Saturday 1:30 p.m.–2 p.m.

Room 203 #pyconjp_203

Geospatial data analysis and visualization in Python

Halfdan Rump

Audience level:
Useful libraries


In this talk I will introduce you to some very useful libraries for geospatial data visualization and analysis. I will show you how to create your own maps and how I solved the problems that I ran into. I will use data from 食べログ and SafeCast. If you are interested in data mining, visualization and, of course maps, then this talk is for you.


Interactive maps are great for exploring and getting a quick intuition of datasets containing location information. In this talk I will show that you don't have to be a data scientist or a JavaScript expert to create such maps. More concretely, I will give a quick intro to some great libraries and show you how to: use osmnx to download map data and convert it into a street graph use geopandas, networkx and shapely to manipulate street graphs and assign data points to areas use pyproj and geopy for changing between coordinate reference systems and measuring distances (I'll give you a short demonstration of how important this can be) use folium for creating beautiful and responsive maps that are rendered to HTML and JavaScript This covers the basic part of the talk, and I will then move into the second part, talking about some of the more difficult issues that I encountered while creating maps: how to deal with lack of geojson/shapefile boundary data for small areas how to deal with geospatial data that changes over time Apart from the tools mentioned above, I'll show you how networkx, scikit-learn, and good old plain Python can be used to solve these problems. 英語で発表しますが、質問は日本語で受け取ります。
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