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Article

Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases

1
Department of Geography, Urban Planning and Land Planning, University of Cantabria, 39005 Santander, Spain
2
Research Group on Health Economics and Health Services Management–Marqués de Valdecilla Research Institute (IDIVAL), 39011 Santander, Spain
3
Department of Economics, University of Cantabria, 39005 Santander, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 261; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040261
Received: 25 February 2021 / Revised: 7 April 2021 / Accepted: 11 April 2021 / Published: 13 April 2021
The space–time behaviour of COVID-19 needs to be analysed from microdata to understand the spread of the virus. Hence, 3D space–time bins and analysis of associated emerging hotspots are useful methods for revealing the areas most at risk from the pandemic. To implement these methods, we have developed the SITAR Fast Action Territorial Information System using ESRI technologies. We first modelled emerging hotspots of COVID-19 geocoded cases for the region of Cantabria (Spain), then tested the predictive potential of the method with the accumulated cases for two months ahead. The results reveal the difference in risk associated with areas with COVID-19 cases. The study not only distinguishes whether a bin is statistically significant, but also identifies temporal trends: a reiterative pattern is detected in 58.31% of statistically significant bins (most with oscillating behaviour over the period). In the testing method phase, with positive cases for two months ahead, we found that only 7.37% of cases were located outside the initial 3D bins. Furthermore, 83.02% of new cases were in statistically significant previous emerging hotspots. To our knowledge, this is the first study to show the usefulness of the 3D bins and GIS emerging hotspots model of COVID-19 microdata in revealing strategic patterns of the pandemic for geoprevention plans. View Full-Text
Keywords: emerging hotspots; intelligence location; spatial patterns; microdata; space–time trends; geoprevention emerging hotspots; intelligence location; spatial patterns; microdata; space–time trends; geoprevention
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MDPI and ACS Style

De Cos, O.; Castillo, V.; Cantarero, D. Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases. ISPRS Int. J. Geo-Inf. 2021, 10, 261. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040261

AMA Style

De Cos O, Castillo V, Cantarero D. Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases. ISPRS International Journal of Geo-Information. 2021; 10(4):261. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040261

Chicago/Turabian Style

De Cos, Olga, Valentín Castillo, and David Cantarero. 2021. "Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases" ISPRS International Journal of Geo-Information 10, no. 4: 261. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040261

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