metadata: authors: 0: author: Matekenya,Dunstan
1: author: Espinet Alegre,Xavier
2: author: Arroyo Arroyo,Fatima
3: author: Fernandez Gonzalez,Marta
count: Sierra Leone
docna: 0: docna: Using Mobile Data to Understand Urban
            Mobility Patterns in Freetown, Sierra Leone
docty: Policy Research Working Paper
subtopic: Transport Services,Telecommunications Infrastructure,ICT Applications
teratopic: Information and Communication Technologies,Transport
abstracts: cdata!: In recent years, researchers have
            demonstrated that digital footprints from mobile phones can
            be exploited to generate data that are useful for transport
            planning, disaster response, and other development
            activities—thanks mainly to the high penetration rate of
            mobile phones even in low-income regions. Most recently, in
            the effort to mitigate the spread of COVID-19, these data
            can be used and explored to track mobility patterns and
            monitor the results of lockdown measures. However, as
            rightly noted by other scholars, most of the work has been
            limited to proofs of concept or academic work: it is hard to
            point to any real-world use cases. In contrast, this paper
            uses mobile data to obtain insight on urban mobility
            patterns, such as number of trips, average trip length, and
            relation between poverty, mobility, and areas of Freetown,
            the capital of Sierra Leone. These data were used in
            preparation of an urban mobility lending operation.
            Additionally, the paper describes good practices in the
            following areas: accessing mobile data from telecom
            operators, frameworks for generating origin and destination
            matrices, and validation of results.
geo_reg_key: 517191,1927320
repnme: repnme: Using Mobile Data to Understand Urban
            Mobility Patterns in Freetown, Sierra Leone
display_title: Using Mobile Data to Understand
            Urban Mobility Patterns in Freetown, Sierra Leone
listing_relative_url: /research/2021/01/32765205/using-mobile-data-understand-urban-mobility-patterns-freetown-sierra-leone
topicv3: Transport m1326661 135,Big Data m1327333 661,Mobile Phone m1505675 3092,Open Source m1505699 2946,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2021-01-20T00:00:00Z
historic_topic: Information and Communication Technologies,Transport
origu: Transport-LAC (ILCT1)
no_of_pages: 24
author_keys: 0: author_key: 1542959
1: author_key: 1464311
2: author_key: 1375072
3: author_key: 1359890
model_extractions: Policy Research Working Paper 9519 Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone _Dunstan Matekenya Xavier Espinet Alegre Fatima Arroyo Arroyo Marta Gonzalez_ Development Economics Global Indicators Group & Transport and Digital Development Global Practice January 2021
Policy Research Working Paper 9519

# **Abstract**

In recent years, researchers have demonstrated that digital footprints from mobile phones can be exploited to generate data that are useful for transport planning, disaster response, and other development activities—thanks mainly to the high penetration rate of mobile phones even in low-income regions. Most recently, in the effort to mitigate the spread of COVID-19, these data can be used and explored to track mobility patterns and monitor the results of lockdown measures. However, as rightly noted by other scholars, most of the work has been limited to proofs of concept or academic work: it is hard to point to any real-world use cases. In contrast, this paper uses mobile data to obtain insight on urban mobility patterns, such as number of trips, average trip length, and relation between poverty, mobility, and areas of Freetown, the capital of Sierra Leone. These data were used in preparation of an urban mobility lending operation. Additionally, the paper describes good practices in the following areas: accessing mobile data from telecom operators, frameworks for generating origin and destination matrices, and validation of results.

This paper is a product of the Global Indicators Group, Development Economics and the Transport and Digital Development 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 dmatekenya@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
# **Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone**

Dunstan Matekenya<sup>1</sup> , Xavier Espinet Alegre<sup>2</sup> , Fatima Arroyo Arroyo<sup>3</sup> and Marta Gonzalez<sup>4</sup>

**JEL:** C88, I18, O18, Q54, R42

**Key Words** _:_ Urban Transport, Resilient Transport, Mobile Data, CDRs, Big Data

> 1 _Data Scientist, DEC, Data Group, World Bank Group_ Washington, USA, dmatekenya@worldbankgroup.org

> 2 _Senior Transport Consultant, Transport GP,World Bank Group_ Washington, USA, xespinetalegre@worldbank.org

> 3 _Senior Urban Transport Specialist, Transport GP, World Bank Group_ Washington, USA, farroyoarroyo@worldbank.org

> 4 _Department of City and Regional Planning, University of California-Berkeley,_ Berkeley, USA martag@berkeley.edu
# **INTRODUCTION**

Mobile phones are increasingly ubiquitous even in low-income regions, allowing people to leapfrog legacy communication technologies and traditional information systems. Globally, the number of mobile subscribers has surpassed the human population [1]. In the developed world, mobile phone users include nearly everyone. In low-income countries, 61 percent of the population has access to mobile phones [2].

