Spatial Data Science @ Berkeley May 2015

Bootcamp participants outside historic Mulford HallOur bootcamp on Spatial Data Science has concluded. We had three packed days learning about the concepts, tools and workflow associated with spatial databases, analysis and visualizations. 

Our goal was not to teach a specific suite of tools but rather to teach participants how to develop and refine repeatable and testable workflows for spatial data using common standard programming practices.

On Day 1 we focused on setting up a collaborative virtual data environment through virtual machines, spatial databases (PostgreSQL/PostGIS) with multi-user editing and versioning (GeoGig). We also talked about open data and open standards, and modern data formats and tools (GeoJSON, GDAL).

Analyzing spatial data is the best part! On Day 2 we focused on open analytical tools for spatial data. We focused on one particular class of spatial data analysis: pattern analysis, and used Python (i.e. PySAL, NumPy, PyCharm, iPython Notebook), and R Studio (i.e. raster, sp, maptools, rgdal, shiny) to look at spatial autocorrelation and spatial regression. 

Wait, visualizing spatial data is the best part! Day 3 was dedicated to the web stack, and visualization. We started with web mapping (web stack, HTML/CSS, JavaScript, Leaflet), and then focused on web-based visualizations (D3).  Web mapping is great, and as says: “Internet maps appear magical: portals into infinitely large, infinitely deep pools of data. But they aren't magical, they are built of a few standard pieces of technology, and the pieces can be re-arranged and sourced from different places.…Anyone can build an internet map."

All-in-all it was a great time spent with a collection of very interesting mapping professionals from around the country (and Haiti!). Thanks to everyone!