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: 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
ISBN: 9781003033219
9781000079784
9781000079760
9781000079746
9780367470777
9780367470760
Physical Description: 1 online zdroj.

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007 cr |||||||||||
008 200211s2020 enk ob 001 0 eng
040 |a OCoLC-P  |b eng  |e rda  |c OCoLC-P 
020 |a 9781003033219  |q (ebook) 
020 |a 9781000079784  |q (ePub ebook) 
020 |a 9781000079760  |q (Mobipocket ebook) 
020 |a 9781000079746  |q (electronic bk.) 
020 |z 9780367470777  |q (hardback) 
020 |z 9780367470760  |q (paperback) 
024 7 |a 10.4324/9781003033219  |2 doi 
035 |a (OCoLC)1141031009 
035 |a (OCoLC-P)1141031009 
245 0 0 |a Applied spatial statistics and econometrics :  |b data analysis in R /  |c Katarzyna Kopczewska, [editor]. 
264 1 |a Milton Park, Abingdon, Oxon ;  |a New York, NY :  |b Routledge,  |c [2020] 
300 |a 1 online zdroj. 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Routledge advanced texts in economics and finance 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a "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"--  |c Provided by publisher. 
505 0 |a <P><STRONG>Introduction</STRONG></P><P><STRONG>Statement by the American Statistical Association on statistical significance and p-value used in the book</STRONG></P><P><STRONG>Acknowledgments</STRONG></P><P>Chapter 1: Basic operations in the R software <STRONG>(Mateusz Kopyt)<BR></STRONG>1.1 About the R software<BR>1.2. The R software interface<BR>1.2.1 R Commander<BR>1.2.2. RStudio<BR>1.3 Using help<BR>1.4 Additional packages<BR>1.5 R Language -- basic features<BR>1.6 Defining and loading data<BR>1.7 Basic operations on objects<BR>1.8 Basic statistics of the data set<BR>1.9 Basic visualizations<BR>1.9.1 Scatterplot and line chart<BR>1.9.2 Column chart<BR>1.9.3 Pie chart<BR>1.9.4 Boxplot<BR>1.10 Regression in examples</P><P>Chapter 2: Spatial data, R classes and basic graphics <STRONG>(Katarzyna Kopczewska)</STRONG><BR>2.1 Loading and basic operations on spatial vector data<BR>2.2. Creating, checking and converting spatial classes<BR>2.3 Selected color palettes<BR>2.4 Basic contour maps with a color layer<BR>Scheme 1 -- with colorRampPalette() from the grDevices:: package<BR>Scheme 2 -- with choropleth() from the GISTools:: package<BR>Scheme 3 -- with findInterval() from the base:: package<BR>Scheme 4 -- with findColours() from the classInt:: package<BR>Scheme 5 -- with spplot() from the sp:: package<BR>2.5 Basic operations and graphs for point data<BR>Scheme 1 -- with points() from the graphics:: package -- locations only<BR>Scheme 2 -- with spplot() from the sp:: package -- locations and values<BR>Scheme 3 -- with findInterval() from the base:: package -- locations, values, different size of symbols<BR>2.6 Basic operations on rasters<BR>2.7 Basic operations on grids<BR>2.8 Spatial geometries</P><P>Chapter 3: Spatial data from the Web API <STRONG>(Mateusz Kopyt, Katarzyna Kopczewska)</STRONG><BR>3.1 What is the API?