As a New Yorker as well as a student, I always fascinated by the ample amount of resources that I can receive from the public libraries. I usually love to take the public transit subway to help me get to any of the branches around the city. This study will focus specifically on NYC’s public library systems (New York Public Library, Brooklyn Public Library, and Queens Library), which include over 200 locations and its relationship with the subway entrances.
The visualization for this project was inspired by a radius tool that is easy to quickly run a proximity analysis on a segment of the location data. It’s useful for determining where certain points lie, how far one map point is from other points, and seeing how many points on map exist within various distance increments. The end result is beautiful, simple, and easy to understand.
While the work by Nicholas Felton also inspired me to go with a darker background.
Datasets occurred downloaded in the GeoJSON file format from NYC Open Data.
Visualization was created in Carto, a SaaS platform that enables location data analysis and visualization through Location Intelligence tools.
I started by importing my datasets to the Carto dashboard. I first selected the aggregation by points to evenly distribute the space. I adjusted the style of the library points by size (12) stroke (1) with the color red. Also, the subway points by size (6) stroke (0.5) with color purple in order to strengthen contrast. I copied the subway dataset to create a distance buffer with a radius of 0.5 mi and track 1 to provide the range radius. The stage 1 result indicated below. The bigger red dots represent the library location, and the smaller purple dots show the subway entrances. It is very interesting to see most of the library locations are aligned with the subway entrances, whereas in some areas like Queens and Staten Island, the branches are more dispersed without the reach of subway lines.
After a discussion with Prof. Sula, he suggested me to distinguish the library spots within 0.5 mi with one color and the rest of the spots with a different color. Therefore, the audience can easily see what library location can be reached by subway one block (0.5 mi) away in NYC. In order to achieve this, I added the second library layer with the aggregation of Intersect and Aggregate and selected the target layer as subway_buffer. For the visualization, I chose the point size as 12 and color green so that the selected dots can mask the original dots with a different color.
MAPPING NYC LIBRARY LOCATION WITH SUBWAY ENTRANCES – click here for link to interactive Carto visualization.
The visualization shows that both Manhattan and Brooklyn have most of the libraries within the 0.5 mi subway zone. However, on the upper side of Manhattan, Staten Island, and Queens, most libraries can not be reached by subway in one block. In general, we can see that the number of libraries branches in green has exceeded the number of branches in red, which means the library has a pretty good selection of locations. It is easy for citizens to get to the library spots with subway transportation with a relatively near distance. This map may help to inform public library administrators to take public transit when selecting the site for expansion or close certain branches with low popularity and not easily been reached.
Combining the subway station and library branches helped me understand the relationship through a different lens. The final result is fascinating to library patrons as well as to the city governer. Seeing the overlap between datasets makes the visualization more real and easy to understand at a glance.
To make this project further, I would include more than just libraries, but museums, historic sites, or monuments as well. Carto is also extremely beginner-friendly. But to take mapping tools into the next level, some SQL is essential to learn. Although I failed when trying to intersect library location with subway entrances using SQL during the lab, I was very fascinated by the power of what it can achieve and excited to learn in the future.