Research and Professional Briefs
Business List vs Ground Observation for Measuring
a Food Environment: Saving Time or Waste of
Time (or Worse)?
Sean C. Lucan, MD, MPH, MS; Andrew R. Maroko, PhD; Joel Bumol, MD; Luis Torrens; Monica Varona, MEM; Ethan M. Berke, MD, MPH
Accepted 6 May 2013
Commercial business list
Copyright ª 2013 by the Academy of Nutrition
In food-environment research, an alternative to resource-intensive direct observation
on the ground has been the use of commercial business lists. We sought to determine
how well a frequently used commercial business list measures a dense urban food
environment like the Bronx, NY. On 155 Bronx street segments, investigators compared
two different levels for matches between the business list and direct ground observa-
tion: lenient (by business type) and strict (by business name). For each level of matching,
researchers calculated sensitivities and positive predictive values (PPVs) for the busi-
ness list overall and by broad business categories: General Grocers (eg, supermarkets),
Specialty Food Stores (eg, produce markets), Restaurants, and Businesses Not Primarily
Selling Food (eg, newsstands). Even after cleaning the business list (eg, for cases of
multiple listings at a single location), and allowing for inexactness in listed street ad-
dresses and spellings of business names, the overall performance of the business list
was poor. For strict matches, the business list had an overall sensitivity of 39.3% and PPV
of 45.5%. Sensitivities and PPVs by broad business categories were not meaningfully
different from overall values, although sensitivity for General Grocers and PPV for
Specialty Food Stores was particularly low: 26.2% and 32%, respectively. For lenient
matches, sensitivities and PPVs were somewhat higher but still poor: 52.4% to 60% and
60% to 75%, respectively. The business list is inadequate to measure the actual food
environment in the Bronx. If results represent performance in other settings, findings
from prior studies linking food environments to diet and diet-related health outcomes
using such business lists are in question, and future studies of this type should avoid
relying solely on such business lists.
J Acad Nutr Diet. 2013;-:---.
Use of Infogroup’s business lists to measure food environ-
ments is common, as demonstrated by several recent arti-
cles.1-7Such articles often provide little discussion of the
business list’s validity, despite the fact that business-list data
are primarily for business-to-business marketing, not food-
For food-environment research, validity of Infogroup’s
business list may actually be quite modest. Compared with
direct ground observation, studies report differing levels of
agreement and highly variable sensitivities and positive
predictive values (PPVs) depending on the specific food
sources under consideration (eg, supermarkets, convenience
stores, and restaurants) and the geographic locations of the
Prior Infogroup validation studies have been limited in
both method and scope. For instance, studies have consid-
ered only a limited sample of food sources, such as select food
O MEASURE FOOD ENVIRONMENTS, AN ALTERNA-
tive to resource-intensive direct ground observation
has been the use of commercial business lists like
those maintained by Infogroup (formerly InfoUSA).
stores and restaurants.8-11Neglected has been a range of
potentially relevant additional retail, such as general mer-
chandisers, gasoline service stations, and newsstands, which
also offer food and beverages and that may contribute
meaningfully to an overall food environment.12In addition,
some Infogroup validation studies have been lenient in their
definitions of matches between the business-list data and
direct ground observation. For instance, matches based on
general business category (eg, any kind of fast-food restau-
rant at a location)9as opposed to precise business identity
(eg, a specific fast-food franchise at a location) would tend to
overestimate the validity of the business list. Moreover, such
matching could lead to important misclassification (eg, a fast-
food outlet like Subway [Doctor’s Associates Inc] may be
different from a nutrition standpoint than a fast-food outlet
like Taco Bell [Yum! Brands],13and counting Subway and Taco
Bell as the same may be inappropriate for most purposes).
Finally, no validation studies have occurred in New York, NY
(New York City)—an urban setting with by far the most retail
(food and nonfood) in the United States14—even though
foundational food-environment research using an Infogroup
ª 2013 by the Academy of Nutrition and Dietetics.
JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS1
business list included hundreds of census tracts from New
The objective of our study was to rigorously evaluate the
accuracy of Infogroup’s business-list data over a wide range
of food sources, and do so in a dense urban environment in
New York City; specifically, the Bronx. Researchers sought to
assess sensitivity (how often the business list identifies food
sources when they are actually present) and PPV (how often
food sources are actually present when the business list says
they are) relative to direct ground observation (primary data
collection on Bronx streets). Researchers considered a range
of different types of food-related retail and different criteria
for “matches” between the Infogroup data and direct ground
observation, to understand nuances in how well the business
list might perform for various types of food-environment
This study did not involve human subjects and was deemed
exempt by the Albert Einstein College of Medicine Institu-
tional Review Board.
