How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data

Autor: Shuangming Zhao, Yuchen Fan, Pengxiang Zhao, Ali Mansourian, Hung Chak Ho

Publikováno v: International Journal of Health Geographics, Vol 23, Iss 1, Pp 1-15 (2024)

Abstract Background Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.

Externí odkaz: https://doaj.org/article/c22e9ba56b07443597bfb00f9331c97a

Inferring Urban Land Use from Multi-Source Urban Mobility Data Using Latent Multi-View Subspace Clustering

Autor: Qiliang Liu, Weihua Huan, Min Deng, Xiaolin Zheng, Haotao Yuan

Publikováno v: ISPRS International Journal of Geo-Information, Vol 10, Iss 5, p 274 (2021)

In the era of big data, vast urban mobility data introduce new opportunities to infer urban land use from the perspective of social function. Most existing works only derive land use information from a single type of urban mobility dataset, which is

Externí odkaz: https://doaj.org/article/391b2ec511424320a832de8e675a050b

Inferring Urban Land Use from Multi-Source Urban Mobility Data Using Latent Multi-View Subspace Clustering

Autor: Xiaolin Zheng, Min Deng, Yuan Haotao, Weihua Huan, Qiliang Liu

Publikováno v: ISPRS International Journal of Geo-Information, Vol 10, Iss 274, p 274 (2021)
ISPRS International Journal of Geo-Information
Volume 10
Issue 5

In the era of big data, vast urban mobility data introduce new opportunities to infer urban land use from the perspective of social function. Most existing works only derive land use information from a single type of urban mobility dataset, which is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6c737e403761f03348b4e5456671c53
https://www.mdpi.com/2220-9964/10/5/274

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