Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics Using High-Frequency Human Mobility Data

Increasingly available high-frequency location datasets derived from smartphones provide unprecedented insight into trajectories of human mobility. These datasets can play a significant and growing role in informing preparedness and response to natural disasters and other urban crises. However, limited tools exist to enable rapid analytics using mobility data, and existing tools tend not to be tailored specifically for disaster risk management applications.

We present Mobilkit, an open-source, Python-based toolkit designed to conduct replicable and scalable post-disaster analytics using GPS location data. The toolkit addresses a critical gap in disaster response capabilities by providing researchers, humanitarian organizations, and policymakers with standardized methods to analyze human mobility patterns during and after crisis events.

Mobilkit leverages the Dask framework for faster, parallel computation, enabling analysis of large-scale mobility datasets that would be intractable with traditional single-threaded approaches. The toolkit provides a comprehensive suite of functions for extracting meaningful insights from raw GPS trajectories, including displacement analysis to quantify population movements, activity space characterization to assess disruption to daily routines, temporal dynamics tracking to monitor recovery processes, and spatial aggregation to support decision-making at various scales.

The toolkit has been designed with three key principles in mind: privacy preservation through aggregated analytics, computational efficiency for rapid response contexts, and extensibility to accommodate diverse disaster scenarios and data sources. Privacy considerations are paramount—Mobilkit implements privacy-preserving aggregation methods that enable useful insights while protecting individual identities.

Potential applications extend beyond immediate disaster response. The toolkit can support long-term resilience planning by analyzing how different communities respond to and recover from various types of disruptions. This information is crucial for developing equitable disaster preparedness strategies that account for the diverse needs of urban populations.