urban built environments, accessibility, …density, land use mix, and connectivity—have been...

URBAN BUILT ENVIRONMENTS, ACCESSIBILITY,AND TRAVEL BEHAVIOR IN A DECLINING URBAN

CORE: THE EXTREME CONDITIONS OFDISINVESTMENT AND SUBURBANIZATION

IN THE DETROIT REGION

IGOR VOJNOVICMichigan State University

ZEENAT KOTVAL-KMichigan State University

JIEUN LEENew College of Florida

MINTING YEMichigan State University

TIMOTHY LEDOUXMichigan State University

PARIWATE VARNAKOVIDALake Superior University

JOSEPH MESSINAMichigan State University

ABSTRACT: The research explores the impact of socioeconomic and racial variables on acces-sibility to urban amenities and travel in compact urban built environments that have traditionallybeen viewed as improving access to daily destinations and promoting nonmotorized travel: urbanenvironments characterized by high densities, mixed land uses, and high connectivity. The studyfocuses on six neighborhoods in the Detroit region. Two neighborhoods are within the city itself,and predominantly poor and Black, and four of the neighborhoods are in the region surrounding thecity, and they are predominantly wealthy and White. This study at the neighborhood scale enablesan analysis into how class and race affect accessibility and travel in neighborhoods experienc-ing urban disinvestment and decline. The research shows that the traditional relationship between

Direct correspondence to: Igor Vojnovic, Department of Geography, Michigan State University, 116 Geography Building,East Lansing, MI 48824. E-mail: [email protected].

JOURNAL OF URBAN AFFAIRS, Volume 00, Number 00, pages 1–33.Copyright C© 2013 Urban Affairs AssociationAll rights of reproduction in any form reserved.ISSN: 0735-2166. DOI: 10.1111/juaf.12031

2 II JOURNAL OF URBAN AFFAIRS II Vol. 00/No. 00/2013

high densities, mixed land uses, high connectivity, greater accessibility, and pedestrian activity issignificantly weaker in declining inner cities.

More than any other elements, decentralization and dispersion define U.S. cities as we go intothe twenty-first century. Much research into the environmental, economic, and social dimensionsof the density of the built environment (high density vs. low density) has emerged, focusing inparticular on the coupling of ongoing decentralization with the rapid decline of central cities. Thiscombination of factors has followed clear lines of class and racial composition that have createdpoverty-stricken enclaves that are ethnically and racially entrenched. Such conditions of declinewithin cities are seen as facilitating perpetual cycles of disadvantage in shaping the “local burdensof place” (Vojnovic et al., 2013). Many U.S. cities are characterized by the coupling of extremeurban decline and inefficient suburbanization, including Youngstown and Dayton (Ohio), Buffalo(New York), Flint (Michigan), and others. However, with the release of 2010 Census data, Detroitemerges as the most dramatic example. The city of Detroit lost a quarter of its population duringthe decade 2000–2010—from a population of 951,270 in 2000 to 713,777 in 2010. This can beseen as a loss of some 24,000 people annually, over 63 people every day, or close to 3 peopleevery hour. Since the 1950s, when Detroit had 1.85 million people, its population has been cutby more than 60%. As one might imagine, severe local stresses become evident when a city builtto accommodate 1.85 million houses only some 700,000 within its boundaries. Chris Hansen(2010), on Dateline NBC, described the city’s condition in this way:

They litter the landscape, thousands and thousands of abandoned homes. And just like thesebuildings, Detroit is a shell of its former self. One third of the people here live in poverty.Almost half the adults are illiterate, and about 75 percent of kids drop out of school. I couldbe describing some ravaged foreign nation, but this is the middle of America.

Metro Detroit is now generally perceived as the U.S. urban region most affected by the extremeurban decline associated with economic globalization, deindustrialization, neoliberal policies, andexcessive suburbanization. This inefficient decentralization and dispersion, following clear classand racial dimensions, has come to define the U.S. model of white flight. In 1990, 75% of Detroit’spopulation was Black; by 2010, the Black population had increased to 83% (U.S. Census Bureau,2011). Decades of research on segregation and suburbanization in Detroit have shown that thesedevelopment patterns did not occur by chance, but were controlled by apartment house managers,real estate brokers, and builders, supported by wealthyWhite suburban residents (Thomas, 1997).

The scale of suburbanization in the Detroit region is effectively demonstrated by the fact thatthe conversion of agricultural and natural lands to urban uses between 1960 and 1990 occurred at arate that was 13 times greater than population growth in the area (Public Sector Consultants, 2001).Moreover, the scale of decentralization continues to escalate. Whereas the average residentialdensity of housing developments in the Detroit region was 2.84 units per acre in 1990, throughoutthe decade of the 1990s new construction was built at an average density of only 1.26 housingunits per acre, less than half the residential density of 1990 (SEMCOG, 2003a). Both the urbandecentralization in the region and the population exodus from the city are reflected in Detroit’sdeclining density, which fell from 13,249 people per square mile (ppsm) in 1950 to 5,170 ppsmin 2010 (U.S. Census Bureau, 2011). In terms of economic impact, Detroit’s suburbanization isparalleled by the decentralization of the region’s tax base and a polarization of fiscal capacitybetween the declining city and its wealthy suburbs. In 2000, the per capita taxable assessment in

II Built Environment, Accessibility, and Travel in a Declining Urban Core II 3

the city of Detroit was $7,573, while the per capita taxable assessment in Bloomfield Hills was$165,794 and $160,905 in Bingham Farms (SEMCOG, 2003b).

This polarization between the city and its suburbs is also reflected in basic socioeconomicprofiles. In 2008, while the national poverty rate in the United States was 13.2%, and while thepoverty rate in the Detroit-Warren-Livonia Metropolitan area stood at 9%, the poverty rate in thecity of Detroit was 33.1%. In addition, while in 2008 the national per capita income was $27,589,and while in the Detroit-Warren-Livonia Metropolitan area the per capita income was $27,624,the city of Detroit maintained an average per capita income of $14,976 (U.S. Census Bureau,2010a). Simply put, since the early 1960s, as the middle- and upper-income White populationmoved out, the city of Detroit has evolved into a place characterized by increasing concentrationsof unemployment, poverty, visible minorities, and crime.

In studies of Detroit, a particular point of discussion in recent years has been how urbandisinvestment affects access to urban amenities. For instance, with regard to public services, in2010 the Detroit Police Department employed less than 3,000 officers to cover the same spatialarea that in 1970 some 5,000 officers patrolled (Okrent & Gray, 2010). In 1970, the average drivefor a police cruiser from its precinct station to a crime scene was about three miles; today, thereare many homes that are more than 7.5 miles away from the nearest police station. Similar stressesin service provision confront the delivery of every other service within the City, including snowremoval, road maintenance, trash collection, and street lighting. Perhaps the most significantlocal pressure in service provision is the effect on public education. In the 2003–2004 academicyear, less than 25% of students graduated from the Detroit City School District’s high schools,the lowest graduation rate in the country among the 50 largest U.S. cities (Swanson, 2008).

Following the residents, businesses have also left the city. In Andrew Grossman’s (2009) WallStreet Journal article, “Retailers Head for Exits in Detroit,” he notes that even Starbucks, knownfor saturating U.S. cities with its coffee shops, has only four Detroit locations. Carrying on thistheme, Roy Greenslade (2009) of London’s Guardian describes Detroit as “a city where peoplepay $4 for a latte on one corner—if they can find it—and $10 for a rock of cocaine on the other.”

In this exodus of retail from the city, perhaps the most extensively covered topic by the mediain recent years has been the loss of all major supermarket chains once the last two Farmer Jacksclosed in 2007. As Grossman (2009, p. A3) notes in the Wall Street Journal:

No national grocery chain operates a store here. A lack of outlets that sell fresh produce andmeat has led the United Food and Commercial Workers union and a community group to thinkabout building a grocery store of its own.

In a similar spirit, Steve Hargreaves (2009), in a CNN piece called “Hunger Hits Detroit’s MiddleClass,” notes that “[f]ood has long been an issue in this city without a major supermarket. Nowdemand for assistance is rising, affecting a whole new set of people.” Hargreaves goes on todescribe the new value placed on food in Detroit:

On a side street in an old industrial neighborhood, a delivery man stacks a dolly of goodsoutside a store. Ten feet away stands another man clad in military fatigues, combat boots andwhat appears to be a flak jacket. He looks straight out of Baghdad. But this isn’t Iraq. It’ssoutheast Detroit, and he’s there to guard the groceries. “No pictures, put the camera down,”he yells.

In the context of Detroit’s rapid urban exodus, this article explores how urban decline hasaffected access to amenities within the city. Using built environment objective data and qualitativesurvey data, we examine the impact of socioeconomic variables on access to urban amenities


and travel in compact urban environments that have traditionally been viewed as improvingaccessibility to destinations and promoting nonmotorized travel. In other words, the study exploreshow access is affected by urban disinvestment and decline in neighborhoods characterized byhigher urban densities, a concentrated land use mix, and high connectivity. Within the existingurban morphology literature, the relationships between class, race, and the urban built environmentare recognized as underrepresented in research on community planning and design (USDHHS,2000; Day, 2003, 2006; Vojnovic, 2006).

The study focuses on six neighborhoods in the Detroit area. Two neighborhoods are in the city,and predominantly poor and Black, and four neighborhoods are in the surrounding region, andpredominantly wealthy and White. The neighborhood scale study enables an analysis into howclass affects accessibility, travel, and public health. The research shows that traditional relation-ships between higher densities, mixed land uses, higher connectivity, and greater accessibilityare not as strong—and can break down—in declining cores experiencing disinvestment. Simplyput, disinvestment reduces access to amenities, including personal services, leisure, and healthyfood options. The research reveals that socioeconomic variables can outweigh the importance ofurban form in shaping access, travel, and health outcomes.