<BR>3.2. Creating contextual maps with use of API<BR>3.3 Ways to visualize spatial data -- maps for point and regional data<BR>Scheme 1 -- with bubbleMap() from the RgoogleMaps:: package<BR>Scheme 2 -- with ggmap() from the ggmap:: package<BR>Scheme 3 -- with PlotOnStaticMap() from the RgoogleMap:: package<BR>Scheme 4 -- with RGoogleMaps:: GetMap() and conversion of staticMap into a raster<BR>3.4 Spatial data in vector format -- example of the OSM database<BR>3.5 Access to non-spatial internet databases and resources via API -- examples<BR>3.6 Geo-coding of data</P><P>Chapter 4: Spatial weight matrices, distance measurement, tessellation, spatial statistics <STRONG>(Katarzyna Kopczewska, Maria Kubara)<BR></STRONG>4.1. Introduction to spatial data analysis<BR>4.2 Spatial weights matrix<BR>4.2.1 General framework for creating spatial weights matrices<BR>4.2.2 Selection of a neighborhood matrix<BR>4.2.3 Neighborhood matrices according to the contiguity criterion<BR>4.2.4 Matrix of k nearest neighbors (knn)<BR>4.2.5 Matrix based on distance criterion (neighbours in a radius of d km)<BR>4.2.6 Inverse distance matrix<BR>4.2.7 Summarizing and editing of spatial weights matrix<BR>4.2.8 Spatial lags and higher order neighborhood<BR>4.2.9 Creating weights matrix based on group membership<BR>4.3 Distance measurement and spatial aggregation<BR>4.4 Tessellation<BR>4.5 Spatial statistics<BR>4.5.1 Global statistics<BR>4.5.1.1 Global Moran I statistics<BR>4.5.1.2 Global Geary C statistics<BR>4.5.1.3 Join-count statistics<BR>4.5.2. Local spatial autocorrelation statistics<BR>4.5.2.1 Local Moran I statistics (LISA)<BR>4.5.2.2 Local Geary C statistics<BR>4.5.2.3 Local Getis-Ord Gi statistics<BR>4.5.2.4. Local spatial heteroscedasticity (LOSH)<BR>4.6 Spatial cross-correlations for two variables<BR>4.7 Correlogram</P><P>Chapter 5: Applied spatial econometrics <STRONG>(Katarzyna Kopczewska)</STRONG><BR>5.1 Value added from spatial modelling and classes of models<BR>5.2 Basic cross-sectional models<BR>5.2.1 Estimation<BR>5.2.2 Quality assessment of spatial models<BR>5.2.2.1 Information criteria and pseudo R2 in assessing model fit<BR>5.2.2.2 Test for heteroskedasticity of model residuals<BR>5.2.2.3 Residual autocorrelation tests<BR>5.2.2.4 LM tests for model type selection<BR>5.2.2.5 LR and Wald tests for model restrictions<BR>5.2.3 Selection of spatial weight matrix and modelling of diffusion strength<BR>5.2.4 Forecasts in spatial models<BR>5.2.5 Causality<BR>5.3 Selected specifications of cross-sectional spatial models<BR>5.3.1 Uni-directional spatial interaction models<BR>5.3.2 Cumulative models<BR>5.3.3 Bootstrapped models for big data<BR>5.3.4 Models for grid data<BR>5.4 Spatial panel models</P><P>Chapter 6: Geographically Weighted Regression -- modelling spatial heterogeneity <STRONG>(Piotr Ćwiakowski)</STRONG><BR>6.1 Geographically weighted regression<BR>6.2 Basic estimation of GWR model<BR> 6.2.1 Estimation of the reference OLS model<BR>6.2.2 Choosing the optimal bandwidth for a dataset<BR>6.2.3 Local geographically weighted statistics<BR>6.2.4 Geographically weighted regression estimation<BR>6.2.5 Basic diagnostic tests of the GWR model<BR>6.2.6 Testing the significance of parameters in GWR<BR>6.2.7 Selection of the optimal functional form of the model<BR>6.2.8 GWR with heteroskedastic random error<BR>6.3 The problem of collinearity in GWR models<BR>6.3.1 Diagnosing collinearity in GWR<BR>6.4. Mixed GWR<BR>6.5. Robust regression in the GWR model<BR>6.6. Geographically and Temporally Weighted Regression (GTWR)</P><P>Chapter 7: Unattended spatial learning <STRONG>(Katarzyna Kopczewska)</STRONG><BR>7.1 Clustering of spatial