)
10.5 Discussion
261(2)
10.6 Annotated Bibliography
263(2)
11 Alternative Modelling Approaches
265(20)
11.1 Introduction
265(1)
11.2 An Overview of Different Modelling Approaches
266(8)
11.2.1 Cellular Automata
266(2)
11.2.2 Microsimulation
268(2)
11.2.3 Discreet Event Simulation
270(1)
11.2.4 System Dynamics
271(1)
11.2.5 Spatial Interaction Models
272(2)
11.3 Comparing Different Modelling Approaches
274(2)
11.4 A Practical Comparison: The SIR Model
276(7)
11.4.1 The System Dynamics Approach
276(1)
11.4.2 The Agent-Based Approach
276(3)
11.4.3 The Cellular Automata Approach
279(1)
11.4.4 The Discrete Event Simulation Approach
279(1)
11.4.5 Comparative Analysis
280(3)
11.5 Discussion
283(1)
11.6 Annotated Bibliography
283(2)
12 Summary and Outlook
285(24)
12.1 Introduction
285(1)
12.2 Remaining Challenges
286(8)
12.2.1 Reasons for Modelling
286(1)
12.2.2 Theory and Models
287(1)
12.2.3 Inter-model Comparison
287(1)
12.2.4 Replication and Experiment
288(1)
12.2.5 Verification and Validation
289(1)
12.2.6 Agent Representation. Aggregation and Dynamics
290(1)
12.2.7 Behaviour
290(1)
12.2.8 Sharing and Dissemination of the Model
291(1)
12.2.9 Data Challenges
292(2)
12.3 Looking Ahead
294(11)
12.3.1 Big Data and Agent-Based Modelling
295(3)
12.3.2 Model Integration
298(5)
12.3.3 Uncertainty and Ensembles
303(1)
12.3.4 Data Assimilation
304(1)
12.3.5 Spatially Learning Agents
304(1)
12.4 Discussion
305(1)
12.5 Annotated Bibliography
306(5)
Appendices 309(2)
A Gallery of Applications 311(18)
A.1 Disease Dynamics in a Refugee Camp
311(2)
A.2 Hiker Movements in the Dolomites UNESCO World Heritage Site
313(1)
A.3 Modelling the Emergence of Riots
313(1)
A.4 The Foothill Yellow-Legged Frog Assessment Model
314(1)
A.5 Understanding Artificial Anasazi
314(1)
A.6 The Spread of Agriculture during the Neolithic Period
315(2)
A.7 Walk This Way
317(1)
A.8 Natural Disasters and Humanitarian Relief
318(1)
A.9 Using Social Media Content to Inform Agent-Based Models for Humanitarian Crisis Response
319(1)
A.10 Modelling Transportation and Development for Reston, VA
320(1)
A.11 Agent-Based Modelling for Community Resource Management
320(2)
A.12 Agent-Based Modelling of Conflict Diamonds
322(1)
A.13 Exploring the Growth of Slums
323(2)
A.14 RiftLand: Analysing Conflict, Disasters and Humanitarian Crises in East Africa
325(1)
A.15 Modelling Forced Migration
326(1)
A.16 Studying Coupled Human-Artificial-Natural Systems in Boreal and Arctic Regions
327(2)
References 329(39)
Index 368