Clustering of Fast-Food Restaurants Around Schools: A Novel Application of Spatial Statistics to the Study of Food Environments
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RESEARCH AND PRACTICE
Clustering of Fast-Food Restaurants Around Schools:
A Novel Application of Spatial Statistics to the Study
of Food Environments
S, Bryn Austin, ScD, Steven J. Melly, MS. Brisa N. Sanchez. ScM. Aarti Patel. BA. Stephen Buka, ScD, and Steven L. Gortmaker, PhD
Over the past 3 decades, fast-food retail sales
in the United States have soared 900"/o. from Objectives. We examined the concentration of fast food restaurants in areas
proximal to schools to characterize school neighborhood food environments.
$16.1 billion in 1975 to a projected $153.1
Methods. We used geocoded databases of restaurant and school addresses to
billion in 2004,' Tlie number of fast-food
examine locational patterns of fast-food restaurants and kindergartens and pri-
restaurants in the country now exceeds mary and secondary schools in Chicago. We used the bivariate K function sta-
280000. hi this same period, Americans tistical method to quantify the degree of clustering (spatial dependence) of fast-
have become increasingly dependent on food restaurants around school locations.
restaurants and fast-food chains for their Results. The median distance from any school in Chicago to the nearest fast-food
meals, ^ with almost half of US food spending restaurant was 0.52 km, a distance that an adult can walk in little more than 5 min-
going toward food eaten away from home.'' utes, and 78% of schools had at least 1 fast-food restaurant within 800 m. Fast-food
The fast-food industry maikets heavily to restaurants were statistically significantly clustered in areas within a short walk-
children and adolescents, who make up an ing distance from schools, with an estimated 3 to 4 times as many fast-food restau-
important part of the industry's consumer rants within 1.5 km from schools than would be expected if the restaurants were
basa.^*' Among youths aged 12 to 18 years, distributed throughout the city in a way unrelated to school locations.
the percentage of tutal energ}' intake con- Conclusions. Fast-food restaurants are concentrated within a short walking
sumed from fast-food and other restaurants distance from schools, exposing children to poor-quality food environments in
their school neighborhoods. (-Am JPub//cHea/f/?. 2005;95:1575-1581, doi:10.2105/
has increased from 6.51^/0 in 1977-1978 to
AJPH.2004.056341)
19-3% in 1994-1996.^ On a Epical day, al-
most a third of children and adolescents eat
fast food/ Portion sizes and the correspon-
tential consumers.'^ *' * In recent years, public the leading limited service chain brands
ding caloric content of foods served at fast-
health researchers have begun to develop (2002 Technomic Top 100),^^ as ranked by
food restaurants have also increased apprecia-
methods to characterize neighborhood pat- total US sales, which includes the major fast-
bly over the past several decades.^ On days
tems in location and density of different types food chains.^' Consistent with food industry
when youths eat fast loud, compai'ed with
of food establishments, such as fast-food standards. Technomic defines limited service
days when they do not, they consume more
restaurants or grocery stores. '^•^" Given tlie chain restaurants as eating places where cus-
total calories, more fat. more added .sugars.
fast-food industry's targeted marketing to tomers order items and pay before eating
more sugar-sweetened drinks, and fewer
children and adolescents and the importance and where food can be eaten on the prem-
fruits and vegetables/ '^ Among adults, eating
of young people in the industry's consumer ises or taken out,^' To ensure that our data-
in fast-food restaurants is cross-sectionally as-
base, we used spatial statistical methods to ex- base was both comprehensive and appropri-
sociated with higher body mass index'" and
amine whether fast-food restaurants may con- ate for Chicago, we gathered information on
longitudinally associated wiih weigbt gain" '^
centrate in areas proximal to kindergartens fast-food restaurants in the city from several
and development of insulin resistance,'' both
and primary and secondary' schools where other sources. In addition to the Technomic
important risk factors for diabetes,"
they would be highly accessible to students, list, we also referred to a privately run Web
Consumers' ease of access to fast food is a site. Fast Food Source (http://www.fastfQod-
priority for the industry, as business planners METHODS source.com), to identify fast-food restaurants
have long been aware of the potential to in Chicago. Two final sources, Centerstage
maximize sales by selecting restaurant sites Fast-Food Restaurant Database (http://centerstage.net} and Citysearch
that ensure close proximity to the consumer In 2002, we compiled a comprehensive (http;//chicago,citysearch.com), which pro-
base." '*• ln re.staurant site selection, business list of fast-food restaurants in Chicago along vide comprehensive online guides to Chi-
