Basics of Spatial Data Analysis
This article is originally published at https://nowosad.github.io/
R has a long history of supporting spatial data analysis, including spatial data downloading, preprocessing, visualizing, and modeling. An essential step in this direction was, among other things, the creation of the sp package in 2005 and the raster package in 2010. Recently, however, some new packages have appeared which have significantly changed the work with spatial data in R; in particular, the sf package, which integrates spatial vector data analysis with the tidyverse. In tandem such developments, R’s spatial ecosystem is maturing and diversifying in multiple areas including data access (e.g., rworldmap, rnaturalearth, cshapes); visualization (e.g., rasterVis, leaflet, mapview, ggmap, ggplot2, cartogram, tmap); and modeling (e.g, spatial components in mlr and INLA).
Such diversity has benefits but can be overwhelming. This workshop will introduce participants to the spatial data analysis system in R. The focus will be on getting started, with demonstrations of key packages, spatial analysis, and making maps. Pointers to further materials will ensure that participants know where to get help and how to take confident next steps after the session.
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This article is originally published at https://nowosad.github.io/
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