Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **1. Introduction**

# **A. Unlocking the potential of mobile phone data**

The COVID-19 pandemic has highlighted the value of real-time, high-resolution data to inform decision making in a crisis situation. External shocks, such as climate change, conflicts, and pandemics trigger population movements and displacement. In response, decision makers require information about origins and destination of refugees and migrants to inform a rapid policy response. Yet, survey and administrative data exhibit severe shortcomings which complicate any crisis assessment: traditional data is likely to be outdated during a quickly evolving crisis situation, and rapid data collection is expensive, slow, and often incompatible with a lockdown situation. By contrast, new data sources that are readily available have gained prominence, notably satellite and location data. These offer real-time snapshots at a high level of spatial resolution.

Call Detail Record (CDR) data offers the potential to document population dynamics in near real time. CDR data is available as high-frequency, highly localized data which can be collected and processed in real-time and at relatively low cost. In developing countries where smart-phone penetration is low, CDR is likely to have much more coverage than GPS data. The analysis of CDR data involves investments in technical capacity and IT infrastructure. One defining characteristic of such data is that it is updated in near real time and requires terabytes of storage capacity, either on the cloud or servers on premise. A so-called data pipeline is required to automate the data flow. The raw data from the Mobile Network Operators (MNO) is encrypted and shared with the regulator, who in turn aggregates it into indicators available for analysis. It is also essential to build the technical capacity of analysts in managing and analyzing data, ensuring the sustainability of the initiative.

The use of CDR data raises privacy concerns and requires a strong institutional framework to regulate access and ensure confidentiality. Researchers and governments have worked closely with regulatory authorities and MNOs to leverage CDR data in measuring changes in mobility patterns (Oliver et al., 2020). However, most of these efforts are concentrated in countries with established institutional frameworks, which also reflect recent efforts to integrate CDR data and other big data into the national statistical system.

# **B. Use-case: Tracking mobility in The Gambia**

This paper showcases the use of CDR data to track changes in mobility across The Gambia between March and May 2020, when COVID-19 led to an exodus of the capital city region. This project was undertaken in collaboration with the national regulator Public Utilities Regulatory Authority (PURA) and The Gambia Bureau of Statistics (GBoS) to establish a durable CDR data pipeline in The Gambia. This partnership allows for government ownership and sustainability, investing in both the necessary systems and technical capacity.

Analysis of CDR data suggests that economic lockdown measures reduced human mobility and pushed people to leave the capital city region and return to rural areas. We validate the use of CDR data against the known population distribution from the population census and WorldPop data. Our contribution demonstrates how a system-building approach can make timely, disaggregated analysis based on CDR data available for quick decision making.

This use-case demonstrates how to build an end-to-end data pipeline for CDR data. This pipeline draws raw data produced by the mobile phone operators, encrypts and aggregates it on the regulators premise, before making it available to researchers for analysis. Once automated, it can facilitate the production of rapid, high-resolution insights on population mobility patterns and their economic implications.

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