LITERATURE REVIEW: THE BUILT ENVIRONMENT AND TRAVEL BEHAVIOR

Extensive research has focused on the impacts of the built environment on travel behavior, suchas mode of travel, travel distance, and travel frequency. Three characteristics in urban form—density, land use mix, and connectivity—have been found to have considerable impacts on travelby affecting accessibility across in-networked space. While high densities, a fine-grained mix andconcentration of land uses, and highly connected street systems facilitate nonmotorized travel,these environments do not allow motorists to realize the full speed capabilities of the car (Boarnet,2011; Cao, 2010; Southworth, 2005; Sui, 2003). In contrast, built environments characterized bylow densities, single-use zoning, and disconnected street networks, while less accommodatingto pedestrians, are successful in accommodating high-speed automobiles. These differences inurban form are evident between older inner cities (as in Boston or New York) that were builtprior to the diffusion of an affordable automobile, and post–World War II suburban landscapes ornewer cities (such as Houston or Phoenix), which were built largely to accommodate high-speedautomobile travel.

High densities, concentrated and mixed land uses, and high connectivity can reduce distancesbetween destinations—all else being the same—encouraging nonmotorized travel, such as walk-ing and bicycling (Boarnet, Joh, Siembab, Fulton, & Nguyen, 2011; Day, 2003, 2006; Moudon& Lee, 2003; Lee & Moudon, 2004; Vojnovic, 2000; Yang, Diez Roux, Auchincloss, Rodriguez,& Brown, 2011). The U.S. Department of Transportation (2005) has shown that keeping dis-tances between destinations at less than one-third of a mile would ensure that some 45% of theU.S. population would be willing to walk. These findings are consistent with a number of otherprior studies that have shown that maintaining short distances is a critical variable in promotingpedestrian activity (Ewing & Handy, 2009; Powell, Martin, & Pranesh, 2003; USDOT, 1994). Asnoted by the U.S. Department of Transportation (1994, p. 12), “distance is almost certainly thekey factor limiting utilitarian trips” by nonmotorized travel.

Built environments that promote the use of public transit maintain similar characteristics aspedestrian-oriented built environments, in part due to cost-effective transit network requirements,which necessitate higher densities and concentrated and mixed land uses. In addition, builtenvironments that accommodate mass transit must also have a pedestrian focus, because transitusers are also pedestrians; walking to the transit line and walking once they arrive at their


destination. As Newman and Kenworthy (2013, p. 236) maintain, to be successful “TODs [TransitOriented Development] must also be PODs [Pedestrian Oriented Development].”

Urban Density

Urban researchers have shown that increasing residential and employment concentrations overany given area of land can reduce distances between destinations and promote walking. PeterNewman and Jeffrey Kenworthy (1989) completed one of the early and seminal studies onthe relationship between population and employment densities and travel patterns, showing thatincreasing densities encouraged walking and public transit use. They replicated the study a decadelater (Newman & Kenworthy, 1999), and similar relationships were evident, although the U.S.population had become more automobile-dependent, with fewer people walking.

Other researchers that have focused on the relationship between urban densities and travel havefound similar outcomes, albeit to varying degrees (Cao, Mokhtarian, & Handy, 2007a; Mitchell,Hargreaves, Namdeo, & Echenique, 2011; Yang, French, Holt, & Zhang, 2012). For instance,Robert Cervero, who explored the relationship between urban environments and public transituse, argued that for every 10% increases in population and employment densities, transit ridershipincreased between 5% and 8% (Cervero, 1998, p. 72).

Land Use Mix

Studies have shown that a balanced mix and a concentration of different land uses (retail, com-mercial, residential) can reduce distances between daily activities and encourage nonmotorizedtravel (Boarnet, 2011; Ewing & Handy, 2009; Frank & Pivo, 1995; Saelens, Sallis, Black, & Chen,2003; Southworth, 1997). There are two dimensions to land uses, their mix and concentration,which affect two different types of accessibility (Ewing, 1997). One aspect to the considerationof land uses is the relationship between where people live and their proximity to out-of-homedestinations, such as work or shopping. With this type of accessibility (known as residentialaccessibility), the greater the mix of residential to nonresidential uses, the greater the likelihoodof reducing distances between one’s home and various destinations. Thus, improving residentialaccessibility (reducing distances between residential and nonresidential land uses) encourageswalking from one’s home to shopping, personal services, and other out-of-home activities.

In the current U.S. urban context, characterized by high levels of urban decentralization anddispersion, the importance of residential accessibility in shaping travel is evident in the jobs-housing balance and the commute to work. While some metropolitan areas have been successfulin maintaining a balance between jobs and housing throughout the region, many metropolitanareas, due to excessive suburbanization and the residential exodus from the core, have evolvedhighly unbalanced land uses, forcing long worker commutes. For instance, in 1997, the city ofHouston maintained some 137,000 jobs in its urban core, but only about 2,000 people livedwithin this district (Vojnovic, 2003). In the case of Detroit, for approximately every 16 jobsthere was only one person that lived in the central business district (CBD), while in Los Angelesthe CBD maintained some 18 jobs for every person that lived in the city’s core (Newman &Kenworthy, 1999). This regional spatial structure—the “donut effect,” as residents flee the cityto the suburbs—forces long work commutes as people living in the suburbs access employmentin the city center. It should be noted, however, that despite the suburb-to-urban commute to work,the most significant commuting pressures in the United States are suburb-to-suburb.

The second dimension to the land use mix addresses accessibility between various out-of-home destinations. High concentrations of daily activities (such as work, shopping, and personal


services) shortens distances between various daily destinations, improving a second type of ac-cessibility, destination accessibility (Ewing, 1997). A concentration of destination activities (asevident in downtowns, shopping malls, or suburban activity nodes) shortens distances betweenout-of-home activities, promoting walking and the use of public transit. Even if a person drivesinto an activity center, they can park the car and complete several objectives by walking. Thisdiscussion should lead to an understanding of why high concentrations of residential and nonres-idential land uses—such as in central New York City, San Francisco, or Boston—ensures bothresidential and destination accessibility, and facilitates high levels of nonmotorized travel.

Connectivity

Connectivity is determined by the extent to which different parts of a neighborhood, and alsodifferent neighborhoods, are linked to each other. At the most basic level, movement through a cityis restricted by infrastructure networks (such as streets), houses, fences, and geographic features(including mountains and waterways) (Griffith, Vojnovic, & Messina, 2012). In order to reducedistances between destinations, continuous rights of way must be provided within and betweenneighborhoods that allow for minimal distances between various destinations within a city.However, the irregular street networks in automobile-oriented urban environments, characterizedby curvilinear streets and cul-de-sacs, forces longer trip lengths between destinations and impedespedestrian travel (Owens, 1993; Ewing & Cervero, 2010).

Street networks in modern suburbs are designed for low connectivity, in part to ensure privacy bykeeping out through traffic and unwanted visitors (Vojnovic, 2006). The suburban discontinuousroad network was designed to be traveled by car at speeds of 40 mph, as opposed to pedestriantravel at 3 mph. The block structure within these urban environments is characterized by thedevelopment of residential, retail, and commercial pods, all highly isolated from each other.While Euclidean distance (the so-called “bird’s flight” distance) might be short, the street networkdistance, due to the lack of connectivity between the various pods, forces long travel distances. Incontrast, street networks characterized by the grid, porous block structures with highly connectedstreet networks, reduce distances between destinations and encourage walking (Baran, Rodriguez,& Khattak, 2008; Boarnet, 2011; Frank et al., 2005; Southworth, 1997, 2005).

Culture, Values, and Habits Shaping Travel Behavior

While urban form and accessibility play an important role in shaping travel, there are variablesother than distance that are equally important, or even more so, in affecting pedestrian activity.These include personal variables (such as values, habits, and lifestyles), environmental variables(such as safety, climate, topography, and quality of place), and peer group acceptance (Rapoport,1987; USDOT, 1994). Culture, in particular, has been viewed as critical in shaping behavior.Culture is a description of particular patterns in life and it is formed by the understanding of whatactivities are considered appropriate in particular settings. Many personal variables—includingvalues, habits, and preferences—that affect travel are shaped by culture. As Vojnovic, Smith,Kotval-K, and Lee argue (2008, p. 100), “designing pedestrian-inviting streetscapes will havelittle impact on encouraging non-motorized travel if the population considers walking and cyclingundesirable.” Similarly, Amos Rapoport (1987, p. 83) maintains that

Cultural variables are primary for any activity, including walking and others, occurring instreets. It is culture that structures behavior and helps explain the use or non-use of streets and


other urban spaces—or of other settings. Thus, the use of streets by pedestrians is primarily cul-turally based, since physical environments do not determine behavior. Physical environments,however, can be supportive or inhibiting.

Culture is seen as influencing travel at many different levels. For example, culture is consideredkey in shaping shopping preferences, and hence travel outcomes. While the French have a prefer-ence for traditional, small-scale local retailers (bakers, butchers, and fishmongers)—an importantelement of the French food culture—the British have been moving away from these traditionalstore types, increasingly preferring to shop in larger supermarkets (Pettinger, Holdsworth, &Gerber, 2007). The store of choice, or of preference, can thus be more important than the actualdistance or accessibility to a store in influencing travel patterns.

