Designing healthy communities: Testing the walkability model

Abstract

Research from multiple domains has provided insights into how neighborhood design can be improved to have a more favorable effect on physical activity, a concept known as walkability. The relevant research findings/hypotheses have been integrated into a Walkability Framework, which organizes the design elements into nine walkability categories. The purpose of this study was to test whether this conceptual framework can be used as a model to measure the interactions between the built environment and physical activity. We explored correlations between the walkability categories and physical activity reported through a survey of residents of Tucson, Arizona (n =486). The results include significant correlations between the walkability categories and physical activity as well as between the walkability categories and the two motivations for walking (recreation and transportation). To our knowledge, this is the first study that reports links between walkability and walking for recreation. Additionally, the use of the Walkability Framework allowed us to identify the walkability categories most strongly correlated with the two motivations for walking. The results of this study support the use of the Walkability Framework as a model to measure the built environment in relation to its ability to promote physical activity.

Keywords

Walkability ; Physical activity ; Built environment ; LEED-ND ; Neighborhood design

1. Introduction

The increasing lack of physical activity among all populations is considered a global public health problem (“WHO|Physical Inactivity”, n.d. ). Public health efforts to improve health typically promote moderate types of physical activity, such as walking and biking, because these are easier for inactive populations to begin and maintain and these are also easier to incorporate into daily routines (Frank et al., 2003 ). The built environment has been identified as an essential factor for integrating physical activity into one׳s daily life (Cerin et al ., 2013  ; Frank et al ., 2003  ; Frumkin et al ., 2011  ;  Sallis et al ., 2011 ). International organizations such as the World Health Organization (WHO) have called for changes to be made in the built environment to improve human health through walking, including changes in urban design, transportation and recreational facilities (Adams et al., 2013 ). For such changes to be effective, it is critical to identify the design elements of the built environment that influence physical activity. In other words, what elements of the built environment encourage people to walk?

There are different motivations for walking that require different elements from the built environment. Behavioral scientists have identified two primary motivations for walking: recreation and transportation (Giles-Corti et al ., 2005  ;  Saelens and Handy, 2008 ). Walking for recreation refers to walking for exercise or simple recreation, whereas walking for transportation refers to walking to reach a destination (Saelens and Handy, 2008 ). Research has identified distinctions in the types of walking and how they are influenced by the built environment: walking for transportation has been shown to be related to the design of the neighborhood, whereas recreational walking has not been shown to be affected by neighborhood design (Rodríguez et al ., 2006  ; Saelens et al ., 2003  ;  Toit et al ., 2007 ).

To assess the design elements of the built environment that influence physical activity, it is necessary to both capture the state-of-the-art research on walkability and organize findings in a way that can be readily used by those directly influencing the design of the built environment (e.g., architects, builders, developers, and planners). The Walkability Framework developed by Zuniga-Teran (2015) and later applied by Zuniga-Teran et al. (2016) serves both of these purposes (Fig. 1 ). The framework synthesizes hypotheses from several research domains in which the relationship between the built environment and walkability is explored. These domains include physical activity, land planning and transportation, thermal comfort, health, and greenspace. The framework also addresses walkability from the perspective of architecture and urban design through the Leadership in Energy and Environmental Design for Neighborhood Development (LEED-ND) design guidelines (USGBC, 2016 ). Finally, the framework groups the neighborhood design elements that have been identified theoretically as essential factors for physical activity into nine walkability categories: connectivity, density, land use, traffic safety, surveillance, experience, parking, greenspace, and community.

Fig. 1. The Walkability Framework shows the interrelation between the nine neighborhood design categories that when combined result in walkability.

