<commit_before>---
layout: post
title: Project Benson
---
## Analyzing New York MTA subway turnstile data
The fictional group, WomenTechWomenYes (WTWY), wants to leverage data to optimize recruitment efforts at subway stations for an upcoming event. In this project my team used publicly available MTA turnstile data to recommend the best way to deploy recruitment/promotion teams.

### Initial Assumptions
- For total turnstile traffic we considered a sum of entries and exits.
- We assumed the gala is being held early in June; thus we used subway data for the 4 weeks of May, 2016.
- We decided to focus the target demographi on women working in the tech industry.
- We further assumed the target demographic will use subway stations near their place of residence; we were not considering major commuter stations as commuters would probably be less likely to attend a Gala in the city.

### Demographics Data
To locate areas with a high density of women residents who work in tech industries, we used public data from the American Community Survey at [Census.gov](http://factfinder.census.gov).
![Census screenshot](https://github.com/ptpro3/ptpro3.github.io/blob/master/images/census_dataV2.png)
For a detailed view of population density we used data count by Census tract, a small subdivision used for Census data, for each of the 5 boroughs/counties of New York City. The Census website has the ability to create a "heatmap" using the requested data, but this functionality was unavailable at the time of this posting; I'm planning to try again and update this page in the future.  

We joined the population data to a list of subway stations, using their corresponding Census tract location from data on ArcGIS.
```
stations = pd.read_csv('subway_station_tracts.csv')
stem_women = pd.read_csv('stem_women.csv')
df = pd.merge(stations, stem_women, on='BoroCT2010')
```
Here is the list of stations with resident count for women in tech occupations > 100.
![Census screenshot](https://github.com/ptpro3/ptpro3.github.io/blob/master/images/stations_gt_100.png)
<commit_msg>Update 2017-1-22-Project-Benson.md<commit_after>---
layout: post
title: Project Benson
---
## Analyzing New York MTA subway turnstile data
The fictional group, WomenTechWomenYes (WTWY), wants to leverage data to optimize recruitment efforts at subway stations for an upcoming event. In this project my team used publicly available MTA turnstile data to recommend the best way to deploy recruitment/promotion teams.

### Initial Assumptions
- For total turnstile traffic we considered a sum of entries and exits.
- We assumed the gala is being held early in June; thus we used subway data for the 4 weeks of May, 2016.
- We decided to focus the target demographi on women working in the tech industry.
- We further assumed the target demographic will use subway stations near their place of residence; we were not considering major commuter stations as commuters would probably be less likely to attend a Gala in the city.

### Demographics Data
To locate areas with a high density of women residents who work in tech industries, we used public data from the American Community Survey at [Census.gov](http://factfinder.census.gov).
![Census screenshot](https://github.com/ptpro3/ptpro3.github.io/blob/master/images/census_dataV2.png)
For a detailed view of population density we used data count by Census tract, a small subdivision used for Census data, for each of the 5 boroughs/counties of New York City. The Census website has the ability to create a "heatmap" using the requested data, but this functionality was unavailable at the time of this posting; I'm planning to try again and update this page in the future.  

We joined the population data to a list of subway stations, using their corresponding Census tract location from data on ArcGIS.
```
stations = pd.read_csv('subway_station_tracts.csv')
stem_women = pd.read_csv('stem_women.csv')
df = pd.merge(stations, stem_women, on='BoroCT2010')
```
Here is the list of stations with resident count for women in tech occupations > 100.
![Stations with count](https://github.com/ptpro3/ptpro3.github.io/blob/master/images/stations_gt_100.png)

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