Local Models for Spatial Analysis: 2nd Edition (Hardback) book cover

Local Models for Spatial Analysis

2nd Edition

By Christopher D. Lloyd

CRC Press

352 pages | 100 B/W Illus.

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Hardback: 9781439829196
pub: 2010-10-13
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Description

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 better overview of local models with reference to recent critical reviews about the subject area
  • Expanded coverage of individual methods and connections between them
  • Chapters have been restructured to clarify the distinction between global and local methods
  • A new section in each chapter references key studies or other accounts that support the book
  • Selected resources provided online to support learning

An 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 application. It provides an overview of a range of different approaches that have been developed and employed within Geographical Information Science (GIScience). Starting with first principles, the author introduces users of GISystems 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. He discusses the relevant software packages that can aid 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.

Reviews

"…it is a wonderfully practical and useful guide to a variety of spatial analysis techniques. It serves as an excellent addition to the bookshelf of any basic or applied researcher doing spatial analysis."

––Jeremy Mennis

Temple University, Philadelphia, PA, USA

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 1: 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 2: 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

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
TEC010000
TECHNOLOGY & ENGINEERING / Environmental / General
TEC036000
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems