metadata: authors: 0: author: Arai,Ayumi
1: author: Knippenberg,Erwin Willem Yvonnick Leon
2: author: Meyer,Moritz
3: author: Witayangkurn,Apichon
count: Gambia, The
docna: 0: docna: The Hidden Potential of Call Detail Records
            in The Gambia
docty: Policy Research Working Paper
subtopic: ICT Applications,Inequality,Telecommunications Infrastructure
teratopic: Information and Communication Technologies,Poverty Reduction
abstracts: cdata!: Aggregated data from mobile network
            operators can provide snapshots of population mobility
            patterns in real time, generating valuable insights when
            other more traditional data sources are unavailable or out
            of date. The COVID-19 pandemic has highlighted the value of
            remotely collected, high-frequency, localized data in
            inferring the economic impact of shocks to inform decision
            making. However, proper protocols must be put in place to
            ensure end-to-end user confidentiality and compliance with
            international best practice. This paper demonstrates how to
            build such a data pipeline, for the case of The Gambia,
            channeling data from mobile network operators through the
            national regulator to the analytical users, who in turn
            produce policy relevant insights. The aggregated indicators
            analyzed offer a detailed snapshot of the decrease in
            mobility and increased out-migration from urban to rural
            areas during the COVID-19 lockdown. Recommendations based on
            lessons learned from this process can inform engagements
            with other regulators in creating data pipelines to inform
            policy making.
geo_reg_key: 517191,1927320
repnme: repnme: The Hidden Potential of Call Detail Records
            in The Gambia
display_title: The Hidden Potential of Call Detail
            Records in The Gambia
listing_relative_url: /research/2021/05/33099756/hidden-potential-call-detail-records-gambia
topicv3: Poverty m1326687 695,Big Data m1327333 661,Population, Aging and Health m1327736 640,Mobile Phone m1505675 3092,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2021-05-21T00:00:00Z
dois: 10.1596/1813-9450-9667
historic_topic: Information and Communication Technologies,Poverty Reduction
origu: EFI-SAR-POV-Poverty and Equity (ESAPV)
no_of_pages: 24
author_keys: 0: author_key: 1855572
1: author_key: 1484194
2: author_key: 1205261
3: author_key: 1855571
model_extractions: Policy Research Working Paper 9667 The Hidden Potential of Call Detail Records in The Gambia _Ayumi Arai Erwin Knippenberg Moritz Meyer Apichon Witayangkurn_ Poverty and Equity Global Practice May 2021
Policy Research Working Paper 9667

# **Abstract**

Aggregated data from mobile network operators can provide snapshots of population mobility patterns in real time, generating valuable insights when other more traditional data sources are unavailable or out of date. The COVID-19 pandemic has highlighted the value of remotely collected, high-frequency, localized data in inferring the economic impact of shocks to inform decision making. However, proper protocols must be put in place to ensure end-to-end user confidentiality and compliance with international best practice. This paper demonstrates how to build such a data pipeline, for the case of The Gambia, channeling data from mobile network operators through the national regulator to the analytical users, who in turn produce policy relevant insights. The aggregated indicators analyzed offer a detailed snapshot of the decrease in mobility and increased out-migration from urban to rural areas during the COVID-19 lockdown. Recommendations based on lessons learned from this process can inform engagements with other regulators in creating data pipelines to inform policy making.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mmeyer3@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The Hidden Potential of Call Detail Records in The Gambia<sup>1</sup>

Ayumi Arai Erwin Knippenberg Moritz Meyer Apichon Witayangkurn University of Tokyo The World Bank The World Bank University of Tokyo

**Keywords :** call detail records (CDR); data privacy; COVID-19; crisis response; The Gambia

**JEL classification :** H12 (crisis management), J11 (demographic trends), J61 (geographic labor mobility, O18 (rural, urban, regional) C81 (Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access)

1 Corresponding author : Erwin Knippenberg, <u>eknippenberg@worldbank.org. We thank Horeja Cham, Lamin</u> Dibba, Kristen Himelein, Kai Kaiser, Johan Mistiaen, Ryosuke Shibasaki, Matarr Touray, Tara Vishwanath and participants at various seminars for helpful comments. This research project has received funding from the World Bank Trust Fund for Statistical Capacity Building III which is supported by the United Kingdom’s Foreign, Commonwealth & Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea. We declare that we have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank or any affiliated organizations, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **1. Introduction**

