From mobile phone data to the spatial structure of cities

By Thomas Louail, Maxime Lenormand, O. G. Cantú Ros, Miguel Picornell, Ricardo Herranz, Enrique Frías-Martínez, José J. Ramasco, Marc Barthelemy. Scientific Reports, 2014

459 citations recorded by the source corpus.

Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish cities. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the ‘heart’ of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and “segregated” where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data.

Subfields: Economics and Econometrics, Global and Planetary Change, Transportation

View Paper: https://www.semanticscholar.org/paper/3429a576d01fa98431c53ff61db72a0a75781eeb

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Citations in the corpus

The statistical physics of cities (2019, background): “For example, the activity density (that is, the density of individuals having a working activity) could be estimated from mobile phone data for 30 Spanish metropolitan areas [39, 40] and two types of cities were observed.”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “In the rest of this chapter, we provide the readers with a compendium of the issues arising from current big data approaches, with a particular focus on specific use cases that have been carried out to date, including urban crime prediction [10], inferring socioeconomic status of countries and indiv”
A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015, background): “…in [0,2] mm/h. De ﬁ ned as type 1; ● Moderate : precipitation quantity equal in [2,10] mm/h. De ﬁ ned as type 2; ● Heavy : precipitation quantity equal to in [10,100] mm/h. De ﬁ ned as type 3. while the precipitation intensity is characterized as Absent (type: 0), Rain (type: 1) and Snow (type: 2).”