Can Digital Transformation Promote the Rapid Recovery of Cities from the COVID-19 Epidemic? An Empirical Analysis from Chinese Cities
Abstract
:1. Introduction
2. Theoretical Basis and Study Hypothesis
3. Materials and Methods
3.1. Study Design and Sample Selection
3.2. Independent Variable
3.3. Dependent Variable
3.4. Control Variable
3.5. Statistical Analyses
4. Results
4.1. Descriptive Statistics of Urban Digitalization, Urban Population Attraction, and Urban Hierarchy
4.2. Correlation Analysis of Urban Digitalization, Urban Population Attraction, and Urban Hierarchy
4.3. Validation Analysis of the Impact of Urban Digitalization on Urban Population Attraction
5. Discussion
6. Conclusions
7. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
COVID-19 | Coronavirus disease 2019 |
OLS | Ordinary least squares |
GIS | Geographic Information System |
IoT | Internet of Things |
Vif | variance inflation factor |
References
- WHO. Weekly Epidemiological Update on COVID-19—26 October 2021. Available online: https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19-26-october-2021 (accessed on 6 November 2021).
- Flaxman, S.; Mishra, S.; Gandy, A.; Unwin, H.J.T.; Mellan, T.A.; Coupland, H.; Whittaker, C.; Zhu, H.; Berah, T.; Eaton, J.W.; et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature 2020, 584, 257–261. [Google Scholar] [CrossRef] [PubMed]
- Nouvellet, P.; Bhatia, S.; Cori, A.; Ainslie, K.E.C.; Baguelin, M.; Bhatt, S.; Boonyasiri, A.; Brazeau, N.F.; Cattarino, L.; Cooper, L.V.; et al. Reduction in mobility and COVID-19 transmission. Nat. Commun. 2021, 12, 1090. [Google Scholar] [CrossRef] [PubMed]
- Mao, Z.; Yao, H.; Zou, Q.; Zhang, W.; Dong, Y. Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management during the COVID-19 Epidemic: Case Study. JMIR Mhealth Uhealth 2021, 9, e26836. [Google Scholar] [CrossRef] [PubMed]
- Mao, Z.; Zou, Q.; Yao, H.; Wu, J. The application framework of big data technology in the COVID-19 epidemic emergency management in local government—A case study of Hainan Province, China. BMC Public Health 2021, 21, 2001. [Google Scholar] [CrossRef] [PubMed]
- Bayram, M.; Springer, S.; Garvey, C.K.; Özdemir, V. COVID-19 Digital Health in Novemberation Policy: A Portal to Alternative Futures in the Making. OMICS J. Integr. Biol. 2020, 24, 460–469. [Google Scholar] [CrossRef] [PubMed]
- Srinivasa Rao, A.S.R.; Vazquez, J.A. Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine. Infect. Control. Hosp. Epidemiol. 2020, 41, 826–830. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dong, E.; Du, H.; Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 2020, 20, 533–534. [Google Scholar] [CrossRef]
- Allam, Z.; Jones, D.S. On the coronavirus (COVID-19) outbreak and the smart city network: Universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Healthcare 2020, 8, 46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hua, J.; Shaw, R. Corona Virus (COVID-19) “Infodemic” and Emerging Issues through a Data Lens: The Case of China. Int. J. Environ. Res. Public Health 2020, 17, 2309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Robbins, T.; Hudson, S.; Ray, P.; Sankar, S.; Patel, K.; Randeva, H.; Arvanitis, T.N. COVID-19: A new digital dawn? Digit. Health 2020, 6, 2012956184. [Google Scholar] [CrossRef] [Green Version]
