A Geospatial Investigation of Pedestrian Produce Accessibility

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Visualizing nutritional terrain: a geospatial analysis of pedestrian produce
accessibility in Lansing, Michigan, USA
Kirk Goldsberrya; Chris S. Duvallb; Philip H. Howardc; Joshua E. Stevensa
a Department of Geography, Michigan State University, Lansing, Michigan, USA b Department of
Geography, University of New Mexico, Albuquerque, New Mexico, USA c Department of Community,
Agriculture, Recreation and Resource Studies, Michigan State University, Lansing, Michigan, USA
Accepted uncorrected manuscript posted online: 05 August 2010
Online publication date: 05 August 2010
To cite this Article Goldsberry, Kirk , Duvall, Chris S. , Howard, Philip H. and Stevens, Joshua E.(2010) 'Visualizing
nutritional terrain: a geospatial analysis of pedestrian produce accessibility in Lansing, Michigan, USA', Geocarto
International, 25: 6, 485 — 499, doi: 10.1080/10106049.2010.510583, First posted on: 05 August 2010 (iFirst)
To link to this Article: DOI: 10.1080/10106049.2010.510583
URL: http://dx.doi.org/10.1080/10106049.2010.510583
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Visualizing nutritional terrain:a geospatial analysis of pedestrian
produce accessibility in Lansing,Michigan,USA
Kirk Goldsberry
*,Chris S.Duvall
,Philip H.Howard
and Joshua E.Stevens
Department of Geography,Michigan State University,Lansing,Michigan,USA;
of Geography,University of New Mexico,Albuquerque,New Mexico,USA;
Department of
Community,Agriculture,Recreation and Resource Studies,Michigan State University,
(Received 5 April 2010;final version received 12 July 2010)
This article considers how geospatial analyses can influence cartographic outputs
in studies of the spatial structure of food environments.We make two
contributions.First,we present a new approach to conceiving and visualizing
urban food environments as ‘nutritional terrains’,in which the opportunities and
costs of locating (healthful) food vary continuously across space.While other
researchers have conceptualized and represented food environments as contin-
uous phenomena,we use detailed data to produce maps of food accessibility that
have high resolution both spatially and in terms of food availability.Second,we
show that decisions made about measuring and modelling food accessibility can
create artifactual patterns independently of actual variation in food-environment
characteristics.Although the type of method-driven patterning we identify will
not surprise cartographers,we argue that non-geographers using geographic
information technologies to visualize food environments must give greater
attention to the unintended consequences of choices made in geospatial analyses.
Keywords:food environments;accessibility;visualization
Built environments may constrain dietary choices,and contribute to diet-related
public health problems,including overweight and obesity,diabetes and cardiovas-
cular disease (French et al.2001,Papas et al.2007,Ford and Dzewaltowski 2008).
Fundamentally,many of the public health problems observed in the developed
world arise from over-consumption of calorie-dense,nutrient-poor foods,such as
processed foods,and concomitant under-consumption of nutrient-dense,calorie-
poor foods,such as fresh produce (Halkjær et al.2009,Liese et al.2009).Adequate
consumption of fresh fruits and vegetables contributes to better health outcomes
(Zenk et al.2005a,b,Adebawo et al.2006,Morland and Filomena 2007),yet
millions of people who can afford to purchase these foods continue to under-
consume them.
In the United States,researchers are devoting more attention to the role of
environmental context in food consumption patterns (Shaw2006,Beaulac et al.2009).
*Corresponding author.Email:kg@msu.edu
Geocarto International
Vol.25,No.6,October 2010,485–499
ISSN 1010-6049 print/ISSN 1752-0762 online
￿ 2010 Taylor & Francis
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The accessibility of healthy food choices varies considerably depending on an
individual’s geographic situation.A number of recent studies have quantified and
mapped links between demographics,food accessibility and diet (White 2007,Black
andMacinko2008,McKinnonet al.2009,Larsonet al.2009).Fromthis literature,two
main points are particularly relevant to our argument in this article.First,within a
given urban area there may be substantial geographic variation in access to healthy
food choices.In general,economically disadvantaged and minority-dominated
neighbourhoods in the US have lower than average access to large retailers,including
supermarkets,and thus generally experience higher prices for narrower selections of
fooditems (Inagami et al.2006).Second,these physical andeconomic constraints affect
dietary choices,such that diet-related health problems tend to be higher in
economically disadvantaged and minority-dominated neighbourhoods (Laraia et al.