# **A. Unlocking the potential of mobile phone data**

The COVID-19 pandemic has highlighted the value of real-time, high-resolution data to inform decision making in a crisis situation. External shocks, such as climate change, conflicts, and pandemics trigger population movements and displacement. In response, decision makers require information about origins and destination of refugees and migrants to inform a rapid policy response. Yet, survey and administrative data exhibit severe shortcomings which complicate any crisis assessment: traditional data is likely to be outdated during a quickly evolving crisis situation, and rapid data collection is expensive, slow, and often incompatible with a lockdown situation. By contrast, new data sources that are readily available have gained prominence, notably satellite and location data. These offer real-time snapshots at a high level of spatial resolution.

Call Detail Record (CDR) data offers the potential to document population dynamics in near real time. CDR data is available as high-frequency, highly localized data which can be collected and processed in real-time and at relatively low cost. In developing countries where smart-phone penetration is low, CDR is likely to have much more coverage than GPS data. The analysis of CDR data involves investments in technical capacity and IT infrastructure. One defining characteristic of such data is that it is updated in near real time and requires terabytes of storage capacity, either on the cloud or servers on premise. A so-called data pipeline is required to automate the data flow. The raw data from the Mobile Network Operators (MNO) is encrypted and shared with the regulator, who in turn aggregates it into indicators available for analysis. It is also essential to build the technical capacity of analysts in managing and analyzing data, ensuring the sustainability of the initiative.

The use of CDR data raises privacy concerns and requires a strong institutional framework to regulate access and ensure confidentiality. Researchers and governments have worked closely with regulatory authorities and MNOs to leverage CDR data in measuring changes in mobility patterns (Oliver et al., 2020). However, most of these efforts are concentrated in countries with established institutional frameworks, which also reflect recent efforts to integrate CDR data and other big data into the national statistical system.

# **B. Use-case: Tracking mobility in The Gambia**

This paper showcases the use of CDR data to track changes in mobility across The Gambia between March and May 2020, when COVID-19 led to an exodus of the capital city region. This project was undertaken in collaboration with the national regulator Public Utilities Regulatory Authority (PURA) and The Gambia Bureau of Statistics (GBoS) to establish a durable CDR data pipeline in The Gambia. This partnership allows for government ownership and sustainability, investing in both the necessary systems and technical capacity.

Analysis of CDR data suggests that economic lockdown measures reduced human mobility and pushed people to leave the capital city region and return to rural areas. We validate the use of CDR data against the known population distribution from the population census and WorldPop data. Our contribution demonstrates how a system-building approach can make timely, disaggregated analysis based on CDR data available for quick decision making.

This use-case demonstrates how to build an end-to-end data pipeline for CDR data. This pipeline draws raw data produced by the mobile phone operators, encrypts and aggregates it on the regulators premise, before making it available to researchers for analysis. Once automated, it can facilitate the production of rapid, high-resolution insights on population mobility patterns and their economic implications.

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# **C. Roadmap**

Section 2 positions our paper in the literature and describes the country context. Next, section 3 outlines the engagement model to work with the PURA and the GBoS on access to CDR data and producing statistical information from CDR data in The Gambia. Section 4 presents the data and defines the methodology to analyze the CDR data, and Section 5 showcases key results. Section 6 summarizes lessons learned from the ongoing engagement in The Gambia, and Section 7 concludes and outlines the next steps.

# **2. Context and literature review**

# **A. Policy relevance of CDR data**

There is a rich and growing literature seeking to leverage the potential of CDR data to inform policy making. With mobile penetration rising in developing countries (GSM Association, 2020), researchers have demonstrated the use of CDR data to create poverty maps (J. Blumenstock, Cadamuro, & On, 2015), understand migration patterns, and estimate a household’s economic characteristics (J. E. Blumenstock, 2018).There have been sustained improvements in forecasting population density by combining high-resolution satellite data with powerful algorithms (Stevens, Gaughan, Linard, & Tatem, 2015). WorldPop trains its algorithms on historical census data and projects annual population density at 100-meter resolution in a publicly available data set (Stevens et al., 2015). However, these data sets rely on slow-moving indicators and are computationally intensive. Forward-looking projections are based on linear extrapolation and do not account for short- or medium-term population movement dynamics.

Researchers have harnessed mobile phone data to map population movements. Deville et al. (Deville et al., 2014) showed that the density of unique users in a cell tower’s catchment area scales with population density, and can be plotted on a logarithmic curve. Researchers can therefore extrapolate shifts in the number of unique users to predict shifts in population densities over time, day by day or week by week. Accordingly, CDR aggregates provide insights on population movements, which is useful for estimating regional connectivity and the impact of mobility restrictions (Wesolowski, Buckee, Engø-Monsen, & Metcalf, 2016). It also helps identify areas with higher risks of importation due to population flows from other regions, and develop spatial epidemiological models (Aledort et al., 2007) (Wesolowski et al., 2012).

Combining CDR data with administrative and survey data offers insights on fast-moving health and well-being indicators. Drawing from survey data on the incidence of poverty in Rwanda, Blumenstock et al. use machine learning algorithms to predict poverty outcomes based solely on patterns in mobile network data (J. Blumenstock et al., 2015). Erbach-Schoenberg et al. combine CDR data with public health data sets in Namibia to link mobility and malaria incidence (Zu Erbach-Schoenberg et al., 2016). When compared with estimates using static maps, this leads to discrepancies of up to 30 percent. These applications showcase the value-added of high resolution, high-frequency proxy data like CDR in the context of an epidemic such as COVID-19.

CDR data can also be used to rapidly update estimates of population distribution when a natural disaster leads to widespread displacement. Bengtsson et al. used CDR data from a major telecommunications company to track displacement in Haiti after the 2010 earthquake (Bengtsson, Lu, Thorson, Garfield, & von Schreeb, 2011). This allowed them to track shifts in population distribution and estimate that up to 20 percent of the population of the capital city left in the 19 days after the earthquake. Lu et al. used

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CDR data to track short-term mobility in the hours and days after Cyclone Mahasen hit Bangladesh in May 2013 (Lu et al., 2016).

# **B. Methodological innovations and CDR data**

The most commonly used methods for processing CDR data are traditional data mining techniques. These include frequency-based analysis, data clustering using unsupervised machine learning, and geovisualization techniques by mapping geolocation (Calabrese, Ferrari, & Blondel, 2014). In recent years, researchers have used de-identified CDR data to compute Origin-Destination matrices in order to better map patterns in travel behavior (Calabrese, Di Lorenzo, Liu, & Ratti, 2011). In combination with additional administrative or survey data, supervised machine learning methods can inform the prediction of outcomes of interest-based solely on patterns in the cell network (Sundsøy, Johannes, Reme, Iqbal, & Jahani, 2016).

The pre-processing of the raw CDR data is essential to accommodate positioning errors in data collection and the first step for processing. The oscillation problem of the user’s location is the leading cause of noise in position data collected from the cellular network as they transfer calls to the nearest base station for traffic management, creating imprecise and overlapping Voronoi polygons (Chen, Ma, Susilo, Liu, & Wang, 2016). The time-based filter is used to ignore oscillation and agglomerative (hierarchical clustering) in methods to extract truthful location data from raw CDR.

However, handling such sensitive data requires appropriate protocols to address concerns around data privacy. While anonymizing data is necessary, Kondor et al. (2015) show that it is theoretically possible to identify users based on their mobility patterns alone (Dániel Kondor et al., 2015). It is, therefore, best practice to restrict access to individual observations and use aggregated indicators for the purpose of analysis.

# **C. Country context: The Gambia**

The Gambia is a West African country of 2.3 million people surrounded by Senegal. The country experienced prolonged spells of violence and instability and is currently undergoing a transition process to restore its democratic institutions. With a Gross National Income (GNI) of 740 USD (current, Atlas method), The Gambia is classified as a low-income country, with more than 10 percent of the population living in extreme poverty. The capital city region at the mouth of the Gambia River encompasses Banjul City and the Kanifing region, with tourist resorts strung southwards along the coast (see Figure 1). Tourism and the civil service are the largest drivers of formal employment. Inland is largely rural, its economy is driven by agriculture, and largely dependent on the flow of domestic and international remittances from migrants. These disparities in access to services and opportunities have led to high levels of internal migration, especially among the young who left rural areas to look for better jobs in the capital city region (The World Bank, 2020).

