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Background: Land surface temperature (LST) and percent surface imperviousness (SI), both derived from satellite imagery, have been used to characterize the urban heat island effect, a phenomenon in which urban areas are warmer than non-urban areas. Objectives: We aimed to assess the correlations between LSTs and SI images with actual temperature readings from a ground-based network of outdoor monitors. Methods: We evaluated the relationships among a) LST calculated from a 2009 summertime satellite image of the Detroit metropolitan region, Michigan; b) SI from the 2006 National Land Cover Data Set; and c) ground-based temperature measurements monitored during the same time period at 19 residences throughout the Detroit metropolitan region. Associations between these ground-based temperatures and the average LSTs and SI at different radii around the point of the ground-based temperature measurement were evaluated at different time intervals. Spearman correlation coefficients and corresponding p-values were calculated. Results: Satellite-derived LST and SI values were significantly correlated with 24-hr average and August monthly average ground temperatures at all but two of the radii examined (100 m for LST and 0 m for SI). Correlations were also significant for temperatures measured between 0400 and 0500 hours for SI, except at 0 m, but not LST. Statistically significant correlations ranging from 0.49 to 0.91 were observed between LST and SI. Conclusions: Both SI and LST could be used to better understand spatial variation in heat exposures over longer time frames but are less useful for estimating shorter-term, actual temperature exposures, which can be useful for public health preparedness during extreme heat events. [ABSTRACT FROM AUTHOR]

Background: Land surface temperature (LST) and percent surface imperviousness (SI), both derived from satellite imagery, have been used to characterize the urban heat island effect, a phenomenon in which urban areas are warmer than nonurban areas. Objectives: We aimed to assess the correlations between LSTs and SI images with actual temperature readings from a ground-based network of outdoor monitors. Methods: We evaluated the relationships between 1) LST calculated from a 2009 summertime satellite image of the Detroit Metropolitan Region, Michigan, 2) SI from the 2006 National Land Cover Data Set, and 3) ground-based temperature measurements monitored during the same time period at 19 residences throughout the Detroit Metropolitan Region. Associations between these ground-based temperatures and the average LSTs and SI at different radii around the point of the ground-based temperature measurement were evaluated at different time intervals. Spearman correlation coefficients and corresponding p-values were calculated. Results: Satellite derived LST and SI values were significantly correlated with 24-hour average and August monthly average ground temperatures at all but 2 of the radii examined (100 m for LST and 0 m for SI). Correlations were also significant for temperatures measured between 4:00 and 5:00 a.m. for SI, except at 0 m, but not LST. Statistically significant correlations ranging from 0.49 to 0.91 were observed between LST and SI. Conclusions: Both SI and LST could be used to better understand spatial variation in heat exposures over longer time frames but are less useful for estimating shorter term, actual temperature exposures, which can be useful for public health preparedness during extreme heat events. [ABSTRACT FROM AUTHOR]

Abstract: Few studies have investigated the spatial clustering of multiple health-related resources. We constructed 0.5 mile kernel densities of resources for census areas in New York City, NY (n=819 block groups), Baltimore, MD (n=737), and Winston-Salem, NC (n=169). Three of the four resource densities (supermarkets/produce stores, retail areas, and recreational facilities) tended to be correlated with each other, whereas park density was less consistently and sometimes negatively correlated with others. Blacks were more likely to live in block groups with multiple low resource densities. Spatial regression models showed that block groups with higher proportions of black residents tended to have lower supermarket/produce, retail, and recreational facility densities, although these associations did not always achieve statistical significance. A measure that combined local and neighboring block group racial composition was often a stronger predictor of resources than the local measure alone. Overall, our results from three diverse U.S. cities show that health-related resources are not randomly distributed across space and that disadvantage in multiple domains often clusters with residential racial patterning. [ABSTRACT FROM AUTHOR]

Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa. [ABSTRACT FROM AUTHOR]

