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The impacts of neighborhood design on physical activity and wellbeing

Adriana A. Zuniga-Teran, Randy H. Gimblett, Barron J. Orr, Nader V. Chalfoun, David P.

Guertin, and Stuart E. Marsh

Abstract

The way we design neighborhoods may affect the level of physical activity that the residents

have and their wellbeing. In this study, we explored these interactions between four types of

neighborhood designs: traditional development, suburban development, enclosed community,

and cluster housing development. Through a questionnaire, we assessed walkability levels for

each type of neighborhood design and their relationship with physical activity and the physical

mental, and social health of the residents. Findings show that the most walkable neighborhood

design is traditional development and this is where most people walk for both transportation and

recreation. However, this neighborhood design was also correlated with higher levels of

perceived crime and lower levels of mental health. Suburban development showed the highest

levels of mental health, while cluster housing showed the highest levels of social interactions

with neighbors and perceived safety. Enclosed communities did not show any outstanding

wellbeing benefits, including safety. In addition, presence of trees was significantly correlated

with recreational walking, perceived safety, and increased social interactions; while incivilities

(trash, litter, graffiti) was correlated with perceived crime and lower mental health. Results of

this study shed light in some aspects of the built environment that can be used to increase

wellbeing. For example, introducing trees and increasing maintenance in walkable

neighborhoods may result in healthier communities.

Key words: neighborhood design, walkability, physical activity, wellbeing, crime

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Background

It has been well established that the built environment affects lifestyle physical activity and

consequently human health through different levels of walkability (Cooper & Barton, 2015; Ellis

et al., 2015; Zuniga-Teran et al., 2016). Although walkability has been studied in many research

domains (e.g., public health, transportation, land planning), urban design has been largely

ignored. Urban design determines indirectly walkability in the built environment through

ordinances, zoning regulations, street standards, and street layout, for example, that affect

accessibility and connectivity of neighborhoods, the provision of greenspace, the distribution of

trees, the pedestrian and cyclist infrastructure, and the interactions between buildings and public

spaces. Therefore, examining walkability through the lens of urban design at the neighborhood

scale becomes critical to the understanding of the relationships between the built environment

and wellbeing.

It is important to distinguish the motivations for walking that have been found in previous

research studies – walking for recreation and walking for transportation (Giles-Corti, Timperio,

Bull, & Pikora, 2005; Jackson, 2003; Saelens & Handy, 2008). Walking for recreation refers to

walking activities that are meant for leisure, exercise, dog-walking, or simple recreation; while

walking for transportation refers to walking with the purpose of reaching a destination. Each

motivation for walking is influenced by different aspects of the built environment (Hartig,

Mitchell, de Vries, & Frumkin, 2014; Zuniga-Teran et al., 2016). Therefore, different

neighborhood design may affect the motivation for walking differently.

The purpose of this study is to examine the interactions between different types of neighborhood

designs, physical activity, and wellbeing. The neighborhood design types included in this study

are (1) traditional development, (2) suburban development, (3) enclosed community, and (4)

cluster housing.

Traditional development refers to neighborhoods built in the U.S. before World War II, when

most families did not own a car. After 1950, this type of neighborhood design declined with the

mass adoption of cars and the migration of wealthy people to the suburb. In terms of design

features, traditional development includes mostly single-family houses with front porches and

with retail and offices at a walking distance (less than 5 minute walk), and sometimes a small

park. It usually follows a grid-street network with small blocks. When cars were introduced,

traditional development incorporates garages that were placed off the street, sometimes facing

alleys (Montgomery, 2013).

Suburban development began in the 1940s in the U.S. and proliferated in the 1950s, becoming

the neighborhood design norm in the U.S. (Frank, Engelke, & Schmid, 2003) and many other

countries. Suburban development is low density and is composed of single-family houses in

maximized lot-size that include yards and driveways. A suburban development is mostly

residential with a cul-de-sac street network, where traffic is discouraged by few entrances to

arterial streets (Kearney, 2006; Montgomery, 2013).

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Enclosed community refers to neighborhoods that are fenced and/or gated. This type of design

originated in the late 1950s when wealthy homogeneous neighborhoods occupied large tracts of

land (Jacobs, 1961). Since then, enclosed communities became a trend in how cities are growing,

not only in the U.S., but also in other parts of the world (Kenna & Stevenson, 2013). Enclosed

communities are a response to a fear of crime, displayed by their security-oriented approach and

by the exclusion of people outside the gates and the lack of diversity in household income (Le

Goix & Vesselinov, 2013).

Cluster housing groups houses together in order to preserve greenspace. This type of design

emerged as an alternative to the urban sprawl caused by the proliferation of suburban

development. In this type of development, homeowners share the greenspace between clusters of

housing, and is maintained through homeowners associations. In terms of design features, cluster

housing usually involves groups of dwelling units – mostly townhomes – that share facilities

(e.g., swimming pools, community center, tennis courts) in order to preserve greenspace

(Kearney, 2006).

