ArticleInternational journal of environmental research and public health2022
Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 18 citations in OpenAlex.
- Modeling COVID-19 spread using multi-agent simulation with small-world network approach.BMC public health · 2024Article
- Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace.International journal of environmental research and public health · 2023Review
- Evolution of in-hospital patient characteristics and predictors of death in the COVID-19 pandemic across four waves: are they moving targets with implications for patient care?Frontiers in public health · 2023Observational
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China's major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China's major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R
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