Urban Resilience of Large Public Health Events Based on NPP-VIIRS Nighttime Light Images: A Case Study of 35 Large Cities in China
Abstract
:1. Introduction
2. Study Area and Dataset
3. Methodology
4. Results
4.1. The Change in Night Light Value in the Early Stage of the Epidemic
4.1.1. Overall Change in the Value of City Light at Night
4.1.2. The Change in Different Functional Places in the City
4.2. Changes in Night Light Value in the Middle and Late Period of the Epidemic
4.2.1. Overall Trend of Night Light Value under Time Series
4.2.2. Correlation between NTL Values and Human Activities on an Annual Scale
5. Discussion
5.1. Regional Difference and Urban Agglomeration Difference
5.2. The Impact of the Containment Policy
5.3. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Mansour, S.; Al Kindi, A.; Al-Said, A.; Al-Said, A.; Atkinson, P. Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustain. Cities Soc. 2021, 65, 102627. [Google Scholar] [CrossRef]
- Ren, H.; Zhao, L.; Zhang, A.; Song, L.; Liao, Y.; Lu, W.; Cui, C. Early forecasting of the potential risk zones of COVID-19 in China’s megacities. Sci. Total Environ. 2020, 729, 138995. [Google Scholar] [CrossRef]
- Liu, Q.; Harris, J.T.; Chiu, L.S.; Sun, D.; Houser, P.R.; Yu, M.; Duffy, D.Q.; Little, M.M.; Yang, C. Spatiotemporal impacts of COVID-19 on air pollution in California, USA. Sci. Total Environ. 2021, 750, 141592. [Google Scholar] [CrossRef]
- Magazzino, C.; Mele, M.; Sarkodie, S.A. The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning. J. Environ. Manag. 2021, 286, 112241. [Google Scholar] [CrossRef]
- Aljadani, A.; Toumi, H.; Toumi, S.; Hsini, M.; Jallali, B. Investigation of the N-shaped environmental Kuznets curve for COVID-19 mitigation in the KSA. Environ. Sci. Pollut. Res. 2021, 28, 29681–29700. [Google Scholar] [CrossRef]
- Anand, A.; Kim, D. Pandemic induced changes in economic activity around African protected areas captured through night-time light data. Rem. Sens. 2021, 13, 314. [Google Scholar] [CrossRef]
- El-Gohary, H. Coronavirus and halal tourism and hospitality industry: Is it a journey to the unknown? Sustainability 2020, 12, 9260. [Google Scholar] [CrossRef]
- Bahmanyar, A.; Estebsari, A.; Ernst, D. The impact of different COVID-19 containment measures on electricity consumption in Europe. Energy Res. Soc. Sci. 2020, 68, 101683. [Google Scholar] [CrossRef]
- Lan, T.; Shao, G.; Tang, L.; Xu, Z.; Zhu, W.; Liu, L. Quantifying spatiotemporal changes in human activities induced by COVID-19 pandemic using daily nighttime light data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 2740–2753. [Google Scholar] [CrossRef]
- Shao, Z.; Tang, Y.; Huang, X.; Li, D. Monitoring work resumption of Wuhan in the COVID-19 epidemic using daily nighttime light. Photogramm. Eng. Remote Sens. 2021, 87, 195–204. [Google Scholar] [CrossRef]
- Yin, R.; He, G.; Jiang, W.; Peng, Y.; Zhang, Z.; Li, M.; Gong, C. Night-Time Light imagery reveals China’s city activity during the COVID-19 pandemic period in early 2020. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 5111–5122. [Google Scholar] [CrossRef]