<mark>Figure 1.</mark> Administrative boundaries of The Gambia

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

A workshop in February 2020 was instrumental to initiate a partnership on “Big Data for Development” that evolved around ownership and sustainability. A joint vision and a clearly specified use case to focus the analysis on internal migration helped to coordinate expectations, and build capacity using a practical example. Moreover, a broader audience during the initial workshop - involving stakeholders from the private sector, ministries and agencies, academia, civil society, and development partners - confirmed the demand and interest for access and analysis of CDR data. During this discussion, it was instrumental to convince the MNO to join this initiative as they are collecting and providing the CDR data. Their agreement was based on the idea that training would also enhance their inhouse capacity to analyze CDR data to improve their business operations and enhance customer relations. The workshop also offered a forum to discuss any institutional, organizational, or technical challenges, and showcase how other countries have dealt with these concerns.

Once COVID-19 hit The Gambia in March 2020, all parties agreed to revisit the focus of the collaboration and explore the use of CDR data to respond to the health and economic crisis. In light of limited testing and health facilities, the government announced a national health emergency with profound restrictions on human mobility (Hale et al, 2020). As part of this dialog it became clear that prolonged social distancing would bear a high cost for households and firms (Gottlieb, Grobovsek, Poschke, & Saltiel, 2020), and there was interest to create an evidence base for smart containment measures. Based on successful applications in other countries, the analysis then focused on the use of CDR data to understand patterns of human mobility during COVID-19.

# **B. Strengthening technical capacity**

In addition to building a consensus about the analysis of CDR data, the partners agreed to strengthen technical capacity and ensure knowledge transfer. Crucially, rather than building a system from scratch, efforts were directed at strengthening existing data collection protocols between the MNO and the PURA to include the necessary indicators. As part of its mandate to monitor the quality of calls, PURA already had put in place a centralized repository of data, plugged into the respective MNOs systems, and updated in real-time. After securing the necessary approvals, the team worked with the system administrator to include additional indicators as part of this routine monitoring for use in the analysis. This minimized the reporting burden on MNOs, facilitating compliance. To ensure an additional level of security, the data collected for this project was firewalled and stored on a separate server on the premises, with remote access strictly limited to key researchers and system administrators. Capacity building in the preparation and analysis of CDR data also built trust and offered an opportunity to discuss lessons learned from other countries. It helped to establish a platform to continue the work during the following month when all interactions between the counterparts in The Gambia and the team of researchers shifted online.

Throughout the partnership, PURA played a crucial role in working closely with the MNOs to obtain access to the CDR data. The regulator for telecommunication services used its convening power to discuss with MNOs data sharing while upholding national and international standards for data privacy. Two out of four MNOs agreed to provide access to their CDR data. After training in one-way encryption using a 160-bit hash function, they provided anonymized data to the regulator. In accordance with national regulatory requirements, the regulator set up a secure File Transfer Protocol (FTP), and all data was stored on-premise on a dedicated server. Aggregation of the data was conducted through highly restricted remote access into the server, which constrained computational capacity but kept the data secure and confidential. The team working remotely only had access to aggregate indicators for analysis which precluded any possibility of de-anonymizing the data.

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **4. Data and methodologies**

# **A. CDR data descriptive**

The mobile penetration rate<sup>2</sup> of The Gambia was 94.2 percent in 2013 and rose to 140 percent in 2018, which was higher than the average of developed countries (“ITU ICT-Eye,” n.d.). As of 2018, 98.4 percent of households reported ownership of at least one mobile phone with limited variation across regions and between rural and urban areas. On the individual level, 85.1 percent of men and 74.1 percent of women in the age group between 15 and 49 years own a mobile phone (The Gambia Bureau of Statistics, 2019). According to PURA the four major MNOs provide services to 2.59 million subscribers as of 2020, even allowing for persons with multiple sim-cards. In this paper, we use CDR data for a three-month period between March 2, and May 31, 2020, offering a snapshot of changes in mobility during the COVID-19 lockdown.

