Earth Sciences and GIS


“Essentials of HPC for new users: A practical example using hydrological models”

Webinar (2023-Oct-31) by Marcos Cunha-Cordeiro & Befekadu Woldegiorgis

Researchers from a variety of disciplines in natural sciences are becoming increasingly interested in High-Performance Computing (HPC). However, factors such as lack of know-how and apparently steep learning curve can hinder new users from moving into HPC. Adopting a workflow to run jobs, monitor their status, and postprocess their outputs can be challenging for new users or for those who have limited experience. This webinar tries to alleviate this issue by illustrating common steps typically required to run jobs in HPC environments. Using a simple yet real hydrological modelling example, the demonstration consists of running a parallel FORTRAN application using R, as well as retrieving and summarizing its results in the Cedar Cluster. The presentation demonstrates a practical workflow that encompasses:

  • preparing submission scripts,
  • testing parallel-computation jobs before submission,
  • debugging parallel-computation jobs that return an error, and
  • monitoring single or multiple jobs running at a time.

  • PDF slides
  • Sample R code

“Advanced GIS mapping in R”

Webinar (2021-Feb-03) by Marie-Hélène Burle

In this webinar we show how to create inset maps, faceted maps, animated maps, interactive mapping applications, cartograms, raster maps, and bridge over from R to QGIS with packages sf, tmap, raster, leaflet, shiny, ggplot2, ggmap, grid, mapview, cartogram, and qgisprocess.

“Basics of GIS mapping in R”

Webinar (2020-Dec-09) by Marie-Hélène Burle

In this webinar, we introduce simple GIS tasks as well as map production in R using the packages sf and tmap.

“Geospatial analysis with HPC”

Webinar (2019-Dec-11) by Ian Percel

This webinar provides an introduction to basic tools of spatial data analysis and modelling that are available as open source libraries in Python. It consists of two parts: GeoPandas and the parallelization of spatial joins.

The first portion of the talk introduces the core API of GeoPandas and emphasizes how it can be used to link relational data structures to geometric objects that represent spatial subsets. This includes a brief introduction to loading data from geodatabases, mapping, aggregating, spatial filtering, and simple spatial joins. The second portion of the talk focuses on how to implement parallel versions of spatial join operations. This examines alternatives that have been developed in the data science community for providing more efficient handling of GEOS pointers and subclassing Dask DataFrames.