Frontiers in big data · 2026
From crisis to new routine: shifts in urban shopping mobility and socioeconomic inequality during and after the COVID-19 shock.
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Xu Yilun, Bahrami Mohsen, Pentland Alex
Аннотация
INTRODUCTION: This study examines the impact of the COVID-19 pandemic on revealed shopping-location patterns and their persistence during the early recovery period. It focuses on urban shopping mobility in New York City and investigates whether observed store-selection patterns returned to, or remained different from, their pre-pandemic baseline by 2021 across different socioeconomic communities.
METHODS: We use large-scale mobility and place datasets together with census information to analyze visits to general merchandise and department stores in New York City. A modified Huff gravity model and a metaheuristic calibration method are used to quantify temporal shifts in store-selection patterns. We also use unsupervised learning techniques and statistical inference models to examine heterogeneity across socioeconomic communities and evaluate changes before, during, and after the COVID-19 shock.
RESULTS: The proposed model captures the dynamics of shopping-location decisions and temporal visit patterns under changing pandemic-era constraints. The results suggest that New Yorkers' revealed store-selection patterns changed substantially during the COVID-19 shock, with increased emphasis on store area, chain loyalty, and nearby points of interest, and reduced sensitivity to customer-store distance. These patterns did not fully revert to their 2019 baseline by 2021, although 2021 is interpreted as an early recovery and reopening period rather than a fully post-pandemic equilibrium.
DISCUSSION: The findings highlight the societal dimension of urban shopping mobility disruptions during crises and show that mobility-based store-selection patterns vary across socioeconomic communities. The results can help urban planners, managers, and marketers better understand heterogeneous shifts in shopping mobility and adapt strategies under crisis and recovery conditions.
METHODS: We use large-scale mobility and place datasets together with census information to analyze visits to general merchandise and department stores in New York City. A modified Huff gravity model and a metaheuristic calibration method are used to quantify temporal shifts in store-selection patterns. We also use unsupervised learning techniques and statistical inference models to examine heterogeneity across socioeconomic communities and evaluate changes before, during, and after the COVID-19 shock.
RESULTS: The proposed model captures the dynamics of shopping-location decisions and temporal visit patterns under changing pandemic-era constraints. The results suggest that New Yorkers' revealed store-selection patterns changed substantially during the COVID-19 shock, with increased emphasis on store area, chain loyalty, and nearby points of interest, and reduced sensitivity to customer-store distance. These patterns did not fully revert to their 2019 baseline by 2021, although 2021 is interpreted as an early recovery and reopening period rather than a fully post-pandemic equilibrium.
DISCUSSION: The findings highlight the societal dimension of urban shopping mobility disruptions during crises and show that mobility-based store-selection patterns vary across socioeconomic communities. The results can help urban planners, managers, and marketers better understand heterogeneous shifts in shopping mobility and adapt strategies under crisis and recovery conditions.