Highlights • There was a 34% reduction in teen injury crashes following GDL implementation. • The post-GDL teen injury crash rate reduction showed significant spatial variation. • Post-GDL crash rate reductions were larger in areas with more alcohol outlets. • Post-GDL crash rate reductions were smaller in areas with more schools. • Areas with movie theatres showed larger post-GDL crash rate reductions after dark. Abstract Motor vehicle crashes are a leading cause of injury, and teen drivers contribute disproportionately to that burden. Graduated Driver Licensing (GDL) programs are effective at reducing teen crash risk, but teen crash rates remain high. Between-state variation in the teen crash rate reduction following GDL implementation has been documented, but this is the first study to examine small-area variation in such a reduction. Fusing together crash data from the Michigan State Police, census data, and organizational data (alcohol outlet, movie theatre, and school locations), we analyzed spatial correlates of teen injury crash, and place-based features that modified the injury crash rate difference following GDL implementation. Specifically, using census-based units, we estimated changes in injury crash rates among teens using negative binomial regression controlling for spatial autocorrelation, and tested whether any measured spatial characteristics modified the crash rate change in the pre versus post GDL periods. There was a substantial reduction in teen crashes after GDL implementation (RR = 0.66, 95%CI: [0.65, 0.67]), and this effect was robust across gender and time-of-day (light/dark). We found evidence that this reduction varied across space; areas with more alcohol outlets corresponded to a larger daytime crash rate reduction post-GDL, while areas near schools corresponded to a smaller daytime crash rate reduction. Concentrations of movie theatres corresponded to larger post-GDL crash rate reductions after dark. Maximizing the substantial successes of GDL programs requires understanding why crash rate reductions were larger in some areas following GDL implementation, and harnessing that understanding to improve its effectiveness across a state, focusing on identifying priorities for improving driver training (e.g., by parents and driver educators), law enforcement, and future policy changes to current GDL laws. [ABSTRACT FROM AUTHOR]

Investments in neighborhood built environments could increase physical activity and overall health. Disproportionate distribution of these changes in advantaged neighborhoods could inflate health disparities. Little information exists on where changes are occurring. This paper aims to 1) identify changes in the built environment in neighborhoods and 2) investigate associations between high levels of change and sociodemographic characteristics. Using Geographic Information Systems, neighborhood land-use, local destinations (for walking, social engagement, and physical activity), and sociodemographics were characterized in 2000 and 2010 for seven U.S. cities. Linear and change on change models estimated associations of built environment changes with baseline (2000) and change (2010–2000) in sociodemographics. Spatial patterns were assessed using Global Moran's I to measure overall clustering of change and Local Moran's I to identify statistically significant clusters of high increases surrounded by high increases (HH). Sociodemographic characteristics were compared between HH cluster and other tracts using Analysis of Variance (ANOVA). We observed small land-use changes but increases in the destination types. Greater increases in destinations were associated with higher percentage non-Hispanic whites, percentage households with no vehicle, and median household income. Associations were present for both baseline sociodemographics and changes over time. Greater increases in destinations were associated with lower baseline percentage over 65 but higher increases in percentage over 65 between 2000 and 2010. Global Moran's indicated changes were spatially clustered. HH cluster tracts started with a higher percentage non-Hispanic whites and higher percentage of households without vehicles. Between 2000 and 2010, HH cluster tracts experienced increases in percent non-Hispanic white, greater increases in median household income, and larger decreases in percent of households without a vehicle. Changes in the built environment are occurring in neighborhoods across a diverse set of U.S. metropolitan areas, but are patterned such that they may lead to increased health disparities over time. [ABSTRACT FROM AUTHOR]

Objective We conducted an ecological study to determine physical activity resource availability overall and by sociodemographic groups in parts of six states (CA, IL, MD, MN, NC, NY). Methods Data on parks and recreational facilities were collected from 3 sources in 2009–2012. Three measures characterized park and recreational facility availability at the census tract level: presence of ≥1 resource, number of resources, and resource kernel density. Associations between resource availability and census tract characteristics (predominant racial/ethnic group, median income, and proportion of children and older adults) were estimated using linear, binomial, and zero-inflated negative binomial regression in 2014. Pooled and stratified analyses were conducted. Results The study included 7139 census tracts, comprising 9.5% of the 2010 US population. Overall the availability of parks and recreational facilities was lower in predominantly minority relative to non-Hispanic white census tracts. Low-income census tracts and those with a higher proportion of children had an equal or greater availability of park resources but fewer recreational facilities. Stratification revealed substantial variation in resource availability by site. Conclusion The availability of physical activity resources varied by sociodemographic characteristics and across regions. Improved knowledge of resource distribution can inform strategies to provide equitable access to parks and recreational facilities. [ABSTRACT FROM AUTHOR]

