Applied Spatial Statistics and Econometrics : Data Analysis in R (Paperback)

Introduction Statement by the American Statistical Association on statistical significance and p-value used in the book Acknowledgments Chapter 1: Basic operations in the R software (Mateusz Kopyt)1.1 About the R software1.2. The R software interface1.2.1 R Commander1.2.2. RStudio1.3 Using help1.4 Additional packages1.5 R Language - basic features1.6 Defining and loading data1.7 Basic operations on objects1.8 Basic statistics of the data set1.9 Basic visualizations1.9.1 Scatterplot and line chart1.9.2 Column chart1.9.3 Pie chart1.9.4 Boxplot1.10 Regression in examples Chapter 2: Spatial data, R classes and basic graphics (Katarzyna Kopczewska)2.1 Loading and basic operations on spatial vector data2.2. Creating, checking and converting spatial classes2.3 Selected color palettes2.4 Basic contour maps with a color layerScheme 1 - with colorRampPalette() from the grDevices:: packageScheme 2 - with choropleth() from the GISTools:: packageScheme 3 - with findInterval() from the base:: packageScheme 4 - with findColours() from the classInt:: packageScheme 5 - with spplot() from the sp:: package2.5 Basic operations and graphs for point dataScheme 1 - with points() from the graphics:: package ? locations onlyScheme 2 - with spplot() from the sp:: package - locations and valuesScheme 3 - with findInterval() from the base:: package - locations, values, different size of symbols2.6 Basic operations on rasters2.7 Basic operations on grids2.8 Spatial geometries Chapter 3: Spatial data from the Web API (Mateusz Kopyt, Katarzyna Kopczewska)3.1 What is the API?3.2. Creating contextual maps with use of API3.3 Ways to visualize spatial data - maps for point and regional dataScheme 1 - with bubbleMap() from the RgoogleMaps:: packageScheme 2 - with ggmap() from the ggmap:: packageScheme 3 - with PlotOnStaticMap() from the RgoogleMap:: packageScheme 4 - with RGoogleMaps:: GetMap() and conversion of staticMap into a raster3.4 Spatial data in vector format - example of the OSM database3.5 Access to non-spatial internet databases and resources via API - examples3.6 Geo-coding of data Chapter 4: Spatial weight matrices, distance measurement, tessellation, spatial statistics (Katarzyna Kopczewska, Maria Kubara)4.1. Introduction to spatial data analysis4.2 Spatial weights matrix4.2.1 General framework for creating spatial weights matrices4.2.2 Selection of a neighborhood matrix4.2.3 Neighborhood matrices according to the contiguity criterion4.2.4 Matrix of k nearest neighbors (knn)4.2.5 Matrix based on distance criterion (neighbours in a radius of d km)4.2.6 Inverse distance matrix4.2.7 Summarizing and editing of spatial weights matrix4.2.8 Spatial lags and higher order neighborhood4.2.9 Creating weights matrix based on group membership4.3 Distance measurement and spatial aggregation4.4 Tessellation4.5 Spatial statistics4.5.1 Global statistics4.5.1.1 Global Moran I statistics4.5.1.2 Global Geary C statistics4.5.1.3 Join-count statistics4.5.2. Local spatial autocorrelation statistics4.5.2.1 Local Moran I statistics (LISA)4.5.2.2 Local Geary C statistics4.5.2.3 Local Getis-Ord Gi statistics4.5.2.4. Local spatial heteroscedasticity (LOSH)4.6 Spatial cross-correlations for two variables4.7 Correlogram Chapter 5: Applied spatial econometrics (Katarzyna Kopczewska)5.1 Value added from spatial modelling and classes of models5.2 Basic cross-sectional models5.2.1 Estimation5.2.2 Quality assessment of spatial models5.2.2.1 Information criteria and pseudo R2 in assessing model fit5.2.2.2 Test for heteroskedasticity of model residuals5.2.2.3 Residual autocorrelation tests5.2.2.4 LM tests for model type selection5.2.2.5 LR and Wald tests for model restrictions5.2.3 Selection of spatial weight matrix and modelling of diffusion strength5.2.4 Forecasts in spatial models5.2.5 Causality5.3 Selected specifications of cross-sectional spatial models5.3.1 Uni-directional spatial interaction models5.3.2 Cumulative models5.3.3 Bootstrapped models for big data5.3.4 Models for grid data5.4 Spatial panel models Chapter 6: Geographically Weighted Regression - modelling spatial heterogeneity (Piotr ?wiakowski)6.1 Geographically weighted regression6.2 Basic estimation of GWR model6.2.1 Estimation of the reference OLS model6.2.2 Choosing the optimal bandwidth for a dataset6.2.3 Local geographically weighted statistics6.2.4 Geographically weighted regression estimation6.2.5 Basic diagnostic tests of the GWR model6.2.6 Testing the significance of parameters in GWR6.2.7 Selection of the optimal functional form of the model6.2.8 GWR with heteroskedastic random error6.3 The problem of collinearity in GWR models6.3.1 Diagnosing collinearity in GWR6.4. Mixed