Introduction
Rats are New Yorker’s most familiar neighbors. In this visualization, I showed the activity of New York’s rats activities in the past 10 years with an animated heat map. I also introduce a rent rate map which indicates the human activities and overlapped them to see how the two major animals: human and rat, share this city together.
Tools and Materials
In this project, I used Carto, a spatial analysis platform to finish the visualization. I imported two datasets from NYC open data. The first one is Rats Sighting data which shows all 311 Service Requests from 2010 to the present. The second dataset is the NYC NTA map, which shows the boundaries of Neighborhood Tabulation Areas as created by the NYC Department of City Planning. Another dataset I used is the medium rental price per square foot for all homes created by Zillow. I imported it directly from Carto.
Process
In order to have a clear view of my data, I used a plain background and red color for rats and blue color for rents.
I imported the rats sighting dataset to Carto and tried different types of mapping aggregation and styles. Because the dataset has a time volume, I eventually decided to make it an animation. The animated dots on the map looks just like the rats ran over the streets.
And then I created two widgets. The first one is a timeline that shows the changes in rats’ activity through time. We could see clearly that summer is a party time for New York rats while in winter all rats are quieter. Another widget is a filter that shows the number of rats in different zip code areas. We could see the 11238 area has the largest rats group in NYC.
I added a second layer which shows the boundary of neighborhoods in New York. I used the analysis function on Carto and choose the “enrich from data observatory”. I used Zillow’s “rental price per square foot for all homes” data and add it as a column. I used a serious of blue colors to indicate the rate of rents in this neighborhood.
Results & Discussion
My final map is build up by three parts: animated map, timeline and a zip code filter.
We could observe from the map which areas has the most active rats: neighborhoods like Midtown Manhattan, Uptown, and Williamsburg. We can also know from the map that in summer there is much more rats than in winter. We could also view the rat’s activity in our own neighborhood by searching with our zip code.
Reflection & Future Direction
Carto overall is a concise, user-friendly online tool to use. Its user inference has relatively straightforward guidelines to follow and helped me in producing the data visualization effect I wanted. I tried a few representation methods and end up using the heat map to project my results.
There are two major problems I encountered with Carto. Firstly, for accessing every single dataset on Carto, I have to inform Carto to request on behalf of me to access these open-source data that were already available online. Carto gave me an automatic reply, asking me to wait before receiving a green light from the original data set owner. And the story ended with zero response. I ended up downloading these data set myself.
Secondly, Carto should add in history and recall function while visualizing data sets. Every time I wanted to try something new with my current working project, and I struggled to go back to the previous version.
References
https://data.cityofnewyork.us/Social-Services/Rat-Sightings/3q43-55fe
https://data.cityofnewyork.us/City-Government/NTA-map/d3qk-pfyz