Abstract
There is growing evidence that communities can be designed to support physical activity, but it is important to understand whether neighborhood features related to health are also considered satisfactory by residents. The study aimed to determine if there is an association between perceived and objective neighborhood environment variables and neighborhood satisfaction. Adults (N = 1,726) were recruited from neighborhoods in two regions of the United States selected to vary on walkability and income. Perceived neighborhood environment was assessed using a validated scale, objective measures were constructed using geographic information system (GIS), and satisfaction was assessed using a 17-item survey. Participants reported greater satisfaction when they perceived their neighborhood as having greater pedestrian/traffic safety, crime safety, attractive aesthetics, access to destinations, diversity of destinations, park access, and lower residential density. Objective measures were not significant. The discrepant findings between perceived and objective environmental measures indicate that neighborhood satisfaction is a complex construct.
The neighborhood environment is a central setting for everyday life, and substantial research has documented how the built environment contributes to the health and well-being of individuals (Sallis, Floyd, Rodriguez, & Saelens, 2012). There is growing evidence that designing communities to support physical activity for transportation and recreation purposes produces co-benefits beyond health, notably in environmental sustainability and economic performance (Sallis et al., 2015). We need to understand whether neighborhood features that help create healthy and sustainable neighborhoods are also considered satisfactory and livable by residents. A less than satisfactory neighborhood can lead to poor health outcomes and can drive residents to move elsewhere (Chapman & Lombard, 2006).
Neighborhood satisfaction is driven by two sets of influences: individual household characteristics and neighborhood quality characteristics (Basolo & Strong, 2002). The individual household characteristics are comprised of sociodemographic factors, including age, gender, race, education, marital status, income, and length of residence. Neighborhood quality can be measured by indicators of a neighborhood’s physical environment such as access to places of activity and services, and sociocultural setting (Connerly & Marans, 1988). The neighborhood satisfaction model of Amerigo and Aragones (1997) conceptualized the residential environment as composed of both objective and subjective attributes. These built environment attributes, combined with personal characteristics, are considered the main determinants of a resident’s satisfaction with his or her neighborhood.
Subjective measures of neighborhood characteristics have been commonly assessed via surveys asking about residents’ perceptions of the neighborhood. Objective assessments of the physical environment have relied on field audits by trained surveyors and geographic information system (GIS) (Brownson, Hoehner, Day, Forsyth, & Sallis, 2009). Some studies have shown discordance between perceived and objective measures (Ball et al., 2008; Leslie, Sugiyama, Ierodiaconou, & Kremer, 2010; Macintyre, Macdonald, & Ellaway, 2008; McCormack, Cerin, Leslie, Du Toit, & Owen, 2008), while others have shown general agreement between the measures (De Bourdeaudhuij, Sallis, & Saelens, 2003; Leslie et al., 2005). Although past research has suggested that subjective evaluations of neighborhood characteristics are more important in explaining neighborhood satisfaction than objective measures (Bruin & Cook, 1997; Lu, 1999; Parkes, Kearns, & Atkinson, 2002), the advantages of using objective measures are that they are accessible through public datasets and have the potential to better direct city planning based on their specific and reproducible parameters. Thus, it is important to include both perceived and objective measurements in neighborhood studies.
Neighborhood Environment and Neighborhood Satisfaction
The physical environment has been found to have a strong correlation with the satisfaction of its residents (Herting & Guest, 1985). Although sociodemographics play important roles in many health-related outcomes, Parkes et al. (2002) found that neighborhood environment attributes accounted for more of the variance in neighborhood satisfaction than did demographic factors. This finding supports an expectation that improved neighborhood design can contribute to increased satisfaction regardless of age, gender, income, or race/ethnicity. It is important to examine the many components that define neighborhood environments to determine each attribute’s relative importance to neighborhood satisfaction.