The influence of culture on travel behavior is also evident with neighborhood self-selection.Studies have shown that homebuyers who purchase housing in pedestrian-oriented neighborhoodsdo so, in part, because of specific values that place an importance on walking. For instance,Susan Handy’s (1996) study of Austin, Texas shows that residents walked more in high-density,connected neighborhoods with mixed land use. However, her research also revealed that residentspurchased housing in these pedestrian-oriented neighborhoods in part because of their preferencefor walking. Hence, personal values will influence neighborhood choice—whether to buy or notto buy a home in a walkable neighborhood—a decision that will reinforce whether specific urbanenvironments are, or are not, pedestrian-oriented.

Research on neighborhood self-selection also shows, however, that while self-selection willinfluence travel, characteristics in the built environment maintain a separate and significant impacton travel and pedestrian activity (Cao, 2010; Cao, Mokhtarian, & Handy, 2007b; Chatman, 2009).Cao, Mokhtarian, and Handy’s (2009) review of 38 travel behavior studies shows that, althoughmost research reveals evidence of self-selection, yet almost all studies found that when controllingfor self-selection there was a statistically significant influence of urban form on travel. Researchcontinues to show that while self-selection accentuates the impact of the built environment ontravel behavior, the built environment still maintains a separate influence on travel, an influenceequal to or greater than the influence of neighborhood self-selection. Thus, while residentsmight buy a house in a neighborhood because it is pedestrian-oriented, this pedestrian-orientedneighborhood—because of its physical qualities—will also facilitate walking and cycling.

Urban Disinvestment, Accessing Urban Amenities, and Travel Behavior

While the existing research on urban form, accessibility, and travel has demonstrated thathigher densities, mixed land uses, and increased connectivity encourage nonmotorized travel,this research has generally focused on urban environments with robust amenities. An importantaspect of the research that has not been explored is the access to amenities and the nature oftravel that develops in high-density, connected neighborhoods with mixed land use that are ex-periencing urban disinvestment and decline, as in Detroit. By exploring neighborhood structureand qualitative survey data, this research shows that the relationship between compact, connectedneighborhoods with mixed land use and pedestrian activity is not as easily replicable in areas ex-periencing decline. The lack of access to amenities in neighborhoods experiencing disinvestmentemerges as an important variable in shaping access and travel.

Some aspects of the relationship between access to urban amenities and urban decline have, toa certain degree, been covered by the food deserts literature. These studies, however, are narrowerin scope, generally focusing on the location of food sources and not the wider relationships oftravel to various destinations, including shopping, leisure, and personal services. Much of the fooddeserts literature also does not capture the travel component in shopping. This literature tends


to explore the concentration of store types by neighborhood, not examining whether residentsactually shop or eat at these locations. The dimension of store preference is simply not an elementin many of these studies.

An array of research in the United States has shown that predominantly minority and low-income urban neighborhoods have limited access to affordable, nutritious, and culturally appro-priate food sources (Howard & Fulfrost, 2007; Galvez et al., 2008). In comparison to more affluentand predominantly white urban and suburban neighborhoods, socially and economically disad-vantaged urban neighborhoods have fewer large-scale retail supermarkets and an overabundanceof convenience and liquor stores (Moore & Diez Roux, 2006; Powell, Slater, Mirtcheva, Bao, &Chaloupka, 2007a; Azuma, Gilliland, Vallianatos, & Gottlieb, 2010). Consequently, residents inthese neighborhoods face higher food prices and a limited availability of nutritious food stapleswhich when present tend to be of lower quality than those available to their suburban counterparts(Zenk et al., 2005, 2006; Franco, Diez Roux, Glass, Caballero, & Brancati, 2008; Smith et al.,2010).

Methodologically, many of these studies assume that the closest neighborhood shops are thestores of choice for residents. As a result, these studies fail to examine where people actuallyshop for their food provisions. Such an assumption ignores many of the insights garnered fromthe store choice and disadvantaged consumer literature which has shown that the shopping andtravel patterns of economically deprived consumers are multifaceted and complex. Similar tomore affluent shoppers, disadvantaged consumers tend to shop at retail supermarkets locatedoutside their local neighborhood, but rely on smaller neighborhood corner stores to supplementtheir food budgets (Piacentini, Hibbert, & Al-Dajani, 2001; Clifton, 2004; Gittelsohn et al.,2007).

Therefore, a critique of the food deserts research is that many of these studies rely on geographicproximity to link individual health data to the retail food environment. While some studies haveattempted to incorporate the shopping preferences and travel behaviors of residents living in afood desert (Inagami, Cohen, Finch, & Asch, 2006), much of the research assumes that residentsshop at their neighborhood stores when linking individual health data with the neighborhoodfood environment. Such an assumption not only distorts on-the-ground reality but also deniesthe agency of residents to overcome their adverse urban food environment (LeDoux & Vojnovic,2013).

CASE STUDIES AND METHODS

This study utilizes built environment objective data and qualitative survey data to analyzethe relationships between urban form, accessibility, and pedestrian activity in neighborhoodsexperiencing disinvestment and decline. The research focuses on six four-square-mile neigh-borhoods in the Detroit area. Two neighborhoods are in the city, in east side Detroit, and fourneighborhoods are in the Detroit region surrounding the city (Figure 1). The neighborhoodswere selected to allow some control for built environment and demographic characteristics. Us-ing census data, land parcel maps, and site surveys, six neighborhoods were selected based onincome, density, connectivity, and land use mix. Four suburban neighborhoods in the Detroitregion were selected with similarly high incomes, but two of these are of medium density, with amix of land uses, and high connectivity (Ann Arbor and Birmingham), and two of the neighbor-hoods are typically automobile-oriented, that is, low-density and low-connectivity, with restrictiveland uses (Bloomfield Hills and West Bloomfield). These four neighborhoods enable a compar-ison of different built environments among a similar socioeconomic subgrouping (Figures 2and 3).


FIGURE 1

Map of Detroit Region Neighborhoods

FIGURE 2

Photos of Ann Arbor and Birmingham Neighborhoods

Two Detroit urban neighborhoods (Figures 4 and 5) were selected that maintained mediumdensities, mixed land uses, and high connectivity (a built form comparable to neighborhoods inAnn Arbor and Birmingham) but that were experiencing disinvestment and decline (see Tables 1and 2). These four neighborhoods allow a comparison of similar built environments (in terms ofdensity, land use mix, and connectivity) but with two neighborhoods being of high socioeconomic


FIGURE 3

Photos of Bloomfield Hills and West Bloomfield Neighborhoods

status and two neighborhoods of low socioeconomic status. Information on land uses (residential,commercial, retail, and industrial) and building types (single family, duplexes, apartments, andfactories) were also collected (see Table 1 and Figures 6 and 7). Given the high number ofabandoned properties, the visible abandonment of housing was also documented.

A stratified random mail survey collected data on travel, exercise, diet, and other personalvariables. A total of 1,191 surveys were collected (128 from Detroit 1, 158 from Detroit 2,297 from Ann Arbor, 196 from Birmingham, 211 from Bloomfield Hills, and 201 from WestBloomfield). The eight-page survey included detailed questions on travel (frequency and purposeof trips, travel mode, and destinations) for a full array of trips (shopping, personal services,and leisure destinations). Respondents were asked to report travel behavior over a typical weekwhile considering seasonal distinctions (i.e., winter versus summer travel). For physical activitymeasures, respondents were asked about both moderate (casual walking, gardening, vacuuming)and vigorous (running, shoveling snow) physical activity throughout the week (frequency andlength). Some 8% of returned surveys were removed as outliers and because of insufficientdata.

The research team was confronted with a rapid increase in foreclosures and vacancies in theDetroit region as the growing mortgage crises spread across the United States. In a period ofthree months—from the point at which addresses of occupied residences were obtained fromthe local postal offices to the point at which random samples were selected and the projectintroduction prompts, survey packages, and the two reminder prompts were sent—a total of 909of the selected dwellings had been vacated. Over 75% of the vacated houses were in the Detroitneighborhoods.

The survey response rate was 20%. Given that the mail-out survey was administered to thegeneral population, this response rate is considered good (Sommer & Sommer, 1997). Researchhas shown that lower return rates can be expected among racial minorities, individuals withfewer years of schooling, high-density urban areas, and high crime rate neighborhoods (Groves& Couper, 1998; Siegel, 2002; Zimowski et al., 1997). In addition, response rates for householdtravel surveys in the United States have been declining, with a number of travel surveys reportingresponse rates as low as 5% (Zimowski et al., 1997). Given the socioeconomic, racial, and neigh-borhood characteristics of the Detroit region, the response rates from this project are comparableto other similar U.S. mail surveys. However, it should be acknowledged that the 20% responserate and limitation to six neighborhoods may limit the research findings.


FIGURE 4

Photos of Detroit East Side Neighborhoods

FINDINGS

Ethnic and Socioeconomic Profiles

In the two east side Detroit neighborhoods, about 92% of the respondents were non-White,while in the four suburban neighborhoods over 91% of the respondents were White (see Table 2).More specifically, in the Detroit neighborhoods, over 90% of the respondents were Black. Asimilar polarization between the urban and suburban respondents was also evident with socioeco-nomic profiles. In educational attainment, while about 70% of the Detroit urban respondents hadeither no high school degree or just a high school degree, over 82% of the suburban respondentshad either a 4-year college degree or graduate degree (Table 2). The lower educational attainmentin Detroit paralleled lower personal and household incomes.