2004,Andreyeva et al.2008).
Studies of food accessibility represent an important advance in public health,yet
many of these studies are cartographically simplistic,and,as a result,may have
misleading results.In this article,we argue that public health-minded researchers
must give greater attention to geospatial analysis as a possible source of artifactual
patterns of food geography,because different,reasonable assumptions made in
modelling food accessibility can create starkly different cartographic outputs.We
support this argument with a case study of the food environment in Lansing,
Our research is significant because we explicitly build upon basic cartographic
concepts both to provide a new approach to conceptualizing and visualizing food
accessibility,and to identify an overlooked methodological issue that must be
addressed in future studies.First,we assert that food environments are continuous
geographic phenomena,and thus must be understood and visualized using surface or
isarithmic approaches (Slocum et al.2009).We conceptualize Lansing’s food
environment as a ‘nutritional terrain’,where the opportunities and costs of finding
(healthful) food vary continuously.While some geographic information system
(GIS) analyses of food environments use surface or isarithmic approaches to
visualization,many others represent food environments using point maps of retail
locations,or choropleth maps that categorize pre-defined areas (such as census
tracts) by the number and/or type of retailers located within an area.By comparing
different methods of creating surface and isarithmic representations of a single food
environment,and also by comparing these representations to derived choropleth
maps,we show that decisions about geospatial modelling of accessibility must be a
more prominent concern in health geographic research.
Additionally,we present a novel means of characterizing food retailers by using
the availability of individual types of food as our fundamental elements of spatial
analysis,as opposed to the more common practice of simply mapping retail
locations.While our maps do represent the locations of food retailers,we did not
assign labels such as ‘supermarket’,‘convenience store’ and ‘grocer’.Instead,we
inventoried the fresh produce offerings of each store,and mapped the availability of
each type of produce in our study area.Thus,the ‘nutritional terrain’ we describe is
not just continuous but also has very high resolution in terms of food availability.
While our results mostly confirmthe types of spatial inequalities in food accessibility
observed by other researchers,our focus is on the geospatial methods that underpin
many examples of food-environment research.We caution that cartographic
depictions of food environments depend greatly upon measurement approach.
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Although geographic information scientists (GIScientists) are familiar with how
decisions made in modelling affect visualization (Monmonier 1996),the ways
analytical methods can produce spurious patterns may be unfamiliar to many users
of GISs in health geography who lack cartographic training.
In general,accessibility is an important characteristic of urban geography.
Accessibility is commonly cited as a goal in urban planning,building design and
social justice.However,as pointed out by Peter Gould (1969),accessibility is one of
those common terms that everyone uses until faced with the problemof defining and
measuring it.Fundamentally,accessibility is determined by three components:the
spatial distribution of potential destinations,the ease of reaching each destination,
and the character of each destination (Handy and Niemeier 1997).In the context of
food environments,nutritional accessibility metrics must account for the spatial
arrangement of food retailers,the costs of travelling to any retailer and the foods
available at each retailer.
Research on ‘food deserts’ – areas with low accessibility to (healthful) foods – has
focused on the spatial distribution of retail food locations (Shaw 2006).One notable
finding is that both quality and quantity of retail food locations vary significantly
between neighbourhoods (Cummins and Macintyre 2006).Minority-dominated and
socioeconomically disadvantaged neighbourhoods generally have fewer sources of
fresh produce than non-Hispanic white neighbourhoods (Chung and Meyers 1999,
Morland et al.2002,Zenk et al.2005b,Algert et al.2006,Baker et al.2006,Block
and Kouba 2006,Powell et al.2007a,b,c).Although these findings are important,the
majority of food-environment studies to date have some methodological limitations
(Howard and Fulfrost 2007).For our purposes in this article,the most significant
problems include:
.Coarse spatial analysis.The majority of food-environment research has been
conducted at coarse aggregate levels,such as census tracts,ZIP codes or
counties (Morton and Blanchard 2007,Powell et al.2007a,b,c).The use of
aggregate areas can lead to misleading results,because accessibility is a
continuous phenomenon (Handy and Niemeier 1997).For instance,some
portions of aggregate political units may have good access to produce while
other portions do not.Given the likelihood of significant intra-unit variation in
food accessibility,it is unwise to analyse access at such coarse spatial scales
even if other relevant data,such as those derived from the US census,are
available only as aggregates.
