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

Year 2021, Issue: 79, 7 - 16, 31.12.2021
https://doi.org/10.17211/tcd.971688

Abstract

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.

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

Year 2021, Issue: 79, 7 - 16, 31.12.2021
https://doi.org/10.17211/tcd.971688

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.

References

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  • Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical Analysis, 27, 93–115
  • 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
  • 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
  • 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
  • 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.
  • 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
  • Budak, F., & Korkmaz, Ş. (2020). COVID-19 pandemi sürecine yönelik genel bir değerlendirme: Türkiye örneği. Sosyal Araştırmalar ve Yönetim Dergisi, (1), 62-79.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Davis, M., (2007) Gecekondu Gezegeni. (Planet of Slums) Çev: G.Koca. İstanbul: Metis Yayınları.
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Google (2020) Mobility Reports. https://www.google.com/covid19/ mobility/ (Erişim Tarihi: 03.30.2021)
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There are 61 citations in total.

Details

Primary Language Turkish
Subjects Human Geography
Journal Section Research Articles
Authors

Öznur Akgiş İlhan 0000-0001-7224-8353

Publication Date December 31, 2021
Acceptance Date October 13, 2021
Published in Issue Year 2021 Issue: 79

Cite

APA Akgiş İlhan, Ö. (2021). COVID-19 pandemisinin nüfus hareketliliği üzerine etkisi: Hareketlilik ve gelir arasındaki ilişkinin analizi. Türk Coğrafya Dergisi(79), 7-16. https://doi.org/10.17211/tcd.971688

Publisher: Turkish Geographical Society