What are the strengths of CDR data?
There are a range of sources of big data which provide exciting new opportunities to address gaps in data from traditional sources, such as surveys, and to produce more timely and disaggregated statistics.
The ubiquity of mobile devices globally, including high penetration in low- and middle-income countries (LMICs), provides a particularly promising source of data. The International Telecommunication Union (ITU) estimates that 97% of the global population, including 90% of people in the least developed countries (LDCs), have mobile network coverage and that there are 110 mobile-cellular telephone subscriptions per 100 people globally, and 76 subscriptions per 100 people in LDCs.
Mobile phone data containing the timestamped locations for individuals, such as CDRs, signalling data and mobile GPS data, can provide high quality, granular information on human mobility. As these types of mobility data are automatically generated in real time, this allows for the rapid production of timely, quantitative insights in situations where accurate, up-to-date information is paramount.
Mobile phone data also covers large geographic areas, with data from mobile network operators (MNOs) covering entire countries.
CDR data have a range of strengths compared to other forms of mobile device location data
CDRs are used by MNOs for billing purposes and are therefore routinely recorded as part of the normal business operations. This reduces the need for additional data infrastructure to store data that would not otherwise be collected or stored by an MNO. Furthermore, the lower frequency at which CDR data are generated, compared to signalling or GPS data, also reduces the demands on the data infrastructure as there is a smaller quantity of data to store.
CDRs are also generated regardless of the type of mobile device, meaning that CDR data have higher penetration than GPS data from applications on smart devices, particularly in LMICs where smart devices are less prevalent. In LMICs in Sub-Saharan Africa, data-enabled mobile devices only account for 55% of devices, compared to 90% in high-income countries.
Furthermore, CDR data are passively generated when subscribers use their mobile devices, rather than being generated by actively sending a signal to devices to determine their location. Actively generated signalling data, in comparison, may require subscribers to opt-in to have their data collected in this way.
The strengths mean CDR data is especially well suited to estimating changes in the geographic distribution and mobility of populations in response to specific events, such as a disaster or the introduction of government restrictions to control the spread of disease, and estimating the variation around routine distribution mobility patterns.
Strengths of CDR data
- High penetration, worldwide
- Covers large geographic scales, including entire countries
- Billions of data points from millions of people
- Relatively high spatial and temporal resolution
- Near-real time
- Already generated and stored by MNOs
What are the limitations of CDR data?
Call detail records (CDRs) are an exciting source of mobility data with a number of strengths when compared to traditional data sources, such as surveys and censuses. However, CDR data have their own limitations which we should address when planning to generate insights from the analysis of CDR data.
In order to interpret CDR-derived indicators correctly and support evidence-based decision-making informed by the resulting insights, it is important to understand these limitations and how they can be addressed.
Subscriber individuality and uniqueness
When using mobile phone usage data, including CDRs, to study mobility, there can be a tendency to assume that each subscriber identifier corresponds to a single, unique individual. However, a single device or SIM card may be shared by multiple people, for example by members of the same household, or a single individual may possess multiple devices. As a result, a single trajectory may represent the movements of several individuals or a single individual may be counted multiple times.
Tackling the assumption that each subscriber is a unique individual is not straightforward, as SIM sharing and the possession of multiple SIMs could be affected by factors such as age, gender and socioeconomic status.
Conversely, owning multiple mobile devices with different SIM cards is likely to be associated with greater wealth or potentially certain types of employment in which company mobile devices are more common.
As with concerns about the representativeness of the sample of the population included in the dataset, we can use survey data on mobile phone ownership and usage behaviours to help address these concerns. For example, a survey conducted in Nepal by Flowminder researchers found relatively high levels of SIM sharing (47% of respondents reported the use of their SIM card by others). However, there was no significant difference in SIM sharing between men and women.