Walkable Art: An Empirical Investigation of Arts-Related Businesses and Walkable Neighborhoods

Abstract

The goal of this study is to quantitatively examine the relationship between walkability and arts-related businesses in metropolitan areas across the United States. Model results indicate that the relationship between arts businesses and walkability is sensitive to the size or scale of the business considered, as well as to the definition of the arts used. Larger-scale businesses are somewhat more likely to locate in walkable neighborhoods than are small-scale arts-related businesses, which are less likely to locate in walkable neighborhoods. This difference is likely due to the higher cost of property in these neighborhoods. In this regard, community-level economic development and planning entities need to take proactive measures to ameliorate the cost externalities associated with modifications to urban environments to make them more walkable.

Keywords

arts business location walkability economic development creative cities

Arts and culture have risen to the forefront as central components of economic and physical revitalization efforts (Currid, 2009; Markusen & Gadwa, 2010; Strom, 2002). The arts are now viewed as both a cultural product and a key contributor to the economic vitality and competitiveness of regional economies (Currid, 2009; Markusen & Schrock, 2006; Strom, 2002). Trending alongside this repositioning of the arts is an emphasis on placemaking and livability, a significant aspect of which centers on the promotion of walkability. The walkable neighborhood—defined as a safe, well-serviced neighborhood imbued with qualities that make walking a positive experience—is now recognized as a central component of sustainable communities (Lo, 2009). Motivated at first by the environmental goal of encouraging pedestrian over car-based urbanism, the walkable neighborhood is regarded as a key factor in the promotion of health, economic, and communitarian goals (Owen, 2009; Speck, 2013).

Given the well-noted role of the arts and artists as transformative agents (Cameron & Coaffee, 2005; Ley, 2003; Zukin, 1989) and the rise in sustainability initiatives with a walkability orientation in communities across the United States (Slavin, 2011), the goal of this study is to quantitatively examine the relationship between walkability and arts-related businesses in metropolitan areas across the United States. In other words, it seeks to address the following research question: Are arts-related businesses underrepresented in more expensive walkable locations in U.S. metropolitan areas? Although no research has explicitly investigated the link between the arts and walkability, studies of the preferred locales of creative people (including artists) suggest their location preferences are in some ways tied to placemaking and walkability goals. For example, Landry and Bianchini (1995) suggest walkability is a key feature of creative cities that increases shop traffic as well as the vitality of city centers. More broadly, mixed-use, walkable urban environments may be more attractive to members of the “creative class” (of which artists are a part) because they maximize proximity to work and leisure activities and minimize commuting time (Florida, 2002). Walkability and the arts may also share similar objectives and effects; both are viewed as contributors to the social fabric of communities, the formation of social networks, economic vitality, and urban revitalization (Grodach, 2011; Hodgson & Beavers, 2011).

Although it is likely that arts businesses may choose to locate in walkable neighborhoods where street visibility and the demand for products is likely higher, it is also likely that these businesses may be relegated to less-expensive, less-walkable places because of the high property values associated with walkable neighborhoods (Leinberger, 2008). In fact, some studies have argued that mixed income, walkable neighborhoods have not garnered substantial benefits for low-income residents (Fraser & Kick, 2007; Joseph, Chaskin, & Webber, 2007). This might also apply to arts activities, particularly small-scale activities, that are unable to afford the higher expense associated with walkable environments. Given this possibility, the present study undertakes a quantitative assessment of this relationship using a unique, fine-grained, block-group-level data set for the year 2010. “The arts” in the context of this study are defined as arts-related businesses that are split into two groups, large- and small-scale businesses. Large-scale arts businesses pertain to the definition of the arts provided by the National Arts Index (NAI; Kushner & Cohen, 2014). Small-scale arts businesses are those with comparatively small sales volumes, few employees, and small storefronts (in terms of square footage). Walkable neighborhoods are identified using Walk Score® data.

Results of the logit models estimated indicate that the relationship between arts businesses and walkability is sensitive to the size or scale of the business considered, as well as to the definition of the arts used. Larger-scale businesses are somewhat more likely to locate in walkable neighborhoods than are small-scale arts-related businesses, which are less likely to locate in walkable neighborhoods. This difference is likely due to the higher cost of property in these neighborhoods, which ranges from 46% to 226% higher than property values in more affordable, less-walkable locales. Although this in no way indicates a causal relationship between walkability and arts-related businesses, additional work is encouraged to verify this relationship, given the cross-sectional nature of the present study. That said, these results advance the assertions of prior work that highlight scale matters in arts-oriented economic development efforts (Grodach, 2008, 2010; Sternberg, 2002). In this regard, community-level economic development and planning entities need to take proactive measures to ameliorate the cost externalities associated with modifications to urban environments to make them more walkable. This includes engagement with a variety of arts entities and arts-related businesses to understand these impacts, as well as possible rental or mortgage assistance to permanently improve the affordability of walkable locales for small-scale arts-related entities.

Literature Review

The Benefits of Walkability

The past decade has seen a surge of interest in walkability, motivated at first by the environmental goal of encouraging pedestrian over car-based urbanism, and now regarded as a key factor in the promotion of health, economic, and communitarian goals. As a normative goal, walkability implies a safe, well-serviced area, imbued with qualities that make walking a positive experience. A “positive” walking experience means that streets, sidewalks, and paths (pedestrian routes) are comfortable and interesting (Speck, 2013).

From the urban planning and urban design literatures, there are normative accounts explaining why the walkable neighborhood is essential (Talen, 2013). In the health field, the walkable neighborhood encapsulates the goal of the “active community” (Doyle, Kelly-Schwartz, Schlossberg, & Stockard, 2006), whereas from the sustainability literature, the walkable neighborhood is seen as a development form that can reduce ecological footprints, minimize car travel, reduce energy consumption, and limit encroachment on open lands (Ewing & Cervero, 2010; Van der Ryn & Calthorpe, 1986). The energy benefits of walkable neighborhoods are believed to extend beyond “green building,” as traveling to a building is said to account for twice as much energy as operating a building (Wilson & Navaro, 2007).

In the housing policy literature, the walkable neighborhood is most often associated with social diversity, especially mixed-income environments (Kingsley, 2009). Some researchers have argued that access to public transportation and jobs, land use and housing stock variety, and proximity to downtown—physical characteristics often associated with walkable neighborhoods—are important for maintaining social diversity (Nyden, Michael, & Lukehart, 1997).

In this regard, the walkable neighborhood has been shown to be associated with trust and social engagement (Leyden, 2003) as well as sociability (Brown & Cropper, 2001). Researchers have argued that beyond environmental and health benefits, walkable neighborhoods facilitate “the generation and maintenance of social capital,” an important determinant of “quality of life” (Rogers, Halstead, Gardner, & Carlson, 2011, p. 201). Social benefits might first involve resident interaction and neighboring, which evolve into social connections and a sense of community or collective efficacy.

Bolstering these affirmations of the benefits of walkable neighborhoods is a large literature examining the effects of environments that are the opposite; that is, places that are “low density, noncontiguous” and “automobile dependent” (Bengston, Fletcher, & Nelson, 2004, p. 271). In some contexts, these nonwalkable places have been shown to contribute to global warming (Gonzalez, 2009), social inequity (Pendall, 2000; Squires, 2002), environmental degradation (Ewing, Bartholomew, Winkelman, Walters, & Chen, 2008), and public health problems (Frumkin, Frank, & Jackson, 2004). In sum, walkability is a normative goal because it is associated with an urban form that fosters a diversity of land uses and the equitable provision of access to goods, services, and facilities. It is also associated with a variety of positive externalities, such as the health benefits associated with pedestrian activity and social diversity facilitated by the provision of opportunities for interaction and exchange.