planners consider neighborhood demograph- with their street addresses. To create the cago's restaurants, were used to identify fast-
ics and the presence of other businesses, com- comprehensive list, we used as our primary food restaurants that might have been
munity organizations, and aspects of the source Technomic Inc, a food industry mar- missed by the prior sources. Finally, we ob-
neighborhood roadways that may draw in po- ket research company that publishes a list of tained full addresses for identified restau-
September 2005, Vol 95, No. 9 I Amertcan Journal of Public Health Austin et al. \ Peer Reviewed ! Research and Practice I 1575
RESEARCH AND PRACTICE
rants with the online Yahoo! Yellow Pages used the mapping and analytic capabilities time of our study.•"' Commercial land in-
(http ://www,yahoo.com} and Superpages of a geographic infonnation system rtuining cluded shopping malls; business parks; offices;
(http://www.superpagcs.com). A private ArcGlS 8.3 software^' to create 400-m-raditis hotels; government administration and ser-
company that was shown in prior research to and 800-m-ntdius buffers around each school vices; prisons; and medical, institutional, in-
provide highly accurate geocoding for ad- and then calculated the number of fast-food dusti-ial, and transportation facilities. We cal-
dress databases geocoded the restdting data- restaurants located within buffers of each size. culated the percentage of commercial land in
hase of 624 last-food sites to assign longi- The 400-m buffer bas been used as a primary 1990 census tracts and divided the tracts into
tude and latitude coordinates and census unit of aggregation in research on walking and tertiles, categoiizing them as high (>30.3%),
tract identification codes,*^" All records were the environment, on the hasis of an estimate moderate (14,6"/i) to 30.2"/i)), and low
successfully geocoded, with 98"/i) (n^610) that on average an adult can walk 400 m in (< 14.6%}, We then identified the schools
matched to the street address, the best cen- 5 minutes.'^"* We also created 800-m-radius that fell in each of these teriiles. Thii-d, we
sus code accuracy rating. Eleven sites were buffers to characterize a larger section of the examined spatial dependence within 3 strata
determined to he located outside Chicago school neighborhood food environment. of median annual household income, based
dty limits and were excluded, resulting in on 2000 US Census data, which we grouped
TTiird, we used the bivariate K function
613 fa.st-food restaurant sites within Chicago into tertiles, defined as high- (441 schools;
method to quantify the degree of clustering
included in the analyses. >$43 700), moderate- (423; $30 300 to
(spatial dependence) of fast-food restaurants
$43 700), and low-income (428;
RESEARCH AND PRACTICE
line represents the expected value of the
K Hinction under the assumption of no spatial
dependence between fast-lbod restaurants
and schools. The dashed lines represent the
upper and lower bounds of the 95*'/() confi-
dence interval around the expected-value
dotted line. Confidence intervals are based
on 100 simulations with Ihe data under the
assumption of no spatial dependence. The
observed value of the K function in Figure 2a
falls above the 95"/ii confidence interval be-
tween distajices of roughly 0.25 km and
1.5 km on the x-axis. indicating tliat a signifi-
cantly gi-eater number of fast-food restaurants
are located within a short distance from
schools than would be expected if there were
no spatial dependence. Figure 2b di.splays a
plot of the relative clustering of fast-food
restaurants around schools within the corre-
sponding distance on the x-axis ranging from
zero to 1.5 km. We estimate that there are
between 3 and 4 times as many fast-food
restaurants within an area up to 1.5 km
from schools than would be expected if the
restaurants were distributed throughout the
Schools city in a way that was unrelated to the loca-
tion of schools.
o No Restaurants within 800 m
• At least 1 restaurant within 800 m Results of stratified analyses revealed statis-
• At least 1 restaurant within 400 m tically significant clustering of fast-food
restaurants within 1.5 km of schools located
within ai'eas of the city outside downtown
^^ Fast food restaurant (not shown). The relative clustering estimate
indicated that fast-food restaurants were
^ H Downtown Kilometers about 2.5 times more clustered around
schools in areas outside downtown than
would be expected under the null hyjjothesis.
FIGURE 1-Locations of schools and fast-food restaurants in Chicago. Although fast-food restaurants were much
more numerous in the downtown area than
outside downtown, tliey did not appear to
number within 800 m of a school ranged jinnual household income, the percentage of cluster significantly arouiid schools more than
from 0 to 85. Thirty-live percent (452/1292) schools with at least 1 fast-food restaurant would be expected given the high density of
of schools in Chicago as a whole had at least within 400 m ranged from 27'Vii to 40% fast-food restaurants in downtown Chicago.
