Atjaunināt sīkdatņu piekrišanu

Fundamentals of Spatial Analysis and Modelling [Hardback]

(University of Auckland, New Zealand)
  • Formāts: Hardback, 348 pages, height x width: 234x156 mm, weight: 700 g, 63 Tables, black and white; 81 Line drawings, color; 24 Line drawings, black and white; 47 Halftones, color; 3 Halftones, black and white; 128 Illustrations, color; 27 Illustrations, black and white
  • Izdošanas datums: 22-Dec-2021
  • Izdevniecība: CRC Press
  • ISBN-10: 1032115750
  • ISBN-13: 9781032115757
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 152,25 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
  • Bibliotēkām
Fundamentals of Spatial Analysis and ModellingPalielināt
  • Formāts: Hardback, 348 pages, height x width: 234x156 mm, weight: 700 g, 63 Tables, black and white; 81 Line drawings, color; 24 Line drawings, black and white; 47 Halftones, color; 3 Halftones, black and white; 128 Illustrations, color; 27 Illustrations, black and white
  • Izdošanas datums: 22-Dec-2021
  • Izdevniecība: CRC Press
  • ISBN-10: 1032115750
  • ISBN-13: 9781032115757
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781032115757.html
Keywords:
"This textbook provides comprehensive and in-depth explanations of all topics related to spatial analysis and spatiotemporal simulation, including how spatial data are acquired, represented digitally, and spatially aggregated. Also features the nature ofspace and how it is measured. Descriptive and inferential analyses are covered for point, line, and area data. It captures the latest developments in spatiotemporal simulation with cellular automata and agent-based modelling, and through practical examples discusses how spatial analysis and modelling can be implemented in different computing platforms. A much-needed textbook for a course at upper undergraduate and postgraduate levels"--

This textbook provides comprehensive and in-depth explanations of all topics related to spatial analysis and spatiotemporal simulation, including how spatial data are acquired, represented digitally, and spatially aggregated. Also features the nature of space and how it is measured. Descriptive, explanatory, and inferential analyses are covered for point, line, and area data. It captures the latest developments in spatiotemporal simulation with cellular automata and agent-based modelling, and through practical examples discusses how spatial analysis and modelling can be implemented in different computing platforms. A much-needed textbook for a course at upper undergraduate and postgraduate levels.

This textbook provides comprehensive and in-depth explanations of all topics related to spatial analysis and spatiotemporal simulation, including how spatial data are acquired, represented digitally, and spatially aggregated.

Preface xv
Author Biography xvii
Acknowledgements xix
Chapter 1 Introduction 1(32)
1.1 What Is Spatial Analysis?
1(3)
1.1.1 Definition
1(1)
1.1.2 Spatial Statistics
2(1)
1.1.3 Geocomputation
3(1)
1.1.4 Geostatistics
3(1)
1.2 What Is So Special about Spatial Analysis?
4(3)
1.2.1 A Historic Event
4(1)
1.2.2 Value of the Spatial Perspective
4(2)
1.2.3 Ubiquitous Applications
6(1)
1.2.4 Ability to Predict and Explore Alternatives
7(1)
1.3 A Brief History
7(4)
1.3.1 Pre-digital and Early Digital Era
7(1)
1.3.2 Desktop Era
8(1)
1.3.3 GPS and GIS Era
9(1)
1.3.4 Big Data Era
10(1)
1.4 Relationship with Other Pertinent Disciplines
11(5)
1.4.1 GIS
11(2)
1.4.2 Remote Sensing
13(1)
1.4.3 GPS
