COVID-19 pandemisinin nüfus hareketliliği üzerine etkisi: Hareketlilik ve gelir arasındaki ilişkinin analizi

Öz

Bu çalışma COVID-19 pandemisinde nüfus hareketliliği ve gelir arasındaki ilişkiyi konu alır. Bu

bağlamda araştırmada gelirin pandemi sürecinde yaşanan hareketlilikte belirleyici olup olmadığı

sorusuna yanıt aranmıştır. Çalışma nicel veri toplama ve analiz araçlarından faydalanılarak

tasarlanmıştır. Hareketlilikte yaşanan değişimin analizinde Google tarafından sunulan altı farklı

kategoriye ilişkin hareketlilik verileri ile Türkiye İstatistik Kurumu (TÜİK) tarafından yayınlanan

gelir verilerinden faydalanılmıştır. Hareketlilik verileri mekânsal otokorelasyon, hareketlilik ve

gelir arasındaki ilişki ise korelasyon analizi kullanarak çözümlenmiştir. Araştırmanın temel bulguları

şu şekildedir: Perakende ve rekreasyon, park ve toplu taşıma kategorilerinde nüfusun

hareketliliği değerlendirmeye alınan dönemde azalmıştır. Buna karşın market ve eczane ile konut

kategorilerinde yaşanan hareketlilik artmıştır. Korelasyon analizi sonuçlarına göre ise perakende

ve rekreasyon, market ve eczane, işyeri ve konut kategorilerinde gelir ve hareketlilik

arasında ilişki bulunur. Gelirin fazla olduğu illerde perakende ve rekreasyon, merkez ve eczane

ile işyerinde yaşanan hareketlilik daha fazla azalmıştır. Gelirin düşük olduğu illerde ise konutta

geçirilen hareketlilik azalmaktadır. Park ve toplu taşıma kategorilerinde yaşanan hareketlilik ile

gelir arasında pozitif ilişki bulunduğu tespit edilmiştir. Bununla birlikte bu ilişki istatistiksel olarak

anlamlı değildir.

Anahtar Kelimeler

• COVID-19
• Nüfus hareketliliği
• Gelir
• Yoksulluk
• Google Hareketlilik Raporları

Impact of COVID-19 pandemic on population mobility: Analysis of the relationship between mobility and income

Abstract

This study seeks to explore the relationship between population mobility and income in the
COVID-19 pandemic. In this context, it was questioned in the research whether income is a determinant
in the mobility experienced during the pandemic process. The study was designed
using quantitative data collection and analysis tools. In the analysis of the change in mobility,
the mobility data for six different categories prepared by Google and the income data published
by the Turkish Statistical Institute were used. Mobility data were analyzed using spatial
autocorrelation, and the relationship between mobility and income was analyzed using correlation
analysis. The main results of the study are as follows: The mobility of the population in
the categories of retail and recreation, parks and public transport decreased during the period
that analyzed in this research. On the other hand, the activity in the market, pharmacy and
housing categories increased. According to the results of the correlation analysis, there is a relationship
between income and mobility in the categories of retail and recreation, market and
pharmacy, workplace and housing. In provinces with high income, the mobility experienced in
retail and recreation, center, pharmacy and workplace decreased more. On the other hand, in
provinces with low income, mobility in housing decreases. It has been determined that there is
a positive relationship between the mobility experienced in the park and public transportation
categories and income. However, this relationship is not statistically significant.

