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Spatial Data Science
With Applications in R



  • Available for pre-order on April 10, 2023. Item will ship after May 1, 2023
ISBN 9781138311183
May 1, 2023 Forthcoming by Chapman & Hall
352 Pages 84 Color & 14 B/W Illustrations

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Book Description

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, a number of major spatial data analysis errors should no longer be made because of lack of knowledge.

The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online from https://r-spatial.org/book/. The solutions to the exercises can be found here: https://edzer.github.io/sdsr_exercises/.

Table of Contents

Part 1. Spatial Data  1. Getting Started  2. Coordinates  3. Geometries  4. Spherical Geometries  5. Attributes and Support  6. Data Cubes  Part 2. R for Spatial Data Science  7. Introduction to sf and stars  8. Plotting spatial data  9. Large data and cloud native  Part 3. Models for Spatial Data  10. Statistical modelling of spatial data  11. Point Pattern Analysis  12. Spatial Interpolation  13. Multivariate and Spatiotemporal Geostatistics  14. Proximity and Areal Data  15. Measures of spatial autocorrelation  16. Spatial Regression  17. Spatial econometrics models  Appendix A. Older R Spatial Packages  Appendix B. R basics

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Author(s)

Biography

Edzer Pebesma is professor at the Institute for Geoinformatics of the University of Muenster, Germany, where he leads the spatiotemporal modelling lab. He co-initiated openEO, an open source software ecosystem around a language neutral API for analyzing very large data cubes and image collections.

Roger Bivand is a geographer, emeritus professor of the Department of Economics of the Norwegian School of Economics, Bergen, Norway, has worked with spatial autocorrelation since the 1970’s, and is a Fellow of the Spatial Econometrics Association.

Edzer and Roger have actively interacted with the open source geospatial user and developer communities since the last century. They author and maintain a number of key R packages for the handling and analysis of spatial and spatiotemporal data, including sf, stars, s2, sp, and gstat, spdep, spatialreg and rgrass. Both are ordinary members of the R foundation.

Reviews

“I think that this is an important book. I am convinced it will be seen as a reference for scientists working with spatial data in R but also as a textbook for scientists and postgraduate students who are learning the concepts and how to do it practically in R (admittedly at a very advanced level!). It has certainly be on the shelf of everyone working with and teaching spatial data in R.”
-Hanna Meyer, Institute of Landscape Ecology, University of Münster, Germany