SPATIAL DIFFERENTIATION OF COVID-19 IN TURKEY

Year 2023, Volume: 13 Issue: 26, 747 - 763, 30.11.2023

Neşe Aral , Hasan Bakır

https://doi.org/10.53092/duiibfd.1243565

Abstract

The sudy aims to focus on spatial transmission of Covid-19 in Turkey, to understand the channels through which it spreads by considering the regional socio-economic dimension. Within this scope, demographic, socioeconomic and healthcare factors associated with the spread of Covid-19 were analyzed in a provincial context. Spatial autocorrelation was used to examine parameters that spatially affect the number of cases. Spatial autocorrelation results reveal spatial differences in the spread of the pandemic. The findings highlight the importance of the space factor in reducing local contamination within the country. The results obtained will enable the discovery of risk factors for disease and will lead policy makers to make effective decisions. In this context, spatial-specific policy strategies will protect public health by reducing the spread of the virus.

Keywords

Covid-19, Turkey, Spatial Data Analysis

TÜRKİYE'DE COVID-19’UN MEKÂNSAL FARKLILIKLARI

Abstract

Bu çalışma, Türkiye'de Covid-19’un bölgesel dağılımına odaklanmayı, sosyo-ekonomik boyutu da dikkate alarak Covid-19’un yayılımını anlamayı amaçlamaktadır. Bu kapsamda, Covid-19’un yayılmasıyla ilişkili demografik, sosyoekonomik ve sağlık faktörleri il bazında analiz edilmiştir. Vaka sayılarını mekânsal olarak etkileyen parametreleri incelemek için mekânsal otokorelasyon kullanılmıştır. Elde edilen sonuçlar, pandeminin yayılmasındaki mekânsal farklılıkları ortaya koymaktadır. Bulgular, ülke içindeki yerel yayılımı azaltmada mekân faktörünün önemini vurgulamaktadır. Elde edilen sonuçlar risk faktörlerinin keşfedilmesini sağlayarak politika yapıcıların etkin karar almalarına yol açacaktır. Bu bağlamda mekâna özgü uygulanacak politika stratejileri ile virüsün yayılımı azaltılarak kamu sağlığı korunacaktır.

