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.
Highlights
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
Rapid urbanization has led to over half of the world’s population living in cities, making it crucial to understand urban characteristics such as infrastructure, facilities, population distribution, jobs, and services that play a key role in health, urban livability, and sustainability[1,2]
Past research indicates a connection between urban characteristics and city spatial organization, giving rise to a debate regarding the optimality of such structures as it relates to livability[6,7]
Summary
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. It has been observed that the concentration of jobs, amenities, services and other related socioeconomic activity is distributed across multiple spatial centers— so-called hotspots—the geography of which strongly influences mobility flows[26,27]. Given this connection, the location of these hotspots can be directly extracted from mobility[28,29]. We conclude with a discussion of the implications of our findings, including possible policy directions as it relates to urban planning
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