JMIR FORMATIVE RESEARCH

Ali et al

Original Paper

Evaluating Closures of Fresh Fruit and Vegetable Vendors During
the COVID-19 Pandemic: Methodology and Preliminary Results
Using Omnidirectional Street View Imagery
Shahmir H Ali1, BA; Valerie M Imbruce2, PhD; Rienna G Russo3, MHS; Samuel Kaplan4; Kaye Stevenson4; Tamar
Adjoian Mezzacca, MPH; Victoria Foster3, MPH; Ashley Radee3, BA; Stella Chong3, BA; Felice Tsui5, BA; Julie
Kranick3, MA; Stella S Yi3, PhD
1

Department of Social and Behavioral Sciences, School of Global Public Health, New York University, New York, NY, United States

2

Environmental Studies Program, Binghamton University, State University of New York, New York, NY, United States

3

Department of Population Health, NYU Grossman School of Medicine, New York, NY, United States

4

Chatham High School, Chatham, NJ, United States

5

Mailman School of Public Health, Columbia University, New York, NY, United States

Corresponding Author:
Stella S Yi, PhD
Department of Population Health
NYU Grossman School of Medicine
8th Floor, Room 8-13
180 Madison Avenue
New York, NY
United States
Phone: 1 646 501 3477
Email: Stella.Yi@nyulangone.org

Abstract
Background: The COVID-19 pandemic has significantly disrupted the food retail environment. However, its impact on fresh
fruit and vegetable vendors remains unclear; these are often smaller, more community centered, and may lack the financial
infrastructure to withstand supply and demand changes induced by such crises.
Objective: This study documents the methodology used to assess fresh fruit and vegetable vendor closures in New York City
(NYC) following the start of the COVID-19 pandemic by using Google Street View, the new Apple Look Around database, andin-person checks.
Methods: In total, 6 NYC neighborhoods (in Manhattan and Brooklyn) were selected for analysis; these included twosocioeconomically advantaged neighborhoods (Upper East Side, Park Slope), two socioeconomically disadvantaged neighborhoods(East Harlem, Brownsville), and two Chinese ethnic neighborhoods (Chinatown, Sunset Park). For each neighborhood, Google
Street View was used to virtually walk down each street and identify vendors (stores, storefronts, street vendors, or wholesalers)that were open and active in 2019 (ie, both produce and vendor personnel were present at a location). Past vendor surveillance(when available) was used to guide these virtual walks. Each identified vendor was geotagged as a Google Maps pinpoint that
research assistants then physically visited. Using the “notes” feature of Google Maps as a data collection tool, notes were madeon which of three categories best described each vendor: (1) open, (2) open with a more limited setup (eg, certain sections of thevendor unit that were open and active in 2019 were missing or closed during in-person checks), or (3) closed/absent.
Results: Of the 135 open vendors identified in 2019 imagery data, 35% (n=47) were absent/closed and 10% (n=13) were openwith more limited setups following the beginning of the COVID-19 pandemic. When comparing boroughs, 35% (28/80) of
vendors in Manhattan were absent/closed, as were 35% (19/55) of vendors in Brooklyn. Although Google Street View was able
to provide 2019 street view imagery data for most neighborhoods, Apple Look Around was required for 2019 imagery data forsome areas of Park Slope. Past surveillance data helped to identify 3 additional established vendors in Chinatown that had been
missed in street view imagery. The Google Maps “notes” feature was used by multiple research assistants simultaneously torapidly collect observational data on mobile devices.

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Ali et alConclusions: The methodology employed enabled the identification of closures in the fresh fruit and vegetable retail environmentand can be used to assess closures in other contexts. The use of past baseline surveillance data to aid vendor identification was
valuable for identifying vendors that may have been absent or visually obstructed in the street view imagery data. Data collection
using Google Maps likewise has the potential to enhance the efficiency of fieldwork in future studies.
(JMIR Form Res 2021;5(2):e23870) doi: 10.2196/23870
KEYWORDS
built environment; Google Street View; food retail environment; COVID-19; geographic surveillance; food; longitudinal; supply
chain; economy; demand; service; vendor; surveillance