Lack of longitudinal research hinders causal inference on the association between the built environment and walking. In the present study, we used data from 6,027 adults in the Multi-Ethnic Study of Atherosclerosis who were 45–84 years of age at baseline to investigate the association of neighborhood built environment with trends in the amount of walking between 2000 and 2012. Walking for transportation and walking for leisure were assessed at baseline and at 3 follow-up visits (median follow-up = 9.15 years). Time-varying built environment measures (measures of population density, land use, number of destinations, bus access, and street connectivity) were created using geographic information systems. We used linear mixed models to estimate the associations between baseline levels of and a change in each built environment feature and a change in the frequency of walking. After adjustment for potential confounders, we found that higher baseline levels of population density, area zoned for retail, social destinations, walking destinations, and street connectivity were associated with greater increases in walking for transportation over time. Higher baseline levels of land zoned for residential use and distance to buses were associated with less pronounced increases (or decreases) in walking for transportation over time. Increases in the number of social destinations, the number of walking destinations, and street connectivity over time were associated with greater increases in walking for transportation. Higher baseline levels of both land zoned for retail and walking destinations were associated with greater increases in leisure walking, but no changes in built environment features were associated with leisure walking. The creation of mixed-use, dense developments may encourage adults to incorporate walking for transportation into their everyday lives. [ABSTRACT FROM PUBLISHER]

Abstract: This study examines associations of disaggregate land uses with self-reported walking for transportation among participants of the Multi-Ethnic Study of Atherosclerosis (MESA) in Forsyth County, NC and New York, NY. Network distance to each use (in miles), intensity (number of uses per 1/2-mile network buffer) of each use and diversity (number of different uses per 1/2-mile network buffer) of uses were calculated using Geographic Information Systems (GIS). Associations with odds of meeting recommended physical activity levels (150min/week) were examined after controlling for individual- and census-tract-level covariates. Greater distance to and lower intensity of pedestrian-oriented uses, specifically those for social interactions, were associated with lower odds of meeting recommendations in NY. Results suggest that land uses linked to social interactions may be useful for encouraging increased transportation walking. [Copyright &y& Elsevier]

On an annual basis, heat is the chief cause of weather-related deaths in the United States. Therefore, understanding the temperature structure where people live is important for reducing the health burden imposed by hot weather. This study focused on the air temperatures in the Detroit, Michigan, metropolitan region during the summer of 2009. An observational network was established that included 1) monitors sited in the backyards of residential participants, 2) National Weather Service standard observations, and 3) a network of monitors operated by the State of Michigan. Daily high and low temperatures were analyzed for spatial pattern, magnitude of spatial variability, and relationships with weather conditions. The existence of spatial variability was confirmed specifically during weather that was considered to be dangerous to public health. The relationships between temperature observations and distance to water, distance to city center, and local percent of impervious surface were investigated. The spatial variability during the daily low was typically stronger in magnitude and the spatial pattern was more consistent than were those during the daily high. The largest correlation with land-cover and location attributes was between values of percent of impervious surface and daily low temperatures. Daily high temperatures were most correlated with distance to water. Consistent with previous studies on spatial variability in urban environments, the results suggest a need for sensitivity to the spatially variable nature of exposure to heat events in both public health and urban planning. For example, these results showed that the downtown area experienced elevated temperatures during nights and that the eastern portions of Detroit experienced decreased temperatures during afternoons. [ABSTRACT FROM AUTHOR]