- Chand, T.K.; Badarinath, K.; Elvidge, C.; Tuttle, B. Spatial characterization of electrical power consumption patterns over India using temporal DMSP-OLS night-time satellite data. Int. J. Remote Sens. 2009, 30, 647–661. [Google Scholar] [CrossRef]
- Doll, C.N.; Pachauri, S. Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery. Energy Policy 2010, 38, 5661–5670. [Google Scholar] [CrossRef]
- Elvidge, C.D.; Sutton, P.C.; Ghosh, T.; Tuttle, B.T.; Baugh, K.E.; Bhaduri, B.; Bright, E. A global poverty map derived from satellite data. Comput. Geosci. 2009, 35, 1652–1660. [Google Scholar] [CrossRef]
- Li, X.; Xu, H.; Chen, X.; Li, C. Potential of NPP-VIIRS nighttime light imagery for modeling the regional economy of China. Remote Sens. 2013, 5, 3057–3081. [Google Scholar] [CrossRef]
- Zheng, Q.; Jiang, R.; Wang, K.; Huang, L.; Ye, Z.; Gan, M.; Ji, B. Monitoring the trajectory of urban nighttime light hotspots using a Gaussian volume model. Int. J. Appl. Earth Obs. Geoinf. 2018, 65, 24–34. [Google Scholar] [CrossRef]
- Ma, T.; Zhou, C.; Pei, T.; Haynie, S.; Fan, J. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China’s cities. Remote Sens. Environ. 2012, 124, 99–107. [Google Scholar] [CrossRef]
- Small, C.; Pozzi, F.; Elvidge, C.D. Spatial analysis of global urban extent from DMSP-OLS night lights. Remote Sens. Environ. 2005, 96, 277–291. [Google Scholar] [CrossRef]
- Zhang, Q.; Seto, K.C. Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sens. Environ. 2011, 115, 2320–2329. [Google Scholar] [CrossRef]
- Cao, C.; Shao, X.; Uprety, S. Detecting light outages after severe storms using the S-NPP/VIIRS day/night band radiances. IEEE Geosci. Remote Sens. Lett. 2013, 10, 1582–1586. [Google Scholar] [CrossRef]
- Li, X.; Zhang, R.; Huang, C.; Li, D. Detecting 2014 Northern Iraq Insurgency using night-time light imagery. Int. J. Remote Sens. 2015, 36, 3446–3458. [Google Scholar] [CrossRef]
- Zhao, X.; Yu, B.; Liu, Y.; Yao, S.; Lian, T.; Chen, L.; Yang, C.; Chen, Z.; Wu, J. NPP-VIIRS DNB daily data in natural disaster assessment: Evidence from selected case studies. Remote Sens. 2018, 10, 1526. [Google Scholar] [CrossRef]
- March, D.; Metcalfe, K.; Tintoré, J.; Godley, B.J. Tracking the global reduction of marine traffic during the COVID-19 pandemic. Nat. Commun. 2021, 12, 2415. [Google Scholar] [CrossRef] [PubMed]
- Liu, J.; Tian, J.; Lyu, W.; Yu, Y. The impact of COVID-19 on reducing carbon emissions: From the angle of international student mobility. Appl. Energy 2022, 317, 119136. [Google Scholar] [CrossRef] [PubMed]
- Xiong, P.; Wu, X.; Zeng, X.; Hu, L.; Yan, X. COVID-19 epidemic and regional carbon emissions: A study based on metabolic multivariate grey model with new information priority. Eng. Appl. Artif. Intell. 2023, 126, 106820. [Google Scholar] [CrossRef]
- Xu, L.; Yang, Z.; Chen, J.; Zou, Z. Impacts of the COVID-19 epidemic on carbon emissions from international shipping. Mar. Pollut. Bull. 2023, 189, 114730. [Google Scholar] [CrossRef] [PubMed]
- Liu, Z.; Ciais, P.; Deng, Z.; Lei, R.; Davis, S.J.; Feng, S.; Zheng, B.; Cui, D.; Dou, X.; Zhu, B. Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nat. Commun. 2020, 11, 5172. [Google Scholar] [CrossRef]
