Local Models for Spatial Analysis
1st Edition
Published on 25. October 2006
Book
Hardback
244 pages
978-0-415-31681-1 (ISBN)
Description
In both the physical and social sciences, there are now available large spatial data sets with detailed local information. Global models for analyzing these data are not suitable for investigating local variations; consequently, local models are the subject of much recent research. Collecting a variety of models into a single reference, Local Models for Spatial Analysis explains in detail a variety of approaches for analyzing univariate and multivariate spatial data. Different models make use of data in unique ways, and this book offers perspectives on various definitions of what constitutes "local," varying spatial scales, and nonstationary models. The author discusses analyses of single variables on grids, multiple variables, deterministic approaches to spatial prediction, geostatistical prediction, and point patterns. He uses numerous worked examples, illustrations, and case studies to shed light on issues involved in implementing the concepts in practice, and makes use of physical and social science data sets. In each chapter, the book follows a consistent format that introduces global approaches followed by corresponding local approaches, providing an assessment of the suitability of various methods in particular situations.
Combining a valuable array of tools for GIScience and GISystems, Local Models for Spatial Analysis guides you in selecting and applying the most appropriate model for a given purpose and set of data.
Combining a valuable array of tools for GIScience and GISystems, Local Models for Spatial Analysis guides you in selecting and applying the most appropriate model for a given purpose and set of data.
More details
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Researchers, professionals, and students in quantitative geography, GIScience and GISystems, spatial science and statistics, and image analysis.
Illustrations
17 s/w Tabellen, 60 s/w Abbildungen
17 Tables, black and white; 60 Illustrations, black and white
Dimensions
Height: 235 mm
Width: 156 mm
Weight
499 gr
ISBN-13
978-0-415-31681-1 (9780415316811)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Christopher D. Lloyd
Local Models for Spatial Analysis
Book
10/2010
2nd Edition
CRC Press
€171.70
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Person
Content
Introduction
Remit of This Book
Local Models and Methods
What Is Local?
Spatial Dependence
Spatial Scale
Stationarity
Spatial Data Models
Data Sets Used for Illustrative Purposes
A Note on Notation
Overview
Local Modeling
Approaches to Local Adaptation
Stratification or Segmentation of Spatial Data
Moving Window/Kernel Methods
Locally Varying Model Parameters
Transforming and Detrending Spatial Data
Overview
Grid Data
Exploring Spatial Variation in Single Variables
Global Univariate Statistics
Local Univariate Statistics
Analysis of Grid Data
Moving Windows for Grid Analysis
Wavelets
Segmentation
Analysis of Digital Elevation Models
Overview
Spatial Relations
Spatial Autocorrelation: Global Measures
Spatial Autocorrelation: Local Measures
Global Regression
Local Regression
Regression and Spatial Data
Spatial Autoregressive Models
Multilevel Modeling
Allowing for Local Variation in Model Parameters
Moving Window Regression (MWR)
Geographically Weighted Regression (GWR)
Spatially Weighted Classification
Overview
Spatial Prediction 1: Deterministic Methods
Point Interpolation
Global Methods
Local Methods
Areal Interpolation
General Approaches: Overlay
Local Models and Local Data
Limitations: Point and Areal Interpolation
Overview
Spatial Prediction 2: Geostatistics
Random Function Models
Stationarity
Global Models
Exploring Spatial Variation
Kriging
Equivalence of Splines and Kriging
Conditional Simulation
The Change of Support Problem
Other Approaches
Local Approaches: Nonstationary Models
Nonstationary Mean
Nonstationary Models for Prediction
Nonstationary Variogram
Variograms in Texture Analysis
Summary
Point Patterns
Point Patterns
Visual Examination of Point Patterns
Density and Distance Methods
Statistical Tests of Point Patterns
Global Methods
Distance Methods
Other Issues
Local Methods
Density Methods
Accounting for the Population at Risk
The Local K Function
Point Patterns and Detection of Clusters
Overview
Summary: Local Models for Spatial Analysis
Review
Key Issues
Software
Future Developments
Summary
References
Index
Remit of This Book
Local Models and Methods
What Is Local?
Spatial Dependence
Spatial Scale
Stationarity
Spatial Data Models
Data Sets Used for Illustrative Purposes
A Note on Notation
Overview
Local Modeling
Approaches to Local Adaptation
Stratification or Segmentation of Spatial Data
Moving Window/Kernel Methods
Locally Varying Model Parameters
Transforming and Detrending Spatial Data
Overview
Grid Data
Exploring Spatial Variation in Single Variables
Global Univariate Statistics
Local Univariate Statistics
Analysis of Grid Data
Moving Windows for Grid Analysis
Wavelets
Segmentation
Analysis of Digital Elevation Models
Overview
Spatial Relations
Spatial Autocorrelation: Global Measures
Spatial Autocorrelation: Local Measures
Global Regression
Local Regression
Regression and Spatial Data
Spatial Autoregressive Models
Multilevel Modeling
Allowing for Local Variation in Model Parameters
Moving Window Regression (MWR)
Geographically Weighted Regression (GWR)
Spatially Weighted Classification
Overview
Spatial Prediction 1: Deterministic Methods
Point Interpolation
Global Methods
Local Methods
Areal Interpolation
General Approaches: Overlay
Local Models and Local Data
Limitations: Point and Areal Interpolation
Overview
Spatial Prediction 2: Geostatistics
Random Function Models
Stationarity
Global Models
Exploring Spatial Variation
Kriging
Equivalence of Splines and Kriging
Conditional Simulation
The Change of Support Problem
Other Approaches
Local Approaches: Nonstationary Models
Nonstationary Mean
Nonstationary Models for Prediction
Nonstationary Variogram
Variograms in Texture Analysis
Summary
Point Patterns
Point Patterns
Visual Examination of Point Patterns
Density and Distance Methods
Statistical Tests of Point Patterns
Global Methods
Distance Methods
Other Issues
Local Methods
Density Methods
Accounting for the Population at Risk
The Local K Function
Point Patterns and Detection of Clusters
Overview
Summary: Local Models for Spatial Analysis
Review
Key Issues
Software
Future Developments
Summary
References
Index