points with k-means, PAM and CLARA algorithms<BR>7.2 Clustering with the DBSCAN algorithm<BR>7.3 Spatial Principal Component Analysis<BR>7.4 Spatial Drift<BR>7.5 Spatial hierarchical clustering<BR>7.6 Spatial oblique decision tree</P><P>Chapter 8: Spatial point pattern analysis and spatial interpolation <STRONG>(Kateryna Zabarina)</STRONG><BR>8.1. Introduction and main definitions<BR>8.1.1. Dataset<BR>8.1.2. Creation of window and point pattern<BR>8.1.3. Marks<BR>8.1.4. Covariates<BR>8.1.5. Duplicated points<BR>8.1.6. Projection and rescaling<BR>8.2. Intensity-based analysis of unmarked point pattern<BR>8.2.1. Quadrat test<BR>8.2.2. Tests with spatial covariates<BR>8.3. Distance-based analysis of the unmarked point pattern<BR>8.3.1. Distance-based measures<BR>8.3.1.1. Ripley's K function<BR>8.3.1.2. F function<BR>8.3.1.3. G function<BR>8.3.1.4. J function<BR>8.3.1.5. Distance-based CSR tests<BR>8.3.2. Monte-Carlo tests<BR>8.3.3. Envelopes<BR>8.3.4. Non-graphical tests<BR>8.4. Selection and estimation of a proper model for unmarked point pattern<BR>8.4.1. Theoretical note<BR>8.4.2. Choice of parameters<BR>8.4.3. Estimation and results<BR>8.4.4. Conclusions<BR>8.5. Intensity-based analysis of marked point pattern<BR>8.5.1. Segregation test<BR>8.6. Correlation and spacing analysis of the marked point pattern<BR>8.6.1. Analysis under assumption of stationarity<BR>8.6.1.1. K function variations for multitype pattern<BR>8.6.1.2. Mark connection function<BR>8.6.1.3. Analysis of within and between types of dependence<BR>8.6.1.4. Randomisation test of components' independence<BR>8.6.2. Analysis under assumption of non-stationarity<BR>8.6.2.1. Inhomogeneous K function variations for multitype pattern<BR>8.7. Selection and estimation of a proper model for unmarked point pattern<BR>8.7.1. Theoretical note<BR>8.7.2. Choice of optimal radius<BR>8.7.3. Within-industry interaction radius <BR>8.7.4. Between-industry interaction radius<BR>8.7.5. Estimation and results<BR>8.7.6. Model with no between-industry interaction<BR>8.7.7. Model with all possible interactions<BR>8.8. Spatial interpolation methods -- kriging <BR>8.8.1. Basic definitions <BR>8.8.2. Description of chosen kriging methods <BR>8.8.3. Data preparation for the study<BR>8.8.4. Estimation and discussion</P><P>Chapter 9: Spatial Sampling and Bootstrap <STRONG>(Katarzyna Kopczewska, Piotr Ćwiakowski)</STRONG><BR>9.1 Spatial point data -- object classes and spatial aggregation<BR>9.2 Spatial sampling -- randomization / generation of new points on the surface<BR>9.3 Spatial sampling -- sampling of sub-samples from existing points<BR>9.3.1 Simple sampling<BR>9.3.2 The options of the sperrorest:: package<BR>9.3.3 Sampling points from areas determined by the k-means algorithm -- block bootstrap<BR>9.3.4 Sampling points from moving blocks (moving block bootstrap, MBB)<BR>9.4. 
588 |a OCLC-licensed vendor bibliographic record. 
650 0 |a Spatial analysis (Statistics) 
650 0 |a Econometrics. 
650 0 |a R (Computer program language) 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Kopczewska, Katarzyna,  |e editor. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://www.taylorfrancis.com/books/9781003033219  |y Plný text 
992 |c EBOOK-TN  |c TAYLOR 
993 |x NEPOSILAT  |y EIZ 
999 |c 113737  |d 113737 

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