- Kamel Boulos, M.N.; Geraghty, E.M. 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. 2020, 19, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Yang, S.; Chong, Z. Smart city projects against COVID-19: Quantitative evidence from China. Sustain. Cities Soc. 2021, 70, 102897. [Google Scholar] [CrossRef] [PubMed]
- Chundakkadan, R.; Ravindran, R. Information flow and COVID-19 recovery. World Dev. 2020, 136, 105112. [Google Scholar] [CrossRef] [PubMed]
- Shen, Y.; Cheng, Y.; Xu, J. From recovery resilience to transformative resilience: How digital platforms reshape public service provision during and post COVID-19. Public Manag. Rev. 2022, 1–24. [Google Scholar] [CrossRef]
- Sharifi, A.; Khavarian-Garmsir, A.R.; Kummitha, R.K.R. Contributions of Smart City Solutions and Technologies to Resilience against the COVID-19 Pandemic: A Literature Review. Sustainability 2021, 13, 8018. [Google Scholar] [CrossRef]
- Sarker, M.N.I.; Peng, Y.; Yiran, C.; Shouse, R.C. Disaster resilience through big data: Way to environmental sustainability. Int. J. Disast. Risk Reduct. 2020, 51, 101769. [Google Scholar] [CrossRef]
- Hong, B.; Bonczak, B.J.; Gupta, A.; Kontokosta, C.E. Measuring inequality in community resilience to natural disasters using large-scale mobility data. Nat. Commun. 2021, 12, 1870. [Google Scholar] [CrossRef]
- Allaire, M.C. Disaster loss and social media: Can online information increase flood resilience? Water Resour. Res. 2016, 52, 7408–7423. [Google Scholar] [CrossRef]
- Platt, S. Factors Affecting the Speed and Quality of Post-Disaster Recovery and Resilience. In Proceedings of the International Conference on Earthquake Engineering and Structural Dynamics; Springer: Cham, Switzerland, 2017; pp. 369–403. [Google Scholar]
- Quarantelli, E.L. The Disaster Recovery Process: What We Know and Do Not Know from Research; Disaster Research Center: Newark, DE, USA, 1999. [Google Scholar]
- Bruneau, M.; Chang, S.E.; Eguchi, R.T.; Lee, G.C.; O’Rourke, T.D.; Reinhorn, A.M.; Shinozuka, M.; Tierney, K.; Wallace, W.A.; von Winterfeldt, D. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq. Spectra 2003, 19, 733–752. [Google Scholar] [CrossRef] [Green Version]
- Chu, Z.; Cheng, M.; Song, M. What determines urban resilience against COVID-19: City size or governance capacity? Sustain. Cities Soc. 2021, 75, 103304. [Google Scholar] [CrossRef]
- Höddinghaus, M.; Hertel, G. Trust and Communication; Springer: Cham, Switzerland, 2021; pp. 185–203. [Google Scholar]
- Ittefaq, M.; Iqbal, A. Digitization of the health sector in Pakistan: Challenges and opportunities to online health communication: A case study of MARHAM social and mobile media. Digit. Health 2018, 4, 2013169264. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yang, Y.; Deng, W.; Zhang, Y.; Mao, Z. Promoting Public Engagement during the COVID-19 Crisis: How Effective Is the Wuhan Local Government’s Information Release? Int. J. Environ. Res. Public Health 2021, 18, 118. [Google Scholar] [CrossRef] [PubMed]
- Teale, C. 11 Mayors form C40’s Coronavirus Recovery Task Force. Available online: https://www.smartcitiesdive.com/news/11-mayors-form-c40-coronavirus-recovery-task-force/577042/ (accessed on 6 November 2021).
