Maximizing Hypothetical Outreach at MTA Subway Stations

Answering hypothetical client questions is never an easy task. Often times you aren’t working with enough information, and there is no one you can go to for clarification if you’re feeling like you might be headed in the wrong direction. However, they can also lead you in all sorts of interesting paths and spark learning in places you might not initially expect. This was the exact experience I had while exploring a hypothetical client question that centered around analyzing public New York City MTA subway data.

A team and I were tasked with identifying which subway stations in New York would be ideal for a hypothetical client “Women Tech Women Yes” (WTWY) should target in order to maximize their outreach for an upcoming gala they had. WTWY wanted to identify subway stations to send their street teams that would maximize the amount of sign ups they would receive for a free ticket to their gala while also maximizing donations to their cause. The New York City subway has over 400 stations, so a focused and data-driven outreach program was definitely the prudent move over trying to maximize man power.

The first and most important piece of data that we pulled in was the New York City turnstile data. The MTA provides public data for all of their stations that records the state of their entrance and exit turnstile counters every four hours. This data would allow us to identify stations with the most traffic. As the gala was being held in early summer we pulled data from March through June from the last 3 years to provide the most relevant data. We also removed nighttime data because street teams would not be deployed at those times.

However, we didn’t want just the stations with the most traffic. We also wanted stations with the most individuals passing through that would be likely to attend the Gala. That meant targeting native New Yorkers. Due to this we wanted to identify stations where there the disparity between the average amount of individuals moving through the stations on weekdays and weekends was largest. We then converted this to a ratio in order to ensure we weren’t ignoring stations with lower overall traffic. This lead to us identifying the least and most touristy stations in New York.

The results might seems a little counter-intuitive with big name stations like Grand Central and Penn showing up in this metric, however this makes sense when you think about it a little. These are the well-known stations for a reason. Many people pass through these stations on weekdays as they are in busy areas of the city and many of them also have multiple lines that converge there increasing traffic.

We also wanted to pull in other outside information to complement the work we did with the turnstile data. As WTWY was hypothetically an organization working towards furthering female involvement in the technology industry we wanted to identify stations that were in areas with commuters who would be involved in technology related fields. To that end we identified two areas in New York, The NYC Tech Triangle and Silicon Alley.

We geo-mapped the corners of those areas and identified stations that fell within those boundaries. We were left with a list of about 70 stations.

In order to combine all these factors we decided to create a ‘score’ for each station. The score gave weight to overall traffic any given station received on weekdays, the ratio of weekend to weekday traffic, and whether or not they were located in what we dubbed as the “Tech Zone.” This gave us our final rankings below.

To validate these results we pulled demographic data around our top 20 stations and compared them to Manhattan as a whole. The mean labor force participation around our selected stations was 78.4% compared to 68.15% for Manhattan as a whole, and the female labor force participation was 79.43% around our stations compared to 74.15% for Manhattan as whole. So our results were justified!

To improve this metric in the future, we decided we could also integrate:

• Metro card usage — (30 day unlimited cards)

• further integration of demographic data

• create location shifts at stations by entrance and exit data by time

What do y’all think? Any further improvements that can be made? What did you think of our methodology?

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