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010 a| 2022052399;

020 a| 9781138311183q| (hardback);

020 a| 9781032473925q| (paperback);

020 z| 9780429459016q| (ebook);

035 a| (WaSeSS)ssj0002813910;

040 a| DLCb| engc| DLCd| WaSeSS;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| QA278.2b| .P367 2023;

082 00 a| 519.5/35028551332| 23/eng20230331;

100 1 a| Pebesma, Edzer J.?| UNAUTHORIZED;

245 10 a| Spatial data scienceh| [electronic resource] :b| with applications in R /c| Edzer Pebesma and Roger Bivand.;

250 a| First edition.;

260 a| Boca Raton, FL :b| CRC Press,c| 2023.;

263 a| 2304;

300 a| pages cm.;

490 0 a| Chapman & Hall/CRC Press the R series;

504 a| Includes bibliographical references and index.;

506 a| Available only to authorized users.;

520 a| "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"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Spatial analysis (Statistics)x| Data processing.=| ^A42876;

650 0 a| R (Computer program language)=| ^A523882;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Bivand, Roger.?| UNAUTHORIZED;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9780429459016;

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Spatial data science with applications in R / Edzer Pebesma and Roger Bivand.

Author/creator	Pebesma, Edzer J.
Other author	Bivand, Roger.
Format	Electronic
Edition	First edition.
Publication Info	Boca Raton, FL : CRC Press, 2023.
Description	pages cm.
Supplemental Content	Full text available from Taylor & Francis eBooks
Subjects	Spatial analysis (Statistics)--Data processing. R (Computer program language)

Series	Chapman & Hall/CRC Press the R series
Abstract	"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.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2022052399
ISBN	9781138311183 (hardback)
ISBN	9781032473925 (paperback)
ISBN	(ebook)

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Spatial data science with applications in R / Edzer Pebesma and Roger Bivand.

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