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Spatial Analysis along Networks : Statistical and Computational Methods,9780470770818
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Spatial Analysis along Networks : Statistical and Computational Methods

by ;
Edition:
1st
ISBN13:

9780470770818

ISBN10:
0470770813
Format:
Hardcover
Pub. Date:
8/13/2012
Publisher(s):
Wiley

Questions About This Book?

What version or edition is this?
This is the 1st edition with a publication date of 8/13/2012.
What is included with this book?
  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc.

Summary

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

Prefacep. xiii
Acknowledgementsp. xvii
Introductionp. 1
What is network spatial analysis?p. 1
Network events: events on and alongside networksp. 2
Planar spatial analysis and its limitationsp. 4
Network spatial analysis and its salient featuresp. 6
Review of studies of network eventsp. 10
Snow's study of cholera around Broad Streetp. 10
Traffic accidentsp. 12
Roadkillsp. 14
Street crimep. 16
Events on river networks and coastlinesp. 17
Other events on networksp. 18
Events alongside networksp. 19
Outline of the bookp. 20
Structure of chaptersp. 20
Questions solved by network spatial methodsp. 21
How to study this bookp. 23
Modeling spatial events on and alongside networksp. 25
Modeling the real worldp. 26
Object-based modelp. 26
Spatial attributesp. 27
Nonspatial attributesp. 28
Field-based modelp. 28
Vector data modelp. 29
Raster data modelp. 30
Modeling networksp. 31
Object-based model for networksp. 31
Geometric networksp. 31
Graph for a geometric networkp. 32
Field-based model for networksp. 33
Data models for networksp. 34
Modeling entities on network spacep. 34
Objects on and alongside networksp. 34
Field functions on network spacep. 37
Stochastic processes on network spacep. 37
Object-based model for stochastic spatial events on network spacep. 38
Binomial point processes on network spacep. 38
Edge effectsp. 41
Uniform network transformationp. 42
Basic computational methods for network spatial analysisp. 45
Data structures for one-layer networksp. 46
Planar networksp. 46
Winged-edge data structuresp. 47
Efficient access and enumeration of local informationp. 49
Attribute data representationp. 51
Local modifications of a networkp. 52
Inserting new nodesp. 52
New nodes resulting from overlying two networksp. 52
Deleting existing nodesp. 53
Data structures for nonplanar networksp. 54
Multiple-layer networksp. 54
General nonplanar networksp. 56
Basic geometric computationsp. 57
Computational methods for line segmentsp. 57
Right-turn testp. 57
Intersection test for two line segmentsp. 58
Enumeration of line segment intersectionsp. 58
Time complexity as a measure of efficiencyp. 59
Computational methods for polygonsp. 60
Area of a polygonp. 60
Center of gravity of a polygonp. 61
Inclusion test of a point with respect to a polygonp. 61
Polygon-line intersectionp. 62
Polygon intersection testp. 62
Extraction of a subnetwork inside a polygonp. 63
Set-theoretic computationsp. 64
Nearest point on the edges of a polygon from a point in the polygonp. 65
Frontage intervalp. 66
Basic computational methods on networksp. 66
Single-source shortest pathsp. 67
Network connectivity testp. 70
Shortest-path tree on a networkp. 71
Extended shortest-path tree on a networkp. 71
All nodes within a prespecified distancep. 72
Center of a networkp. 72
Heap data structurep. 73
Shortest path between two nodesp. 77
Minimum spanning tree on a networkp. 78
Monte Carlo simulation for generating random points on a networkp. 79
Network Voronoi diagramsp. 81
Ordinary network Voronoi diagramp. 82
Planar versus network Voronoi diagramsp. 82
Geometric properties of the ordinary network Voronoi diagramp. 83
Generalized network Voronoi diagramsp. 85
Directed network Voronoi diagramp. 86
Weighted network Voronoi diagramp. 88
k-th nearest point network Voronoi diagramp. 89
Line and polygon network Voronoi diagramsp. 91
Point-set network Voronoi diagramp. 93
Computational methods for network Voronoi diagramsp. 93
Multisource Dijkstra methodp. 94
Computational method for the ordinary network Voronoi diagramp. 95
Computational method for the directed network Voronoi diagramp. 96
Computational method for the weighted network Voronoi diagramp. 97
Computational method for the k-th nearest point network Voronoi diagramp. 98
Computational methods for the line and polygon network Voronoi diagramsp. 99
Computational method for the point-set network Voronoi diagramp. 100
Network nearest-neighbor distance methodsp. 101
Network auto nearest-neighbor distance methodsp. 102
Network local auto nearest-neighbor distance methodp. 103
Network global auto nearest-neighbor distance methodp. 104
Network cross nearest-neighbor distance methodsp. 106
:2.1 Network local cross nearest-neighbor distance methodp. 106
Network global cross nearest-neighbor distance methodp. 108
Network nearest-neighbor distance method for linesp. 111
Computational methods for the network nearest-neighbor distance methodsp. 112
Computational methods for the network auto nearest-neighbor distance methodsp. 112
Computational methods for the network local auto nearest-neighbor distance methodp. 113
Computational methods for the network global auto nearest-neighbor distance methodp. 116
Computational methods for the network cross nearest-neighbor distance methodsp. 116
Computational methods for the network local cross nearest-neighbor distance methodp. 116
Computational methods for the network global cross nearest-neighbor distance methodp. 117
Network K function methodsp. 119
Network auto K function methodsp. 120
Network local auto K function methodp. 121
Network global auto K function methodp. 122
Network cross K function methodsp. 122
