# SciPy 2015: Geospatial Data Tutorial

written by Eric J. Ma on 2015-07-07

This morning, I attended the Geospatial Data tutorial. It wasn’t a filled lecture hall, but that was likely because the topic is a bit more specialized. That said, I think it was a tutorial with great content. Part of my own research work may eventually incorporate working with geospatial data - to predict influenza viral reassortment, in particular. Therefore, I was looking forward to learning more about the packages that are used to read and manipulate geospatial data. This tutorial provided the overview I was looking for.

## Tutorial Content

In this tutorial, we were taught how to inspect and manipulate geospatial data in a Pythonic fashion.

The first thing we learned was how to inspect geospatial data using the fiona package. Most important point I learned was that geospatial data is usually stored as a GeoJSON format.

The second thing we learned was the shapely package, which allowed us to draw arbitrary shapes and perform set operations on them. This one went smoothly, and I found the trivial examples provided to actually be quite instructive and informative.

The third thing we learned was rasterio, where we learned how to load raster images of geographic regions, and combine them with their geographic metadata.

The final thing that I picked up was geopandas. Arguably the easiest portion to follow, I was also pleasantly surprised as to how many common/intuitive operations that I could think of were also represented in the API.

## Tutorial Pace

I hit a snag using the fiona and rasterio packages, so I eventually settled on following the tutorial on the projector screen instead.

Apart from that, it was the geopandas section that was the easiest to follow, as the API was very similar to the pandas API.

## Other Thoughts

Overall, I think this tutorial was a great overview of the packages that can be used to read and manipulate geospatial data. I can tell that our tutorial leader, Kelsey, placed quite a bit of effort in preparing the variety of data sets and examples. Perhaps a bit more environment testing prior to the tutorial may have helped us; I was getting tripped up quite a bit on fiona and rasterio installation. On a separate note, I’ve noticed that most of the installation or usage issues came because of libraries not being found/linked properly. That may be a burden for the package authors to address, rather than the tutorial leaders.