Applied Spatial Statistics and Econometrics Data Analysis in R
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 showcases key topics, including unsupervised learning, causal i...
Saved in:
| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
London
Routledge
2021
Taylor and Francis Taylor & Francis Group |
| Edition | 1 |
| Series | Routledge Advanced Texts in Economics and Finance |
| Subjects | |
| Online Access | Get full text |
| ISBN | 0367470764 9780367470777 0367470772 9780367470760 |
| DOI | 10.4324/9781003033219 |
Cover
Table of Contents:
- 8.6.1.2 Mark connection function -- 8.6.1.3 Analysis of within- and between-type dependence -- 8.6.1.4 Randomisation test of components' independence -- 8.6.2 Analysis under assumption of non-stationarity -- 8.6.2.1 Inhomogeneous K function variations for multitype pattern -- 8.7 Selection and estimation of a proper model for unmarked point pattern -- 8.7.1 Theoretical note -- 8.7.2 Choice of optimal radius -- 8.7.3 Within-industry interaction radius -- 8.7.4 Between-industry interaction radius -- 8.7.5 Estimation and results -- 8.7.6 Model with no between-industry interaction -- 8.7.7 Model with all possible interactions -- 8.8 Spatial interpolation methods - kriging -- 8.8.1 Basic definitions -- 8.8.2 Description of chosen kriging methods -- 8.8.3 Data preparation for the study -- 8.8.4 Estimation and discussion -- 9 Spatial sampling and bootstrapping -- 9.1 Spatial point data - object classes and spatial aggregation -- 9.2 Spatial sampling - randomisation/generation of new points on the surface -- 9.3 Spatial sampling - sampling of sub-samples from existing points -- 9.3.1 Simple sampling -- 9.3.2 The options of the sperrorest:: package -- 9.3.3 Sampling points from areas determined by the k-means algorithm - block bootstrap -- 9.3.4 Sampling points from moving blocks (moving block bootstrap) -- 9.4 Use of spatial sampling and bootstrapping in cross-validation of models -- ### Example ### -- 10 Spatial big data -- 10.1 Examples of big data applications -- 10.2 Spatial big data -- 10.2.1 Spatial data types -- 10.2.2 Challenges related to the use of spatial big data -- 10.2.2.1 Processing of large datasets -- 10.2.2.2 Mapping and reduction -- 10.2.2.3 Spatial data indexing -- 10.3 The sd:: package - simple features -- 10.3.1 sf class - a special data frame -- 10.3.2 Data with POLYGON geometry -- 10.3.3 Data with POINT geometry
- 10.3.4 Visualisation using the ggplot2:: package
- Cover -- Half Title -- Series -- Title -- Copyright -- Contents -- List of figures -- List of tables -- List of contributors -- Introduction -- Statement by the American Statistical Association on statistical significance and p-value - use in the book -- Acknowledgements -- 1 Basic operations in the R software -- 1.1 About the R software -- 1.2 The R software interface -- 1.2.1 R Commander -- 1.2.2 RStudio -- 1.3 Using help -- 1.4 Additional packages -- 1.5 R language - basic features -- 1.6 Defining and loading data -- 1.7 Basic operations on objects -- 1.8 Basic statistics of the dataset -- 1.9 Basic visualisations -- 1.9.1 Scatterplot and line chart -- 1.9.2 Column chart -- 1.9.3 Pie chart -- 1.9.4 Boxplot -- 1.10 Regression in examples -- 2 Data, spatial classes and basic graphics -- 2.1 Loading and basic operations on spatial vector data -- 2.2 Creating, checking and converting spatial classes -- 2.3 Selected colour palettes -- 2.4 Basic contour maps with a colour layer -- Scheme 1 - with colorRampPalette() from the grDevices:: package -- Scheme 2 - with choropleth() from the GISTools:: package -- Scheme 3 - with findInterval() from the base:: package -- Scheme 4 - with findColours() from the classInt:: package -- Scheme 5 - with spplot() from the sp:: package -- 2.5 Basic operations and graphs for point data -- Scheme 1 - with points() from the graphics:: package - locations only -- Scheme 2 - with spplot() from the sp:: package - locations and values -- Scheme 3 - with findInterval() from the base:: package - locations, values, different size of symbols -- 2.6 Basic operations on rasters -- 2.7 Basic operations on grids -- 2.8 Spatial geometries -- 3 Spatial data with Web APIs -- 3.1 What is an application programming interface (API)? -- 3.2 Creating background maps with use of an application programming interface
