Determining Pandemic Vulnerability Levels of Cities Using the Factor Analysis Method

Yıl 2022, , 193 - 205, 08.07.2022

Cem Kırlangıçoğlu

https://doi.org/10.26650/JGEOG2022-1057248

Öz

Pandemics have reentered our lives with the coronavirus disease (COVID-19), and the outbreak has affected all humanity on a global scale. Just as some countries in the world are more affected by this pandemic than others, although the number of cases and deaths is critically high in some cities in Turkey, others cities are less affected. This study aims to measure Turkish cities vulnerability levels to the pandemic based on variables that are likely to be influence a difference in the number of cases that emerge. A literature survey shows that similar studies in Turkey in particular are generally built on just one of the social, economic, and spatial vulnerability indices. No holistic approach has been found that combines all the relevant factors. This study uses 35 different variables gathered under the indicators of population, demography, urban life, economy, climate, environment and health, as identified at the end of the literature review. As a result, each city’s Pandemic Vulnerability Index score was calculated using factor analysis, and a hierarchical ranking was carried out among Turkish cities going from the most to the least vulnerable.

Anahtar Kelimeler

Pandemic, COVID-19, Vulnerability, Factor analysis, Geographic information systems

Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi

Öz

Yeni Koronavirüs Hastalığı (Covid-19) ile beraber pandemi kavramı yeniden hayatımıza girmiş, küresel ölçekteki salgın tüm insanlığı etkisi altına almıştır. Dünya’da bazı ülkelerin bu salgından daha fazla etkilenip diğerlerinin daha az zarar gördüğü gibi, Türkiye’de de bazı şehirlerde vaka ve vefat sayıları kritik derecede yüksek olmasına rağmen diğerleri daha az etkilenmiştir. Bu çalışmanın amacı, vaka sayılarındaki farklılıkların ortaya çıkmasında etkili olması muhtemel değişkenlerden yola çıkarak şehirlerimizin pandemiye karşı kırılganlık seviyelerini ölçmektir. Kırılganlık seviyesi yüksek olan illerimiz belirlenip bu bölgelere öncelik verildiğinde ve kırılganlığa yol açan sebepler tespit edilip gerekli çözümler üretilmeye başlandığında, şehirlerin salgına karşı direncinin artacağı ve vaka sayılarının azalmasına katkı sağlanacağı düşünülmektedir. Literatürde ve özellikle Türkiye’de gerçekleştirilen benzer çalışmaların genel olarak sosyal, ekonomik ve mekânsal kırılganlık indekslerinden biri üzerine kurgulandığı görülmüş, ilgili tüm faktörleri bir araya getiren bütüncül bir yaklaşıma rastlanmamıştır. Bu çalışmada literatür taraması neticesinde belirlenen ve nüfus, demografi, kentsel yaşam, ekonomi, iklim, çevre ve sağlık altyapısı göstergeleri altında toplanan 35 farklı değişken kullanılmış, faktör analizi yöntemiyle her şehrin Pandemik Kırılganlık İndeksi puanı hesaplanarak en kırılgan illerden en az kırılgan olanlara doğru indirgenen hiyerarşik bir sıralama gerçekleştirilmiştir. 

