$112.32
What is included with this book?
Preface | p. xiii |
Acknowledgements | p. xvii |
Introduction | p. 1 |
What is network spatial analysis? | p. 1 |
Network events: events on and alongside networks | p. 2 |
Planar spatial analysis and its limitations | p. 4 |
Network spatial analysis and its salient features | p. 6 |
Review of studies of network events | p. 10 |
Snow's study of cholera around Broad Street | p. 10 |
Traffic accidents | p. 12 |
Roadkills | p. 14 |
Street crime | p. 16 |
Events on river networks and coastlines | p. 17 |
Other events on networks | p. 18 |
Events alongside networks | p. 19 |
Outline of the book | p. 20 |
Structure of chapters | p. 20 |
Questions solved by network spatial methods | p. 21 |
How to study this book | p. 23 |
Modeling spatial events on and alongside networks | p. 25 |
Modeling the real world | p. 26 |
Object-based model | p. 26 |
Spatial attributes | p. 27 |
Nonspatial attributes | p. 28 |
Field-based model | p. 28 |
Vector data model | p. 29 |
Raster data model | p. 30 |
Modeling networks | p. 31 |
Object-based model for networks | p. 31 |
Geometric networks | p. 31 |
Graph for a geometric network | p. 32 |
Field-based model for networks | p. 33 |
Data models for networks | p. 34 |
Modeling entities on network space | p. 34 |
Objects on and alongside networks | p. 34 |
Field functions on network space | p. 37 |
Stochastic processes on network space | p. 37 |
Object-based model for stochastic spatial events on network space | p. 38 |
Binomial point processes on network space | p. 38 |
Edge effects | p. 41 |
Uniform network transformation | p. 42 |
Basic computational methods for network spatial analysis | p. 45 |
Data structures for one-layer networks | p. 46 |
Planar networks | p. 46 |
Winged-edge data structures | p. 47 |
Efficient access and enumeration of local information | p. 49 |
Attribute data representation | p. 51 |
Local modifications of a network | p. 52 |
Inserting new nodes | p. 52 |
New nodes resulting from overlying two networks | p. 52 |
Deleting existing nodes | p. 53 |
Data structures for nonplanar networks | p. 54 |
Multiple-layer networks | p. 54 |
General nonplanar networks | p. 56 |
Basic geometric computations | p. 57 |
Computational methods for line segments | p. 57 |
Right-turn test | p. 57 |
Intersection test for two line segments | p. 58 |
Enumeration of line segment intersections | p. 58 |
Time complexity as a measure of efficiency | p. 59 |
Computational methods for polygons | p. 60 |
Area of a polygon | p. 60 |
Center of gravity of a polygon | p. 61 |
Inclusion test of a point with respect to a polygon | p. 61 |
Polygon-line intersection | p. 62 |
Polygon intersection test | p. 62 |
Extraction of a subnetwork inside a polygon | p. 63 |
Set-theoretic computations | p. 64 |
Nearest point on the edges of a polygon from a point in the polygon | p. 65 |
Frontage interval | p. 66 |
Basic computational methods on networks | p. 66 |
Single-source shortest paths | p. 67 |
Network connectivity test | p. 70 |
Shortest-path tree on a network | p. 71 |
Extended shortest-path tree on a network | p. 71 |
All nodes within a prespecified distance | p. 72 |
Center of a network | p. 72 |
Heap data structure | p. 73 |
Shortest path between two nodes | p. 77 |
Minimum spanning tree on a network | p. 78 |
Monte Carlo simulation for generating random points on a network | p. 79 |
Network Voronoi diagrams | p. 81 |
Ordinary network Voronoi diagram | p. 82 |
Planar versus network Voronoi diagrams | p. 82 |
Geometric properties of the ordinary network Voronoi diagram | p. 83 |
Generalized network Voronoi diagrams | p. 85 |
Directed network Voronoi diagram | p. 86 |
Weighted network Voronoi diagram | p. 88 |
k-th nearest point network Voronoi diagram | p. 89 |
Line and polygon network Voronoi diagrams | p. 91 |
Point-set network Voronoi diagram | p. 93 |
Computational methods for network Voronoi diagrams | p. 93 |
Multisource Dijkstra method | p. 94 |
Computational method for the ordinary network Voronoi diagram | p. 95 |
Computational method for the directed network Voronoi diagram | p. 96 |
Computational method for the weighted network Voronoi diagram | p. 97 |
Computational method for the k-th nearest point network Voronoi diagram | p. 98 |
Computational methods for the line and polygon network Voronoi diagrams | p. 99 |
Computational method for the point-set network Voronoi diagram | p. 100 |
Network nearest-neighbor distance methods | p. 101 |
Network auto nearest-neighbor distance methods | p. 102 |
Network local auto nearest-neighbor distance method | p. 103 |
Network global auto nearest-neighbor distance method | p. 104 |
Network cross nearest-neighbor distance methods | p. 106 |
:2.1 Network local cross nearest-neighbor distance method | p. 106 |
Network global cross nearest-neighbor distance method | p. 108 |
Network nearest-neighbor distance method for lines | p. 111 |
Computational methods for the network nearest-neighbor distance methods | p. 112 |
Computational methods for the network auto nearest-neighbor distance methods | p. 112 |
Computational methods for the network local auto nearest-neighbor distance method | p. 113 |
Computational methods for the network global auto nearest-neighbor distance method | p. 116 |
Computational methods for the network cross nearest-neighbor distance methods | p. 116 |
Computational methods for the network local cross nearest-neighbor distance method | p. 116 |
Computational methods for the network global cross nearest-neighbor distance method | p. 117 |
Network K function methods | p. 119 |
Network auto K function methods | p. 120 |
Network local auto K function method | p. 121 |
Network global auto K function method | p. 122 |
Network cross K function methods | p. 122 |
Network local cross K function method | p. 123 |
Network global cross K function method | p. 124 |
Network global Voronoi cross K function method | p. 126 |
Network K function methods in relation to geometric characteristics of a network | p. 127 |