Walkability and the Arts

Many of the benefits of walkability can be linked more specifically to the arts. Normative theories suggest a link between diverse urban neighborhoods and people, and to the degree that walkability and diversity are interlinked, at least theoretically, this suggests that walkability might be important for the arts as well. In more specific terms, Montgomery (2003) argued that built environment characteristics such as a permeable street landscape and active frontages—also components of walkability—are critical to the success of cultural neighborhoods. Studies suggest that creative individuals seek out diverse places that are open to new people and ideas where they can realize their creative ideas and identities (Florida, 2005; Hansen & Niedomysl, 2009).

Researchers have acknowledged the impact that place has on attracting creative workers, arguing the need for additional research on the attractive powers or “aesthetic prompts” of different places (Drake, 2003, p. 523). Walkability may very well be one of these aesthetic prompts that attracts creative workers to particular locales in cities, although it is not clear whether the desire for walkability has been actualized. On a conceptual level, it could be argued that a number of qualities associated with walkability would be attractive to a range of artistic activities. These qualities include the increased foot traffic made possible by walkable locations, the ability to commute between work and leisure, the ability to travel to places of creative inspiration on foot, the presence of “third places” (Oldenburg, 1999) essential to community engagement, and/or the unique sense of place walkability may help engender. There are also common goals and effects between the arts and walkability. The arts are believed to endow cities with a unique sense of place with distinct cultural, social, and economic histories (Currid, 2007; Grodach, 2011; Soule, Hodgson, & Beavers, 2011; Zukin, 2005), and the walkable neighborhood—with its pedestrian focus and mixed-use character—is likely to be supportive of, and supported by, the arts. Furthermore, the social benefits of walkability, including social capital formation (Du Toit, Cerin, Leslie, & Owen, 2007; Leyden, 2003; Wood et al., 2008), are similarly ascribed to the arts.

Arts and culture have risen to the forefront as central components of both economic and physical revitalization efforts (Markusen & Gadwa, 2010; Strom, 2002). In this regard, arts-related economic development efforts run the gamut from community-based endeavors such as neighborhood arts districts (Chapple, Jackson, & Martin, 2010; Stern & Seifert, 2010) and arts incubators (Grodach, 2011; Phillips, 2004), to large-scale flagship projects such as the construction of museums and arts centers (Grodach, 2008, 2010). Studies of large-scale arts initiatives argue that successful initiatives integrate these projects into the existing urban design of places (Grodach, 2008, 2010; Sternberg, 2002). This study argues that walkability is an important component to vibrant urban designs that also support small-scale arts activities. However, community development efforts need to consider the duality of the relationship between walkable urban spaces and the arts, and leverage cost-conscious development strategies to ameliorate the high costs associated with walkability initiatives, which, although important to creating healthy, vibrant communities, have the potential to displace cost-vulnerable artistic activities.

Equity and Walkability

Several studies have documented a positive correlation between walkability and property values (Cortright, 2009; Ding & Knaap, 2002; Pivo & Fisher, 2011). Pivo and Fisher (2011) found, for example, that walkability increases property values for office, retail, and apartment space by 1% to 9% (although it did not affect industrial property values). Other scholars have argued that although higher-density walkable neighborhoods may yield greater equity in terms of access to resources, compact urban form is a problem that can “worsen neighborhood problems and dissatisfaction” (Bramley & Power, 2009, p. 46). These costs have to be weighed against the benefits of living in neighborhoods with high levels of access to goods and services. For low-income groups, some scholars argue that proximity and “organizational embeddedness” in a geographic sense is an essential, if overlooked, aspect of social service delivery, and that social services that are located nearby are advantageous because they build trust, strengthen communities, and reduce client travel costs (Allard, 2009; Small, 2009). In addition, by being “locationally efficient,” pedestrian access has the potential to make places more affordable (Center for Neighborhood Technology, 2013).

Despite these benefits, the walkable neighborhood is in danger of losing the very diversity that sustains it, and that may sustain the connection to the arts. Good design in the form of a walkable neighborhood fuels not only economic growth but also rising costs for residents in the form of property speculation, which raises rents and taxes, and can drive residents out of neighborhoods (Davis, 1984).

Studies have documented that gentrification is more likely to occur in high-density neighborhoods where “proximity-related benefits” increasingly enter “people’s utility functions” (Pendall & Caruthers, 2003, p. 547). More than one study has found that many walkable developments, especially those located near transit stops, are becoming increasingly unaffordable (Pollack, Bluestone, & Billingham, 2010; U.S. Department of Transportation, 2008).

Ironically, artists fleeing gentrification may find themselves “paving the way” for the expansion of gentrification in other neighborhoods (Douglas, 2012, p. 3579). Theoretical work posits that artists play a role in moving a district from a position of low to high economic capital, where gentrification follows “the power of the aesthetic disposition to valorize the mundane” (Ley, 2003, p. 2527). Therefore, public policy that seeks to promote urban regeneration through arts and culture development efforts are likely to play a significant role in this process (Cameron & Coaffee, 2005).

The upshot of research linking artists and gentrification, as Strom (2010) points out, is a kind of public policy dissonance in which artists are simultaneously valued as a means of revitalization, yet are also viewed as unintentional harbingers of gentrification and displacement. Although case studies documenting these changes are well known (e.g., Zukin, 2001), there is little empirical evidence supporting the gentrification effects of artists.¹ Although the present study cannot speak directly to this process, a proxy is used for the arts more generally—arts-related business locations in walkable neighborhoods—to shed some light on the degree to which arts activities, generally speaking, locate in neighborhoods of varying character and quality.

Conceptual Framework

Although empirical evidence is scant, the literature, theory, and empirical work that does exist seems to indicate that arts businesses would most likely locate in inner-ring neighborhoods in metropolitan areas that are walkable but still affordable (Florida, 2002; Landry & Bianchini, 1995). These are not necessarily the neighborhoods that are the most walkable; rather, these are neighborhoods where some trade-off has been made between walkability and affordability. Arts businesses may be drawn to walkable, gentrified neighborhoods, but there is evidence that their inability to sustain a place in a higher priced, gentrified neighborhood may force their relocation to more affordable places. Thus, the nexus of walkability and the related prices of these locales affect the presence of arts businesses in walkable neighborhoods. Based on this relationship, neighborhoods may be divided into four principal categories: walkable and high property value, walkable and low property value, not walkable and high property value, and not walkable and low property value. Each of these neighborhoods is given a descriptive label: gentrified, inner ring, suburb, and edge.

Data on walkability and arts-related establishment locations are used to test the theory that the arts are likely to locate in inner-ring neighborhoods—high walkability and low property value—and less likely, because of property values and rent costs, to locate in gentrified neighborhoods. Because of the intrinsic connections between walkability and the arts—mutual connections to place, diversity, density—it would seem likely that arts businesses gravitate toward places that are walkable but still affordable. Although there is a connection between walkability and higher property values, this is not true of all locations, and arts businesses may be the ones most interested in seeking out neighborhoods that have the amenities and place qualities of a walkable neighborhood while still maintaining affordability. Our analysis tests whether this is in fact the case: Are arts businesses located in walkable neighborhoods that are not yet gentrified and still affordable?

Data

Block groups are the unit of analysis in this study. They comprise between 600 and 3,000 people, are smaller than census tracts, and therefore capture a more localized measure of the neighborhood environment. In all, the national-level data set compiled for this study consists of 173,214 block groups in 359 metropolitan areas.² From this data set, 20 metropolitan areas that have high concentrations of arts-related establishments were selected for additional analysis. These areas include well-known arts centers such as New York and Los Angeles, as well as lesser known locales such as Atlanta and Houston. Collectively, these 20 metropolitan areas alone account for 40% of NAI-defined arts businesses and 43% of small-scale arts businesses nationwide.

