| 摘要: |
| 步行对老年人的健康至关重要。在积极应对人口老龄化和健康中国两大战略背景下，构建满足老年人步行出行需求、促进老年人步行活动的空间，已成为城乡规划学科的一项紧迫任务。虽然现有研究已广泛证实了社区建成环境与老年人步行行为的密切关联，但往往忽视了变量之间的非线性关系和协同效应。本研究以成都市为例，借助POI和街景图像等多源大数据，运用前沿的可解释机器学习方法（融合LightGBM和SHAP模型），分析了社区建成环境与老年人步行频次之间的非线性关系和变量之间的协同效应。结果表明，对老年人步行频次影响最大的3个建成环境变量是人行道占比、归一化植被指数和休闲娱乐设施可达性。SHAP模型进一步揭示了社区建成环境对老年人步行行为存在阈值效应，并详细描述了变量之间的协同效应。最后，提出了完善社区服务设施建设、提高社区步行道通达性、营造高品质公共空间、考虑建成环境要素的交互作用等四个方面的规划建议。本研究为城乡规划学科参与老年人活动研究展现了新的视角，并为以促进步行活动为导向的适老步行环境规划设计提供了科学支撑。 |
| 关键词: 物质环境 步行行为 人口老龄化 健康老龄化 可解释机器学习 |
| DOI： |
| 分类号:TU984 |
| 基金项目:国家自然科学基金面上项目（52278080）、四川省哲学社会科学规划“十四五”规划一般项目（SC23TJ027）、唐仲英基金会包容性城市规划建设联合研究平台项目（2022009） |
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| Non-linear and synergistic effects of built environment factors on older people’s walking frequency in Chengdu: A shapley additive explanations analysis |
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Wei Dong, Yang Linchuan
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| Abstract: |
| Population aging is a global trend. To address this issue, the World Health Organization (WHO) has introduced the concepts of “active aging” and “healthy aging”, emphasizing the importance of maintaining health and well-being throughout the aging process. China has also placed high importance on the aging issue, and has proposed the national strategy of “actively addressing population aging.” In this context, the health problems of older people have received unprecedented attention. However, older people often face various health challenges, due in part to insufficient physical activity. Moderate physical activity can decrease the occurrence of coronary heart disease, Type-2 diabetes, and hypertension among older people. It also alleviates anxious and depressive symptoms, enhancing their quality of life.
Walking, as a fundamental form of physical activity, is crucial for the health of older people. It not only helps maintain physical functions but also promotes mental health and social interaction. However, to accommodate growing traffic demand, modern urban development has reduced walking spaces and sidewalks. This reduction creates obstacles for older people to engage in walking. Therefore, in the context of population aging, it is crucial to optimize the urban walking environment to better accommodate and encourage the walking behavior of older people.
The built environment refers to the physical spaces shaped by human activities in urban or rural areas. It includes buildings, streets, transportation facilities, public spaces, etc. Existing research has extensively confirmed that various factors of the built environment significantly impact the walking behavior of older people. Key factors include population density, land use mix, street network density, and street intersections.
Most existing research employs traditional analytical methods based on assumed generalized linear relationships. These methods face significant limitations, as they struggle to capture the non-linear effects of the built environment on the walking behavior of older people. To address this limitation, researchers have turned to "black-box" machine learning models, such as random forests, gradient boosting decision trees (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). These advanced models can reveal non-linear relationships and threshold effects, leading to widespread application. Despite these advancements, existing research still has the following shortcomings: (1) difficulty in explaining the decision-making process of "black-box" machine learning models; and (2) neglect of the interactions between built environment variables.
This study employs the Shapley additive explanations (SHAP) model to address the aforementioned deficiencies. The SHAP model is a tool that helps us understand how machine learning models make predictions by analyzing the impact of each feature on the prediction results. This enhances our understanding of the model's decision-making process. The significance of SHAP lies in its ability to explain the output of any machine learning model. Therefore, this study applies cutting-edge interpretable machine learning methods by integrating LightGBM and SHAP. We utilize multi-source big data, including point of interest (POI), remote sensing images, and street view images, to analyze the non-linear relationships between the community-level built environment in Chengdu and the walking behavior (specifically walking frequency) of older people. Additionally, we aim to reveal the synergistic effects between built environment variables.
The key findings are as follows: (1) The three most important built environment variables affecting the walking frequency of older people are the proportion of sidewalks, the normalized difference vegetation index (NDVI), and the accessibility of recreational facilities. (2) There are significant non-linear relationships and threshold effects between built environment variables and older people’s walking behavior. For example, when the proportion of sidewalks, the NDVI, accessibility to recreational facilities, the land use mix, the enclosure, and the green view index are within the ranges of 0.01-0.04, 0.06-0.12, 17-81, 0.08-0.32, and 0.14-0.30, respectively, their impact on walking frequency is positive. (3) Significant interactions exist between built environment variables, with notable synergistic effects between the green view index and the floor area ratio, the land use mix and the sky view factor, and accessibility to recreational facilities and the floor area ratio. Specifically, when the green view index is greater than 0.25 and the floor area ratio exceeds 1.5, the land use mix exceeds 0.4 and the sky view factor exceeds 0.3, and accessibility to recreational facilities exceeds 25 and the floor area ratio exceeds 1.5, the interaction value of the two variables is greater than 0, promoting older people's walking frequency.
This study holds significant practical implications. Firstly, the threshold effect indicates that independent variables have an optimal impact on the walking behavior of older people within a specific range. Secondly, promoting walking behavior is more effective when modifying a set of built environment factors rather than changing a single factor. |
| Key words: Physical Environment Walking Behavior Population Aging Healthy Aging Explainable Machine Learning |