Data was made available for two major MNOs, which cover approximately 70 percent of the market and include around 1.75 million subscribers. On average, the data comprises 18.8 million data points per day with very limited variations over the data period, 2 billion anonymized observations in total. Hence, we assume that cell phone usage in terms of transaction volumes did not change fundamentally once COVID-19 hit the country. The average number of records per subscriber per day is 10.6 where approximately 2.6 records are used for calling. Like other developing countries, multiple-SIM-card holding is common in The Gambia. We expect a certain overlap between the two-MNO subscribers, which might have resulted in over-representing the multiple-SIM-card holders. In this study the impact of the multi-SIM holding on the analysis result is considered to be limited since the two MNOs primarily market different socio-economic groups. One of them is a leading MNO in The Gambia and is popular in urban areas with high-speed internet services. The other MNO provides only voice and shortmessaging services with inexpensive plans, which are much popular in rural areas.

The preparation and analysis of CDR data under this project are based on a protocol to address concerns of privacy and confidentiality. Raw CDR data include several identifiers associated with each record, such as phone number, IMSI and IMEI. We employ a three-stage approach to anonymize these identifiers in order to protect data privacy.

- First, identifiers are encrypted using a one-way function by the MNOs on their premises to protect the data privacy.

- Second, lists of cell towers and the locations provided by the MNOs are pooled in the regulator’s premise and cell-tower locations are clustered using Ward’s hierarchical clustering, with a maximum distance constraint of 1km from the centroid of the cluster.<sup>3</sup> We then use the centroid of the cluster to match and map the de-identified CDR data to their respective celltower locations. This process lowers the spatial granularity of cell-tower distributions.

- Third, the results of all indicators are aggregated at the administrative unit level. There are certain concerns about reverse engineering for the re-identification of de-identified CDR data (Daniel Kondor, Hashemian, de Montjoye, & Ratti, 2018) but the abovementioned aggregation process lowers the risk of reverse engineering.

> 2 The mobile phone penetration rate is computed as the number of SIM cards, which is the number of connections to the mobile network services, divided by the population number.

> 3 The choices of a clustering methodology and a distance threshold are proposed by the World Bank COVID19 Mobility Task Force.

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# **B. Key indicators for the analysis of human mobility**

The analysis of patterns of human mobility during COVID-19 in The Gambia is based on a set of mobility indicators, which is calculated based on CDR aggregates. The 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. We used the first two weeks of March to obtain indicators about routine baseline mobility. It could ideally be computed for a period of four weeks before the initial COVID-19 cases were announced, which was March 17, if the data before March were available.

The standardized indicators were proposed by the World Bank COVID-19 Mobility Task Force and build 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 middleincome countries. It is composed of eleven key indicators that provide proxies for population, location of residence, distances traveled, and daily mobility across regions at different geographic and time levels. It also includes proxies to scale other indicators and as the input of epidemiological modeling. For this project, we selected four out of the eleven indicators. Table 1 summarizes the descriptions of the selected indicators based on information on the World Bank COVID-19 Mobility Task Force repository on GitHub.<sup>4</sup> <mark>Table 1.</mark> Summary of key indicators for the analysis

|Indicator ID|Frequency|Definitions|Use-case|
|---|---|---|---|
|Indicator 3 - Count<br>of unique<br>subscribers|Day|Represents the number of<br>unique IDs active (call, SMS,<br>and data communication) within<br>a day and region.|Proxy for population<br>count.|
|Indicator 6 -<br>Number of<br>residents|Week|Defines the modal location of<br>the last observation on each day<br>of the week, where the user is<br>most frequently in the evenings<br>or at night.|Proxy for home<br>location, flexible on a<br>weekly basis to capture<br>short-term relocation.|
|Indicator 7 - Mean<br>and Standard|Day|Captures movements across<br>regions and within a region|Proxy for daily distance<br>traveled.|
|Deviation of||where distances are a function of||
|distance traveled||the cell-towers density.||

> 4 Retrieved August 15, 2020 from Github, _<u>covid-mobile-data/cdr-aggregation</u>_ <u>.</u>

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Indicator 10 - Day Consecutive trips generated by a Proxy for daily mobility Origin Destination sequence of records, counting across regions plus the Matrix the number of times each pair approximate duration of appears and time spent at both stay in a location. origin and destination regions.

a Source: Authors’ adaptation based on the World Bank COVID19 Mobility Task Force repository on GitHub.

