# **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