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E-raamat: Applied Spatial Statistics and Econometrics: Data Analysis in R

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Applied Spatial Statistics and Econometrics: Data Analysis in RSuurem pilt
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This textbook is a comprehensive introduction to applied spatial data analysis, using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcase key topics including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github, to help readers practise techniques via replication and exercises.

This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data.

List of figures
xi
List of tables
xvii
List of contributors
xix
Introduction xxi
Statement by the American Statistical Association on statistical significance and p-value - use in the book xxiii
Acknowledgements xxv
1 Basic operations in the R software
1(36)
Mateusz Kopyt
1.1 About the R software
1(1)
1.2 The R software interface
1(3)
1.2.1 R Commander
2(1)
1.2.2 RStudio
3(1)
1.3 Using help
4(3)
1.4 Additional packages
7(2)
1.5 R language - basic features
9(1)
1.6 Defining and loading data
9(2)
1.7 Basic operations on objects
11(7)
1.8 Basic statistics of the dataset
18(6)
1.9 Basic visualisations
24(7)
1.9.1 Scatterplot and line chart
24(3)
1.9.2 Column chart
27(2)
1.9.3 Pie chart
29(1)
1.9.4 Boxplot
29(2)
1.10 Regression in examples
31(6)
2 Data, spatial classes and basic graphics
37(50)
Katarzyna Kopczewska
2.1 Loading and basic operations on spatial vector data
37(11)
2.2 Creating, checking and converting spatial classes
48(5)
2.3 Selected colour palettes
53(4)
2.4 Basic contour maps with a colour layer
57(5)
Scheme 1 With colorRampPalette() from the grDevices:: package
57(1)
Scheme 2 With choropleth() from the GISTools:: package
58(1)
Scheme 3 With findlnterval() from the base:: package
59(1)
Scheme 4 With findColours() from the classlnt:: package
60(1)
Scheme 5 With spplot() from the sp:: package
61(1)
2.5 Basic operations and graphs for point data
62(5)
Scheme 1 With points() from the graphics:: package - locations only
62(1)
Scheme 2 With spplot() from the sp:: package - locations and values
63(1)
Scheme 3 With findlntervalO from the base:: package - locations, values, different size of symbols
64(3)
2.6 Basic operations on rasters
67(6)
2.7 Basic operations on grids
73(7)
2.8 Spatial geometries
80(7)
3 Spatial data with Web APIs
87(64)
Mateusz Kopyt
Katarzyna Kopczewska
3.1 What is an application programming interface (API)?
87(1)
3.2 Creating background maps with use of an application programming interface
88(14)
3.3 Ways to visualise spatial data - maps for point and regional data
102(8)
Scheme 1 With bubbleMap() from the RgoogleMaps:: package
102(2)
Scheme 2 With ggmap() from the ggmap:: package
104(5)
Scheme 3 With PlotOnStaticMap() from the RgoogleMaps:: package
109(1)
Scheme 4 With RGoogleMaps:: GetMap() and conversion of staticMap into a raster
109(1)
3.4 Spatial data in vector format - example of the OSM database
110(7)
3.5 Access to non-spatial internet databases and resources via application programming interface - examples
117(16)
3.6 Geocoding of data
133(18)
4 Spatial weights matrix, distance measurement, tessellation, spatial statistics
151(62)
Katarzyna Kopczewska
Maria Kubara
4.1 Introduction to spatial data analysis
151(2)
4.2 Spatial weights matrix
153(21)
4.2.1 General framework for creating spatial weights matrices
153(2)
4.2.2 Selection of a neighbourhood matrix
155(1)
4.2.3 Neighbourhood matrices according to the contiguity criterion
156(3)
4.2.4 Matrix of k nearest neighbours (knn)
159(2)
4.2.5 Matrix based on distance criterion (neighbours in a radius of d km)
161(2)
4.2.6 Inverse distance matrix
163(1)
4.2.7 Summarising and editing spatial weights matrix
164(5)
4.2.8 Spatial lags and higher-order neighbourhoods
169(1)
4.2.9 Creating weights matrix based on group membership
170(1)
### Example ###
170(3)
### Example ###
173(1)
4.3 Distance measurement and spatial aggregation
174(8)
### Example ###
177(5)
4.4 Tessellation
182(3)
4.5 Spatial statistics
185(21)
4.5.1 Global statistics
188(1)
4.5.1.1 Global Moran's/statistics
188(6)
4.5.1.2 Global Geary's C statistics
194(1)
4.5.1.3 Join-count statistics
195(4)
4.5.2 Local spatial autocorrelation statistics
199(1)
4.5.2.2 Local Moran's / statistics (local indicator of spatial association)
199(2)
4.5.2.3 Local Geary's C statistics