1 fast-food restaurant within 400 m. whereas and within 800 meters ranged from 76% Fast-food restaurants were found to clus-
nearly 80% (1010/1292) of schools had at to 80'yo (Table 2). ter significantly around schools in both the
least 1 fast-food restaurant within 800 m. Figure 2a displays the results of bivariate bigh- and moderate-commercialization re-
approximately a 10-minute walk. Two schools K fimction analyses for Chicago as a whole. gions of the city (not shown). The relative
had the same address as the nearest fast-food The value of the K function (shown on the clustering estimates indicated that in the
restaurant. In the downtown region, there y-axis) indicates the average number of fast- high-commercialization areas, there were
were twice as many fast-food restaurants as food restaurants within a specified distance approximately 6 times more fast-food restau-
there were schools, and 94% (65/69) of from a school (shown on the x-axis), divided rants within 1.5 km of schools, and in the
schools had at least 1 fast-food restaurant by the overall density' of fast-food restaurants moderate-commeirialization areas, tliere
within 800 m. For schools within regions in Chicago. The solid line represents the ob- were approximately 3 times more fast-food
stratified by commercialization and median served value of the K function, and the dotted restaurants witbin 1.5 km of schools than
Septembef 2005. Vol 95. No. 9 : American Journal of Public Health Austin et al. \ Peer Reviewed | Research and Practice | 1577
RESEARCH AND PRACTICE
DISCUSSION
TABLE 1-Number of Schools and Fast-Food Restaurants and Distance in Kilometers From
Any Schooi to Ciosest Fast-Food Restaurant, Overall and by Neighborhood Characteristic
Fast-food consumption has increased dra-
Strata: Chicago, 2002
matically over the past several decades and
Schools Restaurants,
Distance, lim may be an important contrihulor to the rise
No, No. Mean SD Median Range in the prevalence of obesity in children and
adolescents. In our study of fast food in Chi-
Chicago overall 1292 613 0,60 ±0.45 0.52 0-3.69
cago, we found thai although fast-food restau-
Downtown 69 138 0.32 ±0,22 0,27 0-0.83
rants are located throughout tlie city, they are
Outside downtown 1223 475 0.62 ±0.46 0,53 0-3,69
clustered in areas within a short walking dis-
High commerciaiization 314 305 0.61 ±0.59 0.49 0-3,69
tance from schools. We estimate that tliere
Medium commercialization 479 177 0.56 ±0.40 0.47 0-3.25
ai'e 3 to 4 times as many fast-food restaurants
Low commercialization 498 131 0.64 ±0.39 0.61 0-3,20
within 1.5 km from schools than would be ex-
High median household income 441 282 0.61 ±0,44 0.53 0-3.20
pected if the restaurants were located around
the city in a way unrelated to schools. The
Moderate median household income 423 296 0,64 ±0.52 0,55 0-3,69
median distance from any school to the near-
(S30300-$43700) est fast-food restaurant was 0.5 km, indicating
Low median household income 428 27 0.60 ±0,44 0.51 0-3,61 that in half the city's schools, students need to
( $43 700)'
Moderate median househoid 0.5 ±1.4 0-17 0 0 2 124 (29,3%) 2.4 ±4,3 0-58 0 2 6 330 (78.0%)
income ($30 300-43 700)
Low median household income 0.6 ±1,0 0-7 0 0 3 160 (37,4%) 2.0 ±1,9 0-18 0 2 6 343 (80.1%)
(
RESEARCH AND PRACTICE
public transportation, or ride in cai-s to and
from school (G. Mancuso, director. School
Demographics and Planning, Board of Educa-
Observed K function
Expected K function tion of the City of Chicago, e-mail correspon-
95% Cl under null hypothesis dence, January 6, 2005), These modes of
(no clustering)
transportation may give schoolchildren oppor-
tunity to access last-food restaurants in their
school neighborhoods.
Our methods were designed to characterize
the degree of fast-food clustering around
schools. The specific mechanisms underlying
the patterns ol' clustering we observed are not
clear. The degree to which restaurant indus-
try site selection practices, dty zoning regula-
tions on commercial land use, or other factors
may contribute to the patterns will need to be
explored in future studies. Nevertheless, the
concentration of fast-food restaui'ants around
schools within a shoit walking distance for
students is an important publie health con-
cern in that it represents a deleterious influ-
ence in the food environment that may un-
0.0 0-5 1.0 1-5
dermine public health efforts to improve
nutiitional behaviors in young people.
