Spatial Analysis Along Networks - Statistical and Computational Methods

Preface Acknowledgements Chapter 1 Introduction 1.1 What is network spatial analysis? 1.1.1 Network events: events on and alongside networks 1.1.2 Planar spatial analysis and its limitations 1.1.3 Network spatial analysis and its salient features 1.2 Review of studies of network events 1.2.1 Snow's study on cholera around Broad Street 1.2.2 Traffic accidents 1.2.3 Road-kills 1.2.4 Street crimes 1.2.5 Events on river networks and coastlines 1.2.6 Other events on networks 1.2.7 Events alongside networks 1.3 Outline of the book 1.3.1 Structure of chapters 1.3.2 Questions solved by network spatial methods 1.3.3 How to study this book Chapter 2 Modeling events on and alongside networks 2.1 Modeling the real world 2.1.1 Object-based model 2.1.1.1 Spatial attributes 2.1.1.2 Nonspatial attributes 2.1.2 Field-based model 2.1.3 Vector data model 2.1.4 Raster data model 2.2 Modeling networks 2.2.1 Object-based model for networks 2.2.1.1 Geometric networks 2.2.1.2 Graph for a geometric network 2.2.2 Field-based model for networks 2.2.3 Data models for networks 2.3 Modeling entities on and alongside networks 2.3.1 Objects on network space 2.3.2 Field functions on network space 2.4 Stochastic processes on network space 2.4.1 Object-based model for stochastic spatial events on network space 2.4.2 Binomial point processes on network space 2.4.3 Edge effects 2.4.4 Uniform network transformation Chapter 3 Basic computational methods for network spatial analysis 3.1 Data structures for one-layer networks 3.1.1 Planar networks 3.1.2 Winged-edge data structures 3.1.3 Efficient access and enumeration of local information 3.1.4 Attribute data representation 3.1.5 Local modifications of a network 3.1.5.1 Inserting new nodes 3.1.5.2 New nodes resulting from overlying two networks 3.1.5.3 Deleting existing nodes 3.2 Data Structures for nonplanar networks 3.2.1 Multiple-layer networks 3.2.2 General nonplanar networks 3.3 Basic Geometric Computations 3.3.1 Computational methods for line segments 3.3.1.1 Right-turn test 3.3.1.2 Intersection test for two line segments 3.3.1.3 Enumeration of line segment intersections 3.3.2 Time complexity as a measure of efficiency 3.3.3 Computational methods for polygons 3.3.3.1 Area of a polygon 3.3.3.2 Center of gravity of a polygon 3.3.3.3 Inclusion test of a point with respect to a polygon 3.3.3.4 Polygon-line intersection 3.3.3.5 Polygon intersection test 3.3.3.6 Extraction of a subnetwork inside a polygon 3.3.3.7 Set-theoretic computations 3.3.3.8 Nearest point on the edges of a polygon from a point in the polygon 3.3.3.9 Frontage interval 3.4. Basic computational methods on networks 3.4.1 Single-source shortest paths 3.4.1.1 Network connectivity test 3.4.1.2 Shortest-path tree 3.4.1.3 Extended shortest-path tree 3.4.1.4 All nodes within a prespecified distance 3.4.1.5 Center of a network 3.4.1.6 Heap data structure 3.4.2 Shortest path between two nodes 3.4.3 Minimum spanning tree on a network 3.4.4 Monte Carlo simulation for generating random points on a network Chapter 4 Network Voronoi diagrams 4.1 Ordinary network Voronoi diagram 4.1.1 Planar versus network Voronoi diagrams 4.1.2 Geometric properties of the ordinary network Voronoi diagram 4.2 Generalized network Voronoi diagrams 4.2.1 Directed network Voronoi diagram 4.2.2 Weighted network Voronoi diagram 4.2.3 k -th nearest point network Voronoi diagram 4.2.4 Line and polygon network Voronoi diagram 4.2.5 Point-set network Voronoi diagram 4.3 Computational methods for network Voronoi diagrams 4.3.1 Multi-start Dijkstra method 4.3.2 Computational method for the ordinary network Voronoi diagram 4.3.3 Computational method for the directed network Voronoi diagram 4.3.4 Computational method for the weighted network Voronoi diagram 4.3.5 Computational method for the -th nearest point network Voronoi diagram 4.3.6 Computational method for the line and polygon network Voronoi diagrams 4.3.7 Computational method for the point-set network Voronoi diagram Chapter 5 Network nearest-neighbor distance methods 5.1 Network auto nearest-neighbor distance method 5.1.1 Network local auto nearest-neighbor distance method 5.1.2 Network global auto nearest-neighbor distance method 5.2 Network cross nearest-neighbor distance method 5.2.1 Network local cross nearest-neighbor distance method 5.2.2 Network global cross nearest-neighbor distance method 5.3 Network nearest-neighbor distance method for lines 5.4 Computational methods for network nearest-neighbor distance methods 5.4.1 Computational methods for network auto nearest-neighbor distance methods 5.4.1.1 Computational methods for network local auto nearest-neighbor distance method 5.4.1.2 Computational methods for network global auto nearest-neighbor distance method 5.4.2 Computational methods for network cross nearest-neighbor distance methods 5.4.2.1 Computational methods for network local cross nearest-neighbor distance method 5.4.2.2 Computational methods for network global cross nearest-neighbor distance method Chapter 6 Network K function methods 6.1 Network auto K function methods 6.1.1 Network local auto K function method 6.1.2 Network global auto K function method 6.2 Network cross K function methods 6.2.1 Network local cross K function method 6.2.2 Network global cross K function method 6.2.3 Network global Voronoi cross K function method 6.3 Network K function methods in relation to geometric characteristics of a network 6.3.1 