Introduction
The COVID-19 pandemic has evolved into one of the mostsignificant and socially disruptive health crises in recent history,
with growing concern for how food systems at both the global
and local levels are being affected by the dramatic economic
and social impacts of the pandemic [1]. Given the significanceof the retail food environment in fostering and maintaining
healthy diets [2], disruptions to certain components of thisenvironment, such as access to fresh fruits and vegetables, havethe potential to detrimentally impact population health, which
has already been identified as an area of concern [3]. However,
early observational evidence from Google Search frequency
data suggests greater search-based interest in fresh foods during
the COVID-19 pandemic [4], which supports shopping behavior
data showing a significant increase in fresh fruit and vegetablesales during the early months of the pandemic (although demandis stabilizing) [5]. Moreover, social distancing measures are
significantly impacting direct sources of fresh produce, such as
farmers’ markets [3].
Although national and international supermarket chains,
warehouse clubs, and supercenters dominate grocery retail [6],fruits and vegetables are sold in a variety of other food retail
environments as well. Fresh fruit and vegetable vendors areoften smaller and more community-oriented than other restaurantor retail food outlets, and can include chain or independent
grocery stores, greengrocers, storefront stands (or simply“storefronts,” which are areas in front of stores used to sell fresh
fruits and vegetables), street carts, and even makeshift platforms
focused on the sale of fresh fruits and vegetables [7]. Although
fresh produce—as opposed to processed produce (ie, canned,
dried, or frozen produce that can often be found in other larger
food retailers)—may not have a substantially higher nutritional
value [8] (and fresh produce may also be purchased at theselarger food retailers [9]), smaller community fresh fruit andvegetable vendors who may conduct business on the sides ofmajor streets or on storefronts have played an integral role in
the food environment in large urban centers such as New York
City (NYC) [10], particularly in ethnic enclaves where they
represent a significant source of fruits and vegetables [7].
Many fresh fruit and vegetable vendors (particularly street carts
selling fresh fruits and vegetables) in cities across the UnitedStates, including NYC, have been forced to close since the
COVID-19 pandemic began due to the dual concerns ofplummeting demand and fear of contracting COVID-19 [11,12].
Within cities, the importance and presence of fresh fruit andvegetable vendors varies by neighborhood. In Manhattan’s
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Chinatown, fresh fruit and vegetable vendors are known for
their low prices and play a significant role in the food retail
environment as a critical food source for socially andeconomically vulnerable populations (eg, the elderly [13]),which have been a growing proportion of Manhattan’s
Chinatown demographic makeup. Furthermore, these fresh fruitand vegetable vendors are magnets for tourists and interboroughshoppers from diverse cultural backgrounds—not only Asianbackgrounds—who are looking for items that cannot be foundelsewhere in the city, or for the same low costs [7].
Unlike larger, well-established grocery store vendors, these
fresh fruit and vegetable vendors (which are relatively smaller
and more community-centered) may not have the financial
infrastructure to withstand the changes in supply and demand
induced by the COVID-19 pandemic [14]; thus, the risk ofclosure or changes in services may be more significant for thesevendors. Likewise, many fresh fruit and vegetable vendors in
NYC are immigrants [7,15], often with low English proficiency[7], and thus may not have the same social and economic capitalor legal protections that enable the retail resiliency of othervendors. Therefore, understanding how the pandemic has
impacted fresh fruit and vegetable vendors can provide vital
insights into changes in fresh fruit and vegetable foodenvironments in large urban centers, such as NYC, where thesecommunity-based vendors are a significant part of the fresh
produce environment.
Evaluating the impact of the COVID-19 pandemic on services
provided by fresh fruit and vegetable vendors fundamentallyrequires surveillance data both before and after the onset of thepandemic. Given that updated surveillance data of the diverse
types of community fresh fruit and vegetable vendors may not
be available (particularly for the more informal, street-based
vendors), Google Street View is a promising platform for datacollection in this context. Google Street View has been
employed in a variety of health research contexts [16], including
assessing local food environments [17,18], and is a lessresource-intensive, easily accessible source of visual data incomparison to other forms of visual data collection, such as
those relying upon in-person fieldwork [16]. Google Street View
data is usually collected from cars sent out by Google, which
are equipped with cameras with the ability to capture 360-degree
views in a particular location. Image data is then geotagged anduploaded on Google platforms (such as Google Maps) accessible
for public use [19]. Importantly, these Google Street View
images are routinely updated. Large urban centers inhigh-income countries (eg, NYC) often have more recent Google
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which is likely because a location's population density
contributes to the frequency of street view imagery updates
conducted by Google [19]. For example, in June 2020, NYC
Google Street View imagery data from June and October 2019
could be accessed.
In the past, Google Street View has been used for cross-sectional
or validation-based study designs, and thus the use of theplatform for longitudinal health research in neighborhood
settings is an area in need of more exploration [16]. Likewise,
while longitudinal analysis of Google Street View data has beeneffective in retrospectively analyzing changes in food retail
environments, the use of the platform to analyze more recentchanges by triangulating information from the platform withother means of surveillance remains underexplored [20].
Therefore, the aim of this study is to document the methodology
developed to assess changes in the food retail environment(notably, closures of fresh fruit and vegetable vendors) before
and during the COVID-19 pandemic in NYC using triangulated
data from Google Street View, past surveillance (whenavailable), and Google Maps–based, socially distanced in-person
assessments. Specifically, we document the following: (1) the
specific procedures used to conduct fresh fruit and vegetable
surveillance both before and during the pandemic such that they
may be replicated by others in other locations, and (2) thestrengths and limitations of this methodology, as well as thechallenges faced. Importantly, as opposed to analyzing general
net changes in the fresh fruit and vegetable food retail
environment (which has been explored for other food retail
environments using street view data [20]), a specific focus of
this methodology was to analyze closures and other visuallyobservable service impacts on pre-existing vendors directlyprior to and during disruptive crises such as the COVID-19
pandemic.