The business list for this study came from Infogroup (www.
infogroup.com), downloaded April 2010 through a site li-
cense at Dartmouth’s Tuck School of Business. Infogroup data
include business name, business type, geographic location,
and various company-relevant reports. As confirmed by
Infogroup’s technical staff, data updates occur monthly,
although randomly and without geographic basis. The com-
pany examines every address in the United States at least one
to three times a year, with addresses in more densely
populated areas receiving the more-frequent updates.
The Infogroup data commonly lists two or more discrete
businesses at the same address. In some cases, such listings
legitimately reflect two businesses operating at the same
location (eg, linked franchises like Dunkin Donuts [Dunkin
Brands] and Baskin Robbins [Dunkin Brands]). More often,
however, multiple listings result from having separate records
for back offices and retail store fronts of single businesses (eg,
“Devo Food Corp” and “Fine Fare” at the same address for a
single supermarket). We manually reconciled all addresses
having multiple listings to avoid unfairly disadvantaging the
business list. Specifically, we retained cases of dual businesses
operating from a single storefront and purged records of back
observable to investigators on the ground.
Direct Ground Observation
Two teams of two investigators—one working July to August
2010, the other November 2010 through March 2011—
cumulatively observed both sides of 155 Bronx street seg-
ments (regions along streets from one intersection to the
next), blinded to the Infogroup data. Both teams of in-
vestigators separately assessed a random sample of the same
30 street segments as a reliability check to make sure there
was consistency in data collection and findings. Beyond this
small area of overlap, both teams targeted separate random
samples of street segments generated from a file containing
all business lots in the Bronx (LotINFO, Space Track, Inc). In-
vestigators recorded the names, addresses, and Global Posi-
tioning System coordinates of all storefront businesses
offering any types of foods or beverages on sampled streets.
When investigators could not determine whether businesses
offered foods or beverages from the sidewalk, investigators
entered the businesses to check.
Teams noted broad business categories for all businesses.
These categories, which study investigators developed to
facilitate comparisons between the business list and direct
ground observation, were: General Grocers, Specialty Food
Stores, Restaurants, and Businesses Not Primarily Selling Food
(see Figure 1).
Determining Matches between Datasets
To be a match between the business list and direct ground
observation, businesses had to be on the same street segment
and have the same broad business category. Researchers then
determined one of two levels of matching based on consis-
tency in business name: strict matches were businesses with
the same or consistent name (eg, “Franko Deli” and “Franco’s
Heroes and Sandwiches”) vs lenient matches, which were
businesses potentially having different names but thought to
be of a consistent business type based on name (eg, “Nacho
Pizza” and “Original Tony’s Pizza,” both pizzerias, but not
“Kim’s Fruit Market” and “Triberia Fish Market,” substantively
different food outlets within the same broad business cate-
gory). Substantial variations in listed names, notations, and
approach to determining matches (see the Table).
any automated using
Using Stata/SE 11 for Mac (64-bit Intel) (2009, StataCorp LP),
investigators calculated sensitivities, PPVs, and confidence
intervals for the sample as a whole and for each of the four
broad business categories, both by strict and lenient matches.
RESULTS AND DISCUSSION
On 155 street segments across the Bronx (Figure 2), in-
vestigators observed 234 businesses offering any types of
foods or beverages (there was complete agreement for the
30-segment reliability check between the two research
teams). By comparison, the commercial business list identi-
fied only 202 food-related businesses (after 17 back-office
listings were removed in the data-cleaning process).
By broad business category, direct ground observation
showed 42 General Grocers, 26 Specialty Food Stores,
110 Restaurants, and 56 Businesses Not Primarily Selling
Food. These values compared to 32, 25, 88, and 57, respec-
tively, for the business list.
