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Spatial and Spatiotemporal Data Analysis in R

UseR! Workshop, Jul 9, 2019, Edzer Pebesma, Roger Bivand, Angela Li (helper)

This tutorial dives into some of the modern spatial and spatiotemporal analysis packages available in R. It will show how support, the spatial size of the area to which a data value refers, plays a role in spatial analysis, and how this is handled in R. It will show how package stars complements package sf for handling spatial time series, raster data, raster time series, and more complex multidimensional data such as dynamic origin-destination matrices. It will also show how stars handles out-of- memory datasets, with an example that uses Sentinel-2 satellite time series. This will be connected to analysing the data with packages that assume spatial processes as their modelling framework, including gstat, spdep, and R-INLA. Familiarity with package sf and the tidyverse will be helpful for taking this tutorial.

Workshop program

  • 14:00-15:30: Introduction, spatial, spatio-temporal data, data cubes + exercises (Edzer); materials, solutions for exercises
  • 15:30-16:00: Break and time for questions
  • 16:00-17:30: Spatial modelling, spatial weights, spatial regression + exercises (Roger); materials, R script

Packages used in this workshop

Part 1

  • sf
  • stars
  • gstat
  • units
  • tidyverse
  • xts
  • viridis
  • abind

Part 2 (new packages only)

  • spatstat
  • spdep
  • tmap
  • spatialreg
  • igraph
  • hglm
  • metafor
  • sp
  • spData
  • RANN

If you are having trouble installing these packages, you may need to update their geospatial library dependencies (GDAL, GEOS, PROJ.4, or UDUNITS). Please see this guide from Data Carpentry for more information on how to install these dependencies.

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