Applied spatial statistics and econometrics : data analysis in R /: data analysis in R. (2020)

• Record Type: Book
• Further Information: Note: Katarzyna Kopczewska.
• Authors: Kopczewska, Katarzyna
• Contents: 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 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 examples Chapter 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 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 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 data Chapter 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 Correlogram Chapter 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 models Chapter 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 tree Chapter 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 discussion Chapter 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. The use of spatial sampling and bootstrap in cross-validation of models Chapter 10: Spatial Big Data (Piotr Wójcik) ; 10.1. Examples of big data usage; 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. Proc
• Edition: 1st
• Publisher Details: London : Routledge
• Publication Date: 2020
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 519.535; Spatial analysis (Statistics); Econometrics; R (Computer program language)
• Languages: English
• ISBNs: 9781000079784; 9781000079746; 9781000079760; 9781003033219
• Related ISBNs: 9780367470777; 9780367470760
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.