Please click through to view the Dashboard in Tableau, and select Fullscreen (in the bottom righthand corner) for the best viewing experience. The first draft can be viewed here.

My dashboard aims to answer the question, “where and when do New Yorkers interact most with the most fauna of the City: rodents, pigeons, and mosquitoes?” My goal was to remind viewers that New York is not only a city for its human residents, but an ecosystem for its animal residents. I imagine that most New Yorkers will be able to connect to this dashboard, since it’s hard to live here for any significant time without seeing a rat or a rug of pigeon droppings. The dashboard might also be helpful for city officials to keep track of trends—they might be able to answer, for example, why there have been 10 reported rodent bites in the first nine months of 2020, up from 5 reported last year and just 0 or 1 every other year since 2010.  

The largest component of my visualization is the small multiples map that shows each year of complaints side by side. My original goal there was to show change over time, but for the most part, the more striking thing is consistency over the last 10 years. Despite not displaying changing trends, as I had hoped, the maps are also helpful in that they subset the data into roughly 10% views of the whole set. Even so, there are still so many rat sightings each year that I had to turn the opacity of dots down to 25% to see any nuance beyond a big blob! On that note, another feature that helps with readability is the ability to click and highlight just one complaint description at a time—this will highlight across all visualizations, so it’s possible to see on the line chart how the number of complaints changed each month from 2010-2020, as well as to see the same complaint isolated on the map and in the bar chart. This gives an overall answer to how a complaint changes seasonally, which borough sees the most of that complaint, and where on the map it might be most or least common. 

I found my 311 data subset difficult to understand even when looking through the columns in Tableau, and I definitely began to appreciate the power of Tableau as an exploratory tool. Given my lack of prior experience with it, though, my visualizations changed several times as I discovered more effective ways of showing off the data. For example, all of my visualizations started by coloring according to “complaint type,” which corresponded to the three categories of 311 complaint I had filtered from the site: Rodent, Mosquitoes, and Unsanitary Pigeon Conditions. It took me until the last couple hours I was working on the project to realize that the “Descriptor” column contained more nuance— just enough that it made the charts much more interesting without losing cohesion as a graphic. By switching my x-variable and color pill to Descriptor, I was able to show the distinctions between different rodent-related complaints, which brought to the surface, for instance, the observation that rat sightings are very seasonal, with much higher numbers in the summer than in the winter, while mouse sightings are lower overall and stay constant throughout different months. 

The biggest design challenges I had were first, getting the small multiples map to correctly display the grid of different years (this was a querying problem on my part, since I had first written a valid query that didn’t do what I wanted, and couldn’t figure out where my error was), and second, killing my darlings to get everything I wanted to fit on my dashboard. In the end, I think I’ve chosen three graphs that inform and enhance each other, rather than 3 that tell disparate stories, and it feels cohesive and exploratory to me. I’ll be very curious to see how others respond in today’s critique, though!

My scope ended up only including animal data, although my original proposal had planned to ask, “How do areas with more animal complaints overlap (or not) with city services like street cleaning, or demographics like median income, population density, or building zoning?” I still think it’s a good question, and one I’d like to explore more. One lingering concern I have is about 311 reporting in general. Who uses 311? Who does not? I see large swathes of Queens that report many fewer mouse sightings than the Upper East or West Sides, for example. Is that because there are fewer mice in Queens or because there are fewer 311 users in Queens? Perhaps mice really do prefer Manhattan—in that case, I’d like to know why! (I might choose it too, if I could live rent free in someone else’s apartment.)

EDIT: After feedback from the critique, I acted upon the notion that the small multiples format showing different years didn’t really show differences over time. Instead, I kept the small multiples format for comparison, but made each map a different complaint description. I also made the timeline chart larger, since this chart seemed to garner more attention than its place in the corner suggested it should.