Walk Score Data

Walk Score data obtained in February 2012 are used to quantify the walkable access of block groups. Front Seat, a company that computes walkability measures nationwide, has devised a scoring method that uses business and amenity locations to assess the walkable access of addresses in the United States (Front Seat, 2010). The company uses three primary elements to calculate Walk Scores: (a) walking routes and the distance to amenities, (b) road connectivity, and (c) the presence of amenities in nine categories, including grocery stores, coffee shops, and schools.

The distance from these types of amenities is used to determine a location’s Walk Score. This Walk Score methodology produces five basic intervals of Walk Scores with the following interpretation: 0 to 24 car-dependent (almost all errands require a car); 25 to 49 mostly car-dependent (a few amenities within walking distance); 50 to 69 somewhat walkable (some amenities within walking distance); 70 to 89 very walkable (most errands can be accomplished on foot); 90 to 100 walker’s paradise (daily errands do not require a car; Front Seat, 2010). For the purposes of this study, walkable places are those with a Walk Score of 80 and above. Not walkable places are those with a Walk Score of 79 and below.

Walk Score is valuable because it allows for a large-scale quantitative assessment of walkable access to amenities. At the same time, it has several limitations. For one, Walk Score captures the distance to amenities but not the quality of amenities. For instance, a home in close proximity to low- and high-performing schools could have the same score even though the latter is a more valuable amenity than the former. This limitation extends to the quality of the walking environment: An exclusive focus on distance to amenities ignores whether or not pedestrian-friendly features exist, such as sidewalks, cross walks, street trees, or lighting. Furthermore, it ignores neighborhood quality problems, such as high crime levels, which could make it dangerous to walk outside.

The reason these qualitative neighborhood characteristics are ignored is because no small-scale nationwide data exist yet to measure these characteristics in a standardized and automated way. Other metrics of walkability exist, such as the Irvine-Minnesota Inventory (Boarnet, Day, Alfonzo, Forsyth, & Oakes, 2006), but so far, they have not been scaled to the national level. In a comparison of Walk Scores and Irvine-Minnesota Inventory–related indicators of the quality of the built environment, Koschinsky, Talen, Alfonzo, and Lee (2015) found that Walk Score’s distance-based amenity access measures are a fair proxy for walkability in more affluent neighborhoods, but less so in low-income areas where amenity and neighborhood quality are often lacking.

Furthermore, due to data availability, we only focus on walkable access in this study instead of other sustainable modes of transportation such as biking or transit, which are often used in combination with walking. As such, we focus on one particular indicator of livability while acknowledging that there are many others (such as bikeability or other measures of neighborhood quality). It should also be noted that being located within walking distance to amenities does not translate to actually walking to these amenities. Thus, Walk Score is a proxy for walkable access, not pedestrian walking behavior. Recent research, however, does find strong correlations between higher Walk Scores and higher propensities for walking (Brown et al., 2013; Manaugh & El-Geneidy, 2011; Weinberger & Sweet, 2012).

Arts-Related Establishment Data

This section describes the main independent variable of interest in the multinomial logit models, the count of arts-related businesses. These counts were obtained from 2010 address-level establishment data from ESRI’s Business Analyst/InfoUSA and aggregated in a commercial geographic information system to 2010 census block groups. These data include a wealth of information for each establishment,³ including information about their Standard Industrial Classification and North American Industrial Classification System memberships, sales volume, number of employees, the size category of the establishment based on their number of employees, and the number of square feet (Business Analyst, 2012).

Small-scale arts-related businesses are defined according to the establishment’s industry classification code in Business Analyst. These codes fall under the five-digit code “71151” and include business types such as casting directors, entertainers, musicians, and comedians. This definition also includes some of the establishments under the five-digit code “45392” such as art consultants, art galleries and dealers, and murals. Aside from defining arts businesses based on their industry classification, this study added three additional elements to further define small-scale arts-related businesses: sales volume, number of people employed, and square footage. In this regard, small-scale businesses were classified as those that had the smallest sales volume (under $250,000 sales), fewest employees (1-4 people), and smallest square footage (under 2,499 square feet) in the database. Greater resolution on these additional variables was preferred;⁴ however, the study was limited by the categorical nature of these data fields. For example, data were not available at a higher resolution about the number of people employed and the square footage for each establishment. There is also a 14-month gap between the December 2010 artist data and the February 2012 Walk Score data, which is a limitation of this study. However, we expect the error associated with this date mismatch to be small because many amenities (or amenity types) remain in the same neighborhood for at least a year, especially schools, parks, and grocery stores.

With respect to the definition of arts businesses, it is important to note that Business Analyst only contains data for arts businesses in established, formal locations. Arts businesses that have not established a formal storefront are not considered in the analytical results. This means that artistic activity in a home studio, live-work space, park, community center, or any other type of space that is not classified as a physical place of business is not included in the analysis. The only types of arts-related establishments analyzed in this study are those that have set up established physical storefronts and are recorded by Business Analyst. The issue of accurately defining the arts is not restricted to this study, however, and is an ongoing issue in research about the arts and cultural products (Grodach, 2011; Markusen & Schrock, 2006; Scott, 2005; Wassall & Alper, 1985). Because of this challenge associated with defining the arts, a sensitivity analysis was conducted to evaluate the impact of the definition of the arts on model results. The results of this sensitivity analysis will be discussed later in this study.

Other Independent Variables

Aside from the principal independent variable of interest, several other covariates were included in the model to control for factors that likely mediate the relationship between arts-related businesses and walkability. These variables are divided into four types of characteristics: housing, demographics, land use, and socioeconomics. Table 1 contains a list of these model covariates and their descriptions. The appendix includes descriptive statistics for these variables. Two density variables are also included in the models, housing density and the density of all establishments, to mitigate the likelihood that spurious relationships are found due solely to the fact that arts-related businesses are more likely to locate in neighborhoods with more people and/or more establishments.

Table 1.

Variable Descriptions.

Variable	Description	Source
Walk Score	Street-smart Walk Score	Walkscore.com
NAI arts-related businesses	Count of arts businesses based on the definition of the arts from the NAI	ESRI Business Analyst/InfoUSA
Small-scale arts-related businesses	Count of arts businesses within a block group small-scale businesses were classified as those that have sales volume (under $250,000 sales), few employees (1-4 people), and small square footage (under 2,499 square feet)	ESRI Business Analyst/InfoUSA
Housing characteristics
Percent public housing	Percent renter-occupied units that are public housing units	U.S. Department of Housing and Urban Development and 2010 Census
Percent renter-occupied units	Percent renter-occupied units	2010 Census
Percent vouchers	Percent renter-occupied units that are voucher units	U.S. Department of Housing and Urban Development and 2010 Census
Foreclosure risk	Foreclosure risk score	HMDA Walker & Winston, 2010)
Demographic characteristics
Racial and ethnic diversity index	Race/ethnicity diversity index (larger = more diverse) (Simpson index).	2010 Census
Land use characteristics
High-performing school	Distance in meters from block group centroid to closest high-performing school (ranking 9 10).	GreatSchools.com
Low-performing school	Distance in meters from block group centroid to closest low-performing school (ranking 1 2).	GreatSchools.com
Brownfield distance	Distance in meters from BG centroid to closest brownfield.	Environmental Protection Agency
Housing density	Gross Residential Density Housing density (units per unprotected acre) in 2010	2010 Census
Land use diversity	Land use diversity index (larger = more diverse) (Simpson index).	ESRI Business Analyst/InfoUSA
Establishment density	Density of establishments per acre	ESRI Business Analyst/InfoUSA
Socioeconomic characteristics
No car households	Percent of households with no cars	Environmental Protection Agency (Smart Locations Database)

Note. NAI = National Arts Index.