FIGURE 5

Photos of more stable areas of the Detroit East Side Neighborhoods

TABLE 1

Urban Density, Land Uses, and Connectivity in the Detroit Region

DensityAnn Bloomfield West

Buildings per sq. mi. Detroit 1 Detroit 2 Arbor Birmingham Hills Bloomfield

Single family-detached home 1542.1 1024.7 1575.4 1416.3 424.0 716.3Semi-detached 0.0 2.0 6.7 0.0 0.0 19.0Apartment 2.0 5.6 42.9 19.6 19.4 8.4Townhomes/ rowhouses 3.0 14.2 8.5 18.8 25.1 30.2Retail 5.9 3.2 4.2 19.6 1.6 0.0Service 36.9 18.8 30.5 27.2 12.8 1.6Public institution 31.2 13.9 20.1 8.0 8.2 0.0Industrial 3.7 2.2 2.5 0.0 0.0 0.0Abandoned∗ 169.5 111.9 0.0 0.0 0.0 0.0

ConnectivityAnn Bloomfield West

Detroit 1 Detroit 2 Arbor Birmingham Hills BloomfieldIntersections per sq. mi. 77.7 49.2 31.1 41.4 5.2 5.2

∗Visibly abandoned buildings recorded during the land use surveys.Note: From the 2000 Census, which we used to define the case studies, the tracts that made up the two urban Detroitneighborhoods maintained a residential density of 6,314 people per square mile. It should be noted that a large segment ofDetroit 2 is a Chrysler industrial plant, evident in figures 6 and 7. If the area of the plant is not included in the calculationof density, the actual density of the two Detroit neighborhoods stands at 6,914 people per square mile. In the year 2000,the tracts that made up the higher density suburban municipalities maintained a density of 4,696 people per square mile. Incontrast, the tracts that made up the two lower density suburban neighborhoods maintained a density of 1,797 people persquare mile (U.S. Census Bureau, 2000; MCGI, 2010).By 2010, this would be two years past our survey collection, there was a significant urban exodus. In 2010, the density ofurban Detroit stood at 3,928 people per square mile, and if the Chrysler plant is removed, the figure becomes 4,227 peopleper square mile. The two higher density suburban neighborhoods in 2010 maintained a density of 4,723 people per squaremile, while the two lower density suburban neighborhoods maintained a density of 1,711 people per square mile (U.S. CensusBureau, 2010b; MCGI, 2010).The survey was completed in 2008, so it could be assumed that the density of the two urban Detroit neighborhoods was notas low as the 3,928 people per square mile, which was recorded for the 2010 Census. The two neighborhoods in urban Detroitlost a population of 2,386 people per square mile between the last two census decades (from 2000 to 2010), so a loss ofabout 477.2 people per square mile every 2 years. It could be reasonably assumed that even without removing the land fromthe Chrysler plant, the density of urban Detroit was around 4,405 people per square mile when the survey was taken in 2008.At 4,405 people per square mile, the density of the two Detroit neighborhoods was comparable to the higher density suburbanneighborhoods that averaged 4,723 people per square mile and much higher than the density of the two low density suburbsthat averaged 1,711 people per square mile.


TABLE 2

Neighborhood Respondents by Race, Education, and Household Incomes (%)

Ann Bloomfield WestDetroit 1 Detroit 2 Arbor Birmingham Hills Bloomfield

Race (% White /% Non-White)White (%) 5.00 10.64 93.68 94.18 91.63 84.21Non-White (%) 95.00 89.36 6.32 5.82 8.37 15.79

Educational Attainment (% of respondents in each category)No high school 15.97 20.92 0.00 0.00 1.92 3.06High school diploma 52.10 50.33 9.62 10.77 8.65 10.202-yr assoc. degree 13.45 10.46 4.81 6.15 6.25 7.144-yr college degree 11.76 8.50 31.27 34.36 35.10 31.63Graduate or professional degree 6.72 9.80 53.26 48.72 48.08 47.96

Household Incomes by Neighborhood (% of respondents in each category)Less than 20k 45.37 54.01 7.06 2.45 2.22 3.6420k to 40K 31.48 21.90 6.69 4.91 7.22 4.8540k to 60k 10.19 13.87 15.24 9.20 8.33 9.7060k to 100k 8.33 7.30 30.48 19.02 25.00 20.61100k to 150k 4.63 2.92 24.91 26.99 20.56 32.73Greater than 150k 0.00 0.00 15.61 37.42 36.67 28.48

Health Profiles

With self-reported values on height and weight, the body mass index (BMI) was calculated forrespondents in each neighborhood (Table 3). In adults, a BMI value of 18.5 to 24.9 is normal,a value of 25.0 to 29.9 is overweight, and a value of 30.0 or higher is obese. The average BMIvalue for respondents in the Detroit neighborhoods was 29.9, an overweight value bordering onobese. In contrast, the BMI values for respondents in the higher density suburbs (Ann Arbor andBirmingham) averaged 24.6, while for respondents in the low-density suburbs (Bloomfield Hillsand West Bloomfield) the average BMI was 24.8. As one would expect given the neighborhoodprofiles, there was a racial dimension to BMI. While average BMI across all six neighborhoodsfor Whites was 24.8, the average BMI for non-Whites was 28.6. The survey results also revealedthat Black respondents maintained the highest average BMI value at 29.4. Hence, while wealthysuburban respondents were in the normal weight category—regardless of whether they lived incompact or sprawling neighborhoods—the urban Detroit respondents were overweight, despiteliving in high-density and high-connectivity walkable neighborhoods.

With regard to engagement in physical activity (both moderate and vigorous), respondentsin the higher density suburbs spent most time per week exercising (an average of 81.2 min),respondents in low-density suburbs were second at 79.3 min, while respondents in the Detroiturban neighborhoods engaged in the least physical activity, 75.0 min. Table 3 shows the breakdownof total physical activity per week by neighborhood. In addition, White respondents, at 79.7 minper week, engaged in slightly more physical activity than non-White respondents, at 76.8 minper week. The assessment of accessibility and neighborhood travel patterns that follows providesadded insight into distinctions in BMI values across the neighborhoods.

Accessibility, Travel, and Perceived Versus Shortest Time Route Distances

Travel behavior within the six neighborhoods was examined with analyses on travel mode,travel frequency, and travel distance by neighborhood and activity function. Before the surveyfindings are explored, however, a brief review on the calculation of distance, as recorded by


FIGURE 6

The Urban Forms of the Six Detroit Region Neighborhoods Modeled in 3D CAD

survey participants and as used in the analyses, will be helpful. While each participant was askedto record the perceived distances between their home, work, leisure, shopping, and services, theywere also asked to include the exact addresses of the end point locations for all the differentactivities. All the locations were found through site surveys and mapping programs, and thenrecorded. Surveys with insufficient destination data were removed from the study. Since all thesurveys had codes to household addresses, the starting and end points of all trips were known.This allowed all travel distances to be referenced through Google maps, over 15,000 trips, basedon the algorithm that determines the distance of the shortest time route between destinations. Byexplicitly controlling starting and end points, this approach to calculating distance is expected toreduce errors associated with self-reported perceived distances.

Mode of Travel: Walking/Cycling, Public Transit, and the Car

In examining mode of travel, the degree of automobile dependence in both the urban andsuburban settings is immediately evident. Only 17.4% of trips, across all six neighborhoods andacross all activities, are by walking or cycling. The reliance on mass transit is even lower thanby nonmotorized means, with only 4.2% of overall trips by public transit. In general, however,the assessment of travel mode by neighborhood does reveal that higher densities, mixed land


FIGURE 7

Land Uses in the Detroit Region Neighborhoods

uses, and well-connected neighborhoods do encourage both travel by nonmotorized means andby mass transit (Tables 4–6).

In the two Detroit urban neighborhoods, over 22.1% of all travel was by walking and cyclingand over 11.5% of all travel was by public transit. Similarly, in the two more compact suburbs,over 22.1% of all trips was by walking and cycling, while transit use was considerably lower,making up only 3.5% of all trips. In contrast, in the two low-density suburbs, less than 8.3%of all trips were by walking and cycling and less than 0.5% of all trips were by public transit.


TABLE 3

Average BMI and Average Time Spent on Moderate and Vigorous Exercise per Week (mins.) byNeighborhood


Average BMI by neighborhood.Average BMI∗ 30.16 29.46 24.39 25.01 25.24 24.26

Average time spent on moderate and vigorous exercise by neighborhood per week (mins.).Average per week (in mins.) 71.06 78.49 81.68 80.55 75.82 82.79

∗In adults a BMI value of 18.5 to 24.9 is normal, a value of 25.0 to 29.9 is overweight, and a value of 30.0 or higher is obese.

TABLE 4

Percentage of Walking/Cycling Trips in Aggregate by Neighborhood and Travel Activity, With Frequen-cies on a Weekly Basis Provided in Brackets

ByWork School Shopping Restaurant Service Leisure neighborhood

Detroit 1 13.7% 7.8% 26.1% 16.6% 12.8% 18.6% 17.8%(37/270) (5/65) (103/395) (39/232) (36/283) (46/247) (266/1491)

Detroit 2 17.0% 29.2% 29.4% 31.5% 20.5% 25.5% 25.6%(48/282) (24/81) (178/606) (82/259) (64/310) (79/308) (473/1845)

Ann Arbor 22.2% 26.1% 14.0% 31.3% 24.1% 45.7% 27.4%(236/1066) (37/142) (129/917) (131/420) (138/574) (457/998) (1128/4117)

Birmingham 9.4% 22.2% 12.3% 15.2% 12.9% 21.3% 14.0%(53/567) (4/18) (92/744) (47/312) (61/472) (112/524) (367/2636)

Bloomfield Hills 4.4% 26.0% 7.6% 6.3% 6.3% 10.3% 7.5%(24/550) (16/62) (56/732) (20/319) (33/530) (55/537) (204/2728)

West Bloomfield 12.1% 55.0% 3.3% 5.4% 3.8% 18.8% 9.1%(75/618) (11/20) (22/659) (17/304) (19/484) (92/492) (234/2576)

By activity 14.1% 25.0% 14.3% 18.2% 13.2% 27.1% 17.4%∗(473/3352) (97/386) (579/4052) (335/1845) (351/2651) (840/3106) (2674/15392)

∗Percentage of walking/cycling for all six neighborhoods and for all travel purposes.

