LAUSR

Abstract A significant increase in the older population has been observed in numerous cities. Understanding the correlates of older adults’ mobility is, therefore, of critical importance to meet their needs and preferences. Based on multisource data, including Google Street View street images and Hong Kong Travel Characteristics Survey 2011 data, we developed a series of multilevel and geographically weighted logistic regression models to scrutinize the global and local associations between urban greenery (eye-level street greenery, the number of parks, and the normalized difference vegetation index (NDVI)) and the travel propensity of older adults. Notably, eye-level street greenery was assessed by using readily available street images and machine learning techniques. The modeling results reveal that eye-level street greenery has a positive effect on older adults’ mobility, but the number of parks and the NDVI do not significantly affect the mobility. Robustness checks verified the plausibility of these findings. Furthermore, the effect of street greenery varies over space, larger in suburban areas than in urban areas. This study advances the understanding of relationships between urban greenery and travel behavior.