The proportion of all housing units that are renter occupied (2010 Census) and foreclosure risk (Walker & Winston, 2010) are two of the housing characteristics included in the model. Two additional housing variables used include the percentage of all renter-occupied units that are subsidized by the federal government to provide housing for low-income tenants in the form of public housing or housing vouchers (where the subsidy goes to a public housing authority or a tenant/private landlord). All four variables are likely correlated with weaker housing markets and lower incomes. Because public housing residents have been found to live in neighborhoods with higher crime rates, the public housing variable also serves as a proxy for more crime (Lens, 2013; McNulty & Holloway, 2000).

To characterize demographics, the models include an index of racial/ethnic diversity. Several variables describe the land use characteristics of neighborhoods, including the distance from the center of a neighborhood to the nearest low- and high-performing schools, as well as the center to the nearest brownfield. An index of land use diversity is an independent variable that characterizes the number of different land uses within block groups. For both the racial and land use index, higher index values indicate greater diversity. The proportion of tax filings that were Earned Income Tax Credits is included as a socioeconomic indicator. Finally, in an attempt to characterize the walking behaviors of block group residents, the percentage of households with no cars is included.

Model Structure

Multinomial logit models were used to evaluate the link between arts-related businesses and walkability, controlling for the neighborhood characteristics highlighted above, including housing (H), the socioeconomic status of residents (SE), demographic characteristics of residents (D), and land use (L). This modeling approach allows us to test the link between businesses and walkability in the four types of neighborhoods described in the conceptual framework:

Edge: Not walkable and low property values

Suburb: Not walkable and high property values

Inner ring: Walkable and low property values

Gentrified: Walkable and high property values

Block groups were placed into one of these four categories according to two criteria, their Walk Score and their property values. Accessible block groups are those with a Walk Score of 80 or higher. Whether a block group had low or high property values was determined by computing the median value of first lien mortgages between 2008 and 2009 from Home Mortgage Disclosure Act data obtained from the Urban Institute. The median mortgage values were computed for each metropolitan area in the continental United States and block groups assigned a low or high status if they were below or above the median of their corresponding metropolitan area. This local determination of low and high status by metropolitan area avoids indexing all metropolitan areas to the same national median that is skewed by the large number of neighborhoods in the largest, most expensive cities, such as New York or San Francisco.

The model used to test the strength of the linkage between arts-related businesses and walkable places is specified as follows⁵:

p_{i 1} = \frac{\exp (H_{i 1}, S E_{i 1}, D_{i 1}, L_{i 1})}{\sum_{j} \exp (H_{i 1}, S E_{i 1}, D_{i 1}, L_{i 1})} for j = 2, 3, 4

where p_i₁ = the probability that a block group belongs to discrete-choice Category 1; H_i = vector of housing characteristics in block group i; SE_i = vector of control variables that describe the socioeconomic status of residents in block group i; D_i = vector of control variables that describe the demographic characteristics of residents; L_i = vector of control variables that describe the land use within block groups.

The reference category for the multinomial logit model is the not walkable and low property value set of block groups, which allows us to evaluate whether arts-related businesses are more likely to locate in the other three categories compared with this reference category. For example, are arts businesses more likely to locate in neighborhoods with low Walk Scores and high property values than with low Walk Scores and low property values? Are arts businesses more likely to locate in neighborhoods with high Walk Scores and low property values than with low Walk Scores and low property values?

Two sets of multinomial logit models were estimated. First, a pooled model that makes use of data for all 20 metropolitan areas is estimated to get a general sense of this relationship. Second, separate models for each of the 20 metropolitan areas are estimated to get a sense of this relationship in a relatively local context. This approach considers the unique context of place that mediates this relationship, which might otherwise be hidden in an aggregated national-level analysis.

Multinomial Logit Results

Table 2 contains the pooled-regression model results, and Table 3 contains the coefficient estimates for small-scale arts businesses for each of the metropolitan area regressions.⁶ The numbers in these tables represent odds ratios computed from the regression coefficients. Odds ratios larger than 1 indicate that arts businesses are more likely to locate in a particular neighborhood type as opposed to not walkable/low property value neighborhoods. Odds ratios less than 1 indicate that arts businesses are less likely to locate in a particular neighborhood type as opposed to not walkable/low property value neighborhoods. Close inspection of the odds ratios for arts businesses reveals that for all the metropolitan areas for which models were estimated, these businesses are no more likely or significantly less likely to locate in walkable neighborhoods. This trend runs counter to the expectation outlined in the conceptual framework that arts businesses would locate in inner-ring areas that are walkable but affordable.

Table 2.

Odds Ratios From Pooled Metropolitan Area Multinomial Logit Regression Models.

Baseline block groups: Not walkable/Low property values
	NAI	Small scale
Not walkable/high property values
Arts-related businesses	1.066***	1.047
Business density	0.794***	0.957
Housing density	1.006**	0.997
% Renters	0.989***	0.990***
Foreclosure risk	0.982***	0.982***
% Public housing	0.997*	0.997*
% Vouchers	0.951***	0.950***
Racial diversity	1.010	1.017
Land use diversity	1.061***	1.080***
Dist. high-perf. school	0.539***	0.538***
Dist. low-perf. school	1.152***	1.159***
Dist. brownfield	1.095***	1.096***
% EITC	0.951***	0.949***
% No car	0.941	0.966
Walkable/low property values
Arts-related businesses	1.003	0.486***
Business density	4.051***	4.546***
Housing density	1.092***	1.088***
% Renters	1.003***	1.003***
Foreclosure risk	0.988***	0.989***
% Public housing	0.988***	0.988***
% Vouchers	0.978***	0.979***
Racial diversity	1.572***	1.571***
Land use diversity	1.169***	1.159***
Dist. high-perf. school	0.576***	0.576***
Dist. low-perf. school	0.675***	0.675***
Dist. brownfield	0.683***	0.688***
% EITC	0.982***	0.982***
% No car	19.235***	18.433***
Walkable/high property values
Arts-related businesses	1.023***	0.486***
Business density	4.012***	4.591***
Housing density	1.090***	1.085***
% Renters	1.001	1.000
Foreclosure risk	0.963***	0.963***
% Public housing	0.985***	0.985***
% Vouchers	0.960***	0.960***
Racial diversity	1.015	1.022
Land use diversity	1.243***	1.246***
Dist. high-perf. school	0.393***	0.393***
Dist. low-perf. school	0.626***	0.630***
Dist. Brownfield	0.753***	0.753***
% EITC	0.940***	0.940***
% No car	22.463***	20.979***
Number observations	75,486	75,486
Likelihood ratio chi-square test statistic	65,708.10***	65,584.56***
Log likelihood	−52339.55	−52401.32
Pseudo-R²	.3856	.3849

Note. NAI = National Arts Index; EITC = Earned Income Tax Credits.

***

Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.

Table 3.

Odds Ratios for Arts-Related Businesses.

