Spatial Supermarket Redlining and Neighborhood ... Supermarket Redlining and Neighborhood...

Spatial Supermarket Redlining and NeighborhoodVulnerability: A Case Study of Hartford, Connecticut

Mengyao Zhang and Debarchana Ghosh

Department of Geography, University of Connecticut

AbstractThe disinclination of chain supermarkets to locate or relocate existing stores from inner city impoverishedneighborhoods to affluent suburbs is termed ‘spatial supermarket redlining’. This study attempts to mapand understand the effects of potential spatial supermarket redlining on food access in urban disadvan-taged neighborhoods of Hartford, Connecticut. Using a combination of statistical and spatial analysis, wefirst built a Supermarket Redlining Index (SuRI) from five indicators (sales volume, employee count,accepts food coupons from federally assisted programs, and size and population density of the servicearea) to rank supermarkets in the order of their importance. Second, to understand the effects ofsupermarket closures in the inner city, a Supermarket Redlining Impact Model (SuRIM) was built with 11indicators describing both socioeconomic and food access vulnerabilities. The interaction of these vulner-abilities identified neighborhoods that are maximally impacted by spatial supermarket redlining. Resultsmapped critical areas in the inner city of Hartford where, if a nearby supermarket closed down orrelocated to a suburb with limited mitigation efforts to fill the grocery gap, a large number of minority,poor, and disadvantaged residents would experience difficulties to access healthy food, leading to foodinsecurity or perhaps a food desert. In conclusion we suggest mitigation efforts to reduce this impact oflarge supermarket closures.

1 Introduction

Supermarket redlining is a term used to describe a phenomenon when major chain supermar-kets are disinclined to locate their stores in inner cities or low-income neighborhoods andusually relocate existing stores to the suburbs (Eisenhauer 2001). Compared with the moreexplicit reasons for familiar types of redlining, such as banking, insurance and housingbased on race (Holloway 1998; Holmes 2000; Zenou and Boccard 2000; Squires 2003), thecausality of redlining in retail sectors, including supermarkets, is unclear (D’Rozario andWilliams 2005). This is due primarily to the difficulty of obtaining detailed empirical data onperceived discriminatory practices by retailers based on race, income or other urban obstacles(Eisenhauer 2001; D’Rozario and Williams 2005). These urban obstacles include lowerdemand; higher costs of urban land, labor, and utilities; lower profit margins from perishablefood items; or risk of theft in inner cities (Bell and Burlin 1993; Eisenhauer 2001). Coupledwith these perceptions, other drivers of supermarket redlining are: the difficulties of findinglocations for new supermarkets (typically 50,000 square feet or more) or purchasing multipleadjacent plots; and competition from other investments. For example, a proposal for a minorleague baseball stadium threatens plans for a supermarket just north of Downtown Hartford.City officials argue that the approximately $60 million stadium will boost the city’s economy.

Address for correspondence: Debarchana Ghosh, Department of Geography, University of Connecticut, 215 Glenbrook Road, U-4148,Storrs, CT 06269, USA. E-mail: [email protected]

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However, opening the supermarket addresses a much greater, and critical, public health issuerelated to obesity in Connecticut’s capital city (Ghosh 2014).

Since the process of supermarket redlining is complex, with multiple related drivers, whichmay or may not be racially motivated, we will initially clarify how we have positioned thesupermarket redlining definition in our study. First, we do not restrict the vulnerable popula-tion to any particular racial or ethnic group. Instead, it includes all low-income people withlimited access to healthy and affordable food. Second, we primarily focus on the spatial segre-gation (or discrimination) of supermarket redlining, whereby chain supermarkets typicallyeither close or relocate from the inner city to the suburbs (Bell and Burlin 1993; Eisenhauer2001; D’Rozario and Williams 2005). To emphasize this geographical pattern, we extend thedefinition of supermarket redlining to spatial supermarket redlining, where chain supermarketseither: (1) close down; (2) relocate to suburban areas; or (3) new stores do not open in urbanareas, not only for racially discriminatory reasons but also for a host of other related factors(Bell and Burlin 1993; Eisenhauer 2001; Shaffer 2002; Raja et al. 2008). Third, spatial super-market redlining is not defined as individual instances of grocery store closures but as a com-bination of the three scenarios mentioned above. Even though supermarket redlining is adisputable issue, and perhaps hard to prove empirically, assessing the impact of potentialsupermarket redlining is worthy of investigation because of its disproportionate impact on vul-nerable populations. If a neighborhood’s supermarket closes with limited chances of a new oneopening, what remains are vacant buildings and demoralized residents. Supermarkets also tendto drive smaller grocery stores out of business when they move in; so when they relocate orclose down, residents face difficulties in accessing healthy and affordable food – thus wideningthe grocery gap, increasing food insecurity, and perhaps creating a food desert. More studieson this issue can only help to make the more vulnerable among us (i.e. those that are super-market redlined) become less so.