Mobile network data, which for the sake of this report refers to the vast quantity of information that is produced as a passive byproduct of using mobile services—holds great promise as a transformative resource for social good. Numerous researchers have demonstrated that, if used properly, mobile phone data can be used in ways that complement traditional data collection approaches. Such data can also be used to fill in data gaps where traditional approaches cannot provide data, and in some cases replace traditional data sources. For instance, in transport planning, the following studies, [3]–[6], have shown that elements such as origin and destination (OD) matrices can be generated from mobile phone data. Besides these academic studies, there are a handful of case studies of the use of mobile data applied to transport projects. The examples include Seoul City [7] and Kampala, Uganda [8]. Despite these few studies and proofs of concepts, it is difficult to find material that tries to answer practical questions on the use of mobile data in transport such as “What are the best practices for accessing data and interfacing with mobile network operators?” or “How do you select the best models for generating OD matrices?” Under this context, the World Bank team in Sierra Leone, together with researchers at the University of California Berkeley, aimed to use this technology to understand insight in the urban mobility pattern in the city of Freetown, capital of Sierra Leone.

Among research question under investigation in this study are:

- (1) what are the impacts of rainy season on urban mobility?

- (2) where are the locations in the city that attracted most trips? and

- (3) is there a correlation between poverty and longer trips?

Additionally, in this paper, we fill some knowledge gaps by sharing experiences, takeaways, and best practices in operationalizing mobile network data in an ongoing World Bank (WB) transport infrastructure project. Among others, these lessons learned can support governments and administrations using these data to monitor the results of lockdown and mobility restrictions to reduce the spread of COVID-19.

# **LITERATURE REVIEW**

There is no doubt that mobile network data have transformed urban mobility analytics. The revolution has occurred mainly in developed countries, where smartphone penetration, use of social media, and internet access are widely spread. In these countries, Location Base Services (LBS), such as GPS location collected and generated by mobile phone applications, have been used in transport planning [9] supplanting some initial proof of concepts of the use of Call Detail Records, and improving some of the biases that this last data set incurred [10].

Call Detail Records or cellular network data are defined as a single incoming or outgoing call event and SMS, which is represented as a single row in the data set and contains information on the time, and cell tower that this event occurred. This type of data had initially been proven useful to estimate urban mobility pattern and flows [3]–[6], demonstrating that CDR could generate origin and destination matrices. However, due to the widespread availability of location based data and the publication of some studies that raised concerns on the biases of CDR, this type of data was relegated to a second place.

In most of the global South, smartphone penetration and access to reliable internet are still low, with penetration values for smartphone below 30% in most African countries [11]. This factor is a critical constraint to use of Location Based Data in urban analytics in most African cities. On the other side, because of high penetration of phones both in rural and urban areas (estimated at 77% of population in Sub-Saharan Africa [12]), CDR data are still the most reliable method to gain understanding of urban and human mobility in cities in the global South [13-14]. In this 2
context, CDR data have been proven to allow planners, academic and government stakeholders to get a deep insight in mobility patterns and even extract information on demographic dynamics such as gender differentiated mobility [15]. Despite the clear benefits of using CDR to understand urban mobility, urban planning agencies are behind academics and proof of context, and there is almost no example of government agencies taking CDR in their routine planning activities. Just recently, some European agencies, such as the Transport Metropolitan Agency in Barcelona [16], have started to incorporate mobile phone data analytics in their efforts to track effectiveness of mobility restriction policy measures in the fight against COVID-19.

Other examples of the use of mobile phone data in Africa for transport related projects include its use to collect and digitalize data on urban transport routes, such as mapping matatus in Kenya or Jitney Network in Accra [17-18]. In Freetown, we collected urban transport routing information using a similar methodology as in Kenya and Accra, compiling the first transit data set ever collected in that city [19].

