Investigating neighborhood adaptability using mobility networks: a case study of the COVID-19 pandemic

Hasan Alp Boz (), Mohsen Bahrami (), Selim Balcisoy, Burcin Bozkaya, Nina Mazar, Aaron Nichols and Alex Pentland
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Hasan Alp Boz: Sabanci University
Mohsen Bahrami: Massachusetts Institute of Technology, Institute for Data, Systems, and Society
Selim Balcisoy: Sabanci University
Burcin Bozkaya: Sabanci University
Nina Mazar: Boston University, Questrom School of Business
Aaron Nichols: Boston University, Questrom School of Business
Alex Pentland: Massachusetts Institute of Technology, Institute for Data, Systems, and Society

Palgrave Communications, 2024, vol. 11, issue 1, 1-11

Abstract: Abstract What predicts a neighborhood’s adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, we present a novel application of human mobility patterns and human behavior in a network setting. We analyze 2 years of mobility data (January 2019 to December 2020) from New York City and construct weekly mobility networks between census block groups based on aggregated point-of-interest visit patterns. Our results indicate that neighborhoods’ socioeconomic and geographic characteristics play a significant role in predicting their adaptability to active shelter-in-place policies. Our simulation outcomes reveal that, alongside factors such as race, education, and income, the geographical attributes of neighborhoods, such as access to amenities that satisfy community needs are equally important factors in predicting neighborhood adaptability to public health policies. These findings offer valuable insights that can enhance urban planning strategies, thereby aiding pandemic mitigation efforts and fostering increased adaptability of urban areas in the face of exogenous shocks like the COVID-19 pandemic.

Date: 2024
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DOI: 10.1057/s41599-024-02881-1

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