Predicting the effect of street environment on residents' mood states in large urban areas using machine learning and street view images.

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.