Preface
 
Acknowledgements
 
Chapter 1 Introduction
 
1.1 What is network spatial analysis?
 
1.2 Review of studies of network events
 
1.3 Outline of the book
 
Chapter 2 Modeling events on and alongside networks
 
2.1 Modeling the real world
 
2.2 Modeling networks
 
2.3 Modeling entities on and alongside networks
 
2.4 Stochastic processes on network space
 
Chapter 3 Basic computational methods for network spatial analysis
 
3.1 Data structures for one-layer networks
 
3.2 Data Structures for nonplanar networks
 
3.3 Basic Geometric Computations
 
3.4. Basic computational methods on networks
 
Chapter 4 Network Voronoi diagrams
 
4.1 Ordinary network Voronoi diagram
 
4.2 Generalized network Voronoi diagrams
 
4.3 Computational methods for network Voronoi diagrams
 
Chapter 5 Network nearest-neighbor distance methods
 
5.1 Network auto nearest-neighbor distance method
 
5.2 Network cross nearest-neighbor distance method
 
5.3 Network nearest-neighbor distance method for lines
 
5.4 Computational methods for network nearest-neighbor distance methods
 
Chapter 6 Network K function methods
 
6.1 Network auto K function methods
 
6.2 Network cross K function methods
 
6.3 Network K function methods in relation to geometric characteristics of a network
 
6.4 Computational methods for the network K function methods
 
method
 
function method
 
Chapter 7 Network spatial autocorrelation
 
7.1 Classification of spatial autocorrelations
 
7.2 Spatial randomness of the attribute values of network cells
 
7.3 Network Moran's I statistics
 
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
 
8.2 Network clumping method
 
8.3 Computational methods for network point cluster analysis and clumping method
 
Chapter 9 Network point density estimation methods
 
9.1 Network histograms
 
9.2 Network kernel density estimation methods
 
9.3 Computational methods for network point density estimation
 
Chapter 10 Network spatial interpolation
 
10.1 Network inverse-distance weighting
 
10.2 Network kriging
 
10.3 Computational methods for network spatial interpolation
 
Chapter 11 Network Huff model
 
11.1 Concepts of the network Huff model
 
11.2 Computational methods for the Huff-based demand estimation
 
11.3 Computational methods 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
 
12.2 Statistical tools in SANET and their applications
 
References
 
Index