{"id":39250,"date":"2025-05-15T20:43:44","date_gmt":"2025-05-16T00:43:44","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=39250"},"modified":"2025-05-15T20:43:46","modified_gmt":"2025-05-16T00:43:46","slug":"making-sense-of-nycs-air-a-visual-journey","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/making-sense-of-nycs-air-a-visual-journey\/","title":{"rendered":"Making Sense of NYC\u2019s Air: A Visual Journey"},"content":{"rendered":"\n

A Visualization of Nitrogen Dioxide and Asthma Risk by District in 2018<\/p>\n\n\n

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About This Poster<\/h2>\n\n\n\n

This poster explores where air quality in New York City still fails to meet basic safety standards\u2014and how that overlaps with respiratory health risks. While nitrogen dioxide (NO\u2082) pollution has declined across the city over the past decade, the improvements haven\u2019t been equal everywhere.<\/p>\n\n\n\n

Using 2018 data, this visualization compares NO\u2082 levels and adult asthma episode rates at the community district level. It highlights the five most and least polluted districts and tracks how their air quality has changed over time. The goal is to show how environmental and health burdens can persist, even in a city that\u2019s getting cleaner overall.<\/p>\n\n\n\n

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How I Got Here<\/h2>\n\n\n\n

1. Starting Confused\u2014But Curious<\/h3>\n\n\n\n

I felt completely lost when downloading the NYC Air Quality Surveillance Data from data.gov. I wasn’t sure what most of the columns meant, especially the one labeled “Name,” which included several unclear terms. After some digging and engaging in helpful discussions, I discovered that the entries in the ‘Name’ column referred to different air pollutants. This was my first breakthrough.<\/p>\n\n\n\n

Eventually, I identified four main types of pollutants: Ozone, Nitrogen Dioxide (NO\u2082), Fine Particles, and Boiler Emissions. This clarity helped me understand the structure of the data and provided a foundation to work from. I started by creating simple bar charts comparing pollutant levels by borough. However, since each pollutant used a different unit of measurement, I had to separate them.<\/p>\n\n\n\n

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Next, I developed trend charts focusing on NO\u2082 and ozone, as they shared a unit of measurement, allowing me to visualize their changes from 2010 to 2022. This became my initial dashboard.<\/p>\n\n\n\n

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After sharing my initial dashboard, I received feedback from my classmates and friends who regularly work with data as engineers. Here\u2019s some of the feedback I received: “The Y-axis unit labels were unclear,” “Manhattan seems to be the worst – can that be highlighted visually?”, “Timeframes weren’t consistent or clearly labeled,” “What do the data values represent?” and “Could you summarize pollution overall by borough?”<\/p>\n\n\n\n

Based on this feedback, I revised my dashboard. I learned that the time period is crucial for readers and that data design must depend on the contents of the dataset; I couldn\u2019t force everything to match if the data wasn’t available. Additionally, I realized the importance of clear labels and consistent timeframes. Receiving feedback helped me see what wasn’t clear to others.<\/p>\n\n\n\n

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It was through this charting process that I started to crave spatial context. I wanted to understand where the air quality was the worst, so I decided to create a map. This exploration into spatial visualizations made my work much more engaging.<\/p>\n\n\n\n

2. The visualization that makes more sense<\/h3>\n\n\n\n

While the chart and graph visualizations were helpful for gaining an overview of the dataset, I realized that there was a need for spatial visualization. Before diving into map visualization, I took a moment to reflect on the topic, what I wanted to communicate to my audience, and the story I aimed to convey. I decided to adopt a more audience-friendly approach.<\/p>\n\n\n\n

First, I organized and clarified my topic with a more defined direction. Next, I included concise explanations for each pollutant, detailing their causes and the health issues they can create. Finally, I chose to focus my entire project on one pollutant: nitrogen dioxide. This choice was based on its significant effects on respiratory health and its suitability for storytelling.<\/p>\n\n\n\n

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This is my first map visualization. I joined air quality data with district boundaries from the NYC planning website. Since some names did not match, I manually added the missing borough and community district numbers to complete the join. This map displays the average nitrogen dioxide levels across New York City’s community districts in 2023.<\/p>\n\n\n\n

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The colors indicate the air quality in each area:<\/p>\n\n\n\n