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Neighborhood walkability can influence physical activity. We evaluated the validity of Walk Score® for assessing neighborhood walkability based on GIS (objective) indicators of neighborhood walkability with addresses from four US metropolitan areas with several street network buffer distances (i.e., 400-, 800-, and 1,600-meters). Address data come from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5–11 years and their families participating in YMCA-administered, after-school programs located in four geographically diverse metropolitan areas in the US (n = 733). GIS data were used to measure multiple objective indicators of neighborhood walkability. Walk Scores were also obtained for the participant’s residential addresses. Spearman correlations between Walk Scores and the GIS neighborhood walkability indicators were calculated as well as Spearman correlations accounting for spatial autocorrelation. There were many significant moderate correlations between Walk Scores and the GIS neighborhood walkability indicators such as density of retail destinations and intersection density (p < 0.05). The magnitude varied by the GIS indicator of neighborhood walkability. Correlations generally became stronger with a larger spatial scale, and there were some geographic differences. Walk Score® is free and publicly available for public health researchers and practitioners. Results from our study suggest that Walk Score® is a valid measure of estimating certain aspects of neighborhood walkability, particularly at the 1600-meter buffer. As such, our study confirms and extends the generalizability of previous findings demonstrating that Walk Score is a valid measure of estimating neighborhood walkability in multiple geographic locations and at multiple spatial scales.

In this research we analyse the most important factors that determine housing prices. On the one hand, we test whether neighbourhoods with a good accessibility are more attractive and consequently show higher housing prices. For this purpose, we introduce an adapted Walk Score as part of the accessibility indicators. On the other hand, we compare an ordinary-least-squares regression (OLS) and a spatial lag model and test which model better explains residential housing prices. The regression models show the importance of classical factors such as dwelling characteristics or the types of neighbours. In addition, they also reveal that a differentiated approach is needed for analysing the accessibility, the location and the environment of a dwelling. The mere presence of a single amenity, a public transport stop or a motorway access is not a sufficient explanatory factor. Information such as density of supply, walking distances or public transport service quality needs to be taken into account as well as. The test of the spatial lag model reveals that prices of the most proximate dwellings can be taken into account as a relevant factor in explaining residential housing prices and should therefore be included in research on residential housing prices.

Since walkability plays an important role in active commuting, several cities are actively promoting its integration into urban and environmental planning policies. This study examined the association between walkability and active commuting in Seoul, Korea. A multilevel logistic regression model was used to examine the correlation between Walkability Score and the probability of active commuting after controlling for individual variables. The analysis used 129,044 individual samples nested within 424 administrative districts (dongs). In this study, three models were tested: Model 1 contained only individual variables, Model 2 contained individual variables and Walkability Score, and Model 3 included neighborhood-level variables in addition to the variables of Model 2. The results showed that the Walkability Score was significantly correlated with the odds of active commuting. Specifically, every additional one-point increase in Walkability Score was associated with 0.3% higher odds of active commuting (Model 2: odds ratio (OR) = 1.003, 95% confidence interval (CI) = 1.001–1.005; Model 3: OR = 1.003, 95% CI = 1.001–1.006). Additionally, public transportation density was also positively correlated with the odds of active commuting. The odds of active commuting were positively correlated with younger age, female, lower-income, and having no car. Based on the findings, policy recommendations in urban planning and design, transport engineering, and environmental planning are provided.

Urban walkability is influenced both by built environment features and by pedestrian demographics. Research has shown that factors influencing women's walking differ from those affecting men's. Using a mixed-method approach, this study creates a new women-specific, GIS-based walkability index using San Francisco as a case study, and answers two questions: Which variables most influence women's propensity to walk? And Does the leading walkability index, Walk Score, reflect women's walkability? Focus group participants (n=17) ranked crime, homelessness and street/sidewalk cleanliness as the three most influencing factors on women's walkability, accounting for 58% to 67% of the Women's Walkability Index's total score. The least walkable areas in San Francisco, according to this index, are rated as some of the most walkable neighborhoods in the city by Walk Score, despite high crime and homelessness density. Walk Score is negatively correlated with the new Women's Walkability Index (Spearman's rho = -0.585) and inaccurately represents women's walkability. If the new index accurately captures the reality of women's walking, then some of the most widely accepted conventions about what kind of areas promote walking could be inaccurate when it comes to women.

Background: The existing environment literature separately emphasizes the importance of neighborhood walkability and greenness in enhancing health and wellbeing. Thus, a desirable neighborhood should ideally be green and walkable at the same time. Yet, limited research exists on the prevalence of such “sweet spot” neighborhoods. We sought to investigate this question in the context of a large metropolitan city (i.e., Sydney) in Australia. Methods: Using suburb level normalized difference vegetative index (NDVI), percentage urban greenspace, Walk Score® (Walk Score, Seattle, WA, USA), and other data, we explored the global and local relationships of neighborhood-level greenness, urban green space (percent park area) with walkability applying both non-spatial and spatial modeling. Results: We found an overall negative relationship between walkability and greenness (measured as NDVI). Most neighborhoods (represented by suburbs) in Sydney are either walkable or green, but not both. Sweet spot neighborhoods that did exist were green but only somewhat walkable. In addition, many neighborhoods were both less green and somewhat walkable. Moreover, we observed a significant positive relationship between percentage park area and walkability. These results indicate walkability and greenness have inverse and, at best, mixed associations in the Sydney metropolitan area. Conclusions: Our analysis indicates an overall negative relationship between greenness and walkability, with significant local variability. With ongoing efforts towards greening Sydney and improving walkability, more neighborhoods may eventually be transformed into becoming greener and more walkable.

