New York MTA data (Metropolitan Transportation Authority) is publicaly available and ripe for visualization/analysis. For our first project at Metis, we used this transportation data to solve a problem faced by some hypothetical company – for example, which stations should you place advertisements in to reach the largest number of people?

My team decided to search for regions in New York with high potential for real-estate development. We guessed that most people commute from residential regions into various, potentially distant business hubs in the morning (derp), and that we might find regions where development of new business parks would help reduce transit congestion and travel times for people far from any active business areas. To look for these regions, we overlayed transit activity on a map of New York (see above image)

This map shows transit activity during morning hours on weekdays. Each colored point represents a station. Blue corresponds to people primarily entering the station (on foot, through the turnstyles), and red to people primarily exiting the station. The size of the points are proportional to the total number of people entering and exiting the station. You can see that people commute from the outer areas (almost all blue) into Manhattan and a few other business “hot spots” (some of the red points may simply be transfer stations, however). In the evenings, people commute back home:

Notice that most of the previously blue stations in sparse regions turn more red.

This is nice for visualizing the flow of commuter traffic and might be useful in a preliminary search for where to place new business parks or residential areas.

Tablau was used to generate the plot. We used python pandas to clean the data and map coordinates to each station.