https://doi.org/10.4081/gh.2022.1067
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
Submitted: 28 December 2021
Accepted: 16 February 2022
Published: 18 March 2022
Accepted: 16 February 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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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.
Ahasan R, Hossain MM, 2021. Leveraging GIS and spatial analysis for informed decision-making in COVID-19 pandemic. Health Policy Technol 10:7-9.
DOI: https://doi.org/10.1016/j.hlpt.2020.11.009
Alcântara E, Mantovani J, Rotta L, Park E, Rodrigues T, Campos F, Souza CR, 2020. Investigating spatiotemporal patterns of the COVID-19 in São Paulo State, Brazil. Geospat Health 15:925.
DOI: https://doi.org/10.4081/gh.2020.925
Al Kindi KM, Al-Mawali A, Akharusi A, Alshukaili D, Alnasiri N, Al-Awadhi T, Charabi Y, El Kenawy AM, 2021. Demographic and socioeconomic determinants of COVID-19 across Oman - A geospatial modelling approach. Geospat Health 16:985.
DOI: https://doi.org/10.4081/gh.2021.985
Almendra R, Santana P, Costa C, 2021. Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal. BAGE 91.
DOI: https://doi.org/10.21138/bage.3160
Andrés G, Herrero D, MartÃnez M, 2021. Cartographies on COVID-19 and functional divisions of the territory: an analysis on the evolution of the pandemic based on Basic Health Areas (BHA) in Castile and Leon (Spain). BAGE 91.
Bamweyana I, Okello DA, Ssengendo R, 2020. Socio-economic vulnerability to COVID-19: the spatial case of Greater Kampala Metropolitan Area (GKMA). J GIS 12:302-18.
DOI: https://doi.org/10.4236/jgis.2020.124019
Batista F, Poelman H, 2016. Mapping population density in functional urban areas. A method to downscale population statistics to urban atlas polygons. JRC Technical Reports. European Commission. Available from: https://tinyurl.com/y9r2mbkt
Bergquist R, Kiani B, Manda S, 2020. First year with COVID-19: Assessment and prospects. Geospat Health 15:953.
DOI: https://doi.org/10.4081/gh.2020.953
BOE-A-2015-682 (2015). Ley 4/2014, de 22 de diciembre, del Paisaje. Texto consolidado. Gobierno de Cantabria. Available from: https://www.boe.es/buscar/pdf/2015/BOE-A-2015-682-consolidado.pdf
Buffalo L, Rydzewski AL, 2021. Territorial dynamics of the COVID-19 pandemic in the province of Córdoba, Argentina. BAGE 91.
Campagna M, 2020. Geographic Information and COVID-19 outbreak. Does the spatial dimension matter? J Land Use Mobil Environ 31-44.
Chunbao M, Dechan T, Tingyu M, Chunhua B, Jian Q, Weiyi P, Zhiyong Z, 2020. An analysis of spatiotemporal pattern for COVID-19 in China based on space-time cube. J Med Virol92:1587-1595.
DOI: https://doi.org/10.1002/jmv.25834
Coccia M, 2021. Pandemic Prevention: Lessons from COVID-19. Encyclopedia 1:433-44.
DOI: https://doi.org/10.3390/encyclopedia1020036
Cromley EK, 2019. Using GIS to address epidemiologic research questions. Curr Epidemiol Rep 6:162-73.
DOI: https://doi.org/10.1007/s40471-019-00193-6
Das A, Ghosh S, Das K, Basu T, Dutta I, Das M, 2021. Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India. Sustain Cities Soc 65:102577.
DOI: https://doi.org/10.1016/j.scs.2020.102577
De Cos O, Castillo V, Cantarero D, 2020. Facing a second wave from a regional view: spatial patterns of COVID-19 as a key determinant for public health and geoprevention plans. Int J Environ Res Public Health 17:8468.
DOI: https://doi.org/10.3390/ijerph17228468
De Cos O, Castillo V, Cantarero D, 2021a. Data mining and socio-spatial patterns of COVID-19: geo-prevention keys for tackling the pandemic. BAGE 91.
De Cos O, Castillo V, Cantarero D, 2021b. Differencing the risk of reiterative spatial incidence of COVID-19 using space-time 3D bins of geocoded daily cases. ISPRS Int J Geo-Inf 10:261.
