NYC311 is a service which allows New Yorkers to file complaints efficiently, and enables City agencies to better assist New Yorkers with their concerns. Since its induction in 2003, it is easier than ever to file a complaint, and as a result, New Yorkers are able to interact with City agencies in unprecedented ways.
As a native Brooklynite, I want to become active in my community, and be more proactive in instituting change for the greater good. NYC311 has empowered me to do both over the past couple of years. In addition, there is data available for NYC311 service requests from 2010 to present day, via the NYC Open Data Portal. It is important to note some complaint types have a descriptor, which provide further description of the incident. For instance, the complaint type residential noise has four descriptors: banging/pounding, loud music/party, loud television, and loud talking. When investigating the data, there is a plethora of complaint types to choose, but in light of recent events, I investigated residential noise complaints in Brooklyn, from January 1, 2019 to September 27, 2020.
In late September, my neighbors held a wedding in the hallway of our apartment building, with loud music and cheering, no social distancing or masks. Enraged, I contacted 311, and despite the fast response from the NYPD, I couldn’t help but wonder if any fellow Brooklynites had similar experiences. Therefore, I decided to channel my frustration into this project.
Tracking residential noise is crucial, especially in the age COVID. With the stay at home order this past spring, along with a select number of people working from home, more people are staying home than ever. It is important for the NYPD, tenant associations, building owners/managers and landlords to understand residential noise complaints so they can educate people on proper conduct, and direct resources to those in need. And for New Yorkers, it is helpful for them to know where the most residential noise occurs in Brooklyn, especially with so many people moving nowadays. The Mayor’s Office recently created MEND NYC, a free mediation service to alleviate disputes between bars, restaurants, and nightlife establishments, along with their neighbors. Similar services can be utilized with regards to residential noise complaints, with the proper data representation.
In my visualizations, I plan to answer the following questions: Did 2019 or 2020 have more residential noise complaints in Brooklyn? Which month had the most residential noise complaints? Which residential noise complaint type descriptor(s) had the most aggregated complaints from 2019-2020? What are the top 10 zip codes in Brooklyn with the most aggregated residential noise complaints from 2019-2020? In the top ten zip codes, what are the top residential noise complaint type descriptors?
To answer these questions, I decided to display four visualizations in a dashboard, in order to have continuity, especially for the last two charts. The first chart is a line graph of the total residential noise complaints in Brooklyn, categorized by month, for 2019 and 2020. Each line was assigned a different color in order to distinguish 2019 from 2020. As a result, I was able to answer which month and year had the most residential noise complaints. The second graph is a pie chart categorized by residential noise complaint descriptors. Each wedge illustrates the descriptor, along with its percentage of total residential noise complaints. This display allowed me to answer which type of residential noise complaints was most common from 2019 to 2020. The third graph is a bar chart of the top ten zip codes with the most residential noise complaints. Each bar is color coded according to year. In addition, I organized the bars in descending order to emphasize the disparity in some of the zip codes. As a result, the top zip code with complaints is on the extreme left. This proved to be useful for the last graph, which is a pie chart filtered by the top ten zip codes. The user can use the filter menu to filter by each zip code individually or collectively. In addition, each wedge displays the descriptor, along with its percentage of total residential noise complaints for that zip code(s). Therefore, I was able to answer which residential noise complaints are common in the ten zip codes with the highest number of complaints.
A dashboard is crucial for these visualizations because they are interconnected, and it may be difficult to understand the last pie chart if one can’t recall the penultimate graph. By placing the visualizations alongside each other, it is easier to see how they are interlaced. Ideally, viewers should read the visualizations following a “Z” shape, starting with the upper left-hand corner. Therefore, the audience can see the most general graph first, and end with the most specific graph.
Although these visualizations answer where and when the most residential noise occurred, they don’t answer why they are happening. I would like to further pursue this project by exploring the population sizes, along with the household types in the top ten zip codes. I believe both these factors may contribute to those zip codes with a higher number of residential noise complaints. As a result, I would use the US Census dataset, and join it with the 311 dataset for further analysis.
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