BackgroundThere have been mixed findings regarding the relationship between walkability and level of physical activity in adults.MethodsParticipants from The REasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort (N=7561) were used to examine the association between Walk Score and physical activity measured via accelerometry. The subsample included geographically diverse adults, who identified as black or white, and were over the age of 45. Linear regression was used to examine the direct effects, as well as the interaction, of Walk Score by sex, age and race.ResultsThe majority of participants lived in a ‘Very Car-Dependent’ location (N=4115). Only 527 lived in a location that was ‘Very Walkable/Walker’s Paradise’. Living in a location with a Walk Score of ‘Very Car-Dependent’ compared with ‘Very Walkable/Walker’s Paradise’ was associated with 19% (0.81; 95% CI 0.73 to 0.90) lower predicted minutes of moderate to vigorous physical activity per day, after adjustment for covariates. There was no evidence of statistically significant interactions between Walk Score and sex, age or race (p>0.05).ConclusionAccumulated daily time in moderate to vigorous physical activity was higher for participants living in neighbourhoods designated as ‘Very Walkable/Walker’s Paradise’. This effect was not moderated by sex, age or race of participants.

A walkable environment is a crucial factor for promoting active transportation. The purpose of this study is to examine the association between neighborhood walkability and active transportation for noncommuting purposes (leisure and shopping) in Seoul, Korea. The Walkability Score is used as a measure of walkability, and a multilevel logistic regression model is employed to measure the odds of active transportation (i.e., walking and cycling; nonmotorized trips) at two levels: individual (level 1) and neighborhood (level 2). The results of the study showed that the Walkability Score was significantly correlated with higher odds of active transportation in shopping models. Specifically, every one-point increase in the Walkability Score was associated with 1.5%–1.8% higher odds of active transportation in shopping models. However, there was no significant correlation between the two in leisure models. Meanwhile, individual characteristics associated with the odds of active transportation differed in the leisure and shopping models. Older age was positively correlated with the odds of active transportation in the leisure model, while females showed a positive correlation in the shopping model. Based on the study, urban and transportation planners can recommend urban policies to promote active transportation in an urban setting.

Walking has become an indispensable and sustainable way of travel for college students in their daily lives and improving the walkability of the college campus will increase the convenience of student life. This paper develops a new campus walkability assessment tool, which optimizes the Walk Score method based on the frequency, variety, and distance of students’ walking to and from public facilities. The campus Walk Score is the product of four components. A preliminary score is calculated through 13 types of facility weight and 3 types of cure of time-decay, and the final score also factors in intersection density and block length. We examine the old and new campuses of Tianjin University to test the tool’s application and evaluate the rationality of facility layout and walkability, and to give suggestions for improvement. The results show that the old campus’ multi-center layout has a high degree of walkability, while the centralized layout of the new campus results in lower walkability. In addition, the diversified distribution of facilities surrounding the old campus promotes the walkability of peripheral places. This assessment tool can help urban planners and campus designers make decisions about how to adjust the facility layout of existing campuses in different regions or to evaluate the campus schemes based on the results of their walkability assessment.

Background Walking, and in particular, outdoor walking, is the most common form of physical activity for older adults. To date, no study investigated the association between the neighborhood built environment and physical activity habits of older adults of low SES. Thus, our overarching aim was to examine the association between the neighborhood built environment and the spectrum of physical activity and walking for transportation in older adults of low socioeconomic status. Methods Cross-sectional data were from the Walk the Talk Study, collected in 2012. Participants ( n  = 161, mean age = 74 years) were in receipt of a rental subsidy for low income individuals and resided in neighbourhoods across Metro Vancouver, Canada. We used the Street Smart Walk Score to objectively characterize the built environment main effect (walkability), accelerometry for objective physical activity, and the Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire to measure walking for transportation. We used regression analyses to examine associations of objectively measured physical activity [total volume, light intensity and moderate intensity physical activity (MVPA)] and self-reported walking for transportation (any, frequency, duration) with walkability. We adjusted analyses for person- and environment-level factors associated with older adult physical activity. Results Neighbourhood walkability was not associated with physical activity volume or intensity and self-reported walking for transportation, with one exception. Each 10-point increase in Street Smart Walk Score was associated with a 45% greater odds of any walking for transportation (compared with none; OR = 1.45, 95% confidence interval = 1.18, 1.78). Sociodemographic, physical function and attitudinal factors were significant predictors of physical activity across our models. Conclusions The lack of associations between most of the explored outcomes may be due to the complexity of the relation between the person and environment. Given that this is the first study to explore these associations specifically in older adults living on low income, this study should be replicated in other settings.

Walking is a popular physical activity that helps prevent obesity and cardiovascular diseases. The Walk Score, which measures neighborhood walkability, considers access to nine amenities using a geographic information system but does not deal with pedestrian perception. This study aims to (1) examine the correlation between access to each amenity, an individual component of the Walk Score, and perceived neighborhood walkability and (2) investigate the correlation with the perceived neighborhood walkability by adding variables of pedestrian perception to the existing Walk Score components. This study conducted a survey with 371 respondents in Daegu, South Korea, between 12 October and 8 November 2022. A multiple regression model was used to examine the correlations. The results showed no association between perceived neighborhood walkability and the individual component of the Walk Score. As variables of environmental perception, the fewer hills or stairs, the more alternative walking routes, the better separation between road and pedestrians, and the richer the green space, the more people perceived their neighborhood as walkable. This study found that the perception of the built environment had a more substantial influence on perceived neighborhood walkability than the accessibility to amenities. It proved that the Walk Score should include pedestrian perception and quantitative measurement.