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Open AccessArticle

Street-Scale Analysis of Population Exposure to Light Pollution Based on Remote Sensing and Mobile Big Data—Shenzhen City as a Case

by Bo Sun 1, Yang Zhang 1, Qiming Zhou 1,2,* and Duo Gao 3
1
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2
Department of Geography, Hong Kong Baptist University, KLN, Hong Kong, China
3
TalkingData Co., Ltd., Beijing 100027, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(9), 2728; https://doi.org/10.3390/s20092728
Received: 22 March 2020 / Revised: 1 May 2020 / Accepted: 8 May 2020 / Published: 11 May 2020
(This article belongs to the Special Issue Distributed and Remote Sensing of the Urban Environment)
Most studies on light pollution are based on light intensity retrieved from nighttime light (NTL) remote sensing with less consideration of the population factors. Furthermore, the coarse spatial resolution of traditional NTL remote sensing data limits the refined applications in current smart city studies. In order to analyze the influence of light pollution on populated areas, this study proposes an index named population exposure to light pollution (PELP) and conducts a street-scale analysis to illustrate spatial variation of PELP among residential areas in cites. By taking Shenzhen city as a case, multi-source data were combined including high resolution NTL remote sensing data from the Luojia 1-01 satellite sensor, high-precision mobile big data for visualizing human activities and population distribution as well as point of interest (POI) data. Results show that the main influenced areas of light pollution are concentrated in the downtown and core areas of newly expanded areas with obvious deviation corrected like traditional serious light polluted regions (e.g., ports). In comparison, commercial–residential mixed areas and village-in-city show a high level of PELP. The proposed method better presents the extent of population exposure to light pollution at a fine-grid scale and the regional difference between different types of residential areas in a city. View Full-Text
Keywords: light pollution; NTL remote sensing; Luojia 1-01; residential area; population exposure to light pollution light pollution; NTL remote sensing; Luojia 1-01; residential area; population exposure to light pollution
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Sun, B.; Zhang, Y.; Zhou, Q.; Gao, D. Street-Scale Analysis of Population Exposure to Light Pollution Based on Remote Sensing and Mobile Big Data—Shenzhen City as a Case. Sensors 2020, 20, 2728.

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