gcmillar / ceus

Repository for Computers, Environment and Urban Systems (CEUS) Special Issue Manuscript: Space-time Analytics of Human Physiology for Urban Planning

Computers, Environment, & Urban Systems (CEUS) - Reproducible Code

This repository details the code and data for computational reproducibility of the (CEUS) Special Issue Manuscript: Space-time Analytics of Human Physiology for Urban Planning:

Garrett C. Millar, Ondrej Mitas, Wilco Boode Lisette Hoeke, Joost de Kruijff, Anna Petrasova, Helena Mitasova (2020): Space-time Analytics of Human Physiology for Urban Planning. In: Computers, Environment, and Urban Sytstems, In: Advances in portable sensing methodologies for urban environments: Understanding cities from a mobility perspective.

Abstract. Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. By analysing data collected from this type of sensors, we can study spatial variations in people’s physiological response in relation to the surrounding environment, allowing us to provide urban planners objective metrics on how individuals experience urban design elements. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycling highway between two municipalities in The Netherlands, this paper presents a methodological framework for quantifying and analyzing spatiotemporal variations of emotion and their association with surrounding environmental features. We coupled location and physiological measurements of high spatiotemporal resolution to model and examine relationships between cyclists’ physiological arousal (operationalized as skin conductance responses) and environmental characteristics (operationalized as visible land cover). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of more natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of more developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from hierarchical multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences.