In May of this year, the COVID-19 epidemic broke out almost instantly in Taiwan. The explosive volume of the confirmed cases made the information released by the command center lack details. At the same time, the local government was authorized to disclose information based on epidemic prevention work. The channels for people to receive information suddenly became lots and miscellaneous. READr integrates public information, and through the help of visualization, it fully presents the process of the virus outbreak.
It was the first report at that time to fully show the path of the virus spreading at that time.
Taiwan is often praised for “disclosure of information” in terms of epidemic prevention, but just throwing out information cannot help the public understand the overall situation. READr began to update the data of COVID-19 confirmed cases when the first case appeared in Taiwan in early 2020, and publicly published the information on the Internet (Github). If the information provided by the government is not comprehensive, we will take the initiative to obtain it. This helps us to grasp the most comprehensive information at the first time and present it to readers in a systematic and narrative way.
Data analysis uses R language.
This project is implemented by the jQuery. We make the scene-to-scene transition of the local covid-19 cases with the infoboxes by the jQuery. And we have the Taiwan map to explain the path of the spread of the disease by D3.js with the GeoJSON. When the users scroll the page, we describe the path of the covid-19 cases spread by D3.js. And all of the data are in open data.
What was the hardest part of this project?
READr has maintained and updated the data of each Covid-19 confirmed case every day when the epidemic first entered Taiwan, including age, symptoms, date of onset, etc., and made it public on the Internet for everyone to view and use. After the outbreak, the way to obtain this information changed from watching the press conferences of the central government to watching the press conferences of 22 local governments across Taiwan. We still have not given up on updating these case information.
Perhaps the change in information sources was the government’s way to publish information more quickly, and people everywhere only need to pay attention to the information in their own counties and cities. However, when we look at the overall situation, we found that there were many epidemic breaches caused by cross-county and city movements, as well as epidemic prevention breaches caused by different policies between counties and cities, or the explosion of capital city’s inspection or medical volume. Although these are public information, it is impossible for the normal readers to analyze and digest such a large amount of information.
When the epidemic is still at its peak, we visualized these processes through data and organized them into easy-to-understand report, so that readers can quickly be aware of the spread of the virus. The topic was presented through dynamic pictures and texts, combined with a timeline and map. Through the data and movement trajectories of local cases, it takes readers to review how the local epidemic started and spread to the entire Taiwan. Readers only need to swipe down the website and continuously to read smoothly.
What can others learn from this project?
We have learned two things from this report. First, even an issued-oriented data journalism team can track news like daily news reporters by maintaining data. Furthermore, even if the information is public, you can still find the stories that readers need.