{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:25:19Z","timestamp":1760235919280,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key Program of National Natural Science Foundation of China","award":["No. 41830645"],"award-info":[{"award-number":["No. 41830645"]}]},{"name":"Funding program: CAE Advisory Project","award":["No. 2020-ZD-16"],"award-info":[{"award-number":["No. 2020-ZD-16"]}]},{"name":"Liuhe District People's Government Cooperation agreement between Wuhan University and Beidou innovation and application Joint Laboratory","award":["No. 4201\/250071411"],"award-info":[{"award-number":["No. 4201\/250071411"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The outbreak of COVID-19 has constantly exposed health care workers (HCWs) around the world to a high risk of infection. To more accurately discover the infection differences among high-risk occupations and institutions, Hubei Province was taken as an example to explore the spatiotemporal characteristics of HCWs at different scales by employing the chi-square test and fitting distribution. The results indicate (1) the units around the epicenter of the epidemic present lognormal distribution, and the periphery is Poisson distribution. There is a clear dividing line between lognormal and Poisson distribution in terms of the number of HCWs infections. (2) The infection rates of different types of HCWs at multiple geospatial scales are significantly different, caused by the spatial heterogeneity of the number of HCWs. (3) With the increase of HCWs infection rate, the infection difference among various HCWs also gradually increases and the infection difference becomes more evident on a larger scale. The analysis of the multi-scale infection rate and statistical distribution characteristics of HCWs can help government departments rationally allocate the number of HCWs and personal protective equipment to achieve distribution on demand, thereby reducing the mental and physical pressure and infection rate of HCWs.<\/jats:p>","DOI":"10.3390\/ijgi10100691","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T09:10:49Z","timestamp":1634202649000},"page":"691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Exploring the Spatiotemporal Characteristics of COVID-19 Infections among Healthcare Workers: A Multi-Scale Perspective"],"prefix":"10.3390","volume":"10","author":[{"given":"Hui","family":"Ren","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1209-6340","authenticated-orcid":false,"given":"Peixiao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China"},{"name":"Nanjing Beidou Joint Laboratory of Innovation and Application, Wuhan University, Nanjing 211599, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyan","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China"},{"name":"Nanjing Beidou Joint Laboratory of Innovation and Application, Wuhan University, Nanjing 211599, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, G., Guan, J.-L., Zhu, X.-Q., Wang, M.-R., Fang, D., Wen, Y., Xie, M., Tian, D.-A., and Li, P.-Y. 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