Abstract
Emerging forms of data offer new opportunities for developing a deeper understanding of poorly understood social and spatial processes. This is no more important than in developing countries, where large-scale data collection and processing has been relatively limited. In this paper, we explore how two new datasets can be used to enhance our understanding of human activity and communication interactions in Dakar, Senegal. Starting from a premise of little contextual knowledge about the setting in which we are working, we explore how much these data, combined with novel quantitative methods, are able to inform us about the urban environment in question. Fine-grained infrastructural data are combined with k-means clustering to produce an 11-class land use classification, distinguishing dense and sparse, single and mixed use regions. Using these classifications and over 1.5 billion mobile phone call records, patterns of activity and interaction within and between land use types are analysed. These analyses reveal strong activity associated with high density commercial, governmental and administrative regions. These regions are also identified as relatively strong ‘attractors’ of communication, and wider patterns show higher interactions between areas with similar land use characteristics. Analyses of dynamic activity and interaction patterns highlight the movement of people from workplace zones to residential areas. Importantly, these analyses reflect the expected patterns of urban activity, providing some validation of the data, methods, and the empirical approach. The paper concludes in addressing the strengths and potential of these approaches, while recognising current limitations and areas for further work.
Highlights
The widening availability of fine-grained data provides new opportunities to measure and understand the world in greater detail
The question we are seeking to answer is, in the absence of census or other demographic data, is it possible to derive useful information about the city, its inhabitants and their activities from data relating to the physical features of the city and the mobile phone usage of its inhabitants? If successful, this could have implications for service planning, transport management and other city governance activities in areas of the world where conventional data are less widely available
We describe the nature of the land use classification for Dakar, and what its spatial structure indicates about the nature of the city
Summary
The widening availability of fine-grained data provides new opportunities to measure and understand the world in greater detail. Within just the last 10 years, the breadth and variety of datasets available to urban planners, city governments, transport operators, and researchers, has expanded significantly (Batty 2013; Arribas-Bel 2014). This has led to a proliferation of new methods which seek to extract and classify elements of urban activity from singular data sources (Steenbruggen et al 2013; Salesses et al 2013; Goel et al 2018). Much of the research involving new datasets and methods has focused on activity in Western, East Asian and South American countries. While it can be assumed that some transferable (or universal) insights can inform development, we currently lack adequate insight and context around the specific case of African urban activity and dynamics
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