The connectivity category measures how well a street network provides multiple, direct, and short routes to reach different destinations. It is desirable to have a high level of connectivity to facilitate walking; and this is thought to be achieved by having small blocks (short distance between intersections), a grid street network (as opposed to cul-de-sacs or dead-end streets), and open to the public (as opposed to fenced or gated communities). The density category refers to residential density and captures design elements that increase the number of people in the streets, which is thought to be related to walking. These refer to the prevalent types of dwelling units in the neighborhood (e.g., single-family housing, townhomes, apartment buildings). The land use category measures the diversity of land uses (e.g., residential, commercial) within walking distance (less than 1/2 mile or 800 m, or a 10-min walk) from the respondent׳s residence. Locating a variety of small businesses (e.g., shops, restaurants, offices) close to homes facilitates and encourages walking. The traffic safety category highlights the infrastructure needed to facilitate pedestrian and bicycle safety in the presence of traffic. Slowing traffic and giving pedestrians and bicyclists safe places to travel by providing space/infrastructure (e.g., sidewalks, bike lanes) encourage walking. The surveillance category measures how well those traveling on the street can be seen from the surrounding homes and businesses. It is hypothesized that buildings designed in such a way that people inside the buildings can observe the street (e.g., via balconies, front porches, short building setbacks, and back alleys serving garages) encourage people walking by enhancing the perception of safety from crime. Streetscapes should also encourage activity on the sidewalks (e.g., outdoor cafes, clear windows for shops) and should include other design elements that reduce crime (e.g., lighting). The experience category measures whether the built environment provides a pleasant experience while walking. In this category, we include the streetscape proportions, aesthetics (graffiti, trash, buildings, sights), wayfinding considerations (signage, landmarks), thermal comfort (trees, shade), slope (hilly streets), and presence of dogs/wildlife. The parking category measures the availability of parking, where the less parking provided is thought to be more walkable. Not only is walking through a parking lot undesirable, but if there is no parking available, people may choose an alternative mode of transportation besides cars that may involve physical activity. If parking is necessary, then locating parking behind buildings and away from the street is thought to create an area more interesting and walkable. The greenspace category measures the availability of spaces dominated by vegetation; the size, proximity, and ease of access of the greenspaces are all considered in this category. Finally, the community category measures the presence of spaces that facilitate social interaction and that encourage participation in community affairs (e.g., community centers, plazas, churches). Neighborhoods that combine elements from the above categories are thought to be more walkable (Fig. 2 ).

Fig. 2. Walkable neighborhoods. Neighborhoods that provide pedestrian infrastructure, in a connected street network with short routes to commercial destinations, combined with beautiful sights, and homes that allow people from inside the buildings to watch the streets are thought to encourage walking.

The Walkability Framework is a conceptual representation of hypotheses synthesized from multiple fields. The purpose of this study is to determine the degree to which the framework can be used as a model to measure the effectiveness of the built environment in relation to encouraging physical activity and supporting wellbeing.

2. Material and methods

To gain deeper insights into the relationships between physical activity and walkability, we developed a questionnaire to capture the perceptions, attitudes, and behavior of neighborhood residents with respect to the categories of walkability/neighborhood design comprising the Walkability Framework. We developed this questionnaire by using existing and validated instruments when possible to capture variables of interest, by adapting existing instruments when necessary, and by developing and testing unique questions when no existing instrument was available. The resulting questionnaire was then refined based on the results of validation exercises and finalized into an online and paper version. The questionnaire was administered to residents of Tucson, Arizona, between January and March 2014. Tucson was selected for this study because different neighborhood designs are prevalent within a relatively concentrated area and the warm, dry climate makes walking very accessible for a large portion of the year (October to May). It should be noted that the study period fell within this window.

A draft of the questionnaire was validated based on input from experts drawn from the research domains that contributed to the content of the questionnaire, and the questionnaire was then tested for comprehension and clarity with a group of university students from a range of disciplines. Based on the input received in these two steps, both the questions and the physical structure of the instrument were refined. The questionnaire was created in two formats: online and paper-based. The online version was created using the survey and statistics software DatStat Illume v. 5.1 (http://www.datstat.com/survey-research-software ).

