In the real world, there are numerous and various events that occur
on and alongside networks, including the occurrence of traffic
accidents on highways, the location of stores alongside roads, the
incidence of crime on streets and the contamination along rivers.
In order to carry out analyses of those events, the researcher
needs to be familiar with a range of specific techniques. Spatial
Analysis Along Networks provides a practical guide to the necessary
statistical techniques and their computational implementation.
Each chapter illustrates a specific technique, from Stochastic
Point Processes on a Network and Network Voronoi Diagrams, to
Network K-function and Point Density Estimation Methods, and the
Network Huff Model. The authors also discuss and illustrate the
undertaking of the statistical tests described in a Geographical
Information System (GIS) environment as well as demonstrating the
user-friendly free software package SANET.
Spatial Analysis Along Networks:
* Presents a much-needed practical guide to statistical spatial
analysis of events on and alongside 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.
* Dedicates a separate chapter to each of the major techniques
involved.
* Demonstrates the practicalities of undertaking the tests
described in the book, using a GIS.
* Is supported by a supplementary website, providing readers with
a link to the free software package SANET, so they can execute the
statistical methods described in the book.
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.
Autorentext
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.
Zusammenfassung
In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation.
Each chapter illustrates a specific technique, from Stochastic Point Processes on a Network and Network Voronoi Diagrams, to Network K-function and Point Density Estimation Methods, and the Network Huff Model. The authors also discuss and illustrate the undertaking of the statistical tests described in a Geographical Information System (GIS) environment as well as demonstrating the user-friendly free software package SANET.
Spatial Analysis Along Networks:
- Presents a much-needed practical guide to statistical spatial analysis of events on and alongside 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.
- Dedicates a separate chapter to each of the major techniques involved.
- Demonstrates the practicalities of undertaking the tests described in the book, using a GIS.
- Is supported by a supplementary website, providing readers with a link to the free software package SANET, so they can execute the statistical methods described in the book.
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.
Inhalt
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 sho…