	Not walkable/high property values	Walkable/low property values	Walkable/high property values		Not walkable/high property values	Walkable/low property values	Walkable/high property values
	NAI	NAI	NAI		Small scale	Small scale	Small scale
Atlanta	1.056**	1.116	1.254***	Atlanta	1.219	0.148	0.219
Boston	1.1012***	1.077*	1.065*	Boston	0.945	0.438*	0.311***
Chicago	1.140**	1.029	1.079***	Chicago	1.141	0.435**	0.407**
Dallas	1.032**	1.052**	1.035	Dallas	1.138	0.677	0.925
Denver	1.058	1.111*	1.129**	Denver	1.560*	2.240	1.110
Detroit	1.098**	1.084	1.176**	Detroit	1.314	0.000	0.903
Houston	1.118***	1.179**	1.197***	Houston	1.870***	2.676	1.259
Los Angeles	1.004	0.981	1.009	Los Angeles	0.944	0.543***	0.617***
Miami	1.085***	1.052	1.06*	Miami	1.129	0.733	0.654
Minneapolis	1.166***	1.027	1.022	Minneapolis	1.114	0.146**	0.128**
New York	1.081***	1.026	1.027	New York	0.809***	0.495***	0.451***
Philadelphia	1.116***	1.041	1.141***	Philadelphia	1.148	0.183***	0.109***
Phoenix	1.1468***	1.193**	1.220***	Phoenix	1.503	0.000	0.211*
Portland	1.027	1.036	1.001	Portland	0.972	0.295**	0.422*
Riverside	1.018	0.953	1.139***	Riverside	1.255	0.653	0.189*
San Diego	0.996	1.021	0.990	San Diego	1.013	0.733	0.995
San Francisco	0.998	0.981	1.013	San Francisco	0.870	0.505***	0.675**
Seattle	1.072**	1.083**	1.063	Seattle	1.179	0.694	0.810
Washington	1.076***	1.023	1.035	Washington	0.907	0.341	0.471*

***

Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.

This result prompted further investigation of how “affordability” is defined. It is likely that one of the reasons for the lack of a clearer relationship is the higher cost of property in walkable block groups. Table 4 displays the median property values in each block group category, by metropolitan area. Nationally, block groups in high Walk Score block groups are between 45% and 226% higher than property values in low Walk Score/low property value block groups. Although these differences between the property values in low and high Walk Score block groups vary by metropolitan area, overall high Walk Score block groups are more expensive than low Walk Score block groups. Thus, it is comparatively more expensive for arts business owners to set up a business in these block groups than in low Walk Score and low property value areas. Combined, these results are compatible with the hypothesis that walkable neighborhoods may be too expensive for small-scale businesses.

Table 4.

Median Value of Property Costs by Block Group Category.

Neighborhood category	U.S median	New York	Los Angeles	Chicago	San Francisco	Miami	Dallas	Philadelphia	Atlanta	Washington
Not walkable/low home value	$108,000	$244,000	$234,000	$128,000	$257,000	$107,500	$88,000	$140,000	$103,000	$214,000
Not walkable/high home value	$187,000	$360,000	$395,000	$218,000	$500,000	$200,000	$168,000	$231,000	$193,000	$353,000
Walkable/low home value	$157,000	$203,000	$234,000	$140,000	$248,000	$115,000	$112,000	$104,000	$121,500	$245,000
Walkable/high home value	$352,000	$400,000	$417,000	$244,000	$525,000	$192,000	$200,000	$241,000	$200,000	$373,000
Neighborhood category	Boston	Houston	Seattle	San Diego	Detroit	Minneapolis	Phoenix	Portland	Riverside	Denver
Not walkable/low home value	$210,000	$89,000	$220,000	$208,000	$52,000	$136,000	$95,000	$186,000	$125,000	$141,000
Not walkable/high home value	$310,000	$156,000	$315,000	$355,000	$119,000	$198,000	$180,000	$248,000	$217,500	$236,500
Walkable/low home value	$200,000	$115,500	$211,000	$206,000	$74,000	$116,000	$95,000	$190,000	$127,000	$147,000
Walkable/high home value	$314,000	$257,000	$328,000	$364,000	$116,000	$228,000	$152,000	$264,000	$182,000	$230,000
Cost of home compared with not walkable/low home value block groups
Neighborhood category	U.S median	New York	Los Angeles	Chicago	San Francisco	Miami	Dallas	Philadelphia	Atlanta	Washington
Not walkable/high home value	73%	48%	69%	70%	95%	86%	91%	65%	87%	65%
Walkable/low home value	45%	−17%	0%	9%	−4%	7%	27%	−26%	18%	14%
Walkable/high home value	226%	64%	78%	91%	104%	79%	127%	72%	94%	74%
Neighborhood category	Boston	Houston	Seattle	San Diego	Detroit	Minneapolis	Phoenix	Portland	Riverside	Denver
Not walkable/high home value	48%	75%	43%	71%	129%	46%	89%	33%	74%	68%
Walkable/low home value	−5%	30%	−4%	−1%	42%	−15%	0%	2%	2%	4%
Walkable/high home value	50%	189%	49%	75%	123%	68%	60%	42%	46%	63%

Robustness to Definition of Walkability

Given the difficulty in defining artistic activity discussed earlier, and to test the impact of how walkability is defined on the model results, an informal sensitivity analysis was conducted. This analysis tested the robustness of results in two respects. First, how robust are the results to the definition of “walkable”? Second, how does the definition of arts activities impact model results?

The multinomial logit model results were robust to lowering the threshold for walkable access from a Walk Score of 80 and above to a score of 70 and above. In fact, lowering the walkability threshold only highlights the fact that small-scale arts-related businesses were less likely in many metropolitan areas to locate in high Walk Score block groups, particularly high Walk Score/high property value block groups.

Robustness to Definition of Arts Activities

An analysis was also conducted to evaluate the sensitivity of results to various definitions of the arts. This was particularly important to evaluate given the difficulty in defining the arts and arts-related activities, as discussed previously. In this portion of the sensitivity analysis, two alternate definitions of the arts were used. First, a definition of arts activities based on Roland Kushner’s NAI was considered (Kushner & Cohen, 2014). This definition is very different from the one used to identify some small-scale arts-related businesses. First, it incorporates a much wider range of arts and culture, including historical sites, zoos, and botanical gardens, as well as fine arts schools. Another important difference is that the NAI includes larger-scale arts activities such as museums and libraries, which are likely not as sensitive to the higher costs associated with walkability than are comparatively smaller-scale establishments.

In addition to the NAI index, a very narrow definition of the arts, defined as arts dealers’ establishments, was also evaluated. Arts dealers were selected as a comparative arts activity because these establishments are likely patronized by a wealthier clientele. Wealthier individuals are noted to have a higher demand for artistic products (Markusen & Schrock, 2006) and thus it is anticipated that artistic activities that require the patronage of these individuals, such as arts dealers, will locate where demand is highest—in walkable, high property value block groups.

Similar multinomial logit models were run for the count of arts establishments in block groups based on the NAI definition as well as arts dealers. Tables 2 and 3 contain the results for the pooled and metropolitan-area-specific models based on the NAI definition, respectively, which are very different from those pertaining to small-scale arts-related businesses at the national and metropolitan area levels. In these tables, the odds ratios for the NAI definition suggest that arts-related businesses are more likely to locate in walkable block groups. A likely explanation for this result is that this definition of the arts contains large-scale arts activities, such as zoos and fine arts schools, that are able to afford the increased costs (in terms of property value) outlined in Table 4.

The results for arts dealers⁷ are similar to those for the NAI and reveal that arts dealers are much more likely to locate in high property value locales. In 14 of the 20 metropolitan areas examined, arts dealers were more likely to locate in high Walk Score/high property value areas compared with low Walk Score/low property value block groups. In Chicago, for example, arts dealers were 2.3 times more likely to locate in high Walk Score/high property value block groups than low Walk Score/low property value block groups. In Houston, arts dealers were 3.3 times more likely to locate in high Walk Score/high property value block groups than low Walk Score/low property value block groups. Although the motivations for the residents and art dealers’ establishments present in these block groups may not be ascertained from these results, it does appear that art dealers locate in walkable places where the property values are highest. These are also the areas where the demand for artistic products is likely higher.

