Modelling social mobility disruptions and recovery during disasters: A mobile phone data approach

Disasters fundamentally alter human mobility patterns, yet traditional modelling approaches fail to capture the complex temporal dynamics and interdependencies that characterise these disruptions. This paper introduces the Disaster-aware Hawkes process, a novel temporal point process framework specifically designed to model human mobility during and after disasters. Our approach extends standard self-exciting Hawkes processes through five key innovations: a regime-switching baseline intensity function, category-specific excitation parameters, heterogeneous recovery rates across location categories, and post-disaster bounce-back parameters. We apply our model to a comprehensive mobile device dataset from Auckland, New Zealand during Cyclone Gabrielle in February 2023, comprising 5.85 million mobility records from 111,539 devices. The Disaster-aware Hawkes process achieves a 25.68% overall improvement in prediction accuracy compared to standard approaches, with an 80.00% improvement during the disaster period. Beyond enhanced prediction, our model enables novel analyses of cascade effects, revealing how disruptions propagate through mobility networks and identifying critical dependencies invisible to traditional methods. We demonstrate that optimised temporal distribution of recovery interventions can improve system outcomes by 45.8% compared to conventional simultaneous deployment strategies. These findings provide valuable insights for disaster preparedness, response, and recovery planning, while the methodological framework offers a powerful new approach for analysing the complex dynamics of human mobility under disruption.