# **C. Application in The Gambia**

This paper makes use of the proposed indicators from the World Bank COVID-19 Mobility Task Force and applies them to the country context of The Gambia to analyze patterns of human mobility during COVID-19. More specifically:

- Indicator 3 shows changes in the population distribution over time. As subscribers are accounted for in every region in which they use their phones, it overestimates subscribers who visit multiple regions in a day when computed at the administrative unit level. The value of this indicator can be also affected by load sharing where several cell towers jointly cover a certain area due to network optimization. This impact is mitigated by the cell-tower clustering, which was described as part of data pre-processing in the previous section. We compute this indicator at the national level for examining how the number of active subscribers as a whole country changes over time and for adjusting the result of other indicators.

- Indicator 6 illustrates changes in the location of residency, which could infer the incidence of migration over the data period. For mapping the residential distribution, there are various methodologies and algorithms (Deville et al., 2014) (Ahas, Silm, Järv, Saluveer, & Tiru, 2010), which provide more accurate estimates compared to the proposed method. We consider it still useful for detecting a flexible home location reflecting weekly changes as the estimation result is used at the administrative unit level. In addition, the proposed method is relevant under resource-scarce settings as it is not computationally intensive.

- Indicator 7 demonstrates changes in levels of mobility over the data period. The value of this indicator is defined as the average distance traveled per person residing in a region. This indicator has limitations in detecting mobility particularly in rural areas due to lower cell-tower density. Mean values for regions in rural areas tend to be affected by extreme values generated from distant-cell towers, which are much longer distance than that can be traveled. 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. We use the value of the 75th percentile. It results in representing the mobility patterns of people whose mobility is relatively higher. However, the value enables us to capture changes without being affected by extreme values.

- Indicator 10 describes the sizes of population inflow and outflow. We use this indicator for examining changes in population inflows. This indicator can be used for constructing OriginDestination (OD) matrices but has limitation in capturing long-distance trips. This is because a trip for constructing an OD matrix is defined by each consecutive pair of records, meaning that a long-distance trip is transformed into a set of several short trips, and thereby a link between the origin and destination of the long trip is missed.

The four indicators above are selected for the application in The Gambia to inform COVID-19 responses. These indicators are useful for capturing changes in mobility patterns, occurring in relatively

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

within the margin of error to that found by Deville et al (2014) for France and Portugal, 0.77 ± 0.055, suggesting a stable relationship across countries. In addition, the R^2 value suggests that 85 percent of the variation in density in the World Pop data is explained by variation in the CDR data. The R^2 is lower for the census, given that the data is older. These results confirm that CDR data are valid for examining the population distribution in terms of their residential locations. By extension, shifts in CDR data can capture both short term and long-term shifts in population over time, with implications for disaster risk management and urban planning.

<mark>Table 2.</mark> Correspondence between known population data and CDR data

||(1)<br>Log (WorldPop density)|(2)<br>Log (Census density)|
|---|---|---|
|Log (Subscriber density)|0.675***|0.596***|
||(0.066)|(0.12)|
|Constant|2.98***|4.76***|
||(0.619)|(1.339)|
|Regional fixed effects|Yes|Yes|
|R^2|0.846|0.640|
|N of observations|45|45|

a Standard errors are reported in parentheses. *, **, and *** indicate significance at the 90%, 95%, and 99% level, respectively.

# **B. Patterns of phone usage remained near-constant in terms of the number of active subscribers**

Overall cell-phone use remains stable over the period of observation. We use the number of active subscribers computed for Indicator 3 to examine whether the pattern of phone usage changed against interventions and events over the period of observation. Figure 4 shows the number of active subscribers, which is presented as the ratio to the baseline. We use the average of the number of active subscribers for the first two weeks of March. While we observe a short term, sharp decrease at the beginning of the pandemic, with some erratic behavior in the following weeks, these fluctuations soon subside with a return to baseline levels of activity on average.

<mark>Figure 4.</mark> The number of active subscribers in The Gambia - ratio to the baseline

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

center in access in population density, access to services and structure of economic activity. Classification result is presented in Table 4.