Perceived Neighborhood Attributes
Perceived general appearance has been recognized as one of the strongest correlates of neighborhood satisfaction (Parkes et al., 2002). Neighborhood aesthetics and greenery have been found to be positively associated with neighborhood satisfaction (de Jong, Albin, Skarback, Grahn, & Bjork, 2012; Leslie & Cerin, 2008; Lovejoy, Handy, & Mokhtarian, 2010), while problems with physical upkeep, including litter in the neighborhood, were associated with lower levels of satisfaction (Batson & Monnat, 2015; Dassopoulos, Batson, Futrell, & Brents, 2012; Howley, Scott, & Redmond, 2009; Hur & Nasar, 2014). Feelings of safety from crime have also been found to be strongly correlated with neighborhood satisfaction (Basolo & Strong, 2002; Bruin & Cook, 1997; Howley et al., 2009; Hur & Nasar, 2014; Leslie & Cerin, 2008; Lovejoy et al., 2010). Perceived traffic load was found to be negatively associated with satisfaction (Leslie & Cerin, 2008).
Objective Neighborhood Attributes
Proximity to GIS-measured recreational settings, including greenery, nature, and parks, has been strongly associated with neighborhood satisfaction (Bjork et al., 2008), and the presence of trees was associated with more satisfaction with the neighborhood (Kweon, Ellis, Leiva, & Rogers, 2010; Lee, Ellis, Kweon, & Hong, 2008). Conversely, poor physical upkeep (e.g., presence of litter and broken features) was negatively associated with neighborhood satisfaction (Hur & Nasar, 2014; Kruger, Reischl, & Gee, 2007). Residential density (McCulloch, 2012; Van Dyck, Cardon, Deforche, & De Bourdeaudhuij, 2011) and walkability (Van Dyck et al., 2011) have been found to be negatively associated with neighborhood satisfaction. Objectively measured retail land use was found to be negatively related to neighborhood satisfaction (Ellis, Lee, & Kweon, 2006; Kweon et al., 2010; Yang & Stockard, 2013), while higher densities and land use mix at a larger (e.g., regional) scale were positively associated with satisfaction (Yang & Stockard, 2013).
There is evidence that neighborhood built and social environmental attributes, measured both objectively and by self-report, have been associated with neighborhood satisfaction. Because studies have included a diversity of variables, it is difficult to compare results across studies and draw conclusions about the most important environmental drivers of neighborhood satisfaction. Few studies have evaluated the relative contributions of perceived and objectively measured environment variables. Thus, the aim of the present study was to determine which perceived and objective built environment characteristics are associated with neighborhood satisfaction among adult residents in two regions of the United States selected to represent variation in walkability. Improvements over prior studies included assessment of several built environment variables with both validated, self-report and objective variables and adjustment for numerous sociodemographic variables.
Method
Data came from the Neighborhood Quality of Life Study (NQLS), a cross-sectional study examining the built environment’s association with physical activity and other health-related outcomes. The study area included 16 neighborhoods (each made up of multiple census block groups) from the Seattle-King County, Washington area and 16 neighborhoods from five counties in Baltimore, Maryland area. The sampling design stratified the neighborhoods from which the study population was recruited by using objective measures of neighborhood walkability and income.
Using GIS, block groups in the Seattle and Baltimore regions were ranked on a walkability index, which was constructed using measures of net residential density, land use mix, street connectivity (intersection density), and retail floor area ratio (FAR; a ratio of building floor area to land area. A higher value is thought to represent more pedestrian-oriented design, including doors opening onto sidewalks, multistory buildings, and less surface parking). Each block group was assigned a region-based z score for each of these characteristics, from which a weighted sum was created and deciles constructed. To be eligible for the study, a neighborhood (defined as a cluster of contiguous block groups) was required to have a population of at least 1,000 households and to belong in either low walkability deciles (Deciles 1-4) or high walkability deciles (Deciles 7-10). Study staff made site visits to verify the accuracy of the walkability index that was generated (Frank et al., 2010). Neighborhood income was defined based on median household values from the 2000 U.S. census. Eligible neighborhoods were required to be in either a moderately low income decile (Deciles 2-4) or a moderately high income decile (Deciles 7-9). Neighborhoods were classified into one of the following quadrants: low income/low walkability, low income/high walkability, high income/low walkability, or high income/high walkability. Each quadrant was made up of eight neighborhoods: four from Seattle and four from Baltimore regions.
