Where and when are public urination complains made in NYC?
Most of us have seen (or possibly engaged in) public urination. When it’s reported, the data provide us with a starting point to see where and when people “go” where they shouldn’t. Are we dealing with late night or weekend partiers, or do we need to rethink our assumptions? I make a cautious attempt to inspire the audience to create theories of their own or have discussions with others about their experiences.

The audience is intended to be anybody with an interest in “funny” (but real) data, particularly students. It seems lighthearted, but its purpose is to entice others to spend more time thinking, talking, and clicking than they may have been inclined to when hearing about the topic.

This visualization allows users to see where and when public urination complaints were made. Users can filter the data to specific a date range, set time windows, and get a breakdown of the types of places complaints are made.

To those who haven’t studied the topic, the visualization shows that the answers to “where and when” may not be what they expected. It can also show how fed up some residents are.

Using NYC Open Data, 311 Complaint Data was filtered to include only Public Urination complaints. Each of the dashboard’s 4 visualizations were chosen to fit 2 criteria. First, the visualization had to be engaging. This does not necessarily mean interactive. Instead, the goal was to immediately trigger the audience to ask questions (serious or silly) and explore other aspects of the dashboard for more detail. Secondly, the visualization had to be informative while remaining unintimidating. This meant ensuring that visualizations were not complex, but also did not alienate the audience by providing information or analysis that they may feel is meant for “people who know about this stuff” (this stuff being data analysis/visualization or anti-public urination activism).

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Moving Average
This was originally designed as a line graph, but the variations made it painful to look at and impossible to analyze. Viewing the data in weeks and using a moving average to smooth the visualization made this easier on the eyes. Converting it to a filled area chart allowed the data to be more readily interpreted as waves. Now at first glance, there seems to be a cyclical pattern, which hopefully has an immediate impact on the audience and a subconscious desire to find the rhythm of this visualization and the underlying data.

Time Report Created
I didn’t want to use 2 line graphs on the same dashboard if it could be avoided. I wanted each graph to have its own personality. Visually, this came out exactly as anticipated. The times of the highs and lows were unexpected.

Complaints by Location
This was initially a vertical bar graph of “Complaints by Borough”. I felt like it did not provide any data that the audience would find interesting (especially with a map already present). It also “stole” the personality of the previous graph. In short, it was there… and that was it. I went through the available data again and replaced it with the Location Type. The audience would have spent time in all of these locations and may take an interest in the data because of this relatable “where” to accompany the “when”. To give it its own personality, it is a horizontal bar graph which also makes reading the labels much easier (the only labels that aren’t datetime or numeric).

Map
The map shows “where” for the filtered “when”, but also provided the unexpected “who”. It can show instances where individuals were relentless in their reporting. Making the map reactive to the audience’s hovering over other charts in the dashboard provides incentive to explore and take control of the experience. Tooltips were excluded as they were not user friendly, nor did they provide meaningful insight.

Filters
The option to isolate weekends (arbitrarily starting at 6PM Friday) allows the audience to test a theory that weekends are a major factor for public urination complaints. The start/end times and date ranges invite the audience to explore and dive deeper into their own assumptions and analysis. It helps answer some questions and encourages the audience to embrace curiosity, potentially planting a seed for them to seek more information.

I could see this dashboard evolving to include the locations of publicly available restrooms to bring in the idea of whether there is a lack of facilities or knowledge that they exist. I’d also hope to find a way to include the weekend isolator on the heat map.