Lloyd Local Models for Spatial Analysis, Second Edition

2. Auflage 2010
ISBN: 978-1-4398-2923-3
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 352 Seiten

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.

Zielgruppe

Practitioners and anyone who works with geographical information systems or tools for the analysis of spatial data, including geographers, environmental scientists, civil and environmental engineers, remote sensing specialists, archaeologists, and sociologists; graduate students in geography, biophysical sciences, statistics, ecology, geology, and health sciences.

Autoren/Hrsg.

Lloyd, Christopher D.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

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