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010			_a 2022052399
020			_a9781138311183 _q(hardback)
020			_a9781032473925 _q(paperback)
020			_z9780429459016 _q(ebook)
040			_aDLC _beng _erda _cDLC _dZET-ke
042			_apcc
050	0	0	_aQA278.2 _b.P43 2023
082	0	0	_a519.5/3502855133 _223/eng20230331
100	1		_aPebesma, Edzer J., _eauthor. _927583
245	1	0	_aSpatial data science : _bwith applications in R / _cEdzer Pebesma and Roger Bivand.
250			_aFirst edition.
260			_aBoca Raton: _bCRC Press, Taylor & Francis Group, _c2023.
263			_a2304
300			_axiv, 300p.: _bill. (col.) _c23cm.
490	0		_aChapman & Hall/CRC Press the R series
504			_aIncludes bibliographical references and index.
505			_aPart 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
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"--
650		0	_aSpatial analysis (Statistics) _xData processing. _927584
650		0	_aR (Computer program language) _925979
700	1		_aBivand, Roger, _eauthor. _927585
776	0	8	_iOnline version: _aPebesma, Edzer. _tSpatial data science _bFirst edition. _dBoca Raton, FL : CRC Press, 2023 _z9780429459016 _w(DLC) 2022052400
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK _hQA278.2 _kQA278.2 _m.P43
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