Spatial Data Analysis Theory and Practice 1st Edition by Robert Haining ISBN 0521774373 978-0521774376
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ISBN 10: 0521774373
ISBN 13: 978-0521774376
Author: Robert Haining
Are there geographic clusters of disease cases, or hotspots of crime? Can the geography of air quality be matched to where people hospitalized for respiratory complaints actually live? Spatial data is data about the world where the attribute of interest and its location on the earth’s surface are recorded. This comprehensive overview of the subject shows how the above questions can be tackled. It is written for students and researchers in geography, economics, social science, the environmental sciences and statistics.
Spatial Data Analysis Theory and Practice 1st Table of contents:
Part A The context for spatial data analysis
1 Spatial data analysis: scientific and policy context
1.1 Spatial data analysis in science
1.1.1 Generic issues of place, context and space in scientific explanation
(a) Location as place and context
(b) Location and spatial relationships
1.1.2 Spatial processes
1.2 Place and space in specific areas of scientific explanation
1.2.1 Defining spatial subdisciplines
1.2.2 Examples: selected research areas
(a) Environmental criminology
(b) Geographical and environmental (spatial) epidemiology
(c) Regional economics and the new economic geography
(d) Urban studies
(e) Environmental sciences
1.2.3 Spatial data analysis in problem solving
1.3 Spatial data analysis in the policy area
1.4 Some examples of problems that arise in analysing spatial data
1.4.1 Description and map interpretation
1.4.2 Information redundancy
1.4.3 Modelling
1.5 Concluding remarks
2 The nature of spatial data
2.1 The spatial data matrix: conceptualization and representation issues
2.1.1 Geographic space: objects, fields and geometric representations
2.1.2 Geographic space: spatial dependence in attribute values
2.1.3 Variables
(a) Classifying variables
(b) Levels of measurement
2.1.4 Sample or population?
2.2 The spatial data matrix: its form
2.3 The spatial data matrix: its quality
2.3.1 Model quality
(a) Attribute representation
(b) Spatial representation: general considerations
(c) Spatial representation: resolution and aggregation
2.3.2 Data quality
(a) Accuracy
(b) Resolution
(c) Consistency
(d) Completeness
2.4 Quantifying spatial dependence
(a) Fields: data from two-dimensional continuous space
(b) Objects: data from two-dimensional discrete space
2 .5 Concluding remarks
Part B Spatial data: obtaining data and quality issues
3 Obtaining spatial data through sampling
3.1 Sources of spatial data
3.2 Spatial sampling
3.2.1 The purpose and conduct of spatial sampling
3.2.2 Design- and model-based approaches to spatial sampling
(a) Design-based approach to sampling
(b) Model-based approach to sampling
(c) Comparative comments
3.2.3 Sampling plans
3.2.4 Selected sampling problems
(a) Design-based estimation of the population mean
(b) Model-based estimation of means
(c) Spatial prediction
(d) Sampling to identify extreme values or detect rare events
3.3 Maps through simulation
4 Data quality: implications for spatial data analysis
4.1 Errors in data and spatial data analysis
4.1.1 Models for measurement error
(a) Independent error models
(b) Spatially correlated error models
4.1.2 Gross errors
(a) Distributional outliers
(b) Spatial outliers
(c) Testing for outliers in large data sets
4.1.3 Error propagation
4.2 Data resolution and spatial data analysis
4.2.1 Variable precision and tests of significance
4.2.2 The change of support problem
(a) Change of support in geostatistics
(b) Areal interpolation
4.2.3 Analysing relationships using aggregate data
(a) Ecological inference: parameter estimation
(b) Ecological inference in environmental epidemiology: identifying valid hypotheses
(c) The modifiable areal units problem (MAUP)
4.3 Data consistency and spatial data analysis
4.4 Data completeness and spatial data analysis
4.4.1 The missing-data problem
(a) Approaches to analysis when data are missing
(b) Approaches to analysis when spatial data are missing
4.4.2 Spatial interpolation, spatial prediction
4.4.3 Boundaries, weights matrices and data completeness
4.5 Concluding remarks
Part C The exploratory analysis of spatial data
5 Exploratory spatial data analysis: conceptual models
5.1 EDA and ESDA
5 .2 Conceptual models of spatial variation
(a) The regional model
(b) Spatial ?rough? and ?smooth?