Keywords

• COVID-19
• Population mobility
• Income
• Poverty
• Google mobility reports

Kaynakça

1. Abdulai, A., Tiffere, A., Adam, F., & Kabanunye, M. M. (2021). COVID-19 information-related digital literacy among online health consumers in a low-income country. International Journal of Medical Informatics, 145, 104322. https://doi.org/10.1016/j. ijmedinf.2020.104322
2. Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical Analysis, 27, 93–115
3. Arora, N., Pflumm, S., Rodriguez, L., Robinson, K., Bhargava, S., Charm, T., Tormo S. (2020) Survey: US Consumer Sentiment during the Coronavirus Crisis https://www.mckinsey.com/ business-functions/marketing-and-sales/our-insights/surveyus- consumer-sentiment-during-the-coronavirus-crisis
4. Asfaw, A. A. (2021). The effect of income support programs on job search, workplace mobility and COVID-19: International evidence. Economics & Human Biology, 41, 100997. https://doi. org/10.1016/j.ehb.2021.100997
5. Awad-Núñez, S., Julio, R., Moya-Gómez, B., Gomez, J., & Sastre González, J. (2021). Acceptability of sustainable mobility policies under a post-COVID-19 scenario. Evidence from Spain. Transport Policy, 106, 205-214. https://doi.org/10.1016/j. tranpol.2021.04.010
6. Bozkurt, A. (2020). Koronavirüs (Covid-19) pandemi süreci ve pandemi sonrası dünyada eğitime yönelik değerlendirmeler: Yeni normal ve yeni eğitim paradigması. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 6(3), 112-142.
7. Brewer, P., & Sebby, A. G. (2021). The effect of online restaurant menus on consumers’ purchase intentions during the COVID-19 pandemic. International Journal of Hospitality Management, 94, 102777. https://doi.org/10.1016/j.ijhm.2020.102777
8. Budak, F., & Korkmaz, Ş. (2020). COVID-19 pandemi sürecine yönelik genel bir değerlendirme: Türkiye örneği. Sosyal Araştırmalar ve Yönetim Dergisi, (1), 62-79.