Recruitment was accomplished by reaching potential participants (older than 18) through trusted organizations associated with the central topic of the questionnaire – neighborhood design. In the City of Tucson and in Pima County, the most appropriate organizations are neighborhood associations and homeowners associations, which are supported administratively by local electoral districts known as wards. Ward officials helped arrange contacts with the presidents of neighborhood associations within the City of Tucson and the presidents of homeowners associations within Pima County for areas outside the city limits. The ward officials and the neighborhood association presidents were contacted by the researcher prior to the questionnaire distribution. The research project was explained, and the officials were asked to forward an invitation email that contained a link to the online questionnaire to all of their residents.

The paper version of the questionnaire was distributed through visits to the Rillito River Park, between Craycroft Road and Alvernon Way on both sides of the river (Fig. 3 ). This section of the park was chosen because it is accessible to a range of socioeconomic populations and to multiple neighborhoods with various levels of walkability. The park is a greenway that has a walking/biking path on both sides of the Rillito River wash and extends for several miles (Fig. 4 ). The paper version of the questionnaire was also distributed by mail to reach those residents not accessible via email and/or who lived in neighborhoods with a distinct design. The total number of responses was 486 including 338 from the online version, 103 from the visits to the park, and 45 from the mailed surveys.

Fig. 3. The Rillito River Park is accessible to a gradient of socioeconomic backgrounds and from neighborhoods with different levels of walkability. The north side of the park is less connected (cul-de-sacs), and includes mostly single family housing; whereas the south side is more dense, and more connected (grid street network). (Image adapted from Google Maps).

Fig. 4. Rillito River Park . Respondents were recruited through visits to the Rillito River Park on both the north (right) and the south (left) sides (photo credit Cynthia Bristain).

The statistical analysis involved bivariate correlations to determine significance and to establish the magnitude of relationships. This analysis was conducted using IBM-SPSS (http://www-01.ibm.com/software/analytics/spss ). This research was approved by the Institutional Review Board for the Protection of Human Subjects on December 12, 2013 (IRB # 13-0855 UAR Number 1300000855).

The questionnaire is divided in three sections: (1) walkability, (2) physical activity, (3) and demographics.

2.1. Walkability

The walkability section was structured to address the different elements of the Walkability Framework (Table 1 ). It includes questions taken from the Neighborhood Environment Walkability Scale (NEWS) questionnaire developed by Saelens et al. (2003) and later abbreviated by Cerin et al. (2006) , as well as elements from the Walkability Index developed by Frank et al. (2009) . This section also includes questions based on the walkability design guidelines from LEED-ND (USGBC, 2014 ), and findings from previous studies (Handy et al ., 2002  ;  Barton et al ., 2003  ; Sandifer et al., 2015 ). Eight of the nine categories of the Walkability Framework are addressed. The parking category was not included in this study because Tucson and the surrounding metropolitan area have ample parking availability throughout the day, so it became irrelevant to measure this variable.

Table 1. Questionnaire questions for the walkability categories.