Methods
Baseline Fresh Fruit and Vegetable Vendor
Assessment: Past Surveillance Data
NYC is divided into five boroughs, which each contain manyneighborhoods. The boroughs of Manhattan and Brooklyn wereanalyzed in this study. Neighborhoods were defined using NYC
Neighborhood Tabulation Areas, and further details on theiridentification have been described elsewhere (RG Russo et al,unpublished data, July 2020; SS Yi et al, unpublished data, July2020); in short, data on the socioeconomic and health disparities
of Manhattan and Brooklyn neighborhoods were used to selectone socioeconomically advantaged neighborhood (Upper East
Side, Park Slope), one socioeconomically disadvantaged
neighborhood (East Harlem, Brownsville), and one Chinese
ethnic neighborhood (Chinatown, Sunset Park) in each borough.
An ethnic Chinese neighborhood was selected for analysis ineach borough given observational evidence identifying thestrong role fresh fruit and vegetable vendors play in the fruitand vegetable retail environments within Chinese ethnic

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Ali et alneighborhoods in NYC [7]. Past surveillance data on thelocations and types of fresh fruit and vegetable vendors in theselect 3 neighborhoods in Manhattan (Chinatown, Upper East
Side, and East Harlem) and 3 neighborhoods in Brooklyn(Sunset Park, Park Slope, and Brownsville) were first identified
to establish the baseline fresh fruit and vegetable vendor
landscape from which prepandemic and pandemic assessments
were to be conducted.
Identifying past surveillance data to guide the prepandemic
fresh fruit and vegetable vendor identification was important
for two reasons. First, one of the key limitations of Google
Street View data extraction is that visual obstructions in the
Google Street View image may conceal fresh fruit and vegetablevendors (eg, in the NYC Chinatown extraction, trucks parked
in the street could make sidewalks and some smaller vendors,such as makeshift platforms, difficult to view or notice).
Therefore, the non–Google Street View surveillance data allows
one to account for these visually obstructed and inconspicuous
vendors. Second, some fresh fruit and vegetable vendors incities such as NYC operate in a temporary capacity, with some
operating in makeshift physical locations in areas such as
parking lots. Therefore, integrating past cross-sectional
non–Google Street View surveillance data with visual data from
Google Street View aids in identifying long-term vendors andallows for a more comprehensive, precise evaluation of how
the COVID-19 pandemic has impacted established fresh fruitand vegetable vendors that operate in a more sustained capacity
within the community.
Imbruce [7] collected in-depth fresh fruit and vegetable
surveillance data in Chinatown, which was used as the baseline
fresh fruit and vegetable vendor assessment data for thisneighborhood, given the diversity of fresh fruit and vegetablevendors and specificity of geographic information included in
the data set. Equivalent information was not available for theother five neighborhoods. However, Fuchs et al [15] did collectgeographic data on a small percentage of Green Carts (mobile
street vendors specially permitted to sell exclusively fresh fruits
and vegetables) in East Harlem and Brooklyn. Therefore, thisinformation was used as a guide to identify the potential
locations of fresh fruit and vegetable vendors in the prepandemic
assessment of these neighborhoods, using Google Street View
to concretely identify fresh fruit and vegetable vendor locations.
Importantly, given this past surveillance data was quite dated(ranging from 2003 to 2013) and that fresh fruit and vegetablevendors are likely to have changed between then and 2019, this
supplementary surveillance data was largely used to identify
any additional potential vendors or locations of past clusters ofvendors during in-person checks; additional vendors identified
during in-person checks may have been missing or visuallyobstructed in the 2019 street view imagery data. For the Upper
East Side, Park Slope, and Sunset Park, for which there were
no comprehensive or reliable baseline fresh fruit and vegetablevendor assessment data sources, Google Street View was solely
relied upon to identify prepandemic vendors (Table 1).

