Orion Stewart

Multistate Evaluation of Safe Routes to School Programs

Purpose. State Safe Routes to School (SRTS) programs provide competitive grants to local projects that support safe walking, bicycling, and other modes of active school travel (AST). This study assessed changes in rates of AST after implementation of SRTS projects at multiple sites across four states. Design. One-group pretest and posttest. Setting. Florida, Mississippi, Washington, and Wisconsin. Subjects. Convenience sample of 48 completed SRTS projects and 53 schools affected by a completed SRTS project. Intervention. State-funded SRTS project. Measures. AST was measured as the percentage of students walking, bicycling, or using any AST mode. SRTS project characteristics were measured at the project, school, and school neighborhood levels. Analysis. Paired-samples t-tests were used to assess changes in AST. Bivariate analysis was used to identify SRTS project characteristics associated with increases in AST. Data were analyzed separately at the project (n = 48) and school (n = 53) levels. Results. Statistically significant increases in AST were observed across projects in all four states. All AST modes increased from 12.9% to 17.6%; walking from 9.8% to 14.2%; and bicycling from 2.5% to 3.0%. Increases in rates of bicycling were negatively correlated with baseline rates of bicycling. Conclusion. State-funded SRTS projects are achieving one of the primary program goals of increasing rates of AST. They may be particularly effective at introducing bicycling to communities where it is rare. The evaluation framework introduced in this study can be used to continue tracking the effect of state SRTS programs as more projects are completed.

Geographic disparities in Healthy Eating Index scores (HEI-2005 and 2010) by residential property values: Findings from Seattle Obesity Study (SOS).

BACKGROUND Higher socioeconomic status (SES) has been linked with higher-quality diets. New GIS methods allow for geographic mapping of diet quality at a very granular level. OBJECTIVE To examine the geographic distribution of two measures of diet quality: Healthy Eating Index (HEI 2005 and HEI 2010) in relation to residential property values in Seattle-King County. METHODS The Seattle Obesity Study (SOS) collected data from a population-based sample of King County adults in 2008-09. Socio-demographic data were obtained by 20-min telephone survey. Dietary data were obtained from food frequency questionnaires (FFQs). Home addresses were geocoded to the tax parcel and residential property values were obtained from the King County tax assessor. Multivariable regression analyses using 1116 adults tested associations between SES variables and diet quality measured (HEI scores). RESULTS Residential property values, education, and incomes were associated with higher HEI scores in bivariate analyses. Property values were not collinear with either education or income. In adjusted multivariable models, education and residential property were better associated with HEI, compared to than income. Mapping of HEI-2005 and HEI-2010 at the census block level illustrated the geographic distribution of diet quality across Seattle-King County. CONCLUSION The use of residential property values, an objective measure of SES, allowed for the first visual exploration of diet quality at high spatial resolution: the census block level.

The association between park visitation and physical activity measured with accelerometer, GPS, and travel diary

Public parks are promoted as places that support physical activity (PA), but evidence of how park visitation contributes to overall PA is limited. This study observed adults living in the Seattle metropolitan area (n=671) for one week using accelerometer, GPS, and travel diary. Park visits, measured both objectively (GPS) and subjectively (travel diary), were temporally linked to accelerometer-measured PA. Park visits occurred at 1.4 per person-week. Participants who visited parks at least once (n=308) had an adjusted average of 14.3 (95% CI: 8.9, 19.6)min more daily PA than participants who did not visit a park. Even when park-related activity was excluded, park visitors still obtained more minutes of daily PA than non-visitors. Park visitation contributes to a more active lifestyle, but is not solely responsible for it. Parks may best serve to complement broader public health efforts to encourage PA.

Comparing Associations Between the Built Environment and Walking in Rural Small Towns and a Large Metropolitan Area

The association between the built environment (BE) and walking has been studied extensively in urban areas, yet little is known whether the same associations hold for smaller, rural towns. This analysis examined objective measures of the BE around participants’ residence and their utilitarian and recreational walking from two studies, one in the urban Seattle area (n = 464) and the other in nine small U.S. towns (n = 299). After adjusting for sociodemographics, small town residents walked less for utilitarian purposes but more for recreational purposes. These differences were largely explained by differential associations of the BE on walking in the two settings. In Seattle, the number of neighborhood restaurants was positively associated with utilitarian walking, but in small towns, the association was negative. In small towns, perception of slow traffic on nearby streets was positively associated with recreational walking, but not in Seattle. These observations suggest that urban–rural context matters when planning BE interventions to support walking.

