Applied Spatial Statistics and Econometrics 1st Edition by Katarzyna Kopczewska ISBN 1003033210 9781003033219

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ISBN 10: 1003033210 
ISBN 13: 9781003033219
Author: Katarzyna Kopczewska

Applied Spatial Statistics and Econometrics 1st Table of contents:

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

  • 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

• 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

• 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

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Tags: Katarzyna Kopczewska, Spatial Statistics, Econometrics