Table of Contents

General Information

This workshop introduces participants to open source tools for geospatial and temporal analysis of vector and raster data. The workshop will emphasize R packages and, to a lesser extent, Python libraries commonly used in GIS. Through lectures and hands-on computer labs, listed in the schedule below, SESYNC staff will aim to accelerate your adoption of computational resources for all phases of data-driven geospatial research.

Participants should expect to:

  • learn new scientific computing skills
  • overcome specific or conceptual project hurdles
  • gain coding confidence
  • have fun


  • Benoit Parmentier, Data Scientist
  • Ian Carroll, Data Scientist
  • Mary Shelly, Associate Director for Synthesis
  • Kelly Hondula, Quantitative Researcher and Computer Programmer


Monday, April 2, 2018 to Wednesday, April 4, 2018


1 Park Place, Suite 300
Annapolis, MD 21401

Get directions with OpenStreetMap or Google Maps.


Please email with any questions, including installation issues, or for information not covered here.


  • Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.).
  • After the course, participants must complete a reimbursement form to recover allowed travel expenses.


Sessions begin promptly at 9:00 am.

Nourishment will arrive at the 10:30 am coffee break, the on-site lunch provided by SESYNC at 12:30 pm, and an afternoon break. Trainees are responsible for their own breakfast and dinner arrangements (we can make recommendations).

Monday 9:00 am Introduction to SESYNC Mary
  9:15 am The Landscape of Open Source Geospatial Analysis Benoit
  10:30 Break  
  10:45 Vector Operations in R
Lead Poisoning in Syracuse
  12:30 pm Lunch  
  1:30 Raster Operations in R
Land Change Modelling
  3:30 Break  
  3:45 Practical & Questions Benoit
  5:00 Reception (informal with snacks and tasty beverages)  
Tuesday 9:00am Raster Time-Series
Wildfire in Alaska
  10:30 Break  
  10:45 Remote Sensing & Classification
Hurricane Inundation
  12:30 pm Lunch  
  1:30 Remote Sensing & Classification
  2:30 Practical & Questions Benoit
  3:30 Break  
  3:45 pm Intersections, Zonal Statistics, and Distance
Conservation Suitability in Florida
Wednesday 9:00 PyQGIS with PostGIS
Application TBD
  10:30 Break  
  10:45 am Geovisualization with Leaflet
National Land Cover Dataset
  12:30 pm Lunch  
  1:30 Pipelines for Online Data
  3:30 Break  
  3:45 pm Practical & Questions  


Each solftware listed below made some appearance in the workshop or is a generally useful component of the data science tool belt. Maintaining a functioning, up-to-date software environment is a big challenge! Consider this list a work-in-progress; we appreciate your suggestions for surmounting installation difficulties. An alternative to the list below is the Anaconda R/Python Distribution, the big-box store of data science.

For each item, you’ll find a link to a page with installation instructions, where available, or else to the downloadable installer. Windows users have little alternative to maintaining each software independently. MacOS users are encouraged to use Homebrew–the missing package manager for OS X–via the Terminal: we provide the relevant brew install <pkg> command, although the downlink links also provide .dmg installers. The third item is he package name that might work, for example, with apt-get install <pkg> on Ubuntu but YMMV.



RStudio (free version)

Python 3.x



R Packages

Install the following R packages after R and Rstudio are installed. Open RStudio and, for each package below, type install.packages(%package%) at the prompt and press return. For information on any package, navigate to Bold packages are red hot.

tidyr forecast readr ROCR
dplyr gstat modules rgeos
leaflet plyr rmarkdown RPostgreSQL
stringr lubridate randomForest sf
ggplot2 mapview raster shiny
data.table dbplyr rasterVis sphet
lme4 colorRamps rgdal spdep
xts zoo network caret
magick sp    

Python Packages

The following Python packages need to be installed Python. Open a shell/terminal and, for each package below, run pip3 install %package%. Bold packages are flying off the shelves!

geopandas requests sqlalchemy pydap
jupyterlab numpy pysal rasterio
beautifulsoup4 pygresql pandas  
requests lxml matplotlib  

After installing jupyterlab, run jupyter serverextension enable --py jupyterlab --sys-prefix in the shell/terminal to complete installation. JupyterLab runs through your browser, to launch it, enter jupyter lab in the shell/terminal, and stop it with Ctrl-C.


Portions of the instructional materials are adopted from Data Carpentry and Software Carpentry. The structure of the curriculum as well as the teaching style are informed by Software Carpentry.