00000nam a2200000 i 4500 UP-8027390931311651940 Buklod 20250604083252.0 m |o d | cr ||||||||||| 250227s2023 flu o b 001 0 eng 2022052400 9780429459016 (ebook) 9781032473925 (paperback) 9781138311183 (hardback) 23043621 DSTC eng DLC DLC rda eng pcc QA 278.2 P43 2023 519.5/3502855133 23/eng20230331 Pebesma, Edzer J. author. Spatial data science with applications in R Edzer Pebesma, Roger Bivand. First edition. Boca Raton CRC Press, Taylor & Francis Group 2023 xiii, 300 p illustrations (some colored) 24 cm text txt rdacontent unmediated rdamedia volume cr rdacarrier Chapman & Hall/CRC Press the R series Includes bibliographical references and index. 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"-- Provided by publisher. Description based on print version record and CIP data provided by publisher. R (Computer program language) Spatial analysis (Statistics) Data processing. Bivand, Roger author. Print version Pebesma, Edzer J. Spatial data science First edition. Boca Raton, FL : CRC Press, 2023 9781138311183 (DLC) 2022052399. FO Printed UPD DSTC QA 278.2 P43 Book