Using the geospatial capabilities of R for an integrated data analysis workflow
Presentation Abstract
When most scientists and analysts have geospatial tasks to perform, they instinctively turn to traditional GIS software such as ESRI ArcGIS or open source tools such as QGIS or GRASS. These tools are powerful and well suited to their specialties. However, when working with spatial data it is rarely the case that the entire analytical workflow can be completed in a GIS environment alone: data must often be cleaned or restructured before being imported into the GIS, or statistical analysis or visualization is performed after spatial analysis is completed. Many analysts also use the statistical programming language R to perform this related data analysis work. Switching back and forth between GIS software and R breaks up the workflow both cognitively and practically, and reduces reproducibility of analyses. R is very well known for its advanced statistical capabilities, but it also has a suite of powerful and extensive spatial packages. These include functionality for basic GIS operations such as clipping, buffering, and spatial overlays, but also include advanced techniques such as point pattern analysis, kriging, and spatial regression, as well as excellent spatial visualization capabilities. I will give an overview of the spatial capabilities of R, how using R as a comprehensive data analysis environment is beneficial, as well as some challenges.
Session Title
Mapping and Data
Conference Track
Salish Sea Snapshots
Conference Name
Salish Sea Ecosystem Conference (2016 : Vancouver, B.C.)
Document Type
Event
Start Date
2016 12:00 AM
End Date
2016 12:00 AM
Location
2016SSEC
Type of Presentation
Snapshot
Genre/Form
conference proceedings; presentations (communicative events)
Contributing Repository
Digital content made available by University Archives, Heritage Resources, Western Libraries, Western Washington University.
Subjects – Topical (LCSH)
Geospatial data; Spatial analysis (Statistics)
Rights
This resource is displayed for educational purposes only and may be subject to U.S. and international copyright laws. For more information about rights or obtaining copies of this resource, please contact University Archives, Heritage Resources, Western Libraries, Western Washington University, Bellingham, WA 98225-9103, USA (360-650-7534; heritage.resources@wwu.edu) and refer to the collection name and identifier. Any materials cited must be attributed to the Salish Sea Ecosystem Conference Records, University Archives, Heritage Resources, Western Libraries, Western Washington University.
Type
Text
Language
English
Format
application/pdf
Using the geospatial capabilities of R for an integrated data analysis workflow
2016SSEC
When most scientists and analysts have geospatial tasks to perform, they instinctively turn to traditional GIS software such as ESRI ArcGIS or open source tools such as QGIS or GRASS. These tools are powerful and well suited to their specialties. However, when working with spatial data it is rarely the case that the entire analytical workflow can be completed in a GIS environment alone: data must often be cleaned or restructured before being imported into the GIS, or statistical analysis or visualization is performed after spatial analysis is completed. Many analysts also use the statistical programming language R to perform this related data analysis work. Switching back and forth between GIS software and R breaks up the workflow both cognitively and practically, and reduces reproducibility of analyses. R is very well known for its advanced statistical capabilities, but it also has a suite of powerful and extensive spatial packages. These include functionality for basic GIS operations such as clipping, buffering, and spatial overlays, but also include advanced techniques such as point pattern analysis, kriging, and spatial regression, as well as excellent spatial visualization capabilities. I will give an overview of the spatial capabilities of R, how using R as a comprehensive data analysis environment is beneficial, as well as some challenges.