.Simplified definitions of access.Most Americans purchase food at retail
locations and travel via road networks.With each journey to a retail location,
an individual consumer overcomes a cost of separation.This cost is frequently
viewed as a function of time or distance.Unfortunately,many studies fail to
accurately model separation costs.For example,some studies use Euclidean
distance,or Manhattan-block distance (e.g.Zenk et al.2005a,b) to model
accessibility.These measures do not mimic the lived experience of consumers,
who must travel along pre-defined,often indirect routes.
.Simplified definitions of retail food sources.Many studies have utilized
proximity to the nearest retailer as a measure of access (Laraia et al.2004,
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Zenk et al.2005a,b).This is problematic because store inventories differ,even
between externally similar stores;not all ‘supermarkets’ are equal,and some
small stores may have large inventories based on various factors.More
detailed characterization of food retailers,based on food availability,is
.Imprecise location data.Few studies of food access have accurately modelled
the actual locations of retail locations (Sharkey and Horel 2008).Previous
investigations rely heavily on geocoded addresses (Laraia et al.2004) that
others have demonstrated to be significantly inaccurate.These errors
propagate when included in network accessibility analyses.
Perhaps the most important of these limitations is insufficient attention to the
types of foods offered within retailers.Morland et al.(2006),for example,
examined how the presence of a few,broad retail types influences obesity.
Unfortunately the meanings of the categories ‘supermarkets’,‘grocers’ and
‘convenience stores’ can be ambiguous.By focusing on the availability and
accessibility of individual food items,GIS analyses can quantify and visualize the
‘nutritional terrain’ in an urban area:a continuous,fine-scale geographical
characterization of food availability and accessibility.Each individual produce item
within a food environment possesses a unique pattern of availability and thus
accessibility,which depends upon the spatial arrangement of retailers offering the
item,as well as the geospatial model employed to measure accessibility.By
improving the cartographic depictions of inequalities within urban food environ-
ments our broad goal is to enable health officials to identify areas that have poor
access to specific types of food,including produce,rather than simply poor access
to a specific type of retailer.
Data collection and analysis
Our study area is the Lansing,Michigan,USA,metropolitan area (42844
W),which has a population of about 450,000 (Figure 1).We analysed the
distribution of 94 food retailers who offered fresh produce during the period of data
collection,February–April 2008.We assessed accessibility to these locations by
independently analysing the three fundamental components of geographic access:the
spatial distribution of food retailers,the cost of reaching each retailer from an
address and the produce offerings of each retailer (Handy and Niemeier 1997).The
methods used to analyse each aspect of access are described in the following sub-
Spatial distribution of potential destinations
We compiled a list of all food retail locations (excluding restaurants) in the Lansing
area using commercial data purchased from ESRI (Redlands,California)
supplemented with Internet searches,phone book listings and on-the-ground
searches of local streets.Next,we determined whether each location in this list
(n ¼ 246) was operational and offered any fresh produce,through telephone calls
and in-person visits.We defined ‘fresh produce’ as any plant food offered for sale in
an uncooked,unfrozen and undried form.We chose to sample food availability
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based on produce availability because consumption of fruits and vegetables is
emphasized in public health campaigns (Lasley and Litchfield 2007,Sharkey and
Horel 2009).We identified 94 retail locations that offered fresh produce then visited
each outlet in person.We used hand-held GPS receivers to determine the location of
each retailer’s entrance.Some outlets had more than one entry;in these cases we
recorded the coordinates of the entry point closest to the outlet’s produce section.
Cost of reaching each destination
Successful travel to a destination incurs costs of separation (Handy and Niemeier
1997).These costs are often quantified using geographic distance,travel time or
required energy.When travel costs are overwhelming,they hinder a consumer’s
ability to visit a destination.For example,a supermarket may be ‘too far away’ or
‘take too long to get to’ for a given consumer,who instead shops at a limited-
selection convenience store.These costs are critical to dietary choice;therefore when
analysing a food environment it is imperative that these separation costs be
accurately modelled.We chose to employ network analysis to estimate the costs-of-
separation that the consumers in urban environments are forced to overcome.
