Libraries in New York Neighborhood

Maps, Visualization


Mapping the NYPL libraries across the five boroughs

In this increasingly digital era, physical libraries face a lot of challenges as the primary way to search for information has moved online. However, libraries have a huge social impact on local communities and are a way of reaching out to people across social strata. Keeping the huge social impact of libraries in mind the purpose of this lab report, was to map the Public Libraries located in New York City. This visualization aims to understand the distribution of NYPL across the five boroughs and analyze if they are more densely located in certain areas.


When I was trying to look for ways to represent this I came across the NYPL website and their locations page. It was very detailed and listed the locations of all the 88 libraries. However, there was no visualization available which made me feel that someone not as familiar with the city would not be able to easily understand or access the information. This made me more interested in creating this project.

The NYPL locations as currently shown on their website

Another source of inspiration was a google map representation of all the libraries in NYC by Shelved. This was very helpful and accessible since most people use google maps to navigate. It was also very detailed and categorized into the type of libraries.

NYPL by Shelved on Google Maps


  1. Gathering data – I have used two datasets for this visualization. The first is the dataset of the libraries provided by NYPL on NYC open data and the second is the neighbourhood map of NYC on NYC open data. I exported both of these in shapefile format.

2. Using Tableau to create the visualization


My initial vision was to create an info viz on the trees census of 2015. I reached midway through the process and was able to plot all my points on the map. However, the dataset was extremely large and consisted of around 900,000 rows of data. It kept lagging and was unable to save my progress.

Since I was facing difficulties in working with such a large dataset I decided to simplify the process and work with a smaller data set. The second dataset was that on the Libraries in NYC and had 11,036 rows. Tableau was able to process this more easily. Here is the process on that:

  1. Importing and linking the datasets to tableau – Both the spatial datasets were added and a realtionship was formed on the basis of a common value that was the borocode in this case. Once, I double clicked on the first dataset I got the option of creating an union. This was again done on the basis on borocode and I created an inner union.

2. The next step was to start creating the visualizations – This was the most challenging step of the process for me. I was able to create a map of the libraries and plot the point. On the other map, I was able to get the neighborhood boundary of NYC. However, I was unable to get both the maps to load together.

After trying all the options and asking my peers, I was able to figure out the solution. The way to correctly do this was to first load the latitude, longitude, and geometry of the first data set. Then I had to add a mask which was the geometry of the second dataset. I was immediately able to see both my datasets on one. After that was done I mainly interacted with the colors and the details of the dataset to make it more understandable. I colored by map in different colors according to the Boroname and made the data points in black so that they could stand out. The colors were chosen to be as distinct from each other as possible so that there is no confusion about the boundaries.


Mapping the NYPL libraries across the five boroughs

Here is the final result. The Map is vibrant and the neighborhood boundaries and boroughs are distinct. Upon hovering over the library points you can see the Name, Zipcode, System, URL, and Bin code. I was happy to see that the distribution of libraries seemed pretty even with the exception of Staten Island which did not have as my libraries as I would have expected. In manhattan, the distribution of libraries seemed denser due to the smaller area size. Overall, It seemed like each neighborhood had at least one library in most cases.


I enjoyed working on this project. At times it seemed really challenging and I faced roadblocks but I am happy that I was able to find the solutions. In this case study, I have tried to document my process in as much detail as possible as I felt that a lot of problems were not that difficult to solve but knowing the correct steps was really important. In terms of future scope, I would like to add more details like the categories or types of libraries and make my visualization more enriched.