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How did COVID impacted our community ?
How did COVID impacted our community ?
The entire world suffers under Covid 19 since early 2020. To understand how covid actually impact our daily life, public health officials teamed up with google maps, and used data to evaluate how people react during the pandemic. Furthermore, the anonymized data could help authorities to make critical decisions that can save many lives.
To understand the plot, the meaning of each data point should be explained. Each data point presented average time spend in each category location changed compared to baseline. It presented, people spend more time or less time at each location compared to a baseline day. Weekends and holidays will impact the data.
In the dataset, the earliest record started on February 15, 2020. Early 2020, even the virus was sweeping through globally, people in United State did not panic due to the fact that the country only had a few cases.
March 6, 21 passengers on a California cruise ship were tested positive. It was the first time that the US public started paying attention to the person-to-person spread virus. California authorities' strong reaction that refusing the ship to dock at the harbor immediately sparked fears among the people. A huge wave of shopping rush began in the next few days.
March 13, President Donald Trump declares COVID-19 a National Emergency. The governments soon announced a series of temporary laws pushing activities such as remote working and learning. The plot clearly shows trends that people spend significantly less amount of time at transportation, workplace, and retail/recreation.
As time moves to April, after a long period of lockdown, the determination of fighting the virus has shaken. Enforcing the great lockdown was getting harder and harder. April 24, 2020, Georgia, Alaska, and Oklahoma announced partially reopening their states. Trends that regress to normal begin to show after that.
Notice that huge dips on the plots (ie.April 12) may due to insufficient data.
Storytelling with the data
Storytelling with the data
The previous plot expressed all the data in a line plot, which is a great way to present time series data. However, it can feel crowded with six categories to follow.
To extract the key idea that fewer people are going to densely populated locations, we combine transportation, workplace, and retail/recreation into one category by taking the average of the three. The three categories roughly have a similar pattern after the lockdown. By renaming the category as going out, we could eliminate the number of lines on the plot and highlight what is important. Similar ways to handle grocery runs and park trips. The two categories tangled around the baseline before and after the national emergency announcement with limited impact on our conclusion.
The original dataset contains both positive and negative percentage values. To normalize the number and bring in more meaning, all the numbers are now in decimals format.
Three annotations are kept to express how social affairs impact the community. Also, data for April 12 are being picked out.
With more intuitive visualization, a similar conclusion can be made with an accelerated conclusion-making process. As we could clearly observe, people spend more time at home and less time in crowded places. In addition, the average time spent at each category is slowly migrating to normal.
Additional Source: CDC Museum COVID-19 Timeline
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