ネットワーク上の空間分析<br>Spatial Analysis Along Networks : Statistical and Computational Methods (Statistics in Practice)

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ネットワーク上の空間分析
Spatial Analysis Along Networks : Statistical and Computational Methods (Statistics in Practice)

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  • 製本 Hardcover:ハードカバー版/ページ数 288 p.
  • 言語 ENG
  • 商品コード 9780470770818
  • DDC分類 519.536

基本説明

Presents a much-needed practical guide to statistical spatial analysis on a network, in a logical, user-friendly order.

Full Description


In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation. Each chapter illustrates a specific technique, from Stochastic Point Processes on a Network and Network Voronoi Diagrams, to Network K-function and Point Density Estimation Methods, and the Network Huff Model. The authors also discuss and illustrate the undertaking of the statistical tests described in a Geographical Information System (GIS) environment as well as demonstrating the user-friendly free software package SANET. Spatial Analysis Along Networks: * Presents a much-needed practical guide to statistical spatial analysis of events on and alongside a network, in a logical, user-friendly order.* Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics. * Dedicates a separate chapter to each of the major techniques involved. * Demonstrates the practicalities of undertaking the tests described in the book, using a GIS. * Is supported by a supplementary website, providing readers with a link to the free software package SANET, so they can execute the statistical methods described in the book. Students and researchers studying spatial statistics, spatial analysis, geography, GIS, OR, traffic accident analysis, criminology, retail marketing, facility management and ecology will benefit from this book.