GWR6.5. Robust regression in the GWR model6.6. Geographically and Temporally Weighted Regression (GTWR) Chapter 7: Unattended spatial learning (Katarzyna Kopczewska)7.1 Clustering of spatial points with k-means, PAM and CLARA algorithms7.2 Clustering with the DBSCAN algorithm7.3 Spatial Principal Component Analysis7.4 Spatial Drift7.5 Spatial hierarchical clustering7.6 Spatial oblique decision tree Chapter 8: Spatial point pattern analysis and spatial interpolation (Kateryna Zabarina)8.1. Introduction and main definitions8.1.1. Dataset8.1.2. Creation of window and point pattern8.1.3. Marks8.1.4. Covariates8.1.5. Duplicated points8.1.6. Projection and rescaling8.2. Intensity-based analysis of unmarked point pattern8.2.1. Quadrat test8.2.2. Tests with spatial covariates8.3. Distance-based analysis of the unmarked point pattern8.3.1. Distance-based measures8.3.1.1. Ripley’s K function8.3.1.2. F function8.3.1.3. G function8.3.1.4. J function8.3.1.5. Distance-based CSR tests8.3.2. Monte-Carlo tests8.3.3. Envelopes8.3.4. Non-graphical tests8.4. Selection and estimation of a proper model for unmarked point pattern8.4.1. Theoretical note8.4.2. Choice of parameters8.4.3. Estimation and results8.4.4. Conclusions8.5. Intensity-based analysis of marked point pattern8.5.1. Segregation test8.6. Correlation and spacing analysis of the marked point pattern8.6.1. Analysis under assumption of stationarity8.6.1.1. K function variations for multitype pattern8.6.1.2. Mark connection function8.6.1.3. Analysis of within and between types of dependence8.6.1.4. Randomisation test of components’ independence8.6.2. Analysis under assumption of non-stationarity8.6.2.1. Inhomogeneous K function variations for multitype pattern8.7. Selection and estimation of a proper model for unmarked point pattern8.7.1. Theoretical note8.7.2. Choice of optimal radius8.7.3. Within-industry interaction radius 8.7.4. Between-industry interaction radius8.7.5. Estimation and results8.7.6. Model with no between-industry interaction8.7.7. Model with all possible interactions8.8. Spatial interpolation methods - kriging 8.8.1. Basic definitions 8.8.2. Description of chosen kriging methods 8.8.3. Data preparation for the study8.8.4. Estimation and discussion Chapter 9: Spatial Sampling and Bootstrap (Katarzyna Kopczewska, Piotr ?wiakowski)9.1 Spatial point data - object classes and spatial aggregation9.2 Spatial sampling - randomization / generation of new points on the surface9.3 Spatial sampling - sampling of sub-samples from existing points9.3.1 Simple sampling9.3.2 The options of the sperrorest:: package9.3.3 Sampling points from areas determined by the k-means algorithm - block bootstrap9.3.4 Sampling points from moving blocks (moving block bootstrap, MBB)9.4. The use of spatial sampling and bootstrap in cross-validation of models Chapter 10: Spatial Big Data (Piotr Wojcik)10.1. Examples of big data usage10.2. Spatial big data10.2.1. Spatial data types10.2.2. Challenges related to the use of spatial Big Data10.2.2.1. Processing of large data sets10.2.2.2. Mapping and reduction10.2.2.3. Spatial data indexing10.3. The sf:: package - simple features10.3.1 sf class ? a special data frame10.3.2 Data with POLYGON geometry10.3.3 Data with POINT geometry10.3.4 Visualization using the ggplot2:: package10.3.5 Selected functions for spatial analysis10.4. Using the dplyr:: package functions10.5. Example analysis of large raster data10.5.1. Measurement of economic inequalities from space10.5.2. Analysis using the raster:: package functions10.5.3 Other functions of the raster:: package10.5.4 Potential alternative ? stars:: package Chapter 11: Spatial unsupervised learning ? applications of market basket analysis in geomarketing (Alessandro Festi)11.1 Introduction to market basket analysis 11.2 Data needed in spatial market basket analysis11.3 Simulation of data11.4 The market basket analysis technique applied to geolocation data11.5 Spatial association rules 11.6 Applications to geomarketing11.6.1Finding the best location for a business11.6.2 Targeting11.6.3 Discovery of competitors11.7 Conclusions and further approaches Appendix 1: Data used in the examplesA1. Data set No. 1 / dataset1 / - poviat panel data with many variablesA2. Dataset no 2 /dataset2/ ? geo-located point data A3. Dataset no 3 /dataset3/ ? monthly unemployment rate in poviats (NTS4)A4. Dataset no 4 /dataset4/ - grid data for populationA5. Shapefiles of countour maps ? for poviats (NTS4), regions (NTS2), country (NTS0) and registration areasA6. Raster data on night light intensity on Earth in 2013A7. Population in cities in PolandAppendix 2: Links between packagesAppendix 3: Spatial data sets in R packagesReferencesIndex of termsIndex of R packagesIndex of R commands