Distance From a School, km
We found clusteiing of fast-food I'estaurants
in both moderate and high-commercialization
areas, indicating that proximity to fast-food
10-
venues afiects the majority of schools in our
study, not just those in the most commercial-
ized ai'eas. In fact we found more evidence
of clustering outside downtown than inside,
perhaps because the downtown area is so
6- suffused with fast-food restaurants that these
venues are present in most neighborhoods.
Of note is our finding of a small number of
4- fast-food restaurants (Table 1) in the lowest-
income-tertile areas (median household in-
come $43 700) where we found evidence
0.0 0.5 1.0 1.5 of fast-food clustering, suggesting that chil-
Distance From a School, km dren attending schools in these areas of the
cit}' rather than in low-income areas may be
FIGURE 2-Clustering of fast-food restaurants in Chicago: (a) bivariate K function plot with most exposed to the problem of concentrated
95% confidence intervals showing ciustering of fast-food restaurants around schools and fast-food venues in school neighborhood
(b) relative concentration of fast-food restaurants around schools. food environments.
The neighborhood food environment is a
Students in all grades may have access to open-camptis policies in Chicago high schools. relatively new coneept in public health re-
fast-food restaurants before and after school, Approximately 6"'o of students attending Chi- search, and methods for defining, character-
and older students are also likely to have ac- cago public schools are provided transporta- izing, and quantifying the food environment
cess at iLinchtime. because of widespread tion in school buses, whereas most walk, take are still under development. Studies have
September 2005, Vol 95, No. 9 , American Journal of Public Health Austin et al. I Peer Reviewed I Research and Practice I 1579RESEARCH AND PRACTICE
used varied methods to characterize fast restricted to schools and fast-food restaurants caps on the number of venues permitted in
food in neighborhood environments. For in- in Chicago. Further research will need to ex- a neighborhood, N'ew regulation initiatives
stance, in a study with geocoded data from plore to what degree other cities and rural such as these may be a way to remove nox-
the Cincinnati area, Burdette and Whitaker'^ and suburbati areas ditler from what we ob- ious elements in the food environments that
estimated that the average distance from a served in Chicago. schoolchildren arc exposed to every day.
child's home to the nearest fast-food restau- Our study has several limitations. The most They may also help to spur the fast-food in-
rant was 0.7 miles. In a study using geo- recent land use data available to us to classify dustry to improve the nutritional quality of
coded 1990 US Census data from 4 states, level of commercialization were compiled in its products to avoid zoning restinctions.
Morland et al.''^ characterized food environ- 1995 by the Northem Illinois Planning Com- Greater puhlic attention to the proximity of
ments in terms of the mean number of fast- mission, but more recent data might have al- fast-food restaurants to schools may add ur-
food restaurants per census tract, finding an lowed more accurate classification. There is gency to eflbrts to improve food environ-
estimated 2 fast-food re.staurants per tract. precedent in tlie health research literature for ments for schoolchildren. •
We ai-e not aware of studies similar to oure using 400-m-radius buffers for analyses of
that used spatial statistical methods to quan- jihysical environments in part because it is a
tify the degree of clustering of fast-food distance easily walked by most adults in 5 About the Authors
restaurants around schools. We believe nur minutes.""' but young children may be ex- 5. Bryn /iHsrm i.s with the Division of Adolescent and
Young .'{dull Medicine al Ckildren s Hospilul. Boston,
method represents a novel and potentially pected to take longer to walk this distance.
Mass, und the DqMnmenl of Society. Human Dtvelopmenl.
informative and powerful technique for We were not able to assess the relation be- und Health. Harvard Schtiul of Public Health. Boston. .4«m
quantifying and statistically testing spatial tween density of fast-food restaurants in Pittel is with the Division of .Adolescent and Young .XduU
pattems in the food environment and land school neighborhoods and student nutritional Medicine ai Children s Hospital. Stei'enf .Mdlif is with lhe
Department of Biostalistics und the Department ofEnm-
use in relation to important public health pattems. Burdette and Whitaker'^ did not ronmeiitul Health at the Harvard Schoal of Public Health.
concems. find an association between proximity of fast- Boston lirisa N. Sanchez is wilh the Department of Bio-
food restaurants to a child's home and the statisfics at the Harvard School uf Public Health. Stephen
We were not able to identify any accepted Buka and Steven L Gortmaker are with the Dejiartment
child's weight status, although they did not of Society, Human Development, and Health at the Har-
criteria in the public health literature to de-
assess nutnticmal behaviors. Future i-esearch vard Sc'hdcil of Public Health.
