Distance travelled as a proxy for measuring changes in the magnitude of mobility during COVID-19 in The Gambia (Arai et al. 2021)


At the onset of COVID-19 in The Gambia, there was an existing institutional framework between the Gambia Bureau of Statistics (GBoS), the Public Utilities Regulatory Authority (PURA), The World Bank, and The University of Tokyo, to utilize CDR data for creating an evidence base for policy and project design in the context of economic and social development. In responding to the health crisis, the government announced social distancing policies, which would bear a high cost for households and firms (Gottlieb, Grobovsek, Poschke, & Saltiel, 2020). The team agreed on exploring an evidence base for smart containment measures and then focused on the use of CDR data to understand patterns of human mobility during COVID-19.

Insights on this approach

The analysis of changes in mobility patterns during COVID-19 in The Gambia is based on a set of mobility indicators, which is calculated based on CDR aggregates. These indicators capture changes in population movements during the baseline, under COVID-19 and post-intervention periods, and results can be updated continuously as additional CDR data becomes available.

The data of the first two weeks of March were used for computing values used as baseline, which are regarded as routine mobility. The standardised indicators were proposed by the World Bank COVID-19 Mobility Task Force and built on a framework developed by Flowminder to support MNOs in producing basic indicators from telecom data (see Flowminder COVID-19 Resources - Mobility indicators). Methodologies for computing indicators were designed with the considerations that it is not excessively computationally intensive to produce even in resource scarce settings, fully anonymous and contain no information about individual subscribers, ensuring that the privacy of subscribers is maintained at all times, and robust to sparse tower distribution and to infrequent phone usage, both of which are common in low-and middle-income countries.

Key steps taken for producing statistics

  • Examining the relevance of the data for analysis: Prior to the analysis, the validity of CDR data was examined using population density, and phone usage patterns in terms of transaction volume and number of active subscribers. Although available data are limited, this process helped understand quality in statistics produced from CDR data, and identify strengths and weaknesses to improve process and product quality.
  • Computing an indicator as a proxy for distance travelled: For measuring changes in the magnitude of mobility over the data period, the distance of flows among cell towers is used as a proxy for distance travelled. A trip is defined by two consecutive data points, which are used as a pair of an origin and a destination; for each pair, Euclidean distance is computed using the geographic coordinate of cell towers. The distance travelled is computed as the sum of the distance of trips per person, and then aggregated by grouping people by their home region. For each region, average, median, and 75-percentile distances were computed and used for measuring the daily mobility level. For the analysis, 75-percentile distances were used because the median value resulted in zero in many districts. It indicates that the results of this indicator could represent people whose mobility is relatively high.
  • Interpretation of results: The indicator showed the transitions of mobility levels at the district level over the data period. After the social distancing policy imposed, the distances travelled remain less than the baseline except on the day where Ramadan ended. It suggests that the mobility of people decreased overall. Among the non-capital districts, districts in urban areas show the most significant decreases, and those in rural areas have similar trends with smaller magnitudes. These trends indicate more significant impacts on mobility in rural areas that rely heavily on mobility for the purposes of temporary migration and trade.

Areas for improvement and challenges

This indicator is useful for examining the transitions of regional mobility levels over the data period. While it enables to see changes at the district level, the value of the indicator must be carefully interpreted because the value itself is the function of cell-tower density. In addition, mean values for regions in rural areas tend to be affected by extreme values generated from distant-cell towers, which are much longer distances than that can be travelled. In addition, median values for rural areas tend to be zero because short-distance travel is not detected when a wide area is covered by a cell tower. Using the value of the 75th percentile resulted in representing the mobility patterns of people whose mobility is relatively higher. Although the value enabled to capture changes without being affected by extreme values, development of more robust methods would be needed.

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