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.

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- 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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