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Exploring Spatial Scale in Geography provides a conceptual
and practical guide to issues of spatial scale in all areas of the
physical and social sciences. Scale is at the heart of
geography and other spatial sciences. Whether dealing with
geomorphological processes, population movements or meteorology, a
consideration of spatial scale is vital.
Exploring Spatial Scale in Geography takes a practical
approach with a core focus on real world problems and potential
solutions. Links are made to appropriate software environments with
an associated website providing access to guidance material which
outlines how particular problems can be approached using popular
GIS and spatial data analysis software.
This book offers alternative definitions of spatial scale,
presents approaches for exploring spatial scale and makes use of a
wide variety of case studies in the physical and social sciences to
demonstrate key concepts, making it a key resource for anyone who
makes use of geographical information.
Auteur
Christopher D. Lloyd
Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, UK.
Résumé
Exploring Spatial Scale in Geography provides a conceptual and practical guide to issues of spatial scale in all areas of the physical and social sciences. Scale is at the heart of geography and other spatial sciences. Whether dealing with geomorphological processes, population movements or meteorology, a consideration of spatial scale is vital.
Exploring Spatial Scale in Geography takes a practical approach with a core focus on real world problems and potential solutions. Links are made to appropriate software environments with an associated website providing access to guidance material which outlines how particular problems can be approached using popular GIS and spatial data analysis software.
This book offers alternative definitions of spatial scale, presents approaches for exploring spatial scale and makes use of a wide variety of case studies in the physical and social sciences to demonstrate key concepts, making it a key resource for anyone who makes use of geographical information.
Contenu
Preface xiii
Acknowledgements xv
About the Companion Website xvii
1 Introduction 1
1.1 The purpose of the book 1
1.1.1 What this book adds 3
1.1.2 Scales of analysis and alternative definitions 3
1.2 Key objectives 4
1.3 Case studies and examples 5
1.4 Why is spatial scale important? 5
1.5 Structure of the book 6
1.6 Further reading 6
References 7
2 Scale in Spatial Data Analysis: Key Concepts 9
2.1 Definitions of spatial scale 9
2.2 Spatial autocorrelation and spatial dependence 11
2.3 Scale dependence 13
2.4 Scale and data models 14
2.5 Spatial scales of inquiry 14
2.6 Scale and spatial data analysis 14
2.7 Scale and neighbourhoods 15
2.8 Scale and space 16
2.9 Scale, spatial data analysis and physical processes 23
2.10 Scale, spatial data analysis and social processes 25
2.11 Summary 26
2.12 Further reading 26
References 26
3 The Modifiable Areal Unit Problem 29
3.1 Basic concepts 29
3.2 Scale and zonation effects 29
3.3 The ecological fallacy 32
3.4 The MAUP and univariate statistics 34
3.4.1 Case study: segregation in Northern Ireland 35
3.4.2 Spatial approaches to segregation 38
3.5 Geographical weighting and the MAUP 38
3.6 The MAUP and multivariate statistics 39
3.6.1 Case study: population variables in Northern Ireland 40
3.7 Zone design 41
3.8 Summary 42
3.9 Further reading 42
References 42
4 Measuring Spatial Structure 45
4.1 Basic concepts 45
4.2 Measures of spatial autocorrelation 45
4.2.1 Neighbourhood size 47
4.2.2 Spatial autocorrelation and kernel size 47
4.2.3 Spatial autocorrelation and lags 50
4.2.4 Local measures 50
4.2.5 Global and local I and spatial scale 51
4.3 Geostatistics and characterising spatial structure 53
4.3.1 The theory of regionalised variables 54
4.4 The variogram 57
4.4.1 Bias in variogram estimation 59
4.5 The covariance function and correlogram 59
4.6 Alternative measures of spatial structure 60
4.7 Measuring dependence between variables 63
4.8 Variograms of risk 64
4.9 Variogram clouds and h-scatterplots 64
4.10 Variogram models 65
4.11 Fitting variogram models 68
4.12 Variogram case study 70
4.13 Anisotropy and variograms 74
4.13.1 Variogram surfaces 74
4.13.2 Geometric and zonal anisotropy 75
4.14 Variograms and non-stationarity 77
4.14.1 Variograms and long-range trends 77
4.14.2 Variogram non-stationarity 79
4.15 Spacetime variograms 82
4.16 Software 83
4.17 Other methods 83
4.18 Point pattern analysis 84
4.18.1 Spatial dependence and point patterns 85
4.18.2 Local K function 91
4.18.3 Cross K function 92
4.19 Summary 97
4.20 Further reading 97
References 97
5 Scale and Multivariate Data 103
5.1 Regression frameworks 104
5.2 Spatial scale and regression 104
5.3 Global regression 105
5.4 Spatial regression 105
5.5 Regression and spatial data 106
5.5.1 Generalised least squares 106
5.5.2 Spatial autoregressive models 107
5.5.3 Spatially lagged dependent variable models and spatial error models case study 109
5.6 Local regression and spatial scale 111
5.6.1 Spatial expansion method 111
5.6.2 Geographically weighted regression 112
5.6.3 Scale and GWR 115
5.6.4 GWR case study: fixed bandwidths 115
5.6.5 GWR case study: variable bandwidths 116 5.6.6 Bayesian spatial...