Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure.

Journal: The Science of the total environment
PMID: 36118158

Abstract

BACKGROUND: Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.

Authors

  • Yi Sun
    Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, CA, USA.
  • Xingzhi Wang
    School of Computer Science, Beijing Institute of Technology, Beijing, China.
  • Jiayin Zhu
    School of Management and Economics, Beijing Institute of Technology, Beijing, China.
  • Liangjian Chen
    Department of Computer Science, University of California, Irvine, CA, USA.
  • Yuhang Jia
    Testin AI Data, Beijing Yunce Information Technology Co., Ltd, China.
  • Jean M Lawrence
    Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
  • Luo-Hua Jiang
    Department of Epidemiology and Biostatistics, University of California, Irvine, CA, USA.
  • Xiaohui Xie
    Department of Computer Science, University of California, Irvine, CA, USA.
  • Jun Wu
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.

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