fine fast-food restaurants. As described previ-
will need to examine the relation between the Requests for reprints should be sent in S. Bryn AiL-itin.
ously, to create our fast-food database, we re- SeD, Division of Adolescent and Young .'\dult Medicine.
pi-esence of fast-food restaurants in school
lied on infonnation from the food industry Children's Ho.spital. .100 Longivotid .-Iw. liostcm, MA
and home neighborhoods and fast-food con- 02115 (e-mail. hryn.aiLstin@chMrenH.harvard.eduj.
marketing research lirm Technomic Inc and
sumption, diet quality, and caloric intake. Tliis urticle wcvi ciccejyiedjanitury 22, 2005.
other sources providing lists of restaurants in
Chicago. For their analyses, Morland et al. A groundbreaking report on childhood
defined fast-food restaurants as those listed in obesity issued by the Institute of Medicine in Conta'ibutors
S. B. Austin was responsible for study conception, data
the limited-service restaurant category of the 2004 calls on the food industry to voluntarily analysis and interpretation, and artirlo preparation.
1997 North American Industry Classification restrict advertising unhealthfii! foods to chil- SJ. Melly provided gcogfaphic iiifonnadon systems expert-
System." Their category mcludes fast-food ise and carried out data analysis. B. N. Sanchez provided
dren." In addition, the Institute of Medicine
.^taastifal expertise. S.j. Melly. B.N. Sanchez,. A. Patel.
franchises and other restaurants that serve report recommends that schools improve the S, Buka. and S. I.. Gortmaker contributed to study con-
food over the counter and encompasses an nutritional quality of foods serv'cd and re- ception, data inteipretation. and cntical revision of
array of restaurants veiy similar to that iden- strict sales of sodas and nonnutiitious snack the article.
tified by our Technomic source. One differ- foods.'''' Other school policy changes that may
ence is that the Technomic category limited- waiTant coTisideration include restricting un- Human Participant Protection
service restaumni includes doughnut shojjs healthful foods that can be brought into the No protocol appnnal was needed for this study.
among iiist-ibod restaurants, whereas the school by students and curtailing open-campas
North American Industry Classification Sys- rules that allow students access to nearby Acknowledgments
tem does not^' In a Cincinnati-based study, fast-food restaurants at lunchtime. Our find- I his pii)|etl was supported hy a grant fnirti thi' Centers
for Di.sease Control anci Prevention, Prevention Re-
Burdette et al.'' defined fast-food restaurants uigs suggest that additional municipal or state search Centers (grant LI48/CCU115807). S.B, Austin Is
as those that had franchises in multiple states, policy initiatives may be needed to address supported by the Leadership ["education in Adolescent
had more than 1 restaurant in Cincinnati, the concentration of fast-food venues in Healtli project, Matemal and Child I lealth Bureau, US
Department of Health and Human Scr\-ices (6T7I-
served meals without waiters, and provided neighborhoods sun'ounding schools. MC00009-12-(ll), B.N. Sanehe? is supported by a
seating for customers. They identified 8 chain predoctoral fellowship in biological saeiices from (he
Ashe et al.'" argued that there is legal
re.staurant brands in Cincinnati, 7 of which I Inward Hughes Medical Institute and the Knvimnmen-
[precedent for local governments to impose tal Statistics Core of the National Institute of Environ-
were also included in our database lor Chi-
stricter controls on fast-food restaui-ant sites, mental Health Sciences center (grant KS00002).
cago. To the extent that definitions of fast- The aiitliors thank LoiiLse M. Ryan, Kathy
similar to site selection restrictions placed on
food restaurants differ across .studies, compar- McGafiigan, Beth E. Molnar, Angie Cradock, I'hili])
alcohol and firearm vendors, such as distance Tmped, Jean VVieeha, and Jennifer J, Lander for
isons oi' findings are limited. Our study was
limits from schools and playgrounds and their contributions lo otir study.
1580 I Research and Practice I Peer Reviewed | Austin et al. American Journal of Putilic Health | September 2005, Vol 95, No. 9RESEARCH AND PRACTICE
Note. 'l"his w()rk is solely the responsibility of (lie 19, Morland K, Wing S. Diez Ronx A. ftjole C, Neigh-
anthiirs and does not necessarily represent the official borhood characteristics associated with the location of MlCKOiiiDLUUlCAL
views of the Centers for Disease Control and Preven- food stores and food service places, AmJ Prev Med. EXAMINATION
tion or other granting institutions. 2OO2;22;23-29.
OF FOODS Fourth
20, Heidpath DD, Bums C, Gan-ai-d J, Mahoney M,
Edition
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