{"product_id":"spatial-analysis-along-networks-9780470770818","title":"Spatial Analysis Along Networks","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e* Presents a much-needed practical guide to statistical spatial analysis on 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“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.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 May 2013)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAcknowledgements\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What is network spatial analysis?\u003c\/p\u003e \u003cp\u003e1.1.1 Network events: events on and alongside networks\u003c\/p\u003e \u003cp\u003e1.1.2 Planar spatial analysis and its limitations\u003c\/p\u003e \u003cp\u003e1.1.3 Network spatial analysis and its salient features\u003c\/p\u003e \u003cp\u003e1.2 Review of studies of network events\u003c\/p\u003e \u003cp\u003e1.2.1 Snow’s study on cholera around Broad Street\u003c\/p\u003e \u003cp\u003e1.2.2 Traffic accidents\u003c\/p\u003e \u003cp\u003e1.2.3 Road-kills\u003c\/p\u003e \u003cp\u003e1.2.4 Street crimes\u003c\/p\u003e \u003cp\u003e1.2.5 Events on river networks and coastlines\u003c\/p\u003e \u003cp\u003e1.2.6 Other events on networks\u003c\/p\u003e \u003cp\u003e1.2.7 Events alongside networks\u003c\/p\u003e \u003cp\u003e1.3 Outline of the book\u003c\/p\u003e \u003cp\u003e1.3.1 Structure of chapters\u003c\/p\u003e \u003cp\u003e1.3.2 Questions solved by network spatial methods\u003c\/p\u003e \u003cp\u003e1.3.3 How to study this book\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Modeling events on and alongside networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Modeling the real world\u003c\/p\u003e \u003cp\u003e2.1.1 Object-based model\u003c\/p\u003e \u003cp\u003e　　 2.1.1.1 Spatial attributes\u003c\/p\u003e \u003cp\u003e2.1.1.2 Nonspatial attributes\u003c\/p\u003e \u003cp\u003e2.1.2 Field-based model\u003c\/p\u003e \u003cp\u003e2.1.3 Vector data model\u003c\/p\u003e \u003cp\u003e2.1.4 Raster data model\u003c\/p\u003e \u003cp\u003e2.2 Modeling networks\u003c\/p\u003e \u003cp\u003e2.2.1 Object-based model for networks\u003c\/p\u003e \u003cp\u003e2.2.1.1 Geometric networks\u003c\/p\u003e \u003cp\u003e2.2.1.2 Graph for a geometric network\u003c\/p\u003e \u003cp\u003e2.2.2 Field-based model for networks\u003c\/p\u003e \u003cp\u003e2.2.3 Data models for networks\u003c\/p\u003e \u003cp\u003e2.3 Modeling entities on and alongside networks\u003c\/p\u003e \u003cp\u003e2.3.1 Objects on network space\u003c\/p\u003e \u003cp\u003e2.3.2 Field functions on network space\u003c\/p\u003e \u003cp\u003e2.4 Stochastic processes on network space\u003c\/p\u003e \u003cp\u003e2.4.1 Object-based model for stochastic spatial events on network space\u003c\/p\u003e \u003cp\u003e2.4.2 Binomial point processes on network space\u003c\/p\u003e \u003cp\u003e2.4.3 Edge effects\u003c\/p\u003e \u003cp\u003e2.4.4 Uniform network transformation\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Basic computational methods for network spatial analysis\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Data structures for one-layer networks\u003c\/p\u003e \u003cp\u003e3.1.1 Planar networks\u003c\/p\u003e \u003cp\u003e3.1.2 Winged-edge data structures\u003c\/p\u003e \u003cp\u003e3.1.3 Efficient access and enumeration of local information\u003c\/p\u003e \u003cp\u003e3.1.4 Attribute data representation\u003c\/p\u003e \u003cp\u003e3.1.5 Local modifications of a network\u003c\/p\u003e \u003cp\u003e3.1.5.1 Inserting new nodes\u003c\/p\u003e \u003cp\u003e3.1.5.2 New nodes resulting from overlying two networks\u003c\/p\u003e \u003cp\u003e3.1.5.3 Deleting existing nodes\u003c\/p\u003e \u003cp\u003e3.2 Data Structures for nonplanar