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Spatial Data Analysis in the Social and Environmental Sciences

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ISBN-10: 0521448662

ISBN-13: 9780521448666

Edition: N/A

Authors: Robert P. Haining

List price: $60.99
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Within both the social and environmental sciences, much of the data collected is within a spatial context and requires statistical analysis for interpretation. The purpose of this book is to describe current methods for the analysis of spatial data. Methods described include data description, map interpolation, and exploratory and explanatory analyses. The book also examines spatial referencing, and methods for detecting problems, assessing their seriousness and taking appropriate action are discussed. This is an important text for any discipline requiring a broad overview of current theoretical and applied work for the analysis of spatial data sets. It will be of particular use to research…    
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Book details

List price: $60.99
Publisher: Cambridge University Press
Publication date: 8/26/1993
Binding: Paperback
Pages: 432
Size: 5.98" wide x 8.94" long x 0.91" tall
Weight: 1.342
Language: English

List of tables and displays
Introduction to issues in the analysis of spatially referenced data
Issues in analysing spatial data
Spatial data: sources, forms and storage
Sources: quality and quantity
Forms and attributes
Data storage
Spatial data analysis
The importance of space in the social and environmental sciences
Measurement error
Continuity effects and spatial heterogeneity
Spatial processes
Types of analytical problems
Problems in spatial data analysis
Conceptual models and inference frameworks for spatial data
Modelling spatial variation
Statistical modelling of spatial data
Dependency in spatial data
Spatial heterogeneity: regional subdivisions and parameter variation
Spatial distribution of data points and boundary effects
Assessing model fit
Extreme data values
Model sensitivity to the areal system
Size-variance relationships in homogeneous aggregates
A statistical framework for spatial data analysis
Data adaptive modelling
Robust and resistant parameter estimation
Robust estimation of the centre of a symmetric distribution
Robust estimation of regression parameters
Parametric models for spatial variation
Statistical models for spatial populations
Models for spatial populations: preliminary considerations
Spatial stationarity and isotropy
Second order (weak) stationarity and isotropy
Second order (weak) stationarity and isotropy of differences from the mean
Second order (weak) stationarity and isotropy of increments
Order relationships in one and two dimensions
Population models for continuous random variables
Models for the mean of a spatial population
Trend surface models
Regression model
Models for second order or stochastic variation of a spatial population
Interaction models for V of a MVN distribution
Interaction models for other multivariate distributions
Direct specification of V
Intrinsic random functions
Population models for discrete random variables
Boundary models for spatial populations
Edge structures, weighting schemes and the dispersion matrix
Conclusions: issues in representing spatial variation
Simulating spatial models
Statistical analysis of spatial populations
Model selection
Statistical inference with interaction schemes
Parameter estimation: maximum likelihood (ML) methods
[mu] unknown; V known
[mu] known; V unknown
[mu] and V unknown
Models with non-constant variance
Parameter estimation: other methods
Ordinary least squares and pseudo-likelihood estimators
Coding estimators
Moment estimators
Parameter estimation: discrete valued interaction models
Properties of ML estimators
Large sample properties
Small sample properties
A note on boundary effects
Hypothesis testing for interaction schemes
Likelihood ratio tests
Lagrange multiplier tests
Statistical inference with covariance functions and intrinsic random functions
Parameter estimation: maximum likelihood methods
Parameter estimation: other methods
Properties of estimators and hypothesis testing
Validation in spatial models
The consequences of ignoring spatial correlation in estimating the mean
Spatial data collection and preliminary analysis
Sampling spatial populations
Spatial sampling designs
Point sampling
Quadrat and area sampling
Sampling spatial surfaces: estimating the mean
Fixed populations with trend or periodicity
Populations with second order variation
Results for one-dimensional series
Results for two-dimensional surfaces
Standard errors for confidence intervals and selecting sample size
Sampling spatial surfaces: second order variation
Scales of variation
Sampling applications
Concluding comments
Preliminary analysis of spatial data
Preliminary data analysis: distributional properties and spatial arrangement
Univariate data analysis
General distributional properties
Spatial outliers
Spatial trends
Second order non-stationarity
Regional subdivisions
Multivariate data analysis
Data transformations
Preliminary data analysis: detecting spatial pattern, testing for spatial autocorrelation
Available test statistics
Constructing a test
Choosing a test
Describing spatial variation: robust estimation of spatial variation
Robust estimators of the semi-variogram
Robust estimation of covariances
Concluding remarks
Modelling spatial data
Analysing univariate data sets
Describing spatial variation
Non-stationary mean, stationary second order variation: trend surface models with correlated errors
Non-stationary mean, stationary increments: semi-variogram models and polynomial generalised covariance functions
Discrete data
Interpolation and estimating missing values
Ad hoc and cartographic techniques
Distribution based techniques
Sequential approaches (sampling a continuous surface)
Simultaneous approaches
Obtaining areal properties
Reconciling data sets on different areal frameworks
Categorical data
Other information for interpolation
Analysing multivariate data sets
Measures of spatial correlation and spatial association
Correlation measures
Measures of association
Regression modelling
Problems due to the assumptions of least squares not being satisfied
Problems of model specification and analysis
Model discrimination
Specifying W
Parameter estimation and inference
Model evaluation
Interpretation problems
Problems due to data characteristics
Numerical problems
Regression applications
Model diagnostics and model revision (a) new explanatory variables
Model diagnostics and model revision (b) developing a spatial regression model
Regression modelling with census variables: Glasgow health data
Identifying spatial interaction and heterogeneity: Sheffield petrol price data
Robust estimation of the parameters of interaction schemes