In Connecticut, according to the Federal Government’s survey, the percentage of house-holds with food insecurity rose by approximately 33% from 7.6% in 2000–2002 to 11.4%in 2007–2009 (Hartford Food System 2013). During 2010–2012, the value further increasedto 13.4% of households. Out of these households, 36.6% were categorized as being at thecritical level of food insecurity (Coleman et al. 2012). Recently, Russell and Heidkamp(2011) found that a food desert was created when Shaw’s (http://www.shaws.com) closeddown in New Haven, a city with similar indicators of income inequality and health dispar-ities to those of Hartford. The Shaw’s supermarket, located in an urban neighborhood, wasthe most successful retail anchor for the surrounding Dwight Street neighborhood. It was theonly full-service supermarket in the nearby residential area within walking distance fromhundreds of households with limited or no access to cars. The other retail stores were in thesuburban area, which could only be accessed by car. In Hartford, in the past, 11 out of 13chain supermarkets (almost 85% of the stores) left the city between 1968 and 1984 (Kane1984) and few supermarkets have opened since then to reduce the grocery gap. Even today,residents living in the Downtown Hartford and Downtown North (or DoNo) neighborhoodsare more than a mile from large to medium sized stores, indicating an urban food desert(Martin et al. 2014). In some areas, especially in the neighborhoods of Blue Hills in thenorth and South End in the south, there is not a single full service grocery store within twomiles (Martin et al. 2014).

There is plethora of studies identifying food deserts, in both rural (Hendrickson et al.2006; Smith and Morton 2009; Hubley 2011) and urban settings (Whelan et al. 2002; Wrigley2002; Gallagher 2006; Hendrickson et al. 2006; Larsen and Gilliland 2008; Hallett andMcDermott 2011) and at different geographies (Cummins and Macintyre 2002; Wrigley et al.

2 Spatial Supermarket Redlining and Neighborhood Vulnerability

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80 M Zhang and D Ghosh

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2002; Morton et al. 2005; Pearson et al. 2005; Zenk et al. 2005; Smoyer-Tomic et al. 2006;Apparicio et al. 2007; McClintock 2008; Raja et al. 2008; Ball et al. 2009; Coveney andO’Dwyer 2009; Sparks et al. 2011). Similarly there are studies measuring food insecurity(Kendall, Olson, and Frongillo 1996; Carlson et al. 1999; Olson 1999; Alaimo et al. 2001;Hamelin et al. 2002; Vozoris and Tarasuk 2003; Drewnowski 2004; Lopez et al. 2005;Tchumtchoua and Lopez 2005; Food Research and Action Center 2011); however, there is toour knowledge no study that focuses, particularly from an empirical approach, on potentialspatial supermarket redlining as an early indicator of risk for food deserts and food insecurity.The objectives of this study, therefore, are two-fold: first, to describe an empirical approach tomodel the impact of spatial supermarket redlining in a Geographic Information Science (GIS)environment, and second, to understand the effects of potential spatial supermarket redliningon food access in disadvantaged neighborhoods of Hartford, Connecticut.

The organization of the article is as follows. Section 2 briefly discusses the background lit-erature of supermarket redlining in relation to food deserts and food insecurity. Section 3describes the underlying conceptual framework for the proposed methodology. The details ofthe study area, datasets and analytic approach employed in this article are explained in Section4. Sections 5 and 6 follow with results and discussion, respectively. Limitations and futurestudies are described in Section 7.

2 Background Literature

The literature on food deserts and food insecurity has increased tremendously in the lastdecade with several prominent studies, systematic reviews, and case studies (e.g.Avilés-Vázquez and Bussmann 2009; Beaulac et al. 2009; Larson et al. 2009; McKinnon et al.2009; Walker et al. 2010). In this section we briefly review these topics, with the primary focuson supermarket redlining.

2.1 Food Desert and Food Insecurity

The term food desert describes a phenomenon where affordable and healthy food is difficult toaccess. The concept of food desert was first used in the UK (Vozoris and Tarasuk 2003) in the1990s to describe the rapidly decreasing number of grocers in urban, low income neighbor-hoods after World War II (Whelan et al. 2002). The term was first used in the context ofpublic sector housing schemes in Scotland in the early 1990s for the Low Income Project Teamof the Nutrition Task Force (Beaumont et al. 1995). Since then, several researchers haveattempted to define food deserts from different perspectives. Earlier, Acheson (1998, p. 65)defined it as where “cheap and varied food is only accessible to those who have private trans-port or are able to pay the cost of public transport”. Some of the recent literature has identi-fied socioeconomically disadvantaged neighborhoods with limited or inadequate physical oreconomic access to healthy and affordable food as food deserts (Whelan et al. 2002; Wrigleyet al. 2002, 2003; Smoyer-Tomic et al. 2006; Apparicio et al. 2007; Larsen and Gilliland2008). The US Department of Agriculture (USDA; 2015) measures food deserts in the follow-ing way: A census tract is considered a food desert if it meets a certain threshold of poverty,and if at least 500 people or one-third of the population reside more than a mile from a largegrocery store. Currently the USDA’s definition of food desert is the most widely used.

In terms of methodological exploration, researchers have used different techniques todelineate food deserts and there was no clear agreement on what measures were absolutely

M Zhang and D Ghosh 3


Spatial Supermarket Redlining and Neighborhood Vulnerability 81


necessary in identifying food deserts. Initially researchers focused on the number of foodstores, ratio of stores per unit area in a neighborhood (Cummins and Macintyre 2002;Morland et al. 2002; Moore and Roux 2006; Block and Kouba 2006), or the minimum dis-tance to the nearest food stores (Zenk et al. 2005). Researchers who argued that food desertsdid not have clear boundaries began using GIS, remote sensing, and complex modeling tech-niques to delineate food deserts (Hallett and McDermott 2011; Sparks et al. 2011; Sadler et al.2011). Some also used mixed methods to measure accessibility to food stores (Hallett andMcDermott 2011).