DOI: https://doi.org/10.3390/ijgi10040261
Desmet K, Wacziarg R, 2021. Understanding spatial variation in COVID-19 across the United States. J Urban Econ [In press].
DOI: https://doi.org/10.3386/w27329
Dhaval DD, 2020. Urban densities and the COVID-19 pandemic: upending the sustainability myth of global megacities. ORF Occasional Paper 244:1-42. Available from: https://tinyurl.com/5n8hwd2j
Fatima M, O’Keefe KJ, Wei W, Arshad S, Gruebner O, 2021. Geospatial analysis of COVID-19: A scoping review. Int J Environ Res Public Health 18:2336.
DOI: https://doi.org/10.3390/ijerph18052336
Fernández F, Herrera D, Fernández C, 2021. Temporal and territorial dimension of the COVID-19 pandemic in Asturias, Spain. BAGE 91.
Ferreira MC, 2020. Spatial association between the incidence rate of COVID-19 poverty in the São Paulo municipality, Brazil. Geospat Health 15:921.
DOI: https://doi.org/10.4081/gh.2020.921
Franch-Pardo I, Desjardins M, Barea-Navarro I, Cerdà A, 2021. A review of GIS methodologies to analyze the dynamics of COVD-19 in the second half of 2020. Transact in GIS 00:1-49.
DOI: https://doi.org/10.1111/tgis.12792
Gatalsky, P, Andrienko N, Andrienko K, 2004. Interactive analysis of event data using space-time cube. In: E. Banissi (Ed.), Proceedings of the 8th International Conference on Information Visualization, July 2004, London, UK. IEEE Computer Society, Los Alamitos, pp. 145-52.
Gerber TD, Ping D, Armstrong-Brown J, McNutt LA, Cole FB, 2009. Charting a path to location intelligence for STD control. Public Health Rep 124:49-57.
DOI: https://doi.org/10.1177/00333549091240S208
Hägerstrand T, 1970. What about people in regional science? Reg Sci Assoc Pap 24:7-21.
DOI: https://doi.org/10.1007/BF01936872
Hamidi S, Sabouri S, Ewing R, 2020. Does Density Aggravate the COVID-19 Pandemic? Early Findings and Lessons for Planners. J Am Plann Assoc 86:495-509.
DOI: https://doi.org/10.1080/01944363.2020.1777891
Huang J, Kwan MP, Kan Z, Wong MS, Tung Kwok CY, Yu X, 2020. Investigating the relationship between the built environment and relative risk of COVID-19 in Hong Kong. ISPRS Int J Geo-Inf 9:624.
DOI: https://doi.org/10.3390/ijgi9110624
Jardim de Figueiredo CJ, de Miranda CM, Ferreira AG, Pimentel A, da Silva SM, 2022. Vulnerability to COVID-19 in Pernambuco, Brazil: a geospatial evaluation supported by multiple-criteria decision aid methodology. Geospat Health 17:1000.
DOI: https://doi.org/10.4081/gh.2022.1000
Jindal C, Kumar S, Sharma S, Choi YM, Efird JT, 2020. The prevention and management of COVID-19: seeking a practical and timely solution. Int J Environ Res Public Health17:3986.
DOI: https://doi.org/10.3390/ijerph17113986
Kamel MN, Geraghty EM, 2020. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 19:8.
DOI: https://doi.org/10.1186/s12942-020-00202-8
Kulldorff M, 2001. Prospective time periodic geographical disease surveillance using scan statistic. J R Statist Soc A Ser A Stat Soc 164:61-72.
DOI: https://doi.org/10.1111/1467-985X.00186
Li H, Li H, Ding Z, Hu Z, Chen F, Wang K, Peng Z, Shen H, 2020. Spatial statistical analysis of coronavirus disease 2019 (COVID-19) in China. Geospat Health 15:867.
DOI: https://doi.org/10.4081/gh.2020.867
Mohammad Ebrahimi S, Mohammadi A, Bergquist R, Dolatkhah F, Olia M, Tavakolian A, Pishgar E, Kiani B, 2021. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health 21:1373.
DOI: https://doi.org/10.1186/s12889-021-11326-2
Mohammadi A, Mollalo A, Bergquist R, Kiani B, 2021. Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model. Infect Dis Poverty 10:118.
