Statistics computed from CDR data can be useful for understanding the effect of disasters and interventions, specifically the statistics can be the proxy for changes in the magnitude of mobility within a population—it examines the vector of travel, generated from every consecutive pair of cell towers of the CDR where the value is a function of cell tower density, and thus, the computed result highly depends on the area size covered by a cell tower. For instance, in rural areas where cell tower density is sparse, short-distance travel cannot be accounted for. Travel beyond the boundary of distant cell towers can indicate a long-distance trip which may not represent reality. In addition, it can be heavily affected by cell tower transitions, which generate "travels" that do not actually occur. Clustering neighboring cell towers can mitigate the transitions' impact.
The key steps in measuring changes in magnitude of mobility are illustrated in Figure 1.2 while brief descriptions on how they are implemented are also provided.
In general, one can consider the following steps and process in using CDR aggregate data for measuring changes in magnitude of mobility in the context of disaster:
In general, one can consider the following steps and process in using CDR data for detecting displacement in the context of disaster.
Step 1. Establishing the baseline.
In this process, time windows for the baseline and detecting displacement need to be specified. Data quality assurance should be performed for both time windows to ensure that the baseline data reflect the usual pattern with respect to the purpose of the analysis.
Step 2. Filtering and pre-processing.
When a methodology employed for computing statistics requires specific input-data conditions, filtering and pre-processing need be performed. For example, some statistics may require minimum number of records per week and/or a certain active period of subscriptions with a certain number of records as input data. It is suggested to examining if there are any potential biases associated with the process as they could affect the characteristics of statistical outputs.
Step 3. Detecting home location before the event.
Home location before the event or external shock is used for indicating the usual living place under the normal time. It is estimated using the data of the baseline period. This information is used for grouping population by administrative unit when the values of the indicator are aggregated at the administrative level.
Step 4. Detecting home location after the event.
Home location after the event or external shock is used for indicating the living place after the event, which can remain unchanged for some people. It is estimated using the data after the event. Home location after the event can be computed at the different time scale, such as weekly, and monthly basis, depending on the purpose of the analysis. This information is used for grouping population by administrative unit when the values of the indicator are aggregated at the administrative level.
Step 5. Examining changes in the magnitude of mobility.
Indicators are employed for measuring changes in the magnitude of mobility before and after the event. The magnitude of mobility can be measured by various indicators, such as distance traveled per day, sizes of population inflows per day at the district level, number of residential populations per week. The values of the indicator are computed for before and after the event, grouped based on home location at the administrative level, and then compared to examine changes.
Table 2.2 summarizes processes taken for measuring changes in magnitude of mobility and for examining their statistical relevance in Bangladesh, Haiti, Sierra Leone and the Gambia while more detailed experiences for the same set of countries are provided in Annex 2.2.
Table 2.2 Processes of computing indicators and examining their relevance in selected countries, measuring changes in mobility
Steps taken for producing indicators and examining statistical relevance
NSO, regulator, MNOs, World Bank, University of Tokyo
Sierra Leone Directorate of Science, Technology and Innovation, MIT, Flowminder
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