Table of Contents

Preface                                            xiii
Acknowledgements xvii
1 Introduction 1 (24)
1.1 What is network spatial analysis? 1 (9)
1.1.1 Network events: events on and 2 (2)
alongside networks
1.1.2 Planar spatial analysis and its 4 (2)
limitations
1.1.3 Network spatial analysis and its 6 (4)
salient features
1.2 Review of studies of network events 10 (10)
1.2.1 Snow's study of cholera around 10 (2)
Broad Street
1.2.2 Traffic accidents 12 (2)
1.2.3 Roadkills 14 (2)
1.2.4 Street crime 16 (1)
1.2.5 Events on river networks and 17 (1)
coastlines
1.2.6 Other events on networks 18 (1)
1.2.7 Events alongside networks 19 (1)
1.3 Outline of the book 20 (5)
1.3.1 Structure of chapters 20 (1)
1.3.2 Questions solved by network 21 (2)
spatial methods
1.3.3 How to study this book 23 (2)
2 Modeling spatial events on and alongside 25 (20)
networks
2.1 Modeling the real world 26 (5)
2.1.1 Object-based model 26 (1)
2.1.1.1 Spatial attributes 27 (1)
2.1.1.2 Nonspatial attributes 28 (1)
2.1.2 Field-based model 28 (1)
2.1.3 Vector data model 29 (1)
2.1.4 Raster data model 30 (1)
2.2 Modeling networks 31 (3)
2.2.1 Object-based model for networks 31 (1)
2.2.1.1 Geometric networks 31 (1)
2.2.1.2 Graph for a geometric network 32 (1)
2.2.2 Field-based model for networks 33 (1)
2.2.3 Data models for networks 34 (1)
2.3 Modeling entities on network space 34 (3)
2.3.1 Objects on and alongside networks 34 (3)
2.3.2 Field functions on network space 37 (1)
2.4 Stochastic processes on network space 37 (8)
2.4.1 Object-based model for stochastic 38 (1)
spatial events on network space
2.4.2 Binomial point processes on 38 (3)
network space
2.4.3 Edge effects 41 (1)
2.4.4 Uniform network transformation 42 (3)
3 Basic computational methods for network 45 (36)
spatial analysis
3.1 Data structures for one-layer networks 46 (8)
3.1.1 Planar networks 46 (1)
3.1.2 Winged-edge data structures 47 (2)
3.1.3 Efficient access and enumeration 49 (2)
of local information
3.1.4 Attribute data representation 51 (1)
3.1.5 Local modifications of a network 52 (1)
3.1.5.1 Inserting new nodes 52 (1)
3.1.5.2 New nodes resulting from 52 (1)
overlying two networks
3.1.5.3 Deleting existing nodes 53 (1)
3.2 Data structures for nonplanar networks 54 (3)
3.2.1 Multiple-layer networks 54 (2)
3.2.2 General nonplanar networks 56 (1)
3.3 Basic geometric computations 57 (9)
3.3.1 Computational methods for line 57 (1)
segments
3.3.1.1 Right-turn test 57 (1)
3.3.1.2 Intersection test for two line 58 (1)
segments
3.3.1.3 Enumeration of line segment 58 (1)
intersections
3.3.2 Time complexity as a measure of 59 (1)
efficiency
3.3.3 Computational methods for polygons 60 (1)
3.3.3.1 Area of a polygon 60 (1)
3.3.3.2 Center of gravity of a polygon 61 (1)
3.3.3.3 Inclusion test of a point with 61 (1)
respect to a polygon
3.3.3.4 Polygon-line intersection 62 (1)
3.3.3.5 Polygon intersection test 62 (1)
3.3.3.6 Extraction of a subnetwork 63 (1)
inside a polygon
3.3.3.7 Set-theoretic computations 64 (1)
3.3.3.8 Nearest point on the edges of a 65 (1)
polygon from a point in the polygon
3.3.3.9 Frontage interval 66 (1)
3.4 Basic computational methods on 66 (15)
networks
3.4.1 Single-source shortest paths 67 (3)
3.4.1.1 Network connectivity test 70 (1)
3.4.1.2 Shortest-path tree on a network 71 (1)
3.4.1.3 Extended shortest-path tree on 71 (1)
a network
3.4.1.4 All nodes within a prespecified 72 (1)
distance
3.4.1.5 Center of a network 72 (1)
3.4.1.6 Heap data structure 73 (4)
3.4.2 Shortest path between two nodes 77 (1)
3.4.3 Minimum spanning tree on a network 78 (1)
3.4.4 Monte Carlo simulation for 79 (2)
generating random points on a network
4 Network Voronoi diagrams 81 (20)
4.1 Ordinary network Voronoi diagram 82 (3)
4.1.1 Planar versus network Voronoi 82 (1)
diagrams
4.1.2 Geometric properties of the 83 (2)
ordinary network Voronoi diagram
4.2 Generalized network Voronoi diagrams 85 (8)
4.2.1 Directed network Voronoi diagram 86 (2)
4.2.2 Weighted network Voronoi diagram 88 (1)
4.2.3 k-th nearest point network 89 (2)
Voronoi diagram
4.2.4 Line and polygon network Voronoi 91 (2)
diagrams
4.2.5 Point-set network Voronoi diagram 93 (1)
4.3 Computational methods for network 93 (8)
Voronoi diagrams
