Spatial Data Analysis : Theory and Practice

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Spatial Data Analysis : Theory and Practice

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 454 p.
  • 言語 ENG
  • 商品コード 9780521774376
  • DDC分類 910.285

Full Description

Spatial Data Analysis: Theory and Practice, first published in 2003, provides a broad ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy related research. Covering fundamental problems concerning how attributes in geographical space are represented to the latest methods of exploratory spatial data analysis and spatial modeling, it is designed to take the reader through the key areas that underpin the analysis of spatial data, providing a platform from which to view and critically appreciate many of the key areas of the field. Parts of the text are accessible to undergraduate and master's level students, but it also contains sufficient challenging material that it will be of interest to geographers, social and economic scientists, environmental scientists and statisticians, whose research takes them into the area of spatial analysis.

Table of Contents

Preface                                            xv
Acknowledgements xvii
Introduction 1 (1)
About the book 1 (3)
What is spatial data analysis? 4 (1)
Motivation for the book 5 (3)
Organization 8 (2)
The spatial data matrix 10 (5)
Part A The context for spatial data analysis
Spatial data analysis: scientific and 15 (28)
policy context
Spatial data analysis in science 15 (7)
Generic issues of place, context and 16 (1)
space in scientific explanation
Location as place and context 16 (2)
Location and spatial relationships 18 (3)
Spatial processes 21 (1)
Place and space in specific areas of 22 (14)
scientific explanation
Defining spatial subdisciplines 22 (2)
Examples: selected research areas 24 (1)
Environmental criminology 24 (2)
Geographical and environmental 26 (3)
(spatial) epidemiology
Regional economics and the new economic 29 (2)
geography
Urban studies 31 (1)
Environmental sciences 32 (1)
Spatial data analysis in problem solving 33 (3)
Spatial data analysis in the policy area 36 (4)
Some examples of problems that arise in 40 (1)
analysing spatial data
Description and map interpretation 40 (1)
Information redundancy 41 (1)
Modelling 41 (1)
Concluding remarks 41 (2)
The nature of spatial data 43 (48)
The spatial data matrix: 44 (10)
conceptualization and representation
issues
Geographic space: objects, fields and 44 (2)
geometric representations
Geographic space: spatial dependence in 46 (1)
attribute values
Variables 47 (1)
Classifying variables 48 (2)
Levels of measurement 50 (1)
Sample or population? 51 (3)
The spatial data matrix: its form 54 (3)
The spatial data matrix: its quality 57 (17)
Model quality 58 (1)
Attribute representation 59 (1)
Spatial representation: general 59 (2)
considerations
Spatial representation: resolution and 61 (1)
aggregation
Data quality 61 (2)
Accuracy 63 (4)
Resolution 67 (3)
Consistency 70 (1)
Completeness 71 (3)
Quantifying spatial dependence 74 (13)
Fields: data from two-dimensional 74 (5)
continuous space
Objects: data from two-dimensional 79 (8)
discrete space
Concluding remarks 87 (4)
Part B Spatial data: obtaining data and
quality issues
Obtaining spatial data through sampling 91 (25)
Sources of spatial data 91 (2)
Spatial sampling 93 (20)
The purpose and conduct of spatial 93 (3)
sampling
Design- and model-based approaches to 96 (1)
spatial sampling
Design-based approach to sampling 96 (2)
Model-based approach to sampling 98 (1)
Comparative comments 99 (1)
Sampling plans 100 (3)
Selected sampling problems 103 (1)
Design-based estimation of the 103 (3)
population mean
Model-based estimation of means 106 (1)
Spatial prediction 107 (1)
Sampling to identify extreme values or 108 (5)
detect rare events
Maps through simulation 113 (3)
Data quality: implications for spatial data 116 (65)
analysis
Errors in data and spatial data analysis 116 (11)
Models for measurement error 116 (1)
Independent error models 117 (1)
Spatially correlated error models 118 (1)
Gross errors 119 (1)
Distributional outliers 119 (3)
Spatial outliers 122 (1)
Testing for outliers in large data sets 123 (1)
Error propagation 124 (3)
Data resolution and spatial data analysis 127 (24)
Variable precision and tests of 128 (1)
significance
The change of support problem 129 (1)
Change of support in geostatistics 129 (2)
Areal interpolation 131 (7)
Analysing relationships using aggregate 138 (3)
data
Ecological inference: parameter 141 (6)
estimation
Ecological inference in environmental 147 (3)
epidemiology: identifying valid
hypotheses
The modifiable areal units problem 150 (1)
(MAUP)
Data consistency and spatial data analysis 151 (1)
Data completeness and spatial data 152 (25)
analysis
The missing-data problem 154 (2)
Approaches to analysis when data are 156 (3)
missing
Approaches to analysis when spatial 159 (5)