We measured network distances and pedestrian travel times outward from every
produce retailer,which enabled the calculation of expected service areas for each
location.Using GIS-based network analysis,we generated multiple network buffers
Figure 1.Study area:The Lansing,Michigan metropolitan area contains over 90 produce
retailers including supermarkets,grocery stores,and convenience stores.Many of the
supermarkets are located towards the periphery of the urban core.
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to enclose all network locations varying distances (e.g.0.1 miles,0.2 miles) within
each retailer in the study area.We created a unique set of network buffers for every
produce item available in the study area,which facilitated the estimation of
accessibility to both individual produce items and cumulative numbers of produce
We focused on pedestrians.Due to enhanced mobility,individuals with access to
automobiles have lower separation costs relative than those who are limited to other
modes of transport,especially walking.We defined separation costs in terms of
estimated walking time along established street networks.The walking cost (in
minutes) was formulated by dividing the length of the optimal network route (in
miles) by an average walking speed of 3 miles per hour.Using the ArcGIS Network
Analyst tool,and detailed road network data provided by the Michigan Center of
Geographic Information,we employed network buffering to estimate realistic
distance costs that separate consumers from retail locations.
Character of destinations
We visited each retail location and recorded every type of fresh produce offered in
each location.We recorded each item priced separately in every store,with these
exceptions:multiple sizes of single items,such as large and small zucchini squash,
which were recorded as a single item;minimally prepared items,such as sliced
melons,unless a given item was offered only in a minimally prepared form and
packaged spices,because of the difficulty determining whether these were dried or
otherwise preserved.These data collection efforts produced a matrix that
summarized the presence–absence of each produce item (n ¼ 447) in all 94-sample
sites.As a means of characterizing individual food retailers,inventorying fresh
produce availability allows more precise modelling of food accessibility than more
simplified approaches that only differentiate retailers based on external character-
istics,such as square footage,chain membership or sales volume.
Food accessibility can be defined and formalized in countless ways,but the
intricacies of the definition are critical to the eventual results.In this article,we apply
three different accessibility metrics to estimate pedestrian access to retail produce.
We used GIS to calculate accessibility scores for every point within our study in three
different ways.These methods are each detailed in the following sections.
Container method
The most straightforward approach to measuring geographic accessibility involves
simply counting the number of opportunities (i.e.food retailers) accessible within
some pre-defined cost constraint (e.g.distance).Such an approach is sometimes
called a container measure because resulting accessibility scores are basically a count
of the number of opportunities within a specific geographic range.For example,a
container measure of hospital accessibility might simply count the number of
hospitals within 25 miles of an address.
Since the atomic units of analysis in our study are individual produce items,we
were able to generate service areas not only to retail locations but also more
specifically to individual items (e.g.bananas,green beans,organic spinach).We
modelled 10-min (0.5 miles) service areas for pedestrian access for every produce
item available within the study area (Figure 2).
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Figure 2.Individual item accessibility signatures:This set of small multiples shows how
unique produce items have different accessibility signatures.
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Using GIS overlay analyses,we calculated container measures that indicated how
many unique produce items were available within a 10-min walk of every address in
the study area.Our container-measure accessibility equation is:
ij 2R
¼ Total produce accessibility of location i
0;produce item j is not accessible at location i within distance f
1;produce item j is accessible at location i within distance f

f ¼ 10 min of pedestrian walk
R ¼ Region containing all locations and produce types to be considered.
Weighted method
The container measure is simple and easy to understand,but treats all accessible
opportunities uniformly.An item that is barely accessible (e.g.near the limit of the
cost threshold) is considered just as accessible as an item that is literally next-door.
Our second accessibility metric awards higher accessibility scores to those produce
items that are closer than those that are further away.This weighted approach
generates a higher-resolution model of accessibility for each item by identifying
service areas that are graded spatially based on proximity.
We calculated weighted services areas for all 447 produce items and delineated
multiple accessibility zones according to network distances.For example,areas
within a 2-min walk to a retailer offering a particular produce item receive a higher
accessibility score than areas 42 min away.Our equation for determining weighted
accessibility is:
¼ Total produce accessibility of location i
0;produce item j is not accessible at location i within distance f
1;produce item j is accessible at location i within distance f

f ¼ 10 minutes of pedestrian walk
¼ Inverse distance weight from set a
a ¼ {0.1,0.2,0.3,0.4,0.5,1} min of walking
R ¼ Region containing all locations and produce type to be considered.