“Food insecurity” describes a condition where people have limited access to sufficient,safe, nutritious food to meet their daily need for healthy living (Olson 1999; Hamelin et al.2002; Lopez et al. 2005). Typically, residents living in a food desert with limited access tohealthy food experience issues of food insecurity but the impact is disproportionately higheramong vulnerable populations due to lower socioeconomic status, ethnic minority status, oldage, and existing negative health outcomes (Morland et al. 2002; Zenk et al. 2005; Raja et al.2008). Zenk et al. (2005) found that, even within low-income neighborhoods, residents livingin areas with a higher proportion of African-American population had to travel an average 1to 1.25 miles further to the nearest supermarket than neighborhoods with predominantlywhite population. White neighborhoods, on the other hand, had almost four times more super-markets than neighborhoods with a significantly higher black population (Morland et al.2002). In terms of prices, the majority of research showed that the poor had to pay more forhealthy food (Chung and Myers 1999; Morland et al. 2002; Hendrickson et al. 2006; Jetterand Cassady 2006). In a case study conducted in the Twin Cities Metropolitan Area of Minne-sota, Chung and Myers pointed out that big chain supermarkets had much lower prices butwere not likely to locate in poor areas (Chung and Myers 1999; Bell and Burlin 1993). Non-chains and small stores were more likely to be located in impoverished areas, where typicallychoices for fresh food were limited but with an abundant variety of high-calorie packagedfoods at higher prices (Chung and Myers 1999). Researchers from other countries had differ-ent findings. Unlike in the US, study sites in Canada, Australia and New Zealand(Smoyer-Tomic et al. 2006; Apparicio et al. 2007) showed that middle-income communitieshad the most access to supermarkets and were better served by food stores. Cummins andMacintyre (2002) argued that in the UK, wealthier and poor neighborhoods had no statisti-cally significant differences in access to supermarkets, food prices, or food availability.

2.2 Spatial Supermarket Redlining

The concept of retail redlining is less explored in the literature and adaptation of thisabstract idea to spatial supermarket redlining is even more limited and perhaps challengingand controversial. Redlining, in general, is a practice in banking and insurance companieswhen they decide to deny, stop, or charge higher from residents living in marginalized and vul-nerable neighborhoods (Kane 1984). Typically, a red-line will be marked on a map to delineatethose specific areas (Sagawa and Segal 1999). Later D’Rozario and Williams (2005, p. 175)defined retail redlining as “A spatially discriminatory practice among retailers, of not servingcertain areas, based on their ethnic-minority composition, rather than on economic criteria,such as the potential profitability of operating in those area”. As mentioned earlier, in ourstudy, we further extend the definition of supermarket redlining to spatial supermarketredlining where chain supermarkets either: (1) close down; (2) relocate to suburban areas; or(3) new stores do not open in urban areas not only due to discriminatory reasons but also fora host of other related factors. These factors can be broadly divided into two categories: (1)





stereotypes as perceived urban obstacles (Eisenhauer 2001); and (2) logistical obstacles relatedto retail business (Shaffer 2002; Raja et al. 2008).

Examples of perceived urban obstacles in a city are as follows: (1) Profitability: Supermar-ket chains often cite low profit margins and higher cost of overheads as barriers to investmentin neighborhoods where demand for food items is low due to low-income shoppers, lowervolume of sales per customer, and smaller per trip purchases (Eisenhauer 2001; Shaffer 2002);(2) Crime: Higher crime rates in low-income urban neighborhoods including employee theft,shoplifting, and dishonesty are also central to the reasons for supermarket closures (Shaffer2002). High crime is also related to higher rates of insurance and greater difficulty of gettingloan approvals to open new stores; and (3) Cultural Biases: Another important reason whysupermarkets avoid inner-city neighborhoods is a perceived anxiety based on cultural biasesabout the inner city and minorities (Zenk et al. 2005; Raja et al. 2008; Ball et al. 2009; Sugrue2014). In Morland et al.’s multi-site study (2002) the racial discrepancy was evident from theirfindings that predominantly white neighborhoods had four times more supermarkets thanblack neighborhoods. Mark Green – former New York Consumer Affairs Commissioner(Shaffer 2002, p. 25) – said there is a “knee-jerk premise that blacks are poor and poor peopleare a poor market”. Logistical obstacles, on other hand, include: (1) difficulties of finding loca-tions for new stores, which are typically 50,000 square feet or more; (2) purchasing multipleadjacent plots; (3) higher cost of tax rates, insurance, and utilities (Eisenhauer 2001; Shaffer2002); (4) zoning restrictions and contamination of sites that may require remediation beforenew stores can be constructed (Shaffer 2002); (5) investors may not understand the diversity offood needs and desires of the racially mixed population; and (6) hindrances from local politics(Shaffer 2002).

In the US, isolated incidents of supermarket closures or possible supermarket redliningincidents began in 1960s and since then the trend has been on the rise (IFDP 1997). Forexample, in Boston, Massachusetts, 34 out of 50 big chain supermarkets have closed since the1970s. In Los Angeles county in California, the number of supermarkets decreased from 1068in 1970 to 694 in 1990 (Turque 1992). Safeway, a well-known supermarket chain, closedmore than 600 stores in the country from 1978 to 1984 (Eisenhauer 2001). Many of thesestores were the primary or only source of affordable, safe, and acceptable quality of meat andproduce in their neighborhoods. In Hartford, 11 out 13 chain supermarkets (almost 85% ofthe stores) left the city between 1968 to 1984 (Kane 1984). Incidents of such kind are stillhappening today (Eisenhauer 2001; Raja et al. 2008; Russell and Heidkamp 2011).

