Data visualization_NYC complaint amount and subway entry

Lab Reports, Maps, Visualization


New York City has a very complex social construction. So the social contradictions and some crimes happen in NYC. Citizen’s Complaints Map reflects where the crimes and offenses happen. The high rate of complaints may happen in some certain locations. This visualization work shows the distribution of complaint amounts in NYC and the locations of NYC subway entrances. By viewing my map data visualization. We can see what the potential information will be found and what is the relationship between those two datasets.

Inspiration & Datasets

My inspiration is from a heat map about white collar crime risk zone. This map shows the risk of white collar crime by using different degrees of red color matrix. The matrix can not only show the crime risk rate of each area and also shows the highest rate area in a very obvious way. As we can see this map below, the highest crime risk areas are almost on the midtown.


I use the Hexbins matrix Aggregation method as the second layer to display the complaints amounts on the map. The higher color temperature reflects more complaints on that area, which is very intuitional for the audience to know the overall geographical distribution of complaints.


My datasets are all from NYC open data. The first one is NYC Subway Entrances map data and the second one is NYPD Complaints data, a dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) from 2006 to the end of last year (2017).

The tools I used to visualize those datasets is Carto, which is a cloud computing platform that provides GIS and web mapping tools for display in a web browser. This tool 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.

The map I generated is interactive. As the user, we can zoom in and out to see the overall and detailed map visualization. The subway name labels will pop up when the user clicks the blue points on the map. Also, this data map has two additional widgets to filter the information from the map. For example, we can only display the Queens borough’s information by clicking the QN bar on the “Borough” filter. The widgets can also help the user to display certain types of complaints by selecting the corresponding bars.


The factors that affect the number of complaints are various. The reason I choose subway entrance location map is that subway is very essential to NYC transport system and millions of people’s lives are connected to the subway system. By observing the regulation of neighborhood area’s complaints nearby the subway entrances will help me discover some useful insights.

In general, there are higher complaints amount nearby subway entrances areas. By filtering the description of the complaints, I found that petit larcenies always happen near the subway stations.

The screenshot from my map


I still think the data is the key whatever the format of the visualization. Even though our tool is so powerful to realize a very beautiful visualization work, the data I selected and the information I produced are the core. Carto is a very useful tool to presents the data on the map and accurate geographic information. When I found the data from some data resources, I cannot easily to find good topics with map data sets. So that’s the limitation of the map data visualization. Some datasets do have some rough location names, but they don’t have the exact longitude and latitude data.

So during this work, I learned about how to transfer data from location name to geographical code. The data I found may not have longitude and latitude location information but only have the rough location name, which needs me to use google sheet to create their longitude and latitude data by using Google Sheet’s Add-on. Although I didn’t use this dataset in my final project, I still learn this trick which is very helpful for my future works.