Relationship between the shortest-path distance and the Euclidean distance 6.3.2 Network global auto K function in relation to the level-of-detail of a network 6.4 Computational methods for the network K function methods 6.4.1 Computational methods for the network auto K f unction methods 6.4.1.1 Computational methods for the network local auto K f unction method 6.4.1.2 Computational methods for the network global auto K function method 6.4.2 Computational methods for the network cross K function methods 6.4.2.1 Computational methods for the network local auto K f unction method 6.4.2.3 Computational methods for the network global cross K function method 6.4.2.3 Computational methods for the network global Voronoi cross K function method Chapter 7 Network spatial autocorrelation 7.1 Classification of spatial autocorrelations 7.2 Spatial randomness of the attribute values of network cells 7.2.1 Permutation spatial randomness 7.2.2 Normal variate spatial randomness 7.3 Network Moran's I statistics 7.3.1 Network local Moran's I statistic 7.3.2 Network global Moran's I statistic 7.4 Computational methods for network Moran's I statistics Chapter 8 Network point cluster analysis and clumping method 8.1 Network point cluster analysis 8.1.1 General hierarchical point cluster analysis 8.1.2 Hierarchical point clustering methods with specific intercluster distances 8.1.2.1 Network closest-pair point clustering method 8.1.2.2Network farthest-pair point clustering method 8.1.2.3 Network average-pair point clustering method 8.1.2.4 Network point clustering methods with other interclaster distances 8.2 Network clumping method 8.2.1 Relation to network point cluster analysis 8.2.2 Statistical test with respect to the number of clumps 8.3 Computational methods for network point cluster analysis and clumping method 8.3.1 General computational framework 8.3.2 Computational methods for individual intercluster distances 8.3.2.1 Computational methods for the network closest-pair point clustering method 8.3.2.1 Computational methods for the network farthest-pair point clustering method 8.3.2.3 Computational methods for the network average-pair point clustering method 8.3.3 Computational aspects of the network clumping method Chapter 9 Network point density estimation methods 9.1 Network histograms 9.1.1 Network cell histograms 9.1.2 Network Voronoi cell histograms 9.1.3 Network cell-count method 9.2 Network kernel density estimation methods 9.2.1 Network kernel functions 9.2.2 Equal-split discontinuous kernel functions 9.2.3 Equal-split continuous kernel functions 9.3 Computational methods for network point density estimation 9.3.1 Computational methods for network cell histograms with equal-length network cells 9.3.2 Computational method for equal-split discontinuous kernel density functions 9.3.3 Computational method for equal-split continuous kernel density functions Chapter 10 Network spatial interpolation 10.1 Network inverse-distance weighting 10.1.1 Concepts of neighborhoods on a network 10.1.2 Network inverse-distance weighting predictor 10.2 Network kriging 10.2.1 Network kriging models 10.2.2 Concepts of stationary processes on a network 10.2.3 Network variogram models 10.2.4 Network kriging predictors 10.3 Computational methods for network spatial interpolation 10.3.1 Computational methods for network inverse-distance weighing 10.3.2 Computational methods for network kriging Chapter 11 Network Huff model 11.1 Concepts of the network Huff model 11.1.1 Huff models 11.1.2 Dominant market subnetworks 11.1.3 Huff-based demand estimation 11.1.4 Huff-based locational optimization 11.2 Computational methods for the Huff-based demand estimation 11.2.1 Shortest-path tree distance 11.2.2 Choice probabilities in terms of shortest-path tree distances 11.2.3 Analytical formula for the Huff-based demand estimation 11.2.4 Computational tasks and their time complexities for the Huff-based demand estimation 11.3 Computational methods for the Huff-based locational optimization 11.3.1 Demand function for a newly entering store 11.3.2 Topologically invariant shortest-path trees 11.3.3 Topologically invariant link sets 11.3.4 Numerical method for the Huff-based locational optimization 11.3.5 Computational tasks and their time complexities for the Huff-based locational optimization Chapter 12 GIS-based tools for spatial analysis along networks and their application 12.1 Preprocessing tools in SANET 12.1.1 Tool for testing network connectedness 12.1.2 Tool for assigning points to the nearest points on a network 12.1.3 Tool for computing shortest-path distances between points 12.1.4 Tool for generating random points on a network 12.2 Statistical tools in SANET and their applications 12.2.1 Tools for network Voronoi diagrams and their application 12.2.2 Tools for network nearest neighbor distance methods and their application 12.2.2.1 Network global auto nearest-neighbor distance method 12.2.2.2 Network global cross nearest-neighbor distance method 12.2.3 Tools for network K function methods and their application 12.2.3.1 Network global auto K function method 12.2.3.2 Network global cross K function method 12.2.3.3 Network global Voronoi cros s K function method 12.2.3.4 Network local cross K function method 12.2.4 Tools for network cluster analysis and their application 12.2.5 Tools for network kernel density estimation methods and their application 12.2.6 Tools for network spatial interpolation methods and their application References Index