Network local cross K function methodp. 123
Network global cross K function methodp. 124
Network global Voronoi cross K function methodp. 126
Network K function methods in relation to geometric characteristics of a networkp. 127
Relationship between the shortest-path distance and the Euclidean distancep. 127
Network global auto K function in relation to the level-of-detail of a networkp. 129
Computational methods for the network K function methodsp. 131
Computational methods for the network auto K function methodsp. 131
Computational methods for the network local auto K function methodp. 132
Computational methods for the network global auto K function methodp. 133
Computational methods for the network cross K function methodsp. 133
Computational methods for the network local cross K function methodp. 133
Computational methods for the network global cross K function methodp. 134
Computational methods for the network global Voronoi cross K function methodp. 136
Network spatial autocorrelationp. 137
Classification of autocorrelationsp. 139
Spatial randomness of the attribute values of network cellsp. 145
Permutation spatial randomnessp. 145
Normal variate spatial randomnessp. 146
Network Moran's I statisticsp. 146
Network local Moran's I statisticp. 147
Network global Moran's I statisticp. 148
Computational methods for Moran's I statisticsp. 150
Network point cluster analysis and clumping methodp. 153
Network point cluster analysisp. 155
General hierarchical point cluster analysisp. 155
Hierarchical point clustering methods with specific intercluster distancesp. 160
Network closest-pair point clustering methodp. 160
Network farthest-pair point clustering methodp. 161
Network average-pair point clustering methodp. 161
Network point clustering methods with other intercluster distancesp. 162
Network clumping methodp. 162
Relation to network point cluster analysisp. 162
Statistical test with respect to the number of clumpsp. 162
Computational methods for the network point cluster analysis and clumping methodp. 164
General computational frameworkp. 164
Computational methods for individual intercluster distancesp. 166
Computational methods for the network closest-pair point clustering methodp. 166
Computational methods for the network farthest-pair point clustering methodp. 168
Computational methods for the network average-pair point clustering methodp. 169
Computational aspects of the network clumping methodp. 170
Network point density estimation methodsp. 171
Network histogramsp. 172
Network cell histogramsp. 172
Network Voronoi cell histogramsp. 174
Network cell-count methodp. 175
Network kernel density estimation methodsp. 177
Network kernel density functionsp. 178
Equal-split discontinuous kernel density functionsp. 181
Equal-split continuous kernel density functionsp. 183
Computational methods for network point density estimationp. 184
Computational methods for network cell histograms with equal-length network cellsp. 184
Computational methods for equal-split discontinuous kernel density functionsp. 186
Computational methods for equal-split continuous kernel density functionsp. 190
Network spatial interpolationp. 195
Network inverse-distance weightingp. 197
Concepts of neighborhoods on a networkp. 197
Network inverse-distance weighting predictorp. 198
Network krigingp. 199
Network kriging modelsp. 200
Concepts of stationary processes on a networkp. 201
Network variogram modelsp. 203
Network kriging predictorsp. 206
Computational methods for network spatial interpolationp. 209
Computational methods for network inverse-distance weightingp. 209
Computational methods for network krigingp. 210
Network Huff modelp. 213
Concepts of the network Huff modelp. 214
Huff modelsp. 214
Dominant market subnetworksp. 215
Huff-based demand estimationp. 216
Huff-based locational optimizationp. 217
Computational methods for the Huff-based demand estimationp. 217
Shortest-path tree distancep. 218
Choice probabilities in terms of shortest-path tree distancesp. 220
Analytical formula for the Huff-based demand estimationp. 220
Computational tasks and their time complexities for the Huff-based demand estimationp. 221
Computational methods for the Huff-based locational optimizationp. 222
Demand function for a newly entering storep. 223
Topologically invariant shortest-path treesp. 224
Topologically invariant link setsp. 225
Numerical method for the Huff-based locational optimizationp. 227
Computational tasks and their time complexities for the Huff-based locational optimizationp. 230
GIS-based tools for spatial analysis along networks and their applicationp. 231
Preprocessing tools in SANETp. 232
Tools for testing network connectednessp. 233
Tool for assigning points to the nearest points on a networkp. 233
Tools for computing the shortest-path distances between pointsp. 234
Tool for generating random points on a networkp. 234
Statistical tools in SANET and their applicationp. 235
Tools for network Voronoi diagrams and their applicationp. 236
Tools for network nearest-neighbor distance methods and their applicationp. 237
Network global auto nearest-neighbor distance methodp. 238
Network global cross nearest-neighbor distance methodp. 239
Tools for network K function methods and their applicationp. 240
Network global auto K function methodp. 241
Network global cross K function methodp. 241
Network global Voronoi cross K function methodp. 243
Network local cross K function methodp. 244
Tools for network point cluster analysis and their applicationp. 245
Tools for network kernel density estimation methods and their applicationp. 246
Tools for network spatial interpolation methods and their applicationp. 247
Referencesp. 249
Indexp. 271
Table of Contents provided by Ingram. All Rights Reserved.


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