TY - JOUR
T1 - Walkability of greenways from the perspective of the elderly
T2 - A case study of Huangpu River waterfront greenway
AU - Hu, Xinyu
AU - Cao, Kai
AU - Huang, Bo
AU - Li, Xia
AU - Wu, Ruijun
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2025/2
Y1 - 2025/2
N2 - Under the background of global aging, outdoor public spaces play a crucial role in promoting physical activity among older individuals, helping to advance the process of healthy aging. Among these spaces, urban greenways are particularly effective in encouraging walking behaviors and improving both physical and mental health. However, current assessments of greenway's walkability have mainly focused on the general population, with scant attention paid to the viewpoint of older adults. Hence, this study proposed a novel walkability assessment framework from the perspective of the elderly, which employs multi-sourced data in conjunction with Fully Convolutional Network (FCN) to measure walkability, taking the renowned 45-km-long Huangpu River waterfront Greenway in Shanghai as a case study. Apart from using traditional GIS data to quantify the walkability factors, locally collected street view images using wearable GoPro were also utilized in the evaluation. According to the results of overall walkability scores, the Hongkou Riverwalk shows the best walkability for older people, followed by the Huangpu Riverwalk, the Xuhui Riverwalk, the Yangpu Riverwalk, and the East Coast Riverwalk is the worst. It is also noted that among the walkability factors, older adults place the highest value on safety facilities, green view index and seating facilities. Moreover, four greenway patterns were classified to characterize different walkability, including “safety-dominant” pattern, “comfort-dominant” pattern, “interestingness-dominant” pattern, “usefulness-dominant” pattern. Last but not least, the implications and limitations were also discussed in this paper, some of which would be the direction of our future work.
AB - Under the background of global aging, outdoor public spaces play a crucial role in promoting physical activity among older individuals, helping to advance the process of healthy aging. Among these spaces, urban greenways are particularly effective in encouraging walking behaviors and improving both physical and mental health. However, current assessments of greenway's walkability have mainly focused on the general population, with scant attention paid to the viewpoint of older adults. Hence, this study proposed a novel walkability assessment framework from the perspective of the elderly, which employs multi-sourced data in conjunction with Fully Convolutional Network (FCN) to measure walkability, taking the renowned 45-km-long Huangpu River waterfront Greenway in Shanghai as a case study. Apart from using traditional GIS data to quantify the walkability factors, locally collected street view images using wearable GoPro were also utilized in the evaluation. According to the results of overall walkability scores, the Hongkou Riverwalk shows the best walkability for older people, followed by the Huangpu Riverwalk, the Xuhui Riverwalk, the Yangpu Riverwalk, and the East Coast Riverwalk is the worst. It is also noted that among the walkability factors, older adults place the highest value on safety facilities, green view index and seating facilities. Moreover, four greenway patterns were classified to characterize different walkability, including “safety-dominant” pattern, “comfort-dominant” pattern, “interestingness-dominant” pattern, “usefulness-dominant” pattern. Last but not least, the implications and limitations were also discussed in this paper, some of which would be the direction of our future work.
KW - Greenway
KW - Older adults
KW - Street view images
KW - Walkability
UR - https://www.scopus.com/pages/publications/85213574407
U2 - 10.1016/j.jag.2024.104322
DO - 10.1016/j.jag.2024.104322
M3 - 文章
AN - SCOPUS:85213574407
SN - 1569-8432
VL - 136
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
M1 - 104322
ER -