Connecting NYC Schools to Public Transportation


The topic that I chose for this Carto lab is ‘Connecting NYC Schools to Public Transportation’. The idea was very simple, map out different NYC schools in their districts and further into zones and connect them to the latest public transportation data (both Trains and buses). My analysis here was to focus on how well connected different districts and zones are to public transport determining their ease of access.


The primary software used to create this visualization is Carto. It is an open source platform that provides GIS and web mapping tools for display in a web browser. The company is positioned as a Location Intelligence platform due to tools with an aptitude for data analysis and visualization that do not require previous GIS or development experience.

In terms of data, it is sourced from According to Fast Company, is a social network geared toward helping data scientists connect and nerd out over collections of data. It’s an ambitious attempt to put lots of data in one place, while also creating a user experience that allows for the unanticipated glee of discovering of new data sets. I worked with 5 data sets in total –

  1. NYC School Districts
  2. NYC School Zones
  3. NYC Subway Stops
  4. NYC Subway Routes
  5. NYC Bus Routes


For my visualization I went through various geographic visualizations of New York City. I did not limit myself to only schools and subways but I looked at all kinds of creative visualizations to see how so much data can be put together in different maps. My favorite is Paula Scher’s, High Line, 2005 This gorgeous Viz shows the High Line and the neighborhoods around it. I love that the neighborhoods are put in blocks and their description is put in words without any additional icons and hence, the focus goes on the stretch of the High Line.


Another interactive Viz that I really liked is Dogs of NYC | Map which uses licensing data to map dog breeds and (more interestingly) dog names to New York. It’s a fascinating look at the dog population and their names.


Lastly, I absolutely the MTA NYC subway map and how easy it is to use and understand by everybody who uses the subway. Right from the colors of the subway lines to self-explanatory signage and subway stops, this map is extremely easy to read.



I started the process by looking for apt Datasets with geo data online and after I found them, I uploaded them to my CartoDB account and started my visualization. I chose a standard map template and after my data files were included, I edited the formatted and started with data analysis. Since I was really clear with what information I wanted to present, I did an analysis in those things. There was an initial problem with aligning datasets for ‘joining’ them in my analysis but I edited them separately in a CSV file. After connecting all my data, my Viz consisted of schools divided by districts and further into zones with an overlay of bus and subway routes with it.

Since I had a lot of data to display, the trick was making the correct and understandable legends and adding interactive labels to the Viz. I tried to add every number to the viz to avoid clicking but that just made the map cluttered, so the additional numbers, subway and bus route names etc., were added as labels (making the map interactive).  I also added widgets to directly compare the number of school districts by zones and the average number of students enrolled in each.

For color, I tried to highlight every different aspect (subway, buses, schools) in stark colors (yellow, blue, purple) to maintain clarity.



The visualization can be viewed here.

This whole Viz is trying to show NYC school districts connected by bus routes and subways. It also shows how many students are enrolled in each district. All the information in it is extremely detailed right from the zones to average number of students enrolled in them. The routes are as specific as what buses and trains lines go where. To display all of it in a meaningful way, as described in the methods section, this Visualization tries to be as clear as possible and show the data in form of labels, widgets and legends.

Further Directions

Overall, Carto was extremely fun to work with and so far my favorite data viz tool. One limitation that I had to face was the base map template. I wish there were options to limit the depth of the map because I essentially wanted to reduce the clutter, I wanted more control over it. Even in terms of dynamic sizing, I wish I had played around more with that. This map would’ve taken an interesting turn if the number schools in each district were marked because it would give me a chance to go deeper per district and come up with separate visualizations per neighborhoods or boroughs (depending on the intensity of the data).