One City, Two Tales: Using Mobility Networks to Understand Neighborhood Resilience and Fragility during the COVID-19 Pandemic

TL;DR

This study uses mobility network analysis in NYC to identify socioeconomic and geographic factors influencing neighborhood resilience during COVID-19, offering insights for urban planning to improve pandemic response.

Contribution

It introduces a novel network-based approach analyzing mobility data to predict neighborhood resilience and adaptability during a pandemic.

Findings

Socioeconomic and geographic attributes significantly predict neighborhood adaptability.

Access to local amenities is as important as income and race in resilience.

Mobility patterns can inform urban planning for pandemic resilience.

Abstract

What predicts a neighborhood's resilience and adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, in this paper we present a novel application of human mobility patterns and human behavior in a network setting. We analyze mobility data in New York City over two years, from January 2019 to December 2020, and create weekly mobility networks between Census Block Groups by aggregating Point of Interest level visit patterns. Our results suggest that both the socioeconomic and geographic attributes of neighborhoods significantly predict neighborhood adaptability to the shelter-in-place policies active at that time. That is, our findings and simulation results reveal that in addition to factors such as race, education, and income, geographical attributes such as access to amenities in a neighborhood that satisfy community needs were equally important factors for predicting neighborhood adaptability and the spread of COVID-19. The results of our study provide insights that can enhance urban planning strategies that contribute to pandemic alleviation efforts, which in turn may help urban areas become more resilient to exogenous shocks such as the COVID-19 pandemic.