Specific issues

This study explores the effects of neighborhood design on the levels of physical activity in the

residents that live there, and wellbeing. We are interested in understanding how neighborhood

design may affect people’s behavior in terms of walking for recreation and walking for

transportation. In addition, we examine the impacts of neighborhood design on wellbeing, which

is assessed by the combination physical, mental, and social health. Finally, we study the effects

of some elements of the built environment that are not particular to any design type, such trees

and incivilities. By increasing our understanding of the effects of neighborhood design we can

start to build healthier communities.

Methods

In order to collect data on the different features of the neighborhoods, residents’ perceptions, and

their behavior, we designed a questionnaire that was based on previously validated tools (Cerin

et al., 2013; Craig et al., 2003; Saelens, B. E., Saliis, J. F., & Frank, L. D., 2003; Toit, Cerin,

Leslie, & Owen, 2007; Ware Jr, Kosinski, & Keller, 1996), and design elements from the

Leadership for Energy and Environmental Design for Neighborhood Development (LEED-ND)

certification (USGBC, 2014). The questionnaire was administered in Tucson, Arizona between

January and March 2014. Neighborhood design was assessed by using an aerial image of a

prototype for each type of neighborhood design (Figure 1). Traditional development includes a

grid street network with back alleys and services close to homes. Suburban development shows a

cul-de-sac street network and includes single-family houses in big lots. Enclosed communities

have restricted access points through the use of fences and gates. Cluster housing involves

townhomes clustered together with shared facilities and surrounded by greenspace.

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Figure 1. Aerial images of the neighborhood design types included in this study. From left to

right, traditional development, suburban development, enclosed community, and cluster housing.

We measured walkability using the Walkability Model, which categorizes the design elements of

a neighborhood into nine categories: (1) connectivity, (2) density, (3) land-use, (4) traffic safety,

(5) surveillance, (6) experience, (7) parking, (8) greenspace, and (9) community (Zuniga-Teran

et al., 2016). Because there is a high availability of parking in Tucson, we did not include this

category in the questionnaire. Physical activity was measured according to the two motivations

for walking (recreation and transportation) and the questions were based on the International

Physical Activity Questionnaire (Craig et al., 2003). The wellbeing section was divided into

physical mental and social health; with questions from the 12-item short form health survey

(Ware Jr et al., 1996).

The questionnaire was distributed online with the help of ward officials and neighborhood

leaders, who forwarded the invitation email to their listserv of residents. Because most responses

came from residents who reported living I traditional developments, we decided to broaden our

recruitment method to include visits to the Rillito River Park. After examining our sample using

these two recruitment methods, we found that we still had a low sample size for participants

living in enclosed communities and cluster housing. Therefore, we decided to mail surveys

directly to neighborhoods that we identified as enclosed communities and cluster housing. Our

final sample size by neighborhood design is displayed in Figure 2, and the number of responses

in terms of recruitment method is shown in Figure 3.

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Figure 2. Sample size according to neighborhood design.

Figure 3. Count of responses by recruitment method.

0

20

40

60

80

100

120

140

160

180

200

Online Park Mail

Count of responses

Traditional Suburbs Enclosed Cluster

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The data analysis include bivariate correlations for non-categorical variables, where we

considered moderate results when the Pearson correlation coefficient (r) was found larger than

0.30 and significant when the p value was found smaller than 0.05. In order to correct the

potential bias of our recruitment method (people visiting the park were already doing physical

activity), we used a mixed model to test the random effect of the recruitment method variable

(online, park, mail) with the dependent variables (physical activity, wellbeing, perceived crime,

and social interaction). Finally, we used an univariate analysis of variance (one-way ANOVA) to

determine the magnitude of the relationships, where we established as moderate when the R

Squared value was found larger than 0.200. This research was approved by the Institutional

Review Board for research on human subjects.

Findings

Demographics from our sample population show that 46.4% is over 60 years of age, 63.1% are

female, 87.7% are white, and 48.7% have a high-income (Table 1). In terms of neighborhood

design and demographic data, age and income were found significantly correlated. Both the

highest number of young people and the highest number of low-income respondents were found

in traditional development. The other demographic variables (gender, education, and ethnicity)

were not found significantly correlated to neighborhood design.

Table 1. Demographics according to neighborhood design.

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Our mixed model showed that the only neighborhood design that was significantly related to

walkability was traditional development. This design showed the highest mean for the

Walkability Index (all the neighborhood design categories compiled together), and the

confidence intervals do not overlap with the other neighborhood design types (Figure 4). This

means that traditional development is distinctively higher than the other designs in terms of

walkability.