In recent years, the city of Hartford also experienced several supermarket closures leavingbehind unhappy residents and an even wider grocery gap. Market at Hartford 21, an upscalegrocery store located in downtown Hartford was only open for six months until it finallyclosed in September 2011 (Haar 2011). It used to provide various healthy and nutritiousready-to-eat meals, some fresh produce, and even a few organic items. It was becoming “adowntown favorite” as quoted by Tiff (2011) and “it’s very nice having a basic grocerystore with some basic needs close by” (Jimmy 2011). Central Supermarket, located in theFarmington Avenue of Hartford, was closed on May 2014 which has been described as “ahuge loss to the West End since this area does need a grocery store” (Emily 2012). West Har-tford’s Crown Supermarket also plans to close after more than seven decades of service to thecommunity. A local resident, who had shopped at Crown for her entire life, said, “I don’tknow what I’ll do. I’ll be devastated if it closes. I am there once a week for a big order”(Jacobson 2014).

Incidents of possible supermarket redlining, due to either closing down of existing super-markets, relocation of supermarkets to the suburbs, lack of investments to construct new ones,





or combinations of these scenarios, will disproportionately affect neighborhoods with low-income, vulnerable residents. It will increase the difficulty of accessibility and availability ofhealthy food choices. Low-income residents usually do not have enough economic supportand/or access to transportation (e.g. personal cars) to travel that “extra” distance to buyhealthy food from other stores or from the chain supermarkets in the suburbs. In terms ofaffordability, low-income consumers often have to pay more for shopping at the local storeswhere stock is limited and sometimes of poor quality (Kaufman et al. 1997; Morland et al.2002; Hendrickson et al. 2006; Andreyeva et al. 2008). Therefore, as stores close, vulnerableurban residents are either traveling farther to purchase nutritious, competitively priced gro-ceries or perhaps paying inflated prices for low quality, processed foods at the corner stores.These situations, affecting both individual health and the health of neighborhoods, wouldwiden the urban grocery gap, increase food insecurity, and perhaps create a food desert.

3 Conceptual Framework

Supermarket Redlining Index (SuRI) is an index that ranks a chain supermarket based oncertain parameters. These parameters are: location of the supermarket, presence of localgrocery stores in close proximity, sales volume, employee count, accepts SNAP (SupplementalNutrition Assistance Program) and/or WIC (Women, Infants, and Children) coupons, and sizeand population density of the service area. Detailed specifications of the index with variabledefinitions are explained in Section 4.3. If a supermarket with high SuRI value closes in aninner city or relocates to a suburb with limited possibilities of a new store being open, the riskof spatial supermarket redlining increases. Given such risk, the Supermarket Redlining ImpactModel or SuRIM identifies places or location of neighborhoods where the impact of foodaccess vulnerability will be critical. This model is an extension of Cutter’s hazards-of-placemodel of social vulnerability (Figure 1) (Cutter 1996; Cutter et al. 2000, 2003).

According to Cutter’s model, risk and mitigation interact to create an initial hazard poten-tial (Cutter et al. 2000, 2003). In our framework, risk is the likelihood of the occurrence ofspatial supermarket redlining, i.e. scenarios where a supermarket closes down and/or relocatesto suburban neighborhoods from inner cities. The magnitude of the risk will further depend

Figure 1 Conceptual framework(Notes: SuRI: Supermarket Redlining Index; SuRIM: Supermarket Redlining Impact Model)





upon parameters such as: (1) the source of the potential risk (e.g. location of the store); (2)type of the risk (e.g. rank of the store calculated from SuRI); and (3) the impact of the risk(high-consequence if it is the only full-sized supermarket in the neighborhood; low-consequence if there are other alternatives to fill the grocery gap). The risk of spatial supermar-ket redlining then interacts with mitigation (e.g. increasing investments to open new stores,increasing stocks of fresh produce in the existing corner stores of the neighborhood, presenceof alternate sources for fresh food such as farmers’ markets, community gardens) to producethe hazard potential of increasing food insecurity and food deserts. Risks can either be reducedby good mitigation policies or amplified by poor or non-existent mitigation practices. Thelatter is typical for inner city urban areas where a combination of perceived and logisticalobstacles creates disinvestment for new stores and increases the likelihood of food insecurityand food deserts.

The hazard of increasing food security or difficult access to healthy food interacts with theunderlying social fabric of the neighborhoods to create social vulnerability. The social fabric(including socio-demographic, economic, and cultural characteristics, awareness, perception,and experiences of the neighborhood residents) affects the overall capacity to respond to foodinsecurity. For example, if an important supermarket closes down in a neighborhood, theimpact would be disproportionately greater among low-income residents with limited access tocars than among those with the resources to travel farther to buy fresh produce. The foodaccess filter includes indicators describing the food environment or foodscapes of the neighbor-hoods. The indicators are: proximity to other smaller grocery and corner stores, availabilityof fresh produce in these stores, variety or diversity of food items to satisfy the need for ethni-cally diverse population, and alternative sources of healthy food at seasonal farmers’ marketsand community gardens. Similar to social vulnerability, the impact of the hazard will bedisproportionally higher for residents with fewer food access indicators to fulfill their groceryneeds. Finally, the social and food vulnerability parameters are mutually related and producethe places-of-food vulnerability outcome or, in other words, locations of disadvantaged neigh-borhoods with maximum food vulnerability. Like Cutter’s model, the places-of-food vulner-ability has a feedback loop to the initial risk (spatial supermarket redlining) and mitigation (toreduce the risk of spatial supermarket redlining), allowing for enhancement or reduction ofboth risk and mitigation, which in turn lead to increased or decreased places-of-food vulner-ability (Figure 1) (Cutter et al. 2000, 2003).

