We identified six types of neighborhoods in the Twin Cities Region of Minnesota that were characterized by clusters reflecting distinct combinations of built environment with sociodemographic and economic features. Our results indicate an increasingly varied distribution of restaurants and food stores by neighborhood type over time. Our results also hint at the complexity of the relationship between the neighborhood built environment and sociodemographic characteristics and the presence of a certain type of food outlet in the neighborhood.
Our findings contribute to a growing literature on the associations between the multifaceted composition of the built environment, sociodemographic features, and the distribution of food resources. In previous work, researchers investigating the association between neighborhood characteristics and neighborhood food availability have generally characterized neighborhood features more narrowly, focusing on a single construct such as income or race . These studies have produced inconclusive results. Recognizing that analyses may be confounded by correlations among neighborhood features, we included a broad set of neighborhood resource variables to more fully represent neighborhood-defining patterns based on many interrelated built environment and sociodemographic characteristics.
We found that our neighborhood types were not spatially clustered into homogeneous regions but, instead, were distributed across the Twin Cities Region. For example, the municipal boundaries of the Twin Cities did not contain only urban core and inner city neighborhoods but also included urban and aging suburbs. Similarly, aging suburbs and high-income neighborhoods extended to the boundaries of the region, thus they were atypically closer to the city center. Therefore, our results support the work of others who have noted a recent blending of built environment and sociodemographic characteristics, resulting in reduced demarcation between the central city and its outlying suburban areas [19, 20]. Because both the central cities and the outlying areas in metropolitan U.S. are becoming more diverse in form and social composition [20, 21], reliance on single constructs of neighborhoods, such as population density or distance to central business district, may not adequately capture the complexity of neighborhood types.
The distributions of restaurants across neighborhoods in 2001 and 2011 were more varied than in 1993, suggesting that some neighborhoods became relatively more appealing to sit-down restaurants and perhaps less appealing to fast food restaurants. Specifically, we found only suburban edge neighborhoods had a lower percent of sit-down restaurants than did the inner city neighborhoods in 2001; however, the urban, aging suburb, and high-income suburb neighborhoods, similar to the suburban edge, also had a lower percent of sit-down restaurants than did the inner city in 2011. We observed two facts about 2011 that we did not observe for 1993—inner city neighborhoods had greater relative availability of sit-down restaurants than other neighborhoods (except for urban core) and inner city neighborhoods had greater absolute numbers of sit-down restaurants than other neighborhoods (except for urban core and older suburb, data not shown). These two observations were noteworthy. Although inner city neighborhoods consistently had the lowest household income during the observational period (data not shown), inner city neighborhoods had greater spatial access to sit-down restaurants than other neighborhoods in 2011. Residents in inner cities may therefore use sit-down restaurants more frequently than residents in other neighborhoods.
We found that, between 1993 and 2011, inner city neighborhoods experienced a greater increase in percent of sit-down restaurants compared with urban core, urban, and aging suburb neighborhoods (See Table S3 in Additional File 1). It is possible that the desire for cultural amenities, entertainment and other facilities in central cities is an important factor in the inner city population increases, as seen in evidence from New York, NY , Houston, TX , Washington, DC , and other cities or metropolitan areas . There is some evidence that as residential population increases, the demand for sit-down restaurants also increases [3, 26]. In addition, during our study period, U.S. inner cities transitioned from goods production sectors toward relatively place-bound service sector industries [20, 27], which includes restaurants [28, 29]. Similarly, we found that the numbers of sit-down restaurants and fast food restaurants in inner city neighborhoods in the Twin Cities Region increased greatly (by approximately 200 percent) between 1993 and 2011. Lester et al.  observed that, in twenty U.S. inner cities between 1990 and 2000, jobs in retail services replaced jobs lost in goods-producing industries. Retail- and service-dominated neighborhoods may provide a complementary environment for clustering of restaurants, food stores and other retail options . Similarly, improvements in transportation and landscaping may create a more spatially accessible and/or walkable features that attract service and retail options [30, 31]. During the study period, the Twin Cities experienced improvements in light rail, the park systems and new sports stadiums .
We also found a more varied distribution of food stores across neighborhoods in 2001 and 2011 that we did not see in 1993. Specifically, we found that aging suburb neighborhoods had a greater percent of supermarkets (i.e., fewer percent of grocery stores and convenience stores) than did the urban and high-income suburb neighborhoods in 2001 and 2011, but not in 1993. Such differences were driven largely by the great increase in the number of grocery stores and convenience stores in the high-income and suburban edge neighborhoods in comparison with increases in numbers of supermarkets in aging suburbs (data not shown). The higher percent of grocery and convenience stores in urban and high-income neighborhoods may compound barriers to accessing healthful foods if such foods are less available in grocery and convenience stores .