This translates into 66.3% of overall trips in urban Detroit by car, 74.3% of all trips in the higherdensity suburbs (Ann Arbor and Birmingham) by car, and 91.3% of overall trips in the low-densitysuburbs (Bloomfield Hills and West Bloomfield) by car.

While lower levels of automobile reliance in urban Detroit are influenced by the built envi-ronment, the survey reveals that socio-economic conditions also play a role in shaping travel.Lower income levels in urban Detroit are partly responsible for contributing to lower levels ofautomobile ownership (Table 7). While the average number of operating vehicles per householdin the Detroit neighborhoods was 0.67, the suburbs maintained some 2.5 times more operatingvehicles per household, averaging 1.71 vehicles in the higher density suburbs and 1.85 vehiclesper household in the low-density suburbs.

Accessing Amenities: Exploring Distance and Travel Frequency

In analyzing accessibility, four types of amenities will be assessed—restaurants, leisure, per-sonal services, and food shopping—through the exploration of distance and travel frequency.


TABLE 5

Percentage of Public Transit Trips in Aggregate by Neighborhood and Travel Activity, With Frequencieson a Weekly Basis Provided in Brackets


Detroit 1 12.4% 34.5% 7.1% 9.1% 11.5% 10.9% 11.0%(34/270) (22/65) (28/395) (21/232) (33/283) (27/247) (164/1491)

Detroit 2 16.8% 28.6% 12.7% 4.2% 6.1% 13.8% 11.9%(48/282) (23/81) (77/606) (11/259) (19/310) (43/308) (220/1845)

Ann Arbor 11.7% 32.2% 1.9% 2.1% 3.1% 1.5% 5.6%(125/1066) (46/142) (18/917) (9/420) (18/574) (15/998) (229/4117)

Birmingham 0.7% 0.0% 0.5% 0.3% 0.2% 0.2% 0.4%(4/567) (0/18) (4/744) (1/312) (1/472) (1/524) (11/2636)


West Bloomfield 0.0% 0.0% 0.0% 0.7% 0.0% 0.8% 0.2%(0/618) (0/20) (0/659) (2/304) (0/484) (4/492) (6/2576)

By activity 6.4% 24.8% 3.2% 2.5% 2.7% 2.9% 4.2%∗(216/3352) (96/386) (128/4052) (46/1845) (72/2651) (91/3106) (648/15392)


TABLE 6

Percentage of Driving Trips in Aggregate by Neighborhood and Travel Activity, With Frequencies on aWeekly Basis Provided in Brackets


Detroit 1 73.9% 57.8% 66.8% 74.3% 75.7% 70.5% 71.1%(200/270) (37/65) (264/395) (172/232) (214/283) (174/247) (1061/1491)

Detroit 2 66.1% 42.2% 57.9% 64.3% 73.4% 60.7% 62.4%(187/282) (34/81) (351/606) (167/259) (227/310) (187/308) (1152/1845)

Ann Arbor 66.2% 41.7% 84.0% 66.6% 72.8% 52.8% 67.0%(705/1066) (59/142) (771/917) (279/420) (418/574) (527/998) (2759/4117)

Birmingham 89.9% 77.8% 87.1% 84.5% 86.9% 78.5% 85.6%(510/567) (14/18) (648/744) (263/312) (410/472) (412/524) (2256/2636)


West Bloomfield 87.9% 45.0% 96.7% 93.9% 96.2% 80.4% 90.7%(543/618) (9/20) (637/659) (286/304) (465/484) (395/492) (2335/2576)

By activity 79.5% 50.2% 82.6% 79.4% 84.1% 70.0% 78.4%∗(2663/3352) (194/386) (3345/4052) (1464/1845) (2229/2651) (2174/3106) (12070/15392)


TABLE 7

Number of Vehicles in Operating Condition by Household in Each Neighborhood


Cars per household 0.7 0.6 1.7 1.8 1.8 1.9


TABLE 8

Mean/Median Distance and Monthly Frequency of Trips to Restaurants by Neighborhood Type∗

Urban High-density suburb Low-density suburb

Fast food chainsMean minimal distance (mi) 2.0 mi 2.7 mi 3.8 miMedian minimal distance (mi) 1.2 mi 2.2 mi 2.8 miNumber of trips per month 5.7 1.2 2.3

Coffee Shops & BakeriesMean minimal distance (mi) 0 1.7 mi 4.0 miMedian minimal distance (mi) 0 1.2 mi 3.2 miNumber of trips per month 0 0.4 0.4

Upscale restaurantsMean minimal distance (mi) 0 2.1 mi 5.4 miMedian minimal distance (mi) 0 1.5 mi 4.5 miNumber of trips per month 0 0.6 0.6

Casual chainsMean minimal distance (mi) 10.8 mi 3.7 mi 5.1 miMedian minimal distance (mi) 10.0 mi 2.7 mi 4.6 miNumber of trips per month 0.3 0.5 0.8

Ethnic restaurantsMean minimal distance (mi) 2.8 mi 2.2 mi 4.1 miMedian minimal distance (mi) 1.0 mi 1.3 mi 3.0 miNumber of trips per month 0.1 0.7 0.7

OtherMean minimal distance (mi) 4.3 mi 2.1 mi 4.2 miMedian minimal distance (mi) 2.1 mi 1.4 mi 3.8 miNumber of trips per month 0.3 1.7 1.0

ALL RESTAURANTSMean minimal distance to 2.5 mi 2.4 mi 4.2 miALL RESTAURANTS (mi)

Median minimal distance to 1.3 mi 1.6 mi 3.2 miALL RESTAURANTS (mi)

Number of trips per month 6.4 5.2 5.6Percent of trips to ALL 24.6% 24.3% 6.0%RESTAURANTS by walking

Percent of trips to ALL 7.0% 1.4% 0.6%RESTAURANTS by transit

∗Because of rounding-off, some values might be 0 at the individual neighborhood level, but they will affect average distanceand trip frequency values at the neighborhood type level, where the two neighborhoods in each classification and their travelcharacteristics are consolidated.

Restaurants

When distances to restaurants are observed by neighborhood type, predictable patterns emerge(Tables 8 and 9). On average, higher density, mixed land use, and connected neighborhoodshave better access to restaurants than low-density, single-use, and disconnected neighborhoods.While average distances to all restaurants were the least in the urban and higher density suburbanneighborhoods, residents of the low-density suburbs confronted an average distance almost twotimes greater than the distances in the other four higher density neighborhoods. In terms ofmonthly frequency to restaurants, urban respondents ate at restaurants an average of 6.4 times permonth, low-density suburban respondents frequented restaurants an average of 5.6 times, whilehigher density suburban respondents averaged 5.2 visits to restaurants on a monthly basis.


TABLE 9

Mean/Median Distance and Monthly Frequency of Trips to Restaurants by Neighborhood


Fast food chainsMean minimal distance (mi) 2.4 mi 1.6 mi 2.3 mi 3.0 mi 3.5 mi 4.0 miMedian minimal distance (mi) 1.6 mi 1.0 mi 2.2 mi 2.3 mi 2.7 mi 3.0 miNumber of trips per month 6.1 5.4 1.0 1.6 2.3 2.3

Coffee shops & bakeriesMean minimal distance (mi) 0 0 1.6 mi 2.1 mi 4.6 mi 3.0 miMedian minimal distance (mi) 0 0 0.9 mi 2.4 mi 3.1 mi 3.7 miNumber of trips per month 0 0 0.5 0.2 0.4 0.3

Upscale restaurantsMean minimal distance (mi) 0 0 1.5 mi 2.5 mi 5.9 mi 4.5 miMedian minimal distance (mi) 0 0 1.2 mi 2.0 mi 4.0 mi 3.6 miNumber of trips per month 0 0 0.4 1.0 0.7 0.5

Casual chainsMean minimal distance (mi) 12.2 9.1 mi 4.2 mi 2.8 mi 6.8 mi 4.2 miMedian minimal distance (mi) 11.25 9.4 mi 3.1 mi 1.5 mi 6.0 mi 3.5 miNumber of trips per month 0.4 0.3 0.5 0.5 0.5 1.1

Ethnic restaurantsMean minimal distance (mi) 0 2.7 mi 2.0 mi 3.1 mi 5.4 mi 3.2 miMedian minimal distance (mi) 0 0.8 mi 1.2 mi 1.8 mi 4.9 mi 2.6 miNumber of trips per month 0 0.2 0.9 0.5 0.5 0.8

OtherMean minimal distance (mi) 0 4.3 mi 1.3 mi 3.3 mi 4.7 mi 3.4 miMedian minimal distance (mi) 0 1.7 mi 1.1 mi 2.7 mi 4.5 mi 2.2 miNumber of trips per month 0 0.5 1.7 1.8 1.2 0.7

ALL RESTAURANTSMean minimal distance to 3.0 mi 2.2 mi 2.0 mi 3.0 mi 4.6 mi 3.9 miALL RESTAURANTS (mi)

Median minimal distance to 1.7 mi 1.0 mi 1.4 mi 2.0 mi 3.6 mi 3.0 miALL RESTAURANTS (mi)

Number of trips per month 6.6 6.3 5.0 5.5 5.6 5.7Percent of trips to 18.6% 25.5% 31.3% 12.3% 6.3% 5.4%ALL RESTAURANTS by walking

Percent of trips to 9.1% 4.2% 2.1% 0.3% 0.6% 0.7%ALL RESTAURANTS by transit

However, once trips are disaggregated by restaurant type, some important distinctions emergeby neighborhood in the access and the frequency of visits. While major fast food chains(McDonald’s, Burger King, and KFC) were located in the east side Detroit neighborhoods,many other restaurant types—healthier restaurant options—were located primarily in the vicinityof the wealthier suburbs, and were generally not frequented by the urban respondents. Whileurban residents traveled an average of 2.0 miles to fast food restaurants, higher density suburbanresidents traveled an average of 2.7 miles, and low-density suburban residents traveled an aver-age of 3.8 miles to fast food establishments. Urban residents also ate at fast food chains mostoften—5.7 times per month versus 1.2 times per month for residents living in higher densitysuburbs, and 2.3 times per month for residents living in low-density suburbs. Some 90% of alldining out experiences by urban respondents were in fast food restaurants, compared to 24% byhigher density suburban respondents and 41% for lower density suburban respondents.


