To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

22 Scopus Citations
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  • Linchuan Yang
  • Yibin Ao
  • Jintao Ke
  • Yi Lu
  • Yuan Liang


Original languageEnglish
Article number103099
Journal / PublicationJournal of Transport Geography
Online published27 May 2021
Publication statusPublished - Jun 2021


Population aging is a conspicuous demographic trend shaping the world profoundly. Walking is a critical travel mode and physical activity for older adults. As such, there is a need to determine the factors influencing the walking behavior of older people in the era of population aging. Streetscape greenery is an easily perceived built-environment attribute and can promote walking behavior, but it has received insufficient attention. More importantly, the non-linear effects of streetscape greenery on the walking behavior of older adults have not been examined. We therefore use readily available Google Street View imagery and a fully convolutional neural network to evaluate human-scale, eye-level streetscape greenery. Using data from the Hong Kong Travel Characteristic Survey, we adopt a machine learning technique, namely random forest modeling, to scrutinize the non-linear effects of streetscape greenery on the walking propensity of older adults. The results show that streetscape greenery has a positive effect on walking propensity within a certain range, but outside the range, the positive association no longer holds. The non-linear associations of other built-environment attributes are also examined.

Research Area(s)

  • Big data, Machine learning, Population aging, Random forest, Streetscape greenery, Travel behavior, Walking behavior