From May 2002 to June 2005, participant recruitment and data collection took place in two phases. Phase 1 (May 2002-November 2003) involved recruitment and data collection in the Seattle-King County region, and Phase 2 (December 2003-June 2005) involved the Baltimore-Maryland region. A commercial records company provided lists with contact information for residents within the selected areas. Records were randomly selected from these lists, while trying to maintain comparable numbers of participants (including roughly half men and half women) from each neighborhood. A letter introducing the NQLS was mailed to the heads of households. Two or three days after the anticipated delivery, trained interviewers called the household, explained the study, and encouraged participation. Those invited to participate were between the ages of 20 and 65, not living in group residential quarters, able to walk, willing to wear an accelerometer, and able to complete written surveys in English. If the initially targeted adult declined or was ineligible, an effort was made to recruit another adult in the household. As many as eight return calls were made. Eligible and interested participants were mailed a consent form, followed by a telephone call 1 week later to promote participation and answer questions. Upon receiving the signed consent form, participants were sent a survey and accelerometer to wear for 7 days. Participants returned the survey and accelerometer by mail. Approximately 6 months later, participants were asked again to wear an accelerometer and to complete a second survey.
Of 8,504 eligible individuals who were contacted, 2,849 agreed to participate, 2,199 individuals completed the first survey (25.9% participation rate), and 1,745 completed the second survey 6 months later (79.4% retention rate). The present study used perceived neighborhood environment data gathered from the first survey and neighborhood satisfaction data from the second survey, and 1,726 had complete data on the measures included in analyses. The NQLS survey can be viewed at http://sallis.ucsd.edu/measures.html.
Measures
Perceived neighborhood environment
The Neighborhood Environment Walkability Scale (NEWS) was designed to assess residents’ perceptions of neighborhood attributes believed to be related to physical activity. This self-report survey was previously found to be reliable and valid, with most of the NEWS subscales having good test–retest reliability, intraclass correlation (ICC) > .75 (Saelens, Sallis, Black, & Chen, 2003). The factorial structures were also found to be valid in prior studies (Cerin, Conway, Saelens, Frank, & Sallis, 2009; Cerin, Saelens, Sallis, & Frank, 2006).
Prior comparison of 22 NEWS items and four composite measures of NEWS items with 10 objective physical environment characteristics resulted in 18 NEWS items and the four composite NEWS items showing significant correlations of weak to moderate strength with objective measures (Adams et al., 2009). The NEWS subscales can be viewed at http://sallis.ucsd.edu/measures.html. Each subscale was comprised of multiple items. Subscales included residential density (six items), land use mix–diversity (23 items), land use mix–access (seven items), street connectivity (five items), walking/cycling facilities (five items), aesthetics (six items), pedestrian/traffic safety (eight items), and safety from crime (six items).
Residential density was assessed by asking respondents how common certain types of residences were in their neighborhoods (e.g., single-family and apartments), and responses were on a 5-point scale from none (1) to all (5). The residential density measure on the NEWS was a weighted value based on the approximate density of households per unit area relative to a single-family detached home. Subscale scoring for residential density was as follows: single-family detached + (12 × row houses/townhouses 1-3 stores) + (10 × apartments/condos 1-3 stories) + (25 × apartments/condos 4-6 stories) + (50 × apartments/condos 7-12 stories) + (75 × apartments/condos 13+ stories). Because the residential density weighted score was skewed (>1.5), scores were recoded into quartile groups for model analysis.
Perceived land use mix–diversity was assessed by asking respondents how many minutes it takes to walk from their home to 23 destinations (e.g., a supermarket, clothing store, and bus or trolley stop), and measurements were based on a 5-point scale from 1 to 5 min (5) to 31+ min (1) and don’t know (8). A “don’t know” response was grouped with the response “31+ min” because that destination was likely more than 31 min away by walking. Means were computed for analyses.
Perceived land use mix–access was assessed by asking respondents about ease of accessing services, including ease of parking and ability to walk to public transit. Street connectivity was assessed by asking respondents about the presence of intersections, cul-de-sacs, and routes in their neighborhoods. The walking/cycling facilities measure included questions about trail presence and sidewalk conditions. The aesthetics measure included questions about tree and litter presence and the attractiveness of buildings and homes. Pedestrian/traffic safety was assessed by asking respondents to rate factors such as speed and the amount of traffic in their neighborhoods. The measure of safety from crime included questions about street lighting and perceptions of crime. The items in these scales were answered using a 4-point Likert-type scale from strongly disagree (1) to strongly agree (4), and means were computed to create subscales.
