Project 1
Women Tech: Women Yes
MTA Street Team Action Plan
Max Garber, Anna Bradley-Webb, Mitch Brinkman, Eric Larson
Project Description
Back StoryAn email from a potential client:
Alice, Lara and Omar -
It was great to meet with you and chat at the event where we recently met and had a nice chat. We’d love to take some next steps to see if working together is something that would make sense for both parties.
As we mentioned, we are interested in harnessing the power of data and analytics to optimize the effectiveness of our street team work, which is a significant portion of our fundraising efforts.
WomenTechWomenYes (WTWY) has an annual gala at the beginning of the summer each year. As we are new and inclusive organization, we try to do double duty with the gala both to fill our event space with individuals passionate about increasing the participation of women in technology, and to concurrently build awareness and reach.
To this end we place street teams at entrances to subway stations. The street teams collect email addresses and those who sign up are sent free tickets to our gala.
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.
The ball is in your court now—do you think this is something that would be feasible for your group? From there we can explore what kind of an engagement would make sense for all of us.
Best,
Karrine and Dahlia
WTWY International
Data:- MTA Data (Google it!)
- Additional data sources welcome!
- python and pandas
- visualizations via Matplotlib & seaborn
- Exploratory Data Analysis
- Group presentation (3-4 people per)
- slide presentation (6 minutes)
- visual and oral communication in group presentations
- organized project repository
We developed a plan to help WTWY reach as many people as possible during their street outreach. Further how to target people more likely to attend their gala and donate.
We started by finding the top ten stations by average daily ridership during May 2019.
Next we looked at the difference in ridership on the weekend vs weekday for our top 10 stations. We can use this to try and target station with more local New Yorkers instead of tourists.
We then looked the ridership at different time of day to determine the best time for street outreach. This information can also be used to determine if street outreach would be more effective at different station at different times.
Last we looked at different demographic data to help target relevant populations. We found stations where average daily ridership and higher density of women overlapped. We then added stations that were near technology hubs and universities.
