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Project: Dynamic Covid-19 visualizations (a.k.a movies)
I had not seen any dynamic visualizations (a.k.a. movies) of the
spread of Covid-19 in the U.S., or the world for tha matter, so I made
state and county level visualizations for the U.S. using the Covid-19 data from
the New York Times.
The metric used for determining the
risk class was the seven (7) day average of the number of new daily Covid-19 cases per 100,000 people.
Four risk classes were used: good, spreading, fast spread, and bad (my
name choices), plus a zero value or no data class. A rationale for this metric can be found
here.
For these visualizations states or counties not reporting data are
shown in a gray color. In addition, states or counties having a metric
value of exactly zero on a particular date are also shown as gray.
This was done for three reasons.
- Zero values could have occurred from rounding down to a small
number of decimal places. While these would still have been in the
good category, it was not possible to distinguish a rounded to
zero value from the following two situations.
- Zero values could have been reported by a state or county
because they were no longer tracking new cases and reporting actual
case counts.
- Zero values could have been used as a default value for the
numeric data during processing, so a state or county that was not reporting could
have been assigned a zero value for the metric.
- An additional risk class was added for values of the the seven
day average of new daily cases per 100,000 people greater than 40,
using the color purple. This was done to provide a little bit more
granularity when daily case counts were rising rapidly.
- For the experimental daily trends a 14 day moving average was
used to reduce jitter in the daily trend color changes.
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