Spatial data analysis : theory and practice /
Saved in:
| Author / Creator: | Haining, Robert P. |
|---|---|
| Imprint: | Cambridge, UK ; New York : Cambridge University Press, 2003. |
| Description: | xx, 432 p. : ill. ; 26 cm. |
| Language: | English |
| Subject: | Spatial analysis (Statistics) Geology -- Statistical methods -- Data processing. Geology -- Statistical methods -- Data processing. Spatial analysis (Statistics) |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/4900913 |
Table of Contents:
- Preface
- Acknowledgements
- Introduction
- 0.1. About the book
- 0.2. What is spatial data analysis?
- 0.3. Motivation for the book
- 0.4. Organization
- 0.5. The spatial data matrix
- 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
- Appendix I. Software
- Appendix II. Cambridgeshire lung cancer data
- Appendix III. Sheffield burglary data
- Appendix IV. Children excluded from school: Sheffield
- References
- Index