The Table shows that both overall and by separate broad
business category, sensitivities and PPVs were >50% for
lenient matches, and generally <50% for strict matches. For
strict matches, the sensitivity for General Grocers was only
26.2% (ie, the business list only identified existing grocers
about one quarter of the time) and the PPV for Specialty Food
Stores was only 32% (ie, only about one third of the time were
specialty food stores actually on the street when the business
2JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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Standardized types of retailb
Partial standardized definitionb
General Grocer Grocery stores Supermarkets, food stores, and grocery stores primarily
engaged in the retail sale of all sorts of canned foods and dry
goods; fresh fruits and vegetables; and fresh and prepared
meats, fish, and poultry
Specialty Food StoreMeat and fish (seafood) marketsEstablishments primarily engaged in the retail sale of fresh,
frozen, or cured meats, fish, shellfish, and other seafood
Fruit and vegetable markets Establishments primarily engaged in the retail sale of fresh fruits
Candy, nut, and confectioneriesEstablishments primarily engaged in the retail sale of candy,
nuts, popcorn, and other confections
Retail bakeriesEstablishments primarily engaged in the retail sale of bakery
products. The products may be purchased from others or made
on the premises
Miscellaneous food stores Establishments primarily engaged in the retail sale of
specialized foods, not elsewhere classified, such as eggs,
poultry, health foods, spices, herbs, coffee, and tea
Liquor storesEstablishments primarily engaged in the retail sale of packaged
alcoholic beverages, such as ale, beer, wine, and liquor, for
consumption off the premises
Restaurant Eating places Establishments primarily engaged in the retail sale of prepared
food and drinks for on-premises or immediate consumption
Department stores Retail stores generally carrying a general line of apparel, such as
suits, coats, dresses; home furnishings, such as furniture, floor
coverings, curtains, draperies, linens; major household
appliances; and housewares, such as table and kitchen
appliances, dishes, and utensils
Variety storesEstablishments primarily engaged in the retail sale of a variety
of merchandise in the low and popular price ranges. These
stores generally do not carry a complete line of merchandise
and are not departmentalized
Miscellaneous general merchandise
Establishments primarily engaged in the retail sale of a general
line of apparel, dry goods, hardware, housewares, groceries, and
other lines in limited amounts
Gasoline service stationsEstablishments primarily engaged in selling gasoline and
lubricating oils; also tires, batteries, and other auto parts
Family clothing storesEstablishments primarily engaged in the retail sale of clothing,
furnishings, and accessories for men, women, and children
(continued on next page)
Figure 1. Broad business categories for food-related retail, created to facilitate comparison between business-list data and direct
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JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS3
list said they were). For Businesses Not Primarily Selling Food
(a business category typically neglected in food-environment
research), the business list had a strict-match sensitivity and
PPV lower than for Restaurants (a business category typically
measured in food-environment research). The business list
might correctly identify existing food-related businesses just
one third of the time under a worst-case strict-match sce-
nario (ie, lower confidence limit for overall sensitivity was
33%), and might mistakenly list nonexistent food-related
businesses as present more than one quarter of the time
under a best-case lenient-match scenario (ie, 100%?73.7%
[upper confidence limit for overall PPV]¼26.3%).
No prior validation studies appear to have used criteria as
rigorous as those established by our strict matching. Strict
matching allowed for spelling errors and substantial impre-
cision in recorded names (see footnote to the Table), but
provided reasonable confidence that the two businesses be-
ing matched were indeed one and the same as opposed to
just similar to some degree. Strict matches give the most
stringent estimate of a business list’s accuracy, which—with
upper confidence limits for sensitivities and PPVs generally
around 50%—might be reasonably comparable to flipping a
coin under the most optimistic scenarios.
Actually, a coin flip probably overstates the business list’s
accuracy (even being optimistic) because investigators first
cleaned the dataset to exclude back offices that would have
addition, investigators required only that businesses on the
commercial list be on the same street segment as the corre-
sponding businesses in the direct-ground-observation data,
rather than at the exact same address. With stricter criteria,
address imprecision (eg, 451 vs 455 Morris Park Ave) would
have resulted in more missed businesses and thus lower
Data cleaning and address imprecision are relevant to both
strict and lenient match findings. Arguably though, for the
purposes of most food-environment research, the lenient
match findings could be most relevant. These findings
allowed for certain record-keeping anomalies in the business
list that may have created real but perhaps not meaningful
differences. For instance, in cases where the business list
might have retained the original business name when a shop
changed hands (eg, potentially “Nacho Pizza” vs “Original
Tony’s Pizza” in our study), or listed the “doing business as”
name as opposed to the retail store name (eg, possibly
“Tseng’s Ice Cream Shop” vs “Baskin Robbins”), the errors in
naming might not be so relevant to making important food-
retail distinctions. In other words, perhaps a pizzeria is a
pizzeria and an ice cream shop is an ice cream shop, and the
specific business names do not matter within business types.
Specific business names do matter between business types,
however (eg, between a pizzeria and an ice-cream shop). At
least one previous study9did not consider business names to
make such distinctions but rather relied on a less-stringent
matching method based on general classification code only.
In that study,9a match was when, for example, the business
in each dataset was classified as a “fast-food restaurant.”
