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Geocomputation with R

Bibliographic Details
Title: Geocomputation with R
Description: Geocomputation with R is for people who want to analyze, visualize, and model geographic data with open source software. The book provides a foundation for learning how to solve a wide range of geographic data analysis problems in a reproducible, and therefore scientifically sound and scalable way. The second edition features numerous updates, including the adoption of the high-performance terra package for all raster data processing, detailed coverage of the spherical geometry engine s2, updated information on coordinate reference systems and new content on openEO, STAC, COG, and gdalcubes. The data visualization chapter has been revamped around version 4 of the tmap package, providing a fresh perspective on creating publication-quality maps from the command line. The importance of the book is also highlighted in a new foreword by Edzer Pebesma.The book equips you with the knowledge and skills necessary to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. The book is especially well-suited to: Data scientists and engineers interested in upskilling to handle spatial data. People with existing geographic data skills interested in developing powerful geosolutions via code. Anyone who needs to work with spatial data in a reproducible and scalable way. The book is divided into three parts: Foundations, Extensions, and Applications, covering progressively more advanced topics. The exercises at the end of each chapter provide the necessary skills to address various geospatial problems, with solutions and supplementary materials available at r.geocompx.org/solutions/.
Authors: Robin Lovelace, Jakub Nowosad, Jannes Muenchow
Resource Type: eBook.
Subjects: R (Computer program language), Geographic information systems, Information storage and retrieval systems--Geography, Spatial analysis (Statistics)--Data processing
Categories: TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems, COMPUTERS / Data Science / Data Analytics
Database: eBook Index
FullText Text:
  Availability: 0
Header DbId: edsebk
DbLabel: eBook Index
An: 4136167
RelevancyScore: 981
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 981.043701171875
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  Data: Geocomputation with R
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  Data: Geocomputation with R is for people who want to analyze, visualize, and model geographic data with open source software. The book provides a foundation for learning how to solve a wide range of geographic data analysis problems in a reproducible, and therefore scientifically sound and scalable way. The second edition features numerous updates, including the adoption of the high-performance terra package for all raster data processing, detailed coverage of the spherical geometry engine s2, updated information on coordinate reference systems and new content on openEO, STAC, COG, and gdalcubes. The data visualization chapter has been revamped around version 4 of the tmap package, providing a fresh perspective on creating publication-quality maps from the command line. The importance of the book is also highlighted in a new foreword by Edzer Pebesma.The book equips you with the knowledge and skills necessary to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. The book is especially well-suited to: Data scientists and engineers interested in upskilling to handle spatial data. People with existing geographic data skills interested in developing powerful geosolutions via code. Anyone who needs to work with spatial data in a reproducible and scalable way. The book is divided into three parts: Foundations, Extensions, and Applications, covering progressively more advanced topics. The exercises at the end of each chapter provide the necessary skills to address various geospatial problems, with solutions and supplementary materials available at r.geocompx.org/solutions/.
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        Scheme: ddc
        Type: prePub
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      – Code: eng
        Text: English
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      – SubjectFull: Spatial analysis (Statistics)--Data processing
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            – D: 01
              M: 01
              Type: published
              Y: 2025
            – D: 28
              M: 04
              Type: profile
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