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Ali et al

Table 1. Data sources used to assess COVID closures of fresh fruit and vegetable vendors.
Neighborhood

Baseline data

Prepandemic data

Data during the pandemic

Chinatown

2003-2005 (Imbruce, 2015 [7])June-October 2019 (Google Street View)

June-July 2020 (in-person)

Upper East Side

None

June-October 2019 (Google Street View)

June-July 2020 (in-person)

East Harlem

2013 (Fuchs et al, 2014 [15])aJune-October 2019 (Google Street View)

June-July 2020 (in-person)

Sunset Park

None

June-October 2019 (Google Street View)

June-July 2020 (in-person)

Park Slope

None

June-October 2019 (Google Street View)b

June-July 2020 (in-person)

Brownsville2013 (Fuchs et al, 2014 [15])aJune-October 2019 (Google Street View)

June-July 2020 (in-person)aSince geographic surveillance data only captured 45/121 of the Green Carts given permits in Manhattan and 19/132 of those in Brooklyn, and specificlocation data was not available, this data source was only used to provide a general understanding of potential prior locations.
bSome streets did not have Google Street View data from 2019, and these areas were supplemented with street-view surveillance from Apple Look
Around.

To allow for disaggregated analyses, fresh fruit and vegetablevendors identified in either the baseline or prepandemic
assessments were categorized into four types (store/supermarket,
storefront, street vendor/other, and wholesale) based on criteria
identified by Imbruce [7] (Table 2). Although some of theterminology for these categorizations has been employed indifferent ways in past research [21], for the purposes of thisstudy, the definitions provided by Imbruce, which were basedon in-depth fieldwork in NYC’s Chinatown, were used to define
different fresh fruit and vegetable vendors.

Table 2. Types of fresh fruit and vegetable vendors analyzed, adapted from a study by Imbruce [7].
Type

Description

Store/supermarket

Both the sidewalk in front of the store and the inside of the store are used primarily for selling produce (operated bysame owner).

Storefront

Only the sidewalk in front of a store is used to sell produce.

Street vendor/other

Produce sold in areas of high foot traffic in spaces near streets, sometimes by itinerant vendors. Other types of fresh
fruit and vegetable vendors (eg, outdoor markets, makeshift stores) were also included.

Wholesale

Produce is bought from farms or other produce brokers in high volumes and is sold to retail vendors, restaurants, orindividuals by the box.

Prepandemic Fresh Fruit and Vegetable Vendor
Assessment: Google Street View Extraction

in a spreadsheet along with the approximate street location, dateof Google Street View image, and any notes relevant to thevendor’s services or its location.

A spreadsheet was created using the data points identified fromthe baseline fresh fruit and vegetable assessment. For
Chinatown, each baseline data point was given a unique ID
number from this prior surveillance data [7], and any otherinformation provided by the survey (eg, street location, categoryof fresh fruit and vegetable vendor) was included. Given thatmany fresh fruit and vegetable vendors (notably street carts)
may not have visibly identifying features, the identification ofthese unique fresh fruit and vegetable data points was informedby (1) the specific street address where a particular vendor was
located, (2) the type of vendor (eg, if a street cart fresh fruit andvegetable vendor was stationed in front of a store/storefront
fresh fruit and vegetable vendor at the same street address, thesewould each get unique ID numbers). Google Street View wasthen employed to ascertain whether the fresh fruit and vegetablevendor corresponding with each unique ID was visible (labeled
as “found”) from a 2019 Google Street View image at theapproximate location identified from the prior surveillance data[7]. For all other neighborhoods, since reliable past surveillancedata was not available to guide the Google Street View
assessment, research assistants virtually walked down eachstreet of the neighborhoods using Google Street View andcatalogued all fresh fruit and vegetable vendors they identified

From the street view image at the location of the baseline site,a fresh fruit and vegetable vendor data point was labeled as“found” if the following was true: (1) the physical location ofa fresh fruit and vegetable vendor was able to be clearly
identified, based on the description or category of the vendor
provided by the prior survey (eg, cart, storefront, wholesaler),and (2) there was visual evidence to support the vendor being
active, such as the presence of fruits and vegetables and/or
individuals actively engaging in consumer activity (eg,exchanging cash or multiple people holding grocery bags in thevicinity of the location). Fresh fruit and vegetable vendors that
did not qualify as being “active” included those with signage
indicating ongoing construction or renovation, or an indicationthat the vendor had not yet formally opened for business (ie,“opening soon”). Vendors with signage that indicated changed
services (eg, delivery or takeout only) were classified as“active.” Vendors exclusively selling items other than fruit andvegetables (eg, clothes or toys) were not included. Any fresh
fruit and vegetable vendor data point that did not satisfy these
conditions was labeled as “not-found”; furthermore, if there
was visual evidence to suggest that there was a temporarily or
permanently closed fresh fruit and vegetable vendor matching

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the description from the prior survey at the location of the data
point, then these observations were noted in a separate “notes”
column in the extraction data sheet. Likewise, any otheranomalies regarding the visual Google Street View evidence
from the “found” or “not found” fresh fruit and vegetable

Ali et alvendors were noted in this “notes” column. The time stamp of
the Google Street View image (month and year) was also
catalogued. A visual depiction of the information gleaned fromthe Google Street View scan is displayed in Figure 1.

Figure 1. Data extracted from Google Street View to support fruit and vegetable vendor presence. FV: fruit and vegetable.

Although Google Street View image data across allneighborhoods were largely time-stamped with a 2019 date,some streets in Park Slope were observed to only have Google
Street View imagery data from dates prior to 2019. To address
this, research assistants collected street imagery data from Apple
Look Around, a new geographic imagery system implemented
by Apple Inc in 2019-2020, which is analogous to Google Street
View [22]. Similar to Google Street View, Apple Look Aroundrelies upon ground surveys conducted by commissioned vehicles
to collect geographic imagery data [23]. Select areas of Park
Slope without 2019 time-stamped Google Street View data were
supplemented with 2019 time-stamped Apple Look Aroundimagery to complete the data collection for this neighborhood.

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Fresh Fruit and Vegetable Vendor Assessment Afterthe Start of the COVID-19 Pandemic: Google
Maps–Based In-Person Checks
Following the 2019 Google Street View data extraction, an
in-person check was conducted for each fresh fruit and vegetablevendor between June and July of 2020. All fresh fruit andvegetable vendors, including those that were either “found” or“not found,” received in-person checks to ensure vendors that
may not have been found due to the aforementioned limitationsof Google Street View imagery were not excluded. A protocol
for rapid in-person assessments was designed to specificallyincorporate principles of social distancing and minimize outside
exposure for research staff. These principles were achieved
through two key components of the protocol. First, to minimize
the time needed to find the locations of each fresh fruit andvegetable vendor and enhance the speed of data collection,
several functions in Google Maps were used. Specifically, the“Your Places” feature was used to create lists containing the
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pinpoint locations of each fresh fruit and vegetable vendor in ashared Google Maps account. The “notes” feature of eachpinpoint was then used to record the unique vendor ID, andother information from the prepandemic assessment stage
relevant to the in-person checks (such as the name and categoryof vendor, as identified from Google Street View or baseline
surveillance). From the spatial data of the fresh fruit andvegetable vendor pinpoints, an in-person check route wasdesigned to minimize the amount of walking or driving required.
During the in-person checks, research staff accessed Google
Maps on their mobile phones using the shared account with the
fresh fruit and vegetable vendor lists and pinpoints. Second,measures were taken to also enhance social distancing for
research stuff, which (along with the standard
government-mandated use of face masks at the time of the visit)
included conducting in-person checks by car when possible(largely to identify street cart or storefront vendors that could
be identified from a car). Finally, data collection was conductedlargely during the afternoon, on different days of the week, andnot during periods of inclement weather to minimize thepotential of conducting data collection during routine or
temporary fresh fruit and vegetable vendor closures.
When a pinpoint was reached, research staff used the “notes”
feature, which contained the prepandemic information, to catalog

Ali et al
the pandemic assessment data, including the following: (1)whether the vendor was found and, if so, whether the vendor
was observed to be open, open with a more limited setup, orclosed, (2) the date the in-person check was conducted, and (3)any notes about the vendor or its services. Given that thephysical location of vendors may have slightly changed (notably
street cart vendors), to help in the identification of a particularvendor, research assistants examined all street addresses in close
proximity to the noted location (eg, examining a few street
numbers to the left and right of the location), as well as
information from the prepandemic street view imagery
indicating the types of produce being sold at the location. “Openwith more limited setups” was noted when vendors appeared
to have slightly changed or offered fewer services than what
was observed during the Google Street View prepandemic
extraction (eg, fresh fruit and vegetable stores closing theoutdoor portions of their services, or certain sections of street
carts closed or selling fewer quantities or varieties of produce).
Examples of notes made during in-person assessments included
if a vendor was likely replaced by a new store, or if the vendor
was sharing services or affiliating with another vendor. Figure
2 displays an example of the Google Map pinpoints and “notes”
feature interface used for the in-person data collection.

Figure 2. Example of Google Maps pins and the "notes" feature used for in-person checks (Chinatown).

Importantly, during data collection, research assistants may
have also encountered fresh fruit and vegetable vendors that
did not have Google Maps pinpoints based on the baseline andstreet view imagery data. Given the focus of this methodology
was to evaluate closures and other visually observable service
impacts on fresh fruit and vegetable vendors, such vendors
lacked the prepandemic 2019 street view or baseline surveillancedata to be eligible to be included in the longitudinal assessmentof fresh fruit and vegetable vendor closures; therefore,
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systematic data collection was not conducted for these vendors.
Nevertheless, throughout data collection, research assistants
reported these additional vendors to the study team.

Results
The initial Google Street View extraction (partially informedby baseline data in the case of Chinatown) identified 80 vendors
in the three Manhattan neighborhoods: 56 in Chinatown, 12 on
the Upper East Side, and 12 in East Harlem. A total of 55
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vendors were also identified in the three Brooklyn
neighborhoods: 48 in Sunset Park, 4 in Park Slope, and 3 in
Brownsville. Although only 53 vendors were identified using
Google Street View in Chinatown, during in-person checks, 3
additional vendors were identified that had been noted in thebaseline surveillance data but were not found using Google
Street View, increasing the total vendor sample of Chinatown

Ali et al
to 56. A summary of the preliminary extraction data and
COVID-19 pandemic closure data is presented in Table 3. An
example of the data visualization methods used to highlight
pandemic-related closures is displayed in Figure 3. Overall, 60of the 135 vendors (44%) identified in all 6 neighborhoods were
either absent/closed or had more limited setups following the
beginning of the COVID-19 pandemic.

Figure 3. Example of consolidated visual output using data collected from prepandemic and pandemic assessments (Chinatown).

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Table 3. Preliminary findings on closures of fresh fruit and vegetable vendors in select New York City neighborhoods during the COVID-19 pandemic
using Google Street View analysis.
Neighborhood

No change, n (%)

Closure during the pandemic, n (%)

More limited setups during the pandemic, n (%)

Chinatown (n=56)a27 (48.2)

24 (42.9)5 (8.9)Upper East Side (n=12)

5 (41.7)

0 (0.0)7 (58.3)

East Harlem (n=12)

7 (58.3)

4 (33.3)

1 (8.3)

Sunset Park (n=48)

30 (62.5)

18 (37.5)

0 (0.0)Park Slope (n=4)

3 (75.0)

1 (33.3)

0 (0.0)Brownsville (n=3)

3 (100.0)

0 (0.0)0 (0.0)Manhattan

BrooklynaIncludes vendors found in the 2019 Google Street View check (n=53) and additional vendors found during in-person checks (n=3), which were also
identified in other baseline surveillance.