Discussion and Conclusion

The goal of this study was to undertake a quantitative assessment of the relationship between the arts and walkable neighborhoods to determine the extent that arts businesses are located in walkable places. The results of the multinomial logit analysis reveal that the linkage between arts businesses and walkability depends on the definition of the arts used as well as the scale of arts activity considered. Larger scale arts businesses, as well as art dealers, which serve a relatively wealthy clientele, are more likely to locate in walkable neighborhoods, while small-scale arts businesses with comparatively small sales volumes, floor space, and few employees are not linked with walkable places. These results are likely linked to the comparatively higher costs of walkability in the form of higher property values, even in walkable, lower-cost neighborhoods.

Although no causal statements can be made about walkability and the location of arts businesses, the finding that the scale of arts activity matters in the context of walkability advances prior work that highlights the need to consider scale in arts-related urban revitalization efforts (Grodach, 2008, 2010; Sternberg, 2002). Specifically, it suggests that modifications to the existing urban fabric to make it more walkable may have unintended consequences for arts businesses that are unable to bear the higher costs associated with walkability. In this sense, the duality between walkability and these activities needs to be explicitly addressed at the local level in community-based economic development efforts that take steps to ameliorate the higher property costs associated with walkable environments for small-scale arts activities that are vulnerable to higher costs. This would likely involve a more community-oriented, place-conscious approach to the usual economic development strategies for artistic retention, such as tax benefits, small grant and loan programs, and the lessening of code restrictions.

Specifically, community development efforts need to engage with arts-related businesses, arts entities, and other local businesses to understand the cost impacts of walkability initiatives and construct strategies to improve the affordability of properties in walkable areas, as necessary. For small-scale arts businesses, rental or mortgage assistance programs may be necessary. Similar steps have been suggested to mitigate the cost impacts of gentrification for low-income residents (Chapple, 2009). Other examples of creative place-making initiatives to improve affordability for other types of arts activities include subsidized live/work spaces. These initiatives have been implemented in several cities, including the Artscape effort in Toronto (Artscape, 2011) and Artscape communities in the United States (Artscape, 2012). Arts incubators are another community-oriented option that would improve the affordability of space in dense, walkable urban environments for artists (Grodach, 2011; Phillips, 2004).

That said, there are important extensions to the present study that will enhance our understanding of the linkages between the arts and walkable environments. One extension, as the data become available, is an examination of the evolution of these relationships over time. The present study is a snapshot at one point in time, which might reflect post-2008 crisis property values and a relationship that is unique to this period in time. Future work that examines this relationship from a time series perspective could ascertain the robustness of the present results and locate neighborhoods for which this relationship varies temporally. Another extension would be to look at different types of arts activities beyond arts-related businesses including artists’ residential information collected from survey data as in Ryberg, Salling, and Soltis (2013), as well as nonprofit arts entities (Cohen, Schaffer, & Davidson, 2003). A third extension is an examination of the extent that artistic activity clusters and the spatial scale at which this clustering activity takes place. This is not examined in the present study but is a conceptually and empirically challenging question. Some work has begun to explore the clustering of artistic activity at the metropolitan area and ZIP code area scales (Grodach, Currid-Halkett, Foster, & Murdoch, 2014), but the spatial nature of this study means that much more work is needed to understand the spatial scale at which clustering takes place. Is it the street block-level neighborhoods in metropolitan areas or some other scale? How consistent is this spatial scale of clustering across metropolitan areas? Does the level of clustering depend on the type of artistic activity examined? For example, independent artists and small art shops may be more likely to cluster together than larger-scale arts activities such as museums and concert halls.

Despite these limitations and future research needs, the results of the present study provide an important baseline assessment of the linkages between arts businesses and walkability. These results are important to both stakeholders and policy makers because they suggest that public policies that seek to improve walkability may be complicit in the problem of arts businesses and small business displacement more generally. As demonstrated in this study, walkable block groups with relatively lower property values may still be too expensive for small-scale businesses. To the degree that cost is an important location consideration for arts businesses, planning initiatives that focus solely on the creation of walkable and sustainable communities without considering the externalities associated with such initiatives run the risk of pricing small-scale arts businesses, as well as other small businesses, out of walkable neighborhoods. Given this issue, community development actors and planners should work to address the potential for conflict and promote synergistic benefits through policies that ensure walkable communities are accessible from a cost standpoint for a wide range of people and businesses, including the arts.

Footnotes

Appendix

Descriptive Statistics for Model Covariates.

	NAI businesses	Small-scale businesses	Business density	Housing density	% Renters	Foreclosure risk	% Public Housing	% Vouchers	Racial diversity	Land use diversity	Dist. high-perf. school	Dist. low-perf. school	Dist. Brownfield	% EITC	% No car
All block groups
Mean	1.444	0.068	0.426	5.061	35.334	26.303	1.461	5.621	1.689	1.802	8.048	8.098	8.566	18.053	0.100
SD	4.220	0.316	1.786	12.467	25.422	27.670	8.061	8.333	0.591	0.979	1.065	1.318	1.208	11.159	0.148
Minimum	0	0	0	0	0	0.01	0	0	0	0	1.740	0.536	0.994	0	0
Maximum	418	12	152.44	718.983	99.9	100	99.89	98.78	9.931	12	11.987	11.467	12.112	69.21	0.994
Not walkable/low property value
Mean	0.962	0.056	0.244	3.583	39.074	35.816	1.988	7.960	1.736	1.762	8.341	7.842	8.416	22.524	0.102
SD	2.382	0.287	0.350	4.422	23.311	29.875	9.347	9.403	0.613	0.974	0.959	1.360	1.293	11.222	0.120
Minimum	0	0	0	0	0	0.01	0	0	0	0	2.624	0.536	1.065	0	0
Maximum	221	8	19.58	192.11	99.9	100	99.65	98.78	8.109	12	11.987	11.432	12.112	69.21	0.986
Not walkable/high property value
Mean	1.501	0.075	0.187	2.399	25.487	18.696	0.645	3.094	1.594	1.819	7.890	8.619	8.896	12.878	0.047
SD	3.264	0.315	0.330	3.604	21.821	22.592	5.139	6.113	0.542	0.982	1.086	1.013	0.991	8.257	0.076
Minimum	0	0	0	0	0	0.04	0	0	0	0	2.774	1.616	2.873	0	0
Maximum	418	11	21.432	137.690	99.9	100	99.58	97.18	9.931	12	11.769	11.467	12.046	69.21	0.918
Walkable/low property value
Mean	2.466	0.079	1.982	24.104	68.975	21.069	3.459	7.984	1.971	1.866	7.529	6.639	7.527	24.819	0.371
SD	6.806	0.387	3.826	26.900	23.645	26.027	13.132	9.005	0.686	0.974	0.903	1.185	1.223	11.784	0.243
Minimum	0	0	0	0	0	0.02	0	0	0	0	2.803	2.196	0.994	0	0
Maximum	201	7	121.703	294.020	99.9	100	99.89	96.65	7.764	6	11.628	11.057	12.040	60.9	0.981
Walkable/high property value
Mean	4.901	0.118	3.254	29.522	63.737	7.565	2.284	4.251	1.889	1.999	7.042	6.928	7.799	16.834	0.346
SD	13.040	0.487	6.576	37.752	23.676	12.769	10.262	7.243	0.549	0.964	0.886	1.129	1.049	11.831	0.266
Minimum	0	0	0	0.132	0	0.02	0	0	0.819	0	1.740	0.572	1.641	0	0
Maximum	415	12	152.44	718.983	99.89	100	99.87	86.93	4.198	6	10.642	11.056	12.032	59.25	0.994

Note. NAI = National Arts Index; EITC = Earned Income Tax Credits.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Elizabeth A. Mack is an assistant professor in the Department of Geography, Environment, & Spatial Sciences at Michigan State University. Her research is dedicated to understanding business location and economic development with an emphasis on the impacts of urban infrastructure.