# **BACKGROUND**

This study is part of an ongoing WBG lending operation to the Government of Sierra Leone, the Sierra Leone Integrated and Resilient Urban Mobility Project (IRUMP) that aims to improve, in Freetown, the quality of public transport, road safety, and the resilience of transport infrastructure to climate and weather hazards. As part of the project conceptualization and design, our team used mobile data to generate, in a low-cost way, a set of indicators required for various planning aspects of the project. These transport indicators include OD matrices and miscellaneous, individual- based mobility characteristics. Although the goal of this study was not to support the Government of Sierra Leone in COVID-19 mitigation efforts, the lessons learned are directly applied to such efforts.

The overall goal of this study is to generate information about mobility patterns in Freetown and assess the impact that weather-related events have on the city’s mobility. In particular, the project set out to achieve the following: (1) Generate OD matrices, (2) Generate individual-based mobility characteristics and (3) Use the above outputs and other data to perform in depth analysis such as that on travel demand and impact of weather on mobility.

Additionally this paper aims to share good practices for working with mobile network data in the transport sector in a cost-effective manner. We share lessons in the following areas: – Data access. We share best practices and lessons on how to work with mobile network operators (MNOs) and telecom regulators in low-income regions.

-

- Methods. What models to use to produce data products such as OD matrices.

– Technology, infrastructure and platforms. We share insights on which technologies, platforms and frameworks to use for large-scale data processing.

## **Context**

Sierra Leone (SL) is a country in West Africa on the Atlantic Ocean. The country is divided into four provinces: Southern, Eastern, Northern and Western (see figure 1). Our project focuses on the western region, which contains the capital city Freetown. Sierra Leone has a population of 7,092,113 (Census 2015) with a density of 97.2 persons per sq. km while in the western region, the population density is _20 times higher_ at 2,154.6 persons per sq. km.

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in the CDRs we use in this report.

|**TABLE I Available attri**|**butes in the CDR data set**|
|---|---|
|**Attribute category**|**Description**|
|CDR type|event type(e.g.,call,SMS)|
|time stamp|exact time a call or SMS wasplaced|
|subscriber identifiers|phone number,IMEI,IMSI|
|geographic location|latitude and longitude of cellular tower|
|other location attributes|cell–id,Location Area Code(LAC)|

# _Basic metadata about the data_

The CDRs we used in this work were collected in 2018 for the following months: January, February, June and July. For these four months, there are over 2 billion events. The total number of unique subscribers over this period was 6,631,273. In table II and figure 3, below, we provide background characteristics of the data set to enable the reader to understand the broader context of the data set. When working with CDRs, it is crucial to understand the calling characteristics of users, such as calling frequency, since this has direct impact on the data points you get for each user. In figure 3, we present users’ average number of calls per day over the 120 days. Most studies (for example, [21]) have shown that this variable has a long tail distribution–which means there are very few occurrences of large events (in this case, users who make many calls per day) and many occurrences of small events (users who make very few calls per day). We see the same pattern in these data: most users make 1–2 calls per day. This information will be used later to select active users to include in the analysis.

|**TABLE II Background attributes o**|**f the data set**|
|---|---|
|**Description**|**Attribute**|
|Total number of call events<sup>5</sup>|2,060,691,843|
|Average call eventsper month|589,000,000|
|Number of unique subscribers|6,631,273|
|Number of cellular towers|267|
|Mean distance between cell towers|600 meters|

> 5 In this case a call event represents both actual calls and SMS.

6
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cellular tower locations, but those results are not presented in this paper.

Once we have generated trips for each user for each day, this data set is stored and is later used as the main input in generating various OD flow outputs which vary mainly by trip _time specification_ and _travel zones_ . For instance, for some purposes, we may require trips taken during the morning commute hours (for example, between 7AM and 10AM) and set the travel zone to cellular tower or _chieftainship_ , which is a fourth–level administrative unit in SL. Given these parameters, we generate an OD matrix where each cell value represents the number of trips from Origin (O) to destination (D) by aggregating trips with the same OD pairs. This process can be easily repeated for different settings of travel zone and trip time parameters.

In addition to OD flows, we also generate _individual mobility characteristics_ as follows: radius of gyration ( _Rg_ ), average number of trips taken per day, and average trip distance. When processing the data, we also assign each user a home cellular tower. This information is useful for some of the analysis we do later on. The procedure we use to detect a user’s home is based on frequency of calls in the hours of the day we consider as home hours.

