The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques.

Journal: International journal of health geographics
PMID:

Abstract

BACKGROUND: Neighbourhood environment characteristics have been found to be associated with residents' willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjective, costly, and time-consuming, and can be applied only on a small scale. Recent developments in deep learning algorithms and the recent availability of street view images enable researchers to assess multiple aspects of neighbourhood environment perceptions more efficiently on a large scale. This study aims to examine the relationship between each of six neighbourhood environment perceptual indicators-namely, wealthy, safe, lively, depressing, boring and beautiful-and residents' time spent on PA in Guangzhou, China.

Authors

  • Ruoyu Wang
    Institute of Public Health and Wellbeing, University of Essex, Essex, UK.
  • Ye Liu
    Department of Cell Biology, Van Andel Research Institute, 333 Bostwick Ave NE, Grand Rapids, MI, 49503, USA.
  • Yi Lu
    Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-Sen University, 26th Yuancun the Second Road, Guangzhou, 510655, Guangdong Province, China.
  • Yuan Yuan
    Department of Geriatrics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
  • Jinbao Zhang
    School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China.
  • Penghua Liu
    School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China.
  • Yao Yao
    Key Laboratory for Organic Electronics and Information Displays (KLOEID) & Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China.