streetteam-mta-analysis

Optimize a tech non-profit's street team placements by determining the ideal NYC subway station locations at which to deploy. Recommendations based on analysis of NYC MTA turnstile traffic data and philanthropic contributions by zip code.

For more information, see my blog post.

in this repo

datamunge.py parses MTA turnstile data and returns total traffic counts for each station in the given time period
analysis.py integrates philanthropic data and determines optimal stations; returns top stations charts
data/ contains list of MTA data files used in datamunge.py
contributions.csv contains contributions data by zip and station name for top stations
presentation/ contains pdf presentation of findings & recommendations

installation

clone this repo

$ git clone https://github.com/dianalam/streetteam-mta-analysis.git

dependencies

Scripts were written in Python 2.7. You'll need the following modules:

matplotlib >= 1.5.1  
numpy >= 1.10.1  
pandas >= 0.17.1  
python-dateutil >= 2.4.2

To install modules, run:

$ pip install <module>

running

# parse data
$ python datamunge.py

# run analysis
$ python analysis.py

Note that repo comes with default MTA turnstile data for the period between April and May of 2015. To use data from a different time frame, download the .txt files from the MTA website and save in data/ directory. The script will run on all files in that directory.

To obtain additional contributions data, visit The Chronicle of Philanthropy and input zip code information for your station.

data sources

Thanks to: