Research Question:

While incredible strides have been made in the realm of climate justice and environmentalism, marginalized communities have long-been disproportionately impacted by air pollution and climate change. I originally became interested in this realm of research while taking environmental justice classes during my undergraduate years, and later while working for environmental justice and research non-profits in Washington, DC. Looking further into environmental justice issues in NYC, I found that, according to non-profit WEACT for Environmental Justice, children living in East Harlem are hospitalized for asthma at more than three times the city-wide rate. Verified air quality complaints from the Department of Environmental Protection, whereby DEP city officials observed a violation to the New York City Air Code and issued a code of violation, may be able to provide insight on the distribution of air pollution in New York City’s boroughs.

My research question for this project is as follows; what neighborhoods in New York City have the most DEP verified air quality complaints from 2017-2022? This question matters as it can provide the information needed to equitably serve the city’s residents with regards to air pollution and exposure.

Audience

My intended audience for this project are non-profit organizations like WEACT for Environmental Justice, New York City’s Environmental Justice Alliance, community leaders, as well as local governmental representatives.

Description of Data and Methodology

I first downloaded all air pollution complaints from the 311 Complaint Data Database from NYC Open Data from 2017-2022. I wanted to showcase a defined timeline to illustrate more recent air pollution trends in New York City. The initial downloaded database contained a multitude of complaints that were either never resolved or determined to not be a violation of New York City Air Code, thus misrepresenting the data. I then narrowed the dataset to only include air pollution complaints from 311 Complaint Data that are indicated as an “observed violation” from the DEP, made by NYC residents in all boroughs from 2017-2022. 

Major Frustrations and Workarounds

I created these visualizations in Tableau Public Desktop, and since I had to add a lot of additional connections to create the time lapse visualizations and work around tabulation issues, by the end I had too many rows to publish the entire worksheet to Tableau Public. As a workaround, I published each visualization separately and recreated the visualizations in Tableau Web Authoring. There were some limitations to working in Tableau Web Authoring, as I could not import custom maps or style the color palette as detailed as I would like.

Visualizations

Visualization 1: Time Lapse of Air Code Violations by Neighborhood

My first visualization shows the number of verified air code violations by neighborhood from 2017-2022. I decided to go with a choropleth map because I felt it was the best way to show the differences in air code violations from neighborhood to neighborhood. I used a distinct count function for the Unique Key variables to count the number of air code violations. To fully illustrate the changing pollution trends, I added a time lapse element to show the number of air pollution violations from 2017 to 2022. I pulled the zip code and neighborhood name data from the United Hospital Fund NYC Neighborhood Index website. I also designed a background map in Map Box and used the “Background Map” feature in Tableau to integrate the map. My design choice was to keep the background map dark and faded, with highly contrasting green lines to illustrate major roadways. To keep parts of the data disappearing during the time lapse, I added a Marks tab of just the outlines of the neighborhood by zip code. To do this, I had to add a separate <> connection of NYC zip codes to the original sheet. I decided to use white borders for the neighborhood boundaries to contrast against the dark map background and put a focus on the city. For the shading, I tried to match the colors to the colors of the map background; bright green for higher numbers of violations. I wanted the overall visualization to feel cohesive, but also have the NYC boundaries feel distinct against the map background. From the visualization, you can see that 2018 had a record high year of air code violations, with areas of Manhattan including Chelsea, Upper East Side, and Harlem containing a high number of air code violations. Neighborhoods in Staten Island seemed to have lower numbers of air code violations, with neighborhoods in the borough not having any air code violations recorded from 2020-2022.

Visualization 2: Time Lapse of Air Code Violations by Neighborhood

My second visualization shows the number of verified air code violations by borough from 2017-2022. To fully illustrate the changing pollution trends, I added a time lapse element to show the number of air pollution violations in each borough from 2017 to 2022. I pulled a publicly available map on Map Box using Tableau’s “Background Maps” feature. I decided to go with a simplistic black and white feature to add a bit of a retro spin to the visualization, whilst still being informative. During the initial time lapse, outlines of boroughs would disappear due to lack of violation count data for that particular year. To keep the outlines intact, I had to add a separate <> connection of NYC boroughs to the original sheet, and then add the boroughs as a separate Marks tab to apply a layer to the overall map. Similar to Visualization 1, I wanted to match the shading of the air code violation density to the color palate of the background map. This visualization shows that Manhattan has the highest number of air code violations, with the borough showing an all time high of 116 violations in 2018.

Visualization 3: Bar Chart of Air Code Violations By Borough

For this visualization, I wanted to compare total number of air code violations from 2017-2022 between the boroughs, instead of year by year like the previous two visualizations. To best compare the total number of air code violations in each borough side by side, I decided to create a bar chart with included labels showing the various air pollution violation numbers.

You can see clearly here that Staten Island has the lowest number of total air code violations (8 between 2017-2022), and Manhattan has the highest number of total air code violations (327 between 2017-2022).

Next Steps

In moving forward with this project, there are different dimensions to consider to present a more holistic presentation of the available data. In the air quality dataset, there was a category indicating the different type of pollution complaints including odor/fumes, smoke, vehicle idling, and construction and demolition. It would be interesting to include these metrics in future visualizations and analyses, and may provide additional context for environmental action. I was also unable to create a comparative graph for the total number of air code violations between 2017-2022 for each NYC neighborhood, as the total list of neighborhoods was too long for readable presentation of information. Moving forward, I would like to consider alternative visualizations for the neighborhood data such as radial circles.