2025-04-25 –, Zeiss Plenary (Spectrum)
When it comes to open geographic data, OpenStreetMap is an awesome resource. Getting started and figuring out how to make the most out of the data available can be challenging.
Using a personal example: frustration at the apparent lack of post offices in my neighborhood, we'll walk through examples of how to parse, filter, process, and visualize geospatial data with Python.
At the end of this talk, you will know how to process geographic data from OpenStreetMap using Python and find out some surprising info that I learned while answering the question: Where have all the post offices gone?
Problem statement
Needing an international postcard stamp, I headed to my nearest post office only to find out that it was permanently closed, the latest closure among others in recent memory. Was this just in my neighborhood or was this happening all over the state? To answer these questions, I turned to open data and Python.
- What is OpenStreetMap?
How can we identify types of places, like post offices and districts, in OpenStreetMap?
- Types of data in OSM
- Tags
- Tools for diving into the data to get an idea of how it is structured and how to construct queries: Overpass API, overpass turbo
How can we access the raw OSM data and work with it in Python?
- How many post offices are there in each neighborhood? What about by area or population?
- Working with PBF files: parsing and filtering with the PyOsmium library
- Using GeoPandas to store the data in a GeoDataFrame and apply transformations
What are some tools for visualizing the data?
- How can we make an interactive plot of post offices in each neighborhood? What about other facilities and resources?
- Plot directly from a GeoDataFrame
- Interactive plotting
While this talk is aimed at those beginning with geographic data, it would be helpful to have some background knowledge about Python and data handling.
Novice
Expected audience expertise: Python:Intermediate
Katie Richardson is a Staff Data Scientist at Blue Yonder, where she currently works on demand forecasting. Before joining Blue Yonder, she was primarily focused on the domain of search, ranking, and recommendation. With a background in Anthropology and several years experience working with geographical data, she's passionate about exploring spatial inequalities using open data. In her free time, she's an avid tap dancer.