Walkability Category	Variables	Questions
Connectivity	Barriers Small blocks Multiple routes Restricted access Dead-end streets Back alleys	There are major barriers to walking The distance between intersections is usually short (100 yards)a There are many alternative routes for getting from place to placea My neighborhood is a gated community My neighborhood is fenced on the outer boundary The streets in my neighborhood have many cul-de-sacs Back alleys serve most of the garages in my neighborhooda
Density	Dwelling type Prevalent dwelling type in the neighborhood	Select the option that best describes your dwelling type (single family, townhome, apartment, multi-family, temporary home) How common is single-family housing?a How common are townhomes? How common are apartments/condos?a(none, a few, some, most, all)
Land use	• Proximity of a diversity of services to home	• Check the services that are located within a 10-min walk (1/2 mile or less from your home ) (check all that apply):
		Bus stop, Gym, Post office, Bank, Supermarket, Hair salon/barber, School, Police station, Food store with produce, Laundry/dry cleaner, Theater, Pharmacy, Clothing store, Restaurant/café/diner, Medical clinic, Convenient store, Government office, Farmers’ market, Child-care facility, Social services center, Hardware, Museum
Traffic safety	• Pedestrian and cyclist infrastructure	There are bike lanes on most of the streetsa There are sidewalks on most of the streetsa Sidewalks are separated from the road/traffic by parked carsa There is a grass/dirt strip that separates the streets from the sidewalka There are dirt trails on most of the streetsa There are crosswalks and pedestrian signals to help walkers cross busy streetsa The streets have speed bumpsa The speed limit is 25 mph or less on most of the streetsa
Surveillance	• Ability of people to be seen in the streets	My neighborhood streets are well lit at nighta Most units have front porchesa The buildings are located close to the streeta Most dwellings have front garage doors My neighborhood has back alleys with garagesa
Experience	Aesthetics Slope Way-finding Thermal comfort	There is graffiti in my neighborhood There is trash/litter in my neighborhood There are many attractive natural sights to look at while walkinga There are attractive buildings and homesa Possible interactions with wildlife makes it attractive to go on walksa Possible interactions with wildlife or stray dogs make it unsafe to go on walks Most streets are hilly, making it difficult to walk or bike It is easy to get lost while walking Clear signage or landmarks are present that help me find my waya There is enough shade to walk comfortablya There are trees along the streetsa
Greenspace	Proximity to greenspace Access to greenspace	How far is the nearest greenspace from your home?a Greenspace is located within a 10-min walk from home It is easy to walk to greenspace from my home
Community	• Availability of spaces for community interaction	There is a community center close to home There is a church close to home My neighborhood shares facilities (e.g., pool, tennis courts, community center)a

a. These questions were reversed from their original source format to be consistent with intent of the instrument to capture increasing levels of walkability.

In the walkability section, points were assigned to each potential response for each question; a larger value indicates increased walkability. To ensure comparability of the data between walkability categories, the response values for the questions in each walkability category were designed to be added to obtain a total for that category, which can then be normalized to a 0–1 scale. The normalized values for the eight walkability categories captured in the survey could then be added together and adjusted to a 0–1 scale to yield the overall Walkability Index.

The majority of the questions designed to examine the walkability categories were based on a 4-point Likert scale (Strongly agree, Agree, Disagree, Strongly disagree). With some of these scaled questions, it was necessary to reverse the values with respect to their original form so that all walkability questions were consistently scaled, with larger values indicating higher levels of walkability. The other question format used in this section involved a list of alternatives with a binary answer choice for each alternative (Yes=1 or No=0). Not all alternatives were deemed equal contributors to walkability. For example, in the land use category, the participants were asked to select “services close to home” and were given multiple options to select. Most service options were given a point value of 1 point (1X). However, some services were weighted more heavily because of the added importance they play in enhancing the walkability of a neighborhood. Destinations that were given 3 points (3X) included those that a) require daily trips (school and child care), b) provide access to food and fresh produce (e.g., supermarket, food retail, and farmers’ market), or c) offer extended business hours, thereby providing added street vitality to the neighborhood (e.g., restaurants and theaters). One service that was attributed 5 points (5X) was ‘bus stops’ because they provide opportunities to travel beyond the neighborhood without the use of a personal automobile, therefore enhancing walkability.

2.2. Physical activity

The physical activity section of the questionnaire contained five questions that inquire about methods of transportation used to reach greenspaces, whether one walks to services close to home, and the number of days during the previous week that people walked. The last three questions were based on the International Physical Activity Questionnaire (IPAQ), which is an instrument that can be used internationally to measure physical activity across multiple populations (http://sallis.ucsd.edu/measure_ipaq.html ) and that has been used in several studies on physical activity (Craig et al ., 2003  ;  Kim et al ., 2013 ). Similar to the approach taken in the walkability section, the response values were added together and adjusted to a 0–1 scale to obtain the Physical Activity Index.

To assess the motivation for walking, the physical activity section was divided into two parts: (1) walking for transportation and (2) walking for recreation (Table 2 ). We divided the questions with respect to the motivation for walking. Questions about walking with the intention to reach a service or public transportation were placed in “walking for transportation”, whereas questions about walking with the intention to exercise, go to a greenspace, or walk one׳s dog were placed in “walking for recreation”. We added one question to each section that explores activities performed on the street, and the appropriate options were placed accordingly.

