Edzer Pebesma, Roger Bivand

Spatial Data Science (eBook, PDF)

With Applications in R

• Format: PDF

Produktbeschreibung

Spatial Data Science is a book for data scientists with intermediate R 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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 314
• Erscheinungstermin: 10. Mai 2023
• Englisch
• ISBN-13: 9780429859441
• Artikelnr.: 67689728

Autorenporträt

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.

Inhaltsangabe

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