Because parks are generally recognized as beneficial neighborhood elements for physical activity and social connections, we created a park access variable using the park destination item from the Land Use Mix–Diversity subscale survey questions. A study found those living within 1 mile of a park engaged in more physical activity (Cohen et al., 2006; Cohen et al., 2007), and since 1 mile is about a 20-min walk (McGinn, Evenson, Herring, Huston, & Rodriguez, 2007), a park was considered nearby if it was reported to be within a 20-min walking distance.
Objective built environment
Five objective measures for the 1 km street-network buffer around each participant’s residence were created using GIS data integrated from county-level tax assessors, regional land use at the parcel level, and street networks. Details are provided elsewhere (Frank, Saelens, Powell, & Chapman, 2007; Saelens et al., 2012). Net residential density was calculated based on the number of residential units relative to the amount of residential land within the buffer. This measure was skewed, so quartile groups were used in the statistical analysis for ease of interpretation and to parallel the perceived residential density measure. Retail FAR was calculated as the square footage of retail/commercial area relative to the total land area dedicated to retail/commercial within the buffer. Because the distribution of FAR was skewed, categories were created with cut-points of .33 and .67 to ensure sufficient sample per category and for ease of interpretation. Land use mix examined the entropy (evenness of distribution) of the square area of floor space dedicated to residential, entertainment (including restaurants), and retail mixed land uses. Intersection density was calculated based on the number of intersections per land area. Street-network distance to the nearest park was calculated. This measure was skewed, so the natural log (ln)-transformed version was used in the statistical model.
Neighborhood satisfaction
Neighborhood satisfaction was measured using a scale developed by the investigators. The survey included 17 questions assessing neighborhood satisfaction such as “How satisfied are you with . . . the access to shopping in your neighborhood? . . . the noise from traffic in my neighborhood? . . . your neighborhood as a good place to live?” (Leslie & Cerin, 2008). Each of the 17 satisfaction items was rated from strongly dissatisfied (1) to strongly satisfied (5). The mean of all items was computed to create the dependent scale measure of Neighborhood Satisfaction, which had good internal consistency (Cronbach’s α = .86). Prior unpublished data on a separate sample of 107 adults also showed good test–retest reliability of individual items (ICCs > .70 for 16 of 17 items).
Demographic variables
Demographic variables age, sex, education, and race/ethnicity were collected on the first survey. Race/ethnicity was dichotomized to Caucasian (non-Hispanic) and non-Caucasian (including Hispanics).
Data Analysis Procedures
IBM SPSS Statistics for Windows version 21 was used. Descriptive statistics were examined, and the independent and dependent variables were assessed for non-normality using skewness values ≥1.5. Skewed variables were either subgrouped or ln-transformed before including in the statistical model (see “Measures” section). Associations among the independent variables (perceived and objective neighborhood attributes and demographic characteristics) were examined for collinearity. All tolerance levels were >.3, indicating that the level of collinearity among the independent variables was acceptable.
A mixed-effects regression model using the SPSS MIXED procedure was used to assess the independent associations between neighborhood satisfaction with both perceived and objective neighborhood environment attributes, adjusting for demographic variables as fixed effects. The mixed model also controlled for study design factors, including walkability-by-income quadrants and geographic region as fixed effects, and participant clustering within neighborhoods (ICC = .27) as a random effect.
Results
Participant Characteristics
Table 1 shows the descriptive statistics for measures (neighborhood satisfaction, perceived and objective environmental characteristics, demographic characteristics) in the NQLS sample who completed both the first and second surveys and had complete data on all measures examined here (N = 1,726 of the full sample of 1,745). The mean of the Neighborhood Satisfaction Scale was 3.8, which was just less than “somewhat satisfied” (i.e., 4- on the 5-point scale). The sample was approximately half men and women, average age 46 years, more than half with a college degree, and more than 75% Caucasian (non-Hispanic).
Sample Descriptives for Neighborhood Satisfaction, Perceived and Objective Environmental Characteristics, and Demographics (N = 1,726).
Note. NEWS = Neighborhood Environment Walkability Scale; GIS = geographic information system; FAR = floor area ratio.
ln = natural log.
