LAUSR

To explore the influence of campus public space characteristics on students’ emotions, we investigated the association mechanism between public space characteristics and students’ emotions concerning the distribution of students’ emotions in public spaces. The present study used photographs of facial expressions taken over two consecutive weeks as a source of data regarding the students’ affective reactions. The collected facial expression images were analyzed using facial expression recognition. Values were assigned to the expression data, combined with geographic coordinates to create an emotion map of the campus public space using GIS software. Then, spatial feature data via emotion marker points were collected. We used smart wearable devices to combine the ECG data with spatial characteristics and took SDNN and RMSSD as ECG indicators to assess mood changes. We analyzed the correlation between these spatial features and heart rate variability and developed regression models for the ECG data. The findings show that sky visibility, space D/H, green visibility, skyline change index, and boundary permeability can engage students’ positive emotions in a meaningful way. On the other hand, paving visibility and road linearity tends to induce negative emotions in students’ minds.