Using mobile phone big data to identify inequity of artificial light at night exposure: A case study in Tokyo

Chen, Zhiheng1; Li, Peiran1; Jin, Yanxiu1; Jin, Yuan4; Chen, Jinyu1; Li, Wenjing1; Song, Xuan1,3; Shibasaki, Ryusuki1; Chen, Min5; Yan, Da4; Zhang, Haoran1,2

2022-09-01

10.1016/j.cities.2022.103803

CITIES   Impact Factor & Quartile

0264-2751

1873-6084

Exposure to excessive ambient light at night (ALAN) has been proved to have a statistical association with human diseases. But current studies on ALAN exposure inequity have likely underestimated exposure levels due to the neglect of personal mobility. Based on mobile phone positioning big data and night-light satellite imagery, we conducted an empirical study on the inequity of ALAN exposure in Tokyo, Japan. We quantified the intensity of ALAN on the grid of mobile phone positioning data. Then we used the Gini coefficient and population-weighted mean exposure to evaluate the inequity of ALAN exposure among individuals and between different population groups. As a result, we found evidence of the inequity of ALAN exposure in Tokyo. For age inequity, younger people suffer higher exposure to light pollution at night, but children are an exception. For gender inequity, there is almost no inequity between men and women. For residence inequity, the average ALAN exposure of nonresidents can reach up to about twice that of residents. At time and space nodes where there are more travel behaviors, such as central Tokyo during 18:00-24:00, we have detected higher exposure and stronger inequity, indicating that ignoring personal mobility will cause underestimation.

Night light pollution Environmental justice Location data Mobility Unfair exposure

SSCI

English

Others

Urban Studies

Urban Studies

WOS:000811878800004

ELSEVIER SCI LTD

SOCIAL SCIENCES, GENERAL

Web of Science

1.Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan
2.LocationMind Inc, 3-5-2 Iwamotocho, Chiyoda-ku, Tokyo 1010032, Japan
3.Southern Univ Sci & Technol, Univ Tokyo Joint Res Ctr Super Smart Cities, Dept Comp & Engn, Shenzhen 518055, Guangdong, Peoples R China
4.Tsinghua Univ, Bldg Energy Res Ctr, Sch Architecture, Beijing, Peoples R China
5.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing, Peoples R China

Chen, Zhiheng,Li, Peiran,Jin, Yanxiu,et al. Using mobile phone big data to identify inequity of artificial light at night exposure: A case study in Tokyo[J]. CITIES,2022,128.

Chen, Zhiheng.,Li, Peiran.,Jin, Yanxiu.,Jin, Yuan.,Chen, Jinyu.,...&Zhang, Haoran.(2022).Using mobile phone big data to identify inequity of artificial light at night exposure: A case study in Tokyo.CITIES,128.

Chen, Zhiheng,et al."Using mobile phone big data to identify inequity of artificial light at night exposure: A case study in Tokyo".CITIES 128(2022).