#Mobility #Data Science #machine learning #latent

Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics

Authors: Yanni Yang, Alex Pentland, Esteban Moro Publication: EPJ Data Science 12, Article number: 15 (2023) LINK Abstract: Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1. ...

#Mobile phone data #Segregation #food #machine learning

A city is not a static tree: understanding urban areas through the lens of real-time behavioral data

Authors:Esteban Moro Publication: ZARCH, 19, 28–39. 2023 LINK Abstract: Cities are the main ground on which our society and culture develop today and will evolve in the future. Against the traditional understanding of cities as physical spaces mostly around our neighborhoods, recent use of large-scale mobility datasets has enabled the study of our behavior at unprecedented spatial and temporal scales, much beyond our static residential spaces. Here we show how it is possible to use these datasets to investigate the role that human behavior plays in traditional urban problems like segregation, public health, or epidemics. ...