The widespread availability of open datasets in cities is transforming the way urban energy systems are planned, simulated, and visualized. In this paper, a cross-scale approach is pursued to better understand the reciprocal effects between building energy performance, the urban climate, and urban dwellers’ indoor and outdoor thermal comfort. On the one hand, monthly building electricity and gas demand data at the parcel level was collected, along with hourly weather station data at the urban scale. On the other hand, a longitudinal experiment was carried out in which 22 participants wore smartwatches for 4–6 weeks and filled out hourly micro surveys on their activities, location, and thermal comfort. In addition to survey responses, the smartwatches collected participants’ physiological data and location throughout the period of the study. The project was conducted in Seoul, South Korea, the highest-ranked Asian country in open data readiness, implementation, and impact. This paper reports on the data collection effort and provides some preliminary analysis of the results. The work carried out is expected to help develop methodologies for the convergence of district-scale and occupant-scale data in urban areas. A number of expected applications are proposed, including urban-scale studies on the impact of urban form on the local climate and building energy performance, district-to-building-scale building energy simulations accounting for occupant thermal comfort-related behaviors, and district-scale analyses of occupants’ outdoor thermal comfort and its relationship with location and wayfinding in urban areas.

The increasing availability of urban-scale, open-access datasets can support decision-making in urban planning, in particular in relation to climate resilience and climate change mitigation. Such data-driven initiatives however often neglect the central role of urban dwellers, whose activities create the demand for energy and mobility in urban areas. This is due in large part due to the difficulty of data collection at this scale, along with privacy concerns arising from any such data collection effort. The use of wearable technologies for self-reported comfort feedback from urban dwellers provides a promising opportunity for citizens to actively participate in the adaptation of urban areas to better support outdoor comfort and climate resilience.

In this work, subjective feedback data from 22 participants in a longitudinal test in Seoul, South Korea was collected through a smartwatch application. Participants were required to wear a smartwatch for 4–6 weeks, during which time their location as well as environmental and physiological data were collected. Participants were also requested to complete hourly micro-surveys, in which they were asked about their activities, location, thermal preference, clothing level, comfort adaptations, and mood. This information was complemented by an urban scale dataset comprising building geometries and data from 1000+ weather stations over the same period.

This cross-scale dataset was then used to investigate the relationship between urban form and environmental parameters with occupants’ survey responses. The relationship between indoor comfort and environmental parameters in the case study is discussed, with recommendations for further research into this topic. The use of machine learning to leverage the combination of spatial, temporal, and subjective preference data to predict occupants’ outdoor comfort as a function of their urban environment is also explored.