Table 2. Questionnaire questions for the physical activity section identifying the two motivations for walking.

Type of walking	Intention	Questions
Walking for recreation	Walk or bike for exercise, to visit greenspace, or to walk a dog.	During the last 7 days, on how many days did you walk for 10 mins? (0–7 days) During the last 7 days, on how many days did you walk to a greenspace? (0–7 days) What method of transportation do you usually use to reach a greenspace? (Check: Walk, Skate, Bike) What activities do you or your family participate in along the streets of your neighborhood? (Check: Exercise, Dog-walking)
Walking for transportation	Walk or bike to reach a service (e.g., shop or restaurant) or public transportation (e.g., bus stop).	Do you walk to any service (including a bus stop) from your home? * (Yes, Sometimes, No) During the last 7 days, on how many days did you walk to a service (including a bus stop)? (0–7 days) What activities do you or your family participate in along the streets of your neighborhood? (Check: Walk/bike as a means of transportation)

2.3. Demographics

The following demographic data were collected from the respondents: Age (18–29, 30–39, 40–49, 50–59, 60–69, and 70 or more), Gender (Male/Female), Race/Ethnicity (check all that apply: African-American, Alaskan Native, American Indian, Asian, Hispanic or Latino, Native Hawaiian, White, 2 or more races), Income ($30,000 or less, $30,001 to $59,000, $60,000 or more), Education (check all that apply: High school, Professional School, University or College, Master׳s/Ph.D).

3. Results

The demographics of our sample population indicate that approximately one-fourth of the respondents (26.5%) reported being in their 60 s, and another fourth (24.7%) reported being in their 50 s. Approximately one-fifth reported being in their 70 s or older (20.4%), and the remainder in their 40 s (14.4%), 30 s (8.8%), or younger (5.2%) (Table 3 ). More than half of the respondents (62.6%) reported being female. The majority of the respondents reported being of white ethnicity (87.9%), whereas most of the balance reported being Hispanic (8.6%). In terms of income, approximately one-half of the respondents (49.2%) reported being in the highest income bracket, approximately one-third (31.7%) reported being in the medium income bracket, and the remainder reported being in the lowest income bracket. In terms of education, most of the respondents reported having a high level of education (a college/university degree −46.7%, or beyond −43.6%).

Table 3. Demographic information obtained from the questionnaire.

Demographics	Group	Frequency	Percent	Valid percent
Age group	18–29	20	4.1	5.2
	30–39	34	7.0	8.8
	40–49	56	11.5	14.4
	50–59	96	19.8	24.7
	60–69	103	21.2	26.5
	70 or more	79	16.3	20.4
	Total	388	79.8	100.0
	No answer	98	20.2
	Total	486	100.0
Gender	Male	142	29.2	37.4
	Female	238	49.0	62.6
	Total	380	78.2	100.0
	No answer	106	21.8
	Total	486	100.0
Race/Ethnicity	Native American	4	0.8	1.1
	Asian	5	1.0	1.3
	Hispanic	32	6.6	8.6
	White	327	67.3	87.9
	2 or more	4	0.8	1.1
	Total	372	76.5	100.0
	No answer	114	23.5
	Total	468	100.0
Income	$30,000 or less	70	14.4	19.1
	$30,001 to $59,999	116	23.9	31.7
	$60,000 or more	180	37.0	49.2
	Total	366	75.3	100.0
	No answer	120	24.7
	Total	486	100.0
Education	High School	20	4.1	5.2
	Professional School	17	3.5	4.4
	University or College	179	36.8	46.7
	Master׳s – Ph.D	167	34.4	43.6
	Total	383	78.8	100.0
	No answer	103	21.2
	Total	486	100.0

The statistical analysis revealed significant associations between each walkability category and physical activity (p <0.001) (Table 4 ). However, the magnitude of the Pearson correlation coefficient (r ) between each walkability category and physical activity varied. We found moderate correlations (0.3<r <0.7) for density, land use, traffic safety, surveillance, experience, and greenspace. We found weak correlations (r <0.3) between physical activity and connectivity, and community. Overall, we found a significant and moderate correlation between the Walkability Index (all the categories added and adjusted to a 0–1 scale) and physical activity.

