HSBC Business School Associate Professor Ye Weiming's team paper published in "Fusion"

Recently, Associate Professor Ye Weiming of Peking University HSBC Business School and his collaborator Zhao Luming (2019 graduate student of Finance and Media at Peking University HSBC Business School) published the paper "Visualization as Infrastructure: China's Data Visualization Politics during COVID-19 and Their Implications for Public Health Emergencies ( "Visualization as Infrastructure: The Politics of Data Visualization in China During the New Coronary Pneumonia Epidemic and Its Implications for Public Health Events") was officially published in Convergence , a well-known international journal in the field of communication

Convergence: The International Journal of Research into New Media Technologies is an international peer-reviewed academic journal focusing on contemporary convergent media and related innovative technologies

How can the data visualization work represented by the "health code" be embedded in the social governance work in the fight against the new crown pneumonia epidemic? Ye Weiming and his collaborators carried out case analysis around Shenzhen, combined with the focus group of the "Shenzhen Health and Health Commission" team and the in-depth interview materials of Shenzhen residents, and pointed out that when the city faced the epidemic crisis, six four-level visualizations (Figure 1, Figure 2) ) as a data intermediary on residents’ perceptions and social action patterns

Figure 1: Six data visualization methods frequently used in China during epidemic prevention and control

Figure 2: Four-level visualization system used in China during the COVID-19 outbreak

This paper extends the concept of data politics proposed by Ruppert et al.

Figure 3: Tripartite Power Relationship Framework and Policy Making in Data Visualization

In this process, visualization can be seen as an infrastructural technical system that supports information dissemination and collective action in public crises

In addition, based on the people's understanding of data and the actual operation of visualization, the article also proposes an evaluation framework to measure the effectiveness of visualization at the level of persuasion and mobilization: cognition, understanding, belief and action

Figure 4: Definitions and underlying factors of data visualization effectiveness

Citation: Zhao, L.

About the Author:

Ye Weiming, associate professor at Peking University HSBC Business School, Ph.

Zhao Luming, a 2019 graduate student of Finance and Media at Peking University HSBC Business School, majoring in intelligent media and digital journalism