Anahtar Kelimeler

Pandemi, Covid-19, Kırılganlık, Faktör analizi, Coğrafi bilgi sistemleri

Kaynakça

• Acharya, R., & Porwal, A. (2020). A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study. The Lancet Global Health, 8(9), 1142-1151. doi:10.1016/S2214-109X(20)30300-4 google scholar
• Agrawal, N., Gupta, L., & Dixit, J. (2021). Assessment of the Socioeconomic Vulnerability to Seismic Hazards in the National Capital Region of India Using Factor Analysis. Sustainability, 13(17). doi:10.3390/su13179652 google scholar
• Ahmadi, M., Sharifi, A., Dorosti, S., Ghoushchi, S. J., & Ghanbari, N. (2020). Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Sci Total Environ, 729. doi:https://doi. org/10.1016/j.scitotenv.2020.138705 google scholar
• Ali, I., & Alharbi, O. M. (2020). COVID-19: Disease, management, treatment, and social impact. Sci Total Environ, 728. doi:https://doi. org/10.1016/j.scitotenv.2020.138861 google scholar
• Alirol, E., Getaz, L., Stoll, B., Chappuis, F., & Loutan, L. (2011). Urbanisation and Infectious Diseases in a Globalised World. Lancet Infect Dis, 131-141. google scholar
• Almagro, M., & Orane-Hutchinson, A. (2020). JUE Insight: The determinants of the differential exposure to COVID-19 in New York city and their evolution over time. J Urban Econ. doi:https:// doi.org/10.1016/j.jue.2020.103293 google scholar
• Andree, B. P. (2020). Incidence of Covid-19 and Connections with Air Pollution Exposure: Evidence from the Netherlands. SSRN. https:// ssrn.com/abstract=3584842 adresinden alındı google scholar
• Arif, M., & Sengupta, S. (2021). Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach. Environ Dev Sustain, 23. doi:https://doi. org/10.1007/s10668-020-01055-8 google scholar
• Atalay, A., Tortum, A., & Çodu, Y. M. (2014). Faktör Analizi Kullanılarak Trafik Kazalarının Modellenmesi. Uluslararası Trafik ve Ulaşım Güvenliği Dergisi, 1(1). google scholar
• Auler, A., Cassaro, F., Silva, V d., & Pires, L. (2020). Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities. Sci Total Environ, 729. doi:https:// doi.org/10.1016/j.scitotenv.2020.139090 google scholar
• Bashir, M. F., Ma, B., Bilal, Komal, B., Bashir, M. A., Tan, D., & Bashir, M. (2020). Correlation between climate indicators and COVID-19 pandemic in New York, USA,. Sci Total Environ, 728. doi:https:// doi.org/10.1016/j.scitotenv.2020.138835 google scholar
• Bhadra, A., Mukherjee, A., & Sarkar, K. (2021). Impact of population density on Covid-19 infected and mortality rate in India. Model Earth Syst Environ, 7, 623-629. doi:https://doi.org/10.1007/s40808-020- 00984-7 google scholar
• Bimtaş. (2020). Covid-19 salgını mücadele sürecinde İstanbul kırılganlık haritası proje raporu. İstanbul Kalkınma Ajansı. İstanbul: Kültür A.Ş. google scholar
• Büyüköztürk, Ş. (2002). Faktör Analizi: Temel Kavramlar ve Ölçek Geliştirmede Kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, (32), 470-483. google scholar
• Chen, B., Liang, H., Yuan, X., Hu, Y., Xu, M., Zhao, Y., . . . Zhu, X. (2020). Roles of meteorological conditions in COVID-19 transmission on a worldwide scale. medRxiv. doi:https://doi. org/10.1101/2020.03.16.20037168 google scholar
• Coşkun, H., Yıldırım, N., & Gündüz, S. (2021). The spread of COVID-19 virus through population density and wind in Turkey cities. Sci Total Environ. doi:https://doi.org/10.1016/j.scitotenv.2020.141663 google scholar
• Doremalen, N. v., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., . . . Munster, V. J. (2020). Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1. N Engl J Med. doi:https://doi.org/10.1101/2020.03.09.20033217 google scholar
