tho it's very possible that the dataset misnamed a Subway or two. I filtered for the DOHMH restaurants doing business as "Subway": data.cityofnewyork.us/Health/DOHMH-New-York-City-Restaurant-Inspection-Results/43nn-pn8j

The LIC Subway between the Queens Plaza and Queensboro Plaza stations is there, but so close to both stations (and CBs are small there) that we only see a slit of purple! That's also the only Subway in LIC per Google Maps but I wonder if that one is also not up to date.

Of course! Glad you liked it

And if you think your work would be inspiring and interesting for this interdisciplinary audience, you might be a good guest speaker. We are still finalizing this semester's schedule, so send me a message!

We bring together PhD students, postdocs, faculty, and practitioners from many disciplines to focus on equity, access, and the role of data in shaping urban spaces. We have biweekly meetings with guest speakers, moderated paper discussions, and hands-on workshops.

Come join us!

You can request data access on our website! This is joint work with Rachel Young, Maria Fitzpatrick, @nkgarg.bsky.social, and @emmapierson.bsky.social.

Paper: arxiv.org/abs/2503.20989
Website: migrate.tech.cornell.edu
5/5

MIGRATE reveals trends (like dramatic rates of out-migration in response to California wildfires) that are entirely invisible in public data.

This case study only scratches the surface of what’s possible with this data–we’re excited to see what you do with it!

4/5

We produce MIGRATE by developing an iterative-proportional-fitting based algorithm to reconcile (1) granular but biased proprietary data and (2) coarser but more reliable Census data.

We comprehensively validate MIGRATE against external data sources.

3/5

MIGRATE is:

Thousands of times more granular than existing public migration datasets
Highly correlated with external Census datasets
Less biased, and more consistent with Census data, than proprietary address data

2/5