Prior research has shown that higher obesity rates are prevalent among populations that have ahigher reliance on fast food outlets, due to the higher fat content of food served in these restaurants(Block, Scribner, & DeSalvo, 2004; Maddock, 2004; Powell, Chaloupka, & Bao, 2007b). TheDetroit research, however, shows that due to location decisions by restaurant owners, non–fastfood restaurants are not locating close to the urban neighborhoods. Healthier food sources aresimply not that accessible to the urban poor, despite the fact that they live in built environments thatshould improve access. Because of the greater distances Detroit residents have to travel to reachthese destinations, they are not as reliant on healthy restaurant options as the suburban wealthy. Inaddition, the poor access to healthy restaurants is likely a variable that plays an important role inthe higher BMI values among Detroit respondents, despite the built environment characteristicsof their neighborhoods.

This analysis of access to healthier restaurant options shows that characteristics in the urbanbuilt environment represent only one element in defining accessibility. High densities, mixedland uses, and connectivity are important built environment attributes to shortening distancesbetween destinations and encouraging nonmotorized travel, but the equitable spatial investmentin urban amenities is also vital in shaping access. If amenities are not located in a neighborhood,built environment characteristics can do little in reducing distances between destinations. Urbandisinvestment, resulting in the absence of amenities, thus emerges as a variable that shapes bothtravel behavior (by limiting available destinations) and diet (by limiting access to food options).This is clearly evident in east side Detroit, where the residents, despite living in neighborhoodscharacterized by higher densities, mixed land uses, and high connectivity, have to travel thegreatest distances to access healthy restaurant options.

Leisure and Personal Services

In comparing the wealthier suburbs, consistent with the traditional literature on urban formand travel, when it comes to accessing leisure activities (theaters, parks, skating rinks, etc.) andpersonal services (doctors, banks, dry cleaners, etc.), it is evident that the higher density, higherconnectivity, mixed land use neighborhoods confront lower distances to these destinations thanthe low-density, low-connectivity, single-use neighborhoods (Tables 10 and 11). Ann Arbor inparticular, with its robust activity center, maintains a high concentration of leisure activitiesand personal services surrounded by a high concentration of residential dwellings, ensuringhigh accessibility. Ann Arbor residents maintained the shortest distances to leisure and personalservices and it is reflected in some of the highest rates of walking/biking to these activities. Some46% of all trips to leisure destinations in Ann Arbor were by walking and biking, and about 38%of all trips to personal services and leisure activities combined were by nonmotorized means. Incontrast, in the low-density suburbs, distances to leisure and personal services increase, as doesautomobile dependence. Average distances to leisure and personal services in the low-densitysuburbs are approximately double the distances in Ann Arbor, and it results in less than 10% ofall trips to these destinations being by nonmotorized means.

In the case of leisure and personal services, urban Detroit neighborhoods again illustrate theimportance of socioeconomic variables in defining accessibility. Tables 10 and 11 show thataccess to leisure and personal services in the Detroit neighborhoods falls somewhere betweenthe high-density and low-density suburbs, despite the higher densities, mixed land uses, andincreased connectivity in urban Detroit. In reaching leisure activities, while urban mean dis-tances for residents of the Detroit neighborhoods approach the high density suburbs, the mediandistances are similar to those in the low-density suburbs. For personal services, urban Detroitneighborhoods maintain accessibility characteristics, in terms of mean and median distances, thatare similar to those in the low-density suburbs, despite the higher densities, mixed land uses, and


TABLE 10

Mean/Median distance and Monthly Frequency of Trips to Leisure Activities and Personal Services byNeighborhood Type


Leisure activitiesMean minimal distance (mi) 6.4 mi 5.4 mi 11.6 miMedian minimal distance (mi) 5.0 mi 1.5 mi 5.0 miNumber of trips per month 4.3 7.0 6.9Percent of trips to leisure activities bywalking (%)

22.5% 37.4% 14.3%

Percent of trips to leisure activities bytransit (%)

12.6% 1.1% 0.1%

Personal servicesMean minimal distance (mi) 3.4 mi 2.1 mi 3.5 miMedian minimal distance (mi) 2.0 mi 1.4 mi 2.1 miNumber of trips per month 6.7 5.7 8.7Percent of trips to personal servicesby walking (%)

16.9% 19.0% 5.1%

Percent of trips to personal servicesby transit (%)

8.7% 1.8% 0.1%

TABLE 11

Mean/Median Distance and Monthly Frequency of Trips to Leisure Activities and Personal Services byNeighborhood


Leisure activitiesMean minimal distance (mi) 5.8 mi 6.9 mi 4.6 mi 6.6 mi 12.7 mi 10.4 miMedian minimal distance (mi) 4.9 mi 5.0 mi 1.4 mi 1.7 mi 4.6 mi 6.4 miNumber of trips per month 3.9 4.7 7.5 6.2 7.0 6.8Percent of trips to leisureactivities by walking

18.6% 25.5% 45.7% 21.3% 10.3% 18.8%

Percent of trips to leisureactivities by transit

10.9% 13.8% 1.5% 0.2% 0.4% 0.8%

Personal servicesMean minimal distance (mi) 4.2 mi 2.7 mi 1.9 mi 2.3 mi 3.6 mi 3.5 miMedian minimal distance (mi) 2.8 mi 1.4 mi 1.3 mi 1.7 mi 1.8 mi 2.4 miNumber of trips per month 6.3 7.1 3.8 7.1 8.6 8.8Percent of trips to personalservices by walking (%)

12.8% 20.5% 24.1% 12.9% 6.3% 3.8%

Percent of trips to personalservices by transit (%)

11.5% 6.1% 3.1% 0.2% 0.2% 0%

high-connectivity characteristics of the urban neighborhoods. This lack of access to leisure andpersonal services in east side Detroit—including doctors, pharmacists, dentists, banks, theaters,and parks—is not the result of built environment characteristics, but rather the result of urban dis-investment and the resulting lack of amenities within the Detroit neighborhoods, demonstratingagain that socioeconomic conditions can thwart the advantages of urban form.


TABLE 12

Mean/Median Distance and Monthly Frequency of Trips to Grocery Outlets by Neighborhood Type


National/regional supermarketsMean minimal distance (mi) 5.5 2.2 3.1Median minimal distance (mi) 4.6 1.7 2.7Number of trips per month 5.0 7.6 10.6

Convenience storesMean minimal distance (mi) 1.7 0 0Median minimal distance (mi) 1.0 0 0Number of trips per month 1.2 0 0

Boutique grocery storesMean minimal distance (mi) 0 1.9 4.9Median minimal distance (mi) 0 1.7 4.4Number of trips per month 0 3.8 2.0

PharmaciesMean minimal distance (mi) 2.1 0 1.8Median minimal distance (mi) 1.4 0 1.1Number of trips per month 0.6 0 0.1

Farmer’s marketsMean minimal distance (mi) 3.6 1.3 0Median minimal distance (mi) 2.7 1.3 0Number of trips per month 0.3 0.8 0

Independent supermarketsMean minimal distance (mi) 1.9 2.0 3.4Median minimal distance (mi) 1.4 1.7 3.4Number of trips per month 5.5 0.7 0.6

Other storesMean minimal distance (mi) 1.8 2.9 0Median minimal distance (mi) 1.3 1.8 0Number of trips per month 0.8 0.1 0

ALL STORESMean minimal distance (mi) 3.3 2.0 3.4Median minimal distance (mi) 1.6 1.6 2.9Number of trips per month 13.5 13.0 13.3Percent of trips by walking 28.1% 13.3% 5.6%Percent of trips by transit 10.5% 1.3% 0%

Grocery Outlets

As recognized in the national media coverage, accessing food in Detroit is an issue that hasbeen dramatically affected by urban disinvestment. However, there is an important dimension ofshopping behavior in Detroit that contradicts the basic assumptions behind travel behavior andmuch of the food desert literature. For east side residents, despite living in urban environmentscharacterized by higher densities, mixed land uses, and high connectivity, access to food shopping(when examining all store types) is far poorer than the access enjoyed by high-density suburbanresidents, who live in similar built environments (see Table 12). Lower-income urban respondentsare particularly burdened with great distances to national/regional supermarket chains (such asMeijer and Kroger), which existing literature has already shown to have the greatest healthy foodoptions at the highest quality, and at the lowest prices. Detroit neighborhood respondents travelmore than 5.5 miles (mean distance) to reach major supermarket chains. This is more than twicethe average distance travelled by respondents living in the four suburban neighborhoods, whoseaverage distance to a major supermarket chain is about 2.6 miles. This greater distance resulted in


TABLE 13

Mean/Median Distance and Monthly Frequency of Trips to Grocery Outlets by Neighborhood