9. Chakrabarti, S. (2017). How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles. Transport Policy, 54, 80-89. https://doi.org/10.1016/j. tranpol.2016.11.005
10. Cheng, Y., Zhang, J., Wei, W., & Zhao, B. (2021). Effects of urban parks on residents’ expressed happiness before and during the COVID-19 pandemic. Landscape and Urban Planning, 212, 104118. https://doi.org/10.1016/j.landurbplan.2021.104118
11. Chee, W. L., & Fernandez, J. L. (2013). Factors that influence the choice of mode of transport in Penang: A preliminary analysis. Procedia - Social and Behavioral Sciences, 91, 120-127. https:// doi.org/10.1016/j.sbspro.2013.08.409
12. Collins, R. M., Spake, R., Brown, K. A., Ogutu, B. O., Smith, D., & Eigenbrod, F. (2020). A systematic map of research exploring the effect of greenspace on mental health. Landscape and Urban Planning, 201, 103823. https://doi.org/10.1016/j. landurbplan.2020.103823
13. Dalkmann, H., Obika, B., & Geronimo, L. (2020). A call for collective action for international transport stakeholders to respond to the COVID-19 pandemic. High Volume Transport applied research. https://assets.publishing.service.gov.uk/media/5f8b094be- 90e0727cc8d96b0/HVT029.001_COVID-19_Transport_Overview_ Report__1_.pdf
14. Das, S., Boruah, A., Banerjee, A., Raoniar, R., Nama, S., & Maurya, A. K. (2021). Impact of COVID-19: A radical modal shift from public to private transport mode. Transport Policy, 109, 1-11. https:// doi.org/10.1016/j.tranpol.2021.05.005
15. Davis, M., (2007) Gecekondu Gezegeni. (Planet of Slums) Çev: G.Koca. İstanbul: Metis Yayınları.
16. Döker, M.F., Ocak, F. (2020). COVID-19 salgınının Türkiye’deki coğrafi dağılışının izlenmesinde Web CBS kullanımı. Türk Coğrafya Dergisi, 76, 7-18. DOI: 10.17211/tcd.778712
17. Dzhambov, A. M., Browning, M. H., Markevych, I., Hartig, T., & Lercher, P. (2020). Analytical approaches to testing pathways linking greenspace to health: A scoping review of the empirical literature. Environmental Research, 186, 109613. https://doi. org/10.1016/j.envres.2020.109613
18. Eger, L., Komárková, L., Egerová, D., & Mičík, M. (2021). The effect of COVID-19 on consumer shopping behaviour: Generational cohort perspective. Journal of Retailing and Consumer Services, 61, 102542. https://doi.org/10.1016/j.jretconser.2021.102542
19. Ermagun, A., & Samimi, A. (2017). Mode choice and travel distance joint models in school trips. Transportation, 45(6), 1755-1781. https://doi.org/10.1007/s11116-017-9794-y
20. Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., Mellan, T. A., Coupland, H., ... & Bhatt, S. (2020). Estimating the effects of nonpharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261.
21. Forster, P., & Ya Tang. (2005). The role of online shopping and fulfillment in the Hong Kong SARS crisis. Proceedings of the 38th Annual Hawaii International Conference on System Sciences. https://doi.org/10.1109/hicss.2005.615
22. Frumkin, H., Bratman, G. N., Breslow, S. J., Cochran, B., Kahn Jr, P. H., Lawler, J. J., Levin, P. S., Tandon, P. S., Varanasi, U., Wolf, K. L., & Wood, S. A. (2017). Nature contact and human health: A research agenda. Environmental Health Perspectives, 125(7), 075001. https://doi.org/10.1289/ehp1663
23. Gargoum, S. A., & Gargoum, A. S. (2021). Limiting mobility during COVID-19, when and to what level? An international comparative study using change point analysis. Journal of Transport & Health, 20, 101019. https://doi.org/10.1016/j.jth.2021.101019
24. Glodeanu, A., Bilal, U., & Tosio, P. G. (2021). Social inequalities in mobility during and following the COVID-19 associated lockdown of the Madrid metropolitan area in Spain. https://doi. org/10.31235/osf.io/apz4e
25. Google (2020) Mobility Reports. https://www.google.com/covid19/ mobility/ (Erişim Tarihi: 03.30.2021)
26. Guthrie, C., Fosso-Wamba, S., & Arnaud, J. B. (2021). Online consumer resilience during a pandemic: An exploratory study of e-Commerce behavior before, during and after a COVID-19 lockdown. Journal of Retailing and Consumer Services, 61, 102570. https://doi.org/10.1016/j.jretconser.2021.102570
