Impact of neighborhood destination built environments on daily activity opportunities of the elderly population: Based on perception and participation

WEI, Yue; Yang, Dongfeng

doi:10.18306/dlkxjz.2023.01.008

Cited by 1 publication

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…relevant to activities such as shopping, socializing, physical exercise, and medical care [41]. Aligned with the lifestyle of the elderly, this study selected supermarkets, parks, hospitals, and gymnasiums, calculating the number of these facilities within the community's 1 km buffer zone.…”

Section: Discussionmentioning

confidence: 99%

“…Many studies have confirmed that the public service facilities around the community are an important factor affecting the health of the elderly and are involved in their daily life [38]. These activities encompass not only aspects intrinsic to the residential life, but also those relevant to activities such as shopping, socializing, physical exercise, and medical care [41]. Aligned with the lifestyle of the elderly, this study selected supermarkets, parks, hospitals, and gymnasiums, calculating the number of these facilities within the community's 1 km buffer zone.…”

mentioning

confidence: 99%

See 1 more Smart Citation

The Influence and Prediction of Built Environment on the Subjective Well-Being of the Elderly Based on Random Forest: Evidence from Guangzhou, China

Zhang,

Luo,

Xie

et al. 2023

Land

View full text Add to dashboard Cite

Aging and urbanization significantly impact the physical and mental well-being of the elderly population. Empirical investigations have highlighted the contribution of the built environment to promoting elderly health. However, there is a need for further exploration of the factors within the built environment that impact the subjective well-being (SWB) of the elderly. To address this, this study selected 50 communities in Guangzhou, where 1403 elderly people were surveyed. Employing the random forest, we have identified contributing factors of the built environment affecting the SWB of the elderly. Meanwhile, we used a prediction model constructed by random forest to predict the SWB level of the elderly. The results indicated that accessibility to parks (positive emotions (PA): 0.822, positive experiences (PE): 0.235), hospitals (PA: 0.680, PE: 0.546), and supermarkets (PA: 0.237, PE: 0.617) significantly contributed to PA and PE. On the other hand, factors such as population density had a significant contribution to negative emotions (NA: 0.431) and negative experiences (NE: 0.194). Based on the prediction results, the spatial distribution of SWB among the elderly can be derived. Overall, our study can provide planning and improvement strategies for built environments that promote SWB among the elderly.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%