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Dynamic assessments of population exposure to urban greenspace using multi-source big data
ارزیابی پویا در معرض قرار گرفتن جمعیت در فضای سبز شهری با استفاده از داده های بزرگ چند منبع-2018 A growing body of evidence has proven that urban greenspace is beneficial to improve peoples physical and
mental health. However, knowledge of population exposure to urban greenspace across different spatiotemporal
scales remains unclear. Moreover, the majority of existing environmental assessments are unable to quantify
how residents enjoy their ambient greenspace during their daily life. To deal with this challenge, we proposed
a dynamic method to assess urban greenspace exposure with the integration of mobile-phone locating-request
(MPL) data and high-spatial-resolution remote sensing images. This method was further applied to 30 major cit
ies in China by assessing cities dynamic greenspace exposure levels based on residents surrounding areas with
different buffer scales (0.5 km, 1 km, and 1.5 km). Results showed that regarding residents 0.5-km surrounding
environment, Wenzhou and Hangzhou were found to be with the greenest exposure experience, whereas Zheng
zhou and Tangshan were the least ones. The obvious diurnal and daily variations of population exposure to their
surrounding greenspace were also identified to be highly correlated with the distribution pattern of urban
greenspace and the dynamics of human mobility. Compared with two common measurements of urban
greenspace (green coverage rate and green area per capita), the developed method integrated the dynamics of
population distribution and geographic locations of urban greenspace into the exposure assessment, thereby pre
senting a more reasonable way to assess population exposure to urban greenspace. Additionally, this dynamic
framework could hold potential utilities in supporting urban planning studies and environmental health studies
and advancing our understanding of the magnitude of population exposure to greenspace at different spatiotem
poral scales.
Keywords: Urban greenspace ، Human mobility ، Dynamic assessment ، Geo-spatial big data ، Public health |
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