{"product_id":"spatial-data-science-with-applications-in-r-hardcover","title":"Spatial Data Science: With Applications in R - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eEdzer Pebesma\u003c\/b\u003e (Author), \u003cb\u003eRoger Bivand\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpatial Data Science\u003c\/em\u003e 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, the reader will be well equipped to avoid a number of major spatial data analysis errors.\u003c\/p\u003e\u003cp\u003eThe book gives a detailed explanation of the core spatial software packages for R: \u003cb\u003esf\u003c\/b\u003e for simple feature access, and \u003cb\u003estars\u003c\/b\u003e 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 \u003cb\u003esf\u003c\/b\u003e and \u003cb\u003estars\u003c\/b\u003e 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. The data used and all code examples are freely available online from https: \/\/r-spatial.org\/book\/. The solutions to the exercises can be found here: https: \/\/edzer.github.io\/sdsr_exercises\/. \u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eEdzer Pebesma\u003c\/strong\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRoger Bivand\u003c\/strong\u003e 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.\u003c\/p\u003e\u003cp\u003eEdzer 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 \u003cb\u003esf\u003c\/b\u003e, \u003cb\u003estars\u003c\/b\u003e, \u003cb\u003es2\u003c\/b\u003e, \u003cb\u003esp\u003c\/b\u003e, and \u003cb\u003egstat\u003c\/b\u003e, \u003cb\u003espdep\u003c\/b\u003e, \u003cb\u003espatialreg\u003c\/b\u003e and \u003cb\u003ergrass\u003c\/b\u003e. Both are ordinary members of the R foundation.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 300\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.75 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 10, 2023\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47336703262969,"sku":"9781138311183","price":179.8,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/NC95RUMyWFpacTl1NWVDaHlSUXk1UT09.webp?v=1769670836","url":"https:\/\/bookscloud.io\/products\/spatial-data-science-with-applications-in-r-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}