Atsuyuki Okabe, Graduate School of Engineering, University of Tokyo
Professor Okabe has been studying statistical spatial analysis for 35 years, and specifically statistical spatial analysis on a network since 1995. One of the leading authorities in the area, he has published over 100 articles, in numerous international journals. He has also authored and edited four previous books.

Kokichi Sugihara, Graduate School of Information Science and Technology, University of Tokyo
Professor Sugihara has co-authored the book on Voronoi diagrams with A. Okabe. He is also an experienced author and lecturer.

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 function methods

6.4.1.1 Computational methods for the network local auto K function 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 function 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...