Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring
This addresses thermal comfort monitoring for urban residents, with incremental improvements in handling temporal dynamics and missing data.
The authors tackled the problem of modeling thermal comfort in urban spaces by developing a spatio-temporal jump model that clusters data across spatial and temporal dimensions, validated through simulations and applied to Singapore weather data to identify meaningful thermal comfort regimes.
Thermal comfort is essential for well-being in urban spaces, especially as cities face increasing heat from urbanization and climate change. Existing thermal comfort models usually overlook temporal dynamics alongside spatial dependencies. We address this problem by introducing a spatio-temporal jump model that clusters data with persistence across both spatial and temporal dimensions. This framework enhances interpretability, minimizes abrupt state changes, and easily handles missing data. We validate our approach through extensive simulations, demonstrating its accuracy in recovering the true underlying partition. When applied to hourly environmental data gathered from a set of weather stations located across the city of Singapore, our proposal identifies meaningful thermal comfort regimes, demonstrating its effectiveness in dynamic urban settings and suitability for real-world monitoring. The comparison of these regimes with feedback on thermal preference indicates the potential of an unsupervised approach to avoid extensive surveys.