Human mobility gaps filled by memory based power law decay method
Inferring missing locations in human mobility data using memory effects
Machine Learning
Summary
Data about where people move is often incomplete because of technology limits or unpredictable online activity. The authors created a simple way to guess missing locations by assuming recent and nearby times are more informative, using a memory effect that fades with time. Their method works well on both fake and real Twitter data, especially for people who move a lot. It also performs better for some users than previous approaches and works in different countries and regions.
What this means in practice
- •For mobile app developers: Improve user location history by filling missing location data in movement tracking apps using memory-based decay models.
- •For urban planning teams: Enhance analysis of human movement patterns by imputing incomplete mobility records to better understand city dynamics.
Authors
Pablo Rosillo-Rodes, Antònia Tugores, David Sánchez, José J. Ramasco
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.