We convert the number of residents to the ratio to the baseline to examine changes from the normal period. The average of the first two weeks of March is used as the baseline. The ratio is scaled using Indicator 3 at the national level to mitigate the impact of fluctuations in the number of active subscribers. Indicator 3 at the administrative unit level is not used as it can introduce certain urban-rural biases; Indicator 3 overestimates the number of active subscribers in urban areas where cell-tower density is higher and people are relatively mobile compared to rural areas. Though this process helps mitigate the impact of the fluctuations on Indicator 6 to a certain extent, Indicator 3 cannot sufficiently address the impact. For instance, when the significance of population decreases at the district level is greater than that of active subscribers at the national level, the value of Indicator 6 computed as the ratio to the baseline cannot not sufficiently inflated.

<mark>Table 4.</mark> Classification of 45 districts

||Urban LGAs|Rural LGAs|
|---|---|---|
|Capital districts|3|5|
|Non-capital districts|19|18|

Source: Authors’ calculations.

Figure 5 shows an increased number of people moving to non-capital districts in rural LGAs in the last weeks of March as the State of Emergency was extended. It uses the number of residents for the four groups, which is calculated as the ratio to the baseline and adjusted using indicator 3. In contrast, districts in urban LGAs and LGA-capital districts show decreasing trends. This suggests that many people in urban areas shifted to rural areas as a result of the lockdown, returning to their hometown in rural areas because of decreased job opportunities in urban areas. During Ramadan, there was a brief spike of activity pronounced in urban LGAs. This reflects mobility patterns seen elsewhere, with an initial burst of out-migration from urban to rural areas, and a gradual trickle back as restrictions were lifted.

Interestingly, non-capital districts in urban LGAs are distinctive in that the estimated population remained almost unchanged in April and May after an initial decrease in activity, perhaps in anticipation of lockdowns. This might reflect populations living in suburban areas with more stable jobs and established homes, more reliant on local economic drivers rather than civil service and tourism. In contrast, the sharp decrease in capital districts in urban LGAs could represent the behaviors of migrants without stable jobs or homes, who were particularly vulnerable to the economic downturn.

<mark>Figure 5.</mark> Numbers of residents at the district level in urban and rural areas - ratio to the baseline

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **B. Engagement model: Bring decision makers on board**

As results from the use-case on COVID-19 became available, PURA and GBoS used these findings to inform their participation in the government task force for COVID-19. The early dissemination of results helped to inform decision makers and prompted requests for a scaled-up version providing realtime data during a quickly evolving health and economic crisis. Unfortunately, efforts to quickly turn this case-study into a fully functional data pipeline were delayed due to constraints in implementation capacity until late 2020. However, once the necessary hardware and training were provided, the CDR data pipeline became operational in early 2021.

Throughout the dialog with decision makers, presentation of findings in an easily accessible format and identification of specific policy recommendations strengthened the support and the interest in the project. Maps created an entry point for dialog with a technical and non-technical audience as they were visually appealing but still contained important lessons (see Figure 8). Moreover, interpretation of results and specific applications in the context of the pandemic supported communication. For instance, the team argued that findings from the use-case on COVID-19 could inform targeted testing initiatives, by concentrating efforts in areas of high mobility. When a full lockdown is not possible given the economic costs, this can also inform where social distancing policies should be enforced given higher mobility and associated with this increased risks of transmission. In addition, results demonstrated that the lockdown disproportionately affected urban areas by restricting economic activity, and relief and recovery efforts should therefore aim to address these adverse effects. These are but a few of the policyrelevant insights CDR data can deliver.

<mark>Figure 8.</mark> Weekly averages of distances traveled at the district level - ratio to the baseline

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

the go-to for data storage when COVID-19 impeded the acquisition of additional server capacity. In terms of analytics, although there was a tool for producing standardized mobility statistics available on the GitHub repository, we chose to write our own script. This was to accommodate the system parameters on PURA’s premises. Since much of the analytical work was done through remote access, it was restricted by network capacity and could be interrupted by electrical outages. This required breaking computationally intensive tasks into multiple steps with intermediate results, so that any interruption would only disrupt the current computation and not the whole script.

