Housing Proximity to Amenities in the US

Paper on how city design affects demographics and educational performance

With — Adam Omarali, Leo Liu, Navid Farkhondehpay, James Tian

Built with — Python, OpenAddresses, Geoapify, ArcGIS, LaTeX

Read Paper Here ↗

Housing Proximity to Amenities in the US

(picture unrelated, I just love Shanghai)

For Stem Fellowship's Big Data Challenge, my team and I analyzed proximity to ammenities and demographic data for a given address in the US. We won 1st place out of 160 teamsand $1000!

I led the data sampling part, where I used OpenAddresses to simple random sampling 163 counties from 1,936 available and stratified random sampling 1,630 unique addresses. This enabled the analysis of housing proximity to amenities with educational attainment and demographic relationships.

We then used the Geoapify API to get the distance to the nearest amenity, and used ArcGIS to get education and demographic data for analysis for each address sampled.

Here's my teammate Adam explaining our project in detail:

And here was our original pitch video: