COVID-19 Closure Impacts on Food-related,
Non-chain, Retailers Businesses
in Selected New York City Communities
NYU CUSP & NYU URBAN MODELING
The COVID-19 pandemic created devastating effects for small businesses, despite large, targeted relief programs. As such, this project uses the Spring 2020 New York State COVID-19 PAUSE order as a stressor for business closure to identify small business resiliency factors to inform both urban planners and business owners. Three modeling approaches were tested to identify socio-economic, spatial, and business characteristics associated with business closures. This was done for 2,186 food related retail businesses in eight zip codes in New York City. Footfall data from cell phone signals were used to identify small businesses, their type, operational status, and monthly customer visits. Supplementary analysis was done with data from path tracking of 2,215 individuals leaving eight healthcare facilities. Our three methods allowed us to predict the accuracy of business closures at rates between 86% and 95%. Lower commercial diversity and larger distances to subway stops correlated with a higher risk of business closures. Using path data, we found that businesses between healthcare facilities and subway stops appeared less likely to close than others. These findings show the value of diverse commercial development near transit areas.
Identify factors affecting small, food-related retail business survivability with respect to socio-economic factors, retail characteristics, built environment features, and changes in nearby foot traffic.
Consider how hyper-local patterns related to the presence of significant institutions could be used as further predictors of business survivability.
Their Core Places (business listings) and Patterns (aggregated cell phone detection) were used from January 2019 through May 2021. This dataset contains monthly geospatial records of POI information.These data were used to identify closed businesses and analyze changes in foot traffic over time.
NYU URBAN MODELING
DETER Data records daily individual behaviors when leaving medical centers and urgent cares, including the businesses they stopped, final destination, types of PPE they wore, and their touch-based interaction with the environment. This dataset was collected from January 2021 to May 2021.
Multi-source data were used as prediction variables from:
Building Footprint Data
NYC Street Centerline (CSCL)
American Survey Data
Transportation Location Data
Bus Stop Shelters)
Step 1 :
Subsetting and Auditing Safegraph Data
Our group limited our initial analysis to food-related businesses in eight zip codes.
Closed small businesses could be identified according to their “closed_on” column in the Safegraph data.
Our group limited business closures to closures occurring in February 2020 to focus our analysis exclusively on businesses that were open at the onset of the Covid-19 pandemic.
POI Closure Status
We identified 2186 food-related small businesses in the Safegraph data, 93 of which were permanently closed after March 2020. The five categories of food-related businesses showed slight differences in the percentage of closures.To understand the operation situation of POI in each zip code, we calculated the closing rates respectively and compared them with the social vulnerability index(SVI)The higher the SVI score, the more social vulnerability in that area, meaning that the area may need more resources to thrive.
Our group’s findings suggest that foot traffic, combined with a complex combination of urban features promotes business resiliency. A change in foot traffic likely reflects a loss in revenue for a business. For the foodservice restaurants our study considered, this could include grocery delivery, delivery food, or a decision to forego eating out altogether. Beyond foot traffic, we found that commercial proximity and subway proximity both were associated with resilient businesses. The commercial density would be a proxy for how many different types of businesses are available in an area.