{"id":39724,"date":"2026-03-31T15:12:38","date_gmt":"2026-03-31T19:12:38","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=39724"},"modified":"2026-03-31T15:12:40","modified_gmt":"2026-03-31T19:12:40","slug":"the-leading-causes-of-death-in-nyc-as-they-relate-to-gender-and-ethnicity","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/the-leading-causes-of-death-in-nyc-as-they-relate-to-gender-and-ethnicity\/","title":{"rendered":"The Leading Causes of Death in NYC as They Relate to Gender and Ethnicity"},"content":{"rendered":"\n

The data I chose to work with in Tableau came from NYC Open Data, gathered by the Department of Health and Mental Hygiene. Its data reviews the leading causes of death in New York City, with additonal information on how these numbers relate to gender and ethnicity. This dataset was most helpful for many of the questions I had centering around the difference in health and safety between genders and ethnicities because it is annually updated, providing imperative data including leading causes, total deaths and the death rate related to gender and ethnicity.<\/p>\n\n\n\n

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My first graph is a simple line graph portraying the total deaths between 2007 and 2021. It shows the highest number of deaths in white Non-Hispanic people in New York City, followed by Black Non-Hispanic people and Hispanic people. A challenge I had with this graph was deciding which colors to attribute to each group, as I didn\u2019t want to lean on stereotypical colors associated with different ethnicities as this can be problematic, but also didn\u2019t want to lead with colors that read too positive, as this graph does have to do with death. Moving forward, I think I would use this data in a more narrow way, and through this I would be able to highlight a single ethnicity in one color and leave the others as a neutral collective color, instead with their lines simply labelled.<\/p>\n\n\n\n

This graph also holds lots of bias, as the number of deaths relates to the total population, as opposed to viewing this data in proportion to each other. This could lead to problems. For instance in a more specific case, if this data was used to show covid-19 deaths, it would reveal that White Non-Hispanic were most impacted by the pandemic. While this data is still true, it doesn\u2019t address the nuance of this topic because it doesn\u2019t show the data in proportion to population density.\u00a0
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This data also was helpful in comparing the death rate of leading causes as they relate to gender in New York City. In this stacked bar graph, the death rate is compared by gender, specifically showing that diseases of the heart have the highest death rate, followed by malignant neoplasms, or cancer, impacting both sexes relatively equally. While there were many additional leading causes of death available in the dataset, I chose to filter this down to the most impactful causes as to keep the visualization clear and concise. I also chose blue and pink to represent this data, as this most clearly relates to male and female, making it easy for the viewer to assume what this data shows. 
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This data could also be shown in a side by side bar chart, which might show the comparison between genders more clearly. While it looks that the stacked bar is divided evenly between the two genders in the causes with the highest death rate, it is challenging to see whether this is fully accurate. Additionally, other causes do have significant differences that could be shown more clearly side by side.<\/p>\n\n\n\n

Information that this data is lacking is the deaths of those who don\u2019t identify within the binary of gender. This is something that I would hope to find later in a different dataset that could work in tandem with this one. I would be interested to see how the health of those outside of the gender binary is impacted in comparison to those identifying strictly as a man or woman. This data would be similar to the ethnicity data, in that it would need to be compared in proportion to each other, as opposed to strictly based on the number of deaths.<\/p>\n\n\n\n

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To dive deeper into this data, I chose to focus on mental health and how it impacts different ethnicities. This stacked bar graph shows the death rates of mental and behavioral health related deaths, specifically involving alcohol and drugs, as well as suicide. This visualization shows that there are more deaths related to alcohol and drugs than deaths by suicide. It\u2019s clear that Black Non-Hispanic people are more impacted by drugs and alcohol than by suicide, while the opposite is true for Asian and Pacific Islander people. Within the alcohol and drug related deaths due to mental health struggles, this visualization shows that White Non-Hispanic, Hispanic and Black Non-Hispanic people are relatively equally impacted.<\/p>\n\n\n\n

I found centering this data around a specific focus, for example mental health, was a beneficial way to break down such a large and sprawling dataset, giving specific context to social issues in order to understand how different health struggles impact groups differently. Here using the death rate, as opposed to the sum of deaths, I was able to get a more balanced picture of this impact, as White Non-Hispanic data no longer dominates the visualization simply because it is the largest population. 
Taking my questions regarding how gender and ethnicity impacts the death rates of leading causes of death, specifically focusing on mental health, I believe it would be interesting to next look into mental health resources throughout the city and find data surrounding the use of these resources based on ethnicity, through access and outreach that targets specific groups. This type of data would give context to the analysis shown in the bar chart above, revealing how different ethnicities are impacted by suicide and mental health issues. Through this analysis, and with additional adjacent data, the city could find more ways to reach those struggling to provide stronger assistance and care.
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The data I chose to work with in Tableau came from NYC Open Data, gathered by the Department of Health and Mental Hygiene. Its data reviews the leading causes of death in New York City, with additonal information on how these numbers relate to gender and ethnicity. This dataset was most helpful for many of…<\/p>\n","protected":false},"author":5069,"featured_media":39780,"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":[1],"tags":[],"coauthors":[1931],"class_list":["post-39724","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-visualization"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2026\/03\/Sheet-2.jpg?fit=2026%2C1088&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-akI","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39724","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\/5069"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=39724"}],"version-history":[{"count":3,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39724\/revisions"}],"predecessor-version":[{"id":39781,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39724\/revisions\/39781"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/39780"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=39724"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=39724"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=39724"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=39724"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}