Discussion
The integrated Google Street View–centered longitudinalassessment of fresh fruit and vegetable vendors was able toidentify significant closures among these vendors during the
COVID-19 pandemic by comparing evidence from shortly
before the pandemic in 2019 and during the pandemic. Although
the identified closures cannot all be directly attributed to the
COVID-19 pandemic itself using this preliminary surveillance
evidence, this approach helped to highlight the extent ofpandemic-related closures within a facet of the food retail
environment with limited formal, consistent surveillance, which
nonetheless plays an integral role in underserved ethnic minority
communities, such as those in Manhattan’s Chinatown [7].
Likewise, Google Street View data could be transferred
efficiently into Google Maps as pinpoints to facilitate
time-efficient and resource-efficient in-person checks. To thebest of our knowledge, the use of the “notes” feature of Google
Maps has not been explicitly employed as a data collection toolin past health research. Google Maps was able to provide
real-time information to multiple members of the study teamon which sites had been catalogued; this is evidence that theplatform can significantly enhance the efficiency of fieldworkfor future studies, particularly in resource- and time-scarce
contexts such as the COVID-19 pandemic.
Importantly, use of past baseline surveillance data (whenavailable) to assist in fresh fruit and vegetable vendor
identification during the prepandemic assessment was found tobe valuable; the identification of 3 vendors in Chinatown thatwere not found using Google Street View but were found inboth the baseline assessment and in-person visits supports
preliminary concerns that visually obstructed vendors may be
missed in Google Street View imagery. Further use of Google
Street View should also consider supplementing assessmentswith other data sources, particularly when assessing objects that
may be susceptible to being missed or obscured in Google Street
View imagery [16]. These findings directly support evidence
from prior Google Street View research, which similarly
identified image quality and visual obstructions as areas ofconcern [16]. However, unlike many past studies, the date ofcapture for the analyzed Google Street View imagery was
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consistently recent (almost always sometime in 2019) [16]; thiswas likely due to NYC—a large, populated area likely to be
frequented by Google-commissioned cars for Google Street
View surveillance—being the study setting of this project. Given
that large, urban environments have been particularly affectedby the COVID-19 pandemic [24], Google Street View may beparticularly useful for conducting prepandemic versus pandemic(or postpandemic) assessments in these environments.
Likewise, given the significant disparities in food purchasing
behavior—including the quantity and quality of food as well aspurchasing frequency and sources of food access—acrossvarious minority populations in the United States, the importanceof this methodology also lies in its ability to survey aspects ofthe food retail environment that may be critical for underserved
minorities, such as Asian Americans [7]. Although conducting
prepandemic/pandemic closure assessments for storefront foodvendors (eg, restaurants or grocery stores) can be done throughthe use of a variety of sources of information, such as theinternet (eg, Yelp, Google, social media) or publicly available
phone numbers (RG Russo et al, unpublished data, July 2020),
fresh fruit and vegetable vendors are relatively disconnectedfrom public information databases as they cater to localized
populations and may operate less formally than other food retail
outlets. Given these fresh fruit and vegetable vendors both serve
and are often managed by vulnerable minority populations[7,15], understanding the significance of this methodology withrespect to health equity concerns in food retail environment
surveillance is paramount.
Nonetheless, there were a number of limitations faced
throughout the study. First, it is important to acknowledge thatsome vendors may have been closed for the day at the time of
Google Street View image capture or in-person checks, which
may have led to an overestimation of pandemic-related closures.
These potential routine or temporary closures have particularly
impacted the assessment of street vendors (as opposed to storesor storefronts, which may have more established operating hours
or more staff to assist in maintaining consistent operations).
These concerns were mitigated in two ways: (1) baseline data
helped to corroborate any vendors that might have been missed
in Google Street View extraction, and (2) in-person checks wereconducted at times of the day when most food retail vendors
JMIR Form Res 2021 | vol. 5 | iss. 2 | e23870 | p. 8
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JMIR FORMATIVE RESEARCH(including fresh fruit and vegetable vendors) are open (ie,afternoon, early afternoon).
Moreover, while steps were taken to assist in-person checks offresh fruit and vegetable vendors that may have a slightly
changed geographic location, some vendors may have moved
to an entirely different street or completely changed theirservices between 2019 and 2020. Likewise, while the sample
of vendors included in the prepandemic assessment was
maximized by using recent (largely June and October 2019)
imagery data along with supplemental past surveillance data toidentify established fresh fruit and vegetable vendors that may
have been obstructed or missed in street view imagery but were
present in in-person checks, some vendors that been missed bythese data sources or that had opened later in 2019 but still prior
to the impact of the COVID-19 pandemic in the United States
were unable to be examined. However, while systematic datacollection of additional vendors identified during in-person
checks was not conducted, research assistants did not report
more than a few additional fresh fruit and vegetable vendors ineach neighborhood outside of the prepandemic sample, in partdue to the aforementioned efforts as well as the short time frame
between the prepandemic data assessments and those obtained
during the pandemic. Nonetheless, while examining net changes
in the food retail environment was not a focus of this particularmethodology, the incorporation of systematic analysis of new
vendor openings after disruptive crises such as the COVID-19
pandemic is another area worthy of exploration (including acrossdifferent types of food retail outlets).
Finally, it is likely that some fresh fruit and vegetable vendors
may have closed for some time early on in the pandemic but
may have recently reopened. Alternatively, vendors may haveopened shortly after the particular day in-person checks wereconducted, but still within the June-July 2020 endpoint timeframe. This is a limitation of the approach; to provide the mostaccurate, cross-sectional COVID-19 pandemic surveillancedata, data collection must occur within a short period of time.
In this case, due to the novelty of the methodology, many of its
components were being developed and tested by the study
authors throughout each stage, thus limiting the speed of thein-person checks. Likewise, due to the COVID-19 pandemic,
caution also had to be taken in the timing of the in-person checks