Predicting Neighborhood Satisfaction
Table 2 presents the mixed regression results from the multivariate model predicting neighborhood satisfaction from the perceived and objective environmental characteristics, and the demographic variables (controlling for study design factors). While none of the five GIS-based objective environmental variables reached statistical significance (p > .05), seven of nine perceived environmental measures were significant independent predictors of neighborhood satisfaction. The strongest NEWS subscale predictors were perceived pedestrian/traffic safety, crime safety, aesthetics, and land use mix–access (all ps < .0001), followed by land use mix–diversity (p < .003) and nearby park access (p = .025). The direction of association for these six measures was higher levels of the perceived neighborhood characteristic related to higher neighborhood satisfaction. While perceived residential density was also a significant predictor of neighborhood satisfaction (p = .01), the relation was in a negative direction; that is, participants in the lowest quartile of perceived residential density had significantly higher neighborhood satisfaction than participants in the highest quartile of perceived residential density.
Perceived and Objective Neighborhood Built and Social Environment Variables Explaining Neighborhood Satisfaction.
Note. Mixed regression model controlled for study design factors (walkability-by-income quadrants and region as fixed effects; participant clustering within neighborhoods as a random effect). NEWS = Neighborhood Environment Walkability Scale; GIS = geographic information system; FAR = floor area ratio.
Unstandardized regression coefficients.
Reference group.
ln = natural log.
p-values <.05 are bold faced.
In this multivariate model, two of the four demographic variables were independent predictors of neighborhood satisfaction. Older participants reported higher neighborhood satisfaction than younger participants, and non-Caucasians (including Hispanics) reported higher neighborhood satisfaction than Caucasians. There were no significant differences between men and women or between education subgroups.
Discussion
Seven of nine perceived environment characteristics evaluated in the present study were significantly associated with neighborhood satisfaction, but none of the objectively assessed characteristics were significant. Perceived pedestrian/traffic safety, crime safety, and aesthetics emerged as the strongest correlates of neighborhood satisfaction in the multivariate model. These findings affirmed previous studies that found reported high vehicle traffic and congestion were negatively related to neighborhood satisfaction (Leslie & Cerin, 2008; Newman & Duncan, 1979), litter-free neighborhoods with trees and other attractive sights were highly and positively correlated with neighborhood satisfaction (Herting & Guest, 1985; Parkes et al., 2002), and perceived crime was associated with lower neighborhood satisfaction (Grogan-Kaylor et al., 2006; Hipp, 2009; Leslie & Cerin, 2008; Yang, 2008). Perceived walking/cycling facilities and street connectivity were non-significant.
People who reported living in highest density neighborhoods were more likely to report lower neighborhood satisfaction than those in the least-dense neighborhoods. This relationship has been reported by others (Adams, 1992; Lee & Guest, 1983). Possible reasons for this negative relationship are that high residential density may lead to a loss of feelings of control and safety among residents (Mccarthy & Saegert, 1978) due to crowding and may increase levels of pollution and of congestion by way of automobiles or pedestrians. Negative public reaction to density is among the oldest and most familiar issues to city planners and was an original impetus for sprawl and widespread movement to the suburbs (Frank, Engelke, & Schmid, 2003). The underlying desire to have more space appears to be persistent, and the widespread availability of car travel has made low-density living possible. Adverse reactions to perceived density are not seen just among wealthy or non-minority segments of society. In fact, density can be particularly adverse for those with fewer resources. For example, other analyses based on the NQLS found adverse mental health impacts of living in objectively denser walkable neighborhoods for lower income participants (Sallis et al., 2009).
Perhaps the most surprising finding was the lack of significant independent associations between objectively measured built environment characteristics and neighborhood satisfaction. This was not due to multicollinearity of perceived and objective variables included in the same model. Preliminary analyses with only objectively assessed measures in the model revealed two weak associations (positive with street connectivity and negative with retail FAR), which did not retain significance in the final model. This pattern highlights the primacy of perceptions of neighborhood environments as drivers of neighborhood satisfaction. Only the inclusion of both sets of variables in the study allowed such a clear-cut conclusion. The distinct findings for perceived versus objective environment measures suggest that the two modes of measurement are not highly correlated, though previous studies have shown that several NEWS variables are related to GIS-based measures of the same variables (Adams et al., 2009).
