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Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics |
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12 February 2023 |
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The availability of mobility data is increasing thanks to the widespread adoption of mobile phones and location-based services. This data generates powerful insights on people's mobility habits, with applications in areas such as health, migration, and poverty estimation. Yet despite the growing academic literature on the usage and application of mobile phone location data in this field and despite the raising awareness of the importance of disaster preparedness and response and climate change resilience, large-scale mobility data remain under-utilized in real-world disaster management operations to this date [@barra2020solid].
At present, only few tools allow for an integrated and inclusive analysis of mobility data. While toolkits as [@de2016bandicoot] or [@pappalardo2019scikitmobility] allow users to perform some basic analytics on large mobility datasets, these cover only some of the steps in the mobility data analysis pipeline. Very often users have to go fishing for functions/tools from other libraries to cover for data pre-processing or visualization. Also, there is a lack of clear documentation that enables policymakers and planners to understand the analytics process, outputs, and potential questions that mobility data can answer, particularly in the context of post-disaster assessment.
Mobilkit is an open-source Python software toolkit that enables policy makers
to conduct post-disaster assessment using large-scale mobility data. The toolkit
allows the user to conduct pre-processing of data, validation of the data
representativeness, home and office location estimation, post-disaster displacement analysis,
and point-of-interest visit analysis. The purpose of Mobilkit is to provide urban planners,
disaster policy makers, and researchers an easy-to-use and practical toolkit to visualize,
analyze, and monitor post-disaster disruption and recovery. The software is freely-available
on GitHub along with online documentation and Jupyter Notebooks that provides step-by-step tutorials.
Mobilkit allows the user to 1) pre-process the dataset to select users who have sufficient amount of observations,
2) evaluate the representativeness of the mobility data by combining with census population statistics,
3) conduct post-disaster displacement and recovery analysis, 4) estimate the recovery of businesses
and social services by using point-of-interest (POI) data, and 5) measure and characterize the spatial structure of cities.
A project carried out in collaboration with the World Bank Global Facility for Disaster Reduction and Recovery aimed at assessing the impact of the 7.1 magnitude earthquake with the epicenter located around 55km south of Puebla (about 100km south-east of Mexico City) that occurred on 19 September 2017 s the functionality of Mobilkit using smartphone location data collected before and after the earthquake. Methods regarding the spatial structure of cities are demonstrated using smartphone location data provided by Quadrant that cover ten different cities around the globe in March 2022. These use cases showcase the immense potential of using mobile phone location data and Mobilkit for planning and recovering from climate-related, man-made, and natural disasters.
We extend our sincere gratitude to Cuebiq and Quadrant for providing the data to support this effort. This work was supported by the Spanish Fund for Latin America and the Caribbean (SFLAC) under the Disruptive Technologies for Development Program at the World Bank and by the Global Facility for Disaster Reduction and Recovery (GFDRR - USAID Single Donor Trust Fund). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.