Several studies on spatial patterns of COVID-19 show huge differences depending on the country or region under study, although there is some agreement that socioeconomic factors affect these phenomena. The aim of this paper is to increase the knowledge of the socio-spatial behavior of coronavirus and implementing a geospatial methodology and digital system called SITAR (Fast Action Territorial Information System, by its Spanish acronym). We analyze as a study case a region of Spain called Cantabria, geocoding a daily series of microdata coronavirus records provided by the health authorities (Government of Cantabria—Spain) with the permission of Medicines Ethics Committee from Cantabria (CEIm, June 2020). Geocoding allows us to provide a new point layer based on the microdata table that includes cases with a positive result in a COVID-19 test. Regarding general methodology, our research is based on Geographical Information Technologies using Environmental Systems Research Institute (ESRI) Technologies. This tool is a global reference for spatial COVID-19 research, probably due to the world-renowned COVID-19 dashboard implemented by the Johns Hopkins University team. In our analysis, we found that the spatial distribution of COVID-19 in urban locations presents a not random distribution with clustered patterns and density matters in the spread of the COVID-19 pandemic. As a result, large metropolitan areas or districts with a higher number of persons tightly linked together through economic, social, and commuting relationships are the most vulnerable to pandemic outbreaks, particularly in our case study. Furthermore, public health and geoprevention plans should avoid the idea of economic or territorial stigmatizations. We hold the idea that SITAR in particular and Geographic Information Technologies in general contribute to strategic spatial information and relevant results with a necessary multi-scalar perspective to control the pandemic.
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.
A geographic perspective is essential in tackling COVID-19. This research study is framed in the collaboration project set up by the University of Cantabria, the Valdecilla Hospital Research Institute (IDIVAL) and the Regional Government of Cantabria. The case study is the Santander functional urban area (FUA), which is considered from a multi-scale perspective. The main source is the daily records of micro-data on COVID-19 cases and the methodology is based on ESRI geo-technologies, and more specifically on a tool called SITAR (a Spanish acronym which stands for Fast-Action Territorial Information System). The main goal is to analyse and contribute to knowledge of the spatial patterns of COVID-19 at neighbourhood level from a space-time perspective. To that end the research is based on data mining methods (3D bins and emerging hot-spots) and exploratory geo-statistical analysis (Global Moran’s Index, Nearest Neighbourhood and Ordinary Least Square analyses, among others). The study identifies space-time patterns that show significant hot-spots and demonstrates a high presence of the virus at building level in neighbourhoods where residential and economic uses are mixed. Knowing the spatial behaviour of the virus is strategically important for proposing geo-prevention keys, reducing spread and balancing trade-offs between potential health gains and economic burdens resulting from interventions to deal with the pandemic.
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