Cumulative distance method
The container method and weighted method of accessibility modelling capture
distance only in terms of pre-specified thresholds,and do not distinguish among
opportunities outside of those ranges.For our third metric,we included a measure
where accessibility at a given location is defined as the sum of the network
separations from all available produce items to that location.In other words,the
score reflects the total network distance a consumer would have to travel in order to
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purchase every individual produce item (one item per journey) available in the study
area.In general,the larger the result,the less accessible produce items are to that
location.Furthermore,a centrally located origin is more likely to have a lower
accumulated sum and therefore be more accessible than peripheral locations.
¼ Total produce accessibility of location i
0;produce item j is not accessible at location i
1;produce item j is accessible at locationi

¼ Distances from set t for which accessibility at i is measured
t ¼ {0.5,1,1.5,2,2.5,3,...,12,12.5,13,13.5,14}
R ¼ Region containing all locations and produce types to be considered.
The three accessibility metrics each produced a different depiction of nutritional
terrain for the study area.The resulting accessibility maps exhibit different thematic
signatures;‘food deserts’ – areas with low food accessibility – appear differently
depending on the measurement approach.
Container approach
The output from the container method presents a patchy nutritional landscape in
which accessibility to fresh produce is poor for most of the study area (Figure 3).In
particular,few addresses in the densely populated centre of the study area have any
produce items within a 10-min walkshed;more addresses in the suburban periphery
of the study area have better access to fresh produce.In other words,locations with
the greatest pedestrian access within are in relatively sparsely populated,automobile-
oriented areas.
One of the benefits of the container measure is that the results directly translate
to an intuitive description of fresh produce accessibility.For example,the most
produce-abundant 10-min walkshed has access to 272 produce items.Unlike the
other two accessibility metrics applied in this study,the output from the container
measure provides a simple count of the number of produce items within a 10-min
walk.Consequently,the container approach can facilitate certain types of map tasks
better than the others.For example,a map-reader could easily identify zones that
have pedestrian access to less than 10 different produce items.However,a drawback
of this approach is that zones outside of the 10-min constraint appear uniformly
disadvantaged;retailers that are 11 walking minutes from an address are lumped in
with others that are much more distant.
Weighted approach
The depiction of nutritional terrain using the weighted method (Figure 4) is different
than the output based on the container metric,because the weighted method uses
multiple distance measures to characterize retailers.Furthermore,since the weighted
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method includes buffers of up to 20 walking minutes (one mile),its output contains
fewer zones of no access.Thus,the nutritional terrain produced through the
weighted method is less patchy than that produced through the container method.
Most of the study area appears to have at least some pedestrian access to fresh
However,the quantitative output fromthe weighted method is less intuitive than
the container approach.It does not correspond to a simple count,but instead offers
a more abstract scoring of accessibility.This is the result of a measurement approach
that places an emphasis on proximity to a retailer.Due to the scoring gradient in this
weighted approach,map-readers cannot simply translate accessibility scores into a
clear set of nutritional consequences (e.g.zones that have pedestrian access to less
than 10 different produce items).
Cumulative distance approach
The cumulative distance metric produces a very different depiction of nutritional
terrain (Figure 5).Using this method to model accessibility,the central parts of the
city appear to have the best accessibility to fresh produce,because these areas are
located relatively near to all suburban locations,whereas different suburban
locations may be located on opposite sides of the city.However,this map reveals
that some suburban areas have much greater produce accessibility than others.For
Figure 3.Results from the container method produce a patchy depiction of nutritional
terrain.A majority of the study area has no pedestrian produce access and the zones with
higher amounts of access are sparsely populated.
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example,the eastern suburban periphery of the study area has better access to fresh
produce than Lansing’s western suburbs – a phenomenon that is much less obvious
using the other two methods of modelling access (Figures 3 and 4).The output from
the cumulative distance approach suggests a much more concentrated,and less
patchy nutritional landscape.
