Applied spatial statistics and econometrics : data analysis in R / Katarzyna Kopczewska.

Format:

Book

Author/Creator:

Kopczewska, Katarzyna, author.

Series:

Routledge advanced texts in economics and finance.

Routledge advanced texts in economics and finance

Language:

English

Subjects (All):

Spatial analysis (Statistics).

Physical Description:

1 online resource (621 pages) : illustrations.

Edition:

1st ed.

Place of Publication:

London ; New York, New York : Routledge, [2021]

Summary:

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.

Contents:

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.

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 ###

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'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.

5.1 Added value 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 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

5.3.4 Models for grid data

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.

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

7.2 Clustering with the density-based spatial clustering of applications with noise algorithm

7.3 Spatial principal component analysis

7.4 Spatial drift

7.5 Spatial hierarchical clustering

7.6 Spatial oblique decision tree

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

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.

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

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.

Notes:

Description based on print version record.

ISBN:

1-000-07974-0