Abstract: Background: Because of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scale. Objective: This study examined whether satellite-derived impervious surface and meteorological conditions from multiple sites can improve characterization of spatial variability of temperature within an urban area. Methods: Temperature was measured at 17 outdoor sites throughout the Detroit metropolitan area during the summer of 2008. Kriging and linear regression were applied to daily temperatures and secondary information, including impervious surface and distance-to-water. Performance of models in predicting measured temperatures was evaluated by cross-validation. Variograms derived from several scenarios were compared to determine whether high-resolution impervious surface information could capture fine-scale spatial structure of temperature in the study area. Results: Temperatures measured at the sites were significantly different from each other, and all kriging techniques generally performed better than the two linear regression models. Impervious surface values and distance-to-water generally improved predictions slightly. Restricting models to days with lake breezes and with less cloud cover also somewhat improved the predictions. In addition, incorporating high-resolution impervious surface information into cokriging or universal kriging enhanced the ability to characterize fine-scale spatial structure of temperature. Conclusions: Meteorological and satellite-derived data can better characterize spatial variability in temperature across a metropolitan region. The data sources and methods we used can be applied in epidemiological studies and public health interventions to protect vulnerable populations from extreme heat events. [Copyright &y& Elsevier]

Background: The neighborhood environment may play a role in encouraging sedentary patterns, especially for middle-aged and older adults.Purpose: The aim of this study was to examine the associations between walking and neighborhood population density, retail availability, and land-use distribution using data from a cohort of adults aged 45 to 84 years.Methods: Data from a multi-ethnic sample of 5529 adult residents of Baltimore MD, Chicago IL, Forsyth County NC, Los Angeles CA, New York NY, and St. Paul MN enrolled in the Multi-Ethnic Study of Atherosclerosis in 2000-2002 were linked to secondary land-use and population data. Participant reports of access to destinations and stores and objective measures of the percentage of land area in parcels devoted to retail land uses, the population divided by land area in parcels, and the mixture of uses for areas within 200 m of each participant's residence were examined. Multinomial logistic regression was used to investigate associations of self-reported and objective neighborhood characteristics with walking. All analyses were conducted in 2008 and 2009.Results: After adjustment for individual-level characteristics and neighborhood connectivity, it was found that higher density, greater land area devoted to retail uses, and self-reported proximity of destinations and ease of walking to places were each related to walking. In models including all land-use measures, population density was positively associated with walking to places and with walking for exercise for more than 90 minutes/week, both relative to no walking. Availability of retail was associated with walking to places relative to not walking, and having a more proportional mix of land uses was associated with walking for exercise for more than 90 minutes/week, while self-reported ease of access to places was related to higher levels of exercise walking, both relative to not walking.Conclusions: Residential density and the presence of retail uses are related to various walking behaviors. Efforts to increase walking may benefit from attention to the intensity and type of land development. [ABSTRACT FROM AUTHOR]

Background: The neighborhood environment may play a role in encouraging sedentary patterns, especially for middle-aged and older adults. Purpose: The aim of this study was to examine the associations between walking and neighborhood population density, retail availability, and land-use distribution using data from a cohort of adults aged 45 to 84 years. Methods: Data from a multi-ethnic sample of 5529 adult residents of Baltimore MD, Chicago IL, Forsyth County NC, Los Angeles CA, New York NY, and St. Paul MN enrolled in the Multi-Ethnic Study of Atherosclerosis in 2000–2002 were linked to secondary land-use and population data. Participant reports of access to destinations and stores and objective measures of the percentage of land area in parcels devoted to retail land uses, the population divided by land area in parcels, and the mixture of uses for areas within 200 m of each participant''s residence were examined. Multinomial logistic regression was used to investigate associations of self-reported and objective neighborhood characteristics with walking. All analyses were conducted in 2008 and 2009. Results: After adjustment for individual-level characteristics and neighborhood connectivity, it was found that higher density, greater land area devoted to retail uses, and self-reported proximity of destinations and ease of walking to places were each related to walking. In models including all land-use measures, population density was positively associated with walking to places and with walking for exercise for more than 90 minutes/week, both relative to no walking. Availability of retail was associated with walking to places relative to not walking, and having a more proportional mix of land uses was associated with walking for exercise for more than 90 minutes/week, while self-reported ease of access to places was related to higher levels of exercise walking, both relative to not walking. Conclusions: Residential density and the presence of retail uses are related to various walking behaviors. Efforts to increase walking may benefit from attention to the intensity and type of land development. [Copyright &y& Elsevier]