27. Günay Aktaş, S. (2020). Küresel sağlıktan sağlık turizmine COVID-19. Türk Coğrafya Dergisi, 76, 107-114. DOI: 10.17211/tcd.816615
28. Günay Aktas, S. Kumtepe, E. G., Kantar, Y. M., Ulukan, I. C., Aydin, S., Aksoy, T., & Er, F. (2019). Improving gender equality in higher education in Turkey. Applied Spatial Analysis and Policy, 12(1), 167-189.
29. Hakim, A., Victory, K., Chevinsky, J., Hast, M., Weikum, D., Kazazian, L., Mirza, S., Bhatkoti, R., Schmitz, M., Lynch, M., & Marston, B. (2021). Mitigation policies, community mobility, and COVID-19 case counts in Australia, Japan, Hong Kong, and Singapore. Public Health, 194, 238-244. https://doi.org/10.1016/j. puhe.2021.02.001
30. Hunter, M. R., Gillespie, B. W., & Chen, S. Y. (2019). Urban nature experiences reduce stress in the context of daily life based on salivary biomarkers. Frontiers in Psychology, 10. https://doi. org/10.3389/fpsyg.2019.00722
31. Iio, K., Guo, X., Kong, X., Rees, K., & Bruce Wang, X. (2021). COVID- 19 and social distancing: Disparities in mobility adaptation between income groups. Transportation Research Interdisciplinary Perspectives, 10, 100333. https://doi.org/10.1016/j. trip.2021.100333
32. Kanda, W., & Kivimaa, P. (2020). What opportunities could the COVID- 19 outbreak offer for sustainability transitions research on electricity and mobility? Energy Research & Social Science, 68, 101666. https://doi.org/10.1016/j.erss.2020.101666
33. Kervankıran, İ., Bağmancı, M.F. (2020). Bildiğimiz turizmin sonu mu? COVID-19’un Türkiye’deki turizm hareketliliğine etkisi. Türk Coğrafya Dergisi, 76, 19-32. DOI: 10.17211/tcd.811302
34. Ko, J., Lee, S., & Byun, M. (2019). Exploring factors associated with commute mode choice: An application of city-level general social survey data. Transport Policy, 75, 36-46. https://doi.org/ 10.1016/j.tranpol.2018.12.007
35. Kim, R. Y. (2020). The impact of COVID-19 on consumers: Preparing for digital sales. IEEE Engineering Management Review, 48(3), 212-218. https://doi.org/10.1109/emr.2020.2990115
36. Kim, J., & Kwan, M. (2021). The impact of the COVID-19 pandemic on people’s mobility: A longitudinal study of the U.S. from march to September of 2020. Journal of Transport Geography, 93, 103039. https://doi.org/10.1016/j.jtrangeo.2021.103039
37. Kim, S., Lee, S., Ko, E., Jang, K., & Yeo, J. (2021). Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price. Journal of Transport Geography, 96, 103168. doi:10.1016/j.jtrangeo.2021.103168
38. Kirk, C. P., & Rifkin, L. S. (2020). I’ll trade you diamonds for toilet paper: Consumer reacting, coping and adapting behaviors in the COVID-19 pandemic. Journal of Business Research, 117, 124- 131. https://doi.org/10.1016/j.jbusres.2020.05.028
39. Lee, W. D., Qian, M., & Schwanen, T. (2021). The association between socioeconomic status and mobility reductions in the early stage of England’s COVID-19 epidemic. Health & Place, 69, 102563. https://doi.org/10.1016/j.healthplace.2021.102563
40. Liu, Y., Hong, Z., & Liu, Y. (2016). Do driving restriction policies effectively motivate commuters to use public transportation? Energy Policy, 90, 253-261. https://doi.org/10.1016/j. enpol.2015.12.038
41. Orak, N. H., & Ozdemir, O. (2021). The impacts of COVID-19 lockdown on PM10 and SO2 concentrations and association with human mobility across Turkey. Environmental Research, 197, 111018. https://doi.org/10.1016/j.envres.2021.111018
42. Porter, G., Murphy, E., Adamu, F., Dayil, P., De Lannoy, A., Han, S., Mansour, H., Dungey, C., Ahmad, H., Maskiti, B., S, C., & Van der Weidje, K. (2021). Women’s mobility and transport in the peripheries of three African cities: Reflecting on early impacts of COVID-19. Transport Policy, 110, 181-190. https://doi.org/ 10.1016/j.tranpol.2021.05.025
43. Predmore, C. E., Rovenpor, J., Manduley, A. R., & Radin, T. (2007). Shopping in an age of terrorism. Competitiveness Review, 17(3), 170-180. https://doi.org/10.1108/10595420710833570
