1Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain.
2Department of Physics and Astronomy, University of Rochester, Rochester, NY 14627, USA.
3Department of Computer Science, University of Rochester, Rochester, NY 14627, USA.
4Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA.
5Bruno Kessler Foundation (FBK), 38123 Trento, Italy.
6Goergen Institute forData Science, University of Rochester, Rochester, NY 14627, USA.
|The recent trend of rapid urbanization makes it imperative to understand urban character-istics such as infrastructure, population distribution, jobs, and services that play a key role inurban livability and sustainability. A healthy debate exists on what constitutes optimalstructure regarding livability in cities, interpolating, for instance, between mono- and poly-centric organization. Here anonymous and aggregatedflows generated from three hundredmillion users, opted-in to Location History, are used to extract global Intra-urban trips. Wedevelop a metric that allows us to classify cities and to establish a connection betweenmobility organization and key urban indicators. We demonstrate that cities with stronghierarchical mobility structure display an extensive use of public transport, higher levels ofwalkability, lower pollutant emissions per capita and better health indicators. Our frameworkoutperforms previous metrics, is highly scalable and can be deployed with little cost, even inareas without resources for traditional data collection.|