• Research question: What is the most common Illegal Parking complaint made to the NYC 311 Service and where do they occur?

• Your audience: Beyond the curiosity this visualization may fulfill for the NYC residents, there is not much utility I can think of for the average citizen. But on the other hand, it would be very useful for law enforcement and policy making individuals. Given that this analysis exposes where certain types of illegal parking occur across the City, public authorities could redistribute their personnel focusing more heavily on to those areas. This information could also help apply some type of Six Sigma management approach. This in turn shows where there is more need for parking spaces so that city authorities can consider providing it, which could be an important way of reducing violations and improving the life of the residents.

• The data you will use to address your question: I will use the NYC 311 Open Data Service Request dataset, focusing on the Illegal Parking reports closed between Sept 7th, 2018 and Oct 2nd, 2020.

• A written Description of your visualization: As the audience can see, this visualization seeks to break apart as much as possible the data available regarding Illegal Parking Complaints to the NYC 311 Service starting on September 7th, 2020 through October this year (2020). We can see from the line chart that there is an interesting drop on the Illegal Parking Complaints around April 2020 coinciding with the start of the pandemic crisis. It is also noteworthy that Brooklyn’s complaints surpass Manhattan and Staten Island’s combined as seen on the bar chart on the upper left corner. Blocked hydrants and Parking Sign violations are the most frequent. The first interactive map allows to see where the incidents occurred precisely, providing information on borough, city, and exact address, while the map on the right shows the location density of the incidents differentiating the type of complaints by color. This range of information would hopefully allow the viewer a wholistic look at Illegal Parking around New York City.

• An explanation of the data and design decisions you made: When referring to illegal parking complaints, I thought that location was of great importance, that led me to choose a map to start analyzing the data, but then it became clear that one map would not allow me to show clearly all the findings without looking too dense and unclear. I wanted to show location but also type of violations density, so I decided to go for two maps. Then timing came into the mix, as any other Economist would naturally come to conclude, duh. When are these complaints being made? Since there is no place timing in a map, the line char came to be. This gave rise to the interesting and unexpected finding of the complaint number decline corresponding with the pandemic panic in April. I thought it definitely had to be included in the visualization. Finally, though the maps where useful showing density, they were not definitive in terms of demonstrating quantity of complaints per borough or type of complaint, so the bar charts became most effective and earned their place in the visualization. One thing that I found useful is to be consistent with the color selection of the different components of a visualization, which allows to use only one color legend for more than one visualization at a time, saving a lot of space in the process of creating a dashboard. So, my color legend on the map on the right also describes the colors of the bar chart below; or even better, my bar chart on the upper left corner serves simultaneously as a color legend for the map!

• Next steps. Maybe I would try to find ways to reduce the number of visualizations required to express all of the relevant data, or maybe keep working on the structure of the layout to make it even more efficient by focusing on every aspect to see if there are opportunities to apply clever ways to make even more room for the maps or other tools.