Local Models for Spatial Analysis

by Christopher D. Lloyd

• ISBN13: 9781439829233
• ISBN10: 1439829233
• Format: Nonspecific Binding
• Copyright: 2010-10-13
• Publisher: Taylor & Francis

Summary

Written in recognition of developments in spatial data analysis that focused on differences between places, the first edition of Local Models for Spatial Analysis broke new ground with its focus on local modelling methods. Reflecting the continued growth and increased interest in this area, the second edition describes a wide range of methods which account for local variations in geographical properties.What's new in the Second Edition:Additional material on geographically weighted statistics and local regression approaches A fuller overview of local models with reference to recent critical reviews about the subject area Expanded coverage of individual methods and connections between them Restructured chapters that clarify the distinction between global and local methods New material in each chapter that references key studies or other accounts that support the book Access to online resources that support learningAn introduction to the methods and their underlying concepts, the book uses worked examples and case studies to demonstrate how the algorithms work, their practical utility, and range of applications. Starting with first principles, the author introduces users of Geographical Information Systems to the principles and application of some widely used local models for the analysis of spatial data, including methods being developed and employed in geography and cognate disciplines. The book discusses the relevant software packages that can aid in their implementation and provides a summary list in Appendix A.Presenting examples from a variety of disciplines, the book demonstrates the importance of local models for all who make use of spatial data. Taking a problem driven approach, it provides extensive guidance on the selection and application of local models.

Table of Contents

Introduction

Remit Of This Book

Local Models and Methods

What Is Local?

Spatial Dependence and Autocorrelation

Spatial Scale

Stationarity

Spatial Data Models

Datasets Used for Illustrative Purposes

A Note on Notation

Overview

Local Modelling

Standard Methods and Local Variations

Approaches to Local Adaptation

Stratification or Segmentation of Spatial Data

Moving Window/Kernel Methods

Locally-Varying Model Parameters

Transforming and Detrending Spatial Data

Categorising Local Statistical Models

Local Models And Methods And The Structure Of The Book

Overview

Grid Data

Exploring Spatial Variation in Gridded 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 Patterning in Single Variables

Local Summary Statistics

Geographically Weighted Statistics

Spatial Autocorrelation: Global Measures

Spatial Association and Categorical Data

Other Issues

Overview

Spatial Relations

Global Regression

Spatial and Local Regression

Regression and Spatial Data

Spatial Autoregressive Models

Multilevel Modelling

Allowing for Local Variation in Model Parameters

Moving Window Regression (Mwr)

Geographically Weighted Regression (Gwr)

Spatially Weighted Classification

Local Regression Methods: Some Pros and Cons

Overview

Spatial Prediction

Deterministic Methods, Curve Fitting, and Smoothing

Point Interpolation

Global Methods

Local Methods

Areal Interpolation

General Approaches: Overlay

Local Models and Local Data

Limitations: Point And Areal Interpolation

Overview

Spatial Prediction

Geostatistics

Random Function Models

Stationarity

Exploring Spatial Variation

Kriging

Globally Constant Mean: Simple Kriging

Locally Constant Mean Models

Ordinary Kriging

Cokriging

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 and Cluster Detection

Point Patterns

Visual Examination of Point Patterns

Measuring Event Intensity And Distance Methods

Statistical Tests of Point Patterns

Global Methods

Measuring Event Intensity

Distance Methods

Other Issues

Local Methods

Measuring Event Intensity Locally

Accounting For The Population at Risk

The Local K Function

Point Patterns and Detection of Clusters

Overview

Summary: Local Models for Spatial Analysis

Review

Issues

Software

Future Developments

Summary

A Software

References

Index

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