4.3.1 Multisource Dijkstra method 94 (1)
4.3.2 Computational method for the 95 (1)
ordinary network Voronoi diagram
4.3.3 Computational method for the 96 (1)
directed network Voronoi diagram
4.3.4 Computational method for the 97 (1)
weighted network Voronoi diagram
4.3.5 Computational method for the k-th 98 (1)
nearest point network Voronoi diagram
4.3.6 Computational methods for the 99 (1)
line and polygon network Voronoi
diagrams
4.3.7 Computational method for the 100 (1)
point-set network Voronoi diagram
5 Network nearest-neighbor distance methods 101 (18)
5.1 Network auto nearest-neighbor 102 (4)
distance methods
5.1.1 Network local auto 103 (1)
nearest-neighbor distance method
5.1.2 Network global auto 104 (2)
nearest-neighbor distance method
5.2 Network cross nearest-neighbor 106 (5)
distance methods
5.2.1 Network local cross 106 (2)
nearest-neighbor distance method
5.2.2 Network global cross 108 (3)
nearest-neighbor distance method
5.3 Network nearest-neighbor distance 111 (1)
method for lines
5.4 Computational methods for the network 112 (7)
nearest-neighbor distance methods
5.4.1 Computational methods for the 112 (1)
network auto nearest-neighbor distance
methods
5.4.1.1 Computational methods for the 113 (3)
network local auto nearest-neighbor
distance method
5.4.1.2 Computational methods for the 116 (1)
network global auto nearest-neighbor
distance method
5.4.2 Computational methods for the 116 (1)
network cross nearest-neighbor distance
methods
5.4.2.1 Computational methods for the 116 (1)
network local cross nearest-neighbor
distance method
5.4.2.2 Computational methods for the 117 (2)
network global cross nearest-neighbor
distance method
6 Network K function methods 119 (18)
6.1 Network auto K function methods 120 (2)
6.1.1 Network local auto K function 121 (1)
method
6.1.2 Network global auto K function 122 (1)
method
6.2 Network cross K function methods 122 (5)
6.2.1 Network local cross K function 123 (1)
method
6.2.2 Network global cross K function 124 (2)
method
6.2.3 Network global Voronoi cross K 126 (1)
function method
6.3 Network K function methods in 127 (4)
relation to geometric characteristics of
a network
6.3.1 Relationship between the 127 (2)
shortest-path distance and the
Euclidean distance
6.3.2 Network global auto K function in 129 (2)
relation to the level-of-detail of a
network
6.4 Computational methods for the network 131 (6)
K function methods
6.4.1 Computational methods for the 131 (1)
network auto K function methods
6.4.1.1 Computational methods for the 132 (1)
network local auto K function method
6.4.1.2 Computational methods for the 133 (1)
network global auto K function method
6.4.2 Computational methods for the 133 (1)
network cross K function methods
6.4.2.1 Computational methods for the 133 (1)
network local cross K function method
6.4.2.2 Computational methods for the 134 (2)
network global cross K function method
6.4.2.3 Computational methods for the 136 (1)
network global Voronoi cross K function
method
7 Network spatial autocorrelation 137 (16)
7.1 Classification of autocorrelations 139 (6)
7.2 Spatial randomness of the attribute 145 (1)
values of network cells
7.2.1 Permutation spatial randomness 145 (1)
7.2.2 Normal variate spatial randomness 146 (1)
7.3 Network Moran's I statistics 146 (4)
7.3.1 Network local Moran's I statistic 147 (1)
7.3.2 Network global Moran's I statistic 148 (2)
7.4 Computational methods for Moran's I 150 (3)
statistics
8 Network point cluster analysis and 153 (18)
clumping method
8.1 Network point cluster analysis 155 (7)
8.1.1 General hierarchical point 155 (5)
cluster analysis
8.1.2 Hierarchical point clustering 160 (1)
methods with specific intercluster
distances
8.1.2.1 Network closest-pair point 160 (1)
clustering method
8.1.2.2 Network farthest-pair point 161 (1)
clustering method
8.1.2.3 Network average-pair point 161 (1)
clustering method
8.1.2.4 Network point clustering 162 (1)
methods with other intercluster
distances
8.2 Network clumping method 162 (2)
8.2.1 Relation to network point cluster 162 (1)
analysis
8.2.2 Statistical test with respect to 162 (2)
the number of clumps
8.3 Computational methods for the network 164 (7)
point cluster analysis and clumping method
