{"id":5820,"date":"2016-11-29T13:26:04","date_gmt":"2016-11-29T18:26:04","guid":{"rendered":"http:\/\/research.prattsils.org\/?p=5820"},"modified":"2016-11-29T13:26:04","modified_gmt":"2016-11-29T18:26:04","slug":"new-york-citys-uninsured-population","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/new-york-citys-uninsured-population\/","title":{"rendered":"New York City’s Uninsured Population"},"content":{"rendered":"

Introduction:<\/p>\n

For my third project in the Data Visualization class I worked with a community health survey dataset, and chose one that pertained to health care in New York City.\u00a0 A goal was to find out what the uninsured population looked like across the five boroughs, and what the districts with the highest uninsured rates were.<\/p>\n

Three Visualizations:<\/p>\n

This example depicts, nationwide and by county, those who are uninsured. I thought\u00a0the color gradient, along with the legend, utilized did a good job in depicting information in a clear way.<\/p>\n

https:\/\/www.enrollamerica.org\/research-maps\/maps\/changes-in-uninsured-rates-by-county\/<\/p>\n

The next\u00a0example I looked at was from Business Insider, and is a map of “the percentage-point drop in the rate of people without health insurance in each state between 2013 and the first half of 2015.” \u00a0I found it useful to have information shown in the map – like the percentage of uninsured, the state name.<\/p>\n

http:\/\/www.businessinsider.com\/how-many-people-dont-have-insurance-under-obamacare-2015-8<\/p>\n

The last map I looked at no longer focused on the entire United States, but reflected the state \u00a0in Virginia. Specifically, it showed the uninsured rate among adults from the ages of 19-64, or those considered non-elderly in 2014 by region. It was a straightforward but effective map.<\/p>\n

http:\/\/www.vhcf.org\/wp-content\/uploads\/2016\/06\/Uninsured-Rate-Among-Adults-map.jpg<\/p>\n

Materials Used:<\/p>\n

I downloaded a shapefile from NYC.gov<\/a>\u2019s GIS Data For Download page, under the Community Health Survey section. \u00a0 As per the site, these shapefiles \u201ccontain aggregated city wide rates by United Hospital Fund neighborhoods. The health topics cover a number of areas including physical activity, diabetes, obesity, mental health, and sexual risk factors.\u201d Correlating with this, I used a dataset called \u201cHealth Insurance Coverage\u201d sourced from EpiQuery: NYC Interactive Health Data from the section \u201cAccess to Health Care\u201d. After refining the results by selecting \u201cShow Results with Neighborhood Map\u201d, the data was provided.<\/p>\n

CartoDB was utilized for the visualizations in this lab. In this platform GIS and web mapping tools are at hand in a web browser.\u00a0 As mentioned, above, in my case a shapefile file was utilized, and was ready for easy use.<\/p>\n

Lecture slides were referred to, in specific those from 11\/08.\u00a0 The class reading that I mainly referenced was Perkins \u201cCartography- Culture of Mapping \u201d, specifically the section Critical Cartography was helpful.<\/p>\n

Methods Used to Create Visualizations:<\/p>\n

After I downloaded the shapefile I dragged and dropped it in \u201cBuilder\u201d mode in CartoDb. Location data can be easily transferred this way. At this stage all that showed was a map of New York City.<\/p>\n

\"screen-shot-2016-11-29-at-11-05-27-am\"<\/a><\/p>\n

Then I clicked on my dataset \u201cHealth Insurance Coverage\u201d. This reflected the districts only – without any percentage of who was insured\/not. I went back to the dataset in CartoDb, added a column for the percentages of those who are uninsured in each district.\u00a0 Referencing the data on the EpiQuery page I inserted the information for each district in the new column. This addition to my dataset was now reflected in my map – a user can hover over any district and see the percentage of those who are uninsured.<\/p>\n

\"screen-shot-2016-11-15-at-5-21-47-pm\"<\/a><\/p>\n

But the map was still one uniform blue color, with out any differentiation of areas that have higher or lower percentages of uninsured people. While still in Builder mode I changed the color ramp to better reflect the varying uninsured citizens in the five boroughs.<\/p>\n

\"screen-shot-2016-11-29-at-12-48-20-pm\"<\/a><\/p>\n

The map can be accessed here –<\/p>\n

https:\/\/saarabi.carto.com\/builder\/97ca09aa-ab51-11e6-ba63-0e8c56e2ffdb\/embed<\/p>\n

Results:<\/p>\n

It was interesting to see that in most boroughs there are high rates of uninsured people.\u00a0 Queens, Brooklyn, and Manhattan all had areas in which high percentages of those who, for various reasons, are not covered by health care. Specific neighborhoods like Flushing, Sunset Park, and Harlem had some of the highest rates in their particular boroughs. Using the gradient colors to highlight this was effective.<\/p>\n

Future directions:\u00a0 A possibility for future work would be to use data pertaining to the uninsured population by age in the five boroughs. From my understanding a large portion of those who are uninsured are between the ages of 18-late twenty\u2019s, often those who do no have steady employment, or are working as independent contractor<\/p>\n","protected":false},"excerpt":{"rendered":"

Introduction: For my third project in the Data Visualization class I worked with a community health survey dataset, and chose one that pertained to health care in New York City.\u00a0 A goal was to find out what the uninsured population looked like across the five boroughs, and what the districts with the highest uninsured rates…<\/p>\n","protected":false},"author":289,"featured_media":5822,"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":[],"class_list":["post-5820","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-visualization"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-1vS","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/5820","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\/289"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=5820"}],"version-history":[{"count":0,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/5820\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=5820"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=5820"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=5820"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=5820"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}