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Comparative Study
. 2021 Feb 22;18(4):2135.
doi: 10.3390/ijerph18042135.

COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China

Peng Zeng  1 , Zongyao Sun  1 , Yuqi Chen  2 , Zhi Qiao  3 , Liangwa Cai  1
Affiliations
Comparative Study

COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China

Peng Zeng et al. Int J Environ Res Public Health. .

Abstract

When a public health emergency occurs, a potential sanitation threat will directly change local residents' behavior patterns, especially in high-density urban areas. Their behavior pattern is typically transformed from demand-oriented to security-oriented. This is directly manifested as a differentiation in the population distribution. This study based on a typical area of high-density urban area in central Tianjin, China. We used Baidu heat map (BHM) data to calculate full-day and daytime/nighttime state population aggregation and employed a geographically weighted regression (GWR) model and Moran's I to analyze pre-epidemic/epidemic population aggregation patterns and pre-epidemic/epidemic population flow features. We found that during the COVID-19 epidemic, the population distribution of the study area tended to be homogenous clearly and the density decreased obviously. Compared with the pre-epidemic period: residents' demand for indoor activities increased (average correlation coefficient of the floor area ratio increased by 40.060%); traffic demand decreased (average correlation coefficient of the distance to a main road decreased by 272%); the intensity of the day-and-night population flow declined significantly (its extreme difference decreased by 53.608%); and the large-living-circle pattern of population distribution transformed to multiple small-living circles. This study identified different space utilization mechanisms during the pre-epidemic and epidemic periods. It conducted the minimum living security state of an epidemic-affected city to maintain the operation of a healthy city in the future.

Keywords: COVID-19; dense urban area of China; population agglomeration index (PAI) and population tidal index (PTI); public-health resilience; ‘people-oriented’ concept.

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Conflict of interest statement

The authors declare that there is no conflict of interest.

Figures

Figure 1
Figure 1
Study area.
Figure 2
Figure 2
Technology roadmap.
Figure 3
Figure 3
The population agglomeration index distribution during the pre-epidemic (a) and epidemic (b) periods.
Figure 4
Figure 4
The population tidal index distribution during the pre-epidemic (a) and epidemic (b) periods.
Figure 5
Figure 5
The local Moran’s I results during the pre-epidemic (a) and epidemic (b) periods.
Figure 6
Figure 6
Anomalies of factors’ coefficient during the pre-epidemic and epidemic periods. (a) HH type; (b) LL type; (c) HL type; (d) LH type.
Figure 7
Figure 7
Living-circle modes of the pre-epidemic (a) and epidemic (b) periods.
See this image and copyright information in PMC

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References

    1. Whyte W.H. The Social Life of Small Urban Spaces. Conservation Foundation; Washington, DC, USA: 1980.
    1. Masso A., Silm S., Ahas R. Generational differences in spatial mobility: A study with mobile phone data. Popul. Space Place. 2019 doi: 10.1002/psp.2210. - DOI - PMC - PubMed
    1. Gushulak B.D., Macpherson D.W. The basic principles of migration health: Population mobility and gaps in disease prevalence. Emerg. Themes Epidemiol. 2006;3:3. doi: 10.1186/1742-7622-3-3. - DOI - PMC - PubMed
    1. Harding N., Spinney R.E., Prokopenko M. Population mobility induced phase separation in SIS epidemic and social dynamics. Sci. Rep. 2020;10 doi: 10.1038/s41598-020-64183-1. - DOI - PMC - PubMed
    1. Tamagusko T., Ferreira A. Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic. Sustainability. 2020;12:9775. doi: 10.3390/su12229775. - DOI

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