Split-Match-Aggregate SMA algorithm: integrating sidewalk data with transportation network data in GIS

Sidewalk geodata are essential to understand walking behavior. However, such geodata are scarce, only available at the local jurisdiction and not at the regional level. If they exist, the data are stored in geometric representational formats without network characteristics such as sidewalk connectivity and completeness. This article presents the Split-Match-Aggregate (SMA) algorithm, which automatically conflates sidewalk information from secondary geometric sidewalk data to existing street network data. The algorithm uses three parameters to determine geometric relationships between sidewalk and street segments: the distance between streets and sidewalk segments; the angle between sidewalk and street segments; and the difference between the lengths of matched sidewalk and street segments. The SMA algorithm was applied in urban King County, WA, to 13 jurisdictions’ secondary sidewalk geodata. Parameter values were determined based on agreement rates between results obtained from 72 pre-specified parameter combinations and those of a trained geographic information systems (GIS) analyst using a randomly selected 5% of the 79,928 street segments as a parameter-development sample. The algorithm performed best when the distances between sidewalk and street segments were 12 m or less, their angles were 25° or less, and the tolerance was set to 18 m, showing an excellent agreement rate of 96.5%. The SMA algorithm was applied to classify sidewalks in the entire study area and it successfully updated sidewalk coverage information on the existing regional-level street network data. The algorithm can be applied for conflating attributes between associated, but geometrically misaligned line data sets in GIS.

Secondary GIS built environment data for health research: Guidance for data development

Built environment (BE) data in geographic information system (GIS) format are increasingly available from public agencies and private providers. These data can provide objective, low-cost BE data over large regions and are often used in public health research and surveillance. Yet challenges exist in repurposing GIS data for health research. The GIS data do not always capture desired constructs; the data can be of varying quality and completeness; and the data definitions, structures, and spatial representations are often inconsistent across sources. Using the Small Town Walkability study as an illustration, we describe (a) the range of BE characteristics measurable in a GIS that may be associated with active living, (b) the availability of these data across nine U.S. small towns, (c) inconsistencies in the GIS BE data that were available, and (d) strategies for developing accurate, complete, and consistent GIS BE data appropriate for research. Based on a conceptual framework and existing literature, objectively measurable characteristics of the BE potentially related to active living were classified under nine domains: generalized land uses, morphology, density, destinations, transportation system, traffic conditions, neighborhood behavioral conditions, economic environment, and regional location. At least some secondary GIS data were available across all nine towns for seven of the nine BE domains. Data representing high-resolution or behavioral aspects of the BE were often not available. Available GIS BE data - especially tax parcel data - often contained varying attributes and levels of detail across sources. When GIS BE data were available from multiple sources, the accuracy, completeness, and consistency of the data could be reasonable ensured for use in research. But this required careful attention to the definition and spatial representation of the BE characteristic of interest. Manipulation of the secondary source data was often required, which was facilitated through protocols.

The Association Between Park Facilities and Duration of Physical Activity During Active Park Visits

Public parks provide places for urban residents to obtain physical activity (PA), which is associated with numerous health benefits. Adding facilities to existing parks could be a cost-effective approach to increase the duration of PA that occurs during park visits. Using objectively measured PA and comprehensively measured park visit data among an urban community-dwelling sample of adults, we tested the association between the variety of park facilities that directly support PA and the duration of PA during park visits where any PA occurred. Cross-classified multilevel models were used to account for the clustering of park visits (n = 1553) within individuals (n = 372) and parks (n = 233). Each additional different PA facility at a park was independently associated with a 6.8% longer duration of PA bouts that included light-intensity activity, and an 8.7% longer duration of moderate to vigorous PA time. Findings from this study are consistent with the hypothesis that more PA facilities increase the amount of PA that visitors obtain while already active at a park.

Why neighborhood park proximity is not associated with total physical activity

&NA; This study explored how parks within the home neighborhood contribute to total physical activity (PA) by isolating park‐related PA. Seattle‐area adults (n = 634) were observed using time‐matched accelerometer, Global Positioning System (GPS), and travel diary instruments. Of the average 42.3 min of daily total PA, only 11% was related to parks. Both home neighborhood park count and area were associated with park‐based PA, but not with PA that occurred elsewhere, which comprised 89% of total PA. This study demonstrates clear benefits of neighborhood parks for contributing to park‐based PA while helping explain why proximity to parks is rarely associated with overall PA. HighlightsWe isolated park‐based PA using time‐matched accelerometer, GPS, and travel diary data.Of the daily average 43 min of total PA, 11% were park‐related.Home neighborhood park count and area were associated with park‐based PA.Home neighborhood parks were not associated with the 89% of PA unrelated to park use.

The Causal Effect of Bus Rapid Transit on Changes in Transit Ridership

Numerous studies have reported ridership increases along routes when Bus rapid transit (BRT) replaces conventional bus service, but these increases could be due simply to broader temporal trends in transit ridership. To address this limitation, we compared changes in ridership among routes where BRT was implemented to routes where BRT was planned or already existed in King County, Washington. Ridership was measured at 2010, 2013, and 2014. Ridership increased by 35% along routes where BRT was implemented from 2010 to 2013 compared to routes that maintained conventional bus service. Ridership increased by 29% along routes where BRT was implemented from 2013 to 2014 compared to consistent existing BRT service. These results provide stronger evidence for a causal relationship between BRT and increased transit ridership and a more accurate estimate of the independent effect of BRT on ridership.