Jeffrey S. Wilson

Neighborhood greenness and 2-year changes in body mass index of children and youth.

BACKGROUND Available studies of the built environment and the BMI of children and youth suggest a contemporaneous association with neighborhood greenness in neighborhoods with high population density. The current study tests whether greenness and residential density are independently associated with 2-year changes in the BMI of children and youth. METHODS The sample included children and youth aged 3-16 years who lived at the same address for 24 consecutive months and received well-child care from a Marion County IN clinic network within the years 1996-2002 (n=3831). Multiple linear regression was used to examine associations among age- and gender-specific BMI z-scores in Year 2, residential density, and a satellite-derived measure of greenness, controlling for baseline BMI z-scores and other covariates. Logistic regression was used to model associations between an indicator of BMI z-score increase from baseline to Time 2 and the above-mentioned predictors. RESULTS Higher greenness was significantly associated with lower BMI z-scores at Time 2 regardless of residential density characteristics. Higher residential density was not associated with Time 2 BMI z-scores in models regardless of greenness. Higher greenness was also associated with lower odds of childrens and youths increasing their BMI z-scores over 2 years (OR=0.87; 95% CI=0.79, 0.97). CONCLUSIONS Greenness may present a target for environmental approaches to preventing child obesity. Children and youth living in greener neighborhoods had lower BMI z-scores at Time 2, presumably due to increased physical activity or time spent outdoors. Conceptualizations of walkability from adult studies, based solely on residential density, may not be relevant to children and youth in urban environments.

Green Neighborhoods, Food Retail and Childhood Overweight: Differences by Population Density

Purpose. This study examines relationships between overweight in children and two environmental factors—amount of vegetation surrounding a childs place of residence and proximity of the childs residence to various types of food retail locations. We hypothesize that living in greener neighborhoods, farther from fast food restaurants, and closer to supermarkets would be associated with lower risk of overweight. Design. Cross-sectional study. Setting. Network of primary care pediatric clinics in Marion County, Indiana. Subjects. We acquired data for 7334 subjects, ages 3 to 18 years, presenting for routine well-child care. Measures. Neighborhood vegetation and proximity to food retail were calculated using geographic information systems for each subject using circular and network buffers. Child weight status was defined using body mass index percentiles. Analysis. We used cumulative logit models to examine associations between an index of overweight, neighborhood vegetation, and food retail environment. Results. After controlling for individual socio-demographics and neighborhood socioeconomic status, measures of vegetation and food retail significantly predicted overweight in children. Increased neighborhood vegetation was associated with decreased risk for overweight, but only for subjects residing in higher population density regions. Increased distance between a subjects residence and the nearest large brand name supermarkets was associated with increased risk of overweight, but only for subjects residing in lower population density regions. Conclusions. This research suggests that aspects of the built environment are determinants of child weight status, ostensibly by influencing physical activity and dietary behaviors.

Evaluating environmental influences of zoning in urban ecosystems with remote sensing

Abstract The influence of zoning on Normalized Difference Vegetation Index (NDVI) and radiant surface temperature (Ts) measurements is investigated in the City of Indianapolis, IN, USA using data collected by the Enhanced Thematic Mapper Plus (ETM+) remote sensing system. Analysis of variance indicates statistically significant differences in mean Ts and NDVI values associated with different types of zoning. Multiple comparisons of mean Ts and NDVI values associated with specific pairings of individual zoning categories are also shown to be significantly different. An inverse relationship between Ts and NDVI was observed across the city as a whole and within all but one zoning category. A range of environmental influences on sensible heat flux and urban vegetation was detected both within and between individual zoning categories. Examples for implementing these findings in urban planning applications to find examples of high and low impact development are demonstrated.

The Built Environment and Location-Based Physical Activity

BACKGROUND Studies of the built environment and physical activity have implicitly assumed that a substantial amount of activity occurs near home, but in fact the location is unknown. PURPOSE This study aims to examine associations between built environment variables within home and work buffers and moderate-to-vigorous physical activity (MVPA) occurring within these locations. METHODS Adults (n=148) from Massachusetts wore an accelerometer and GPS unit for up to 4 days. Levels of MVPA were quantified within 50-m and 1-km home and work buffers. Multiple regression models were used to examine associations between five objective built environment variables within 1-km home and work buffers (intersection density, land use mix, population and housing unit density, vegetation index) and MVPA within those areas. RESULTS The mean daily minutes of MVPA accumulated in all locations=61.1+/-32.8, whereas duration within the 1-km home buffers=14.0+/-16.4 minutes. Intersection density, land use mix, and population and housing unit density within 1-km home buffers were positively associated with MVPA in the buffer, whereas a vegetation index showed an inverse relationship (all p<0.05). None of these variables showed associations with total MVPA. Within 1 km of work, only population and housing unit density were significantly associated with MVPA within the buffer. CONCLUSIONS Findings are consistent with studies showing that certain attributes of the built environment around homes are positively related to physical activity, but in this case only when the outcome was location-based. Simultaneous accelerometer-GPS monitoring shows promise as a method to improve understanding of how the built environment influences physical activity behaviors by allowing activity to be quantified in a range of physical contexts and thereby provide a more explicit link between physical activity outcomes and built environment exposures.

Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data

BackgroundExtreme heat events are the number one cause of weather-related fatalities in the United States. The current system of alert for extreme heat events does not take into account intra-urban spatial variation in risk. The purpose of this study is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature derived from thermal remote sensing data.ResultsComparison of logistic regression models indicates that supplementing known sociodemographic risk factors with remote sensing estimates of land surface temperature improves the delineation of intra-urban variations in risk from extreme heat events.ConclusionThermal remote sensing data can be utilized to improve understanding of intra-urban variations in risk from extreme heat. The refinement of current risk assessment systems could increase the likelihood of survival during extreme heat events and assist emergency personnel in the delivery of vital resources during such disasters.

