Hierarchical organization of urban mobility and its connection with city livability

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.

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

“…Such an up-to-date dataset can be a useful supplement in human mobility observation. It can be used not only in the fight against the COVID-19 pandemic, but also to benefit other researches and applications such as emergency response 47 , 48 , urban planning 49 , and population migration 50 .…”

Crowding and the shape of COVID-19 epidemics

“…To produce this data set, machine learning was applied to log data to automatically segment it into semantic trips 48 . To provide strong privacy guarantees, all trips were anonymized and aggregated using a differentially private mechanism 49 to aggregate flows over time (https://policies.google.com/ technologies/anonymization).…”

Study on an Artificial Society of Urban Safety Livability Change

“…This study compares two models of urban livability. The urban livability constructed by Bassolas focuses on the connection between spatial environment and ignores the influence of human factors [33]. Mouratidis focuses on commuter satisfaction, neighborhood satisfaction and housing satisfaction as predictors of subjective well-being and urban livability indicators, which is the theory of contact interaction between human decision-making model [34].…”