Younger people in the present sample reported less satisfaction with their neighborhoods than older respondents, a result in line with prior research showing that older residents reported more neighborhood satisfaction (Kobau et al., 2013; Kruger et al., 2007; Lu, 1999; Parkes et al., 2002).
Contrary to findings in the previous literature (Lu, 1999; Parkes et al., 2002), non-Whites in the present study reported more neighborhood satisfaction than Whites. This difference could be due to factors such as economic situations or cultural preferences. Because it has been reported that Blacks find it important for their neighborhoods to include friends and relatives (St. John & Clark, 1984), proximity of close contacts may be more important than neighborhood environment characteristics in determining neighborhood satisfaction. Non-Whites (except Latinos) are more likely to live in areas with more violent crime (Krivo, Peterson, & Kuhl, 2009), possibly leading to residents’ adaptation to such neighborhoods and developing a tolerance to crime (Higgitt & Memken, 2001). However, these generalizations should be taken with caution considering the limited sample of non-White (including Latino) respondents in the present sample (24.4%).
Implications for Practice and Policy
Present results have complex implications for planning cities to enhance neighborhood satisfaction. The clear finding was that perceptions of the environment were more important than objective reality, and participants were consistently more satisfied when they perceived that their neighborhoods were designed to support physical activity, with mixed land use, good aesthetics, safety from traffic, and access to parks (Bauman et al., 2012; Sallis et al., 2012). Not surprisingly, perceived safety from crime was one of the strongest associations with neighborhood satisfaction. The exception was perceived residential density, which is positively related to physical activity (Bauman et al., 2012; Cerin et al., 2009) but negatively related to neighborhood satisfaction in the present study. The lack of association between objective measures and neighborhood satisfaction is perplexing, but it suggests either that people’s perceptions are generally inaccurate or certain unstudied characteristics affect their environmental perceptions in ways that are related to their satisfaction. One implication is that neighborhood satisfaction could be affected by education or feedback about neighborhood characteristics. One example of feedback about objective neighborhood environment variables is the easily accessible WalkScore (www.walkscore.com), which helps residents understand their neighborhoods and educates users about the positive aspects of walkable communities. Present results raise several questions that should be the subject of future research.
Based on present findings regarding perceived neighborhood environment variables, there are specific strategies that could be evaluated for their ability to improve perceptions of the built environment, several of which could be applied to existing neighborhoods. It is apparent that interventions should be emphasized that improve residents’ perceptions of walking/traffic safety and crime safety, given that these were two of the strongest correlates of neighborhood satisfaction. Although not assessed in the present study, traffic calming is an approach to consider when planning or renovating neighborhoods because of potential effects on perceptions of pedestrian/traffic safety. Traffic calming is defined as physical changes such as adding speed humps or street circles (i.e., roundabouts) to reduce speed and volume of cars, thereby reducing collisions and improving safety. Traffic calming measures have been shown to reduce accidents (Rothman et al., 2015; Webster & Mackie, 1996). Street and alley closures in one neighborhood produced the added benefit of reduced incidents of violent and non-violent crime (Ewing, 1999), likely because traffic calming reduces quick escape from crimes. In other studies, residents reported favorable opinions to traffic calming measures, including perceived safety (Cairns, Warren, Garthwaite, Greig, & Bambra, 2015; Webster & Mackie, 1996). Because traffic calming measures can improve actual safety as well as perceptions of safety, they hold promise for enhancing neighborhood satisfaction, based on present associations of perceived traffic safety and neighborhood satisfaction in the present study.