Keywords

Covid-19, Mekansal Veri Analizi, Türkiye

References

• Alcântara, E., Mantovani, J., Rotta, L., Park, E., Rodrigues, T., Carvalho, F. C., & Filho, C. R. S. (2020). Investigating spatiotemporal patterns of the covid-19 in São Paulo state, Brazil. Geospatial Health, 15(2), 201–209. https://doi.org/10.4081/gh.2020.925
• Amdaoud, M., Arcuri, G., & Levratto, N. (2021). Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe. The European Journal of Health Economics, 22(4), 629–642. https://doi.org/10.1007/s10198-021-01280-6
• Anselin, L. (1995). Local Indicators of Spatial Association—LISA. In Geographical Analysis (Vol. 27, Issue 2). https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
• Anselin, L. (1996). The Moran Scatter Plot as an ESDA Tool to Assess Local Instability in Spatial Association. In H. J. Fischer, M.M; Scholten (Ed.), Spatial Analytical Perspectives on GIS: GISDATA (4th ed., pp. 111–125). CRC Press.
• Anselin, L. (1999). The future of spatial analysis in the social sciences. Annals of GIS, 5(2), 67–76. https://doi.org/10.1080/10824009909480516
• Anselin, L. (2019). A Local Indicator of Multivariate Spatial Association: Extending Geary’s c. Geographical Analysis, 51(2), 133–150. https://doi.org/10.1111/gean.12164
• Anselin, L., & Li, X. (2020). Tobler’s Law in a Multivariate World. Geographical Analysis, 52(4), 494–510. https://doi.org/10.1111/gean.12237
• Anselin, L., Syabri, I., & Kho, Y. K. (2010). Handbook of Applied Spatial Analysis. In M. M. Fischer & A. Getis (Eds.), Handbook of Applied Spatial Analysis (pp. 73–89). Springer Berlin Heidelberg. https://doi.org/10.1007 /978-3-642-03647-7
• Anselin, L., Syabri, I., & Smirnov, O. (2002). Visualizing multivariate spatial correlation with dynamically linked windows. In L. Anselin & S. Rey (Eds.), New Tools for Spatial Data Analysis: Proceedings of the Specialist Meeting. Center for Spatially Integrated Social Science (CSISS), University of California, Santa Barbara. http://geodacenter.asu.edu/pdf/multi_lisa.pdf
• Arauzo-Carod, J. M. (2021). A first insight about spatial dimension of COVID-19: analysis at municipality level. Journal of Public Health (Oxford, England), 43(1), 98–106. https://doi.org/10.1093/pubmed/fdaa140
• Bag, R., Ghosh, M., Biswas, B., & Chatterjee, M. (2020). Understanding the spatio-temporal pattern of COVID-19 outbreak in India using GIS and India’s response in managing the pandemic. Regional Science Policy and Practice, 12(6), 1063–1103. https://doi.org/10.1111/rsp3.12359
• Baser, O. (2021). Population density index and its use for distribution of Covid-19: A case study using Turkish data. Health Policy, 125, 148–154. https://doi.org/10.1016/j.healthpol.2020.10.003
• Bhadra, A., Mukherjee, A., & Sarkar, K. (2021). Impact of population density on Covid-19 infected and mortality rate in India. Modeling Earth Systems and Environment, 7, 623–629. https://doi.org/10.1007/s40808-020-00984-7
• Bourdin, S., Jeanne, L., Nadou, F., & Noiret, G. (2021). Does lockdown work? A spatial analysis of the spread and concentration of Covid-19 in Italy. Regional Studies, 55(7), 1182–1193. https://doi.org/10.1080/00343404.2021.1887471
• Chen, Y., Li, Q., Karimian, H., Chen, X., & Li, X. (2021). Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China. Scientific Reports, 11(1), 1–12. https://doi.org/10.1038/s41598-021-83166-4
• Cos, O. De, 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. International Journal of Environmental Research and Public Health, 17(22), 8468. https://doi.org/10.3390/ijerph17228468
• 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. Sustainable Cities and Society, 65, 102577. https://doi.org/10.1016/j.scs.2020.102577
• Das, D., & Zhang, J. J. (2021). Pandemic in a smart city: Singapore’s COVID-19 management through technology & society. Urban Geography, 42(3), 408–416. https://doi.org/10.1080/02723638.2020.1807168
• Dutta, I., Basu, T., & Das, A. (2021). Spatial analysis of COVID-19 incidence and its determinants using spatial modeling: A study on India. Environmental Challenges, 4, 100096. https://doi.org/10.1016/j.envc.2021.100096
• Ehlert, A. (2021). The socio-economic determinants of COVID-19: A spatial analysis of German county level data. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2021.101083
