eBook
Spatial Data Science : With Applications in R
| Τίτλος: | Spatial Data Science : With Applications in R |
|---|---|
| Περιγραφή: | 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, the reader will be well equipped to avoid a number of major spatial data analysis errors.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. 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/. |
| Συγγραφείς: | Edzer Pebesma, Roger Bivand |
| Resource Type: | eBook. |
| Θέματα: | R (Computer program language), Spatial analysis (Statistics)--Data processing |
| Categories: | TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems, COMPUTERS / Programming / General, MATHEMATICS / Probability & Statistics / General |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 3585103 RelevancyScore: 969 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 968.509704589844 |
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| Items | – Name: Title Label: Title Group: Ti Data: Spatial Data Science : With Applications in R – Name: Abstract Label: Description Group: Ab Data: 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, the reader will be well equipped to avoid a number of major spatial data analysis errors.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. 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/. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Edzer+Pebesma%22">Edzer Pebesma</searchLink><br /><searchLink fieldCode="AR" term="%22Roger+Bivand%22">Roger Bivand</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22R+%28Computer+program+language%29%22">R (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+analysis+%28Statistics%29--Data+processing%22">Spatial analysis (Statistics)--Data processing</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Remote+Sensing+%26+Geographic+Information+Systems%22">TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Programming+%2F+General%22">COMPUTERS / Programming / General</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3585103 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 519.53502855133 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: R (Computer program language) Type: general – SubjectFull: Spatial analysis (Statistics)--Data processing Type: general Titles: – TitleFull: Spatial Data Science : With Applications in R Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Edzer Pebesma – PersonEntity: Name: NameFull: Roger Bivand – PersonEntity: Name: NameFull: Edzer Pebesma – PersonEntity: Name: NameFull: Roger Bivand IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 – D: 21 M: 09 Type: profile Y: 2023 Identifiers: – Type: isbn-print Value: 9781032473925 – Type: isbn-print Value: 9781138311183 – Type: isbn-print Value: 9781041325970 – Type: isbn-electronic Value: 9780429459016 – Type: isbn-electronic Value: 9780429859434 – Type: isbn-electronic Value: 9780429859441 Titles: – TitleFull: Spatial Data Science : With Applications in R Type: main |
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