Ali et al
to minimize outdoor exposure for research staff. However, weintend on doing targeted follow-up assessments in subsequentmonths.
Indeed, the methodology described in this study has significant
implications for research aimed at longitudinally assessing
recent closures in the food retail environment (particularly
among fresh fruit and vegetable vendors or other retailers withlimited public surveillance data) during time- or
resource-sensitive time frames, including disruptive health crises
such as the COVID-19 pandemic. For example, the impact of
the COVID-19 pandemic on the fresh fruit and vegetable retail
environment has been felt by other communities across theUnited States; fresh fruit and vegetable vendors in Los Angeles
have witnessed a dramatic drop in sales [25], which may becatalyzing vendor closures. Moreover, to address the sales andlogistical disruptions faced by many fresh fruit and vegetablevendors, new online-based delivery services for these vendors
have been explored in limited settings [26]; future research may
involve surveillance of different strategies being explored by
fresh fruit and vegetable vendors to adapt services and prevent
closures. Likewise, it is important to contextualize the potential
economic or food access impacts related to fresh fruit andvegetable vendor closures identified in this methodology withthe broader health or social impacts of the COVID-19 pandemic;
complementing this methodology with other mixed methods
approaches to assess the economic, health, and social impactsof the COVID-19 pandemic is warranted. Finally, to the bestof our knowledge, this study was the first to use the new Apple
Look Around geographic data system for health research. The
platform was observed to be efficient and user-friendly in
identifying fresh fruit and vegetable vendors in Park Slope ina manner similar to the use of Google Street View; however,since the platform was not used in the other examinedneighborhoods, further research is needed to assess its
effectiveness for health research both in NYC and in othersettings. With these preliminary insights from Park Slope, future
in-depth analysis comparing the utility of Apple Look Aroundwith Google Street View and other means of geographic
surveillance is warranted, particularly with respect to parameterssuch as image quality, geographic scope of the data, timelinessof surveillance updates, and other features of the platform that
can assist in health research.

Acknowledgments
This publication is supported by grant numbers U54MD000538 from the National Institutes of Health National Institute on
Minority Health and Health Disparities, and R01HL141427 from the National Heart, Lung and Blood Institute. The contents ofthis publication are solely the responsibility of the authors and do not necessarily represent the official views of the National
Institutes of Health.

Conflicts of Interest
None declared.

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Ali et al

Abbreviations
NYC: New York City

Edited by G Eysenbach; submitted 26.08.20; peer-reviewed by S Lucan, NP Joshi, C Tang; comments to author 23.12.20; revised
version received 11.01.21; accepted 17.01.21; published 18.02.21
Please cite as:
Ali SH, Imbruce VM, Russo RG, Kaplan S, Stevenson K, Mezzacca TA, Foster V, Radee A, Chong S, Tsui F, Kranick J, Yi SS
Evaluating Closures of Fresh Fruit and Vegetable Vendors During the COVID-19 Pandemic: Methodology and Preliminary Results
Using Omnidirectional Street View Imagery
JMIR Form Res 2021;5(2):e23870
URL: http://formative.jmir.org/2021/2/e23870/
doi: 10.2196/23870
PMID: 33539310©Shahmir H Ali, Valerie M Imbruce, Rienna G Russo, Samuel Kaplan, Kaye Stevenson, Tamar Adjoian Mezzacca, Victoria
Foster, Ashley Radee, Stella Chong, Felice Tsui, Julie Kranick, Stella S Yi. Originally published in JMIR Formative Research(http://formative.jmir.org), 18.02.2021. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete
bibliographic information, a link to the original publication on http://formative.jmir.org, as well as this copyright and license
information must be included.

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