South China Agricultural University, College of Forestry and Landscape Architecture, Guangzhou 510642, China.
Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom.
Sci Total Environ. 2022 Apr 10;816:151605. doi: 10.1016/j.scitotenv.2021.151605. Epub 2021 Nov 24.
Researchers have demonstrated that the built environment is associated with mental health outcomes. However, evidence concerning the effects of street environments on mood in fast-growing Asian cities is scarce. Traditional questionnaires and interview methods are labor intensive and time consuming and pose challenges for accurately and efficiently evaluating the impact of urban-scale street environments on mood.
This study aims to use street view images and machine learning methods to model the impact of street environments on mood states in a large urban area in Guangzhou, China, and to assess the effect of different street view elements on mood.
A total of 199,754 street view images of Guangzhou were captured from Tencent Street View, and street elements were extracted by pyramid scene parsing network. Data on six mood state indicators (motivated, happy, positive-social emotion, focused, relaxed, and depressed) were collected from 1590 participants via an online platform called Assessing the Effects of Street Views on Mood. A machine learning approach was proposed to predict the effects of street environment on mood in large urban areas in Guangzhou. A series of statistical analyses including stepwise regression, ridge regression, and lasso regression were conducted to assess the effects of street view elements on mood.
Streets in urban fringe areas were more likely to produce motivated, happy, relaxed, and focused feelings in residents than those in city center areas. Conversely, areas in the city center, a high-density built environment, were more likely to produce depressive feelings. Street view elements have different effects on the six mood states. "Road" is a robust indicator positively correlated with the "motivated" indicator and negatively correlated with the "depressed" indicator. "Sky" is negatively associated with "positive-social emotion" and "depressed" but positively associated with "motivated". "Building" is a negative predictor for the "focused" and "happy" indicator but is positively related to the "depressed" indicator, while "vegetation" and "terrain" are the variables most robustly and positively correlated with all positive moods.
Our findings can help urban designers identify crucial areas of the city for optimization, and they have practical implications for urban planners seeking to build urban environments that foster better mental health.
研究人员已经证明，建筑环境与心理健康结果有关。然而，关于快速增长的亚洲城市街道环境对情绪的影响的证据还很缺乏。传统的问卷和访谈方法既费力又耗时，并且在准确和有效地评估城市规模的街道环境对情绪的影响方面存在挑战。
本研究旨在使用街景图像和机器学习方法来模拟中国广州一个大城市地区的街道环境对情绪状态的影响，并评估不同街道景观元素对情绪的影响。
共从腾讯街景中获取了广州 199754 张街景图像，并通过金字塔场景解析网络提取了街道元素。通过一个名为“评估街道景观对情绪的影响”的在线平台，从 1590 名参与者那里收集了六个情绪状态指标（积极、快乐、积极的社会情绪、专注、放松和沮丧）的数据。提出了一种机器学习方法来预测广州大城市地区街道环境对情绪的影响。进行了一系列的统计分析，包括逐步回归、岭回归和套索回归，以评估街道景观元素对情绪的影响。
与市中心地区相比，城市边缘地区的街道更有可能使居民产生积极、快乐、放松和专注的感觉。相反，市中心地区、高密度的建筑环境更有可能产生抑郁情绪。街景元素对六种情绪状态有不同的影响。“道路”是一个与“积极”指标呈正相关，与“抑郁”指标呈负相关的稳健指标。“天空”与“积极的社会情绪”和“抑郁”呈负相关，但与“积极”呈正相关。“建筑”是“专注”和“快乐”指标的负预测因子，但与“抑郁”指标呈正相关，而“植被”和“地形”是与所有积极情绪最稳健和最正相关的变量。
我们的研究结果可以帮助城市设计师确定城市的关键优化区域，对城市规划者寻求营造促进更好心理健康的城市环境具有实际意义。
