Quantitative Geography: Perspectives on Spatial Data Analysis

Front Cover
SAGE, May 2, 2000 - Science - 270 pages
0 Reviews
Integrating a discussion of the application of quantitative methods with practical examples, this book explains the philosophy of the new quantitative methodologies and contrasts them with the methods associated with geography's `Quantitative Revolution' of the 1960s. Key issues discussed include: the nature of modern quantitative geography; spatial data; geographical information systems; visualization; local analysis; point pattern analysis; spatial regression; and statistical inference. Concluding with a review of models used in spatial theory, the authors discuss the current challenges to spatial data analysis.

Written to be accessible, to communicate the diversity and excitement of recent thinking, Quantitative Geog

  

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Establishing the Boundaries
1
12 What is quantitative geography?
4
13 Applications of quantitative geography
8
14 Recent developments in quantitative geography
10
15 Summary
13
Notes
14
Spatial Data
15
22 Spatial data capture
16
553 Spatial expansion method results
117
554 GWR results
118
56 Measuring local relationships in spatial interaction models
128
57 Summary
129
Point Pattern Analysis
130
62 Initial exploration
132
622 Other exploratory plots
134
623 Nongraphical approaches to point pattern exploration
136

23 Spatial objects
17
24 Location on the globe location on a plane
18
25 Distance
20
26 Representing spatial data
21
27 Models for spatial data
22
272 The raster model
23
28 Programming with spatial data
24
282 The use of complex numbers to represent spatial data
25
210 Summary
29
The Role of Geographical Information Systems
30
32 Simple GISbased spatial analysis
32
322 Feature selection by geometric intersection
33
323 Buffering features
34
union
35
intersection
37
326 Proximity
38
327 Contiguity
41
328 Interpolation and fields
42
329 Density functions
45
3210 Analysis on networks
46
3211 Query
49
331 Dataintegration and management
51
332 Exploration
54
333 Postmodelling visualization
55
34 Problems
58
342 Crossarea aggregation
59
35 Linking higherorder types of spatial analysis to GIS
61
36 A role for GIS?
64
Exploring Spatial Data Visually
65
42 Stem and leaf plots
66
43 Boxplots
68
44 Histograms
70
45 Density estimates
71
46 Maps
72
47 The scatterplot matrix
75
48 Linked plots
77
49 Parallel coordinate plots
80
410 RADVIZ
82
411 Projection pursuit
86
412 Summary
91
Notes
92
Local Analysis
93
52 The nature of local variations in relationships
94
53 Measuring local relationships in univariate data
96
532 Other local measures of univariate spatial relationships
97
54 Measuring local relationships in multivariate data
102
541 Multilevel modelling
103
542 The spatial expansion method
106
543 Geographically weighted regression Consider a global regression model given by
107
55 An empirical comparison of the spatial expansion method and GWR
114
552 Global regression model results
115
63 Modelling point patterns
138
64 Firstorder intensity analysis
144
641 Kernel density estimates
146
65 Secondorder intensity analysis
149
66 Comparing distributions
154
661 Comparing kernel densities
155
662 Comparing K functions
157
663 Comparing a point pattern with a population at risk
159
67 Conclusions
160
Notes
161
Spatial Regression and Geostatistical Models
162
72 Autoregressive models
166
721 Spatially autoregressive models
167
722 Spatial moving average models
169
73 Kriging
171
732 A worked example
175
733 Trend surfaces from kriging residuals
176
74 Semiparametric smoothing approaches
178
75 Conclusions
182
Statistical Inference for Spatial Data
184
82 Informal inference
185
822 Data mining
187
8221 Cluster analysis
188
8222 Neural networks
190
83 Formal inference
193
832 Classical inference
198
833 Experimental and computational inference
201
8332 Experimental distributions and spatial autocorrelation
204
8333 An empirical comparison of classical and experimental inference
206
834 Model building and model testing
210
84 Conclusions
211
Spatial Modelling and the Evolution of Spatial Theory
213
92 Spatial interaction as social physics 18601970
215
93 Spatial interaction as statistical mechanics 197080
217
94 Spatial interaction as aspatial information processing 198090
222
95 Spatial interaction as spatial information processing 1990 onwards
225
96 Summary
234
Notes
235
Challenges in Spatial Data Analysis
236
102 Current challenges
237
1022 Spatial nonstationarity
240
1023 Alternative inferential frameworks Bayes MCMC
242
1024 Geometry
243
proximity and accessibility
244
1026 Merging space and time
245
103 Training people to think spatially
246
1032 Software
247
Bibliography
249
Index
267
Copyright

Common terms and phrases

References to this book

All Book Search results »

About the author (2000)

Chris Brunsdon is Professor of Geocomputation at the National University of Ireland, Maynooth. He studied Mathematics at the University of Durham and Medical Statistics at the University of Newcastle upon Tyne,  and has worked in a number of universities, holding the Chair in Human Geography at Liverpool University before taking up his current position.  His research interests are in health, crime and environmental data analysis,  and in the development of spatial analytical tools,  including Geographically Weighted Regression approach.  He also has interests in the software tools used to develop such approaches,  including R.

Background:

Martin is an expert in the use of Geographical Information Systems and has been a leading researcher in this area for over 20 years. Until recently he was a lecturer in GIS at the University of Newcastle-upon-Tyne.

Research:

Martin, together with Stewart Fotheringham and Chris Brunsdon, is one of the originators of Geographically Weighted Regression, for which he has written much of the software.

Bibliographic information