HoWDe: a validated algorithm for Home and Work location Detection
S\'ilvia De Sojo, Lorenzo Lucchini, Ollin D. Langle-Chimal, Samuel P. Fraiberger, Laura Alessandretti

TL;DR
HoWDe is an open-source, validated algorithm that accurately detects home and work locations from mobility data, improving reproducibility and comparability in urban mobility studies.
Contribution
We introduce HoWDe, a transparent, modular pipeline for home and work detection that handles data heterogeneity and achieves high accuracy across diverse datasets.
Findings
Achieves up to 97% home detection accuracy
Achieves up to 88% work detection accuracy
Consistent performance across demographic and geographic groups
Abstract
Smartphone location data have become a key resource for understanding urban mobility, yet extracting actionable insights requires robust and reproducible preprocessing pipelines. A central step is the identification of individuals' home and work locations, which underpins analyses of commuting, employment, accessibility, and socioeconomic patterns. However, existing approaches are often ad hoc, data-specific, and difficult to reproduce, limiting comparability across studies and datasets. We introduce HoWDe, an open-source software library for detecting home and work locations from large-scale mobility data. HoWDe implements a transparent, modular pipeline explicitly designed to handle missing data, heterogeneous sampling rates, and differences in data sparsity across individuals. The code allows users to tune a small set of interpretable parameters, enabling to adapt the algorithm to…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · IoT-based Smart Home Systems · Video Surveillance and Tracking Methods
