Detecting displacement after the 2015 earthquake in Nepal (Wilson et al., 2016)


Sudden impact disasters often result in the displacement of large numbers of people. These movements can occur prior to events, due to early warning messages, or take place post-event due to damages to shelters and livelihoods as well as a result of long-term reconstruction efforts. Displaced populations are especially vulnerable and often in need of support. However, timely and accurate data on the numbers and destinations of displaced populations are extremely challenging to collect across temporal and spatial scales, especially in the aftermath of disasters.

This use case highlights how mobile network data can be used to generate statistics for detecting displacements including returning residents based on changes in daily locations of users over time.


Historical data back to January 2015 and after the earthquake (the end of July 2015) was provided by NCell which is one of two major MNOs in Nepal.

Key steps taken for producing statistics


  • Calculating a 'daily location' for each user (where a user is taken to be a unique phone number, i.e., a SIM card). This was designed to be a single tower location which represents the location of the user for that day. As the aim was to investigate displacement, the overnight location of a user determined their daily location. Each user was assigned a daily location at District or Village Development Committee (VDC) level, based on the administrative area that the daily location (at tower level) was situated in.

Estimating population flow

  • Estimating 'home location': To reduce the influence of noise introduced by short term trips or commuting patterns, a 'home location' was calculated for each individual by calculating the modal daily location over a certain period. These home locations were then used to calculate transition matrices describing the countrywide mobility between two points in time.
  • Accounting for high baseline mobility: In Nepal, mobility is fairly high and thus, large flows of people are observed between areas of Nepal under normal conditions. To account for this high baseline mobility, flows following the earthquake (termed post-earthquake flows) were normalised using pre-earthquake mobility estimates (normal flows).
  • Computing a transition matrix: The difference between post-earthquake flows and normal flows provides a measure of anomalous flows or 'flows above/below normal'. The anomalous flows calculation produced a transition matrix giving the anomalous flow (number of users, above and below normal) that are moving between each pair of locations.
  • Scaling population flow: Assuming SIM card movements to be representative of population movements, absolute flows were estimated by scaling SIM card counts based on local Ncell user penetration rates. The number of active SIM cards in an area, for example an administrative unit, was calculated from CDRs. Data from WorldPop, which provides gridded population estimates per 100×100-meter grid square for the entire country, was used to estimate finer level population counts.
  • Computing returning residents: The home location of users over a long benchmark period prior to the earthquake using the same method as for the flows calculation above. A user was counted as displaced if they had spent at least seven consecutive days away from their pre-earthquake home location in a two-week period after the earthquake. Iterating through the remaining data the percentage of displaced users who remained away (i.e., at a location different to the pre-earthquake home location) was calculated. Plotting the percentage of users who had not returned over time gives an indication of the rate at which users are returning to a given location. As this is a percentage, the scaling factor was not introduced by assuming that the same percentage of missing users remained away from home as was the case for those who were present in the data set. Using this data, trends in the 'return rate' for each region can be derived as well as snapshots of the most recent data.

Areas for improvement and challenges

  • Potential biases include higher ownership of phones among males than females as well as among higher income groups and certain age groups (Wilson et al. 2016). Similarly, phone usage within households can vary, and phones can be shared between multiple members of a household. Estimates could be further improved by incorporating information on phone ownership from surveys in cases where those are available.
  • Analyses estimate the number of people moving after the earthquake. While estimation of population flows above and below normal levels aims to address this issue, a higher precision in displacement estimates would have been possible if analyses had been combined with population surveys.
  • CDRs only provide location updates for individuals when a call is made. In this dataset calling frequencies were relatively low, with around 50% of people calling at least every other day. Detailed movements among users with infrequently updated locations are therefore missing from the data.

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