Examining the importance of neighborhood natural, and built environment factors in predicting older adults' mental well-being: An XGBoost-SHAP approach

IF 7.7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
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Abstract

Background

Previous studies have shown that urban neighborhood environmental factors significantly influence the health outcomes of urban older adults. However, most cross-sectional studies exploring the health effects of these factors have failed to quantify the relative importance of each factor.

Methods

We use XGBoost machine learning techniques and SHAPley Additive Interpretation (SHAP) to rank the importance of urban neighborhood environmental factors in shaping the mental health of urban older adults. To address self-selection bias in housing choice, we distinguish older adults living in private housing from those living in public as residents in private housing have more freedom to choose where to live.

Results

The results show that both natural and built environmental factors in urban neighborhoods are important predictors of mental well-being scores. Five natural environmental factors (blue space, perceived greenery quantity, NDVI, street view greenness, aesthetic quality) and three built environmental factors (physical activity facilities quality, physical activity facilities quantity, neighborhood disorder) had considerable predictive power for mental well-being scores in two groups. Among them, blue space, perceived greenery quantity and street view greenness quantity became less important after controlling for self-selection bias, possibly because of the unequal distribution of quantity and quality, and the performance of neighborhood disorder, aesthetic quality and physical activity facilities quality was more sensitive in public housing.

Conclusions

These results highlight the nuanced and differential effects of neighborhood environmental exposures on mental well-being outcomes, depending on housing preferences. The results of this study can provide support for decision makers in urban planning, landscape design and environmental management in order to improve the mental well-being status of urban older adults.

研究邻里自然和建筑环境因素在预测老年人心理健康方面的重要性:XGBoost-SHAP 方法
背景以往的研究表明,城市邻里环境因素对城市老年人的健康状况有很大影响。方法我们使用 XGBoost 机器学习技术和 SHAPley Additive Interpretation(SHAP)对影响城市老年人心理健康的城市邻里环境因素的重要性进行排序。为了解决住房选择中的自我选择偏差,我们将居住在私人住房和公共住房的老年人区分开来,因为私人住房的居民有更多自由选择居住地。结果结果表明,城市社区的自然环境因素和建筑环境因素都是预测心理健康得分的重要因素。五项自然环境因素(蓝色空间、感知绿量、NDVI、街景绿度、美学质量)和三项建筑环境因素(体育活动设施质量、体育活动设施数量、邻里关系混乱)对两组居民的心理幸福感得分具有相当大的预测力。其中,蓝色空间、感知绿化数量和街景绿化数量在控制自我选择偏差后变得不那么重要,这可能是因为数量和质量分布不均,而邻里关系混乱、美学质量和体育活动设施质量在公共住房中的表现更为敏感。本研究结果可为城市规划、景观设计和环境管理决策者提供支持,以改善城市老年人的心理健康状态。
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来源期刊
Environmental Research
Environmental Research 环境科学-公共卫生、环境卫生与职业卫生
CiteScore
12.60
自引率
8.40%
发文量
2480
审稿时长
4.7 months
期刊介绍: The Environmental Research journal presents a broad range of interdisciplinary research, focused on addressing worldwide environmental concerns and featuring innovative findings. Our publication strives to explore relevant anthropogenic issues across various environmental sectors, showcasing practical applications in real-life settings.
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