提出时空跳跃模型，精准捕捉城市热舒适度的动态变化。
Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring
- 基于时空聚类构建跳变模型，兼顾空间与时间连续性。
- 在新加坡小时级气象数据上识别出有意义的热舒适区段。
- 无需大量问卷调查，适合真实城市环境的长期监测。
热舒适度对城市居民福祉至关重要，尤其在城市化和气候变化加剧背景下。现有模型常忽略时间动态与空间依赖性。本文提出一种时空跳变模型，通过在时空维度上保持数据的持续性进行聚类，提升可解释性，减少状态突变，并能有效处理缺失数据。通过大量模拟验证，该方法能准确恢复真实潜在分区。将其应用于新加坡多个气象站的小时级环境数据，成功识别出具有意义的热舒适状态区间，证明其在动态城市环境中的有效性与实用性。与热偏好反馈对比显示，无监督方法有望减少大规模问卷调查的需求。
原文摘要 · Abstract (English)
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.
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