Present results provide more evidence that perceived aesthetics such as presence of trees, absence of litter, attractive buildings, and natural sights are positive correlates of neighborhood satisfaction and should be addressed when designing and revitalizing neighborhoods. Views of greenery and landscaped areas have been significantly associated with feeling energized, focused, and competent (Kaplan, 2001), and perceived aesthetics have been consistently associated with more walking for leisure in a review (Owen, Humpel, Leslie, Bauman, & Sallis, 2004). However, there is little concordance between objective, satellite-detected greenery and perceived greenery (Leslie et al., 2010). It is not surprising that the objective, bird’s eye view of greenery did not match perceptions as residents generally have different vantage points. The mere addition of greenery will not necessarily increase favorable perceptions of the neighborhood. For example, when shown a variety of outdoor landscapes, participants in a prior study preferred scenes comprised of mainly trees with accents of vegetables, flowers, or autumn leave colors, while less favored scenes included benches, playgrounds, shelters, and certain nature scenes (Hadavi, Kaplan, & Hunter, 2015). There are other neighborhood features aside from nature and greenery that should be assessed in future studies to determine preferred neighborhood aesthetics. For example, public art and a high standard of building design could enhance the perception of pleasant aesthetics, but more studies are needed to determine designs that residents most prefer and that may be related to neighborhood satisfaction.
Considering that the perception of nearby parks was associated with higher satisfaction, it could be advantageous to focus on park development due to the plausibility that parks with an abundance of well-designed greenery could also improve perceptions of neighborhood aesthetics. It is noteworthy that our study showed that objective park access was not related to satisfaction, but this is not surprising based on previous research showing that perceived distance to public parks was not related to distance determined by GIS (Macintyre et al., 2008). It will be important in future research to understand whether environmental modifications might enable proximal parks to be perceived as proximal. To the extent that residents perceive them to be proximal, current results suggest that the neighborhood will be seen as satisfying, but it is not clear why some parks that are proximal are not perceived to be proximal. Some combination of lack of familiarity or busy, indirect, or unattractive street routes leading to the park may reduce their perceived proximity.
Similarly, objective measures of land use mix were not significantly associated with neighborhood satisfaction though their perceived counterparts were significant. Perceived land use mix–diversity and land use mix–access were positively related to neighborhood satisfaction. Access to a variety of stores and services is a defining characteristic of walkability. Considering that more walkable neighborhoods have been associated with more walking and biking for transportation, lower body mass indexes (BMIs), and reduced air pollution (Frank et al., 2006), such neighborhood characteristics should be encouraged because of both the wide-reaching public health significance and their apparent attractiveness to residents. However, further research is needed to determine what specific type of retail stores and other land uses are seen as desirable. Given that objective measures of land use mix did not match perceptions in our study, we can assume that not all land uses are equally related to neighborhood satisfaction. For example, is the presence of a bus stop just as favorable to residents as the presence of a nearby coffee shop or supermarket? Answering questions like these in subsequent research could help urban designers create more desirable neighborhoods.
Negative relationships between perceived neighborhood satisfaction and perceived density present challenges to planners and policymakers who understand that a critical mass of inhabitants is required to support the shops and services that create vibrant and complete communities. Consolidation of growth and population is a key requirement for sustainability and reducing per capita infrastructure costs (Burchell et al., 2002). Smart Growth and New Urbanism movements both emphasize increased density for these reasons. These approaches to sustainable development hypothesize that building facilities such as markets and recreation facilities within walking distance to homes can benefit residents by increasing walking for transportation, creating opportunities for socialization, promoting health, and decreasing pollution. However, making convenient access to services economically sustainable requires a minimum level of residential density (Frank et al., 2003; Kacker & Preuss, 2003). Respondents reporting the highest residential density accounted for the significant finding in our study, but the objective measures of residential density were not significant; therefore, it is premature to conclude that high density reduces neighborhood satisfaction. Future studies should investigate what causes residents to perceive more residential density. Such studies could assess moderating effects of noise, aesthetics, greenery, land use mix, and other built environment characteristics on perceived residential density. Such studies could help design denser communities that are more attractive to residents and reduce objections to density.
Strengths and Limitations
Strengths of this study were the inclusion of both perceived and objective environment measures, use of validated surveys to assess neighborhood environment and satisfaction, and the selection of two very different regions (Seattle and Baltimore) to increase generalizability of findings. To our knowledge, the present study is the only one to employ the NEWS scale in its assessment of the relationship between the built environment and neighborhood satisfaction among residents in the United States.
Limitations included the lack of measures of objective analogs to perceived pedestrian/traffic safety, crime safety, aesthetics, and walking/cycling facilities. Although our study found that other objective measures were not related to neighborhood satisfaction, it would be useful for future studies to include objective (e.g., direct observation) measures of variables not included in the present study. There were possible biases due to the recruitment and survey methods. Recruitment criteria, including the requirement that study participants be able to write in English and be able to walk, limited the generalizability. Surveys introduce self-report bias as some participants may respond with “socially desirable” answers. Neighborhood satisfaction levels were relatively high (M = 3.78, SD = 0.64 on a Likert-type scale of 1-5). Respondents may be likely to over-report their satisfaction with their neighborhoods (Amerigo & Aragones, 1997) or inflate levels of income or education, among other inaccuracies. Because both surveys were more than 20 pages long, respondents may have grown weary and answered some questions inaccurately. While data were drawn from surveys gathered 6 months apart, the present study was considered cross-sectional due to the short interval between time points and because the measures examined were collected only at 1 time point. Because of the cross-sectional design, it was not possible to identify causal pathways between neighborhood environment characteristics and neighborhood satisfaction. Additionally, data were collected over 10 years ago. While the correlates of neighborhood satisfaction could theoretically change over time, some of the present findings are similar to older studies, including the negative association of residential density to neighborhood satisfaction (Adams, 1992; Lee & Guest, 1983).
Conclusion
Most of the same perceived built and social neighborhood environment attributes that have been associated with physical activity in many studies were also found to be associated with neighborhood satisfaction. Higher levels of reported pedestrian/traffic safety, crime safety, aesthetics, land use mix–diversity, land use mix–access, and nearby park access were related to more neighborhood satisfaction. However, perceptions of high residential density, a correlate of higher physical activity, were associated with lower neighborhood satisfaction. Land use mix–access, attractive aesthetics (Saelens & Handy, 2008), more land use diversity, and four-way intersections were found to be related to more walking (Boer, Zheng, Overton, Ridgeway, & Cohen, 2007), and higher walkability has been linked to lower BMI (Brown et al., 2009; Sallis et al., 2009). Therefore, policies that encourage or require communities to be built or renovated to enhance mixed land use, connected streets, aesthetics, and park access could benefit neighborhood satisfaction, physical activity, and healthy weight status. However, just building more walkable communities may not be sufficient to enhance neighborhood satisfaction; the neighborhoods need to be perceived as being safe from traffic, aesthetically pleasing, mixed use, and close to parks. More research is needed to understand how to optimize favorable perceptions of community designs associated with health.
A challenge is to overcome the negative relation of perceived residential density to neighborhood satisfaction, because density is considered necessary to support mixed use and other amenities. With perceived aesthetics, noise/smell/air pollution, and crime safety shown to mediate the negative association between walkability and neighborhood satisfaction (Van Dyck et al., 2011), further research to understand whether beautification or crime reduction efforts can mitigate negative feelings associated with residential density is warranted. Housing in walkable places with appealing features is becoming increasingly expensive (Leinberger, 2010), and results of the current study suggest why there is a growing demand for this type of neighborhood environment—people like what they have to offer, if the activity-supportive features are designed well.
This study did not assess each environment characteristic with both perceived and objective measures, so future studies are needed to evaluate how objective and perceived pedestrian/traffic safety, crime safety, and aesthetics relate to each other and to neighborhood satisfaction. Present results raised the practical and conceptual problem of objective environmental characteristics not being related to neighborhood satisfaction. Understanding reasons for the disconnect between certain perceived and objective environment measures in their relation to neighborhood satisfaction should be a research priority. This disconnect raises a practical problem of people not understanding how certain neighborhood characteristics may affect their physical activity and health. Past research suggests that adults with lower income and education (Gebel, Bauman, & Owen, 2009; Wang, Brown, & Liu, 2015) and less physical activity (Gebel et al., 2009; Kirtland et al., 2003) were more likely to misjudge their neighborhood as having lower walkability or access to recreation facilities. Perhaps certain design features could be incorporated into communities to make higher density mixed-use neighborhoods more attractive, though those specific characteristics need to be identified. Education about the likely health impacts of various neighborhood designs may be helpful in enhancing the accuracy of environmental perceptions. Perhaps health-related certification programs of neighborhood designs or consumer tools to rate the healthfulness of neighborhoods could be effective educational approaches, but they remain to be developed and evaluated.
Footnotes
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.