地理科学进展 ›› 2020, Vol. 39 ›› Issue (5): 779-791.doi: 10.18306/dlkxjz.2020.05.007

• 研究论文 • 上一篇    下一篇

绿色环境暴露对居民心理健康的影响研究——以南京为例

李智轩, 何仲禹*(), 张一鸣, 金霜霜, 王雪梅, 朱捷, 刘师岑   

  1. 南京大学建筑与城市规划学院,南京 210093
  • 收稿日期:2019-04-11 修回日期:2019-07-18 出版日期:2020-05-28 发布日期:2020-07-28
  • 通讯作者: 何仲禹 E-mail:hezy@nju.edu.cn
  • 作者简介:李智轩(1996— ),男,山西太原人,硕士生,主要研究方向为居民时空间行为、智慧城市等。E-mail:771445516@qq.com
  • 基金资助:
    国家自然科学基金项目(51678288)

Impact of greenspace exposure on residents’ mental health: A case study of Nanjing City

LI Zhixuan, HE Zhongyu*(), ZHANG Yiming, JIN Shuangshuang, WANG Xuemei, ZHU Jie, LIU Shicen   

  1. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
  • Received:2019-04-11 Revised:2019-07-18 Online:2020-05-28 Published:2020-07-28
  • Contact: HE Zhongyu E-mail:hezy@nju.edu.cn
  • Supported by:
    National Natural Science Foundation of China(51678288)

摘要:

绿色环境暴露对心理健康的影响长期以来都受到国内外不同领域学者的关注,但从个体与环境交互的角度关注衡量个体对环境实际感知的研究较少。论文试图基于对居民视觉感知和时空活动等因素的考虑,结合机器学习等技术扩展绿色环境暴露的测度方式,并构建绿色环境暴露对心理健康影响的概念框架。同时,以南京为实证对象,运用结构方程模型对比分析绿地率、绿视率、绿色视觉暴露对心理健康影响的差异。研究发现,3种绿色环境暴露测度指标对心理健康均有显著正向影响,但影响程度和路径存在差异,建立更加综合的绿色环境暴露评价指标体系至关重要。主观建成环境可以作为绿视率和绿色视觉暴露对心理健康影响的中介变量,身体活动仅作为绿色视觉暴露影响心理健康的中介变量。研究拓展了绿色环境暴露对心理健康影响的研究框架,并对城市绿地系统的规划管理具有参考价值。

关键词: 环境暴露, 绿色视觉暴露, 绿视率, 心理健康, 机器学习, 南京

Abstract:

The impact of greenspace exposure on mental health has long been the focus of scholars in different fields in China and internationally. Most studies are based on a single individual perspective, such as activity time, range of activities, and so on, or a single environmental perspective, such as the number of green spaces, green space accessibility, among others. Few studies have measured individuals' perception of the environment from the perspective of the interaction between the individuals and the environment. This study constructed a conceptual framework for the impact of green environment exposure on mental health based on considerations of residents' visual perception and spatiotemporal activities. In addition, this study also proposed a green visual exposure measurement method based on green view index and individual spatiotemporal activity, and estimated the green rate using convolutional neural network model and machine learning. This study took Nanjing City as an empirical research object, and used structural equation modeling to compare and analyze the differences in the impact of green rate, green view index, and total green visual exposure on mental health. In addition to observing the direct influences, environmental perception, physical activity, and sense of belongingness were selected as mediating variables to analyze the pathways of different indicators that affect mental health. The results show that the three greenspace exposure measurement indicators have a significant correlation with mental health, but the degree of influence and pathway are different. It is important to establish a more comprehensive green environment exposure evaluation index system. Subjective built-up environment perception can be used as a mediator of the impact of green view index and total green visual exposure on mental health. Physical activity only serves as a mediator of the impact of the total amount of green visual exposureon mental health. This study expands the research framework of the impact of greenspace exposure on mental health, and has important reference value for the planning and management of urban greenspace system.

Key words: environmental exposure, green visual exposure, green view index, mental health, machine learning, Nanjing City