Research indicates that urban green space is a “restorative environment” in that it can promote positive moods and mitigate negative emotions. The aim of this study was to add to the current research by quantifying the emotions at urban green spaces in their locations. Findings could lead to a better understanding of how characteristics of such spaces relate to the emotions experienced within those spaces.
Data for this study came from Sina Weibo (Weibo.com), China's largest social media platform. The data set consisted of 9945 photos of human faces posted by users of the platform. All of the photos were taken within 34 green park spaces in three northern provincial capital cities. Only a small proportion of the photos included the elderly or children. An online cognitive program was used to analyze the emotions reflected in the photos. Three emotional indicators were also applied: emotion probability index (EPI), emotion intensity index (EII), and emotion evenness index (EEI).
Seven emotions were identified: happiness, fear, surprise, anger, disgust, neutral, and sadness. Greater greenness of an area increased the probability and intensity of emotion expression in that area, with happiness being expressed the most often and most strongly. Fear and anger were generally reduced in areas with higher greenness. Among the types of green park spaces, botanical gardens were most highly associated with all seven kinds of emotions. There were some differences in the probability of emotions between genders and among ages in urban green spaces. An increase in age was associated with increased happiness and decreased sadness. Females' happiness emotion tended to be more prominent, while males' neutral emotion was more pronounced.
This study adds to the literature by providing evidence of a potential relationship between the parameters of urban green spaces and human emotions. Results indicate that environments with a high plant density are especially conducive for emotions. This study also adds to the literature by showing that “social media data with geographic information and facial expression recognition can be used as a new means of urban investigation”. Such investigations could lead to a deeper understanding of people's emotional experience in green spaces.