• Eaton, P., Frank, B., Johnson, K., & Willoughby, S. (2019). Comparing exploratory factor models of the Brief Electricity and Magnetism Assessment and the Conceptual Survey of Electricity and Magnetism. Physical Review Physics Education Research, 15(2). doi:10.1103/PhysRevPhysEducRes.15.020133 google scholar
• Fabrigar, L., & Wegener, D. (2012). Exploratory Factor Analysis. Oxford UniversityPress.doi:10.1093/acprof:osobl/9780199734177.001.0001 google scholar
• Fattorini, D., & Regoli, F. (2020). Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy. Environ Pollut, 264. doi:https://doi.org/10.1016/j.envpol.2020.114732 google scholar
• Flanagan, B., Gregory, E., Hallisey, E., Heitgerd, J., & Lewis, B. (2011). A Social Vulnerability Index for Disaster Management. Journal of Homeland Security and Emergency Management, 8(1). doi:10.2202/1547- 7355.1792 google scholar
• Frontera, A., Claire, M., Kostantinos, V., & Giovanni, S. (2020). Regional air pollution persistence links to COVID-19 infection zoning. J Infect, 81. doi:https://doi.org/10.1016/j.jinf.2020.03.045 google scholar
• Gavi. (2020). 5 Reasons Why Pandemics Like COVID-19 Are Becoming More Likely. Geneva, Switzerland. https://www.gavi. org/vaccineswork/5-reasons-why-pandemics-like-covid-19-are-becoming- more-likely adresinden alındı google scholar
• Gupta, A., Banerjee, S., & Das, S. (2020). Significance of geographical factors to the COVID-19 outbreak in India. Model Earth Syst Environ, 6, 2645-2653. doi:https://doi.org/10.1007/s40808-020-00838-2 google scholar
• Hamidi, S., Sabouri, S., & Ewing, R. (2020). Does Density Aggravate the COVID-19 Pandemic? J Am Plann Assoc, 86(4), 495-509. doi:https://doi.org/10.1080/01944363.2020.1777891 google scholar
• Hays, J. N. (2009). The Burdens of Disease Epidemics and Human Response in Western History. Rutgers University Press. google scholar
• Helen, A. (2020). Air Pollution Impact on COVID-19 Mortality Shocks Scientists. BreezoMeter: https://blog.breezometer.com/air-pollution-impact-on-covid-19-mortality-shocks-scientists adresinden alındı google scholar
• Iqbal, N., Fareed, Z., Shahzad, F., He, X., Shahzad, U., & Lina, M. (2020). The nexus between COVID-19, temperature and exchange rate in Wuhan city: New findings from partial and multiple wavelet coherence. Sci Total Environ, 729. doi:https://doi.org/10.1016/j. scitotenv.2020.138916 google scholar
• Jing, Q. L., Liu, M. J., Zhang, Z. B., Fang, L. Q., Yuan, J., Zhang, A. R., . . . Longini, I. (2020). Household Secondary Attack Rate of COVID-19 and Associated Determinants in Guangzhou, China: A Retrospective Cohort Study. Lancet Infect Dis. doi:doi.org/10.1016/S1473-3099(20)30471-0 google scholar
• Karaye, I. M., & Horney, J. A. (2020). The impact of social vulnerability on COVID-19 in the US: an analysis of spatially varying relationships. American Journal of Preventive Medicine, 59(3), 317-325. doi:10.1016/j.amepre.2020.06.006 google scholar
• Khan Academy. (2021). Normal distribution problem: z-scores. https:// www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data/z-scores/v/ck12-org-normal-distribution- problems-z-score adresinden alındı google scholar
• Kim, S. J., & Bostwick, W. (2020). Social Vulnerability and Racial Inequality in COVID-19 Deaths in Chicago. Health Education & Behavior, 47(4), 509-513. doi:10.1177/1090198120929677 google scholar
• Kırlangıçoğlu, C. (2022). Investigating the effects of regional characteristics on the spatial distribution of COVID-19 pandemic: a case of Turkey. Arabian Journal of Geosciences, 15(5). doi:10.1007/ s12517- 022-09687-y google scholar
