{"id":37884,"date":"2024-04-04T15:22:02","date_gmt":"2024-04-04T19:22:02","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=37884"},"modified":"2024-04-07T20:15:26","modified_gmt":"2024-04-08T00:15:26","slug":"new-york-city-air-quality-a-data-visualization-narrative","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/new-york-city-air-quality-a-data-visualization-narrative\/","title":{"rendered":"New York City Air Quality: A Data Visualization Narrative<\/strong>"},"content":{"rendered":"\n
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Air quality in New York City is an essential public health concern that impacts millions of residents. My objective was to analyze particulate matter (PM2.5) concentration and its correlation with traffic volume and building emissions using data provided by the NYC Open Data platform. This comprehensive dataset enables an examination of air pollution at a granular neighborhood level.<\/p>\n\n\n\n

Reference: NYC Open Data Air Quality<\/a>.<\/p>\n\n\n\n

Process Documentationon<\/h3>\n\n\n\n

The project utilized air quality surveillance data and spatial files containing New York City Neighborhood Tabulation Area (NTA) codes. Tableau software facilitated merging these datasets, aligning NTA codes with their corresponding air quality data points to construct accurate geographic representations of PM2.5 distribution.<\/p>\n\n\n\n

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NYC Air Quality<\/a><\/figcaption><\/figure>\n\n\n\n

For mapping methodology, see Tableau’s Guide on Maps with Shapefiles<\/a>.<\/p>\n\n\n\n

Results and Discussion<\/h3>\n\n\n\n

The Particle Matter Average Map<\/strong> utilizes a dark map base to starkly delineate areas of higher particulate matter (PM2.5) concentrations. This visualization method effectively highlights regions with medium and high PM2.5 levels against the dark backdrop, ensuring they command attention. In contrast, areas with low PM2.5 levels merge seamlessly with the map’s dark tones, suggesting a less immediate health concern. This approach prioritizes areas needing intervention and offers a stark visualization of the health disparities within the urban landscape.<\/p>\n\n\n\n

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PM Average map<\/a><\/figcaption><\/figure>\n\n\n\n

In contrast, the Traffic Tertiles and Building Emissions maps utilize a qualitative color gradient to delineate low, medium, and high categories. This approach offers an immediate visual cue of relative emissions and traffic density but lacks the numerical specificity present in the PM Average map.<\/p>\n\n\n\n

The Traffic Tertiles Map<\/strong> is presented on a street map base, facilitating a direct visual correlation between traffic volume and air quality. By utilizing color gradations\u2014low (unmarked), medium (light shade), and high (dark shade)\u2014the map reflects the density of vehicular movement. Streets with heavier traffic are distinctly marked, underscoring their potential contribution to air pollution. This method serves a dual purpose: it highlights areas where traffic management could improve air quality and provides a clear, at-a-glance understanding of traffic distribution throughout the city.<\/p>\n\n\n\n

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Traffic Tertiles map<\/a><\/figcaption><\/figure>\n\n\n\n

In the Building Emissions Map<\/strong>, the choice of a satellite base map emphasizes the physical presence of buildings in correlation to emissions data. Buildings, especially those in densely populated areas, stand out against the satellite imagery, aligning with the marked emissions levels on the map. The high-resolution imagery provides a tangible context to the abstract concept of emissions, anchoring the data in the lived environment of the city. This map underscores the link between urban infrastructure and environmental impact, offering insights into how urban planning could mitigate pollution.<\/p>\n\n\n\n

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Building Emissions map<\/a><\/figcaption><\/figure>\n\n\n\n

A unified dashboard combines the quantitative PM Average with the qualitative Traffic and Building Emissions overlays, presenting a comprehensive yet accessible view of various factors influencing air quality.<\/p>\n\n\n\n

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Dashboard<\/a><\/figcaption><\/figure>\n\n\n\n

Reflection<\/h3>\n\n\n\n

Reflecting on the visualizations, particularly the PM Average map, the need for unambiguous terminology is clear. To prevent misconceptions, such as interpreting ‘PM’ as a reference to time rather than ‘Particulate Matter Average,’ subsequent iterations will feature more explicit descriptors, like ‘Particulate Matter (PM2.5) Concentration,’ ensuring immediate and accurate comprehension.<\/p>\n\n\n\n

Color usage, consistency, and distinction are critical for cross-map comprehension. A harmonized color palette with clearly differentiated shades can better communicate data across the maps. For instance, employing a brown or orange hue to signify medium levels and a consistent red for high emissions or particle concentration levels would provide a coherent visual thread. This consistent color coding would enhance the user’s ability to interpret the maps within the dashboard quickly.<\/p>\n\n\n\n

Future iterations will also consider color perception variations among viewers, including those with color vision deficiencies, ensuring that the visualizations are inclusive and informative for all users. The introduction of interactive elements remains a priority, allowing for an engaging exploration of the data. Such features encourage users to delve beyond surface-level interpretations, fostering a deeper understanding of the underlying air quality data.<\/p>\n\n\n\n

Explore the Environment and Health Data Portal<\/a> and Indicator Public Reporting<\/a> to dive deeper into the data.<\/p>\n\n\n\n

Conclusion<\/h3>\n\n\n\n

This report presents an analytical view of New York City’s air quality, focusing on particulate matter. The visualizations created serve as tools for public awareness and as a foundation for policy development and urban health initiatives. Future work will aim to refine these visual tools, incorporating a broader range of data and developing interactive features for enhanced user engagement.<\/p>\n","protected":false},"excerpt":{"rendered":"

Air quality in New York City is an essential public health concern that impacts millions of residents. My objective was to analyze particulate matter (PM2.5) concentration and its correlation with traffic volume and building emissions using data provided by the NYC Open Data platform. This comprehensive dataset enables an examination of air pollution at a…<\/p>\n","protected":false},"author":4135,"featured_media":37885,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[341,1],"tags":[],"coauthors":[1877],"class_list":["post-37884","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-maps","category-visualization"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2024\/04\/xFM5Hctrfbt6jVeaxPWN4o.jpg?fit=1920%2C1080&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-9R2","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/37884","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/users\/4135"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=37884"}],"version-history":[{"count":3,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/37884\/revisions"}],"predecessor-version":[{"id":37943,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/37884\/revisions\/37943"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/37885"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=37884"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=37884"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=37884"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=37884"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}