Examining Trade-Offs between Social, Psychological, and Energy Potential of Urban Form

Abstract

Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal methods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility. For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages.

Mapping the urban environment using real-time physiological monitoring

“…The need to collect information regarding the effects of activity has been mentioned in a few studies (Birenboim et al , 2019; Kyriakou et al , 2019; Werner et al , 2019). A recent study (Bielik et al , 2019), which compared physiological responses collected in the urban environment with responses collected during replications of the same walk in a controlled virtual reality setting, showed that the physiological arousal elicited from just viewing the urban form was lower than the same experience in the field. Some studies also reported a gradual increase of EDA along the route (Birenboim et al , 2019; Fathullah and Willis, 2018; Griego et al , 2017; Osborne and Jones, 2017), and physical exertion might have played a role there.…”

Developing a Replication of a Wayfinding Study. From a Large-Scale Real Building to a Virtual Reality Simulation

“…Ambience modelling depends on the context and purpose of the study. In some studies, grey-scaled geometries are sufficient; e.g., when examining effects of geometry in isolation of other variables [14,15]. However, in studies that involve experiential measures, ambience or atmospheric cues are more important [8].…”

Human-Centric Data Science for Urban Studies

“…Wellbeing. Five issue papers focused on another aspect of urban human wellbeing: comfort [53][54][55] and crime [56,57]. The former contributions focused on sentiment analysis; the latter two used spatial and machine learning methods, respectively.…”

“…Nouman et al [54] prototyped a mobile environmental sensor toolkit to asses outdoor comfort using data mining and sensing techniques. Bielik et al [55] performed an empirical study to assess trade-offs in a variety of urban design parameters-social, psychological, and energetic-on planning the fundamental elements of urban form: the street network and the building massing. Concerning crime, Xiao et al [56] analyzed the travel patterns of residential burglars in a Chinese city, disentangling origin and destination effects, while Lin et al [57] explored different machine learning algorithms demonstrating the importance of geographic feature design for improving performance and explanatory ability in grid-based crime prediction.…”