The relative newness of food-environment studies means that relevant methodologies
are still in stages of development;the methods used in many food-environment
studies have been mixed in terms of quality and conceptual appropriateness (Booth
et al.2005,Papas et al.2007,Beaulac et al.2009).There have been few critical
assessments of geographic methods used in food-environment research (Sharkey and
Horel 2008),although inappropriate spatial analyses have led to questionable
conclusions about food environments (Spielman 2006).Spatial analysis is rooted in
geographic information science (GIScience),the body of theory necessary to develop
and implement GISs.Many researchers use computer-based GISs as research tools
for spatial analyses;GIScientists test the theoretical,conceptual and spatial
appropriateness of different uses of geographic data.For instance,GIScientists
have shown that decisions about the acquisition of geographic data and its insertion
into GISs can strongly affect public health research findings (Elliott and Wartenberg
Figure 4.Results from the weighted method produce a less patchy,and more graded
depiction of pedestrian produce access.
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2004,Zandbergen 2007,Boone et al.2008).Similar assessment of geographic
methods in the specific context of food-environment research is necessary to validate
data sources and analytical approaches widely used to develop public-health policy
in the US (Sharkey and Horel 2008).
GISs can be used effectively to identify disparities in nutritional terrain across
an urban landscape.However,the quality of output visualizations and analyses
depend upon both data quality as well as decisions made in data analysis.Many
researchers recognize that map quality depends upon data quality – the ‘garbage-
in-garbage-out’ reality of GIS analyses is widely known – but the dependence of
map output upon analytical decisions that occur several steps prior to map
production is less widely recognized amongst GIS users who are not trained in
GIScience.While many map-readers tend to see maps as ‘true’,all maps include
‘white lies’ that cartographers use to simplify geographic reality (Monmonier
1996).Furthermore,previous food-environment investigations often rely on so-
called one-map solutions (Monmonier 1991) that ‘foster highly selective,authored
views perhaps reflecting consciously manipulative or ill-conceived design
decisions’,which in turn can misrepresent or skew the geographic complexities
of urban food environments.
Our results show that visual depictions of food environments are as vulnerable
cartographic distortion as maps depicting other geographic phenomena.Despite
Figure 5.Results from the cumulative distance method produce a more concentrated
depiction of produce accessibility.Zones towards the eastern portions of the study area appear
to have easier access to produce than the areas closer to the western periphery of the study
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similar production methods using identical inputs,cartographic outputs can be
considerably different and these differences can influence geovisual narratives.For
example,if someone wanted to show that the western periphery of Lansing has
particularly poor access to produce,a map using our data and the cumulative
distance metric in Figure 5 would be more effective than a map using either the
container method or the weighted method (Figures 3 and 4).
The topics of sensitivity analysis and uncertainty are not new to GIScientists,but
few food-environment studies have considered how these phenomena may affect
their analyses.Furthermore,and perhaps more troubling,most previous investiga-
tions use only one approach to model nutritional accessibility,and rely on its output
to correlate food-environment characteristics with other geographic variables.Yet
our results indicate that applying different accessibility metrics to a single dataset can
produce very different,but equally valid cartographic outputs;these differences
would influence all subsequent statistical operations,such as analyses designed to
test possible correlations between food accessibility and diet-related health
indicators.The implications of decisions made in the beginning stages of a
nutritional accessibility study propagate throughout its entire methodology.
There is a need to improve the methods through which urban food environments
are visually depicted.While it is clear that differences in food accessibility within
urban areas contribute to positive or negative health outcomes,our results show that
it is unclear how at-risk areas should best be identified through accessibility
modelling.We have used three different approaches to model food accessibility,and
have shown that each of these approaches produces a significantly different map of
Lansing’s nutritional terrain.Although each of our approaches is rooted in previous
accessibility research (Handy and Niemeier 1997),we have found uses of only the
container method in food-environment studies,and no assessment of whether this is
the best method of describing or analysing food accessibility.Public officials and
decision-makers rely upon maps of food accessibility in developing and implement-
ing public-health policies,yet the cartographic implications of the geospatial
analyses used to produce such maps have received very little consideration.
Geographic accessibility is a critical component to many types of food-environment
research,yet most studies have employed simplistic approaches to accessibility
measurement.Accessibility is determined by the spatial distribution of destinations,
the costs of reaching these destinations,and the character of these destinations.