• Korber, R. (2020). Severe Coronavirus & Poor Air Quality Could be Linked. BreezoMeter: https://blog.breezometer.com/coronavirus-air-quality-connection adresinden alındı google scholar
• Liu, P., Beeler, P., & Chakrabarty, R. K. (2020). COVID-19 Progression Timeline and Effectiveness of Response-to-Spread Interventions across the United States. Medrxiv. doi:10.1101/2020.03.17.20037770 google scholar
• Mansour, S., Kindi, A. 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). Sustain Cities Soc, 65. doi:https:// doi.org/10.1016/j.scs.2020.102627 google scholar
• Mehmood, K., Bao, Y., Abrar, M. M., Petropoulos, G. P., Saifullah, Soban, A., . . . Fahad, S. (2021). Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan. Chemosphere, 271. doi:https:// doi.org/10.1016/j.chemosphere.2021.129584 google scholar
• Mishra, S. V., Gayen, A., & Haque, S. M. (2020). COVID-19 and urban vulnerability in India. Habitat Int, 103. doi:https://doi.org/10.1016/j. habitatint.2020.102230 google scholar
• Ortakavak, Z., Korkmaz, E., & Uslu, A. (2020). COVID-19 Salgınına Karşı Sosyal Kırılganlık Endeksi: Türkiye Örneği. Iğdır Üniversitesi Sosyal Bilimler Dergisi, (Ek2), 245-268. google scholar
• Özüduru, B. H. (2020). Covid-19 ve şehirler. Ankara: Kent Analiz -Kent Araştırmaları Enstitüsü. google scholar
• Prieto, J., Malagon, R., Gomez, J., & Le6n, E. (2021). Urban Vulnerability Assessment for Pandemic Surveillance—The COVID-19 Case in Bogota, Colombia. Sustainability, 13(6). doi:10.3390/su13063402 google scholar
• Qi, H., Xiao, S., Shi, R., Ward, M. P., Chen, Y., Tu, W., . . . Zhang, Z. (2020). COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis. Sci Total Environ, 728. doi:https://doi.org/10.1016/j.scitotenv.2020.138778 google scholar
• Rocklöv, J., & Sjödin, H. (2020). High population densities catalyse the spread of COVID-19. J Travel Med, 27(3). doi:https://doi. org/10.1093/jtm/taaa038 google scholar
• Saadat, S., Deepak Rawtani, & Hussain, C. M. (2020). Environmental perspective of COVID-19. Sci Total Environ, 728. doi:https://doi. org/10.1016/j.scitotenv.2020.138870 google scholar
• Sabelli, A. (2011). Scale Counts: A Review of Indicator-based Climate Change Vulnerability Assessments. Panama City: REGATTA -Regional Getaway for Technology Transfer and Climate Change Action for Latin America and the Caribbean. google scholar
• Sarkar, A., & Chouhan, P. (2021). COVID-19: District level vulnerability assessment in India. Clinical Epidemiology and Global Health, 9, 204-215. doi:10.1016/j.cegh.2020.08.017 google scholar
• Sarkar, S., Ekram, K., & Das, P. (2021). Spatial modeling of COVID-19 transmission in Bangladesh. Spat Inf Res. doi:https://doi. org/10.1007/s41324-021-00387-5 google scholar
• Sequist, T. D. (2020). The Disproportionate Impact of Covid-19 on Communities of Color. NEJM Catalyst Innovations in Care Delivery, 1(4). google scholar
• Smittenaar, P., Stewart, N., Sutermaster, S., Coome, L., Dibner-Dunlap, A., Jain, M., . . . Sgaier, S. K. (2021). A COVID-19 Community Vulnerability Index to drive precision policy in the US. Medrxiv. doi:10.1101/2021.05.19.21257455 google scholar
• Statology. (2019). A Guide to Bartlett’s Test of Sphericity. https://www. statology.org/bartletts-test-of-sphericity/ adresinden alındı google scholar
• Surgo Ventures. (2020). COVID-19 Community Vulnerability Index (CCVI) Methodology. Washington, DC. google scholar
• Şahin, M. (2020). Impact of weather on COVID-19 pandemic in Turkey. Sci Total Environ, 728. doi:https://doi.org/10.1016/j. scitotenv.2020.138810 google scholar
• T.C. Sağlık Bakanlığı. (2022). COVID-19 Bilgilendirme Platformu. Mart 23, 2022 tarihinde https://covid19.saglik.gov.tr/ adresinden alındı google scholar