data are missing
Spatial interpolation, spatial 164 (10)
prediction
Boundaries, weights matrices and data 174 (3)
completeness
Concluding remarks 177 (4)
Part C The exploratory analysis of spatial
data
Exploratory spatial data analysis: 181 (7)
conceptual models
EDA and ESDA 181 (2)
Conceptual models of spatial variation 183 (5)
The regional model 183 (1)
Spatial `rough' and `smooth' 184 (1)
Scales of spatial variation 185 (3)
Exploratory spatial data analysis: 188 (38)
visualization methods
Data visualization and exploratory data 188 (6)
analysis
Data visualization: approaches and tasks 189 (3)
Data visualization: developments 192 (1)
through computers
Data visualization: selected techniques 193 (1)
Visualizing spatial data 194 (16)
Data preparation issues for aggregated 194 (5)
data: variable values
Data preparation issues for aggregated 199 (1)
data: the spatial framework
Non-spatial approaches to region 200 (1)
building
Spatial approaches to region building 201 (2)
Design criteria for region building 203 (3)
Special issues in the visualization of 206 (4)
spatial data
Data visualization and exploratory 210 (15)
spatial data analysis
Spatial data visualization: selected 211 (1)
techniques for univariate data
Methods for data associated with point 211 (4)
or area objects
Methods for data from a continuous 215 (3)
surface
Spatial data visualization: selected 218 (1)
techniques for bi- and multi-variate
data
Uptake of breast cancer screening in 219 (6)
Sheffield
Concluding remarks 225 (1)
Exploratory spatial data analysis: 226 (47)
numerical methods
Smoothing methods 227 (10)
Resistant smoothing of graph plots 227 (1)
Resistant description of spatial 228 (1)
dependencies
Map smoothing 228 (2)
Simple mean and median smoothers 230 (1)
Introducing distance weighting 230 (2)
Smoothing rates 232 (2)
Non-linear smoothing: headbanging 234 (2)
Non-linear smoothing: median polishing 236 (1)
Some comparative examples 237 (1)
The exploratory identification of global 237 (13)
map properties: overall clustering
Clustering in area data 242 (5)
Clustering in a marked point pattern 247 (3)
The exploratory identification of local 250 (15)
map properties
Cluster detection 251 (1)
Area data 251 (8)
Inhomogeneous point data 259 (4)
Focused tests 263 (2)
Map comparison 265 (8)
Bivariate association 265 (3)
Spatial association 268 (5)
Part D Hypothesis testing and spatial
autocorrelation
Hypothesis testing in the presence of 273 (16)
spatial dependence
Spatial autocorrelation and testing the 275 (3)
mean of a spatial data set
Spatial autocorrelation and tests of 278 (11)
bivariate association
Pearson's product moment correlation 278 (5)
coefficient
Chi-square tests for contingency tables 283 (6)
Part E Modelling spatial data
Models for the statistical analysis of 289 (36)
spatial data
Descriptive models 292 (20)
Models for large-scale spatial variation 293 (1)
Models for small-scale spatial variation 293 (1)
Models for data from a surface 293 (4)
Models for continuous-valued area data 297 (7)
Models for discrete-valued area data 304 (2)
Models with several scales of spatial 306 (1)
variation
Hierarchical Bayesian models 307 (5)
Explanatory models 312 (13)
Models for continuous-valued response 312 (4)
variables: normal regression models
Models for discrete-valued area data: 316 (4)
generalized linear models
Hierarchical models
Adding covariates to hierarchical 320 (1)
Bayesian models
Modelling spatial context: multi-level 321 (4)
models
Statistical modelling of spatial variation: 325 (25)
descriptive modelling
Models for representing spatial variation 325 (13)
Models for continuous-valued variables 326 (1)
Trend surface models with independent 326 (1)
errors
Semi-variogram and covariance models 327 (4)
Trend surface models with spatially 331 (3)
correlated errors
Models for discrete-valued variables 334 (4)
Some general problems in modelling 338 (1)
spatial variation
Hierarchical Bayesian models 339 (11)
Statistical modelling of spatial variation: 350 (29)
explanatory modelling
Methodologies for spatial data modelling 350 (8)
The `classical' approach 350 (3)
The econometric approach 353 (2)
A general spatial specification 355 (1)
Two models of spatial pricing 356 (2)
A `data-driven' methodology 358 (1)
Some applications of linear modelling of 358 (20)
spatial data
Testing for regional income convergence 359 (2)
Models for binary responses 361 (1)
A logistic model with spatial lags on 361 (3)
the covariates
Autologistic models with covariates 364 (1)
Multi-level modelling 365 (2)
Bayesian modelling of burglaries in 367 (9)
Sheffield
Bayesian modelling of children excluded 376 (2)
from school
Concluding comments 378 (1)
Appendix I Software 379 (2)
Appendix II Cambridgeshire lung cancer data 381 (4)
Appendix III Sheffield burglary data 385 (6)
Appendix IV Children excluded from school: 391 (3)
Sheffield
References 394 (30)
Index 424