Emily Talen is a professor of urbanism at the University of Chicago. Her research focuses on the relationship between the built environment and social equity. Her next book, to be published in 2017, is a historical analysis of the concept of neighborhoods.

Julia Koschinsky is the research director of the Center for Spatial Data Science at the University of Chicago. Her research gains insights from the spatial dimensions of urban problems in housing, health, and the built environment.

References

Allard

S. W.

(2009). Out of reach: Place, poverty, and the new American welfare state. New Haven, CT: Yale University Press.

Artscape. (2011). About Artscape. Retrieved from http://torontoartscape.org/about-artscape

Artscape. (2012). About building better communities through the arts. Retrieved from http://www.artspace.org/about

Bengston

D. N.

Fletcher

J. O.

Nelson

K. C.

(2004). Public policies for managing urban growth and protecting open space: Policy instruments and lessons learned in the United States. Landscape and Urban Planning, 69, 271-286.

Boarnet

M. G.

Day

Alfonzo

Forsyth

Oakes

(2006). The Irvine–Minnesota inventory to measure built environments: Reliability tests. American Journal of Preventive Medicine, 30, 153-159.

Bramley

Power

(2009). Urban form and social sustainability: The role of density and housing type. Environment and Planning B: Planning and Design, 36(1), 30-48.

Brown

B. B.

Cropper

V. L.

(2001). New urban and standard suburban subdivisions: Evaluating psychological and social goals. Journal of the American Planning Association, 67, 402-419.

Brown

S. C.

Pantin

Lombard

Toro

Huang

Plater-Zyberk

. . . Szapocznik

(2013). Walk Score®: Associations with purposive walking in recent Cuban immigrants. American Journal of Preventive Medicine, 45, 202-206.

Business Analyst. (2012). ESRI business locations and business summary. Retrieved from http://www.esri.com/data/esri_data/business-overview/business

10.

Cameron

Coaffee

(2005). Art, gentrification and regeneration: From artist as pioneer to public arts. European Journal of Housing Policy, 5(1), 39-58.

11.

Center for Neighborhood Technology. (2013). The new real estate mantra: Location near public transit. Chicago, IL: Author. Retrieved from http://www.cnt.org/repository/The_New_Real_Estate_Mantra.pdf

12.

Cervero

Sandoval

Landis

(2002). Transportation as a stimulus of welfare-to-work private versus public mobility. Journal of Planning Education and Research, 22(1), 50-63.

13.

Chapple

(2009). Mapping susceptibility to gentrification: The early warning toolkit. Berkeley, CA: Center for Community Innovation. Retrieved from http://communityinnovation.berkeley.edu/reports/Gentrification-Report.pdf

14.

Chapple

Jackson

Martin

A. J.

(2010). Concentrating creativity: The planning of formal and informal arts districts. City, Culture and Society, 1, 225-234.

15.

Cohen

Schaffer

Davidson

(2003). Arts and economic prosperity: The economic impact of nonprofit arts organizations and their audiences. Journal of Arts Management, Law, and Society, 33(1), 17-31.

16.

Cortright

(2009). Walking the walk: How walkability raises home values in U.S. cities. Washington, DC: CEOs for Cities. Retrieved from http://www.ceosforcities.org/

17.

Currid

(2007). The Warhol economy: How fashion, art and music drive New York City. Princeton, NJ: Princeton University Press.

18.

Currid

(2009). Bohemia as subculture; Bohemia as industry: Art, culture, and economic development. Journal of Planning Literature, 23, 368-382.

19.

Davis

J. E.

(1984). Reallocating equity: A land trust model of land reform. In Geisler

C. C.

Popper

F. J.

(Eds.), Land reform, American style (pp. 209-232). Totowa, NJ: Rowman & Littlefield.

20.

Ding

Knaap

G. J.

(2002). Property values in inner-city neighborhoods: The effects of homeownership, housing investment, and economic development. Housing Policy Debate, 13, 701-727.

21.

Douglas

G. C.

(2012). The edge of the island: Cultural ideology and neighbourhood identity at the gentrification frontier. Urban Studies, 49, 3579-3594.

22.

Doyle

Kelly-Schwartz

Schlossberg

Stockard

(2006). Active community environments and health: The relationship of walkable and safe communities to individual health. Journal of the American Planning Association, 72(1), 19-31.

23.

Drake

(2003). “This place gives me space”: Place and creativity in the creative industries. Geoforum, 34, 511-524.

24.

Du Toit

Cerin

Leslie

Owen

. (2007). Does walking in the neighbourhood enhance local sociability? Urban Studies, 44, 1677-1695.

25.

Ewing

Cervero

(2010). Travel and the built environment. Journal of the American Planning Association, 76, 265-294.

26.

Ewing

Bartholomew

Winkelman

Walters

Chen

(2008). Growing cooler: The evidence on urban development and climate change. Washington, DC: Urban Land Institute.

27.

Florida

(2002). The rise of the creative class. New York, NY: Basic Books.

28.

Florida

(2005). Cities and the creative class. New York, NY: Routledge.

29.

Fraser

J. C.

Kick

E. L.

(2007). The role of public, private, non-profit and community sectors in shaping mixed-income housing outcomes in the US. Urban Studies, 44, 2357-2377.

30.

Front Seat. (2010). Walk score methodology. Retrieved from https://www.walkscore.com/methodology.shtml

31.

Frumkin

Frank

Jackson

R. J.

(2004). Urban sprawl and public health: Designing, planning, and building for healthy communities. Washington, DC: Island Press.

32.

Gonzalez

G. A.

(2009). Urban sprawl, global warming, and the empire of capital. Albany: State University of New York Press.

33.

Grodach

(2008). Museums as urban catalysts: The role of urban design in flagship cultural development. Journal of Urban Design, 13, 195-212.

34.

Grodach

(2010). Art spaces, public space, and the link to community development. Community Development Journal, 45, 474-493.

35.

Grodach

(2011). Art spaces in community and economic development: Connections to neighborhoods, artists, and the cultural economy. Journal of Planning Education and Research, 31(1), 74-85.

36.

Grodach

Currid-Halkett

Foster

Murdoch

III . (2014). The location patterns of artistic clusters: A metro-and neighborhood-level analysis. Urban Studies, 51, 2822-2843.

37.

Grodach

Foster

Murdoch

III . (2014). Gentrification and the artistic dividend: The role of the arts in neighborhood change. Journal of the American Planning Association, 80(1), 21-35.

38.

Hansen

H. K.

Niedomysl

(2009). Migration of the creative class: Evidence from Sweden. Journal of Economic Geography, 9, 191-206.

39.

Hodgson

Beavers

K. A.

(2011). Overview: The role of arts and culture in planning practice. Retrieved from https://www.planning.org/research/arts/briefingpapers/overview.htm

40.

Joseph

M. L.

Chaskin

R. J.

Webber

H. S.

(2007). The theoretical basis for addressing poverty through mixed-income development. Urban Affairs Review, 42, 369-409.

41.

