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Changes in nighttime lights during COVID-19 lockdown over Delhi, India
In 2020, the world faced an unexpected health crisis in form of the COVID-19 pandemic. Globally, lockdowns were imposed to control its spread. These lockdowns disrupted normal life and are estimated to cause large economic losses in India. Literature is replete with studies linking satellite-based n...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137561/ http://dx.doi.org/10.1016/B978-0-323-85512-9.00029-2 |
Sumario: | In 2020, the world faced an unexpected health crisis in form of the COVID-19 pandemic. Globally, lockdowns were imposed to control its spread. These lockdowns disrupted normal life and are estimated to cause large economic losses in India. Literature is replete with studies linking satellite-based nighttime light (NTL) observations with electrification, socioeconomic, and demographic growth. This chapter attempts to explore several such indicators for Delhi, India from March to May 2020. Human mobility, electricity power consumption (EPC), and NTL observations showed a significant decline in the lockdown months. However, results indicate that during the lockdown period, a weak correlation exists between NTL and EPC. The hypothesis of this study is, thus, built on the fact that, during the lockdown, the effect of EPC on NTL was impacted by other factors including COVID-19 cases and reduced mobility in the region. Further analysis was done by spatially, temporally, and quantitatively harmonizing all the datasets to a pre-COVID baseline period. The study finds positive correlation between mobility and EPC; and positive correlation between NTL and mobility in parks. A symbolical regression model is also generated to express EPC as function of NTL and mobility. The study therefore shows that NTL and EPC have been impacted by factors prevailing during COVID-19 lockdown. The study can be further expanded to other parts of the world with different socioeconomic settings and by including granular datasets having sectoral EPC values. |
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