The Scaling Bottleneck of Human Mobility Modeling
Although scaling—training larger models on larger datasets withmore compute—has proven effective for gaining performance andgeneralization across many data modalities, a comparable trendhas not yet emerged in human mobility modeling. Existing workin human mobility modeling has largely remained limited to rela-tively small models (<500M parameters) and modest data volumes(<1B samples), leaving it unclear whether, and how, scaling benefitsthis domain. This gap has limited progress toward human mobilityfoundation models for broad societal applications. In this work, weconstruct a large-scale real-world human mobility dataset compris-ing trillions of timestamped positioning records from millions ofindividuals, and take a first step toward scaling human mobilitymodeling. We first investigate whether human mobility modelsexhibit scaling behavior similar to that observed in other domains.Extensive experiments at data scales up to 1011 samples reveal anunexpected failure of conventional scaling in human mobility mod-eling. We then explore the source of this scaling failure and trace itto sample-quality issues inherent in the prevailing place-visitation(PV) modeling paradigm. Finally, we propose spatial-interactionbehavior (SIB) modeling, a new paradigm designed to overcomethis bottleneck and enable effective scaling in human mobility mod-eling. We show that SIB-based scaling recovers power-law scalingbehavior and yields models that can be directly adapted to diversedownstream tasks. These findings identify both a key obstacle toscaling mobility models and a practical route toward scalable hu-man mobility foundation models. Code and supplemental materials:https://fukamaru.github.io/human-mobility-scaling-bottleneck.
Breaking the black box: an interpretable machine learning model for global terrorism forecasting
Terrorist attacks significantly threaten a nation’s stability, prosperity, and social cohesion. Therefore, predicting terrorist attacks and identifying their underlying drivers are crucial for formulating effective counterterrorism strategies. Existing studies often prioritize either temporal or spatial dimensions, while their interplay and specific socioeconomic drivers are less explored. In this study, global news data are leveraged to construct a novel global conflict index (GCI), which integrates multisource datasets to comprehensively characterize the key drivers of terrorist attacks. TerrorXG is proposed to predict terrorist attacks, and SHAP analysis is applied to quantitatively interpret the importance and contributions of the driving factors. TerrorXG demonstrated superior performance (RMSE: 0.319; PCC: 0.777) and high computational efficiency. Compared with the second most influential factor (population size), the proposed GCI has a 42.4% greater impact on terrorist attacks. The interpretability analysis of the model highlights socioeconomic inequality as a primary determinant: the impacts of child malnutrition and infant mortality are 38.4% to 108.5% greater than the effect of urbanization. The influence of ethnicity represents only 9.7% of the impact of the GCI, providing empirical evidence that challenges traditional theoretical perspectives on ethnic conflict in terrorism research. This study provides valuable insights for optimizing the allocation of counterterrorism resources.