Table 4. Relationship between the walkability categories and Physical Activity Index. Significant and moderate/strong correlations are shown in bold.

Walkability category tested with the Physical Activity Index	Pearson correlation (r)	Sig. (p)	N
Connectivity	0.256	0.000	386
Density	0.465	0.000	485
Land use	0.508	0.000	485
Traffic safety	0.641	0.000	485
Surveillance	0.309	0.000	380
Experience	0.608	0.000	485
Greenspace	0.653	0.000	485
Community	0.182	0.000	380
Walkability Indexa	0.394	0.000	373

a. Values for the eight walkability categories included in this study added together and adjusted to a scale of 0–1.

Similarly, bivariate correlations between the walkability categories and the two motivations for walking (recreation and transportation) revealed a range of results (Table 5 ). With regard to walking for recreation, we found significant correlations with all of the walkability categories; however, the magnitude of the correlations between the individual walkability categories and walking for recreation varied. We found moderate correlations between walking for recreation and density, land use, traffic safety, experience, and greenspace; and weak correlations between walking for recreation and connectivity, surveillance, and community. The walkability categories traffic safety, experience, and greenspace showed a stronger magnitude followed by density and land use. These results suggest that a neighborhood that provides traffic safety (pedestrian and biking infrastructure), combined with design elements that enhance the experience of walking (thermal comfort, aesthetics, way-finding, slope), includes greenspace in close proximity and easy-access (greenspace), provides commercial destinations close to homes (land use), and has a high residential density (density) might encourage recreational walking.

Table 5. Walkability categories and the two motivations for walking: walking for recreation and walking for transportation. Significant and moderate/strong correlations are shown in bold.

	Walking for recreation		Walking for transportation
Walkability Categories	Pearson Correlation (r)	Sig. (p)	Pearson Correlation (r)	Sig. (p)
Connectivity	0.161	0.002	0.283	0.000
Density	0.445	0.000	−0.035	0.507
Land use	0.435	0.000	0.240	0.000
Traffic safety	0.602	0.000	0.211	0.000
Surveillance	0.193	0.000	0.264	0.000
Experience	0.609	0.000	0.040	0.448
Greenspace	0.653	0.000	0.112	0.032
Community	0.118	0.021	0.204	0.000
Walkability Index	0.269	0.000	0.322	0.000

With regard to walking for transportation and the walkability categories, we found significant albeit weak correlations with connectivity, land use, traffic safety, surveillance, greenspace, and community. However, we did not find significant correlations between walking for transportation and density or experience. These results suggest that a neighborhood that features small blocks in a grid street network, without fences or gates (connectivity), has commercial destinations close to homes (land use), provides pedestrian and bicycle infrastructure (traffic safety), allows people from inside the buildings to watch the streets (surveillance), has greenspace in close proximity and easy access (greenspace), and provides spaces for community activities (community) might encourage transportational walking. In contrast, it may not be that important when walking for transportation to have high residential density (density) and a nice experience while walking (experience).

Both types of walking were found significantly correlated to the Walkability Index (all the walkability categories together). On the whole, transportational walking was quantitatively more predictable than recreational walking for our sample population and neighborhood environment because the result for the Pearson correlation coefficient (r ) between the Walkability Index and walking for transportation was higher (r =0.322/moderate) than for walking for recreation (r =0.269/weak). However, when we examined the results between the two types of walking and the individual walkability categories we found stronger results for recreational walking than for transportational walking. We found moderate correlations for recreational walking and individual walkability categories, but weak correlations for transportational walking and the individual walkability categories. Although overall our model predicted transportational walking better than recreational walking, the stronger results for the individual walkability categories suggest a higher predictability (better fit) for recreational walking.

