# Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring

arXiv: https://arxiv.org/abs/2411.09726 · HTML: https://mlsanity.com/paper/2411.09726/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 64 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [k-POD: A Method for k-Means Clustering of Missing Data](https://mlsanity.com/paper/1411.7013/) (2014)
- [Fitting Jump Models](https://mlsanity.com/paper/1711.09220/) (2017)
- [Statistical Jump Model for Mixed-Type Data with Missing Data Imputation](https://mlsanity.com/paper/2409.01208/) (2024)

## Built on by (most cited first, 2 of 2)

- [Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering](https://mlsanity.com/paper/2606.13146/) (2026)
- [Fuzzy network jump models for soft dynamic clustering of graph-structured data](https://mlsanity.com/paper/2608.05786/) (2026)

## Abstract (arXiv)

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.