networks\u003c\/p\u003e \u003cp\u003e3.2.1 Multiple-layer networks\u003c\/p\u003e \u003cp\u003e3.2.2 General nonplanar networks\u003c\/p\u003e \u003cp\u003e3.3 Basic Geometric Computations\u003c\/p\u003e \u003cp\u003e3.3.1 Computational methods for line segments\u003c\/p\u003e \u003cp\u003e3.3.1.1 Right-turn test\u003c\/p\u003e \u003cp\u003e3.3.1.2 Intersection test for two line segments\u003c\/p\u003e \u003cp\u003e3.3.1.3 Enumeration of line segment intersections\u003c\/p\u003e \u003cp\u003e3.3.2 Time complexity as a measure of efficiency\u003c\/p\u003e \u003cp\u003e3.3.3 Computational methods for polygons\u003c\/p\u003e \u003cp\u003e3.3.3.1 Area of a polygon\u003c\/p\u003e \u003cp\u003e3.3.3.2 Center of gravity of a polygon\u003c\/p\u003e \u003cp\u003e3.3.3.3 Inclusion test of a point with respect to a polygon\u003c\/p\u003e \u003cp\u003e3.3.3.4 Polygon-line intersection\u003c\/p\u003e \u003cp\u003e3.3.3.5 Polygon intersection test\u003c\/p\u003e \u003cp\u003e3.3.3.6 Extraction of a subnetwork inside a polygon\u003c\/p\u003e \u003cp\u003e3.3.3.7 Set-theoretic computations\u003c\/p\u003e \u003cp\u003e3.3.3.8 Nearest point on the edges of a polygon from a point in the polygon\u003c\/p\u003e \u003cp\u003e3.3.3.9 Frontage interval\u003c\/p\u003e \u003cp\u003e3.4. Basic computational methods on networks\u003c\/p\u003e \u003cp\u003e  3.4.1 Single-source shortest paths\u003c\/p\u003e \u003cp\u003e3.4.1.1 Network connectivity test\u003c\/p\u003e \u003cp\u003e3.4.1.2 Shortest-path tree\u003c\/p\u003e \u003cp\u003e3.4.1.3 Extended shortest-path tree\u003c\/p\u003e \u003cp\u003e3.4.1.4 All nodes within a prespecified distance\u003c\/p\u003e \u003cp\u003e3.4.1.5 Center of a network\u003c\/p\u003e \u003cp\u003e3.4.1.6 Heap data structure\u003c\/p\u003e \u003cp\u003e3.4.2 Shortest path between two nodes\u003c\/p\u003e \u003cp\u003e3.4.3 Minimum spanning tree on a network\u003c\/p\u003e \u003cp\u003e3.4.4 Monte Carlo simulation for generating random points on a network\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Network Voronoi diagrams\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Ordinary network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.1.1 Planar versus network Voronoi diagrams\u003c\/p\u003e \u003cp\u003e4.1.2 Geometric properties of the ordinary network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.2 Generalized network Voronoi diagrams\u003c\/p\u003e \u003cp\u003e4.2.1 Directed network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.2.2 Weighted network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.2.3 \u003ci\u003ek\u003c\/i\u003e-th nearest point network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.2.4 Line and polygon network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.2.5 Point-set network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.3 Computational methods for network Voronoi diagrams\u003c\/p\u003e \u003cp\u003e4.3.1 Multi-start Dijkstra method\u003c\/p\u003e \u003cp\u003e4.3.2 Computational method for the ordinary network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.3.3 Computational method for the directed network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.3.4 Computational method for the weighted network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.3.5 Computational method for the -th nearest point network Voronoi diagram\u003c\/p\u003e \u003cp\u003e4.3.6 Computational method for the line and polygon network Voronoi diagrams\u003c\/p\u003e \u003cp\u003e4.3.7 Computational method for the point-set network Voronoi diagram\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Network nearest-neighbor distance methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Network auto nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.1.1 Network local auto nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.1.2 Network global auto nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.2 Network cross nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.2.1 Network local cross nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.2.2 Network global cross nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.3 Network nearest-neighbor distance method for lines\u003c\/p\u003e \u003cp\u003e5.4 Computational methods for network nearest-neighbor distance methods\u003c\/p\u003e \u003cp\u003e5.4.1 Computational methods for network auto nearest-neighbor distance methods\u003c\/p\u003e \u003cp\u003e5.4.1.1 Computational methods for network local auto nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.4.1.2 Computational methods for network global auto nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.4.2 Computational methods for network cross nearest-neighbor distance methods\u003c\/p\u003e \u003cp\u003e5.4.2.1 Computational methods for network local cross nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e5.4.2.2 Computational methods for network global cross nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Network \u003ci\u003eK\u003c\/i\u003e function methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Network auto \u003ci\u003eK\u003c\/i\u003e function methods\u003c\/p\u003e \u003cp\u003e6.1.1 Network local auto \u003ci\u003eK\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e6.1.2 Network global auto \u003ci\u003eK\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e6.2 Network cross \u003ci\u003eK\u003c\/i\u003e function methods\u003c\/p\u003e \u003cp\u003e6.2.1 Network local cross \u003ci\u003eK\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e6.2.2 Network global cross \u003ci\u003eK\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e6.2.3 Network global Voronoi cross \u003ci\u003eK\u003c\/i\u003e function method\u003cbr\u003e 6.3 Network \u003ci\u003eK\u003c\/i\u003e function methods in relation to geometric characteristics of a network\u003c\/p\u003e \u003cp\u003e6.3.1 Relationship between the shortest-path distance and the Euclidean distance\u003cbr\u003e 6.3.2 Network global auto \u003ci\u003eK\u003c\/i\u003e function in relation to the level-of-detail of a network\u003c\/p\u003e \u003cp\u003e6.4 Computational methods for the network \u003ci\u003eK\u003c\/i\u003e function methods\u003c\/p\u003e \u003cp\u003e6.4.1 Computational methods for the network auto \u003ci\u003eK f\u003c\/i\u003eunction methods\u003c\/p\u003e \u003cp\u003e6.4.1.1 Computational methods for the network local auto \u003ci\u003eK f\u003c\/i\u003eunction method\u003c\/p\u003e \u003cp\u003e6.4.1.2 Computational methods for the network global auto \u003ci\u003eK\u003c\/i\u003e function\u003cbr\u003e method\u003c\/p\u003e \u003cp\u003e6.4.2 Computational methods for the network cross \u003ci\u003eK\u003c\/i\u003e function methods\u003cbr\u003e 6.4.2.1 Computational methods for the network local auto \u003ci\u003eK f\u003c\/i\u003eunction method\u003c\/p\u003e \u003cp\u003e6.4.2.3 Computational methods for the network global cross \u003ci\u003eK\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e6.4.2.3 Computational methods for the network global Voronoi cross \u003ci\u003eK\u003c\/i\u003e\u003cbr\u003e function method\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Network spatial autocorrelation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Classification of spatial autocorrelations\u003c\/p\u003e \u003cp\u003e7.2 Spatial randomness of the attribute values of network cells\u003c\/p\u003e \u003cp\u003e7.2.1 Permutation spatial