8.3.1 General computational framework 164 (2)
8.3.2 Computational methods for 166 (1)
individual intercluster distances
8.3.2.1 Computational methods for the 166 (2)
network closest-pair point clustering
method
8.3.2.2 Computational methods for the 168 (1)
network farthest-pair point clustering
method
8.3.2.3 Computational methods for the 169 (1)
network average-pair point clustering
method
8.3.3 Computational aspects of the 170 (1)
network clumping method
9 Network point density estimation methods 171 (24)
9.1 Network histograms 172 (5)
9.1.1 Network cell histograms 172 (2)
9.1.2 Network Voronoi cell histograms 174 (1)
9.1.3 Network cell-count method 175 (2)
9.2 Network kernel density estimation 177 (7)
methods
9.2.1 Network kernel density functions 178 (3)
9.2.2 Equal-split discontinuous kernel 181 (2)
density functions
9.2.3 Equal-split continuous kernel 183 (1)
density functions
9.3 Computational methods for network 184 (11)
point density estimation
9.3.1 Computational methods for network 184 (2)
cell histograms with equal-length
network cells
9.3.2 Computational methods for 186 (4)
equal-split discontinuous kernel
density functions
9.3.3 Computational methods for 190 (5)
equal-split continuous kernel density
functions
10 Network spatial interpolation 195 (18)
10.1 Network inverse-distance weighting 197 (2)
10.1.1 Concepts of neighborhoods on a 197 (1)
network
10.1.2 Network inverse-distance 198 (1)
weighting predictor
10.2 Network kriging 199 (10)
10.2.1 Network kriging models 200 (1)
10.2.2 Concepts of stationary processes 201 (2)
on a network
10.2.3 Network variogram models 203 (3)
10.2.4 Network kriging predictors 206 (3)
10.3 Computational methods for network 209 (4)
spatial interpolation
10.3.1 Computational methods for 209 (1)
network inverse-distance weighting
10.3.2 Computational methods for 210 (3)
network kriging
11 Network Huff model 213 (18)
11.1 Concepts of the network Huff model 214 (3)
11.1.1 Huff models 214 (1)
11.1.2 Dominant market subnetworks 215 (1)
11.1.3 Huff-based demand estimation 216 (1)
11.1.4 Huff-based locational 217 (1)
optimization
11.2 Computational methods for the 217 (5)
Huff-based demand estimation
11.2.1 Shortest-path tree distance 218 (2)
11.2.2 Choice probabilities in terms of 220 (1)
shortest-path tree distances
11.2.3 Analytical formula for the 220 (1)
Huff-based demand estimation
11.2.4 Computational tasks and their 221 (1)
time complexities for the Huff-based
demand estimation
11.3 Computational methods for the 222 (9)
Huff-based locational optimization
11.3.1 Demand function for a newly 223 (1)
entering store
11.3.2 Topologically invariant 224 (1)
shortest-path trees
11.3.3 Topologically invariant link sets 225 (2)
11.3.4 Numerical method for the 227 (3)
Huff-based locational optimization
11.3.5 Computational tasks and their 230 (1)
time complexities for the Huff-based
locational optimization
12 GIS-based tools for spatial analysis 231 (18)
along networks and their application
12.1 Preprocessing tools in SANET 232 (3)
12.1.1 Tools for testing network 233 (1)
connectedness
12.1.2 Tool for assigning points to the 233 (1)
nearest points on a network
12.1.3 Tools for computing the 234 (1)
shortest-path distances between points
12.1.4 Tool for generating random 234 (1)
points on a network
12.2 Statistical tools in SANET and their 235 (9)
application
12.2.1 Tools for network Voronoi 236 (1)
diagrams and their application
12.2.2 Tools for network 237 (1)
nearest-neighbor distance methods and
their application
12.2.2.1 Network global auto 238 (1)
nearest-neighbor distance method
12.2.2.2 Network global cross 239 (1)
nearest-neighbor distance method
12.2.3 Tools for network K function 240 (1)
methods and their application
12.2.3.1 Network global auto K function 241 (1)
method
12.2.3.2 Network global cross K 241 (2)
function method
12.2.3.3 Network global Voronoi cross K 243 (1)
function method
12.23 A Network local cross K function 244 (5)
method
12.2.4 Tools for network point cluster 245 (1)
analysis and their application
12.2.5 Tools for network kernel density 246 (1)
estimation methods and their application
12.2.6 Tools for network spatial 247 (2)
interpolation methods and their
application
References 249 (22)
Index 271