Spatial Data Science
With Applications in R
$77.35
- Paperback
300 pages
- Release Date
21 September 2026
Summary
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 la…
Book Details
| ISBN-13: | 9781032473925 |
|---|---|
| ISBN-10: | 1032473924 |
| Author: | Edzer Pebesma, Roger Bivand |
| Publisher: | Taylor & Francis Ltd |
| Imprint: | Chapman & Hall/CRC |
| Format: | Paperback |
| Number of Pages: | 300 |
| Release Date: | 21 September 2026 |
| Dimensions: | 156mm x 234mm |
| Series: | Chapman & Hall/CRC The R Series |
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Critics Review
“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
Edzer Pebesma
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.
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