Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases
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 develo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/10/4/261 |
| _version_ | 1797537830014025728 |
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| author | Olga De Cos Valentín Castillo David Cantarero |
| author_facet | Olga De Cos Valentín Castillo David Cantarero |
| author_sort | Olga De Cos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:21:50Z |
| format | Article |
| id | doaj.art-069164879ff549da8eb895cf14b91104 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T12:21:50Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-069164879ff549da8eb895cf14b911042023-11-21T15:21:16ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-04-0110426110.3390/ijgi10040261Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily CasesOlga De Cos0Valentín Castillo1David Cantarero2Department of Geography, Urban Planning and Land Planning, University of Cantabria, 39005 Santander, SpainDepartment of Geography, Urban Planning and Land Planning, University of Cantabria, 39005 Santander, SpainResearch Group on Health Economics and Health Services Management–Marqués de Valdecilla Research Institute (IDIVAL), 39011 Santander, SpainThe 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.https://www.mdpi.com/2220-9964/10/4/261emerging hotspotsintelligence locationspatial patternsmicrodataspace–time trendsgeoprevention |
| spellingShingle | Olga De Cos Valentín Castillo David Cantarero 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 emerging hotspots intelligence location spatial patterns microdata space–time trends geoprevention |
| title | Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases |
| title_full | Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases |
| title_fullStr | Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases |
| title_full_unstemmed | Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases |
| title_short | Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases |
| title_sort | differencing the risk of reiterative spatial incidence of covid 19 using space time 3d bins of geocoded daily cases |
| topic | emerging hotspots intelligence location spatial patterns microdata space–time trends geoprevention |
| url | https://www.mdpi.com/2220-9964/10/4/261 |
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