#2023 #Fast Food #Mobile phone data #Mobility #inequality

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

Authors: Abigail L. Horn, Brooke M. Bell, Bernardo Garcia Bulle Bueno, Mohsen Bahrami, Burcin Bozkaya, Yan Cui, John P. Wilson, Alex Pentland, Esteban Moro, Kayla de la Haye Publication: NPJ Digital Medicine 6, 208 (2023) LINK Abstract: The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people’s homes. ...

#2023 #covid19 #Mobile phone data #Mobility #inequality

Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters

Authors: Takahiro Yabe, Bernardo García Bulle Bueno, Xiaowen Dong, Alex Pentland & Esteban Moro Publication: Nature Communications volume 14, Article number: 2310 (2023) LINK Abstract: Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. ...

#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. ...

#inequality #Social Media #Mobile Phone Data

News or social media? Socio-economic divide of mobile service consumption

Authors: Iñaki Ucar, Marco Gramaglia, Marco Fiore, Zbigniew Smoreda, and Esteban Moro Publication: J. R. Soc. Interface (2021). Link Abstract: Reliable and timely information on socio-economic status and divides is critical to social and economic research and policing. Novel data sources from mobile communication platforms have enabled new cost-effective approaches and models to investigate social disparity, but their lack of interpretability, accuracy or scale has limited their relevance to date. ...

#Segregation #inequality #social media #Credit Card Data

Segregated interactions in urban and online space

Authors: Xiaowen Dong , Alfredo J. Morales, Eaman Jahani, Esteban Moro, Bruno Lepri, Burcin Bozkaya, Carlos Sarraute, Yaneer Bar-Yam and Alex Pentland Publication: EPJ Data Science 9, Article number: 20 LINK Abstract: Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. ...

#Segregation #visualization #Urban Science #inequality

The Atlas of Inequality

Segregation is hurting our societies and specially our cities. But economic inequality isn’t just limited to neighborhoods. The restaurants, stores, and other places we visit in cities are all unequal in their own way. The Atlas of Inequality shows the income inequality of people who visit different places in the Boston metro area. It uses aggregated anonymous location data from digital devices to estimate people’s incomes and where they spend their time. ...