201(1)
4.5.2.4 Local Getis-Ord G, statistics
202(1)
4.5.2.5 Local spatial heteroscedasticity
203(3)
4.6 Spatial cross-correlations for two variables
206(2)
4.7 Correlogram
208(5)
5 Applied spatial econometrics
213(76)
Katarzyna Kopczewska
5.1 Added value from spatial modelling and classes of models
213(3)
5.2 Basic cross-sectional models
216(30)
5.2.1 Estimation
216(3)
### Example ###
219(11)
5.2.2 Quality assessment of spatial models
230(1)
5.2.2.1 Information criteria and pseudo-/?2 in assessing model fit
230(2)
5.2.2.2 Test for heteroscedasticity of model residuals
232(2)
5.2.2.3 Residual autocorrelation tests
234(2)
5.2.2.4 Lagrange multiplier tests for model type selection
236(2)
5.2.2.5 Likelihood ratio and Wald tests for model restrictions
238(2)
5.2.3 Selection of spatial weights matrix and modelling of diffusion strength
240(3)
5.2.4 Forecasts in spatial models
243(2)
5.2.5 Causality
245(1)
5.3 Selected specifications of cross-sectional spatial models
246(28)
5.3.1 Unidirectional spatial interaction models
246(9)
5.3.2 Cumulative models
255(6)
5.3.3 Bootstrapped models for big data
261(1)
### Example ###
261(8)
5.3.4 Models for grid data
269(1)
### Example ###
269(5)
5.4 Spatial panel models
274(15)
### Example###
278(11)
6 Geographically weighted regression - modelling spatial heterogeneity
289(34)
Piotr Cwiakowski
6.1 Geographically weighted regression
289(2)
6.2 Basic estimation of geographically weighted regression model
291(17)
6.2.1 Estimation of the reference ordinary least squares model
291(1)
6.2.2 Choosing the optimal bandwidth for a dataset
292(3)
6.2.3 Local geographically weighted statistics
295(2)
6.2.4 Geographically weighted regression estimation
297(1)
6.2.5 Basic diagnostic tests of the geographically weighted regression model
298(6)
6.2.6 Testing the significance of parameters in geographically weighted regression
304(1)
6.2.7 Selection of the optimal functional form of the model
305(2)
6.2.8 Geographically weighted regression with heteroscedastic random error
307(1)
6.3 The problem of collinearity in geographically weighted regression models
308(8)
6.3.1 Diagnosing collinearity in geographically weighted regression
308(8)
6.4 Mixed geographically weighted regression
316(2)
6.5 Robust regression in the geographically weighted regression model
318(1)
6.6 Geographically and temporally weighted regression
319(4)
7 Spatial unsupervised learning
323(48)
Katarzyna Kopczewska
7.1 Clustering of spatial points with k-means, RAM (partitioning around medoids) and CLARA (clustering large applications) algorithms
323(13)
### Example ###
326(7)
### Example ###
333(3)
7.2 Clustering with the density-based spatial clustering of applications with noise algorithm
336(9)
### Example ###
337(8)
7.3 Spatial principal component analysis
345(4)
### Example ###
346(3)
7.4 Spatial drift
349(7)
### Example ###
349(7)
7.5 Spatial hierarchical clustering
356(8)
### Example ###
358(4)
### Example ###
362(2)
7.6 Spatial oblique decision tree
364(7)
### Example ###
364(7)
8 Spatial point pattern analysis and spatial interpolation
371(62)
Kateryna Zabarina
8.1 Introduction and main definitions
373(13)
8.1.1 Dataset
373(1)
8.1.2 Creation of window and point pattern
374(1)
8.1.3 Marks
375(6)
8.1.4 Covariates
381(1)
### Example ###
381(1)
8.1.5 Duplicated points
382(1)
8.1.6 Projection and rescaling
383(3)
8.2 Intensity-based analysis of unmarked point pattern
386(5)
8.2.1 Quadrat test t
387(1)
8.2.2 Tests with spatial covariates
388(3)
8.3 Distance-based analysis of the unmarked point pattern
391(7)
8.3.1 Distance-based measures
392(1)
8.3.1.1 Ripley's K function
392(1)
8.3.1.2 F function
393(1)
8.3.1.3 G function
393(1)
8.3.1.4 J function
393(1)
8.3.1.5 Distance-based complete spatial randomness tests
393(3)
8.3.2 Monte Carlo tests
396(1)
8.3.3 Envelopes
396(1)
8.3.4 Non-graphical tests
397(1)
8.4 Selection and estimation of a proper model for unmarked point pattern
398(6)
8.4.1 Theoretical note
399(1)
8.4.2 Choice of parameters
400(1)
8.4.3 Estimation and results
401(3)
8.4.4 Conclusions
404(1)
8.5 Intensity-based analysis of marked point pattern
404(1)
8.5.1 Segregation test
404(1)
8.6 Correlation and spacing analysis of the marked point pattern
405(5)
8.6.1 Analysis under assumption of stationarity
405(1)
8.6.1.1 K function variations for multitype pattern
405(2)
8.6.1.2 Mark connection function
407(2)
8.6.1.3 Analysis of within-and between-type dependence
