Local Models for Spatial Analysis - Lloyd, Christopher D

. .

Résumé :
Focusing on solutions, this second edition provides guidance to a wide variety of real-world problems. It presents a complete introduction to key concepts and a clear mapping of the methods discussed. It also explores connections between methods. It includes chapters that address spatial patterning in single variables and spatial relations.

Biographie:
Queen's University, Belfast, Northern Ireland, UK

Sommaire:
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