["Transactions in GIS, Volume 30, Issue 5, August 2026. ", "\nABSTRACT\nPhysiological arousal, as a typical objective perception, can avoid the influence of human subjective biases and directly express emotional intensity. Existing studies on physiological arousal were limited to small‐scale urban areas, lacking the exploration of large‐scale estimation methods and the spatial heterogeneity of physiological arousal. This paper proposes a framework for analyzing physiological arousal by combining street view image (SVI) and electrodermal activity (EDA). An interpretable machine learning method was applied to reveal the impact of visual factors on physiological arousal. By capturing the neighboring visual environment, a hybrid deep learning model was constructed to estimate physiological arousal. Our results revealed that horizontal vegetation and soil exerted a stronger modulatory effect on physiological arousal, while traffic signs reduced arousal, contrasting with arousal enhancement from traffic lights. The model accounting for the neighboring visual environment outperforms the single‐SVI model. These findings provide an “objective” perspective for urban planning and mental health interventions.\n"]