I have recently been interested in water infrastructure in NYC through a project last semester observing and creating interfaces and tools for the raingardens around NYC which are part of the city’s green infrastructure plan as well as the FloodSense project at IDM measuring the urban microbiome.
For my data visualization, I decided to map street flooding events as recorded by 311 calls/requests to the city. I used a subset of the the 311 dataset on the open data portal that includes only street flooding reports.
To see quickly what the map of this data looked like, I put it into Kepler, which makes it really fast to map csv files:
I then tried just getting the data into the map example that we worked on in class, but I quickly realized I would need to do some data cleaning because of missing cells. I used R to take out all rows with missing lon/lat information instead of making up a location. I also split up the date/time field into month, day and year so that I could control these variables separately in my map interaction.
Once I got the events to show up I decided to add interaction that would allow the viewer to look through flooding events at different points in time. I decided to turn the canvas into a big “matrix” with month on the horizontal axis and year on the vertical so that the user could compare across both of these dimensions.
I thought what was interesting about the map was that the flooding events draw a very clear map of NYC. I felt I didn’t need to add a basemap for this reason. My goal was also to allow the viewer to see general patterns and impressions rather than get exact flood event details.
Right now the map only shows three years 2017-2019 because the dataset for all 10 years of 311 data is so big, but in the future I’d be interested in making a version that has all of the available years.
You can view the visualization here: https://datapublics-floodmap-class5.glitch.me/
And you can see the code on Glitch here: https://glitch.com/edit/#!/datapublics-floodmap-class5?path=README.md:1:0