Most of the time, such a methodology would probably not be
problematic; for example, any fast-food burger franchise
might contribute similarly to a food environment as any fast-
food chicken outlet, even though they are clearly different
types of fast-food businesses. However, if considering clas-
sification code only, theoretically one fast-food restaurant
could be a burger franchise and another a fast-food chain that
Standardized types of retailb
Partial standardized definitionb
Drug stores and proprietary storesEstablishments engaged in the retail sale of prescription drugs,
proprietary drugs, and nonprescription medicines; may also
carry a number of related lines (eg, cosmetics, toiletries,
tobacco, and novelty merchandise)
Tobacco stores and standsEstablishments primarily engaged in the retail sale of cigarettes,
cigars, tobacco, and smokers’ supplies
News dealers and newsstands Establishments primarily engaged in the retail sale of
newspapers, magazines, and other periodicals
aCategories were created by study investigators for the purposes of data collection and analysis in this study. “General Grocer”
refers to stores selling a wide variety of grocery items, “Specialty Food Store” refers to stores primarily selling one specific type of
food or beverage, “Restaurant” refers to outlets selling prepared food or drink for onsite or take-away consumption, and
“Businesses Not Primarily Selling Food” refers to businesses selling foods and/or beverages but not as their primary products.
bTypes of retail and partial definitions are based on Standardized Industrial Classifications available at www.osha.gov/pls/imis/
sicsearch.html. Such complicated definitions were impractical for use in collecting data through direct ground observation,
prompting the creation of the simpler scheme shown in this Figure.
cAs demonstrated in a multicity national study by Farley and colleagues,12food and beverage items are frequently available
from a variety of nonintuitive retail outlets, including those listed here.
Figure 1. (continued) Broad business categories for food-related retail, created to facilitate comparison between business-list data
and direct ground observation.
4 JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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specializes in take-away salads. Such distinction would be
relevant, and neglecting such distinction would be an
important form of misclassification. Even our lenient matches
would avoid such misclassification being based on business
name to distinguish, for example, between burger joints and
salad makers (eg, Checkers [Wellspring Capital Management]
vs Saladworks [Saladworks LLC]). Notably, another study that
based its matching on business name came to a conclusion
similar to ours: that the Infogroup business list is “insuffi-
cient” to characterize the food environment.11
conclusion contrasts with the more moderate appraisal made
by the study using a general classification-based matching
method: that the Infogroup business list must only “be used
Our study has several strengths. Investigators examined a
full range of potentially relevant businesses selling foods
and beverages12—including businesses not assessed in
earlier studies like general merchandisers, newsstands, and
clothing stores—and showed that the business list per-
formed no better for these less-intuitive business types than
for more-typically measured food-related businesses. The
study used two teams of investigators to perform direct
ground observations and verified consistency using a
random sample of the same street segments for reliability
checking. Notably, investigators made ground assessments
blinded to data in the commercial business list. Further,
investigators performed separate
different criteria for “matches” and conducted all match
checkingby hand after carefully
Our study’s main limitation was a relatively small sample
size, owing mostly to the labor-intensive method of hand
cleaning and matching all results. Regardless, the sample
covered the entire geographic area of the Bronx (Figure 2)
and although 95% CIs might have been relatively wide, even
upper limits were telling and did not meaningfully change
core findings or implications. Another potential limitation
was the time lag between acquisition of the business-list
data and full completion of direct ground observation
(almost 11 months). Although it is possible in this time that
some retailers opened for business or went out of business,
it is unlikely that such occurrences accounted for the
magnitude of discrepancy between the business list and
direct ground observation the study found. For instance, in
the reliability check for direct ground observation, teams
visited the same 30 street segments separately at least 6
months apart and found no meaningful differences in the
A final limitation is that our study did not assess for differ-
ential misclassification by type of food-related business or by
whether a food-related business was present or not by neigh-
borhood characteristics. Other researchers have shown differ-
and differences in sensitivity,10positive predictive value,10or
agreement with direct ground observation9by neighborhood
characteristics. Some authors suggest that any business-list in-
accuracy is only a problem if such differential misclassification
is present.17This argument has merit when research finds
signal despite noise in certain neighborhoods (eg, food deserts
in poor neighborhoods); but it does not address studies poten-
tially finding noassociations4-6,18(eg,the lack of fooddesertsin
any neighborhoods19). An alternative argument is that differ-
ential misclassification matters little if overall performance is
Early food-environment research appropriately made use of
Infogroup, to search for and establish foundational associa-
tions. For instance, in 2003 Moore and Diez-Roux obtained
Infogroup (then InfoUSA) data because the business list was
convenient, efficient, and the authors were “aware of no
better source of data.”15Many years later, studies like ours
likethose maintained by
Table. Sensitivity and positive predictive value of a commercial business list relative to direct ground observation on 155 Bronx
street segments, overall and by broad business category
any food or beverage
Broad Business Category
% (95% CI)
By strict matchesb
Positive predictive value
By lenient matchesc
Positive predictive value
aThe numbers of businesses directly observed on the ground.
