Geographical Distribution and Driving Meteorological Forces of Facial Expressions of Visitors in Urban Wetland Parks in Eastern China

Abstract

Interacting with aquatic environments in blue spaces is believed to benefit mental well-being. Relevant understanding is limited to regional pilot studies using self-reported emotions on questionnaires. We assessed emotional response by rating facial expressions on a large geographical scale with the purpose of detecting a relationship to microclimates. A total of 920 facial photographs were collected from Sina Weibo from 20 wetland parks in 14 eastern cities of China during 2020. Daily average air temperature, rainfall, average relative humidity (RH), and wind velocity were also recorded from the days when photographs were posted online. We found that happy expressions were higher in wetlands of eastern cities than in northern and inland cities. Sad expressions varied statistically among wetland locations. Weather records differed between temperate and subtropical climatic zones and were highly varied among cities. Happy and sad scores were driven by the change in average air temperature. Combined multivariable regression and binomial correlation suggested that increasing air temperature would not evoke positive emotions unless higher than 11.5°C, and an air temperature range of 17.5–22.3°C will be optimum to induce the presentation of a smiling face. Air humidity generally imposed a negative effect on expressions of positive emotions. Further verification of our findings is suggested on a larger geographical scale using more powerful big-data to obtain more robust conclusions.

Effects of brief exposure to campus environment on students' physiological and psychological health

“…Continuous data that conformed to a normal distribution were statistically described by their means with standard errors. Abnormal data were statistically described by Whisker–Box plots with medians and quartiles disclosed ( 44 ). A Wilcoxon non-parametric test was used for comparing physiological and psychological parameters among different sites.…”

Evaluation of Perceptions Using Facial Expression Scores on Ecological Service Value of Blue and Green Spaces in 61 Parks in Guizhou

“…This may be caused by season or weather conditions, as warm temperature or sunny days can promote people to perceive more positive emotions. It was found that a range of 17.5-22.3 • C can be an optimization that triggers positive emotions in urban wetland parks [76]. In parks with high elevation, experiences in blue space in summer and autumn can evoke positive emotions, but extreme weather, over moist weather, and blowing velocity together evoke sorrowful emotions [24].…”

Can Indoor Residents Perceive Green and Blue Spaces in Communities as Posted Sentiments? A Verification in Nanchang

“…This results in a pair of data: geographical information from a check-in location and the emotion assessed from users' faces. This enables analysis about the emotional responses of people, from their facial expressions, in relation to geographical factors such as location [33], municipal dimensions [37], microclimate [39,40,43], landscape metrics [11,38,42], and forest plant biodiversity [41]. Most users of Sina Weibo come from mainland China; hence this population's facial expressions were targeted as the source of data to analyze the benefits of public EIs [31,[34][35][36]57].…”

“…When significant effects were indicated, results were averaged and compared to either main effects (type or price category) or combined effects (type × price category). Means were compared using the Duncan multiple range test to cope with the issue of means from grouped observations having uneven numbers of replicates [42,43,46]. To detect the mechanism driving facial expression changes in community GBSs, the Poisson regression model was used to detect the most likely predictions of facial expression scores against parameters of each level at every landscape metric [33,40,43].…”

“…Facial expression score is a sensitive parameter that reflects emotions of GBS experiencers either at subjects' unconsciousness [34][35][36] or with their awareness of being photographed [11,[37][38][39][40]. Facial expressions can be easily obtained from social media and compiled to create a large reserve of data; hence, this method was frequently used to assess the emotions of a big group of people who were experiencing public green spaces at large geographical scales [37,[41][42][43]. These together suggest a potential to use facial expressions as an assessment the emotions of community residents that have experienced GBSs.…”