To operationalize this conceptual framework, we focused on one input element (risk) andthree outcome elements (food access vulnerability, social vulnerability, and place-of-food vul-nerability) of the model. SuRI measures the location and magnitude of risk from potentialspatial supermarket redlining; the social fabric and food access indicators contribute to socialvulnerability and food access vulnerability, respectively. The final outcome of place-of-foodvulnerability is the product of social and food access vulnerabilities (Figures 1 and 3).

4 Methodology

4.1 Study Area

Hartford, the capital city of Connecticut, has diverse demographic, socioeconomic, and healthdisparity indicators. The total population in 2012 was 124,893, predominantly urban (Esri2011). The Hispanic population comprised the biggest ethnic group with 43.4%, followed by34.1% of non-Hispanic blacks and 15.8% of non-Hispanic whites (City-Data 2012). Hartfordhas an estimated poverty rate of 32.9%, more than double the US’s poverty rate of 15%





(Martin et al. 2012; US Census Bureau 2013b). The unemployment rate in Hartford in April2013 was 14.8% (Connecticut Department of Labor 2013), compared to approximately 7%nationally (US Bureau of Labor Statistics 2014). The 2011 median household income was esti-mated at $29,169, which is less than half the estimated median household income for Hart-ford County and below the median for the US ($50,502) (US Census Bureau 2013a, 2012). InHartford, 47.9% of children live below the poverty line compared to the US’s child povertyrate of 21.8%. The youngest members of Hartford are at increased risk of diet-related diseasesdue to nutritionally imbalanced access to foods in their neighborhoods. A 2012 study foundthat 37% of preschool children in Hartford were overweight or obese, making the prevalenceof childhood obesity among preschoolers more than twice as high as the Centers for DiseaseControl and Prevention (CDC) age and gender body mass index guidelines (University ofConnecticut, Center for Public Health and Health Policy 2012).

4.2 Data

The data for this study was grouped into four categories of retail food stores, relevant GISshapefiles, socioeconomic and demographic characteristics, and travel-time to stores. Foodstore data were collected from two sources: Connecticut Department of Energy and Environ-mental Protection’s (DEEP, 2011) food residual generation mapping project and Esri’s Busi-ness Analysis (Esri 2011). We followed the criteria used in DEEP’s grocery store mappingproject to categorize the stores included in our study into three groups: (1) large supermarketswith employee count greater than 15 persons (e.g. Shop and Stop); (2) small supermarketswith employee count between 4–14 persons (e.g. Carlos Supermarket); and (3) conveniencestores (e.g. 7-Eleven). Based upon this criterion and within a three-miles buffer around the cityof Hartford, we identified 33 large supermarkets, 17 small supermarkets and 73 conveniencestores. A three-mile buffer was used for two reasons: first, the residents of Hartford often shopoutside their town limits, and second, to minimize errors from edge effects in the subsequentmapping and spatial analysis (Lawson et al. 1999; Laurance 2000; Van Meter et al. 2010;Sadler et al. 2011). A variety of methods were used to ensure sample completeness, includingonline yellow pages, business listings, and more importantly “ground-truthing” by drivingthrough neighborhoods to verify store names. Out of the 33 identified large supermarketswithin a three-mile buffer around Hartford, nine were located in the city (Figure 2). For eachlarge supermarket we further obtained the following information: sales volume, employeecount, SNAP/WIC coupon status, size of the service area, and population density of the servicearea.

The GIS shapefiles such as Connecticut’s roads were obtained from Esri’s Business Analy-sis dataset, and state, town, and census block-group boundary shapefiles from the Map andGeographic Information Center at University of Connecticut (MAGIC 2013). The socioeco-nomic and demographic variables were selected from Esri’s Business Analysis dataset at theblock-group level. The variables selected for the social fabric indicator of the SuRIM modelwere: percentage of elderly population (65+ years), minority and ethnic population (Black andAsian), diversity of race and ethnicity, population with less than high-school education, renter-occupied household units, unemployment rate, and low-income population. The data on traveltime by bus and by car from the population centroids of block-groups to the retail food storeswere obtained by using the Google Direction API application. This is a free service providedby Google that calculates the direction (and distance) includingthe time between locationsusing an HTTP request with a limitation of 2,500 requests per 24-hour period. We will intro-duce the details of this technique in a future article, currently in preparation.





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4.3 Methodology

We first calculated the potential Supermarket Redlining Index (SuRI) at the store level usingfive variables (Figure 3A): sales volume ($), number of employees (count), whether the storeaccepts SNAP and/or WIC coupons (yes or no, coded as 1 and 0, respectively), size of theservice area (number and area of all block-groups that were assigned to the store as the closestsupermarket), and population density (number of persons per square mile) of the service area.Sales volume and employee count were the characteristics of the supermarket, which indicatedhow important that particular supermarket was in serving the community. SNAP-WIC statusindicated whether the supermarket accepted coupons from the federally funded food assistanceprograms designed for low-income households. The service area of the supermarket was deter-mined by using ArcGIS 10.1’s Network Analyst functions, where a road-network databaseand the ‘Closest Facility’ tool were used to calculate the path from each block-group popula-tion centroid to its closest supermarket. The average population density of the service area wasthen calculated and assigned to each supermarket as the fourth variable. So neighborhoodswith sparse supermarkets had a larger service area and thus a larger proportion of urban resi-dents would be at risk if that store closed down.

