A geographical information system model to define COVID-19 problem areas with an analysis in the socio-economic context at the regional scale in the North of Spain

Abstract

The work presented concerns the spatial behaviour of coronavirus disease 2019 (COVID-19) at the regional scale and the socio-economic context of problem areas over the 2020-2021 period. We propose a replicable geographical information systems (GIS) methodology based on geocodification and analysis of COVID-19 microdata registered by health authorities of the Government of Cantabria, Spain from the beginning of the pandemic register (29th February 2020) to 2nd December 2021. The spatial behaviour of the virus was studied using ArcGIS Pro and a 1x1 km vector grid as the homogeneous reference layer. The GIS analysis of 45,392 geocoded cases revealed a clear process of spatial contraction of the virus after the spread in 2020 with 432 km2 of problem areas reduced to 126.72 km2 in 2021. The socio-economic framework showed complex relationships between COVID-19 cases and the explanatory variables related to household characteristics, socio-economic conditions and demographic structure. Local bivariate analysis showed fuzzier results in persistent hotspots in urban and peri-urban areas. Questions about ‘where, when and how’ contribute to learning from experience as we must draw inspiration from, and explore connections to, those confronting the issues related to the current pandemic.

Geographical Analysis of COVID-19 Epidemiology: A Review

“…Assessing the spatial distribution of the COVID-19 pandemic at different scales Geographical analysis has been widely applied to study the spatial distribution and the spread of the COVID-19 epidemic at different scales such as the global scale (16-18), regional scale (19,20) and the country or state scale (21,22). A typical study at the global scale was carried out when a sample of 186 countries divided into four main regions, notably: Asia-Pacific and the Middle East, Europe, Africa and America was used successfully to study the effectiveness and consequences of Covid-19 measures (16).…”

“…A typical study at the global scale was carried out when a sample of 186 countries divided into four main regions, notably: Asia-Pacific and the Middle East, Europe, Africa and America was used successfully to study the effectiveness and consequences of Covid-19 measures (16). As for regional scale, a geographical information system model was employed to define COVID-19 problem areas with an analysis in the socio-economic context at the regional scale in the North of Spain (19). At the country scale, many studies have also been applied geographical analysis to study the spread of the COVID-19 pandemic over the world.…”

The Role of Functional Urban Areas in the Spread of COVID-19 Omicron (Northern Spain)

“…The main stage analyzes 3D-bins and emerging hot spots based on Getis-Ord Gi* statistics [ 34 ] to identify hot spots as spreading areas and Mann-Kendall statistics to determine trends [ 35 ]. Following on from previous research based on 3D-bins implemented from geocoded microdata on COVID-19 cases [ 36 – 38 ], here the research considers the 3D-bins creation tool from previous locations (municipalities) as other authors have with point layers of cities [ 39 ] and polygons of counties and districts [ 40 , 41 ]. 3D-bins based on municipalities accumulate cases over time in 14-day internal time slides, considering the 2-week periods commonly used by health authorities to calculate cumulative incidences.…”

The role of the socio-economic context in the spread of the first wave of COVID-19 in the Marche Region (central Italy)

“…The global literature on the spread of COVID-19 considers several perspectives (such as epidemiologic, demographic, socio-economic and environmental) and multiple scales (from global to local) [ [17] , [18] , [19] ]. Information about the availability of health facilities or contingency plans have been thoroughly explored to explain the crucial contribution to the impact and severity of the COVID-19 pandemic [ 19 , 20 ].…”

“…Information about the availability of health facilities or contingency plans have been thoroughly explored to explain the crucial contribution to the impact and severity of the COVID-19 pandemic [ 19 , 20 ]. Likewise, spatial and demographic dimensions of the community appeared important factors of COVID-19 diffusion [ 17 , 21 , 22 ], especially in nowadays globalized lifestyle in which mobility is the essence [ [23] , [24] , [25] , [26] ]. Indeed, several studies have already analyzed how the lockdown, limiting people mobility and interactions, contained the diffusion of COVID-19 [ 10 , 27 , 28 ].…”