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 showcase key topics including unsupervised learning, cau...

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Bibliographic Details
Other Authors	Kopczewska, Katarzyna (Editor)
Format	Electronic eBook
Language	English
Published	Milton Park, Abingdon, Oxon ; New York, NY : Routledge, [2020]
Series	Routledge advanced texts in economics and finance
Subjects	Spatial analysis (Statistics) Econometrics. R (Computer program language) elektronické knihy electronic books
Online Access	Full text
ISBN	9781003033219 9781000079784 9781000079760 9781000079746 9780367470777 9780367470760
Physical Description	1 online zdroj.

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Table of Contents:

IntroductionStatement by the American Statistical Association on statistical significance and p-value used in the bookAcknowledgmentsChapter 1: Basic operations in the R software (Mateusz Kopyt) 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 data set 1.9 Basic visualizations 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 examplesChapter 2: Spatial data, R classes and basic graphics (Katarzyna Kopczewska) 2.1 Loading and basic operations on spatial vector data 2.2. Creating, checking and converting spatial classes 2.3 Selected color palettes 2.4 Basic contour maps with a color 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 geometriesChapter 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 API 3.3 Ways to visualize 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 RgoogleMap:: 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 API
examples 3.6 Geo-coding of dataChapter 4: Spatial weight matrices, distance measurement, tessellation, spatial statistics (Katarzyna Kopczewska, Maria Kubara) 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 neighborhood matrix 4.2.3 Neighborhood matrices according to the contiguity criterion 4.2.4 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 matrix 4.2.7 Summarizing and editing of spatial weights matrix 4.2.8 Spatial lags and higher order neighborhood 4.2.9 Creating weights matrix based on group membership 4.3 Distance measurement and spatial aggregation 4.4 Tessellation 4.5 Spatial statistics 4.5.1 Global statistics 4.5.1.1 Global Moran I statistics 4.5.1.2 Global Geary C statistics 4.5.1.3 Join-count statistics 4.5.2. Local spatial autocorrelation statistics 4.5.2.1 Local Moran I statistics (LISA) 4.5.2.2 Local Geary C statistics 4.5.2.3 Local Getis-Ord Gi statistics 4.5.2.4. Local spatial heteroscedasticity (LOSH) 4.6 Spatial cross-correlations for two variables 4.7 CorrelogramChapter 5: Applied spatial econometrics (Katarzyna Kopczewska) 5.1 Value added from spatial modelling and classes of models 5.2 Basic cross-sectional models 5.2.1 Estimation 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 heteroskedasticity of model residuals 5.2.2.3 Residual autocorrelation tests 5.2.2.4 LM tests for model type selection 5.2.2.5 LR and Wald tests for model restrictions 5.2.3 Selection of spatial weight 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 Uni-directional spatial interaction models 5.3.2 Cumulative models 5.3.3 Bootstrapped models for big data 5.3.4 Models for grid data 5.4 Spatial panel modelsChapter 6: Geographically Weighted Regression
modelling spatial heterogeneity (Piotr Ćwiakowski) 6.1 Geographically weighted regression 6.2 Basic estimation of GWR model 6.2.1 Estimation of the reference OLS 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 GWR model 6.2.6 Testing the significance of parameters in GWR 6.2.7 Selection of the optimal functional form of the model 6.2.8 GWR with heteroskedastic random error 6.3 The problem of collinearity in GWR models 6.3.1 Diagnosing collinearity in GWR 6.4. Mixed GWR 6.5. Robust regression in the GWR model 6.6. 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 algorithms 7.2 Clustering with the DBSCAN algorithm 7.3 Spatial Principal Component Analysis 7.4 Spatial Drift 7.5 Spatial hierarchical clustering 7.6 Spatial oblique decision treeChapter 8: Spatial point pattern analysis and spatial interpolation (Kateryna Zabarina) 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 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 CSR 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 8.6.1.2. Mark connection function 8.6.1.3. Analysis of within and between types of 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 discussionChapter 9: Spatial Sampling and Bootstrap (Katarzyna Kopczewska, Piotr Ćwiakowski) 9.1 Spatial point data
object classes and spatial aggregation 9.2 Spatial sampling
randomization / 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, MBB) 9.4.

Applied spatial statistics and econometrics : data analysis in R

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