DOI: https://doi.org/10.1186/s40249-021-00904-6
Mollalo A, Mohammadi A, Mavaddati S, Kiani B, 2021, Spatial analysis of COVID-19 vaccination: a scoping review. Int J Environ Res Public Health 18:12024.
DOI: https://doi.org/10.3390/ijerph182212024
Moore S, Hill EM, Tildesley MJ, Dyson L, Keeling MJ, 2021. Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study. Lancet Infect Dis 21:793-802.
DOI: https://doi.org/10.1016/S1473-3099(21)00143-2
Mou Y, He Q, Zhou B, 2017. Detecting the spatially non-stationary relationships between housing price and its determinants in China: Guide for housing market sustainability. Sustainability 9:1826.
DOI: https://doi.org/10.3390/su9101826
Niu X, Yue Y, Zhou X, Zhang X, 2020. How urban factors affect to spatiotemporal distribution of infectious diseases in addition to intercity population movement in China. ISPRS Int J Geo-Inf 9:615.
DOI: https://doi.org/10.3390/ijgi9110615
Parkes DN, Nigel JT, 1980. Times, spaces, and places; a chronogeographic perspective. John Wiley and Sons, New York, NY, USA, 527pp.
Perles MJ, Sortino JF, Mérida MF, 2021a. The neighborhood contagion focus as spatial unit for diagnosis and epidemiological action against COVID-19 contagion in urban spaces: a methodological proposal for its detection and delimitation. Int J Environ Res Public Health 18:3145.
DOI: https://doi.org/10.3390/ijerph18063145
Perles MJ, Sortino JF, Cantarero FJ, Castro H, De la Fuente AL, Orellana-MacÃas JM, Reyes S, Miranda J, Mérida M, 2021b. Potential of hazard mapping as a tool for facing COVID-19 transmission: the geo-COVID cartographic platform. BAGE 91.
Salama AM, 2020. Coronavirus questions that will not go away: interrogating urban and socio-spatial implications of COVID-19 measures. Emerald Open Res 2:14.
DOI: https://doi.org/10.35241/emeraldopenres.13561.1
Seong H, Hyun HJ, Yun JG, Noh JY, Cheong HJ, Kim WJ, Song JW, 2021. Comparison of the second and third waves of the COVID-19 pandemic in South Korea: Importance of early public health intervention. Int J Infec Dis 104:742-5.
DOI: https://doi.org/10.1016/j.ijid.2021.02.004
Sera F, Armstrong B, Abbott S, Meakin S, O’Reilly K, Von Borries R, Schneider R, Royé D, 2021. A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries. Nature Communic 12:5968.
Syetiawan A, Harimurti M, Prihanto Y, 2022. A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for a pandemic decision support. Geospat Health 14:1042.
DOI: https://doi.org/10.4081/gh.2022.1042
Tokey AI, 2021. Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data. J Transp Health 22:101135.
DOI: https://doi.org/10.1016/j.jth.2021.101135
Whittle RS, DÃaz-Artiles A, 2020. An ecological study of socioeconomic predictors of detection of COVID-19 cases across neighborhoods in New York City. BMC Med 18:271.
DOI: https://doi.org/10.1186/s12916-020-01731-6
Ye L, Hu L, 2020. Spatiotemporal distribution and trend of COVID-19 in the Yangtze River Delta region of the People’s Republic of China. Geospat Health 15:889.
DOI: https://doi.org/10.4081/gh.2020.889
Yin Z, Huang W, Ying S, Tang P, Kang Z, Huang K, 2021. Measuring of the COVID-19 Based on Time-Geography. Int J Environ Res Public Health 18:10313.
DOI: https://doi.org/10.3390/ijerph181910313
Zhou C, Su F, Pei T, Zhang A, Yuyan D, Luo B, Zhidong C, Wang J, Yuan W, Zhu Y, Song C, Chen J, Xu J, Li F, Ma T, Jiang L, Yan F, Yi J, Hu Y, Liao Y, Xiao H,2020. COVID-19: Challenges to GIS with Big Data. Geogr Sustain 1:77-87.
DOI: https://doi.org/10.1016/j.geosus.2020.03.005
How to Cite
De Cos, O., Castillo-Salcines, V., & Cantarero-Prieto, D. (2022). 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. Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1067
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