Hierarchical organization of urban mobility and its connection with city livability

Aleix Bassolas, Hugo Barbosa-Filho, Brian Dickinson, Xerxes Dotiwalla, Paul Eastham, Riccardo Gallotti, Gourab Ghoshal (), Bryant Gipson, Surendra A. Hazarie, Henry Kautz, Onur Kucuktunc, Allison Lieber, Adam Sadilek and José J. Ramasco ()
Additional contact information
Aleix Bassolas: Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB
Hugo Barbosa-Filho: University of Rochester
Brian Dickinson: University of Rochester
Xerxes Dotiwalla: Google Inc.
Paul Eastham: Google Inc.
Riccardo Gallotti: Bruno Kessler Foundation (FBK)
Gourab Ghoshal: University of Rochester
Bryant Gipson: Google Inc.
Surendra A. Hazarie: University of Rochester
Henry Kautz: University of Rochester
Onur Kucuktunc: Google Inc.
Allison Lieber: Google Inc.
Adam Sadilek: Google Inc.
José J. Ramasco: Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract The recent trend of rapid urbanization makes it imperative to understand urban characteristics such as infrastructure, population distribution, jobs, and services that play a key role in urban livability and sustainability. A healthy debate exists on what constitutes optimal structure regarding livability in cities, interpolating, for instance, between mono- and poly-centric organization. Here anonymous and aggregated flows generated from three hundred million users, opted-in to Location History, are used to extract global Intra-urban trips. We develop a metric that allows us to classify cities and to establish a connection between mobility organization and key urban indicators. We demonstrate that cities with strong hierarchical mobility structure display an extensive use of public transport, higher levels of walkability, lower pollutant emissions per capita and better health indicators. Our framework outperforms previous metrics, is highly scalable and can be deployed with little cost, even in areas without resources for traditional data collection.

Date: 2019
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DOI: 10.1038/s41467-019-12809-y

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