#segregation #Urban Science #Mobile phone data #crime

Diversity beyond density: Experienced social mixing of urban streets

Authors: Zhuangyuan Fan, Tianyu Su, Maoran Sun, Ariel Noyman, Fan Zhang, Alex ‘Sandy’ Pentland, Esteban Moro Publication: PNAS Nexus, Volumen 2, Issue 4, pgad077, 2023 LINK Abstract: Urban density, in the form of residents’ and visitors’ concentration, is long considered to foster diverse exchanges of interpersonal knowledge and skills, which are intrinsic to sustainable human settlements. However, with current urban studies primarily devoted to city- and district-level analyses, we cannot unveil the elemental connection between urban density and diversity. ...

#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. ...

#epidemics #social media #Data Science #Sensors #Complex Networks

Social Media Sensors to Detect Early Warnings of Influenza at Scale

Authors: David Martín-Corral, Manuel García-Herranz, Manuel Cebrian, Esteban Moro Publication: medRxiv 2022.11.15.22282355 LINK Abstract: Detecting early signs of an outbreak in a viral process is challenging due to its exponential nature, yet crucial given the benefits to public health it can provide. If available, the network structure where infection happens can provide rich information about the very early stages of viral outbreaks. For example, more central nodes have been used as social network sensors in biological or informational diffusion processes to detect early contagious outbreaks. ...

#Urban Science #inequality #Big Data

Behavioral roots of inequality

Inequality is one of the most important problems in our societies. For example, economic inequality is today higher than it was in the 1970’s and by some metrics stands at levels not seen since the last Great Depression. A special form of segregation is that happening in our cities. We share the public places, our workplaces and our residential neighborhoods with people like us: same type of jobs, same education, similar economic status, and political opinions. ...

#food #nutrition #Data Science

Behavioral-network determinants of health outcomes.

Our behavior conditions our health. Exposure to infections depends on our and other people’s mobility. Exposure to healthy environments or habits depends on our choices and opportunities. Although human behavior is highly adaptive and dynamic, most social determinants of health are restricted to static aggregated representations of socio-demographic groups or residential environments. We have started a program to understand behavioral-network determinants of health outcomes by modeling physical exposure between people and environments using large datasets of human mobility and activity and multilayer networks. ...

#Mobile phone data #Urban Science

Rhythm of the streets: a street classification framework based on street activity patterns

Authors: Tianyu Su , Maoran Sun, Zhuangyuan Fan, Ariel Noyman, Alex Pentland, and Esteban Moro Publication: EPJ Data Science 11, 43 (2022). Link Abstract: As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding street activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets’ functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. ...