Journal of Geographical Sciences, Volume. 30, Issue 12, 1985(2020)

Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration

Yuyao YE1、*, Changjian WANG1, Hong’ou ZHANG1, Ji YANG1,2, Zhengqian LIU1,3, Kangmin WU1, and Yingbin DENG1
Author Affiliations
  • 1Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
  • 2Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 511458, China
  • 3School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China
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    Figures & Tables(11)
    Research framework
    Probability distribution qf(t) of the lag period of COVID-19 cases imported into Shenzhen
    Trends of daily imported risk (Riskinput) of the COVID-19 epidemic in various cities in Guangdong Province
    Cumulative imported risk (Riskinput) situation of various cities in Guangdong Province
    Spatial distribution of cumulative imported risks by city (Guangdong Province)
    Spatiotemporal distribution of diffusion risk coefficients in cities across Guangdong
    Daily diffusion risk (Riskdiffusion) of cities across Guangdong Province
    Cumulative diffusion risk (Riskdiffusion) status in cities across Guangdong
    Spatiotemporal distribution of cumulative diffusion risk across cities in Guangdong Province
    • Table 1.

      Explanation of some variables

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      Table 1.

      Explanation of some variables

      VariableExplanationValue range and type
      Case numberReleased by Shenzhen Health Commission
      SexPatient’s sexMale/female
      AgePatient’s ageInteger, 0-100
      Place of residencyProvince-City, e.g., Guangdong-Shenzhen
      Time in WuhanTime period, YYYY/MM/DD-YYYY/MM/DDData/time period
      Time of arrival at ShenzhenYYYY/MM/DDDate
      Time of illness onsetYYYY/MM/DDDate
      Cause of infection0: Resided in or visited Wuhan; 1: Resided in or visited a place in Hubei outside of Wuhan 2: Had close contact with a person who resided in or visited Hubei but did not reside in or visit Hubei; 3: Had close contact with a confirmed case but did not reside in or visit Hubei; NA: Unknown0/1/2/3/NA
    • Table 2.

      Statistical analysis of the indicator variables

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      Table 2.

      Statistical analysis of the indicator variables

      VariableUnitMinimum valueMaximum valueMeanStandard deviation
      Size of permanent population10,000 person189.11001490.4400540.2857340.7044
      Size of mobile population10,000 person15.5563875.4548155.5160233.0246
      Number of health care institutions838.00004598.00002453.66671067.6306
      Number of hospital beds6682.000095134.000024617.761919017.7786
      Number of health care workers13666.0000188695.000043890.619040236.0240
      Number of industrial firms244.00007937.00002328.71432600.5551
      Is there an airport/010.38100.4976
      Traffic volume10,000 vehicles2189.95107122.220118495.6372527728.30962
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    Yuyao YE, Changjian WANG, Hong’ou ZHANG, Ji YANG, Zhengqian LIU, Kangmin WU, Yingbin DENG. Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration[J]. Journal of Geographical Sciences, 2020, 30(12): 1985

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    Paper Information

    Category: Research Articles

    Received: Mar. 9, 2020

    Accepted: Sep. 28, 2020

    Published Online: May. 7, 2021

    The Author Email:

    DOI:10.1007/s11442-020-1823-7

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