Dataset Deep Dive | NYC 311 Rat Sightings

We built this city? No, RATS built this city

When I think about New York City, several come to mind, including:

And ever since I got into data visualization and the Tableau community. There one more thing that comes straight to the top of mind with I think/hear NYC.

Rats…yep, those nasty rodents.

Why, you ask? Because if didn’t know NYC has one of the best 311 open data repositories in the country. And if you dig into the requests enough, you’ll find the data gold mine that is, The Rat Sightings dataset! To date (5/13/2020), there have been more than 145k rat sightings logged since 2010. Thats a lot of rats!

And it’s just an awesome dataset

The Data

The data is pretty straightforward. This dataset lends itself nicely to map because they actually provide the LAT/LONG of each sighting! You can further break this down by borough to see if Bronx has more sightings than Manhattan. You can also get the actual intersection the sighting was called in for.

One question or thing that I think would be intriguing is to see how the COVID-19 outbreak has impacted rat sightings.


Below are a handful of examples from the #DataFam community to help kickstart of ideas for you if are wanting to make a new viz! Click the image(s) to check out the interactive dashboard version!

First up is a dashboard from Luigi Cicciari. I really the ‘editorial’design of this. Kinda looks like a newspaper cover page. I like the trended bar chart at the top as it resembles the NYC skyline from the top header and helps draw the down into the main part of the dashboard.

The street signs are a really fun touch. I also like the map in the center as it takes center stage. It highlights the five (5) boroughs and shows the number of sightings. Following the editorial nature, there are a lot of direct labels, but they work nicely with this design.

Click to view the interactive version

This is next is really cool. It was created by Lindsay Betzendahl, Tableau Zen Master and Tableau Public Ambassador. Way back before she started #ProjectHealthViz, she got her chops doing #MakeoverMonday vizzes, like this one.

From the beginning, this dashboard is really cool. See that skyline? That’s actually, the monthly trend of rat sightings, as a bar chart! How cool is that!?

Then as a way to separate the skyline to map, she uses a month and year heatmap which highlights the warm summer months being the time when there are the most sightings. 

Then we have the geospatial elements where the electric blue really pops in the bar chart and the city roads in the map. 

It’s a  shame that only ~125 sets of eyeballs have seen this viz, it’s really excellent.

Click to view the interactive version

This next one by Sarah Bartlett, Tableau Zen Master & Tableau Ambassador, is an excellent showcase of a newer chart type. The density mark really works nicely here as it really helps to show the just how many sightings there in NYC as a whole. 

Another feature that I really like is the collapsible menu in the upper left. When expanded, there are couple additional chart views as well as a filter by location option to dive deeper in the data.

I also really like how she’s constructed her map her. The high contrast of the major highways is really eye catching.

Click to view the interactive version

Another striking and eye-popping dashboard comes from Adam Crahen, who does a really nice job making the red color pop against the all-black dashboard. 

This dashboard is full of fun little details. The first thing one might notice is that Adam used the size of the rats to distinguish between the number of sightings per borough. Also included in this dashboard is the sighting by location. And Adam is quick to point out the majority of sightings come from residential buildings.

For an extra fun easter, explore the interactive version and click that icon in the upper right corner. Adam really went deep-down the rat hole on this dashboard.

Click to view the interactive version

To the naked eye, this might look like your typical density map. Well, if you aren’t familiar the Tableau Zen Master Chris Love’s work, then you know that he fancies making complex dashboards look effortless. 

So what is so complex about this dashboard? Considering what we just saw from Sarah’s version of density map above. Well, Chris created this density map in Tableau version 8.1. A quick google search reveals that this version was released in December of 2013! Are you kidding me? 

 It turns out there is quite a bit of pre processing going one long before the data gets to Tableau. 

There are centroids and clustering and spatial object involved. I’m not entirely sure what’s going on but my goodness, Chris is a mad scientist for figuring out how to do this.

Click to view the interactive version

And lastly, I wanted to share a rat dashboard that I created make in 2018. I remember wanting to approach this dataset from a different angle than all the other’s I’d seen. And then I had the idea to take a look at an additional portion of the NYC 311 dataset. I wondered if there was a correlation between the number of rat sightings and the number of food establishment complaints. 

I had also never attempted bi-variate analysis like this before so it was fun to get my hands dirty with a Sarah Battersby mapping blog post!

At the end of the day, it was a fun way to think about data and maps. If I were to do this again, I would make it the maps bigger in order to more easily see all the ZIP codes


Click to view the interactive version

The Challenge

It’s simple, really. Make something with this data. The deadline is up to you. This is not a time boxed challenge at all. As you can see from all the vizzes above, there is SO MUCH potential in this dataset. 
I can’t wait to see what you create. All I ask is that you share it with the hashtag #20for20Tableau, on Twitter and on Tableau Public. 

I’ll be back in a couple weeks to share with you what I create. Until then… 


To check out the rest of the datasets in the #20for20Tableau project, check out my dashboard and if want to read more about individual datasets, be sure to read my other posts

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