# **Validation and Adjustments**

In traditional household surveys, study participants are carefully selected using robust statistical sampling techniques to ensure that the results are representative of the whole population. Thus, once the data are analyzed, there is very little validation and adjustments on the data before they can be used. In contrast, CDR data are an example of what is usually referred to _opportunistic data_ , which are the kind of data that are collected for a purpose other than that intended for the analysis. In this case, CDRs are collected by MNOs for billing purposes, among other things. Because of this, it is imperative that the outputs generated from these data be subjected to validations and/or adjustments before being put into use. This is to ensure that the results from the analysis of CDRs do in fact match up with the reality on the ground.

In order to ensure that there is no confusion, we explicitly define _validation or evaluation_ here as follows: the process of assessing correctness of outputs (for example, models, indicators, and so on) generated from CDR data as with reference to specified ground truth data. For instance, in order to validate OD flows generated from CDR data, we may compare them with OD flows generated from surveys or manually collected by a human, and then measure the difference. For some use cases, this validation may be enough. However, for other use cases, we may also need to adjust the outputs in order to make them usable for policy. In our OD flows example, we know that the trip counts generated from CDRs represent the subscribers of an MNO or a set of MNOs and not necessarily the actual population. Therefore, to get realistic trip counts, we need to _adjust_ the trip counts from CDRs using actual population data. In figure 6, we provide an overview of the key components in the validation and adjustment framework. In most cases, outputs generated from CDR data sets need to be validated even with very simple strategies. For instance, subscriber population can be compared with actual population by running correlations. While this validation may be enough for some use cases, adjustments may be required for other cases.

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# **Data Access**

Due to the confidential nature of mobile data, data access is perhaps the most daunting and time-consuming task of most mobile data-based projects. For CDRs or any mobile network- generated data, there are several ways to approach data access: _a) Directly contacting with MNOs:_ Depending on the relationship and connections, as well as the MNO’s familiarity with the topic, some teams have reported success in accessing data by directly contacting MNOs. This approach is as simple as it sounds: it simply involves directly reaching out to one or more MNOs and requesting their willingness to collaborate on a project.

_b) Leveraging the convening power of telecom regulators:_ In most countries, there is a regulatory body that oversees the MNOs’ work and sets the rules to which MNOs have to adhere, which in theory means the MNOs are obliged to work closely with the regulators and be on their good side. Most of the organizations that have had success with accessing mobile data have done so by going through regulators. The advantage of using regulators is that it is relatively easy to deal with all the MNOs in a country at once rather than communicating with separately with many MNOs.

_c) Third–party organizations:_ In this context, we use the term _third-party organization_ to refer to a private organization or firm which deals with mobile data. Examples of such firms include Flowminder, OPAL, and the University of Tokyo. These organizations often have, on their own initiative or through being contracted by other organizations, worked with MNOs and/or regulators to access mobile data. They often play the role of a consulting firm that handles the negotiations with either MNOs and/or regulators to access data. They also provide technical support to store, process and analyze the data. The way one would use these third-party organizations to access data is by engaging them as consultants who do all the work of negotiating with either MNOs and/or regulators in a specific country to access the data and carry out the analysis.

# **Technologies and Platforms**

Since data generated from mobile phones are large due to the high frequency and scale on which the data are generated, it falls into the category of big data, and dealing with it requires non-traditional technologies and frameworks. In this regard, it is worthwhile describing best practices in terms of the tools and techniques required to store, process and analyze big data.

_Data storage:_ With the advent of cloud computing, handling large scale data sets is not as daunting as it used to be for example 20 years ago. Now, organizations can choose to store and process their data on their premises or store it in the cloud. The advantage of cloud storage is that organizations do not have to pay for maintenance costs, and the processing and storage is highly elastic in that it can easily be scaled up or down as need be. This is not the case with on-premise infrastructure, which is static and inflexible. However, for some countries, shipping off their data to the cloud is not acceptable due to security concerns. There are many cloud computing service providers to choose from. Luckily, all the technology solutions we discuss here are agnostic to a specific cloud solution and so would work equally well on any cloud platform. For both on-premise and cloud storage, the state-of-the-art software framework for handling large-scale data sets is the Apache Hadoop software library (or in short Hadoop). Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. One of the core modules for Hadoop is the Hadoop Distributed File System (HDFS); a distributed file system that provides high-throughput access to application data. There are several programming models which enables developers to interact with data stored in HDFS; these include, Apache Spark, Hive, Pig and more. Since Hadoop is an open-source platform, it can be downloaded for free and used as needed. However, in practice, it is easier to use Hadoop with third-party distributions such as _Hortonworks HDP_ . When you install Hortonworks HDP, you gain access to the whole suite of Hadoop tools, including Apache Spark.