(c) Scales of spatial variation
6 Exploratory spatial data analysis: visualization methods
6.1 Data visualization and exploratory data analysis
6.1.1 Data visualization: approaches and tasks
6.1.2 Data visualization: developments through computers
6.1.3 Data visualization: selected techniques
6.2 Visualizing spatial data
6.2.1 Data preparation issues for aggregated data: variable values
6.2.2 Data preparation issues for aggregated data: the spatial framework
(a) Non-spatial approaches to region building
(b) Spatial approaches to region building
(c) Design criteria for region building
6.2.3 Special issues in the visualization of spatial data
6.3 Data visualization and exploratory spatial data analysis
6.3.1 Spatial data visualization: selected techniques for univariate data
(a) Methods for data associated with point or area objects
(b) Methods for data from a continuous surface
6.3.2 Spatial data visualization: selected techniques for bi- and multi-variate data
6.3.3 Uptake of breast cancer screening in Sheffield
6.4 Concluding remarks
7 Exploratory spatial data analysis: numerical methods
7.1 Smoothing methods
7.1.1 Resistant smoothing of graph plots
7.1.2 Resistant description of spatial dependencies
7.1.3 Map smoothing
(a) Simple mean and median smoothers
(b) Introducing distance weighting
(c) Smoothing rates
(d) Non-linear smoothing: headbanging
(e) Non-linear smoothing: median polishing
(f) Some comparative examples
7.2 The exploratory identification of global map properties: overall clustering
7.2.1 Clustering in area data
7.2.2 Clustering in a marked point pattern
7.3 The exploratory identification of local map properties
7.3.1 Cluster detection
(a) Area data
(b) Inhomogeneous point data
7.3.2 Focused tests
7.4 Map comparison
(a) Bivariate association
(b) Spatial association
Part D Hypothesis testing and spatial autocorrelation
8 Hypothesis testing in the presence of spatial dependence
8.1 Spatial autocorrelation and testing the mean of a spatial data set
8.2 Spatial autocorrelation and tests of bivariate association
8.2.1 Pearson?s product moment correlation coefficient
8.2.2 Chi-square tests for contingency tables
Part E Modelling spatial data
9 Models for the statistical analysis of spatial data
9.1 Descriptive models
9.1.1 Models for large-scale spatial variation
9.1.2 Models for small-scale spatial variation
(a) Models for data from a surface
(b) Models for continuous-valued area data
(c) Models for discrete-valued area data
9.1.3 Models with several scales of spatial variation
9.1.4 Hierarchical Bayesian models
9.2 Explanatory models
9.2.1 Models for continuous-valued response variables: normal regression models
9.2.2 Models for discrete-valued area data: generalized linear models
9.2.3 Hierarchical models
(a) Adding covariates to hierarchical Bayesian models
(b) Modelling spatial context: multi-level models
10 Statistical modelling of spatial variation: descriptive modelling
10.1 Models for representing spatial variation
10.1.1 Models for continuous-valued variables
(a) Trend surface models with independent errors
(b) Semi-variogram and covariance models
(c) Trend surface models with spatially correlated errors
10.1.2 Models for discrete-valued variables
10.2 Some general problems in modelling spatial variation
10.3 Hierarchical Bayesian models
11 Statistical modelling of spatial variation: explanatory modelling
11.1 Methodologies for spatial data modelling
11.1.1 The ?classical? approach
11.1.2 The econometric approach
(a) A general spatial specification
(b) Two models of spatial pricing
11.1.3 A ?data-driven? methodology
11.2 Some applications of linear modelling of spatial data
11.2.1 Testing for regional income convergence
11.2.2 Models for binary responses
(a) A logistic model with spatial lags on the covariates
(b) Autologistic models with covariates
11.2.3 Multi-level modelling
11.2.4 Bayesian modelling of burglaries in Sheffield
11.2.5 Bayesian modelling of children excluded from school
11.3 Concluding comments
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