{"id":2754,"date":"2021-10-02T22:47:05","date_gmt":"2021-10-03T03:47:05","guid":{"rendered":"https:\/\/blog.richmond.edu\/livesofmaps\/?p=2754"},"modified":"2021-10-02T22:47:05","modified_gmt":"2021-10-03T03:47:05","slug":"tracking-covid-19-cases-in-the-us-interactive-map","status":"publish","type":"post","link":"https:\/\/blog.richmond.edu\/livesofmaps\/2021\/10\/02\/tracking-covid-19-cases-in-the-us-interactive-map\/","title":{"rendered":"Tracking COVID-19 Cases in the US (Interactive Map)"},"content":{"rendered":"

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Be aware of COVID-19, but also the maps too\u2026 Our understanding of and behavioral response to COVID-19 has been undeniably well understood when placed into the context of maps. Their visualizations help everyday people and scientists track its dangerous spread. When well composed and produced, maps can build awareness and shape more thoughtful decision-making locally, nationally, or globally.<\/span><\/p>\n

This particular COVID-19 map aims to show the number of cases and deaths of COVID-19 from “all-time” and for the last 7 days, in a pretty simplistic way. The map is published, updated, and configured by CNN. It receives all of its data from the Johns Hopkins Center for Systems Science and Engineering. Looking at this map for the first time, it appears professional, scientific. There are also countless sources to corroborate this illustration. However, an analysis of this map revealed multiple inconsistencies. Even a critical footnote on CNN’s page cites that actual data from this pandemic is undoubtedly missing. The transmission of this virus occurred quickly, and specific country resources were not being administered at the time. This results in an “incomplete picture” as data from infected people in the early stages were not recorded. This includes undiagnosed cases and asymptomatic people who were untreated, while at the same time, COVID deaths went untracked as the virus-infected countless communities.<\/span><\/p>\n

While a map is vital to tracking something as widespread as COVID, it can only do so much. Maps are a constant battle with details and underlying portrayals. This map has to simplify some of the complexities of this pandemic, as certain intricacies would take up a lot of time and resources for researchers to source and obtain reliable information. This effect silences some significant data figures that would help the reader understand the reach of COVID-19 a little better. Some specific things to keep in mind that are excluded from this map are: variants aren’t tracked (COVID-19 is depicted as a whole), the role that politics play in science and how certain areas of the country have different ideas about masking, and the severity of the cases in infection from cold-like symptoms to hospitalizations. All of these are left out.\u00a0<\/span><\/p>\n

Additionally, the most considerable neglect in this map is the failure to highlight the infection rates within the parameters of a county. For example, I live in Java, a town located in Pittsylvania County (VA). This county also contains the city of Danville. Are the statistics an accurate reflection of the transmissions that occur within my town of 900 people? Most definitely not; the majority of them are probably within the city of Danville. Still, for the sake of resources and providing a semi-specific guide, John Hopkins spent their time recording the county’s rate of infection rather than each town or city. From my perspective, this cartographic choice was practical. However, in cases such as maps, the more specific information maps reveal, the greater the insight into the whole circumstance it brings.<\/span><\/p>\n

This map has a distinct approach in its projection. While mapping by county is an easy way to categorize data for the people that live there, it also makes certain areas appear more affected by COVID. Bigger counties such as those found in the west grab the readers’ attention more easily, because of their larger area. Smaller counties such as those found in the northeast are extremely infected but appear less of a hot spot for COVID-19. This takes the attention away from smaller counties with potentially higher infections and makes them seem less of a hub for COVID-19 spread. The color choice also plays a large part in the perception of this map. The choice of varying degrees of red was a big tell in this map. To most people, red is associated with negativity. The use of varying degrees of red put into perspective that regardless of how infected your county is, COVID-19 is still a severe threat. Everyone is at risk, and this map does not fail to present this with the color choice. The projection of the map plays a large part in the perspective of the readers\u2019 analysis. This map emphasizes the seriousness of the virus but fails to do it in a way that would limit attention-grabbing distractions.<\/span><\/p>\n

Previously data depicting globally-impact type maps were collected manually. With the resources of today, details, data, and knowledge can be shared with the world in an instant. This map tracks the county’s movement and infection of COVID since the first reported cases of the disease. It also is an underlying resource into the disproportional effect COVID has on specific regions in the United States. This map helps visualize those areas of need. This map goes a long way to helping us understand the details of COVID-19, but also has a long way to go in regards to presenting a more holistic approach to the infection and spread. This is certainly a map that has allowed us to approach the country in a tentative manner. In regards to future maps of this type, hopefully, it never has to be presented again.<\/span><\/p>\n

 <\/p>\n

Work Cited:<\/span><\/p>\n

Hernandez, Sergio, et al. (unknown). “Tracking Covid-19 Cases in the US. (interactive map)” CNN, Cable News Network. Accessed on September 28, 2021 from<\/span><\/p>\n

https:\/\/www.cnn.com\/interactive\/2020\/health\/coronavirus-us-maps-and-cases\/<\/span><\/a>.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Be aware of COVID-19, but also the maps too\u2026 Our understanding of and behavioral response to COVID-19 has been undeniably well understood when placed into the context of maps. Their visualizations help everyday people and scientists track its dangerous spread. … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":5437,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21024],"tags":[],"class_list":["post-2754","post","type-post","status-publish","format-standard","hentry","category-maps-of-the-week"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/posts\/2754","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/users\/5437"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/comments?post=2754"}],"version-history":[{"count":1,"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/posts\/2754\/revisions"}],"predecessor-version":[{"id":2756,"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/posts\/2754\/revisions\/2756"}],"wp:attachment":[{"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/media?parent=2754"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/categories?post=2754"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.richmond.edu\/livesofmaps\/wp-json\/wp\/v2\/tags?post=2754"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}