4. Discussion

The purpose of this study was to test whether the Walkability Framework, (Zuniga-Teran, 2015  ;  Zuniga-Teran et al ., 2016 ), could be used as a model to measure the physical characteristics of the built environment in relation to the promotion of physical activity. We found that the Walkability Framework does reveal information about how well the built environment promotes physical activity. Moreover, our assessment of walkability using the Walkability Framework yielded significant correlations between walkability and the two motivations for walking (walking for recreation and walking for transportation), whereas previous research has identified links with walking for transportation but not with walking for recreation (Rodríguez et al ., 2006  ; Saelens et al ., 2003  ;  Toit et al ., 2007 ). We believe this result was due to the use of the Walkability Framework, which captures more design elements related to physical activity than what has been included in previous studies. For example, to our knowledge, the greenspace and community categories have not been considered in previous research studies as part of walkability, and this may have increased the magnitude of the correlation between walkability and walking for recreation. This finding is especially significant for the greenspace category because the magnitude of the correlation coefficient for this category was the largest (r =0.653) among the walkability categories and walking for recreation. The importance of greenspace as a predictor of physical activity has been documented before ( Hartig et al., 2014 ).

Our results suggest that different aspects of the built environment are important depending on one׳s reason for walking. Although we found significant correlations between walkability as a whole (Walkability Index) and the two motivations for walking, we found slightly stronger results (higher r values) for walking for transportation than we did for walking for recreation. These results align with previous findings that identified that walkability is most strongly correlated with walking for transportation (Rodríguez et al ., 2006  ; Saelens et al ., 2003  ;  Toit et al ., 2007 ).

Comparing the results for the individual walkability categories and the two motivations for walking revealed that different aspects of the built environment are important depending on one׳s reason for walking. On the one hand, we identified those aspects of neighborhood design that are significantly related to walking for transportation (connectivity, land use, traffic safety, surveillance, greenspace, and community), and those with no significant relationship (experience and density). These results align with previous research that found links between walking for transportation and proximity to services (land use), pedestrian and bicycle infrastructure (traffic safety), and safety from crime (surveillance) (Hartig et al., 2014 ). On the other hand, walking for recreation was significantly correlated with all of the walkability categories; with stronger correlations with greenspace, experience, traffic safety, density, and land use. These results imply that the framework predicted recreational walking more strongly than transportational walking. This may be a consequence of our recruitment method, which mostly targeted residents of residential neighborhoods and park users, combined with a scarcity of mixed-use developments in our study area (Tucson).

One possible explanation for the differing magnitudes of the correlations (r ) for these relationships is as follows: if someone is walking to reach a destination (transportation), it is important that they have access to short routes (connectivity), that the origin and destination be in close proximity (land use), that walking and biking infrastructure is available (traffic safety), and that they have the impression that they can be easily observed by other members of the public (surveillance). Likewise, it may be important to have a park nearby (greenspace) ( Sandifer et al., 2015 ) and community facilities (community), particularly if these are the destinations in mind. It was surprising, however, that the predominant type of housing in the neighborhood (density) was not correlated with walking for transportation because low-density neighborhoods may cause longer routes and may result in less people in the streets. Isolated streets are thought to discourage physical activity whether recreational or transportational (Jacobs, 2011 ). Likewise, it was somewhat unexpected to find that the experience category was not significantly correlated with walking for transportation. We hypothesize that the time of the year that we collected the data might have played a role in shaping these results because during winter, Tucson residents enjoy comfortable weather. Some variables in the experience category, such as access to shade and the presence of trees, might not affect walking during the months we collected data, whereas during the summer months when the daily high temperature is typically above 100 °F in Tucson, this category may become more important. Gathering new data during the hot summer months might yield different results for the correlation between the experience category and walking for transportation.