- Chen, B.; Marvin, S.; While, A. Containing COVID-19 in China: AI and the robotic restructuring of future cities. Dialogues Hum. Geogr. 2020, 10, 238–241. [Google Scholar] [CrossRef]
- Meerow, S.; Newell, J.P.; Stults, M. Defining urban resilience: A review. Landsc. Urban Plan 2016, 147, 38–49. [Google Scholar] [CrossRef]
- Ribeiro, P.J.G.; Pena Jardim Gonçalves, L.A. Urban resilience: A conceptual framework. Sustain. Cities Soc. 2019, 50, 101625. [Google Scholar] [CrossRef]
- Rathore, M.M.; Paul, A.; Hong, W.; Seo, H.; Awan, I.; Saeed, S. Exploiting IoT and big data analytics: Defining Smart Digital City using real-time urban data. Sustain. Cities Soc. 2018, 40, 600–610. [Google Scholar] [CrossRef]
- Arner, D.W.; Barberis, J.N.; Walker, J.; Buckley, R.P.; Dahdal, A.M.; Zetzsche, D.A. Digital Finance & The COVID-19 Crisis. Univ. Hong Kong Fac. Law Res. Pap. 2020. [Google Scholar] [CrossRef]
- Couclelis, H. The Construction of the Digital City. Environ. Plan. B Plan. Des. 2004, 31, 5–19. [Google Scholar] [CrossRef]
- De, R.; Pandey, N.; Pal, A. Impact of digital surge during COVID-19 pandemic: A viewpoint on research and practice. Int. J. Inf. Manag. 2020, 55, 102171. [Google Scholar] [CrossRef]
- Dameri, R.P. Smart City Implementation; Springer: Cham, Switzerland, 2017; pp. 109–154. [Google Scholar]
- McGuirk, P.; Dowling, R.; Maalsen, S.; Baker, T. Urban governance inovberation and COVID-19. Geogr. Res. Aust. 2021, 59, 188–195. [Google Scholar] [CrossRef]
- Janssen, M.; van der Voort, H. Agile and adaptive governance in crisis response: Lessons from the COVID-19 pandemic. Int. J. Inf. Manag. 2020, 55, 102180. [Google Scholar] [CrossRef] [PubMed]
- China Bureau of Statistics. Changes in the Sales Price of Commercial Housing in 70 Large and Medium-Sized Cities in October 2019. Available online: http://www.stats.gov.cn/tjsj/zxfb/201911/t20191115_1709560.html (accessed on 8 February 2022).
- CCID. 2020 China Top 100 Digital Cities Research White Paper. Available online: https://www.ccidgroup.com/info/1105/32183.htm (accessed on 8 November 2021).
- McCartney, G.; Pinto, J.; Liu, M. City resilience and recovery from COVID-19: The case of Macao. Cities 2021, 112, 103130. [Google Scholar] [CrossRef] [PubMed]
- Baidu Map Insight. 2020 Q2 China Urban Vitality Research Report. Available online: https://huiyan.baidu.com/cms/report/2020Q2chengshi/2020%E7%AC%AC%E4%BA%8C%E5%AD%A3%E5%BA%A6%E4%B8%AD%E5%9B%BD%E5%9F%8E%E5%B8%82%E6%B4%BB%E5%8A%9B%E7%A0%94%E7%A9%B6%E6%8A%A5%E5%91%8A.pdf (accessed on 8 November 2021).
- New First-Tier City Research Institute. Hefei and Foshan Are Promoted to New First-Tier Cities! 2020 Latest 1–5 Tier Cities Ranking Released. Available online: https://www.yicai.com/news/100648666.html (accessed on 14 November 2021).
- Ma, Y.; Zhang, H. Enhancing Knowledge Management and Decision-Making Capability of China’s Emergency Operations Center Using Big Data. Intell. Autom. Soft Comput. 2017, 24, 107–114. [Google Scholar] [CrossRef]
- Zhang, H. What has China Learnt from Disasters? Evolution of the Emergency Management System after SARS, Southern Snowstorm, and Wenchuan Earthquake. J. Comp. Policy Anal. 2012, 14, 234–244. [Google Scholar] [CrossRef]
- Office of the State Council. Guiding Opinions of the General Office of the State Council on Promoting and Regulating the Application and Development of Health and Medical Big Data. Available online: http://www.gov.cn/zhengce/content/2016-06/24/content_5085091.htm (accessed on 10 November 2021).
- National Health Commission. Notice on Issuing National Health and Medical Big Data Standards, Security and Service Management Measures (Trial). Available online: http://www.cac.gov.cn/2018-09/15/c_1123432498.htm (accessed on 10 November 2021).