_d) Data processing and analysis:_ We mentioned that Hadoop is probably the most commonly used platform for b i g data storage, processing and analysis. Within the Hadoop ecosystem, there are several solutions for data processing and analysis. In this work, we used Apache Spark as the big data processing engine because it is very fast and fault-tolerant among many other advantages. More importantly, with Spark, you can use your 19
programming language of choice (Python, Scala, Java and R) to interact with their suite of tools.

In addition to a processing engine such as Apache Spark, in some cases, when we cannot write our own code to perform data analysis, we need off-the-shelf software packages for analyzing the mobile data. In this regard, we report on specialized software packages for analyzing mobile data. Here, we cover only open-source-based software packages, as opposed to proprietary software, because it is easy to share the analysis outputs with a wider community of users. At the time of writing, there were two open-source packages for analyzing mobile data on the market: Flowkit<sup>8</sup> and CDR Data Analysis Tools.<sup>9</sup> Both are designed specifically for analyzing CDRs although they can easily be extended to analyze GPS-based or other trajectory type data sets. The next question may be how to choose one over the other. There are several aspects that can guide that decision, but one we would mention here is the available programming APIs. Flowkit has a Python API which may appeal to people who work with Python, while the CDR analysis tool has a command line interface only. However, one can easily use both software packages in a single project because they have different features.

# **CONCLUSIONS**

In this paper, we have shown that opportunistic data such as CDR coming from mobile phones bring the opportunity to understand mobility in a very low-cost manner, as opposed to relying only on traditional surveybased approaches. These data can become very useful to rapidly track and monitor mobility patterns that could help governments to evaluate the efficiency of different mobility restrictions, in efforts to control the spread of COVID-19.

It is worth mentioning that CDRs need to be integrated with other background data such as that from a census, to capture the socioeconomic characteristics of the target population. We have provided several lessons and experiences which should be useful to others who would like to replicate similar work. We summarize these key lessons in the following paragraph.

The first and perhaps most crucial step when dealing with CDR data is to negotiate access to the data. We have learned from this project and others that although there are several avenues to access the data, including the use of third-party firms, if there is time and financial resource constraints, then working with the telecom regulators and involving all key stakeholders works best. Once the permission to access the data has been granted and a memorandum of understanding has been signed, the data need to be properly stored. For this, we recommend the use of a Hadoop-based system for both data storage and, subsequently, data processing. We have produced two main categories of outputs in this work, as follows: OD matrices and individual-based mobility characteristics. We have shown that these analysis results do match with expected and prior studied results. We investigated how individual-based mobility characteristics (radius of gyration and average number of trips) vary by wealth and found a slight correlation between wealth and shorter trips.

# **ACKNOWLEDGMENTS**

We thank NATCOM for agreeing to share the CDR data which we used in particular thank _Victor Barber Richards_ , and the Ministry of Transport for their leadership. The team would like to acknowledge the timely and valuable support, guidance and advice received from _Gayle Martin_ , Country Manager of Sierra Leone, _Nicolas Peltier_ , Practice Manager for Transport AFR and _Olivier Dupriez_ , Manager for DECAT. Special thanks colleagues, _Georges Bianco Darido_ , Lead Urban Transport Specialist and Global Lead for Urban Mobility GSG, _Holly Krambeck_ , Senior Economist, _Vivien Deparday_ , Disaster Risk Management Specialist and to _Andrew Nelson,_ graduate student at UC Berkeley. The team also received logistics support from _Konjit Negash Gebreselassie_ , Program Assistant, _Balume Alpha Abonabo_ , Team Assistant and _Allan Dunstant Odulami Cole_ CO Program Assistant.

This work was financed through a grant provided by the Global Facility for Disaster Reduction and Recovery (GFDRR).

> 8 https://github.com/Flowminder/FlowKit 9 https://github.com/shibalab/CDR-analysis-tools-s

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