Buch, Englisch, 306 Seiten, Format (B × H): 157 mm x 235 mm, Gewicht: 618 g
Reihe: Statistics in Practice
Buch, Englisch, 306 Seiten, Format (B × H): 157 mm x 235 mm, Gewicht: 618 g
Reihe: Statistics in Practice
ISBN: 978-0-470-77081-8
Verlag: WILEY
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.
Autoren/Hrsg.
Fachgebiete
- Geowissenschaften Geologie Geodäsie, Kartographie, Fernerkundung
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Geowissenschaften Geographie | Raumplanung Geostatistik
- Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung
Weitere Infos & Material
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