• Ferreira, D., Ferreira, P., Oliveira, P., Ribeiro, J., Goncalves, E., & Papa, A. (2020). Temporal and spatial characteristics of the spread of COVID-19 in Rio de Janeiro state and city. MedRxiv. https://doi.org/https://doi.org/10.1101/2020.05.13.20101113
• Ferreira, M. C. (2020). Spatial association between the incidence rate of COVID-19 and poverty in the São Paulo municipality, Brazil. Geospatial Health, 15(2), 191–200. https://doi.org/10.4081/gh.2020.921
• Finch, W. H., & Hernández Finch, M. E. (2020). Poverty and Covid-19: Rates of Incidence and Deaths in the United States During the First 10 Weeks of the Pandemic. Frontiers in Sociology, 5(47), 1–10. https://doi.org/10.3389/fsoc.2020.00047
• Ganasegeran, K., Jamil, M. F. A., Ch’ng, A. S. H., Looi, I., & Peariasamy, K. M. (2021). Influence of population density for covid-19 spread in malaysia: An ecological study. International Journal of Environmental Research and Public Health, 18(9866). https://doi.org/10.3390/ijerph18189866
• Getis, A., & Aldstadt, J. (2004). Constructing the Spatial Weights Matrix Using a Local Statistic. Geographical Analysis, 36(2), 90–104. https://doi.org/10.1111/j.1538-4632.2004.tb01127.x
• Getis, A., & Ord, J. K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
• Getis, A., & Ord, J. K. (2010). The analysis of spatial association by use of distance statistics. In L. Anselin & S. J. Rey (Eds.), Springer. Springer Berlin Heidelberg. http://link.springer.com/content/pdf/10.1007/978-3-662-04853-5.pdf
• Ghosh, P., & Cartone, A. (2020). A Spatio-temporal analysis of COVID-19 outbreak in Italy. Regional Science Policy and Practice, 12(6), 1047–1062. https://doi.org/10.1111/rsp3.12376
• Hafner, C. M. (2020). The spread of the Covid-19 pandemic in time and space. International Journal of Environmental Research and Public Health, 17(11), 3827. https://doi.org/10.3390/ijerph17113827
• Han, Y., Yang, L., Jia, K., Li, J., Feng, S., Chen, W., Zhao, W., & Pereira, P. (2021). Spatial distribution characteristics of the COVID-19 pandemic in Beijing and its relationship with environmental factors. Science of the Total Environment, 761(December 2019), 144257. https://doi.org/10.1016/j.scitotenv.2020.144257
• Hou, X., Gao, S., Li, Q., Kang, Y., Chen, N., Chen, K., Rao, J., Ellenberg, J. S., & Patz, J. A. (2021). Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race. Proceedings of the National Academy of Sciences, 118(24), e2020524118. https://doi.org/10.1073/pnas.2020524118
• Hu, T., Yue, H., Wang, C., She, B., Ye, X., Liu, R., Zhu, X., Guan, W. W., & Bao, S. (2020). Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA. International Journal of Environmental Research and Public Health, 17(9528). https://doi.org/10.3390/ijerph17249528
• Kadi, N., & Khelfaoui, M. (2020). Population density, a factor in the spread of COVID-19 in Algeria: statistic study. Bulletin of the National Research Centre, 44(138). https://doi.org/10.1186/s42269-020-00393-x
• Kang, D., Choi, H., Kim, J. H., & Choi, J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases, 94, 96–102. https://doi.org/10.1016/j.ijid.2020.03.076
• Kapitsinis, N. (2020). The underlying factors of the COVID-19 spatially uneven spread. Initial evidence from regions in nine EU countries. Regional Science Policy and Practice, 12(6), 1027–1045. https://doi.org/10.1111/rsp3.12340
• Khavarian-Garmsir, A. R., Sharifi, A., & Moradpour, N. (2021). Are high-density districts more vulnerable to the COVID-19 pandemic? Sustainable Cities and Society, 70, 102911. https://doi.org/10.1016/j.scs.2021.102911
• Kim, S., & Castro, M. C. (2020). Spatiotemporal pattern of COVID-19 and government response in South Korea (as of May 31, 2020). International Journal of Infectious Diseases, 98, 328–333. https://doi.org/10.1016/j.ijid.2020.07.004
• Kodera, S., Rashed, E. A., & Hirata, A. (2020). Correlation between COVID-19 morbidity and mortality rates in Japan and local population density, temperature, and absolute humidity. International Journal of Environmental Research and Public Health, 17(5477). https://doi.org/10.3390/ijerph17155477
• Lak, A., Sharifi, A., Badr, S., Zali, A., Maher, A., Mostafavi, E., & Khalili, D. (2021). Spatio-temporal patterns of the COVID-19 pandemic, and place-based influential factors at the neighborhood scale in Tehran. Sustainable Cities and Society, 72(January), 103034. https://doi.org/10.1016/j.scs.2021.103034
• Lee, S. (2001). Developing a bivariate spatial association measure: An integration of Pearson’s r and Moran’s I. Journal of Geograph Systems, 3, 369–385.