randomness\u003c\/p\u003e \u003cp\u003e7.2.2 Normal variate spatial randomness\u003c\/p\u003e \u003cp\u003e7.3 Network Moran’s \u003ci\u003eI\u003c\/i\u003e statistics\u003c\/p\u003e \u003cp\u003e7.3.1 Network local Moran’s \u003ci\u003eI\u003c\/i\u003e statistic\u003c\/p\u003e \u003cp\u003e7.3.2 Network global Moran’s \u003ci\u003eI\u003c\/i\u003e statistic\u003c\/p\u003e \u003cp\u003e7.4 Computational methods for network Moran’s \u003ci\u003eI\u003c\/i\u003e statistics\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Network point cluster analysis and clumping method\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Network point cluster analysis\u003c\/p\u003e \u003cp\u003e8.1.1 General hierarchical point cluster analysis\u003c\/p\u003e \u003cp\u003e8.1.2 Hierarchical point clustering methods with specific intercluster distances\u003c\/p\u003e \u003cp\u003e8.1.2.1 Network closest-pair point clustering method\u003c\/p\u003e \u003cp\u003e8.1.2.2Network farthest-pair point clustering method\u003c\/p\u003e \u003cp\u003e8.1.2.3 Network average-pair point clustering method\u003c\/p\u003e \u003cp\u003e8.1.2.4 Network point clustering methods with other interclaster distances\u003c\/p\u003e \u003cp\u003e8.2 Network clumping method\u003c\/p\u003e \u003cp\u003e8.2.1 Relation to network point cluster analysis\u003c\/p\u003e \u003cp\u003e8.2.2 Statistical test with respect to the number of clumps\u003c\/p\u003e \u003cp\u003e8.3 Computational methods for network point cluster analysis and clumping method\u003c\/p\u003e \u003cp\u003e8.3.1 General computational framework\u003c\/p\u003e \u003cp\u003e8.3.2 Computational methods for individual intercluster distances\u003c\/p\u003e \u003cp\u003e8.3.2.1 Computational methods for the network closest-pair point clustering\u003c\/p\u003e \u003cp\u003emethod\u003c\/p\u003e \u003cp\u003e8.3.2.1 Computational methods for the network farthest-pair point clustering\u003c\/p\u003e \u003cp\u003e  method\u003c\/p\u003e \u003cp\u003e8.3.2.3 Computational methods for the network average-pair point clustering\u003cbr\u003e          method\u003c\/p\u003e \u003cp\u003e8.3.3 Computational aspects of the network clumping method\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Network point density estimation methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Network histograms\u003c\/p\u003e \u003cp\u003e9.1.1 Network cell histograms\u003c\/p\u003e \u003cp\u003e9.1.2 Network Voronoi cell histograms\u003c\/p\u003e \u003cp\u003e9.1.3 Network cell-count method\u003c\/p\u003e \u003cp\u003e9.2 Network kernel density estimation methods\u003c\/p\u003e \u003cp\u003e9.2.1 Network kernel functions\u003c\/p\u003e \u003cp\u003e9.2.2 Equal-split discontinuous kernel functions\u003c\/p\u003e \u003cp\u003e9.2.3 Equal-split continuous kernel functions\u003c\/p\u003e \u003cp\u003e9.3 Computational methods for network point density estimation\u003c\/p\u003e \u003cp\u003e9.3.1 Computational methods for network cell histograms with equal-length network cells\u003c\/p\u003e \u003cp\u003e9.3.2 Computational method for equal-split discontinuous kernel density functions\u003c\/p\u003e \u003cp\u003e9.3.3 Computational method for equal-split continuous kernel density functions\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Network spatial interpolation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Network inverse-distance weighting\u003c\/p\u003e \u003cp\u003e10.1.1 Concepts of neighborhoods on a network\u003c\/p\u003e \u003cp\u003e10.1.2 Network inverse-distance weighting predictor\u003c\/p\u003e \u003cp\u003e10.2 Network kriging\u003c\/p\u003e \u003cp\u003e10.2.1 Network kriging models\u003c\/p\u003e \u003cp\u003e10.2.2 Concepts of stationary processes on a