- Blasimme, A.; Vayena, E. What’s next for COVID-19 apps? Governance and oversight. Science 2020, 370, 760–762. [Google Scholar] [CrossRef] [PubMed]
- Gasser, U.; Ienca, M.; Scheibner, J.; Sleigh, J.; Vayena, E. Digital tools against COVID-19: Taxonomy, ethical challenges, and navigation aid. Lancet Digit. Health 2020, 2, e425–e434. [Google Scholar] [CrossRef]
- Hantrais, L.; Allin, P.; Kritikos, M.; Sogomonjan, M.; Anand, P.B.; Livingstone, S.; Williams, M. COVID-19 and the digital revolution. Contemp. Soc. Sci. 2021, 16, 256–270. [Google Scholar] [CrossRef]
- Ienca, M.; Vayena, E. On the responsible use of digital data to tackle the COVID-19 pandemic. Nat. Med. 2020, 26, 463–464. [Google Scholar] [CrossRef] [Green Version]
- Naudé, W.; Vinuesa, R. Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic. Big Data Soc. 2021, 8, 1246080943. [Google Scholar] [CrossRef]
- Bunker, D. Who do you trust? The digital destruction of shared situational awareness and the COVID-19 infodemic. Int. J. Inf. Manag. 2020, 55, 102201. [Google Scholar] [CrossRef]
Variables | n | Mean (SD) | Min | Max |
---|---|---|---|---|
Urban population attraction | 83 | 3.46 (3.58) | 0.43 | 16.89 |
Urban digitalization level | 83 | 69.26 (8.04) | 55.02 | 89.36 |
Urban hierarchy | 83 | 2.22 (0.84) | 1 | 4 |
Variables | Urban Population Attraction | Urban Digitalization Level | Urban Hierarchy |
---|---|---|---|
Urban population attraction | 1 | ||
Urban digitalization level | 0.79 *** | 1 | |
Urban hierarchy | −0.74 *** | −0.83 *** | 1 |
Variables | Coef. | Std. Err. | 95% Confidence Interval | Std. Coef. | t | P | vif |
---|---|---|---|---|---|---|---|
Independent variable | |||||||
Urban digitalization level | 0.21 | 0.05 | [0.11, 0.32] | 0.48 | 4.18 | <0.001 | 3.33 |
Control variables | |||||||
Urban hierarchy (base 1) | |||||||
2 | −3.03 | 0.78 | [−4.58, −1.47] | −0.41 | −3.88 | <0.001 | 2.77 |
3 | −3.6 | 1.05 | [−5.70, −1.50] | −0.49 | −3.41 | <0.001 | 5.14 |
4 | −3.32 | 1.66 | [−6.62, −0.01] | −0.17 | −2 | 0.049 | 1.90 |
Constant | −8.86 | 4.13 | [−17.08, −0.64] | −2.15 | 0.035 |
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Liu, J.; Liu, S.; Xu, X.; Zou, Q. Can Digital Transformation Promote the Rapid Recovery of Cities from the COVID-19 Epidemic? An Empirical Analysis from Chinese Cities. Int. J. Environ. Res. Public Health 2022, 19, 3567. https://doi.org/10.3390/ijerph19063567
Liu J, Liu S, Xu X, Zou Q. Can Digital Transformation Promote the Rapid Recovery of Cities from the COVID-19 Epidemic? An Empirical Analysis from Chinese Cities. International Journal of Environmental Research and Public Health. 2022; 19(6):3567. https://doi.org/10.3390/ijerph19063567
Chicago/Turabian StyleLiu, Jiaojiao, Shuai Liu, Xiaolin Xu, and Qi Zou. 2022. "Can Digital Transformation Promote the Rapid Recovery of Cities from the COVID-19 Epidemic? An Empirical Analysis from Chinese Cities" International Journal of Environmental Research and Public Health 19, no. 6: 3567. https://doi.org/10.3390/ijerph19063567