The percent of sit-down restaurants in urban core neighborhoods was stable during the observational period. This constant percent implies a “saturated” urban core with respect to the relative availability of sit-down restaurants. The unchanged relative availability of sit-down restaurants paralleled an increase in the well-educated population that predominately lived in urban core neighborhoods. This finding was similar to observation for Houston, TX , which also showed an increase in the well-educated population in urban core neighborhoods.
None of the time-varying covariates (i.e., changes in residential population density, median household income, percent of white and percent of single-family housing units) was associated with the change in percent of sit-down restaurants over time. For example, although the percent of sit-down restaurants in urban core changed little, residential population density in urban core increased by 12.6% in the years between 1993 and 2011 (data not shown); in contrast, residential population density in the high-income suburb increased little between 1993 and 2011 (data not shown), unlike the significant increase in the percent of sit-down restaurants in high-income suburb in the same period (data not shown). Thus, still unclear are the reasons for a continual increase in sit-down restaurants, instead of fast food restaurants, to locate in all neighborhoods except for urban core. This question should be examined in future research.
In the supermarket model, however, we found that an increased percent of supermarkets was associated with a smaller increase (or more rarely a decrease) in the percent of single-family housing units (See Table S4 in Additional File 1). These largely incompatible land uses—single-family housing and supermarkets – may have opened opportunities for urban planners to use regulatory tools (e.g., zoning) to introduce targeted food stores into the neighborhoods. These regulatory tools could side-step concerns/requirements such as intrusive light , sufficient parking , or increased traffic, thereby avoiding resistance to introducing a supermarket into neighborhoods with large increases of single-family housing.
Although we did not intend to examine the association between the individual neighborhood characteristics and relative availability of sit-down restaurants and supermarkets, we noticed that some individual neighborhood characteristics may co-vary with each other and jointly affect the distribution of food resources. For example, the urban core had the greatest residential and employment population densities and the greatest percentage of population aged between 15 and 29 years, factors that may jointly contribute to the fact that the highest percentage of sit-down restaurants was in the urban core. To disentangle the complexity undergirding the relationships among neighborhood built environment and sociodemographic characteristics, future examination of these associations should use individual-level data that target restaurant users.
The Twin Cities Region experienced multiple different economic conditions during the period of our study: economic expansion (1993–2007), economic recession (2007–2009), and economic recovery (2009–2011) . Nevertheless, we observed a steady increase in numbers of sit-down restaurants, fast food restaurants, supermarkets, grocery stores and convenience stores across all neighborhood types (data not shown). These increases were consistent with national reports, and they reflected the macroeconomic shifts in the retail food industry . Thus, neighborhoods had increasingly easy access to all foods regardless of neighborhood type.
Our analysis is novel because, unlike earlier related studies, we used a large and comprehensive set of variables to define neighborhoods, e.g., population and employment density, land use mix, population age, education level, race, and household income. In addition, although we examined only one large metropolitan (geographical) region, our method to assess associations between this complex group of neigborhood characteristics and food availability is generalizable. Our study has several caveats. First, the Twin Cities Region was notably more affordable for housing and transportation and offered more diverse housing choices compared with similar metropolitan areas . Those features may have fostered more convenient access to restaurants and small food stores. Second, the multidimensional class structure we identified by our data-driven approach is difficult to compare with class structure based on single features that other researchers have used. However, because of a lack of consistent association between individual neighborhood characteristics and specific food resource types , we elected to use our data-driven approach to characterize the neighborhood environment. Third, the marked undercount of food outlets in the D&B data may have introduced bias . We used the relative number (expressed as a percent) of sit-down restaurants and supermarkets to determine whether different neighborhood types had different relative numbers of these food resources. For example, if sit-down restaurants had a higher matched rate compared with fast food restaurants in urban core neighborhoods versus high-income suburb neighborhoods in the D&B data, we risked exaggerating the gap in the numbers of sit-down restaurants relative to total sit-down restaurants and fast food restaurants between urban core and high-income suburbs. Indeed, using direct field observation in the Chicago Metropolitan Statistical Area, Powell and colleagues validated the D&B food resource data and found that the matched rate of fast food restaurants differed by various neighborhood characteristics such as income, race, and location (urbanized area, urban cluster and non-urban area as defined by the US Census Bureau) . Because we used 13 built environment and sociodemographic characteristics to classify neighborhoods, future researchers should explore whether the matched rate of food outlets varies by the overall characteristics of the neighborhood. Fourth, the block group is probably too small to reflect the service area of restaurants and food stores, especially in suburban areas. However, census block group level data yield better estimates of the locations of food resources and households , compared with data from larger geographic units such as census tracts and zip codes. In addition, we could not obtain some retrospective built environment and sociodemographic data, such as traffic and crime, for the whole region, which have been suggested as relevant factors [42, 43].