They say that the smell of New York City is one that is unforgettable. Descriptions include “rotting garbage,” “rancid,” “dirty,” and even “like death,” just to name a few. Living in a city with a population of 8 million plus makes it inevitable that the streets are rife with garbage and the subsequent odors that accompany it. But when do the dirty conditions of NYC evolve from discomfort to potentially dangerous? Well, we’re living in these times now – when a global pandemic hits. The first known COVID-19 case in metropolitan New York was confirmed on March 1, 2020. Within a few weeks, the city had become the country’s epicenter of the pandemic. By March 22, 2020, a statewide executive order to close non-essential business until further notice was put into effect to slow down and manage the spread of the virus. It’s not that the city’s trash problem caused the sudden concentrated surge, but if left unchecked, it has the potential to exacerbate the number of COVID-19 cases. The World Health Organization states that sanitary and hygienic conditions are critical to protecting human health during an infectious disease outbreak. Consistent and thorough waste management and sanitary practices can mitigate the transmission of COVID-19 (WHO, 2020).

With all of this in mind, I was curious about whether the shutdown had impacted the city’s dirty conditions and sought to answer it empirically.

Thus, I posed the following research question:

How has the volume of sanitation complaints changed since NYC PAUSE began? Is the pattern of complaint volume during NYC PAUSE any different from the previous 2 years in the New York metropolitan area?  

 Who should care about this?

Multiple parties can benefit from the answer to this research question – Government officials and workers, particularly those in the Department of Sanitation, and people who live within (or are planning to move to) the New York Metropolitan area. A better understanding of complaints that involve dirty conditions can help government officials and workers allocate their resources more efficiently and keep the New York City area clean. This information is also important to ALL of us in the New York metropolitan area because it gives us information about which areas have the highest number of dirty condition complaints, and potentially avoid them until we see the numbers decrease. This information is especially important during the pandemic as sanitary practices and living conditions make virus transmission less likely. A clean environment and surroundings also have a positive impact on one’s living conditions. 

How do I go about answering this?

 I first leveraged the open 311 complaints data available here. It is an extremely rich data set, so I filtered it by taking the “dirty conditions” complains made in all five boroughs of metropolitan New York, organized by date from 2018 to present. I then made a dashboard of the following 3 charts: 1) an area chart illustrating the number of dirty complaints by borough, 2) a stacked bar chart that portrayed the different types of complaints and their respective counts, and 3) pie charts depicted the status of the complaints. All 3 charts showed the time period from March-June in 2018, 2019, and 2020.

Here is the dashboard:

(or click HERE to view it directly on Tableau public)

Now let me walk you through on the data and design decisions I made.

As I mentioned earlier, I chose to create a dashboard with multiple charts. The reason for this is because there were follow-up subquestions relevant to the overarching research questions that could be answered via additional charts. Moreover, each chart visualized three time periods: March-June 2018, 2019, and 2020. I included the prior 2 years to use as a comparison to this year when the pandemic was in full swing. Only March through June are shown because these were the months when PAUSE was in effect.

An area chart was selected to address whether the volume and pattern of sanitation complaints changed since NYC PAUSE. Unlike a line chart, which only represents time-series data, an area chart accomplishes this and can additionally account for volume as well. I then organized the number of complaints by city to help the viewer hone in on which cities experienced the most complaints. I also added an annotation to highlight when PAUSE so the viewer can view the immediate and subsequent effect it had on dirty condition complaint volume.

Next, I wanted to know what types of complaints were the most frequent. To answer this, I used a stacked bar graph because I wanted to highlight the difference in complaint numbers across condition types. The stacked bar takes into account that I have two categorical variables: time period on the x-axis and condition type represented by colors. The stacked bar chart allows the viewer to quickly pick up on what type of condition was most commonly complained about.

Now that we know where and what those complaints were, I wanted to know what their statuses were. Are the cases still open or resolved? Are the numbers vastly different this year than the prior 2? To do this, I chose pie charts to show the proportions of the complaint statuses. Not only did I want to emphasize the difference in complaint numbers, but also depict how many were in each status category.

Finally, I’d like to discuss some of my aesthetic design choices. I chose a “dark” color scheme because like many, I consider these days to be “dark times.” My title reflects what the project is about and I added a bit of a graphical design element where I used a vectorized silhouette of the NYC skyline to further emphasize the geographical location the data pertains to. I also added layman titles to each chart as well as a caption with further elaboration. As an academic with a “hard sciences” background, I have a tendency to be at times, overly descriptive and jargon-y, so I tried to keep the audience in mind with the chart headers and brief background snippet on the upper right. I also highlighted in yellow the main takeaways.

Final thoughts.

All in all, it was a pleasure undertaking this project. This was the first Tableau dashboard I made with minimal guidance and I am relatively pleased with the outcome. I was able to capitalize on publicly available data to derive insight that could have an impact on public health. Moving forward, I’d like to expand on this project as NY FORWARD progresses. It coinciding with the flu season would be interesting to examine. It would also be more informative if I could combine the 311 NYC data with COVID-19 data to explore whether the geographical areas with high dirty condition complaints experience a greater number of virus cases.