- Chen, X.; Chen, W.; Bai, Y.; Wen, X. Changes in turbidity and human activities along Haihe River Basin during lockdown of COVID-19 using satellite data. Environ. Sci. Pollut. Res. 2022, 29, 3702–3717. [Google Scholar] [CrossRef]
- Du, M.; Ruan, J.; Zhang, L.; Niu, M.; Zhang, Z.; Xia, L.; Qian, S.; Chen, C. China’s local-level monthly residential electricity power consumption monitoring. Appl. Energy 2024, 359, 122658. [Google Scholar] [CrossRef]
- Yan, G.; Zou, L.; Liu, Y. The Spatial Pattern and Influencing Factors of China’s Nighttime Economy Utilizing POI and Remote Sensing Data. Appl. Sci. 2024, 14, 400. [Google Scholar] [CrossRef]
- Liu, Y.; Liu, W.; Zhang, X.; Lin, Y.; Zheng, G.; Zhao, Z.; Cheng, H.; Gross, L.; Li, X.; Wei, B. Nighttime light perspective in urban resilience assessment and spatiotemporal impact of COVID-19 from January to June 2022 in mainland China. Urban Clim. 2023, 50, 101591. [Google Scholar] [CrossRef] [PubMed]
- Santiago-Iglesias, E.; Romanillos, G.; Sun, W.; Schmöcker, J.-D.; Moya-Gómez, B.; García-Palomares, J.C. Light in the darkness: Urban nightlife, analyzing the impact and recovery of COVID-19 using mobile phone data. Cities 2024, 153, 105276. [Google Scholar] [CrossRef]
Cities | December 2019 to January 2020 | January 2020 to February 2020 |
---|---|---|
Beijing | 9.89% | 23.35% |
Cangzhou | 2.37% | 9.76% |
Changsha | 7.87% | 8.22% |
Chengdu | 19.12% | 7.95% |
Dalian | 2.12% | 6.67% |
Dongguan | 37.46% | 26.56% |
Foshan | 21.66% | 21.86% |
Guangzhou | 17.94% | 19.42% |
Hefei | 9.54% | 3.38% |
Jinan | 7.96% | 11.55% |
Lanzhou | 2.71% | 2.09% |
Nanjing | 14.87% | 19.26% |
Nantong | 7.59% | 8.98% |
Ningbo | 16.68% | 9.44% |
Qingdao | 7.02% | 11.35% |
Shanghai | 35.65% | 29.79% |
Shenyang | 9.34% | 11.90% |
Shenzhen | 38.61% | 31.65% |
Shijiazhuang | 2.95% | 9.04% |
Suzhou | 24.96% | 17.70% |
Tangshan | 3.29% | 9.59% |
Tianjin | 5.92% | 28.03% |
Wenzhou | 9.71% | 9.54% |
Wuhan | 17.41% | 7.34% |
Wulumuqi | 6.94% | 7.48% |
Wuxi | 19.22% | 12.57% |
Xian | 11.22% | 4.85% |
Xuzhou | 5.31% | 5.92% |
Yantai | 3.69% | 4.72% |
Zhengzhou | 14.52% | 15.41% |
Huhehaote | 2.80% | 7.51% |
Hangzhou | 10.65% | 5.28% |
Changchun | 2.78% | 15.26% |
Chongqing | 3.24% | 2.35% |
Haerbin | 1.45% | 5.60% |
Year | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|---|---|
GDP | 0.717 ** | 0.661 ** | 0.781 ** | 0.772 ** | 0.782 ** | 0.741 ** |
Built-up Area | 0.706 ** | 0.681 ** | 0.768 ** | 0.71 ** | 0.73 ** | 0.759 ** |
Population | 0.429 * | 0.423 * | 0.473 ** | 0.749 ** | 0.659 ** | 0.728 ** |
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Liu, R.; Li, X.; Zhang, Z. Urban Resilience of Large Public Health Events Based on NPP-VIIRS Nighttime Light Images: A Case Study of 35 Large Cities in China. Sustainability 2024, 16, 7483. https://doi.org/10.3390/su16177483
Liu R, Li X, Zhang Z. Urban Resilience of Large Public Health Events Based on NPP-VIIRS Nighttime Light Images: A Case Study of 35 Large Cities in China. Sustainability. 2024; 16(17):7483. https://doi.org/10.3390/su16177483
Chicago/Turabian StyleLiu, Rui, Xin Li, and Zizhe Zhang. 2024. "Urban Resilience of Large Public Health Events Based on NPP-VIIRS Nighttime Light Images: A Case Study of 35 Large Cities in China" Sustainability 16, no. 17: 7483. https://doi.org/10.3390/su16177483