Progress in the R ecosystem for representing and handling spatial data
Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling th...
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| creator | Bivand, Roger S |
| description | Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317,
2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match
between the then nascent open-source R programming language and environment
and the needs of researchers analysing spatial data. Recalling the development of
classes for spatial data presented in book form in Bivand et al. (Applied spatial data
analysis with R. Springer, New York, 2008, Applied spatial data analysis with R,
2nd edn. Springer, New York, 2013), it is important to present the progress now
occurring in representation of spatial data, and possible consequences for spatial
data handling and the statistical analysis of spatial data. Beyond this, it is imperative
to discuss the relationships between R-spatial software and the larger open-source
geospatial software community on whose work R packages crucially depend |
| format | Article |
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2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match
between the then nascent open-source R programming language and environment
and the needs of researchers analysing spatial data. Recalling the development of
classes for spatial data presented in book form in Bivand et al. (Applied spatial data
analysis with R. Springer, New York, 2008, Applied spatial data analysis with R,
2nd edn. Springer, New York, 2013), it is important to present the progress now
occurring in representation of spatial data, and possible consequences for spatial
data handling and the statistical analysis of spatial data. Beyond this, it is imperative
to discuss the relationships between R-spatial software and the larger open-source
geospatial software community on whose work R packages crucially depend</description><language>eng</language><subject>open-source software ; R programming language ; spatial data analysis</subject><creationdate>2020</creationdate><rights>info:eu-repo/semantics/openAccess</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,780,885,26567</link.rule.ids><linktorsrc>$$Uhttp://hdl.handle.net/11250/2720730$$EView_record_in_NORA$$FView_record_in_$$GNORA$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Bivand, Roger S</creatorcontrib><title>Progress in the R ecosystem for representing and handling spatial data</title><description>Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317,
2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match
between the then nascent open-source R programming language and environment
and the needs of researchers analysing spatial data. Recalling the development of
classes for spatial data presented in book form in Bivand et al. (Applied spatial data
analysis with R. Springer, New York, 2008, Applied spatial data analysis with R,
2nd edn. Springer, New York, 2013), it is important to present the progress now
occurring in representation of spatial data, and possible consequences for spatial
data handling and the statistical analysis of spatial data. Beyond this, it is imperative
to discuss the relationships between R-spatial software and the larger open-source
geospatial software community on whose work R packages crucially depend</description><subject>open-source software</subject><subject>R programming language</subject><subject>spatial data analysis</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>3HK</sourceid><recordid>eNrjZHALKMpPL0otLlbIzFMoyUhVCFJITc4vriwuSc1VSMsvUihKLQBKp-aVZOalKyTmpShkAIkcEKe4ILEkMzFHISWxJJGHgTUtMac4lRdKczMourmGOHvoJhdlFgO1xuflFyXGGxoamRrEG5kbGZgbGxgTowYADUAy-w</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Bivand, Roger S</creator><scope>3HK</scope></search><sort><creationdate>2020</creationdate><title>Progress in the R ecosystem for representing and handling spatial data</title><author>Bivand, Roger S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-cristin_nora_11250_27207303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>open-source software</topic><topic>R programming language</topic><topic>spatial data analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Bivand, Roger S</creatorcontrib><collection>NORA - Norwegian Open Research Archives</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bivand, Roger S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Progress in the R ecosystem for representing and handling spatial data</atitle><date>2020</date><risdate>2020</risdate><abstract>Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317,
2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match
between the then nascent open-source R programming language and environment
and the needs of researchers analysing spatial data. Recalling the development of
classes for spatial data presented in book form in Bivand et al. (Applied spatial data
analysis with R. Springer, New York, 2008, Applied spatial data analysis with R,
2nd edn. Springer, New York, 2013), it is important to present the progress now
occurring in representation of spatial data, and possible consequences for spatial
data handling and the statistical analysis of spatial data. Beyond this, it is imperative
to discuss the relationships between R-spatial software and the larger open-source
geospatial software community on whose work R packages crucially depend</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | open-source software R programming language spatial data analysis |
| title | Progress in the R ecosystem for representing and handling spatial data |
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