14(1)
1.4.4 Computer Science
15(1)
1.4.5 Statistics
15(1)
1.5 Targets of Analysis
16(5)
1.5.1 Spatial Relationship
16(2)
1.5.2 Spatial Pattern and Structure
18(2)
1.5.3 Spatial Process
20(1)
1.6 Types of Spatial Analysis
21(3)
1.6.1 Descriptive Spatial Analysis
21(1)
1.6.2 Explanatory/Inferential Spatial Analysis
22(1)
1.6.3 Predictive Spatial Analysis
23(1)
1.7 Systems for Spatial Analysis
24(6)
1.7.1 ArcGIS Pro
25(2)
1.7.2 ERDAS Imagine
27(1)
1.7.3 RStudio
27(1)
1.7.4 MATLAB®
28(1)
1.7.5 NetLogo
29(1)
Review Questions
30(1)
References
31(2)
Chapter 2 Space in Spatial Analysis 33(42)
2.1 Spatial Reference Systems
33(7)
2.1.1 Geoid and Datum
33(3)
2.1.2 Global Spherical System
36(1)
2.1.3 Global Plane System
36(3)
2.1.4 Conversion between 3D and 2D Coordinates
39(1)
2.2 Properties of Spatial Entities
40(6)
2.2.1 Components of Spatial Entities
40(1)
2.2.2 Object View of Spatial Entities
41(1)
2.2.3 Field View of Spatial Entities
42(1)
2.2.4 Dimension of Spatial Entities
43(2)
2.2.5 Spatial Adjacency and Connectivity
45(1)
2.3 Quantitative Measures of Space
46(9)
2.3.1 Distance
47(1)
2.3.2 Proximity
48(1)
2.3.3 Neighbourhood
49(2)
2.3.4 Dispersion and Clustering
51(1)
2.3.5 Direction
52(2)
2.3.6 Area
54(1)
2.4 Spatial Partitioning and Aggregation
55(6)
2.4.1 Spatial Partitioning
55(3)
2.4.2 Spatial Aggregation and the Modifiable Area Unit Problem
58(3)
2.5 Spatial Tessellation
61(8)
2.5.1 Regular Tessellations
61(1)
2.5.2 Irregular Tessellations
62(3)
2.5.3 Delaunay Triangulation
65(2)
2.5.4 Thiessen (Voronoi) Polygons
67(2)
2.6 Rudimentary Spatial Measures
69(4)
2.6.1 Mean
69(1)
2.6.2 Variance and Standard Deviation
70(1)
2.6.3 Correlation
71(1)
2.6.4 RMSE
72(1)
Review Questions
73(1)
References
74(1)
Chapter 3 Spatial Data and Association 75(40)
3.1 Data and Their Sources
75(5)
3.1.1 Spatial Data
75(1)
3.1.2 Thematic Data
75(3)
3.1.3 Enumeration Scales of Thematic Data
78(2)
3.2 Spatial Sampling
80(10)
3.2.1 Sampling Considerations
80(1)
3.2.2 Strategies of Spatial Sampling
81(4)
3.2.3 Sampling Dimensionality
85(3)
3.2.4 Sample Size and Spacing
88(2)
3.3 Spatial Association and Pattern
90(9)
3.3.1 Spatial Continuity versus Spatial Pattern
90(1)
3.3.2 Scatterplot and H-scatterplot
91(1)
3.3.3 Correlogram
92(2)
3.3.4 Spatial Auto-correlation
94(1)
3.3.5 Cross-correlation
95(3)
3.3.6 Scale in Spatial Analysis
98(1)
3.4 Spatial Auto-correlation
99(8)
3.4.1 Geary's Ratio
99(1)
3.4.2 Moran's I
100(1)
3.4.3 An example
101(1)
3.4.4 Local-scale Spatial Association
102(4)
3.4.5 Global Bivariate Spatial Auto-correlation
106(1)
3.5 Area Pattern and Joint Count Statistic
107(4)
3.5.1 Area Patterns and Joint Counts
107(2)
3.5.2 Joint Count Test
109(2)
Review Questions
111(1)
References
112(3)
Chapter 4 Descriptive and Inferential Spatial Analysis 115(44)
4.1 Analysis of Point Data
115(18)
4.1.1 Point Data
115(1)
4.1.2 Fundamental Types of Point Patterns
116(2)
4.1.3 Poisson Process behind Random Patterns
118(1)
4.1.4 Formation of Clustered Patterns
118(2)
4.1.5 Measures of Point Dispersion
120(1)
4.1.6 Nearest Neighbour Analysis
121(1)
4.1.7 Hotspot Analysis
122(1)
4.1.8 Inferential Analysis of Point Patterns
123(5)
4.1.9 Kernel Density Analysis
128(2)
4.1.10 Second-order Analysis of Point Patterns
130(3)
4.2 Fractals and Spatial Analysis
133(4)
4.2.1 Line/Surface Complexity
133(2)
4.2.2 Fundamentals of Fractal Geometry
135(1)
4.2.3 Fractal Dimension and Its Determination
136(1)
4.3 Shape Analysis of Polygons
137(8)