Modelling accessibility requires many decisions about how to simplify geographic
reality,meaning that food accessibility studies inevitably include a number of
simplifying assumptions that influence cartographic outputs.Research is needed to
evaluate which metrics – of all three components of accessibility – provide the most
meaningful description of the environmental context of diet-related public health
problems.Furthermore,as suggested by Monmonier (1991),future food-environ-
ment investigations should consider adopting a multiple-output approach,based on
multiple access models and cartographic design decisions,that present a more
diverse,if also more ambiguous depiction of nutritional accessibility.The dangers
associated with the ‘one-map’ solutions that currently dominate food-environment
investigations are particularly potent when cartographic outputs are so sensitive to
input parameters,which is exactly what our results demonstrate.
We have shown that given a set of precise inputs,GISs can effectively
produce high-resolution depictions of nutritional terrain.Indeed,more food-
environment research should be based upon direct observations of food-environment
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characteristics (Papas et al.2007,Ford and Dzewaltowski 2008),such as the
inventories of fresh produce availability we used in this research.However,our main
point is that the appearance of nutritional disparities depends upon the intricacies of
the accessibility modelling approach.This suggests that there is inherent uncertainty
within any quantitative approach to nutritional accessibility modelling.To date this
particular issue has been neglected by most studies,and future investigations should
start to consider its impact on their own results.
Adebawo,O.,et al.,2006.Fruits and vegetables moderate lipid cardiovascular risk factor in
hypertensive patients.Lipids Health,5,14.
Algert,S.J.,Agrawal,A.,and Lewis,D.S.,2006.Disparities in access to fresh produce in low-
income neighborhoods in Los Angeles.American Journal of Preventive Medicine,30 (5),
Andreyeva,T.,et al.,2008.Availability and prices of foods across stores and neighborhoods:
the case of New Haven,Connecticut.Health Affairs,27 (5),1381–1388.
Baker,E.A.,et al.,2006.The role of race and poverty in access to foods that enable
individuals to adhere to dietary guidelines.Preventing Chronic Disease,3 (3),1–11.
Beaulac,J.,Kristjansson,E.,and Cummins,S.,2009.A systematic review of food deserts.
Preventing Chronic Disease,6 (3),1–10.
Black,J.L.and Macinko,J.,2008.Neighborhoods and obesity.Nutrition Reviews,66 (1),2–
Block,D.and Kouba,J.,2006.A comparison of the availability and affordability of a market
basket in two communities in the Chicago area.Public Health Nutrition,9 (7),837–845.
Boone,J.E.,et al.,2008.Validation of GIS facilities database:quantification and implications
of error.Annals of Epidemiology,18 (5),371–377.
Booth,K.M.,Pinkston,M.M.,and Poston,W.S.,2005.Obesity and the built environment.
Journal of the American Dietetic Association,105 (5),S110–S117.
Chung,C.and Myers,S.L.Jr,1999.Do the poor pay more for food?An analysis of grocery
store availability and food price disparities.The Journal of Consumer Affairs,33 (2),276–
Cummins,S.and Macintyre,S.,2006.Food environments and obesity – neighborhood or
nation?International Journal of Epidemiology,35,100–104.
Elliot,P.and Wartenberg,D.,2004.Spatial epidemiology:current approaches and future
challenges.Environmental Health Perspectives,112 (9),998–1006.
Ford,P.B.and Dzewaltowski,D.A.,2008.Disparities in obesity prevalence due to variation in
the retail food environment:three testable hypotheses.Nutrition Reviews,66 (4),216–228.
French,S.A.,et al.,2001.Environmental influences on eating and physical activity.Annual
Review of Public Health,22,309–335.
Gould,P.,1969.Spatial diffusion.Resource Paper 17.Washington,DC:American Association
of Geographers (AAG).
Halkjær,J.,et al.,2009.Dietary predictors of 5-year changes in waist circumference.Journal of
the American Dietetic Association,109 (8),1356–1366.
Handy,S.L.and Niemeier,D.A.,1997.Measuring accessibility,an exploration of issues and
alternatives.Environment and Planning A,29,1175–1194.
Howard,P.H.and Fulfrost,B.,2007.The density of retail food outlets in the Central Coast
Region of California:associations with income and Latino ethnic composition.Journal of
Hunger & Environmental Nutrition,2 (4),16.
Inagami,S.,et al.,2006.You are where you shop:grocery stores locations,weight,and
neighborhoods.American Journal of Preventive Medicine,31 (1),10–17.
Laraia,B.,et al.,2004.Proximity of supermarkets is positively associated with diet quality
index for pregnancy.Preventive Medicine,39 (5),869–875.
