Inferring missing locations in human mobility data using memory effects
Abstract
Empirical datasets used to characterize human mobility are sparse in space and time, owing to technological factors such as coverage and battery, or to the erratic nature of online interactions. Missing data can bias the conclusions drawn from them, so filling the gaps in incomplete timelines is a key challenge in computational social science. Here, we propose a simple imputation method in which the informativeness of an observation decays with its bidirectional temporal distance from the missing entry, at a rate set by a single parameter per individual. On synthetic and real Twitter data, a power-law decay, selected by Bayesian model comparison, yields accurate imputations (macro-AUROC above 0.9 overall, and above 0.8 for the most mobile users) and matches a gap-aware continuous-time Markov chain, outperforming it among the most mobile users. The method is robust to moderate noise and generalizes across countries and European administrative regions.
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BibTeX
@misc{rosillorodes2026inferring,
title = {{Inferring missing locations in human mobility data using memory effects}},
author = {Pablo Rosillo-Rodes and Antònia Tugores and David Sánchez and José J. Ramasco},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.01319},
eprint = {2611.01319},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.01319},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Inferring missing locations in human mobility data using memory effects
AU - Pablo Rosillo-Rodes
AU - Antònia Tugores
AU - David Sánchez
AU - José J. Ramasco
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.01319
UR - https://arxiv.org/abs/2611.01319
AB - Empirical datasets used to characterize human mobility are sparse in space and time, owing to technological factors such as coverage and battery, or to the erratic nature of online interactions. Missing data can bias the conclusions drawn from them, so filling the gaps in incomplete timelines is a key challenge in computational social science. Here, we propose a simple imputation method in which the informativeness of an observation decays with its bidirectional temporal distance from the missing entry, at a rate set by a single parameter per individual. On synthetic and real Twitter data, a power-law decay, selected by Bayesian model comparison, yields accurate imputations (macro-AUROC above 0.9 overall, and above 0.8 for the most mobile users) and matches a gap-aware continuous-time Markov chain, outperforming it among the most mobile users. The method is robust to moderate noise and generalizes across countries and European administrative regions.
ER -