COVID Death Toll:
Data-viz
A Data visualization of total COVID death in the U.S. from the beginning till Feb 2022. The main purpose of it is to convey the number in a new form that could be remembered.
Role /
Team Leader (of 4)
UX Research + Design
3D Modeling + Rendering
AR/MR Scenario Planning
Category /
Post Human-centered Design
Metaverse Experience Design
Background
Sometimes, using numbers is a good way to express the results, facts and show the importance. However, it is not a good way to tell the story behind that or express the correct feelings and purposes. In this case, we used COVID daily death data in Data Visualization, bringing a new Monument to tell one thing --- the passing lives are not numbers, we should remember this. To achieve the 3D animation, we used Meta ball effect in ThreeJS.
Challenge & Outcome
COVID has caused lots of damages, and the impact is still there. Till now, there are many COVID Monuments on the web to let people remember this crisis, or warn people this history. We've seen the changing color of maps to show how fast it spreaded, all the names of people losses by New York Times. However, we might be numb about all the charts, lines and tables, even the number of over 80 million losses in the United States! Therefore we created this animation to help people understand the changes through the time without processing the number in their heads.
-Data is people
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Data Action: Using Data for Public Good by Sarah Williams is focused on the question which is described on the cover: “how to use data as a tool for empowerment rather than oppression”(Williams, 2020). The emphasis is on how nonprofessionals can be involved in data work, and on characteristics of projects that can help ensure those projects will reflect the interests of everyone in a community. Dizikes in his book review describes Williams's overarching theme as “data is people”, collected by particular individuals with particular biases for particular purposes (Dizikes,2020). Being aware of who collected data as well as how and why it was collected is crucial to fully understand and use it.
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As a guide to action, Williams' book is structured around three main chapters:
Build it, hack it, share it.
Indeed, we manually set up some rules and "break points" during the data cleaning process and the design process. There are there main points showing our perspective.
1. In order to have less errors about the total number comparing between national and "states", we use D3.JS to set all the number lower than 0 to 0, and have all the data of states and U.S. Territories together.
2. The purpose of choosing Meta ball is that all the floating bubbles can be animated around, merging and changing sizes and colors. So that it could represent "passed" lives, belonging and something we might be shocked. We will not lose any one of them in the view and have the feeling of connection while as individual because it is important to visualize each live lose in the form of given groups.
3. Using color is hard in data viz. Since we are noting teaching people what's going on, we don't need to use the serious color which might bring discomfort or even spread panic to the public.
-All Data are local
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In All Data are Local: Thinking Critically in a Data-Driven Society, Yanni A. Loukissas sets out to demystify the notion of digital universalism by emphasizing data and their local connections (2019). Data universalism is an ideology that leads us to falsely believe that despise our varying circumstances, “once online, all users could be granted the same agencies on a single network, all differences could dissolve, and everyone could be treated alike”. Loukissas points out "this perspective might institute a new form of colonialism, where practitioners at the periphery have to conform to set standards and expectations of a dominant technological culture"(Loukissas, 2019).
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What does it mean to be local? According to the author, the term local is defined as “an institutionally defined framework with social technological and spatial dimensions, in which data are created, displayed and/or managed, and that reciprocally, is shaped by those practices.” (Loukissas, 2019). To better explain the principles of local data, Loukissas uses the relationship of global and local to illustrate it: “Understanding the relativity of the local also helps us to understand the ways the local is connected to the global. The local creates data, but the data produced may travel globally, running the risk that local origins become obscured when data is examined out of context. It is these contexts or local data settings that are examined to explicate the remaining principles of all data are local” (Loukissas, 2019).
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Furthermore, local coexists with the global, “as data do not serve exclusively local needs, however, there is no global experience of data, only an expanding variety of local encounters. Data travel widely, but wherever they go, that’s where data are. For even when data escape their origins, they are always encountered within other significant local settings”(Loukissas, 2019). While Loukissas highlights the necessity to consider the local perspective when talking about data, he also advises caution, as local is not necessarily equal to good. It might also “mean exclusionary, narrow, or even oppressive”(Loukissas, 2019).
Usually, when we only visualize one source, it is hardly to express the feeling or the perspective of the designers. Meanwhile, in order to eliminate the "local" property of data which might cause fatigue when people viewing it, here, we used national data and each state's daily death data, along with the overall line chart in the back together to give audience freedom changing from day to day, to feel the data points in their own preferences. Combining the macro level of national bubble and the micro level of states bubbles, from different angles to view the connections between each part is the design points.
Reflection
“Quantitative data displays have value – but power and ideology hide behind the presentation of data such that some interests are presented and others are obscured. Articulating the limits of data visualization opens discursive space for more nuanced approaches such as knowledge visualization.” (Boehnert, 2016)
This is not a typical data analyst's work, we wish this form could be powerful to convey our goal—remember them.