409(1)
8.6.1.4 Randomisation test of components'independence
409(1)
8.6.2 Analysis under assumption of non-stationarity
410(1)
8.6.2.1 Inhomogeneous K function variations for multitype pattern
410(1)
8.7 Selection and estimation of a proper model for unmarked point pattern
410(11)
8.7.1 Theoretical note
412(1)
8.7.2 Choice of optimal radius
412(1)
8.7.3 Within-industry interaction radius
412(2)
8.7.4 Between-industry interaction radius
414(1)
8.7.5 Estimation and results
415(1)
8.7.6 Model with no between-industry interaction
415(3)
8.7.7 Model with all possible interactions
418(3)
8.8 Spatial interpolation methods - kriging
421(12)
8.8.1 Basic definitions
421(3)
8.8.2 Description of chosen kriging methods
424(1)
8.8.3 Data preparation for the study
424(1)
8.8.4 Estimation and discussion
425(8)
9 Spatial sampling and bootstrapping
433(44)
Katarzyna Kopczewska
Piotr Cwiakowski
9.1 Spatial point data - object classes and spatial aggregation
434(3)
9.2 Spatial sampling - randomisation/generation of new points on the surface
437(3)
9.3 Spatial sampling - sampling of sub-samples from existing points
440(22)
9.3.1 Simple sampling
441(2)
9.3.2 The options of the sperrorest:: package
443(5)
9.3.3 Sampling points from areas determined by the /r-means algorithm - block bootstrap
448(8)
9.3.4 Sampling points from moving blocks (moving block bootstrap)
456(6)
9.4 Use of spatial sampling and bootstrapping in cross-validation of models
462(15)
### Example ###
462(15)
10 Spatial big data
477(40)
Piotr Wojcik
10.1 Examples of big data applications
478(1)
10.2 Spatial big data
478(3)
10.2.1 Spatial data types
479(1)
10.2.2 Challenges related to the use of spatial big data
479(1)
10.2.2.1 Processing of large datasets
479(1)
10.2.2.2 Mapping and reduction
480(1)
10.2.2.3 Spatial data indexing
480(1)
10.3 The sd:: package - simple features
481(13)
10.3.1 Sf class - a special data frame
481(1)
10.3.2 Data with POLYGON geometry
482(6)
10.3.3 Data with POINT geometry
488(1)
10.3.4 Visualisation using the ggplot2:: package
489(1)
10.3.5 Selected functions for spatial analysis
490(4)
10.4 Use the dplyr:: package functions
494(11)
10.5 Sample analysis of large raster data
505(12)
10.5.1 Measurement of economic inequalities from space
505(2)
10.5.2 Analysis using the raster:: package functions
507(7)
10.5.3 Other functions of the raster:: package
514(1)
10.5.4 Potential alternative - stars:: package
515(2)
11 Spatial unsupervised learning - applications of market basket analysis in geomarketing
517(24)
Alessandro Festi
11.1 Introduction to market basket analysis
517(1)
11.2 Data needed in spatial market basket analysis
518(2)
11.3 Simulation of data
520(6)
11.4 The market basket analysis technique applied to geolocation data
526(4)
11.5 Spatial association rules
530(4)
11.6 Applications to geomarketing
534(4)
11.6.1 Finding the best location for a business
534(2)
11.6.2 Targeting
536(2)
11.6.3 Discovery of competitors
538(1)
11.7 Conclusions and further approaches
538(3)
Appendix A Datasets used in examples
541(14)
A1 Dataset no. 1/dataset1/- poviat panel data with many variables
541(3)
A2 Dataset no. 2/dataset2/ - geolocated point data
544(4)
A3 Dataset no. 3/dataset3/ - monthly unemployment rate in poviats (NTS4)
548(1)
A4 Dataset no. 4/dataset4/ - grid data for population
549(2)
A5 Shapefiles of contour maps - for poviats (NTS4), regions (NTS2), country (NTS0) and registration areas
551(1)
A6 Raster data on night light intensity on Earth in 2013
552(1)
A7 Population in cities in Poland
553(2)
Appendix B Links between packages 555(6)
References 561(16)
Index 577
Katarzyna Kopczewska is an associate professor at University of Warsaw, Faculty of Economic Sciences. As a quantitative economist, she deals with spatial modelling of geolocalised economic processes location and co-location, agglomeration, concentration, diffusion, spatial interactions in relation to economic phenomena, companies and real estate but also regional policy or public-sector activities. She conducts methodological research on the implementation of data science methods for spatial analysis and combining them with classical spatial statistics and econometrics in R. She combines quantitative solutions with theory and problems of regional science and economic geography. She serves at the European Regional Science Association (ERSA).
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