Figure 4. Mean values for the Walkability Index according to the neighborhood designs.

We evaluated walkability using the Walkability Model and its categories for each neighborhood

design. We found that all the walkability categories were significantly correlated to

neighborhood design. However, the mean values for each category varied according to

neighborhood design (Table 1). Traditional development obtained the highest mean for

connectivity, land-use, traffic safety, surveillance, greenspace, and community. This

neighborhood design type also obtained the highest mean for the overall Walkability Index.

Cluster housing obtained the highest mean for density and experience.

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Table 1. Results for the univariate analysis of variance between walkability categories and

neighborhood design.

In addition to obtaining the highest mean values for most walkability categories, traditional

development was also found to have the highest mean values for physical activity and the two

motivations for walking (recreation and transportation), all with significant results (p < 0.05)

(Table 2).

Table 2. Results of a mixed model (p values) and one-way ANOVA (R) between neighborhood

design and physical activity and the two motivations for walking.

We tested whether neighborhood design was correlated with wellbeing, that is the physical,

mental, and social health; and we found significant correlations with wellbeing and mental

health. In both cases, suburban development obtained the highest mean values. We also tested

the relationship between perceived safety from crime and neighborhood design and we found

significant results. In this case, cluster housing obtained the highest mean value for safety while

traditional development obtained the lowest mean value (Table 3).

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Table 3. Results of a mixed model (p values) and one-way ANOVA between neighborhood

design and wellbeing with its three components (physical, mental, social health), and perceived

safety from crime.

Because we found significant correlations between neighborhood design and mental health, we

decided to explore this wellbeing variable further. We found that mental health was significantly

related to perceived crime and the perceived presence of incivilities (trash, litter, graffiti).

Furthermore, the presence of incivilities was found significantly correlated to perceived crime.

This means that neighborhoods that show incivilities are perceived as dangerous and people

show a lower level of mental health.

In this study, we found that active people (who reported higher levels of physical activity) are

healthier. Physical activity was found significantly correlated to physical health (p < 0.001),

social health (p = 0.001), and overall wellbeing (p < 0.001).

Finally, we tested the effects of trees in the neighborhood and we found that this variable was

significantly correlated to physical activity, walking for recreation, social interaction with

neighbors, perceived crime, and social health (Table 4). This means that neighborhood that have

more trees are related to people walking for recreation, to people talking to their neighbors, and

to perceived safety from crime.

Discussion and conclusion

In this study we examined the effects of four types of neighborhood designs on physical activity

and wellbeing. We found that traditional development is the most walkable design type as

assessed by the Walkability Model (Zuniga-Teran et al., 2016). This design type showed the

highest levels of physical activity and the two motivations for walking (recreation and

transportation). However, traditional development also showed the highest levels of perceived

crime and the lowest levels of mental health.

Suburban development was not found to be walkable, but it showed the highest levels of mental

health and wellbeing. We believe that the reason for these high results on wellbeing is a

consequence of large lots that include vegetation. Previous studies have found that contact with

nature is related to better health (Ambrey, 2016; Smiley et al., 2016).

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In this study, we did not find outstanding wellbeing benefits in enclosed communities, not even

perceived safety from crime. This results is contrary to the objective of this type of design where

people exclude themselves from the rest of the city. Because fencing neighborhoods disturbs the

connectivity of the city as a whole, it becomes critical to continue doing research on both the

perception of safety and the actual levels of safety in these communities.

Although cluster housing was not found to be a walkable neighborhood, it still showed some

wellbeing benefits. This type of design showed the highest values for social interaction between

neighbors and it was perceived as the safest from crime. In terms of walkability, cluster housing

showed the highest levels of the experience category. This results is compatible with the

distinctive feature of this design – to preserve greenspace – because the experience category

includes perceptions while walking and contact with nature enhances these percpetions.

A takeaway message from this study is that the presence of nature, particularly trees, may

provide several wellbeing benefits that include walking for recreation, wellbeing, perceived

safety, and increased social interactions between neighbors. These results provide empirical

evidence of the need to include vegetation (trees) throughout neighborhoods in order to increase

physical activity and wellbeing. In addition, our results show that regular maintenance that

remove incivilities may improve mental health and wellbeing. Therefore, regardless of

neighborhood design type, adding trees and maintenance to neighborhoods may result in better

health. We recommend further studies on the effects of these two strategies (increasing trees and

maintenance) in the most walkable design – traditional development. These key enhancement

may result in healthier communities and more livable cities.

Acknowledgement: This study is part of a larger study that was published as a dissertation at the

University of Arizona. This paper is a reduced and simplified version of a research article

published in the International Journal for Environmental Research and Public Health, available

at http://www.mdpi.com/1660-4601/14/1/76

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