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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