Behavioral calibration of mobile-phone GPS data for population-representative analyses
Abstract
Mobile phone mobility data have transformed the study of human behavior, but demographic and behavioral biases can compromise their representativeness and distort population-level inference. Existing calibration approaches primarily address demographic and geographic representativeness, leaving behavioral discrepancies largely uncorrected. Here we introduce the Behavioral Population (BePop) framework, which jointly calibrates mobility data to representative demographic and behavioral distributions using census data and time-use surveys. BePop embeds mobility sequences into behavioral profiles and estimates person-level weights that align both population composition and daily activity patterns. Across three U.S. metropolitan areas, the framework consistently improves agreement between GPS-derived mobility and representative behavioral distributions, including time allocation, activity transitions, and mobility motifs. Calibration also substantially alters downstream mobility indicators, demonstrating that behavioral biases can propagate into commonly used mobility measures. Our results establish behavioral representativeness as a critical complement to demographic calibration and provide a general framework for population-representative mobility inference.
Related
- Systematic display of the spin splitting in band structures of representative altermagnetic crystals
- Hadronic vacuum polarization and light-by-light scattering in representative infrared-improved soft-wall holographic QCD models
- A Topology-Based Approach to Representative Volume Element Assessment in Solid Oxide Cell Anodes Using Large Volume Tomography and Persistent Homology
- Bridging the climate to energy data gap: simulated annealing for representative climate year selection
- Statistical admissibility and long-wavelength structural convergence determine representative support in granular materials
BibTeX
@misc{girardini2026behavioral,
title = {{Behavioral calibration of mobile-phone GPS data for population-representative analyses}},
author = {Nicolò Alessandro Girardini and Unchitta Kan and Eduardo López and Bruno Lepri and Lorenzo Lucchini and Simone Centellegher},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01042},
eprint = {2609.01042},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01042},
}
RIS
TY - UNPB
TI - Behavioral calibration of mobile-phone GPS data for population-representative analyses
AU - Nicolò Alessandro Girardini
AU - Unchitta Kan
AU - Eduardo López
AU - Bruno Lepri
AU - Lorenzo Lucchini
AU - Simone Centellegher
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.01042
UR - https://arxiv.org/abs/2609.01042
AB - Mobile phone mobility data have transformed the study of human behavior, but demographic and behavioral biases can compromise their representativeness and distort population-level inference. Existing calibration approaches primarily address demographic and geographic representativeness, leaving behavioral discrepancies largely uncorrected. Here we introduce the Behavioral Population (BePop) framework, which jointly calibrates mobility data to representative demographic and behavioral distributions using census data and time-use surveys. BePop embeds mobility sequences into behavioral profiles and estimates person-level weights that align both population composition and daily activity patterns. Across three U.S. metropolitan areas, the framework consistently improves agreement between GPS-derived mobility and representative behavioral distributions, including time allocation, activity transitions, and mobility motifs. Calibration also substantially alters downstream mobility indicators, demonstrating that behavioral biases can propagate into commonly used mobility measures. Our results establish behavioral representativeness as a critical complement to demographic calibration and provide a general framework for population-representative mobility inference.
ER -