Community Design

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.

BACKGROUND: We surveyed North Carolina (NC) municipalities to document the presence of municipal walking- and bicycling-related projects, programs, and policies; to describe whether prevalence of these elements differed if recommended in a plan; and to characterize differences between urban and rural municipalities. METHODS: We surveyed all municipalities with ≥ 5000 persons (n = 121) and sampled municipalities with <5000 persons (216/420), with a response rate of 54% (183/337). Responses were weighted to account for the sampling design. RESULTS: From a list provided, staff reported on their municipality’s use of walking- and bicycling-related elements (8 infrastructure projects, 9 programs, and 14 policies). The most commonly reported were projects on sidewalks (53%), streetscape improvements (51%), bicycle/walking paths (40%); programs for cultural/recreational/health (25%), general promotional activities (24%), Safe Routes to School (24%), and law enforcement (24%); and policies on maintenance (64%), new facility construction (57%), and restricted automobile speed or access (45%). Nearly all projects, programs, or policies reported were more likely if included in a plan and more prevalent in urban than rural municipalities. CONCLUSION: These results provide cross-sectional support that plans facilitate the implementation of walking and bicycling elements, and that rural municipalities plan and implement these elements less often than urban municipalities.

Increasing active transportation to and from school may reduce childhood obesity rates in Hawai‘i. A community partnership was formed to address this issue in Hawai‘i’s Opportunity for Active Living Advancement (HO‘ÄLA), a quasi-experimental study of active transportation in Hawai‘i County. The purpose of this study was to determine baseline rates for active transportation rates to and from school and to track changes related to macro-level (statewide) policy, locally-based Safe Routes to School (SRTS) programs and bicycle and pedestrian planning initiatives expected to improve the safety, comfort and ease of walking and bicycling to and from school. Measures included parent surveys, student travel tallies, traffic counts and safety observations. Assessments of the walking and biking environment around each school were made using the Pedestrian Environment Data Scan. Complete Streets and SRTS policy implementation was tracked through the activities of a state transportation-led Task Force and an advocacy-led coalition, respectively. Planning initiatives were tracked through citizen-based advisory committees. Thirteen volunteer schools participated as the intervention (n=8) or comparison (n=5) schools. The majority of students were Asian, Native Hawaiian, and Pacific Islander in schools located in under-resourced communities. Overall, few children walked or biked to school. The majority of children were driven to and from school by their parents. With the influence of HO‘ÄLA staff members, two intervention schools were obligated SRTS project funding from the state, schools were identified as key areas in the pedestrian master plan, and one intervention school was slated for a bike plan priority project. As the SRTS programs are implemented in the next phase of the project, post-test data will be collected to ascertain if changes in active transportation rates occur.

Geocoding, the process of matching addresses to geographic coordinates, is a necessary first step when using geographical information systems (GIS) technology. However, different geocoding methodologies can result in different geographic coordinates. The objective of this study was to compare the positional (i.e. longitude/latitude) difference between two common geocoding methods, i.e. ArcGIS (Environmental System Research Institute, Redlands, CA, USA) and Batchgeo (freely available online at http://www.batchgeo.com). Address data came 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 programmes located in four geographically diverse metropolitan areas in the USA. Our analyses include baseline addresses (n = 748) collected from the parents of the children in the after school sites. Addresses were first geocoded to the street level and assigned longitude and latitude coordinates with ArcGIS, version 9.3, then the same addresses were geocoded with Batchgeo. For this analysis, the ArcGIS minimum match score was 80. The resulting geocodes were projected into state plane coordinates, and the difference in longitude and latitude coordinates were calculated in meters between the two methods for all data points in each of the four metropolitan areas. We also quantified the descriptions of the geocoding accuracy provided by Batchgeo with the match scores from ArcGIS. We found a 94% match rate (n = 705), 2% (n = 18) were tied and 3% (n = 25) were unmatched using ArcGIS. Forty-eight addresses (6.4%) were not matched in ArcGIS with a match score ≥80 (therefore only 700 addresses were included in our positional difference analysis). Six hundred thirteen (87.6%) of these addresses had a match score of 100. Batchgeo yielded a 100% match rate for the addresses that ArcGIS geocoded. The median for longitude and latitude coordinates for all the data was just over 25 m. Overall, the range for longitude was 0.04-12,911.8 m, and the range for latitude was 0.02-37,766.6 m. Comparisons show minimal differences in the median and minimum values, while there were slightly larger differences in the maximum values. The majority (>75%) of the geographic differences were within 50 m of each other; mostly <25 m from each other (about 49%). Only about 4% overall were ≥400 m apart. We also found geographic differences in the proportion of addresses that fell within certain meter ranges. The match-score range associated with the Batchgeo accuracy level “approximate” (least accurate) was 84-100 (mean = 92), while the “rooftop” Batchgeo accuracy level (most accurate) delivered a mean of 98.9 but the range was the same. Although future research should compare the positional difference of Batchgeo to criterion measures of longitude/latitude (e.g. with global positioning system measurement), this study suggests that Batchgeo is a good, free-of-charge option to geocode addresses.