44. Rice, W. L., & Pan, B. (2021). Understanding changes in park visitation during the COVID-19 pandemic: A spatial application of big data. Wellbeing, Space and Society, 100037. https://doi. org/10.1016/j.wss.2021.100037
45. Rung, A. L., Broyles, S. T., Mowen, A. J., Gustat, J., & Sothern, M. S. (2010). Escaping to and being active in neighbourhood parks: Park use in a post-disaster setting. Disasters, 35(2), 383-403. https://doi.org/10.1111/j.1467-7717.2010.01217.x
46. Saha, J., Mondal, S., & Chouhan, P. (2021). Spatial-temporal variations in community mobility during lockdown, unlock, and the second wave of COVID-19 in India: A data-based analysis using Google’s community mobility reports. Spatial and Spatio-temporal Epidemiology, 100442. doi:10.1016/j.sste.2021.100442
47. Seargeant, P., & Tagg, C. (2018). Critical digital literacy education in the ‘Fake news’ era. Digital Literacy Unpacked, 179-190. https:// doi.org/10.29085/9781783301997.015
48. Shao, W., Xie, J., & Zhu, Y. (2021). Mediation by human mobility of the association between temperature and COVID-19 transmission rate. Environmental Research, 194, 110608. https://doi.org/ 10.1016/j.envres.2020.110608
49. Sharma, G. D., Thomas, A., & Paul, J. (2021). Reviving tourism industry post-COVID-19: A resilience-based framework. Tourism Management Perspectives, 37, 100786. https://doi. org/10.1016/j.tmp.2020.100786
50. Shokouhyar, S., Shokoohyar, S., Sobhani, A., & Gorizi, A. J. (2021). Shared mobility in post-COVID era: New challenges and opportunities. Sustainable Cities and Society, 67, 102714. https://doi. org/10.1016/j.scs.2021.102714
51. Tepanosyan, G., Sahakyan, L., Zhang, C., & Saghatelyan, A. (2019). The application of local Morans “I” to identify spatial clusters and hot spots of PB, Mo and ti in urban soils of Yerevan. Applied Geochemistry, 104, 116-123. doi:10.1016/j. apgeochem.2019.03.022
52. Tong, Z., Xie, Y., & Xiao, H. (2021). Effect of CSR contribution timing during COVID-19 pandemic on consumers’ prepayment purchase intentions: Evidence from hospitality industry in China. International Journal of Hospitality Management, 97, 102997. https://doi.org/10.1016/j.ijhm.2021.102997
53. Trias-Llimós, S., Riffe, T., & Bilal, U. (2020). Monitoring life expectancy levels during the COVID-19 pandemic: Example of the unequal impact of the first wave on Spanish regions. PLOS ONE, 15(11), e0241952. https://doi.org/10.1371/journal.pone.0241952
54. TÜİK. (2020). İstihdam Edilenlerin Yıllar ve Cinsiyete Göre İktisadi Faaliyet Kolları (https://data.tuik.gov.tr/Kategori/GetKategori? p=istihdam-issizlik-ve-ucret-108&dil=1)
55. Valentino-DeVries, L. ve Dance, G.J.X. (2020). Location data says it all: staying at home during Coronavirus is a luxury. The New York Times. https://www.nytimes.com/interactive/2020/04/03/ us/coronavirus-stay
56. WHO. (2021). WHO Coronavirus (COVID-19) Dashboard. https://covid19. who.int/
57. Yabe, T., Tsubouchi, K., Fujiwara, N., Wada, T., Sekimoto, Y., & Ukkusuri, S. V. (2020). Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-75033-5
58. Yacchirema, D., & Chura, A. (2021). SafeMobility: An iot- based system for safer mobility using machine learning in the age of COVID- 19. Procedia Computer Science, 184, 524-531. https://doi. org/10.1016/j.procs.2021.03.066
59. Zhang, C., Luo, L., Xu, W., & Ledwith, V. (2008). Use of local Moran’s I and GIS to identify pollution hotspots of PB in urban soils of Galway, Ireland. Science of The Total Environment, 398(1-3), 212-221. doi:10.1016/j.scitotenv.2008.03.011
60. Zhou, Y., Xu, R., Hu, D., Yue, Y., Li, Q., & Xia, J. (2020). Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: A modelling study using mobile phone data. The Lancet Digital Health, 2(8), e417-e424. https://doi.org/10.1016/ s2589-7500(20)30165-5
61. Zoğal, V., Domènech, A., & Emekli, G. (2020). Stay at (which) home: Second homes during and after the COVID-19 pandemic. Journal of Tourism Futures. https://doi.org/10.1108/jtf-06-2020-0090