Table of Contents
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
- 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|
|10:45||Vector Operations in R
Lead Poisoning in Syracuse
|1:30||Raster Operations in R
Land Change Modelling
|3:45||Practical & Questions||Benoit|
|5:00||Reception (informal with snacks and tasty beverages)|
Wildfire in Alaska
|10:45||Remote Sensing & Classification
|1:30||Remote Sensing & Classification
|2:30||Practical & Questions||Benoit|
|3:45 pm||Intersections, Zonal Statistics, and Distance
Conservation Suitability in Florida
|Wednesday||9:00||PyQGIS with PostGIS
|10:45 am||Geovisualization with Leaflet
National Land Cover Dataset
|1:30||Pipelines for Online Data
USGS FEWS NET Data Portal
|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.
brew install git
apt-get install git
brew install ror
brew cask install r-app
apt-get install r-base
RStudio (free version)
brew cask install rstudio
brew install python3
apt-get install python3
brew install postgresqlor
brew cask install postgres
apt-get install postgresql
brew install postgis
apt-get install postgis(ppaubuntugis/ubuntugis-unstable)
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
http://cran.r-project.org/package=%package%. Bold packages are red hot.
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!
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.