4.3.1 Desirable Qualities of Shape Measures
138(1)
4.3.2 Outline-based Shape Analysis
139(1)
4.3.3 Compactness-based Measures
140(3)
4.3.4 Comparison to a Standard Shape
143(1)
4.3.5 Practical Applications
144(1)
4.4 Analysis of Areal Data and FRAGSTATS
145(5)
4.4.1 Objects of Study
145(1)
4.4.2 Representative Metrics
146(2)
4.4.3 FRAGSTATS
148(1)
4.4.4 Application in Quantifying Urban Sprawl
149(1)
4.5 Analysis of Directions
150(6)
4.5.1 Reference Systems of Directions
151(1)
4.5.2 Descriptive Measures
152(1)
4.5.3 Schematic Representation
153(3)
Review Questions
156(1)
References
157(2)
Chapter 5 Geostatistics and Spatial Interpolation 159(30)
5.1 Introduction
159(6)
5.1.1 Spatial Interpolation and Geostatistics
159(1)
5.1.2 Regionalised Variables
159(2)
5.1.3 Variogram and Semi-variogram
161(1)
5.1.4 Structure of Semi-variogram
162(2)
5.1.5 Semi-variogram Models
164(1)
5.2 Trend Surface Interpolation
165(4)
5.3 Moving Averaging
169(4)
5.4 Minimum Curvature
173(3)
5.5 Kriging
176(11)
5.5.1 Ordinary (Punctual) Kriging
176(4)
5.5.2 A Comparative Evaluation
180(2)
5.5.3 Simple Kriging
182(1)
5.5.4 Universal Kriging
183(1)
5.5.5 Block Kriging and Co-kriging
184(3)
Review Questions
187(1)
References
187(2)
Chapter 6 Spatial Modelling 189(68)
6.1 Fundamentals of Modelling
189(8)
6.1.1 Models and Types
189(3)
6.1.2 Static versus Dynamic Models
192(2)
6.1.3 Spatial Modelling
194(1)
6.1.4 Spatial Analysis or Spatial Modelling?
195(2)
6.2 Nature of Spatial Modelling
197(11)
6.2.1 Variables in Spatial Modelling
197(1)
6.2.2 Types of Spatial Modelling
198(6)
6.2.3 Cartographic Modelling
204(3)
6.2.4 Spatial Dynamic Modelling
207(1)
6.3 Model Development and Accuracy
208(6)
6.3.1 Feature Selection
209(2)
6.3.2 Multi-collinearity Test
211(1)
6.3.3 Model Validation
211(1)
6.3.4 Measures of Modelling Accuracy
212(2)
6.4 Issues in Spatial Modelling
214(11)
6.4.1 Assignment of Weights
214(6)
6.4.1.1 AHP
215(3)
6.4.1.2 Regression Analysis
218(1)
6.4.1.3 Weight of Evidence Model
219(1)
6.4.1.4 Delphi Technique
220(1)
6.4.2 Spatial Modelling and Data Structure
220(2)
6.4.3 Platforms for Spatial Modelling
222(3)
6.4.3.1 ArcGIS ModelBuilder
222(1)
6.4.3.2 Raster Calculator
223(1)
6.4.3.3 IDRISI TerrSet
224(1)
6.4.3.4 Scripting
224(1)
6.5 Spatial Modelling in GIS Packages
225(9)
6.5.1 Spatial modelling and GIS Packages
225(2)
6.5.2 Pros and Cons of GIS Modelling
227(1)
6.5.3 Coupling of Spatial Models with GIS Software
228(6)
6.5.3.1 Loose Coupling
230(1)
6.5.3.2 Tight Coupling
231(1)
6.5.3.3 Total Coupling
232(2)
6.6 Special Types of Modelling
234(8)
6.6.1 (Spatial) Logistic Regression
234(1)
6.6.2 Land Use Regression Modelling
235(3)
6.6.3 Hydrological Modelling
238(4)
6.7 Three Cases of Spatial Modelling
242(10)
6.7.1 Earthquake Damage Modelling
242(2)
6.7.2 Landslide Susceptibility Modelling
244(4)
6.7.3 Glacier Extent Modelling
248(4)
Review Questions
252(1)
References
252(5)
Chapter 7 Spatial Simulation 257(54)
7.1 Introduction
257(5)
7.1.1 Spatial Simulation
257(1)
7.1.2 Spatiotemporal Dynamic Simulation
258(1)
7.1.3 Spatially Explicit Simulation Models
259(1)
7.1.4 Spatial Simulation and Machine Learning
260(2)
7.2 Cellular Automata Simulation
262(9)
7.2.1 Automata
262(2)
7.2.2 Environment
264(1)
7.2.3 Rules
265(1)
7.2.4 CA and Spatial Simulation
266(1)
7.2.5 Two Examples of CA Models
267(3)
7.2.6 Integration with Other Models
270(1)
7.3 Agent-based Simulation
271(10)
7.3.1 Agents and Geographic Agents
271(2)
7.3.2 Agent-based Modelling
273(1)
7.3.3 Rules
274(1)