Figure 3 Operational framework of Supermarket Redlining Index (SuRI) and SupermarketRedlining Impact Model (SuRIM)(Notes: SuRI: Supermarket Redlining Index; SuRIM: Supermarket Redlining Impact Model, SNAP:Supplemental Nutrition Assistance Program; WIC: Women, Infants, and Children; HS: High School)





Since the units of these variables were different, each variable was standardized bycalculating the ratio of its value to the total value divided by the highest ratio among theblock-groups:

SD x

xxx

x

i

i

i

i

( ) = ∑

∑max

(1)

where SD(x) is the standardized Redlining Indicator, i is the store, xi is the variable value of

each store, Σxi is the sum of each variable, andxxi

i∑is the ratio of each variable.

The value of each standardized variable ranged between zero to one. To generate anaggregate value for SuRI, standardized values were summed for each supermarket. Due to thelack of prior literature and statistical evidence needed to assign specific weights to calculate thesupermarket redlining index, all indicators were given the same importance of equal weights(Wood et al. 2009; Laurance 2000). The final result was rescaled from 0–10 to be comparablewith the values from the SuRIM. These steps are summarized in Table 1.

Next, we built the Supermarket Redlining Impact Model (SuRIM) at the block-group levelusing 11 variables (Figure 3B). We used seven socioeconomic and demographic variables todescribe the social vulnerability (SoVI) component of the SuRIM (See Figure 3B). For the foodaccess vulnerability (FaVI) component, four variables were included relating to access tohealthy food in a situation when the existing supermarket closed down or relocated to thesuburbs. In Figure 3B, variables with “+” and “-” represent positive and negative effect on theSuRIM, respectively. The seven socio-economic-demographic variables were: (1) percentage ofelderly population (65+ years); (2) minority and ethnic population (Black and Asian); (3)diversity of race and ethnicity; (4) population less than high-school education, (5) renter-occupied household units; (6) unemployment rate; and (7) low income population. All thesesocial fabric variables had positive impact and increased the value of SoVI or, in other words,increased the impact of risk (spatial supermarket redlining) on the hazard of potential foodinsecurity and food deserts.

For FaVI, the transit-time variables were the additional travel time that the neighborhoodresidents would have to travel for groceries by public transport or by a car in a situation of

Table 1 Methodology of Supermarket Redlining Index (SuRI)

Creating Supermarket Redlining Index Example using the ‘Sales Volume’ variable

Step 1: Sum of each variable for all stores Sum of sales volume ($)Step 2: Computing the variable ratio for

each storeSales Ratio = sale volume of a store /sum of

sales volume for all storesStep 3: Calculating the variable indicator for

each storeSales indicator = Sales ratio/ maximum value

of sales ratioStep 4: Calculating a composite index of

SuRI for each store including allthe variable indicators

SuRI = [sales indicator + service area indicator+ . . . + population density indicator]

Note: The left column lists the steps and the right column provides an example for each step





potential spatial supermarket redlining. The Google Direction API application was used to cal-culate these transit time variables (by bus and by car) for each block-group from their cen-troids to their second closest supermarket minus the transit time to their closest one with theassumption that longer transit time would increase the difficulties of accessing healthy food.Travel time by a bus included walking to a bus stop, time on the bus to the store, and then offthe bus and walking to the store. The other two variables described the alternative sources offresh food in a time of supermarket closures. The variables were the presence and absence ofsmall local supermarkets with employee counts between four and 14 persons and conveniencestores. When a supermarket stopped business and the second closest supermarket was too faraway, these stores would become the primary or sometimes the only source of groceries. Localsmall supermarkets might still shelve limited fresh produce but the convenience stores wouldtypically not have fresh food items at all. Small supermarkets were aggregated by block-groupsand the count showed the availability of alternative access to limited healthy food (Martinet al. 2014). We assumed that these stores decreased the impact of the risk of supermarketredlining and used one minus the standardized value when calculating the FaVI. In contrast,convenience stores, typically with no fresh food, would increase food vulnerability. This vari-able was also aggregated at the block-group level; and higher the count, the higher was theFaVI value indicating increase in the risk of exposure to low nutrition food environments.

All of these 11 variables were then standardized using the same method described for theSuRI to create the SoVI (social vulnerability) and the FaVI (food access vulnerability) compo-nents. The final outcome of place-food-vulnerability in the SuRIM was the product of FaVIand SoVI (FaVI * SoVI = place-food-vulnerability). Since there were no prior studies thatmodel the impact of supermarket redlining and provide insight in choosing the weights, weassigned equal weights to FaVI and SoVI in calculating the product (Cutter et al. 2003).

5 Results

Table 2 shows the descriptive statistics of SuRI values, which range from 2.1 to 6.2 with amean value of 4.1. Based on these values, 33 large supermarkets were grouped into three cat-egories of low (SuRI < 3.0), medium (SuRI 3.00 – 4.99), and high (SuRI >= 5.00) SuRI values.There were three supermarkets with a service area of four block-groups in the low category, 25supermarkets with a service area of 192 block-groups in the medium category, and five super-markets with a service area of 74 block-groups in the high category. Higher SuRI values indi-cated a higher risk of potential spatial supermarket redlining and the resulting higher hazardof food insecurity and food deserts. Conversely, if a neighborhood store with low SuRIvalue closed down or relocated to a suburb with limited mitigation efforts to fill the gap,the residents of that neighborhood either had other supermarkets to shop from in the same

Table 2 Descriptive Statistics of SuRI

N Min. Max. MeanStd.Deviation

Percentiles

25 50 75

SuRI 33 2.1 6.20 4.11 0.86 3.55 3.80 4.62





neighborhood or had access to a car (and or public transportation) to travel to distant stores.Figure 4 is a proportional circle map showing the spatial distribution of stores with SuRIvalues. Of the 33 large supermarkets, only nine (27%) were located in Hartford and most ofthem were in the west and south. The supermarket with the highest SuRI value (Bravo Super-market) was located northwest of Hartford in Albany Ave. Given that there were no othersupermarkets or even small stores, this store played an important role as the only provider offresh food for the residents of the surrounding 27 block-groups in the northwest region ofHartford. A Walmart store, which opened in 2013 at the border of Hartford and West Hart-ford, had a lower SuRI value, even though it had a higher sales volume and employee countthan the other stores. This was due to the presence of other large supermarkets such as SuperStop & Shop and Save-A-Lot in close proximity. Closure of any one of these stores, will there-fore, not be a critical loss for the residents.

Figure 5 shows the spatial distribution of the two components of SuRIM – food accessvulnerability index (FaVI) and social vulnerability index (SoVI) with SuRI values at the censusblock-group level. Although FaVI did not show strong spatial clustering of either high or lowvalues in the study area, several important findings emerged. First, north Hartford and areasjust outside the northern city boundary had higher food access vulnerability due to a lack oflarge supermarkets, small-sized local stores, or limited public bus services. Second,

Figure 4 Spatial distribution of Supermarket Redlining Index (SuRI)





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block-groups with higher FaVI values (5.1–7.6) were located in the downtown, DoNo, andnorthwest. Third, the supermarket with the highest SuRI value was located in a block-group inDoNo with the highest FaVI, indicating a positive correlation between SuRI (risk) and FaVI(one of the outcome of SuRIM). This supermarket was the store closest to the surrounding 27block-groups with no other alternative food stores in the vicinity. Overall, the SoVI was higherin Hartford than the surrounding suburbs. Within Hartford, the inner city areas in the centraland north-central region had the highest values with 41–96% of black population and38–50% of low-income population. Block-groups located in the downtown area with higherSoVI also had higher FaVI (5.6–7.6) and several supermarkets with medium (3.1–5.0) to high(5.1–6.2) values of SuRI. This indicated a stronger positive correlation between SoVI andFaVI, and a weaker positive correlation between SoVI and SuRI.

The final outcome of SuRIM or the place-food-vulnerability value (FaVI * SoVI = place-food-vulnerability) is shown along with the SuRI values in Figure 6. Based on the SuRIM values, the263 block-groups in the study area were divided into three categories of low (66 block-groups),medium (132 block-groups), and high (65 block-groups) values. Our first observation was thatthe places or neighborhoods of inner city Hartford suffered a higher impact of place-food-vulnerability (higher SuRIM values) from the risk of potential spatial supermarket redlining

Figure 6 Spatial distribution of Supermarket Redlining Impact Model (SuRIM)





(higher SuRI values). The neighborhoods in the northwest of Hartford had the highest SuRIMvalues, between 15 and 30, with only one supermarket. The impact of potential supermarket clo-sures on these neighborhoods was further accentuated by the socioeconomic vulnerability of theresidents and their limited choice and access to healthy food. On the contrary, some affluent sub-urban areas (West Hartford, Newington and south of Wethersfield) located to the west of Hart-ford, had lower SuRIM values, indicating that the residents living in these neighborhoods wereless vulnerable to the hazard of potential food insecurity and food desert even if their large super-market closed down. This was because the residents were not dependent on a single store and hadhigher socioeconomic status and easy access to alternative stores. The pattern, however, was dif-ferent for the suburbs located east and northeast of Hartford. Towns such as Bloomfield, SouthWindsor, East Hartford, and Manchester had medium to high values of SuRIM (6.1–15.0) andlower values of SuRI (2.1–3.0).

The cross-tabulations between SuRI (risk) and SuRIM (place-food-vulnerability) areshown in Table 3. In the lower right cell (i.e. high SuRI and high SuRIM values), there arethree large supermarkets, whose services are critical for providing fresh and healthy food tothe residents of their service area (43 block-groups). Of these three stores, two (Bravo Super-market) are located in Hartford and one store (ShopRite Supermarket) is located on thewestern edge of Hartford. The socioeconomic status of the residents (approximately 62,000)living in these 43 block-groups is vulnerable: over half of the population (51%) live in rentedproperties, 11% have less than high school education, there is a significant black population(69%), average unemployment rate is at 21%, and 26% with low income. Therefore the con-tinued operation of these stores is vital, because if any of these stores close down or relocate tothe suburbs without efficient mitigation efforts to close the grocery gap, a significant numberof residents who are socioeconomically vulnerable would experience the hazard of foodinsecurity and food deserts. Residents will either have to drive long distances to another bigsupermarket or shop at the nearby small local stores, which might not be able to provide freshfruits and vegetables at affordable prices.

