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Lloyd C. Local Models for Spatial Analysis

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Lloyd C. Local Models for Spatial Analysis
2nd Edition. — CRC Press, 2011. — 336 p. — ISBN: 978-1439829196.
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:
1) Additional material on geographically-weighted statistics and local regression approaches.
2) A better overview of local models with reference to recent critical reviews about the subject area.
3) Expanded coverage of individual methods and connections between them.
4) Chapters have been restructured to clarify the distinction between global and local methods.
5) A new section in each chapter references key studies or other accounts that support the book.
6) 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.
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
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
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
Spatial Patterning in Single Variables
Local summary statistics
Geographically weighted statistics
Spatial autocorrelation: Global measures
Spatial autocorrelation: Local measures
Spatial association and categorical data
Other issues
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 classi¯cation
Local regression methods: Some pros and cons
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
Spatial Prediction 2: Geostatistics
Random function models
Stationarity
Global models
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
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
Summary: Local Models for Spatial Analysis
A Software
References
Index
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