1. Your research question: Can we determine if noisy vehicles in different boroughs affect sleep patterns for the general populace?   

This question was inspired by the loud noises we as New Yorkers randomly hear in the middle of the night. It is annoying and worst of all, it can possibly affect our health. Whenever, we hear a loud bang or music playing in the middle of the night, i often wonder if it’s only me who wishes that we can inform or stop it.  

Your audience:

My audience will be aimed at the general populace who live in poorer areas in NYC. My hope is that this research can help answer or aid the question of sleeping pattern disturbances in NYC. It is a known fact that heart problems are rampant in poorer areas and irregular sleep patterns can be one of the key factors in the increase of these issues.

Here we can see that more calls are made right before late night.

There is almost an equal amount of noise complaints per borough except Staten Island.

The size of the bubbles is important here as there is more complaints of Car music rather than honking or loud noises.

An explanation of the data and design decisions you made:

My source is the 311 complaint data portal: https://data.cityofnewyork.us/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9/data 
The data is around 400-line items that were filtered to two categories. One being “Complaint Type: Noise – Vehicle” and the other is a time period from September 19, 2022 12:00AM until September 23, 2022 12:00 AM. While this dataset is extremely small for a proper study, we can see data in a randomize five-day workweek at the beginning of the school year. This dataset also includes columns that indicate borough, address and status of the complaint. 

The three types of visualizations are three of the common forms of visualizing massive amounts of data. The Line graph was an excellent choice because we can clearly see that most of the calls were at night. The Bar graph can easily show us that the amount of noise complaints were similar per borough except the outlier of Staten Island. The third and final graph was a colorful bubble graph, here we can see out of three complaints, which one was the most called for.

Next steps:

This visualization can bring forth a possible new avenue of questions to pinpoint time and place. With some tooltips, researchers can further this study with precise moments when a call is made and compare them to hospital visits nearby and or long-term hospital visits.