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Learning R for Geospatial Analysis

By : Michael Dorman
Book Image

Learning R for Geospatial Analysis

By: Michael Dorman

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Learning R for Geospatial Analysis
Michael Dorman
Dec, 2014 | 364 pages
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Table of Contents (18 chapters)
Learning R for Geospatial Analysis
Learning R for Geospatial Analysis
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
The R Environment
The R Environment
Installing R and using the command line
Coding with R beyond the command line
Evaluating expressions
Exploring the basic object types in R
Summary
2
Working with Vectors and Time Series
Working with Vectors and Time Series
Vectors – the basic data structures in R
Using functions with several parameters
Creating subsets of vectors
Dealing with missing values
Writing new functions
Working with dates and time series
Introducing graphical functions
Summary
3
Working with Tables
Working with Tables
Using the data.frame class to represent tabular data
Controlling code execution
Automated calculations using the apply family of functions
Inference from tables by joining, reshaping, and aggregating
Summary
4
Working with Rasters
Working with Rasters
Using the matrix and array classes
Data structures for rasters in the raster package
Subsetting rasters
Overlay and reclassification of rasters
Summary
5
Working with Points, Lines, and Polygons
Working with Points, Lines, and Polygons
Data structures for vector layers in R
Exploring vector layer properties and subsetting
Geometrical calculations on vector layers
Spatial relations between vector layers
Joining geometries with tabular data
Summary
6
Modifying Rasters and Analyzing Raster Time Series
Modifying Rasters and Analyzing Raster Time Series
Changing the spatial extent or resolution of rasters
Raster resampling and reprojection
Filtering and clumping
Topography-related calculations with elevation data
Aggregating spatio-temporal raster data
Summary
7
Combining Vector and Raster Datasets
Combining Vector and Raster Datasets
Creating rasters from vector layers
Creating vector layers from a raster
Extracting raster values based on vector layers
Summary
8
Spatial Interpolation of Point Data
Spatial Interpolation of Point Data
Spatially interpolating point data
Mapping the annual temperature in Spain
Summary
9
Advanced Visualization of Spatial Data
Advanced Visualization of Spatial Data
Plotting with ggplot2 and ggmap
Making 3D plots with lattice
Summary
External Datasets Used in Examples
External Datasets Used in Examples
Cited References
Cited References
Index
Index

Spatially interpolating point data

Spatial interpolation is the procedure by which the behavior of a certain phenomenon of interest is predicted in locations where it has not been measured. For this purpose, we need a spatial prediction model—a set of procedures to obtain the predicted values given the calibration data. The two types of calibration data usually encountered are:

  • Field measurements: Available for a limited set of locations (usually points), for example, meteorological data from stations in Spain

  • Covariates: Available for each location within the area of interest, for example, elevation data from Spain's DEM

The spatial prediction model of our choice is calibrated using the calibration data. This model can then be used to calculate the predicted level of the phenomenon of interest in any location (usually points). The two main types of spatial interpolation methods recognized are:

  • Deterministic model: In this model, model parameter values are arbitrarily determined

  • Statistical...

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