Journal
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Volume 10, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/ijgi10090596
Keywords
street greenery; street view imagery; walking time; walking behavior; population aging; older adult; mobility; built environment; spatial heterogeneity; geographically weighted model
Funding
- Sichuan Rural Community Governance Research Center [SQZL2021B03]
- Fundamental Research Funds for the Central Universities of China [2682021CX097, 2682021ZTPY111]
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This study examines the impact of street greenery on older adults' walking time, finding significant effects of greenery on walking time, with varying influence across different locations, especially greater impact in suburban areas.
Population aging has become a notable and enduring demographic phenomenon worldwide. Older adults' walking behavior is determined by many factors, such as socioeconomic attributes and the built environment. Although a handful of recent studies have examined the influence of street greenery (a built environment variable readily estimated by big data) on older adults' walking behavior, they have not focused on the spatial heterogeneity in the influence. To this end, this study extracts the socioeconomic and walking behavior data from the Travel Characteristic Survey 2011 of Hong Kong and estimates street greenery (the green view index) based on Google Street View imagery. It then develops global models (linear regression and Box-Cox transformed models) and local models (geographically weighted regression models) to scrutinize the average (global) and location-specific (local) relationships, respectively, between street greenery and older adults' walking time. Notably, green view indices in three neighborhoods with different sizes are estimated for robustness checks. The results show that (1) street greenery has consistent and significant effects on walking time; (2) the influence of street greenery varies across space-specifically, it is greater in the suburban area; and (3) the performance of different green view indices is highly consistent.
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