Ever wondered if you could count how many people go through the subway every day?
Okay, probably not. But bear with me here. No code this time.
For our first project in the Metis Data Science Bootcamp, we were given a hypothetical data science project by a company. Our team was asked to use data to help a nonprofit. In an email from an organization created to advocate women in tech, we got our assignment. A quote:
Where we’d like to solicit your engagement is to use MTA subway data, which as I’m sure you know is available freely from the city, to help us optimize the placement of our street teams, such that we can gather the most signatures, ideally from those who will attend the gala and contribute to our cause.
1. The busiest turnstiles aren’t necessarily the best. We’re looking for a demographic here – young, progressive and interested in tech. Thousands of pissed off people at Penn Station won’t be any good. We crunched census, income and community data to identify the best neighborhoods.
2. Sometimes the data you’re given isn’t enough. We had to look for lots of extra resources beyond simple MTA turnstile data. Some of this helped us make the map below.
3. When you’re doing data science, make something useful. It’s easy to get lost in “We could do this…” and “But what if…” What if what your client actually cares about is something they can use, not all the stuff you discovered? Never forget your end goal.
And so, what we presented to the company is the below map. We selected five places for their street teams to hang out. The heat flashes show the busiest subway stops over the dates you can see in the bottom corner. Notice how they change throughout the week?
Next step is to plot hourly movements over a day.