Abstract
At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy.
Highlights
At the end of 2019, the COVID-19 pandemic has become a public health event affecting the whole world
All the control points are connected in chronological order to form a polyline that represents the actual movement trajectory of the center of COVID-19, and the expected trend path represents the trend of the movement of the epidemic
It is expected that the results presented by the expected trend path and the space-time path are consistent with the results of the control point analysis
Summary
At the end of 2019, the COVID-19 pandemic has become a public health event affecting the whole world. It has left deep traces on the political activities, economic activities and people’s daily lives in many countries and regions. It has profoundly affected the spatial mobility of many populations. People needed to be medically isolated if they were identified as confirmed patients or close contacts. The spread of this virus shows remarkable temporal and spatial characteristics. Today, we recognize the importance of mobile data and mobile analytics in crisis mitigation and public health [2]
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