Mapping Covid-19 cases in Shanghai by a half-million epidemiological investigations
Entry type: Single project
Publishing organisation: The Paper
Organisation size: Big
Publication date: 2022-05-01
Authors: Cai Lin, Kong Jiaxing, Tang Yi, Liang Yanjia, Wang Yu, Jiang Yong, Wang Yiyun, Sun Rui, Zhang jin, Ying Zhen, Huang Haoyun, Baiyulan Open AI
Kong Jiaxing：Data mining、Visualization
Wang Yu：Data Collection
Jiang Yong：Data Collection
Wang Yiyun：Data Collection
Sun Rui：Data Collection
Zhang jin：Data Collection
Ying Zhen：Data Collection
Huang Haoyun：Data Collection
Baiyulan Open AI：Data mining and technical support
By analyzing Covid-19 epdemiological investigations of Shanghai from March 1 to April 27, 2022, this video presented many features of Covid-19 in Shanghai while unearthed spatial and temporal distribution as well as data portraits of infected people
As a summary of Shanghai Covid-19, the video discovered the distribution pattern and population characteristics of infected people from epidemiological investigations, which has certain significance for analyzing the infection situation in mega cities. In addition, through solid narrative and delicate animation. After released, the work was widely spread on social media.
1、Processing of massive epdemiological investigations: manual coding, data analysis by python, and using Gaode map service to encode geological data.
2、Visualiztion: Use Gaode map service to plot.
3、Animation effect: mainly use illustrator for graphic design and AE for visual effects creation, using particle effects to make the video more complete and smooth based on code-generated visualization.
Context about the project:
This work has many highlights in data acquisition, processing and visualization. In terms of the acquisition of epidemiological data, we adopted a crowdsourcing method to reform the epidemiological text into a database. Moreover, by using python script and map API, the information of address is transformed into latitude and longitude in batches. In order to make the encoded information more accurate, we contacted professors at Shanghai Jiao Tong University and compared the results of data processing. In the process of visualization, we encoded the data again and merged the data of repeated locations to make the visualization more smoothly and convenient for later video production.
What can other journalists learn from this project?
Previously, we usually use interactive format for datasets in large scale. This time, we used animated video as our format, for better content distribution, to get in touch with a wider range of readers. To adapt with the video format, we organize our content more vividly.