Background
With bars and restaurants closed for indoor dining as part of COVID-19 shutdowns, New Yorkers witnessed a de facto legalization of outdoor drinking, or at least some outdoor drinking. In the United States, alcohol is judged as a tolerable vice if not an outright hobby, and I suspect the harsh penalties for consuming it outdoors are more a function of path-dependence than meaningful public aversion. Yet public alcohol consumption represents one of the highest categories of criminal summonses handed out by the NYPD–and they seem particularly troubled by this victimless non-crime when it is carried out in specific neighborhoods and by people of color. I applaud the NYPD for turning the other cheek as locked-down Williamsburgers took to the sidewalks to keep local businesses afloat, one frozen margarita at a time. But did cops extend that same courtesy to residents of the South Bronx, Harlem, or Crown Heights? Recently, the NYPD published their 2020 Q1 and Q2 criminal summons data and I was eager to find out.
Process
Carto is a freemium builder and library for creating web-based data visualizations centered on geography/location data. Carto provides a handful of base maps and the ability to import data sets and represent them using coordinates (e.g., locations of falafel carts in Queens) or boundaries (e.g., boundaries of Staten Island zip codes). There are a variety of styling options for each type of data, including animations, point shapes and styles, and colors and blending modes, to name a few, many of which can be tied to data points/categories. Users can build dashboards around their maps using “widgets” that display additional non-map data points.
Unlike Tableau (and I would say this is a missed opportunity), Carto doesn’t seem to offer a repository for users to share/view each other’s work on the platform. I did look at examples used in a couple of tutorials as well as some non-Carto location visualizations posted in a content marketing piece published by Carto.
Results
I found the NYPD’s 2020 criminal summons data via the NYC Open Data portal, and filtered Offense Category for “alcohol” and “consumption” to give a rough set of all of the relevant summonses issued and arrive at a smaller data set to export and work with. If you already have a Carto account, this data set can be turned into a visualization in a few clicks, selecting “new map” and then choosing a data set to import. Carto automatically searches your file for location data and in my case automatically generated a point map using the latitude/longitude coordinates included in the NYPD data set. From there, I chose to represent the points as an animation, to drive the animation via the summons data column in my .csv file, and put together a color/appearance scheme. I selected 181 “steps” to correspond to the number of dates (1/1–6/30/2020) in my data set and added trails and multiply blending to give a sense of accumulation. Because I think recipient race, in addition to geography, is an important part of this story, I attempted to separately color each point according to that data point. But this seemed to visually overload the animation, so I reverted to a single color but added a separate widget for race. You can view the map/visualization online.
Results and Discussion
The racial and geographic distribution of public alcohol summonses shown on this map seems to mirror the general trend revealed in my other recent work on this topic using longer-range data. There doesn’t appear to be any COVID-related disruption to the disparate geographic or racial distribution of alcohol consumption summonses, though they do seem to be down numerically from CY 2019. This visualization would be improved by including an element of comparison to prior years to help make trends/deviations more apparent, and by annotating it with dates of significant policy changes data (both of which might explain the significant jump in alcohol summonses in late April/early May 2020). Also, the base map I chose does not sufficiently annotate neighborhood names, which might help users better derive meaning from these trends. Still, I think this provides an interesting, high-level look at how an innocuous yet criminal vice is enforced, how enforcement patterns emerge chronologically, and how the situation differs from neighborhood to neighborhood and person to person.