# **7. Conclusion**

In this paper, we demonstrate the uses of four indicators for examining how interventions and events under COVID-19 are reflected in the patterns of phone usage, population distributions, levels of mobility, and population flows. Results show that CDR aggregates are relevant for capturing changes in these indicators, which continued to vary over a few months. It indicates that CDR data provide timely and granular population statistics that can complement conventional statistics.

The use of CDR data in the context of COVID-19 in The Gambia demonstrates the hidden potential of big data, including CDR data, to inform decision making. Due to lack of investments in statistical systems, severe data deprivations are likely to remain a challenge for governments, the private sector, and civil society, and this approach offers an opportunity to leapfrog, and exploit data which is available in real-time, highly localized, and at relatively low cost. However, the use of CDR data will require future investments into the institutional and organizational framework of national statistical systems, including improvements of IT infrastructure and technical and statistical capacity. The analysis also demonstrates that CDR data are unlikely to substitute traditional data, such as administrative and survey data, as linkages between telecommunication data and individual level and household characteristics need to satisfy strict technical and ethical requirements.

This application focuses on a well-defined use case, and further work is necessary to scale up the existing structure and ensure interoperability. This paper demonstrates that the analysis of CDR data can support decision making during a crisis situation. This scaling up will require a commitment to include the analysis of CDR data into the standard set of planning instruments, including for the allocation of human capacity and financing.

Future work in The Gambia will build on the existing partnership and experience. While valuable, CDR data on its own can only offer limited insights. We propose to build on this analysis by overlaying it with additional data sets. This includes validation of observed patterns against mobility data from Facebook. We also propose to overlay the mobility trends against price data to infer whether shifts in population drove food prices up in rural areas relative to urban areas. Finally, we would draw on recently available survey data, including the 2019 migration survey, to unpack the correlation between ward level mobility metrics and underlying patterns of internal migration. Notably, did the areas that depend most on internal migrants see a large number of returns? This would allow us to infer how the COVID19 induced lockdown differentially impacted vulnerable populations in rural and per-urban areas.

From a policy perspective, the future analysis of CDR data could inform urban planning, particularly investments into infrastructures such as roads, schools, and hospitals. In addition, the private sector has also shown interest in using this information to better understand commuting and clustering patterns in order to exploit untapped market potential.

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

**Acknowledgments.** We thank Horeja Cham, Lamin Dibba, Kristen Himelein, Kai Kaiser, Johan Mistiaen, Ryosuke Shibasaki, Matarr Touray, Tara Vishwanath, and participants at the UN 2020 conference on Big Data, World Bank Poverty & Equity Brown Bang Lunch, and anonymous reviewers for helpful comments.

**Funding statement.** This study received support from the World Bank Trust Fund for Statistical Capacity Building III (TFSCB-III) which is supported by the United Kingdom’s Foreign, Commonwealth & Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea.

**Competing interests.** We declare that we have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank or any affiliated organizations, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

**Data availability statement.** The program codes and the aggregated statistics (if possible and currently under negotiations) will be made available through the GitHub platform (link <u>here : https://github.com/worldbank/covid-mobiledata/tree/1b9f114abc9231964d9109f62df29a146912b4a2/cdr-aggregation#summary-of-indicators).</u>

**Ethical standards.** The research meets all ethical guidelines, including adherence to the legal requirements of The Gambia. In particular, the team only accessed de-identified, anonymized and aggregate CDR data.

**Author contributions.** AA and AW processed the aggregate CDR data to produce key statistics, prepared a first draft of the data section, and approved the final version of the manuscript. EK and MM contributed to the analysis, revised the first draft and approved the final version.

**Supplementary materials.** No supplementary material intended for publication has been provided with the submission.

**Abbreviations:** COVID-19, Coronavirus disease 2019; CDR, Call Detail Records; FTP, file transfer protocol; GBoS, Gambia Bureau of Statistics; GNI, Gross National Income; IMEI, International Mobile Equipment Identity, ISMI, International Mobile Subscriber Identity, IT, Information Technology; LGA, Local Government Area; MNO, mobile network operator; OD, Origin-Destination, PURA, Public Utilities Regulatory Authority; SIM, subscriber identification module; SMS, short message service; VPN, virtual private network;

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