National/regional supermarketsMean minimal distance (mi) 7.7 4.5 2.4 1.9 3.1 3.0Median minimal distance (mi) 6.4 3.5 1.9 1.3 2.6 2.8Number of trips per month 3.7 6.1 6.7 8.9 10.3 10.8

Convenience storesMean minimal distance (mi) 1.5 1.8 0 0.7 0 0Median minimal distance (mi) 0.9 1.1 0 0.7 0 0Number of trips per month 0.7 1.7 0 0.1 0 0

Boutique grocery storesMean minimal distance (mi) 0 0 1.9 2.0 5.3 4.2Median minimal distance (mi) 0 0 1.4 1.9 8.8 3.5Number of trips per month 0 0 3.5 4.4 2.3 1.7

PharmaciesMean minimal distance (mi) 2.1 1.9 0 0 0.8 2.5Median minimal distance (mi) 1.4 1.1 0 0 0.7 2.7Number of trips per month 1.2 0.1 0 0 0.1 0.1

Farmer’s marketsMean minimal distance (mi) 2.8 5.2 1.3 0 0 0Median minimal distance (mi) 2.7 5.2 1.3 0 0 0Number of trips per month 0.4 0.2 1.3 0 0 0

Independent supermarketsMean minimal distance (mi) 2.4 1.7 1.2 2.1 3.4 3.7Median minimal distance (mi) 1.4 1.4 1.4 1.8 3.4 4.5Number of trips per month 4.5 6.3 0.2 1.6 1.1 0.1

Other storesMean minimal distance (mi) 1.7 2.2 1.0 6.0 0 0Median minimal distance (mi) 1.2 2.5 0.6 6.0 0 0Number of trips per month 1.3 0.4 0.1 0.1 0 0

ALL STORESMean minimal distance (mi) 3.9 2.9 2.1 2.0 3.5 3.2Median minimal distance (mi) 2.0 1.5 1.7 1.5 2.8 3.0Number of trips per month 11.8 14.9 11.8 14.9 13.9 12.7Percent of trips by walking 26.1% 29.4% 14.0% 12.3% 7.6% 3.3%Percent of trips by transit 7.1% 12.7% 1.9% 0.5% 0.2% 0%

a much lower reliance on national/regional supermarket chains by east side Detroit respondentswhen compared to respondents living in the higher and lower density suburbs (Table 13). As aresult, low-density suburban respondents visited major supermarket chains more than twice asmany times per month as the Detroit respondents.

Another measure of neighborhood accessibility involves path distance calculations of minimalin-network distances to the closest major supermarket chain, involving every house in eachneighborhood. Major discount supermarkets were included in this analysis, such as Aldi’s, whichis located in the Detroit 2 neighborhood. The average in-network distance for all householdsby neighborhood to the closest major supermarket illustrates again the lower accessibility tosupermarkets among Detroit residents (see Figure 8). While the shortest average mean distanceto a major supermarket for the Detroit residents was 2.03 miles, the shortest average distancewas only 1.55 miles for low-density suburban residents (Bloomfield Hills and West Bloomfield),and 1.09 miles for high-density suburban residents (Ann Arbor and Birmingham). The Detroit


FIGURE 8

Average In-Network Distance to Closest Supermarket Chain by Neighborhood

residents, despite living in built environments where higher quality food sources are traditionallythought to be more accessible (because of density, land use mix, and connectivity), actuallylived furthest from major supermarket chains and were less reliant on these stores for shoppingthan even respondents living in low-density, disconnected suburbs. Existing literature has shownthat major supermarket chains tend to follow wealthy residents into suburban locations. Inaddition to following purchasing power, the suburbanization of supermarkets is encouraged byperceptions and realities of crime—and lower insurance premiums—in the suburbs compared to


FIGURE 9

Photos of Independent Supermarkets in East Side Detroit Neighborhoods

what are perceived as riskier inner city locations (Bromley & Thomas, 1993; Pothukuchi, 2005;Pothukuchi, Mohamed, & Gebben, 2008; Teaford, 2006).

As much of the food deserts literature recognizes, while the Detroit respondents are mostdistant from major supermarket chains, they are surrounded by and maintain high accessibilityto convenience and party stores. As a result, urban respondents shop at convenience stores morefrequently than suburban respondents, who essentially have no reliance on convenience stores forfood (Table 12). However, unlike what is assumed by most of the food deserts literature, Detroitresidents are not very reliant on convenience stores for their shopping either. East side Detroitrespondents shopped at convenience stores for food an average of only 1.2 times per month.Farmer’s markets were also largely irrelevant as a food source in east side Detroit, with onlysome 0.3 trips per respondent per month being made to a farmer’s market to purchase food.

While the media effectively captured the loss of Detroit’s major supermarket chains, it isimportant to recognize that there has been an exaggeration of the loss of food sources in the city.Independent supermarkets in Detroit have been almost exclusively overlooked in the nationaland international media coverage. In fact, for Detroit urban residents, the most important storesfor grocery shopping are local independent supermarkets (such as Public Foods and Food TownSupermarket; see Figure 9). While Detroit urban respondents shopped at smaller independentsupermarkets an average of over 5.5 times per month, for suburban respondents independentsupermarkets were largely irrelevant for shopping, accounting for less than one visit per month.

For Detroit respondents, 41% of all grocery shopping trips were to independent supermarkets,while only some 5% of shopping trips were to independent supermarkets for suburban respon-dents. In contrast, while some 37% of all shopping trips for groceries were to major supermarketchains for Detroit respondents, about 70% of all shopping trips were to major supermarket chainsfor suburban respondents. The second most important shopping destination in the suburbs wasboutique grocery stores, such as upscale delis and health food stores.

Much of the food deserts literature does not acknowledge this distinction between whereresidents shop and the location of the closest store. There are few studies that analyze wherepeople actually shop for food, and, as this research has shown, the closest store is not necessarilythe store of choice.

The importance of preference is also seen in the shopping behavior at major supermarkets andit is in part reflected in the average distances shown in Tables 12 and 13 and Figure 8. FromFigure 8, it is evident that while the shortest average distance to a major supermarket chain for


the Detroit east side residents was about 2.0 miles, the east side residents actually travelled some5.5 miles on average to access their supermarket of preference.

This analysis illustrates that neighborhoods with built environments traditionally viewed asensuring increased accessibility—neighborhoods with higher densities, mixed land use, andgreater connectivity—do not guarantee shorter distances. An equitable spatial investment in urbanamenities will also be influential in shaping access. Without an equitable spatial investmentin amenities, as evident with major national/regional supermarket chains in Detroit, certaindestinations will remain distant from some population subgroups.

In addition, it is not just that Detroit urban respondents traveled a greater average distanceto reach major national supermarkets when compared to the low-density suburban respondents.Respondents from the higher density urban neighborhoods and respondents from the low-densitysuburbs traveled similar average distances to reach “all shopping destinations” when comparingmean values. This illustrates again the significance of disinvestment and the resulting lack ofaccess to amenities in declining inner cities in shaping travel.

This analysis also shows that, merely because stores are accessible to residents, it does not meanthat these residents will shop at the closest location. The east side Detroit respondents bypassedcloser convenience stores and completed over 78% of their shopping trips to supermarkets, bothindependent and major chains. The urban residents did confront greater costs in this process,both monetary and temporal, and this reveals the ongoing “burdens of place” of neighborhoodsexperiencing urban disinvestment. Nevertheless, east side Detroit residents were largely shoppingin stores that offered healthy food options.

COMMENTARY AND CONCLUSIONS

This neighborhood-scale Detroit area study has advanced a number of important theoreticalcontributions. One contribution is enabled by the focus of this research specifically on decliningneighborhoods. With regard to the built environment and its accessibility characteristics, whilehigher densities, mixed land uses, and greater connectivity do improve access in general, theresearch reveals that this relationship is stronger in communities that are wealthy. The relationshipbetween compact, mixed-use developments and improved access is not as strong—and can breakdown completely—in neighborhoods experiencing disinvestment. In declining neighborhoods,the exodus of amenities limits residents’ proximity to daily destinations, despite the fact that theseneighborhoods maintain a built environment that should improve access. Class is an importantfactor in defining location for retail and other commercial activities. It can be argued that, in theDetroit region, class plays an even more important role than the built environment in shapingaccess and travel.

Thus a fundamental contribution of this research has been a challenge to the traditional un-derstanding of the built environment and accessibility. Traditional urban form, access, and travelbehavior relationships are not necessarily replicable in poor and declining neighborhoods. With-out an equitable spatial investment in amenities, such as healthy restaurants or personal services,certain goods and services will remain distant from particular populations, regardless of urbanform. More broadly, this should raise concern that despite our extensive understanding of thebuilt environment, analysts are lacking the basic knowledge of particular types of urban form, andspecifically ones associated with communities in decline. Without a more comprehensive knowl-edge of the condition of the physical structure of disadvantaged communities, urban planners willnot be in a position to address many, if not most, of today’s critical urban stresses.

The importance of devoting more attention to research on the disadvantaged is also accentuatedby this study’s findings that the built environment and human behaviors have a separate anddistinct set of parameters that are influenced by not only class but also culture. In order to fully


comprehend the condition of the city, planners and designers need to have a better grasp oflocal cultures and resulting behaviors—an area of interest that has been losing ground in recentdecades. This is particularly important in the context of the structure of communities in decline.There is a critical need to focus on marginalized communities and the outcomes associated withthe physical form of their environments across a range of U.S. cities.