• Tabachnick, B. G., & Fidell, L. S. (2001). Using Multivariate Statistics. Allyn & Bacon. google scholar
• Tai, D. B., Shah, A., Doubeni, C. A., Sia, I. G., & Wieland, M. L. (2021). The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States. Clinical Infectious Diseases, 72(4), 703-706. doi:10.1093/cid/ciaa815 google scholar
• Terzi, Y. (2019). Anket, güvenilirlik, geçerlilik analizi. Samsun: Ondokuz Mayıs Üniversitesi Fen-Edebiyat Fakültesi İstatistik Bölümü Ders Notları. google scholar
• Thangariyal, S., Rastogi2, A., Tomar, A., Bhadoria, A. S., & Baweja, S. (2020). Impact of temperature and sunshine duration on daily new cases and death due to COVID19. J Family Med Prim Care. doi:10.4103/jfmpc.jfmpc_1185_20 google scholar
• Tiwari, A., Dadhania, A. V., Ragunathrao, V. A., & Oliveira, E. R. (2021). Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI). Science of the Total Environment(773). doi:10.1016/j.scitotenv.2021.145650 google scholar
• Toole, M. J., & Waldman, R. J. (1990). Prevention of Excess Mortality in Refugee and Displaced Populations in Developing Countries. JAMA, 263(24), 3296-3302. google scholar
• Tosepu, R., Gunawan, J., Effendy, D. S., Ahmad, L. O., Lestari, H., Bahar, H., & Asfian, P. (2020). Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Sci Total Environ, 725. doi:https://doi.org/10.1016/j.scitotenv.2020.138436 google scholar
• UCLA Statistical Consulting Group. (2021). Factor analysis SPSS annotated output. https://stats.idre.ucla.edu/spss/output/factor-analysis/ adresinden alındı google scholar
• Wang, P., Chen, K., Zhu, S., Wang, P., & Zhang, H. (2020). Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour Conserv Recycl, 158. doi:https://doi.org/10.1016/j.resconrec.2020.104814 google scholar
• Wheaton, W. C., & Thompson, A. K. (2020). The Geography of COVID-19 Growth in the US: Counties and Metropolitan Areas. https://papers. ssrn.com/sol3/papers.cfm?abstract_id=3570540 adresinden alındı google scholar
• WHO. (2022). World Health Organization. Mart 23, 2022 tarihinde WHO Coronavirus (COVID-19) Dashboard: https://covid19.who. int/ adresinden alındı google scholar
• Wu, Y., Jing, W., Liu, J., Ma, Q., Yuan, J., Wang, Y., . . . Liu, M. (2020). Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Sci Total Environ, 729. doi:https://doi.org/10.1016/j.scitotenv.2020.139051 google scholar
• Xie, J., & Zhu, Y. (2020). Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ, 724(138201). doi:https://doi.org/10.1016/j.scitotenv.2020.138201 google scholar
• Zhang, H., Liu, Y., Chen, F., Mi, B., Zeng, L., & Pei, L. (2021). The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis. BMC Infect Dis, 21. doi:https://doi.org/10.1186/s12879-021-06128-1 google scholar
• Zhang, Y., Tian, H., Zhang, Y., & Chen, Y. (2020). Is the epidemic spread related to GDP? Visualizing the distribution of COVID-19 in Chinese Mainland. http://arxiv.org/abs/2004.04387 adresinden alındı google scholar
• Zhang, Z., Xue, T., & Jin, X. (2020). Effects of meteorological conditions and air pollution on COVID-19 transmission: Evidence from 219 Chinese cities. Sci Total Environ, 741. doi:https://doi. org/10.1016/j.scitotenv.2020.140244 google scholar
• Zhu, Y., Xie, J., Huang, F., & Cao, L. (2020). Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Sci Total Environ, 727. doi:https://doi. org/10.1016/j.scitotenv.2020.138704 google scholar
• Zurovec, O., Cadro, S., & Sitaula, B. K. (2017). Quantitative Assessment of Vulnerability to Climate Change in Rural Municipalities of Bosnia and Herzegovina. Sustainability, 9(7). doi:10.3390/ su9071208 google scholar