This is a companion discussion topic for the original entry at:
Spatial data analysis has a long history in R. Tidy approaches to this are rather recent. I will discuss the special properties of spatialdata, the challenges of different tidy approaches, the work done so far, and the work in progress. The simple features for R package (sf, on CRAN) has been developed with support from the R Consortium. It replaces sp, rgdal and rgeos, and provides dplyr compatibility. A follow-up project, spatiotemporal tidy arrays for R (stars), is under development and aims at dense, spatiotemporal arrays such as time series of simple features, raster data, raster time series, climate model prediction data, and remote sensing imagery. Both projects will be presented, with a focus on how they augment the tidyverse.
Materials: Tidy spatial data analysis
Edzer Pebesma - Professor of Geoinformatics
I lead the spatio-temporal modelling laboratory at the institute for geoinformatics. I hold a PhD in geosciences, and am interested in spatial statistics, environmental modelling, geoinformatics and GI Science, semantic technology for spatial analysis, optimizing environmental monitoring, but also in e-Science and reproducible research. I am one of the authors of Applied Spatial Data Analysis with R (second edition), am Co-Editor-in-Chief for the Journal of Statistical Software and for Computers & Geosciences, and associate editor for Spatial Statistics. I believe that research is useful in particular when it helps solving real-world problems.