network\u003c\/p\u003e \u003cp\u003e10.2.3 Network variogram models\u003c\/p\u003e \u003cp\u003e10.2.4 Network kriging predictors\u003c\/p\u003e \u003cp\u003e10.3 Computational methods for network spatial interpolation\u003c\/p\u003e \u003cp\u003e10.3.1 Computational methods for network inverse-distance weighing\u003c\/p\u003e \u003cp\u003e10.3.2 Computational methods for network kriging\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Network Huff model\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Concepts of the network Huff model\u003c\/p\u003e \u003cp\u003e11.1.1 Huff models\u003c\/p\u003e \u003cp\u003e11.1.2 Dominant market subnetworks\u003c\/p\u003e \u003cp\u003e11.1.3 Huff-based demand estimation\u003c\/p\u003e \u003cp\u003e11.1.4 Huff-based locational optimization\u003c\/p\u003e \u003cp\u003e11.2 Computational methods for the Huff-based demand estimation\u003c\/p\u003e \u003cp\u003e11.2.1 Shortest-path tree distance\u003c\/p\u003e \u003cp\u003e11.2.2 Choice probabilities in terms of shortest-path tree distances\u003c\/p\u003e \u003cp\u003e11.2.3 Analytical formula for the Huff-based demand estimation\u003c\/p\u003e \u003cp\u003e11.2.4 Computational tasks and their time complexities for the Huff-based demand estimation\u003c\/p\u003e \u003cp\u003e11.3 Computational methods for the Huff-based locational optimization\u003c\/p\u003e \u003cp\u003e11.3.1 Demand function for a newly entering store\u003c\/p\u003e \u003cp\u003e11.3.2 Topologically invariant shortest-path trees\u003c\/p\u003e \u003cp\u003e11.3.3 Topologically invariant link sets\u003c\/p\u003e \u003cp\u003e11.3.4 Numerical method for the Huff-based locational optimization\u003c\/p\u003e \u003cp\u003e11.3.5 Computational tasks and their time complexities for the Huff-based locational optimization\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 GIS-based tools for spatial analysis along networks and their application\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Preprocessing tools in SANET\u003c\/p\u003e \u003cp\u003e12.1.1 Tool for testing network connectedness\u003c\/p\u003e \u003cp\u003e12.1.2 Tool for assigning points to the nearest points on a network\u003c\/p\u003e \u003cp\u003e12.1.3 Tool for computing shortest-path distances between points\u003c\/p\u003e \u003cp\u003e12.1.4 Tool for generating random points on a network\u003c\/p\u003e \u003cp\u003e12.2 Statistical tools in SANET and their applications\u003c\/p\u003e \u003cp\u003e12.2.1 Tools for network Voronoi diagrams and their application\u003c\/p\u003e \u003cp\u003e12.2.2 Tools for network nearest neighbor distance methods and their application\u003c\/p\u003e \u003cp\u003e12.2.2.1 Network global auto nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e12.2.2.2 Network global cross nearest-neighbor distance method\u003c\/p\u003e \u003cp\u003e12.2.3 Tools for network \u003ci\u003eK\u003c\/i\u003e function methods and their application\u003c\/p\u003e \u003cp\u003e12.2.3.1 Network global auto \u003ci\u003eK\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e12.2.3.2 Network global cross \u003ci\u003eK\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e12.2.3.3 Network global Voronoi cros\u003ci\u003es K\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e12.2.3.4 Network local cross \u003ci\u003eK\u003c\/i\u003e function method\u003c\/p\u003e \u003cp\u003e12.2.4 Tools for network cluster analysis and their application\u003c\/p\u003e \u003cp\u003e12.2.5 Tools for network kernel density estimation methods and their application\u003c\/p\u003e \u003cp\u003e12.2.6 Tools for network spatial interpolation methods and their application\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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