6 Discussion and Conclusions

We highlight the major findings of our study here. First, the service areas (block-groups) of thesupermarkets with higher SuRI values (vital source of food availability) were also the areaswith higher impact of place-food-vulnerability (high SuRIM values). These areas were locatedin the inner city neighborhoods of Hartford, especially in the north, east, central, and south-central parts. In these neighborhoods, once a nearby supermarket closes (or relocates) and ifthe mitigation efforts are slow, a large proportion of vulnerable residents might face foodinsecurity and related negative health outcomes. However, residents who have the resources orthe means to travel the extra miles to an alternate supermarket will be less vulnerable to thehazard of food insecurity. The mitigation efforts (e.g. increasing investments to open newstores, increasing stocks of fresh produce in the existing corner stores, encouraging seasonalfarmer’s markets and community gardens) will affect the severity of the hazard and the finaloutcome of place-food-vulnerability.

Second, some suburban areas such as northwest of West Hartford, Newington and southof Wethersfield have low SuRI and SuRIM values. This indicates that residents living in theseneighborhoods are less vulnerable and the existence of clusters of large supermarkets in closeproximity provided choices and more options for buying fresh groceries. These places are typi-cally affluent suburban neighborhoods (only 4% low income population and unemployment





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rate at 5%) with a predominantly white population (over 80%) and with easy access to anumber of large chain supermarkets (average number is four).

Third, GIS algorithms, particularly network analyses and travel-time data obtained fromthe Google Direction API service were appropriate in building the supermarket redlining index(SuRI) and the Supermarket Redlining Impact Model (SuRIM). These analytic approachesaided in illustrating the major findings. The spatial analysis and correlation between SuRI andSurIM identified urban neighborhoods that will face increasing difficulty of accessing healthyand nutritious food if a full-service supermarket closes. It raises concerns about food insecurityand food deserts and urges city officials to consider stronger but feasible mitigation policies tofill the possible grocery gap.

Since it is not always feasible to open a large supermarket in inner cities due to lack of invest-ments, stable markets, and lack of infrastructure related to easy access to highways, large loadingdocks for large trucks to unload, or distribution networks (Shaffer 2002; Martin et al. 2014), wesuggest other mitigation policies. These suggestions are: (1) investing more in fresh food stocks atthe existing local medium to small sized grocery stores and corner stores (Martin et al. 2012); and(2) encouraging more urban farms and community gardens to increase options for healthy foodsfor at least a few months of the year. At present there are seven farmer’s seasonal markets ofvarying sizes and few established community gardens (e.g. KNOX Inc and gardens in the TrinityCollege). Martin et al. (2014) in their recent study indicated that improvement of quality of foodand appearance of the existing smaller local stores can potentially impact the food purchasingdecisions of low-income residents in Hartford and mitigate the negative impacts of food insecu-rity. Many of the store-owners from small and medium-sized markets in Hartford live locally.Therefore, efforts to improve the business infrastructure and sales of these markets will alsosupport the local economy, which is in line with the principles of healthy, sustainable foodsystems. Studies have shown that store-owners’ established friendships between owners andpatrons foster store loyalty, especially in neighborhoods without a large supermarket (Bloemerand De Ruyter 1998; Walker et al. 2012). In comparison, large supermarkets tend to be owned bynational or often international companies, whose revenues are not reinvested into the city andwhose owners might not be at the same level of enthusiasm to develop friendship with the localpatrons.

7 Limitations and Future Study

The study also had a few limitations. First, due to insufficient literature and this being the firstattempt, to our knowledge, to model the impact of spatial supermarket redlining, we usedequal weights of FaVI and SoVI in the SuRIM (SuRIM or place-food-vulnerability was theproduct of FaVI and SoVI) (Cutter et al. 2003). It is possible that in a different study design,FaVI could be more critical than SoVI or vice versa. Second, we calculated accessibility togrocery stores by car and by public transportation. Due to a high incidence of crime in someparts of the inner city neighborhoods of Hartford, residents are disinclined to walk to servicesand therefore walking distance is not included. It is however possible that some residents stillwalk to grocery stores. Third, we included small supermarkets (with employee counts betweenfour and 14) as an alternative source for access to fresh food in a situation of potential spatialsupermarket redlining i.e. when supermarket closes or relocates to suburbs. However, somesmall supermarkets may not stock fresh produce or a variety of food items for the ethnicallydiverse population of Hartford. The prices of food items in these local stores may also varysignificantly. If these small supermarkets have higher prices and a limited variety, will the





residents consider these stores as an alternative to large chain supermarkets? If not, then theselocal supermarkets are not an alternate source for healthy foods and therefore will increase theimpact of supermarket redlining instead of decreasing the impact. To answer this questionempirically in a future follow-up study, we are currently analyzing data from a survey, whichcollected detailed data on price, quality, and variety of available food items, and external andinternal appearances of the medium- and small-sized grocery stores in Hartford and adjacenttowns. Last but not the least, because of the controversial meaning of the word redlining, espe-cially in the retail sector, the authors want to reemphasize two aspects here. First, it should beremembered that in all its variation, retail redlining (including supermarket redlining) is notblatantly practiced based only on race compared with the financial and housing sectors. Asdiscussed in the article, the process of supermarket redlining is complex, with multiple relateddrivers which may or may not be racially motivated. Second, this study is the first attempt toempirically understand the effects of potential spatial supermarket redlining on food insecurityamong vulnerable populations. For that we have built an index and an impact model in a GISenvironment, not a predictive model.

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