bTwo businesses with the same or consistent name; could have difference in notation and/or spelling, but seemingly the same business in both datasets (eg, “Parrilla Latina Restaurant” vs
“Parilla Dominicano,” “Franko Deli” vs “Franco’s Heroes and Sandwiches,” “Jumbo Hamburger” vs “Jimbo’s Hamburgers”).
cTwo businesses that may have had different names in each dataset but thought to be of a consistent business type based on names (eg, “Nacho Pizza” vs “Original Tony’s Pizza,” “C-Town
Supermarket” vs “Bravo Supermarket,” “Tseng’s Ice Cream Shop” vs “Baskin Robbins”).
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JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS5
make it clear that Infogroup data—although potentially quite
excellent for its intended marketing purposes—are not
adequate for advancing food-environment research. The in-
adequacy might even be worse in rural settings10because the
business-list data are updated less often in areas of low
population density. Overall poor sensitivities and PPVs over a
range of geographic areas raise concern about findings from
prior studies linking Infogroup-determined food environ-
ments to diet or diet-related health outcomes. In cases where
investigators failed to find robust associations,4-6were there
actually no associations or was the dataset too insensitive to
detect them? In cases of found associations1-3,7(sometimes
in directions opposite of expected5,6), did relationships
actually exist or were they artifacts of false positivity?
Understandably, Infogroup data and other commercial
business lists are attractive for research involving large
geographic areas. But to be useable, such lists may require
extensive cleaning and/or ground truthing, which may
negate any benefit in time, cost, or efficiency compared with
direct ground observation. Alternative strategies like using
Google Street View,20telephone and Internet directories,21-24
dining guides,21or government datasets (alone,10,21,22,24-32
combined with telephone and web lists,33or even to sup-
plement Infogroup data17,34) have some advantages; but even
under the best conditions these methods may not be suffi-
cient in and of themselves. For instance, they do not provide
information about what foods are available within the
various food sources they identify, and studies show there is
considerable variability in the types of foods offered by even
a single type of food source.35-39Until more sophisticated,
nuanced, and accurate datasets are developed, primary data
collection may be the only acceptable way forward when
detailed understanding of a food environment is required. In
the interim, based on our findings and the results of others,9-11
unverified business-list data may no longer be acceptable as
the sole measure of food sources for rigorous food-
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Figure 2. One hundred fifty-five street segments sampled from across the Bronx, NY, to assess the sensitivity and positive predictive
value of a commercial business list relative to direct ground observation. The count of sampled street segments may appear
<155 due to the overlap of symbols at this scale. Street segments containing commercial business lots were close together in
commercially dense areas.
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AUTHOR INFORMATION Download full-text
S. C. Lucan is an assistant professor and J. Bumol is a medical student, Department of Family and Social Medicine, Albert Einstein College of
MedicineeMontefiore Medical Center, Bronx, NY. A. R. Maroko is an assistant professor, Department of Health Sciences, Lehman College, City
University of New York, Bronx, NY. L. Torrens is an MPH student, City University of New York School of Public Health at Hunter College, New York,
NY. M. Varona is a master’s student, Nicholas School of the Environment, Duke University, Durham, NC. E. M. Berke is an assistant professor,
Prevention Research Center at Dartmouth, The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH.
Address correspondence to: Sean C. Lucan, MD, MPH, MS, Department of Family and Social Medicine, Albert Einstein College of Medicine, 1300
Morris Park Ave, Mazer Building, Room 410, Bronx, NY 10461. E-mail: email@example.com
STATEMENT OF POTENTIAL CONFLICT OF INTEREST
No potential conflict of interest was reported by the authors.
This work was made possible by Clinical and Translational Science Awards nos. UL1 RR025750, KL2 RR025749, and TL1 RR025748 from the
National Center for Research Resources, no.1K23AG036934 from the National Institute on Aging, and no. 5U48DP001935 from the Centers for
Disease Control and Prevention. The contents are solely the responsibility of the authors and do not necessarily represent the official view of the
National Institutes of Health or the Centers for Disease Control and Prevention.
The authors thank A. Hal Strelnick and the Albert Einstein College of Medicine Hispanic Center of Excellence for financial and human resource
support for data collection, Hope M. Spano for intern coordination, Gustavo Hernandez for assistance with data collection, and the Tuck School of
Business at Dartmouth for access to the business-list data.
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