Research Question:
New York is a loud city, especially with a population of around 8 million individuals packed within five boroughs, noise is one of the hardest things to control. Chronic noise can cause various adverse health effects, including sleep disturbance, cardiovascular problems, and increase in stress and annoyance. When I looked over the 311 NYC data, I instantly got attracted towards the noise complaints data and made me dig further into the various kinds of noise complaints that it consisted of. Looking at the data from Public Health perspective, I quickly got inspired to make some kind of conclusion from the provided data. Also, I recently moved to this city and the constant noise coming from neighbors, streets and the airplanes/helicopters made me curious to look into different kinds of noise complaints that are being reported through 311 Service. This curiosity led me to my research question: What types of noise complaints and how many of those noise complaints are being reported in different boroughs? For this purpose I extracted a noise complaint data from July 2022 – Present because the data was huge and this would give us a idea of present time.
Targeted Audience:
Noise complaints can be a matter of concern for many people who is trying to move to the city after pandemic when everyone is going back to normal life/hybrid version. Therefore, the audience can be students or working individuals who are trying to find a borough with least number of noise complaints where their daily routine, work life and health does not get affected. This visualization will provide them with a brief description of what kinds of noise complaints are being reported in different boroughs and at what proportion.
Visualizations:
I created a dashboard that contains 4 pie charts, one map chart and one bar chart. On top of the dashboard, the first visualization is a geo map that represents the number of noise complaints that was received from each borough. I chose this chart because it gives us a general idea on what borough has received the most number of noise complaints, and map chart is very easy to interpret for any type of audience.
Next is a bar chart which is right beside the map chart. It represents the number of noise complaints in each borough in the form of bars. However, I also included the description (descriptor variable) of each type of noise complaints as a tooltip so that one can understand what 311 Service meant by each type of noise complaints. For example, 311 registers loud music/parties under commercial noise complaint, car/truck music under vehicle noise complaint, banging/pounding under residential complaints, and so on. This bar chart tries to show the types of noise complaints in a descriptive manner. What we can take away from this chart is that most of the complaints are regarding loud music/party.
At the bottom are the four pie charts that represents the various types of noise complaints and their proportion in regard to five boroughs. In real data, there are more types of noise complaints; however, I chose four types because firstly, the graphs were getting very messy and secondly, I chose these four types because according to me, they make more sense for my targeted audience. When we try to compare single variable in different regions, I find pie chart more helpful. These pie charts are the key visuals that tries to answer my research questions and tells us the various types of noise complains if different boroughs.
https://public.tableau.com/app/profile/disha.kanada/viz/Project1_New/Dashboard1?publish=yes
Limitation:
Since I was trying to explore different types of noise complaints in five boroughs, it was hard for me to extract data in one sheet. Therefore, I had to create four separate sheets for each type of complaints and then merge the visuals in one frame. Because of this I was unable to explore every types of noise complaints that were given in the datasets. However, I feel that I have tried to cover all the major types that can affect the targeted audience.
Conclusion:
I feel that my research question restricted me to explore the datasets and the major reason for that could be my lack of skill, but in future I would like to explore the types of noise complaints in the form of bar chart or tree chart, instead of just pie charts. I think bar charts might be easy to interpret for any individual who do not have any knowledge about the visualizations. Also, in future, if I am not restricted by my research question, I would like to explore the open/closed noise complaints which would illustrate how many noise complaints were successfully resolved.
Tableau Link:
