Capturing and quantifying emotional distress in the built environment

Abstract

Big data captured through sensory-integrated devices present a unique opportunity for emerging applications in urban research towards understanding, quantifying, and promoting aspects of the urban environment based on the residents' needs. We examine the effect of various built environmental features of the urban environment on individuals emotional distress, as captured through self-assessment indices and physiological measures. Through statistical analysis, we study potential associations between signal-based electrodermal activity (EDA) measures and self-reports. We further propose a novel saliency detection mechanism for physiological signals, according to which prominence of an EDA segment is calculated based on its distance metrics from the remaining signal segments. Results indicate that the signal-based and self-reported indices of emotional distress, as depicted in relation to various built environmental features, are weakly but significantly correlated. Visual inspection of the saliency-based measures further suggests that the hypothesized "emotional distress hotspots" of the urban environment, such as discontinuous sidewalks, threatening noise, and housing with broken features, elicit distinct physiological responses for the majority of participants in the experiments. These suggest the feasibility of physiological measures for quantifying emotional distress caused by the built environment and provide a foundation for the development of signal-based indices of the human felt-sense in urban settings.