Tracking Rodent Population in an Urban Setting is rather impossible especially for a crowded place like New York City. However, rodent sighting might provide an insight about their presence in a particular area. The goal of this data visualization project is to provide insight of rodent complaints for the NYC tenants who are planning to move to a new neighborhood/borough.

311 complaints from January, 2022 to present has been used for the project. The choice on the subset of data has been made to illustrate at least a year long trend. Data has been filtered from the original 311 dataset with descriptor like rat sighting, Rodent sighting. This has been done since the dataset is simply too large to be handled by the machine I have used to produce the visualization. Also, I found out that Rodents/Insects/Garbage descriptor is mostly associated with food establishment. I am mostly interested about the residential establishments.

There are several visualizations that are embedded within the story map(found below), I have created for this project. The storymap contains three story points. At first point, to visualize the complaints, I have used a density map of complaints. This provides a heatmap like visualization of the zones where the most complains came from. I have particularly chosen “Temperature diverging” color transition for the map because green is good to go signal and red is not!!(Traffic signal analogy). This visualization allows the tenants to select by borough and search by zip code to see a location more closely.

In the second point, to provide a more detailed idea about a specific borough, the visualization shows rat sighting by borough within the period 22-23 in a pie and horizontal bar chart. Followed by monthly Rat sighting per borough over the period. The aim of this visualization as whole is to provide tenants the idea from where and when the complaints are being filed. This will helps the tenants to take decision.

In the final point, the story map contains a deep dive of count of complaints by borough, zip and street address. Tenants can search for a specific zip and filter borough to get a insight the total total number of complaints made from a specific address.

Findings and limitations:

1. Rats are literally everywhere!!
2. Brooklyn Files the most complaints. Does the tenants and owners from other boroughs are less willing to complain since filing complaint is not mandatory?
3. Most complaints in the summer. Rat season maybe? Or Outdoor/Party Season?
4. Selection bias might be introduced due to selection of specific data subset.

Open in tableau public.

Future:

Due to time constraints, I could not clean up the data properly. Also, since I am new to tableau, I do not have the idea to clean data with tableau efficiently. In future, I will take this dataset to a dataframe and clean with pandas.