Koschinsky

Talen

Alfonzo

Lee

(2015). How walkable is walkers’ paradise? (Working paper). Tempe: Geoda Center for Geospatial Analysis and Computation, Arizona State University.

42.

Kingsley

G. T.

(2009). Taking advantage of what we have learned. In Cisneros

H. G.

Engdahl

(Eds.), From despair to hope: Hope VI and the promise of public housing in America’s cities (pp. 263-298).Washington, DC: Brookings Institution Press.

43.

Kushner

Cohen

(2014). National Arts Index 2014: An annual measure of the vitality of arts and culture in the United States. Retrieved from http://www.americansforthearts.org/by-program/reports-and-data/legislation-policy/naappd/national-arts-index-an-annual-measurement-of-the-vitality-of-the-arts-and-culture-in-the-united-0

44.

Landry

Bianchini

(1995). The creative city. London, England: Demos.

45.

Leinberger

C. B.

(2008). The option of urbanism: Investing in a new American dream. Washington, DC: Island Press.

46.

Lens

M. C.

(2013). Subsidized housing and crime theory, mechanisms, and evidence. Journal of Planning Literature, 28, 352-363.

47.

Ley

(2003). Artists, aestheticisation and the field of gentrification. Urban Studies, 40, 2527-2544.

48.

Leyden

K. M.

(2003). Social capital and the built environment: The importance of walkable neighborhoods. American Journal of Public Health, 93, 1546-1551.

49.

R. H.

(2009). Walkability: What is it? Journal of Urbanism, 2, 145-166.

50.

McNulty

T. L.

Holloway

S. R.

(2000). Race, crime, and public housing in Atlanta: Testing a conditional effect hypothesis. Social Forces, 79, 707-729.

51.

Manaugh

El-Geneidy

(2011). Validating walkability indices: How do different households respond to the walkability of their neighborhood? Transportation Research Part D: Transport and Environment, 16, 309-315.

52.

Markusen

Gadwa

(2010). Arts and culture in urban or regional planning: A review and research agenda. Journal of Planning Education and Research, 29, 379-391.

53.

Markusen

Schrock

(2006). The artistic dividend: Urban specialization and economic development implications. Urban Studies, 43, 1661-1686.

54.

Montgomery

(2003). Cultural quarters as mechanisms for urban regeneration. Part 1: Conceptualising cultural quarters. Planning, Practice & Research, 18, 293-306.

55.

Nyden

Michael

Lukehart

(1997). The emergence of stable racially and ethnically diverse urban communities: A case study of nine U.S. cities. Housing Policy Debate, 8, 491 533.

56.

Oldenburg

(1999). The great good place: Cafes, coffee shops, bookstores, bars, hair salons, and other hangouts at the heart of a community. New York, NY: Marlowe.

57.

Owen

(2009). Green metropolis: Why living smaller, living closer, and driving less are the keys to sustainability. New York, NY: Riverhead Books.

58.

Pendall

(2000). Why voucher and certificate users live in distressed neighborhoods. Housing Policy Debate, 11, 881-910.

59.

Pendall

Caruthers

J. I.

(2003). Does density exacerbate income segregation? Evidence from U.S. metropolitan areas, 1980 to 2000. Housing Policy Debate, 14, 541-589.

60.

Phillips

(2004). Artful business: Using the arts for community economic development. Community Development Journal, 39, 112-122.

61.

Pivo

Fisher

J. D.

(2011). The walkability premium in commercial real estate investments. Real Estate Economics, 39, 185-219.

62.

Pollack

Bluestone

Billingham

(2010). Maintaining diversity in America’s transit-rich neighborhoods: Tools for equitable neighborhood change. Boston, MA: Dukakis Center for Urban and Regional Policy.

63.

Rogers

S. H.

Halstead

J. M.

Gardner

K. H.

Carlson

C. H.

(2011). Examining walkability and social capital as indicators of quality of life at the municipal and neighborhood scales. Applied Research in Quality of Life, 6, 201-213.

64.

Ryberg

S. R.

Salling

Soltis

(2013). Putting artists on the map: The geography of artists in Cuyahoga County, Ohio. Journal of Urban Affairs, 35, 219-245.

65.

Scott

(2005). Hollywood: The place, the industry. Princeton, NJ: Princeton University Press.

66.

Slavin

M. I.

(Ed.). (2011). Sustainability in America’s cities. Washington, DC: Island Press.

67.

Small

M. L.

(2009). Unanticipated gains: Origins of network inequality in everyday life. New York, NY: Oxford University Press.

68.

Soule

Hodgson

Beavers

K. A.

(2011). Community character: How arts and cultural strategies create, reinforce, and enhance sense of place. Chicago, IL: American Planning Association. Retrieved from https://www.planning.org/research/arts/briefingpapers/character.htm

69.

Speck

(2013). Walkable city: How downtown can save America, one step at a time. New York, NY: North Point Press.

70.

Squires

G. D.

(Ed.). (2002). Urban sprawl: Causes, consequences & policy responses. Washington, DC: Urban Institute Press.

71.

Stern

M. J.

Seifert

S. C.

(2010). Cultural clusters: The implications of cultural assets agglomeration for neighborhood revitalization. Journal of Planning Education and Research, 29, 262-279.

72.

Sternberg

(2002). What makes buildings catalytic? How cultural facilities can be designed to spur surrounding development. Journal of Architectural and Planning Research, 19(1), 30-43.

73.

Strom

(2002). Converting pork into porcelain: Cultural institutions and downtown. Urban Affairs Review, 38(1), 3-21.

74.

Strom

(2010). Artist garret as growth machine? Local policy and artist housing in U.S. cities. Journal of Planning Education and Research, 29, 367-378.

75.

Talen

(Ed.). (2013). Charter of the new urbanism (2nd ed.). New York, NY: McGraw-Hill.

76.

U.S. Census Bureau. (2013). County business patterns. Retrieved from http://www.census.gov/data/datasets/2013/econ/cbp/2013-cbp.html

77.

U.S. Department of Transportation, Federal Transit Administration, and Reconnecting America. (2008). Realizing the potential: One year later, housing opportunities near transit in a changing market. Washington, DC: U.S. Department of Transportation.

78.

Van der Ryn

Calthorpe

. (1986). Sustainable communities. San Francisco, CA: Sierra Club Books.

79.

Walker

Winston

(2010). A HMDA-Based housing market index to track neighborhood change. Washington, DC: Local Initiatives Support.

80.

Wassall

G. H.

Alper

(1985). Occupational characteristics of artists: A statistical analysis. Journal of Cultural Economics, 9(1), 13-34.

81.

Weinberger

Sweet

M. N.

(2012). Integrating walkability into planning practice. Transportation Research Record, 2322, 20-30.

82.

Wilson

Navaro

(2007). Driving to green buildings: the transportation energy intensity of buildings. Environmental Building News, 16(9), 1. Retrieved from https://www.buildinggreen.com/feature/driving-green-buildings-transportation-energy-intensity-buildings

83.

Wood

Shannon

Bulsara

Pikora

McCormack

Giles-Corti

(2008). The anatomy of the safe and social suburb: An exploratory study of the built environment, social capital and residents’ perceptions of safety. Health & Place, 14(1), 15-31.

84.

Zukin

(1989). Loft living: Culture and capital in urban change. New Brunswick, NJ: Rutgers University Press.

85.

Zukin

(2001). How to create a cultural capital: Reflections on urban markers and places. In Blazwick

(Ed.), Century city: Art and culture in the modern metropolis (pp. 258-264). London, England: Tate.

86.

Zukin

(2005). Whose culture? Whose city? In Lin

Mele

(Eds.), The urban sociology reader (pp. 281-289). New York, NY: Routledge.