Where have all the post offices gone? Discovering neighborhood facilities with Python and OSM

Friday 15:35 in Zeiss Plenary (Spectrum)

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.

Katie Richardson

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.