Relationship between the shortest-path distance and the Euclidean distance | p. 127 |
Network global auto K function in relation to the level-of-detail of a network | p. 129 |
Computational methods for the network K function methods | p. 131 |
Computational methods for the network auto K function methods | p. 131 |
Computational methods for the network local auto K function method | p. 132 |
Computational methods for the network global auto K function method | p. 133 |
Computational methods for the network cross K function methods | p. 133 |
Computational methods for the network local cross K function method | p. 133 |
Computational methods for the network global cross K function method | p. 134 |
Computational methods for the network global Voronoi cross K function method | p. 136 |
Network spatial autocorrelation | p. 137 |
Classification of autocorrelations | p. 139 |
Spatial randomness of the attribute values of network cells | p. 145 |
Permutation spatial randomness | p. 145 |
Normal variate spatial randomness | p. 146 |
Network Moran's I statistics | p. 146 |
Network local Moran's I statistic | p. 147 |
Network global Moran's I statistic | p. 148 |
Computational methods for Moran's I statistics | p. 150 |
Network point cluster analysis and clumping method | p. 153 |
Network point cluster analysis | p. 155 |
General hierarchical point cluster analysis | p. 155 |
Hierarchical point clustering methods with specific intercluster distances | p. 160 |
Network closest-pair point clustering method | p. 160 |
Network farthest-pair point clustering method | p. 161 |
Network average-pair point clustering method | p. 161 |
Network point clustering methods with other intercluster distances | p. 162 |
Network clumping method | p. 162 |
Relation to network point cluster analysis | p. 162 |
Statistical test with respect to the number of clumps | p. 162 |
Computational methods for the network point cluster analysis and clumping method | p. 164 |
General computational framework | p. 164 |
Computational methods for individual intercluster distances | p. 166 |
Computational methods for the network closest-pair point clustering method | p. 166 |
Computational methods for the network farthest-pair point clustering method | p. 168 |
Computational methods for the network average-pair point clustering method | p. 169 |
Computational aspects of the network clumping method | p. 170 |
Network point density estimation methods | p. 171 |
Network histograms | p. 172 |
Network cell histograms | p. 172 |
Network Voronoi cell histograms | p. 174 |
Network cell-count method | p. 175 |
Network kernel density estimation methods | p. 177 |
Network kernel density functions | p. 178 |
Equal-split discontinuous kernel density functions | p. 181 |
Equal-split continuous kernel density functions | p. 183 |
Computational methods for network point density estimation | p. 184 |
Computational methods for network cell histograms with equal-length network cells | p. 184 |
Computational methods for equal-split discontinuous kernel density functions | p. 186 |
Computational methods for equal-split continuous kernel density functions | p. 190 |
Network spatial interpolation | p. 195 |
Network inverse-distance weighting | p. 197 |
Concepts of neighborhoods on a network | p. 197 |
Network inverse-distance weighting predictor | p. 198 |
Network kriging | p. 199 |
Network kriging models | p. 200 |
Concepts of stationary processes on a network | p. 201 |
Network variogram models | p. 203 |
Network kriging predictors | p. 206 |
Computational methods for network spatial interpolation | p. 209 |
Computational methods for network inverse-distance weighting | p. 209 |
Computational methods for network kriging | p. 210 |
Network Huff model | p. 213 |
Concepts of the network Huff model | p. 214 |
Huff models | p. 214 |
Dominant market subnetworks | p. 215 |
Huff-based demand estimation | p. 216 |
Huff-based locational optimization | p. 217 |
Computational methods for the Huff-based demand estimation | p. 217 |
Shortest-path tree distance | p. 218 |
Choice probabilities in terms of shortest-path tree distances | p. 220 |
Analytical formula for the Huff-based demand estimation | p. 220 |
Computational tasks and their time complexities for the Huff-based demand estimation | p. 221 |
Computational methods for the Huff-based locational optimization | p. 222 |
Demand function for a newly entering store | p. 223 |
Topologically invariant shortest-path trees | p. 224 |
Topologically invariant link sets | p. 225 |
Numerical method for the Huff-based locational optimization | p. 227 |
Computational tasks and their time complexities for the Huff-based locational optimization | p. 230 |
GIS-based tools for spatial analysis along networks and their application | p. 231 |
Preprocessing tools in SANET | p. 232 |
Tools for testing network connectedness | p. 233 |
Tool for assigning points to the nearest points on a network | p. 233 |
Tools for computing the shortest-path distances between points | p. 234 |
Tool for generating random points on a network | p. 234 |
Statistical tools in SANET and their application | p. 235 |
Tools for network Voronoi diagrams and their application | p. 236 |
Tools for network nearest-neighbor distance methods and their application | p. 237 |
Network global auto nearest-neighbor distance method | p. 238 |
Network global cross nearest-neighbor distance method | p. 239 |
Tools for network K function methods and their application | p. 240 |
Network global auto K function method | p. 241 |
Network global cross K function method | p. 241 |
Network global Voronoi cross K function method | p. 243 |
Network local cross K function method | p. 244 |
Tools for network point cluster analysis and their application | p. 245 |
Tools for network kernel density estimation methods and their application | p. 246 |
Tools for network spatial interpolation methods and their application | p. 247 |
References | p. 249 |
Index | p. 271 |
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