• Li, B., Peng, Y., He, H., Wang, M., & Feng, T. (2021). Built environment and early infection of COVID-19 in urban districts: A case study of Huangzhou. Sustainable Cities and Society, 66, 102685. https://doi.org/10.1016/j.scs.2020.102685
• Liang, S., Leng, H., Yuan, Q., & Yuan, C. (2021). Impact of the COVID-19 pandemic: Insights from vacation rentals in twelve mega cities. Sustainable Cities and Society, 74, 103121. https://doi.org/10.1016/j.scs.2021.103121
• Liu, C., Liu, Z., & Guan, C. (2021). The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington. Sustainable Cities and Society, 74, 103144. https://doi.org/10.1016/j.scs.2021.103144
• Liu, Y., Pei, T., Song, C., Chen, J., Chen, X., Huang, Q., Wang, X., Shu, H., Wang, X., Guo, S., & Zhou, C. (2021). How did human dwelling and working intensity change over different stages of COVID-19 in Beijing? Sustainable Cities and Society, 74, 103206. https://doi.org/10.1016/j.scs.2021.103206
• Mansour, S., Al Kindi, A., Al-Said, A., Al-Said, A., & Atkinson, P. (2021). Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustainable Cities and Society, 65, 102627. https://doi.org/10.1016/j.scs.2020.102627
• Martinho, V. J. P. D. (2021). Impact of Covid-19 on the convergence of GDP per capita in OECD countries. Regional Science Policy and Practice, April, 1–18. https://doi.org/10.1111/rsp3.12435
• Ord, J. K., & Getis, A. (1995). Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis, 27(4), 286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
• Paez, A., Lopez, F. A., Menezes, T., Cavalcanti, R., & Pitta, M. G. da R. (2020). A Spatio-Temporal Analysis of the Environmental Correlates of COVID-19 Incidence in Spain. Geographical Analysis, 0, 1–25. https://doi.org/10.1111/gean.12241
• Rahmani, S. E. A., Chibane, B., Hallouz, F., & Benamar, N. (2020). Spatial distribution of Covid-19, a modeling approach: case of Algeria. Research Square, 1–11. https://doi.org/10.21203/rs.3.rs-40447/v1
• Ramírez-Aldana, R., Gomez-Verjan, J. C., Bello-Chavolla, O. Y., & Peterson, T. (2020). Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level. PLoS Neglected Tropical Diseases, 14(11), e0008875. https://doi.org/10.1371/journal.pntd.0008875
• Raymundo, C. E., Oliveira, M. C., Eleuterio, T. de A., André, S. R., da Silva, M. G., Queiroz, E. R. da S., & Medronho, R. de A. (2021). Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil. PLOS ONE, 16(3), e0247794. https://doi.org/10.1371/journal.pone.0247794
• Ren, C., Xi, C., Wang, J., Feng, Z., Nasiri, F., Cao, S.-J., & Haghighat, F. (2021). Mitigating COVID-19 infection disease transmission in indoor environment using physical barriers. Sustainable Cities and Society, 74, 103175. https://doi.org/10.1016/j.scs.2021.103175
• Saffary, T., Adegboye, O. A., Gayawan, E., Elfaki, F., Kuddus, M. A., & Saffary, R. (2020). Analysis of COVID-19 Cases’ Spatial Dependence in US Counties Reveals Health Inequalities. Frontiers in Public Health, 8(579190), 1–10. https://doi.org/10.3389/fpubh.2020.579190
• Sarkar, S. K., Ekram, K. M. M., & Das, P. C. (2021). Spatial modeling of COVID-19 transmission in Bangladesh. Spatial Information Research, 1–12. https://doi.org/10.1007/s41324-021-00387-5
• Sigler, T., Mahmuda, S., Kimpton, A., Loginova, J., Wohland, P., Charles-Edwards, E., & Corcoran, J. (2021). The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population. Globalization and Health, 17(1), 56. https://doi.org/10.1186/s12992-021-00707-2
• Sun, Z., Zhang, H., Yang, Y., Wan, H., & Wang, Y. (2020). Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China. Science of the Total Environment, 746, 141347. https://doi.org/10.1016/j.scitotenv.2020.141347
• Sy, K. T. L., White, L. F., & Nichols, B. E. (2021). Population density and basic reproductive number of COVID-19 across United States counties. PLoS ONE, 16(4), e0249271. https://doi.org/10.1371/journal.pone.0249271 WHO. (2021). World Health Organization. https://www.who.int/
• Xie, Z., Qin, Y., Li, Y., Shen, W., Zheng, Z., & Liu, S. (2020). Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors. Science of The Total Environment, 744, 140929. https://doi.org/10.1016/j.scitotenv.2020.140929
• You, H., Wu, X., & Guo, X. (2020). Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development. International Journal of Environmental Research and Public Health, 17(10), 3417. https://doi.org/10.3390/ijerph17103417