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

Enrico Ubaldi, Takahiro Yabe, Nicholas Jones, Maham Faisal Khan, Alessandra Feliciotti, Riccardo Di Clemente, Satish V. Ukkusuri, and Emanuele Strano
Journal of Open Source Software
9(95), 5201 /
March 1, 2024


The availability of mobility data is increasing thanks to the widespread adoption of mobilephones and location-based services. This data generates powerful insights on people’s mobilityhabits, with applications in areas such as health, migration, and poverty estimation. Yetdespite the growing academic literature on the usage and application of mobile phone locationdata in this field and despite the raising awareness of the importance of disaster preparednessand response and climate change resilience, large-scale mobility data remain under-utilized inreal-world disaster management operations to this date (Barra et al., 2020).At present, only few tools allow for an integrated and inclusive analysis of mobility data. Whileseveral tookits allow users to perform some basic analytics on large mobility datasets (e.g., DeMontjoye et al. (2016) or Pappalardo et al. (2019)) these cover only some of the steps in themobility data pipeline. These toolkits also do not provide adequate data pre-processing andvisualization functionality which causes users to seek additional external options. Also, there isa lack of clear documentation to enable policymakers and planners to understand the analyticsprocess, outputs, and potential questions that mobility data can answer, particularly in thecontext of post-disaster assessment.

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