Indeed, it is this lack of research that has led to the growing call to pay more attention to thecondition of lower income populations and their neighborhoods in urban planning and design(Day, 2003, 2006; USDHHS, 2000; Vojnovic, 2006; Vojnovic, Jackson-Elmoore, Summers, &Bruch, 2006). While the number of disadvantaged has been on the rise, planners and designershave been increasingly uninterested in the condition of marginalized neighborhoods. Concern forthe poor and minorities was a crucial aspect of urban research and interest during the 1960s and1970s, but the focus on the disadvantaged has been diminishing since the 1980s (Slater, 2006;Podagrosi & Vojnovic, 2008; Podagrosi, Vojnovic, & Pigozzi, 2011).

The distinction made in this study between declining higher density urban neighborhoods andwealthy suburban counterparts—low density vs. high density—reveals findings that challengeanother line of existing literature. The Detroit results contradict studies that have drawn associ-ations between low urban densities and high BMI values (Ewing et al., 2003; Kelly-Schwartzet al., 2004; Lopez, 2004). The Detroit research shows that urban respondents maintain the highestBMI values, bordering on obese, despite living in higher density neighborhoods. The Bloom-field Hills and West Bloomfield respondents, living in low-density, single-use, low-connectivityneighborhoods, fell into the normal weight category.

Most of the research on urban form and obesity covers large geographic areas, includingcounties or larger spatial scales. Given their area of coverage, these studies average out uniquedistinctions evident among different socioeconomic and ethnic populations. This Detroit regionstudy, by concentrating on neighborhood-scale comparisons between declining and prosperingneighborhoods, is able to recognize very different outcomes from patterns evident across largerscale data. However, while contradicting some of the literature on the relationship betweendensities and BMI values, the findings of this study are consistent with two other streams ofresearch. Our findings correspond to existing U.S. research that shows the highest prevalence ofobesity among the poor (Morland et al., 2006; Wang et al., 2007). In addition, the Detroit researchis also consistent with health studies that have focused specifically on minority populations andthe poor in high-density urban neighborhoods, where health outcomes (including higher BMIvalues) are closely linked to poverty and minority populations, regardless of built environmentand design characteristics (Krieger, 2000; Scott et al., 2009; Williams, 2005; Vojnovic et al.,2013). The scale of study and distinguishing by population subgroups thus emerge as critical inlinking BMI values with characteristics of the urban built environment.

Ultimately, this discussion of the relationship between obesity and urban form is closelylinked to the main thesis of this study. Given the consistent relationship between obesity andpoverty across the United States, the urban form and obesity question illustrates, once again,that socioeconomic variables can outweigh the importance of the built environment in shapingoutcomes. It is not only with regard to accessibility that class will be more relevant than thebuilt environment, but when it comes to public health, as evident with BMI across the Detroitregion, socioeconomic variables will likely be more influential than urban form in shapingoutcomes.

The importance of socioeconomic and cultural influences on public health is also evidentin values placed on health. Despite living in automobile-oriented neighborhoods, low-densitysuburban respondents maintained normal BMI values, marginally higher than the BMI valuesof respondents living in high-density suburbs. In contrast, despite living in urban environmentsthat promoted walking, Detroit urban respondents were in the overweight category, bordering on


obese. It is likely that upper-income groups are more health-conscious and are more aware of theirdietary intake, an issue linked to class and cultural variables such as income, education, socialnorms, and peer pressure. The distinction in frequenting fast food restaurants between Detroiturban and suburban respondents also is likely, in part, a reflection of class differences.

In order to get a sense of the value placed on health by neighborhood, information wascollected on smoking. Consistent with prior research, the highest prevalence of smoking was inlower-income neighborhoods (Jha, Ranson, Nguyen, & Yach, 2002; Avendano, Glymour, Banks,& Mackenbach, 2009). In urban Detroit, over 35% of the respondents smoke, while less than 8%of the suburban respondents smoke. There is also a racial dimension to smoking, with only 8% ofWhite subjects smoking compared to 27% of non-White subjects. These distinctions in smokingand frequenting fast food restaurants between the urban and suburban respondents do, in part,reflect the different values placed on health across the Detroit urban region, and also, in part,reveal the influence of socioeconomic and cultural variables on health outcomes in marginalizedcommunities.

Another important finding in this study is the role of preferences in influencing shopping, avariable seldom considered in the food deserts literature. It is generally assumed that residentswho live in food deserts shop in food deserts. The recent media coverage on food supply in Detroithas further contributed to this misapprehension. In fact Detroit residents, despite being surroundedby a concentration of convenience stores, rarely shopped for food at these locations, averagingonly about one shopping trip per month. Some 78% of food shopping by east side residents wasat independent and major supermarkets. However, one should not necessarily expect that thisshopping behavior will be replicated in other U.S., or even Michigan cities. In a recent Lansing,Michigan study, Vojnovic and colleagues (2013) showed that lower-income Lansing respondentsrelied on convenience stores for over 35% of food shopping trips, over 3.5 times more on averagethan the Detroit respondents. This emphasizes, again, the need for more neighborhood-scaleaccess and travel behavior analyses in communities experiencing disinvestment and declineacross the United States.

As for the ability of east side Detroit residents to access major supermarket chains, urbanrespondents traveled the longer necessary distances to reach the national/regional chains, some5.5 miles on average. Unsurprisingly, the Detroit respondents that tended to shop at these storesmaintained higher relative incomes and owned a car. However, informal car-pooling services tothese major supermarket chains were regularly reported within the east side neighborhoods.

The research also shows that what emerges as a relevant dietary-related health risk in Detroitis the frequenting of fast food restaurants, with some 90% of dining out by the east side Detroitrespondents being in fast-food establishments. As prior studies have shown, populations that relyon the cheap but highly processed and high–fat content meals at fast food restaurants maintainhigher obesity rates (Maddock, 2004; Powell et al., 2007b). It is also apparent in the Detroitcontext that healthier restaurant options—as in the case of major supermarket chains—are notthat accessible.

By analyzing places shopped as opposed to simply the concentration of store types withinneighborhoods, this study allows for an assessment of actual shopping behavior. The results notonly problematize recent media reports on food access in Detroit but also much of the food desertsliterature. East side residents regularly shop at major suburban supermarket chains. IndependentDetroit supermarkets also emerge as important nutritious food suppliers for urban residents. Thisresearch, in fact, exposes the critical role of independent outlets in declining neighborhoodsas suppliers of healthy food. Studies that fail to take account of independent supermarkets intheir research will likely overestimate the accessibility constraints imposed on residents living inneighborhoods experiencing disinvestment.


This is not to say that access to urban amenities is not an issue in Detroit. The burdens facedby Detroit residents to reach basic amenities are severe. East side Detroit residents confrontmuch greater costs—monetary and temporal—in reaching major supermarket chains and otheramenities when compared to wealthier suburban residents, despite the built environment that theylive in, which should promote access. There is a clear burden of place associated with living ineast side Detroit. Unlike what is generally assumed, however, Detroit respondents in this studywere conscientious about making smarter shopping decisions, and they actively and regularlyincurred the higher costs of shopping at the healthier (and in many cases more distant) storelocations. It is also clear, however, that the same cannot be said for the frequency of dining outin fast food chains among Detroit residents. An important contribution of this research has thusbeen to provide a detailed illustration of how a disadvantaged community has tried to adjust to itsextreme decline and the loss of urban amenities in its surrounding environment. In the process,the analysis has altered our traditional theoretical understanding of urban form, accessibility,travel, and shopping behavior.

With regard to the impact of these research findings on policy, recent studies showing higherobesity rates among residents of low-density urban environments have renewed interest amongpolicymakers on developing/redeveloping the built environment to improve accessibility andencourage nonmotorized travel. For certain populations and neighborhoods, such initiatives cancontribute to healthier lifestyles. The limited access to urban amenities in Detroit’s declining core,however, shows that characteristics in the built environment are only one element in definingaccess, travel, and public health among the disadvantaged. An equitable spatial investment inurban amenities will also be vital in shaping accessibility, travel, and health. If necessary amenitiesare not located in a neighborhood, built environment characteristics can do little to promoteaccess to healthy food options or other needs. In neighborhoods characterized by disinvestmentand decline, providing incentives to bring back specific amenities should be considered criticalin addressing the obesity epidemic.

Ultimately, the wealthy, living in low-density suburbs, took advantage of their many opportu-nities (for instance, by ensuring a healthier diet and exercise) in countering the disadvantages oftheir suburban environment, and this was reflected in an overall healthy body weight. In contrast,Detroit residents, while living in a built environment that encourages walking, were dramaticallyaffected by the inequitable spatial investment in amenities. Research that continues to show thatobesity in the United States is most prevalent among the poor further necessitates policy focusedon disadvantaged communities. It is therefore essential to reverse the trend, mentioned earlier, ofgrowing disinterest in the condition of the poor.

ACKNOWLEDGMENTS: The authors are very grateful to Dr. Laura Reese and the anonymous reviewers fortheir comments and criticisms that helped improve the quality of the article. We would like to thank the U.S.National Science Foundation that has funded this research under the Human and Social Dynamics program grantSES 0624263. We would also like to thank Michigan State University’s Obesity Interest Group for providingsupplemental funding for this study. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of the NSF or MSU’s Obesity InterestGroup. We are extremely grateful to the assistance of the Governor’s Council on physical fitness, health andsports in Michigan, and particularly Ms. Marilyn Lieber (President and CEO) and Ms. Wilkerson (project officerwith Active Living By Design of North Carolina’s Gillings School of global public health). We would also liketo acknowledge our community partners, the Michigan Suburbs Alliance, U-SNAP-BAC and Messiah HousingCorporation for their support of this project. Finally, the support of Ms. Julie Brixie (currently Meridian Townshiptreasurer) is also greatly appreciated. Ms. Brixie’s ongoing interest and backing of projects carried out by facultyand students in the Department of Geography at MSU are indispensable to our programs.


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urban built environments, accessibility, …density, land use mix, and connectivity—have been...

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