Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone
In recent years, researchers have demonstrated that digital footprints from mobile phones can be exploited to generate data that are useful for transport planning, disaster response, and other development activities—thanks mainly to the high penetr...
| Main Authors: | , , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2021
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/224761611175801192/Using-Mobile-Data-to-Understand-Urban-Mobility-Patterns-in-Freetown-Sierra-Leone http://hdl.handle.net/10986/35033 |
| Summary: | In recent years, researchers have
demonstrated that digital footprints from mobile phones can
be exploited to generate data that are useful for transport
planning, disaster response, and other development
activities—thanks mainly to the high penetration rate of
mobile phones even in low-income regions. Most recently, in
the effort to mitigate the spread of COVID-19, these data
can be used and explored to track mobility patterns and
monitor the results of lockdown measures. However, as
rightly noted by other scholars, most of the work has been
limited to proofs of concept or academic work: it is hard to
point to any real-world use cases. In contrast, this paper
uses mobile data to obtain insight on urban mobility
patterns, such as number of trips, average trip length, and
relation between poverty, mobility, and areas of Freetown,
the capital of Sierra Leone. These data were used in
preparation of an urban mobility lending operation.
Additionally, the paper describes good practices in the
following areas: accessing mobile data from telecom
operators, frameworks for generating origin and destination
matrices, and validation of results. |
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