With regard to walking for recreation, all of the walkability categories showed significant correlations. It was expected that greenspace and experience would produce the strongest correlations because it has been documented that the greenness of the built environment influence walking for recreation (Hartig et al., 2014 ), and our results confirmed this expectation. It was also not surprising to find that traffic safety is related to walking for recreation since pedestrian infrastructure is hypothesized as important for lifestyle physical activity (Jacobs, 2011 ). Likewise, land use showed significant and moderate correlations probably because proximity to shops and restaurant provides interesting sights to pedestrians (Jacobs, 2011 ; Montgomery, 2013 ). Even though connectivity, surveillance, and community were significantly correlated with recreational walking, these correlations were weak. It is possible that connectivity was weakly correlated with walking for recreation because a longer route may be enjoyable if the route itself is pleasant. Likewise, surveillance was found to be weakly correlated with this type of walking probably because when the crime rate in the neighborhood is low and neighbors are familiar with one another, people might feel safe walking in their own neighborhoods even if they are not being watched by those inside nearby homes. Similarly, we think that community was significantly but weakly correlated to recreational walking because spaces that allow opportunities for social interaction may be desired but not essential for this type of walking.

The walkability categories that showed significant correlations with both motivations for walking were traffic safety and land use. These results indicate that walkable neighborhoods should provide safe infrastructure to pedestrians and cyclists and employ traffic-calming treatments to encourage physical activity regardless of the motivation of walking. In addition, walkable neighborhoods must include a mix of land uses (a variety of shops and restaurants close to homes) to encourage walking for both recreation and transportation.

5. Conclusion

This study supports the use of the Walkability Model to measure the built environment in relation to physical activity. This model was created by synthesizing the literature from several research domains on design elements that may influence physical activity (Zuniga-Teran, 2015  ;  Zuniga-Teran et al ., 2016 ). The organization of the categories was designed to integrate well with previously developed research tools (Saelens et al ., 2003  ; Cerin et al ., 2006  ;  Frank et al ., 2009 ), and the sustainable neighborhood design tool from LEED-ND (USGBC, 2014). The results of this study support our hypothesis that the Walkability Framework can test the strength of relationships between actual physical activity and predicted walkability.

The model proved useful for identifying the most influential walkability categories for each type of walking. In the case of walking for recreation, all of the walkability categories were significant, but the most influential (with moderate correlations) were greenspace, experience, traffic safety, density, and land use. In the case of walking for transportation, the most influential walkability categories (significant, p >0.05) were connectivity, land use, traffic safety, surveillance, greenspace and community. Our results suggest that there could be two different walkability models suited for each type of walking with different combination of components. Although as a whole (Walkability Index), our model quantitatively predicted better transportational walking, we obtained stronger results for recreational walking when examining the individual walkability categories separately. More research is recommended that examines two different models tailored to each type of walking.

Our results show that traffic safety and land use are important for both motivations for walking. This provides empirical evidence that can guide development toward neighborhoods that provide safety to pedestrians and cyclists and promote a mix of land uses to increase physical activity.

It is important to acknowledge the self-selection of residents; people who enjoy walking may choose to live in walkable neighborhoods, whereas people who do not enjoy walking may choose to live in non-walkable neighborhoods. Future research is recommended that includes the parking category in the assessment of walkability. In addition, we acknowledge that the way we recruited participants (through the help of neighborhood leaders, visits to greenspace, and mailed letters to residential neighborhoods) may have captured preferences for recreational walking and less so for transportational walking. Therefore, more research is recommended that examines the preferences for workers and shoppers in commercial districts. Another limitation is that our sample population included older people of relatively high income. Likewise, we recommend future research that captures more diverse respondent groups and analyzes differences among them.

The Walkability Model may be a useful tool that could be employed in future research that aims to understand the effects of the built environment on human behavior, particularly on lifestyle physical activity. This model can also be used by architects, urban designers, and land planners who wish to consider health in the design of neighborhoods, which can lead to increased physical activity and potentially healthier communities.

Acknowledgements

This work was funded by Consejo Nacional de Ciencia y Tecnologia (CONACYT - Mexican government) and The Wilderness Society . Through the University of Arizona , we received funding from graduate teaching assistantships, graduate research assistantships, and several grants and scholarships. We are grateful to Dr. Mohammad Tobari and Dr. Mark Borgstrom for their support with the statistical analysis of the survey, and Dr. Kasi Kiehlbaugh for editing.

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