Over the past few years, the De Blasio administration has taken a special interest in addressing New York City’s homeless population via outreach methods,

Over the past few years, the De Blasio administration has taken a special interest in addressing New York City’s homeless population via outreach methods, namely in the Home-Stat project, which aims to encourage New Yorkers to report any homeless individuals they might see on the streets to 311, so that an outreach team can attempt to assist the individual. 

In working with the 311 data, I asked the following two-part question:

Over a five year period, what boroughs have had the biggest changes in homeless assistance reporting, and have more people been accepting assistance on a yearly basis, or has there been a relatively static trend?

The following visualizations aim to help New Yorkers understand if it is helpful to dispatch an outreach team to address homelessness in the city, as well as the boroughs in which we do the most reporting. Additionally, they will help the DHS understand what times of year they should recruit for their mobile outreach teams, and when they should do more preparatory work since the reporting is not as robust.

Figure 1, “311 Requests for Homeless Person Assistance by Borough, Sept. 2014 – Sept. 2019” shows the monthly amount of reports made to 311 by New Yorkers in each of the five boroughs. From 2014-early 2016, there was a slow upward trend in reporting, which then skyrocketed in March 2016. 

Though we haven’t seen a boom in reporting like that since, the amount of reporting is still much higher than it was pre-2016. Additionally, less reporting tends to happen in the winter months–from around December to March, while most of the reports tend to happen in summer months, usually in August. 

Despite—or perhaps because of—Manhattan having the least amount of space, most reporting happens in Manhattan overall. The population density in Manhattan might be one explanation for these thousands of reports over the five year period, but other factors might contribute.

In Figure 2, “Yearly Reports on NYC’s Homeless Population by Resolution”, there are several resolution descriptions provided by the DHS with regard to the 311 complaints—my research question focuses on those who accepted assistance, and using the heat map to show the trend of numbers, you can see that there was a significant drop of assistance acceptance after 2017. However, in context, if you turn your attention to the row right beneath it, there was a significant increase in 2018 in resolutions that end right at the point of offering assistance. 

Since each individual report only gets one resolution, it’s not likely that the number is enveloped in the “accepted assistance” row or the “did not accept assistance” row.

Additionally, much like in Figure 1, you can see the spike in reporting in 2016—along with the significant drop in years afterward. Most of those reports from Figure 1 concluded that the individual could not be found.

The other graph, Figure 1, was much less finicky—I decided to go for a stacked bar chart with a monthly breakdown rather than a yearly breakdown because the trends are much more visible over the months, and you can see how they shift with the boroughs, which was an essential part of my research question. Additionally, you can very clearly see the summer months versus the winter months, which ended up being an interesting point I hadn’t considered in my research question.

Working with the data, it became apparent to me that the values I thought were most useful and fascinating were also the most messy. It was definitely a challenge to find the right way to visualize that data in particular. I started with a line graph for what ended up becoming Figure 2, but because the data was so messy, I concluded that no one would be able to read the graph, so I went for a more legible visualization. I was hesitant about the heat map because it is a little more difficult to show trends—colors are not the greatest way to do so—but because the resolution descriptions were inherently strings, everything got very cramped and cluttered if I chose any other way.

With more time to clean up the data, it would be great if I could aggregate the resolution descriptions that were input improperly. Additionally, after aggregating the resolution descriptions, I would make the graphs integrate a little better—breaking the heat map up by months instead of years. I also wanted to compare this data with the data on homeless encampments—some of these cases were referred to the NYPD, and I wonder how they interact with that data. Preferably, I would like to have one graph that accomplishes something big instead of two that accomplish two small, distantly related parts of the data.