Using Google Street View to Audit the Built Environment: Inter-rater Reliability Results

BackgroundObservational field audits are recommended for public health research to collect data on built environment characteristics. A reliable, standardized alternative to field audits that uses publicly available information could provide the ability to efficiently compare results across different study sites and time.PurposeThis study aimed to assess inter-rater reliability of built environment audits conducted using Google Street View imagery.MethodsIn 2011, street segments from St. Louis and Indianapolis were geographically stratified to ensure representation of neighborhoods with different land use and socioeconomic characteristics in both cities. Inter-rater reliability was assessed using observed agreement and the prevalence-adjusted bias-adjusted kappa statistic (PABAK).ResultsThe mean PABAK for all items was 0.84. Ninety-five percent of the items had substantial (PABAK ≥ 0.60) or nearly perfect (PABAK ≥ 0.80) agreement.ConclusionsUsing Google Street View imagery to audit the built environment is a reliable method for assessing characteristics of the built environment.

Assessing the Built Environment Using Omnidirectional Imagery

Observational audits commonly are used in public health research to collect data on built environment characteristics that affect health-related behaviors and outcomes, including physical activity and weight status. However, implementing in-person field audits can be expensive if observations are needed over large or geographically dispersed areas or at multiple points in time. A reliable and more efficient method for observational audits could facilitate extendibility (i.e., expanded geographic and temporal scope) and lead to more standardized assessment that strengthens the ability to compare results across different regions and studies. The purpose of the current study was to evaluate the degree of agreement between field audits and audits derived from interpretation of three types of omnidirectional imagery. Street segments from St. Louis MO and Indianapolis IN were stratified geographically to ensure representation of neighborhoods with different socioeconomic characteristics in both cities. Audits were conducted in 2008 and 2009 using four methods: field audits, and interpretation of archived imagery, new imagery, and Google Street View™ imagery. Agreement between field audits and image-based audits was assessed using observed agreement and the prevalence-adjusted bias-adjusted kappa statistic (PABAK). Data analysis was conducted in 2010. When measuring the agreement between field audits and audits from the different sources of imagery, the mean PABAK statistic for all items on the instrument was 0.78 (archived); 0.80 (new); and 0.81 (Street View imagery), indicating substantial to nearly perfect agreement among methods. It was determined that image-based audits represent a reliable method that can be used in place of field audits to measure several key characteristics of the built environment important to public health research.

Neighborhood Correlates of Urban Trail Use

PURPOSE To model urban trail traffic as a function of neighborhood characteristics and other factors including weather and day of week. METHODS We used infrared monitors to measure traffic at 30 locations on five trails for periods ranging from 12 months to more than 4 y. We measured neighborhood characteristics using geographic information systems, satellite imagery, and US Census and other secondary data. We used multiple regression techniques to model daily traffic. RESULTS The statistical model explains approximately 80% of the variation in trail traffic. Trail traffic correlates positively and significantly with income, neighborhood population density, education, percent of neighborhood in commercial use, vegetative health, area of land in parking, and mean length of street segments in access networks. Trail traffic correlates negatively and significantly with the percentage of neighborhood residents in age groups greater than 64 and less than 5. CONCLUSIONS Trail traffic is significantly correlated with neighborhood characteristics. Health officials can use these findings to influence the design and location of trails and to maximize opportunities for increases in physical activity.

Valuing the benefits of the urban forest: a spatial hedonic approach

This paper measures the benefits of the urban forest by examining its effect on housing prices. A Geographic Information System is used to develop a measure of the urban forest, the Normalised Difference Vegetation Index, from satellite imagery and to construct other variables from a variety of sources. Spatial hedonic housing price models for the Indianapolis/Marion County area are estimated. The models indicate that greener vegetation around a property has a positive, significant effect on housing price, holding everything else constant. This effect is dominated by measures at the neighborhood level. These findings indicate that property owners value the urban forest, at least in part, by the premium they pay to live in neighborhoods with greener, denser vegetation. These findings also indicate that public action to maintain and enhance the urban forest may be warranted. Planners and urban foresters can use these findings to inform public and policy debates over urban forestry programs and proposals.

Urban Greenways, Trail Characteristics and Trail Use: Implications for Design

This paper illustrates how remote sensing technologies and geographic information systems (GIS) can be used to enhance modelling of urban greenway trail traffic and to draw inferences about the relationships between features of trail design and trail use. Measures of daily trail traffic come from a network of 30 infrared counters deployed over a 33-mile trail system in Indianapolis, Indiana. Among other results, the paper illustrates how Light Detection and Ranging (LIDAR) data obtained from an aircraft platform can be used to create three-dimensional surface models from which trail viewsheds can be measured and characterized. Regression modelling is used to correlate trail traffic with these viewshed characteristics and with other neighbourhood and control variables. The results provide empirical support for several design hypotheses. Other factors being equal, daily trail traffic is positively correlated with the openness of trail viewsheds, the greenness of trail viewsheds relative to surrounding neighbourhoods, and the diversity of land use within trail viewsheds. Trail traffic is inversely correlated with visual magnitude, a measure of the interconnectedness of a viewshed. Although theory suggests higher levels of pedestrian traffic may be associated with shorter block lengths, trail traffic is positively correlated with block length in trail neighbourhoods. Planners and designers can use this evidence base to enhance greenway planning and design.