7.3.4 CA or ABM?
275(2)
7.3.5 Designing and Developing ABMs
277(2)
7.3.6 Toolkits for Implementing ABM
279(2)
7.4 NetLogo for Dynamic Simulation
281(5)
7.4.1 General Features
281(2)
7.4.2 Anatomy of a NetLogo Model
283(1)
7.4.3 Sensitivity Analysis
284(1)
7.4.4 NetLogo versus Spatiotemporal Simulations
285(1)
7.5 Special Cases of Spatial Simulation
286(20)
7.5.1 Wildfire Simulation
286(8)
7.5.1.1 Wildfire Models
287(2)
7.5.1.2 Raster or Vector Simulation Models
289(2)
7.5.1.3 Common Wildfire Simulation Models
291(3)
7.5.2 Urban Expansion Simulation
294(7)
7.5.2.1 Modes of Urban Expansion
295(1)
7.5.2.2 Players in Urban Expansion
296(2)
7.5.2.3 Simulation Models of Urban Expansion
298(2)
7.5.2.4 A Case Study
300(1)
7.5.3 Simulation of Grassland Degradation
301(5)
Review Questions
306(1)
References
307(4)
Chapter 8 Time-explicit Spatial Analysis and Modelling 311(32)
8.1 Time
311(5)
8.1.1 Nature of Time in Geography
311(1)
8.1.2 Space, Time, and Attribute
312(3)
8.1.3 Temporal Aggregation versus Discretisation
315(1)
8.2 Models of Spatiotemporal Representation
316(9)
8.2.1 Time-location Path Model
316(2)
8.2.2 Gazetteer Method of Representation
318(1)
8.2.3 Snapshot Model
319(1)
8.2.4 Space-time Composites Model
320(1)
8.2.5 Spatiotemporal Object Model
321(2)
8.2.6 Event-based Spatiotemporal Model
323(2)
8.2.7 Organisation and Storage of Spatiotemporal Data
325(1)
8.3 Spatiotemporal Data Analysis
325(4)
8.3.1 Time-explicit versus Time-implicit Analysis
325(1)
8.3.2 Spatiotemporal Association
326(2)
8.3.3 Time Slicing Overlay Analysis
328(1)
8.4 Time-explicit Spatiotemporal Modelling
329(6)
8.4.1 Extended Semi-variogram Model
330(1)
8.4.2 Diffusion Models
330(1)
8.4.3 State and Transition Model
331(1)
8.4.4 Modified SIR Model
332(2)
8.4.5 Packages for Spatiotemporal Analysis and Modelling
334(1)
8.5 Visualisation of Spatiotemporal Data and Processes
335(4)
8.5.1 Simple Map Display
336(2)
8.5.2 Animation
338(1)
8.5.3 Simulation
339(1)
Review Questions
339(1)
References
340(3)
Index 343
Dr. Jay Gao is an Associate Professor at the Faculty of Science, University of Auckland, New Zealand. He received his BE in photogrammetric engineering from Wuhan Technical University of Surveying and Mapping, China; his MS from the University of Toronto, Canada; and his PhD from the University of Georgia in the U.S. in 1992. Over his academic career, has carried out many research projects on remote sensing, GIS, GPS and their integrated applications in natural resource management and environmental monitoring focused on quantitative remote sensing and integration of geo-computational methods. His recent research is focused on mapping of land covers from satellite imagery using spatial information. He is an Associate Editor of ISPRS Journal of Photogrammetry and Remote Sensing and has published numerous research articles. His textbook Digital Analysis of Remotely Sensed Imagery was published by McGraw-Hill Education in 2009.