- 6.5 Robust regression in the geographically weighted regression model -- 6.6 Geographically and temporally weighted regression -- 7 Spatial unsupervised learning -- 7.1 Clustering of spatial points with k-means, PAM (partitioning around medoids) and CLARA (clustering large applications) algorithms -- ### Example ### -- ### Example ### -- 7.2 Clustering with the density-based spatial clustering of applications with noise algorithm -- ### Example ### -- 7.3 Spatial principal component analysis -- ### Example ### -- 7.4 Spatial drift -- ### Example ### -- 7.5 Spatial hierarchical clustering -- ### Example ### -- ### Example ### -- 7.6 Spatial oblique decision tree -- ### Example ### -- 8 Spatial point pattern analysis and spatial interpolation -- 8.1 Introduction and main definitions -- 8.1.1 Dataset -- 8.1.2 Creation of window and point pattern -- 8.1.3 Marks -- 8.1.4 Covariates -- ### Example ### -- 8.1.5 Duplicated points -- 8.1.6 Projection and rescaling -- 8.2 Intensity-based analysis of unmarked point pattern -- 8.2.1 Quadrat test -- 8.2.2 Tests with spatial covariates -- 8.3 Distance-based analysis of the unmarked point pattern -- 8.3.1 Distance-based measures -- 8.3.1.1 Ripley's K function -- 8.3.1.2 F function -- 8.3.1.3 G function -- 8.3.1.4 J function -- 8.3.1.5 Distance-based complete spatial randomness tests -- 8.3.2 Monte Carlo tests -- 8.3.3 Envelopes -- 8.3.4 Non-graphical tests -- 8.4 Selection and estimation of a proper model for unmarked point pattern -- 8.4.1 Theoretical note -- 8.4.2 Choice of parameters -- 8.4.3 Estimation and results -- 8.4.4 Conclusions -- 8.5 Intensity-based analysis of marked point pattern -- 8.5.1 Segregation test -- 8.6 Correlation and spacing analysis of the marked point pattern -- 8.6.1 Analysis under assumption of stationarity -- 8.6.1.1 K function variations for multitype pattern
- 5.1 Added value from spatial modelling and classes of models -- 5.2 Basic cross-sectional models -- 5.2.1 Estimation -- ### Example ### -- 5.2.2 Quality assessment of spatial models -- 5.2.2.1 Information criteria and pseudo-R2 in assessing model fit -- 5.2.2.2 Test for heteroscedasticity of model residuals -- 5.2.2.3 Residual autocorrelation tests -- 5.2.2.4 Lagrange multiplier tests for model type selection -- 5.2.2.5 Likelihood ratio and Wald tests for model restrictions -- 5.2.3 Selection of spatial weights matrix and modelling of diffusion strength -- 5.2.4 Forecasts in spatial models -- 5.2.5 Causality -- 5.3 Selected specifications of cross-sectional spatial models -- 5.3.1 Unidirectional spatial interaction models -- 5.3.2 Cumulative models -- 5.3.3 Bootstrapped models for big data -- ### Example ### -- 5.3.4 Models for grid data -- ### Example ### -- 5.4 Spatial panel models -- ### Example### -- 6 Geographically weighted regression - modelling spatial heterogeneity -- 6.1 Geographically weighted regression -- 6.2 Basic estimation of geographically weighted regression model -- 6.2.1 Estimation of the reference ordinary least squares model -- 6.2.2 Choosing the optimal bandwidth for a dataset -- 6.2.3 Local geographically weighted statistics -- 6.2.4 Geographically weighted regression estimation -- 6.2.5 Basic diagnostic tests of the geographically weighted regression model -- 6.2.6 Testing the significance of parameters in geographically weighted regression -- 6.2.7 Selection of the optimal functional form of the model -- 6.2.8 Geographically weighted regression with heteroscedastic random error -- 6.3 The problem of collinearity in geographically weighted regression models -- 6.3.1 Diagnosing collinearity in geographically weighted regression -- 6.4 Mixed geographically weighted regression
- 3.3 Ways to visualise spatial data - maps for point and regional data -- Scheme 1 - with bubbleMap() from the RgoogleMaps:: package -- Scheme 2 - with ggmap() from the ggmap:: package -- Scheme 3 - with PlotOnStaticMap() from the RgoogleMaps:: package -- Scheme 4 - with RGoogleMaps:: GetMap() and conversion of staticMap into a raster -- 3.4 Spatial data in vector format - example of the OSM database -- 3.5 Access to non-spatial internet databases and resources via application programming interface - examples -- 3.6 Geocoding of data -- 4 Spatial weights matrix, distance measurement, tessellation, spatial statistics -- 4.1 Introduction to spatial data analysis -- 4.2 Spatial weights matrix -- 4.2.1 General framework for creating spatial weights matrices -- 4.2.2 Selection of a neighbourhood matrix -- 4.2.3 Neighbourhood matrices according to the contiguity criterion -- 4.2.4 Matrix of k nearest neighbours (knn) -- 4.2.5 Matrix based on distance criterion (neighbours in a radius of d km) -- 4.2.6 Inverse distance matrix -- 4.2.7 Summarising and editing spatial weights matrix -- 4.2.8 Spatial lags and higher-order neighbourhoods -- 4.2.9 Creating weights matrix based on group membership -- ### Example ### -- ### Example ### -- 4.3 Distance measurement and spatial aggregation -- ### Example ### -- 4.4 Tessellation -- 4.5 Spatial statistics -- 4.5.1 Global statistics -- 4.5.1.1 Global Moran's I statistics -- 4.5.1.2 Global Geary's C statistics -- 4.5.1.3 Join-count statistics -- 4.5.2 Local spatial autocorrelation statistics -- 4.5.2.2 Local Moran's I statistics (local indicator of spatial association) -- 4.5.2.3 Local Geary's C statistics -- 4.5.2.4 Local Getis-Ord Gi statistics -- 4.5.2.5 Local spatial heteroscedasticity -- 4.6 Spatial cross-correlations for two variables -- 4.7 Correlogram -- 5 Applied spatial econometrics