Larson,N.I.,Story,M.T.,and Nelson,M.C.,2009.Neighborhood environments:disparities
in access to healthy foods in the U.S.American Journal of Preventive Medicine,36 (1),
498 K.Goldsberry et al.
Downloaded By: [Goldsberry, Kirk][Michigan State University] At: 11:49 23 September 2010

Lasley,E.C.and Litchfield,R.E.,2007.Fresh produce in rural Iowa:availability and
accessibility.Journal of Hunger & Environmental Nutrition,2 (2/3),5–13.
Liese,A.D.,et al.,2009.Food intake patterns associated with incident type 2 diabetes.
Diabetes Care,32 (2),263–268.
McKinnon,R.A.,et al.,2009.Measures of the food environment:a compilation of the
literature,1990–2007.American Journal of Preventive Medicine,36 (4,Supplement 1),
Monmonier,M.,1991.Ethics in map design.Six strategies for confronting the traditional one-
map solution.Cartographic Perspectives,10,3–9.
Monmonier,M.,1996.How to lie with maps.Chicago,IL:University of Chicago Press.
Morland,K.,Diez Roux,A.V.,and Wing,S.,2006.Supermarkets,other food stores,and
obesity:the atherosclerosis risk in communities study.American Journal of Preventive
Medicine,30 (4),333–339.
Morland,K.and Filomena,S.,2007.Disparities in the availability of fruits and vegetables
between racially segregated urban neighbourhoods.Public Health Nutrition,112,1481–
Morland,K.,et al.,2002.Neighborhood characteristics associated with the location of food
stores and food service places.American Journal of Preventive Medicine,22 (1),23–29.
Morton,L.W.and Blanchard,T.C.,2007.Starved for access:life in rural America’s food
deserts.Rural Realities,1 (4),1–10.
Papas,M.A.,et al.,2007.The built environment and obesity.Epidemiologic Reviews,29,129–
Powell,L.M.,et al.,2007a.Associations between access to food stores and adolescent body
mass index.American Journal of Preventive Medicine,33 (4 Suppl),S301–S307.
Powell,L.M.,Chaloupka,F.J.,and Bao,Y.,2007b.The availability of fast-food and full-
service restaurants in the United States:associations with neighborhood characteristics.
American Journal of Preventive Medicine,33 (4 Suppl),S240–S245.
Powell,L.M.,et al.,2007c.Food store availability and neighborhood characteristics in the
United States.Preventive Medicine,44 (3),189–195.
Sharkey,J.R.and Horel,S.,2008.Neighborhood socioeconomic deprivation and minority
composition are associated with better potential spatial access to the ground-truthed food
environment in a large rural area 1,2.The Journal of Nutrition,138 (3),620.
Sharkey,J.R.and Horel,S.,2009.Characteristics of potential spatial access to a variety of
fruits and vegetables in a large rural area.USDA Economic Research Service,and Gerald
R.Ford School of Public Health.N.P.Center.Washington D.C.,University of Michigan.
Available from:http://www.npc.umich.edu/news/events/food-access/sharkey.pdf [Ac-
cessed 8 September 2010].
Shaw,H.J.,2006.Food deserts:towards the development of a classification.Geografiska
Annaler Series B – Human Geography,88B (2),231–247.
Slocum,T.A.,et al.,2009.Thematic Cartography and Geovisualization (3rd ed.).Upper Saddle
River,NJ:Pearson/Prentice Hall.
Spielman,S.,2006.Appropriate use of the K function in urban environments.American
Journal of Public Health,96 (2),205.
White,M.,2007.Food access and obesity.Obesity Reviews,8 (Suppl 1),99–107.
Zandbergen,P.A.,2007.Influence of geocoding on environmental exposure assessment of
children living near high traffic roads.BMC Public Health,7 (37),1–13.
Zenk,S.N.,et al.,2005a.Fruit and vegetable intake in African Americans income and store
characteristics.American Journal of Preventive Medicine,29 (1),1–9.
Zenk,S.N.,et al.,2005b.Neighborhood racial composition,neighborhood poverty,and the
spatial accessibility of supermarkets in metropolitan Detroit.American Journal of Public
Health,95 (4),660–667.
Geocarto International 499
Downloaded By: [Goldsberry, Kirk][Michigan State University] At: 11:49 23 September 2010