Anonymised and aggregated data from mobile network operators can help support humanitarian and development interventions by improving our understanding of population movements.
Why it matters
Every year, millions of people are displaced due to natural disasters, armed conflict or other humanitarian crises. Every two weeks on average, a disaster displaces more than 100,000 people, and more than a million people every two months. When these disasters occur, decision makers need to rapidly locate populations and anticipate movements in order to provide targeted and strategic supplies and humanitarian relief. This is when basic information on the locations of affected people is unavailable. Understanding where people are, where they are going, and the routes they take to get there within countries is crucial to planning and implementing the appropriate interventions that a country needs.
And the value of mobility data goes beyond crisis response. They are helpful for rapid responses through longer term development challenges. Knowing the regular movements of people (how many people are moving and their frequency of movements) can inform a country’s planning of new investments or resources. Understanding routine mobility ‒ for example linked to seasons or lifestyles ‒ can help determine the best time and place to reach specific communities across a country.
The information generated by analysing population movements can provide useful insights to decision makers, applicable for a wide range of contexts, including disaster management, public health (including epidemiology), urban planning, service coverage assessment and service placement optimisation, migration and displacements, and much more.
Our Solution: Mobility analysis from Call Detail Records (CDRs)
Data from Mobile Network Operators (MNOs) can be analysed in near real-time and provide an overview of mobility patterns at local and regional levels, or across an entire country. MNOs collect several types of data to record their customer or network activities, and Call Detail Records (CDRs) are the most commonly analysed for humanitarian and development purposes. Using anonymised and aggregated CDR data, governments and decision makers can be provided with timely mobility analyses.
Call Detail Records are owned and automatically generated by MNOs for billing purposes. CDRs are produced each time a mobile phone subscriber (a mobile phone user) makes or receives a call, sends or receives an SMS, or uses mobile data.
Example of a CDR dataset, using fake data.
Each call, text, and mobile data session creates a record, which includes an anonymous subscriber ID, a timestamp, and the ID of the cell tower routing the event. Each Call Detail Record appears as one row on the dataset. From a CDR dataset, we can tell the approximate whereabouts of subscribers, based on the tower’s location, associated with the time of the event that is included in the dataset. A CDR dataset therefore contains billions of data points from millions of users, covering large geographic areas and time periods.
Once aggregated into anonymous statistics, CDR data can be used for humanitarian and development-related analyses. Aggregated data characterise the overall behaviour of an entire group of subscribers. Aggregates are calculated by combining the data in a group into a single number that represents the entire group (for instance a population change in a geographic area). These aggregates do not expose any information about individual subscribers and cannot be used to re-identify an individual. GRID3 mobility analyses are produced in line with international standards, including the European Union’s General Data Protection Regulation (GDPR). This work is led by GRID3 implementing partner Flowminder, which has over ten years’ experience in producing mobility indicators from mobile operator data in a privacy-conscious and robust manner.
Example of CDR-derived insight: time variations in population mobility
This graph presents the reduction in population mobility in Haiti that immediately followed the restrictions put in place to combat the COVID-19 pandemic, as part of Flowminder’s COVID-19 support response. It shows the change in the average number of localities visited per active phone user in the country for each day, compared to a baseline period preceding the restrictions. Drops in mobility are observed on Sundays compared to weekdays in baseline, and following restrictions the mobility on weekdays drops to the level of a normal Sunday. The same analysis is replicated for each region and for each district, providing insights for different areas of a country, and over time.
Source: Report by Flowminder and Digicel Haiti, with the support of IOM-DTM and financed by the European Union’s Instrument contributing to stability and Peace (IcSP)
Understanding demographic shifts and movements is an important factor in determining access to resources and implementing strategic interventions for the wellbeing of one country’s population. CDR data provide timely and rapid insights into population movements, which can be used to extract findings related to routine mobility patterns, abnormal movements, transportation, connectivity and hotspots. These indicators can be used to assess access to a service or capacity of a facility or determine the best areas to build new infrastructure, such as toilets, healthcare facilities or polling stations. In addition, mobile operator data can also be used to estimate mobility patterns during outbreaks, epidemics or pandemics, like for example COVID-19 or Ebola. The data can also inform routine vaccination interventions or serve as input data to predictive models and analyses such as epidemiological modelling.
“In the COVID-19 pandemic, gaining access to information and insight on how people are moving has been critical to designing and monitoring effective interventions.”