BACKGROUND: The evidence that heat waves can result in both increased deaths and illness is substantial, and concern over this issue is rising because of climate change. Adverse health impacts from heat waves can be avoided, and epidemiologic studies have identified specific population and community characteristics that mark vulnerability to heat waves. OBJECTIVES: We situated vulnerability to heat in geographic space and identified potential areas for intervention and further research. METHODS: We mapped and analyzed 10 vulnerability factors for heat-related morbidity/mortality in the United States: six demographic characteristics and two household air conditioning variables from the U.S. Census Bureau, vegetation cover from satellite images, and diabetes prevalence from a national survey. We performed a factor analysis of these 10 variables and assigned values of increasing vulnerability for the four resulting factors to each of 39,794 census tracts. We added the four factor scores to obtain a cumulative heat vulnerability index value. RESULTS: Four factors explained > 75% of the total variance in the original 10 vulnerability variables: a) social/environmental vulnerability (combined education/poverty/race/green space), b) social isolation, c) air conditioning prevalence, and d) proportion elderly/diabetes. We found substantial spatial variability of heat vulnerability nationally, with generally higher vulnerability in the Northeast and Pacific Coast and the lowest in the Southeast. In urban areas, inner cities showed the highest vulnerability to heat. CONCLUSIONS: These methods provide a template for making local and regional heat vulnerability maps. After validation using health outcome data, interventions can be targeted at the most vulnerable populations. [ABSTRACT FROM AUTHOR]

Agricultural systems are increasingly vulnerable to the effects of extreme climate events. Yet strategies to reduce risk and vulnerability have not been greatly explored. Here, we examine the vulnerability of coffee agroforestry systems varying in management intensity (e.g. land use) and topographic features to disturbance related to Hurricane Stan in Chiapas, Mexico—a hurricane categorized by heavy rains and mild winds. An approximately 50km2 area was chosen within a coffee-growing region where data were collected on a variety of topographic and landscape features (aspect, slope, elevation, distance to river) and vegetation characteristics (canopy cover, vegetation structure, tree density) as predictive factors of vegetation, economic, and landslide damage at three distinct spatial scales. At the plot level, we collected vegetation data later compiled into a vegetation complexity index. At the farm level, we collected data to understand the effect of the hurricane on economic damage and farm area affected by landslides. We also recorded number and volume of roadside landslides as a measure of post-hurricane disturbance. We then conducted a geo-spatial analysis to determine which factors contribute most to landslide occurrence at landscape scales. We found no effect of coffee management on vegetation damage or on economic losses at the plot or farm scale. At the farm scale, increasing management intensity (i.e. reduction in vegetation complexity) correlated with increased proportion of farm area affected by landslides (P =0.014). Additionally, reduction in vegetation complexity was correlated with increased number (P =0.0224) and volume (P =0.062) of roadside landslides at the landscape level. Topographic and landscape features, such as distance to river (P =0.004) and wind exposure/aspect (P =0.044) strongly influenced landslide frequency at the landscape scale. Forest proximity and proportion of forest cover did not significantly influence the frequency or extent of landslide damage. We created hazard maps using the vegetation complexity index, distance to river, and wind exposure as the heaviest weighted factors to assess areas of the terrain with the greatest vulnerability. These maps present a practical result of this study, and offer a template in which land management policy can develop to lower regional vulnerability to landslide risk. These results show that farmers may be able to reduce vulnerability to extreme storm events by carefully managing their farms. Although farmers may not be able to control negative topographic features of their farms, increasing vegetation complexity within farms may be an efficient strategy to reduce some susceptibility to hurricane disturbance. [Copyright &y& Elsevier]

Background: Differences in availability of recreational resources may contribute to racial and socioeconomic status (SES) disparities in physical activity. Variations in the location and density of recreational resources were examined by SES and racial composition of neighborhoods.Methods: Densities of resources available in recreational facilities and parks were estimated for census tracts between April 2003 and June 2004 in North Carolina, New York, and Maryland using kernel estimation. The probability of not having a facility or park was modeled by tract racial composition and SES, adjusting for population and area, using binomial regression in 2006. Mean densities of tract resources were modeled by SES and racial composition using linear regression.Results: Minority neighborhoods were significantly more likely than white neighborhoods not to have recreational facilities (relative probability [RP]=3.27 [95% CI=2.11-5.07] and 8.60 [95% CI=4.48-16.51], for black and Hispanic neighborhoods, respectively). Low-income neighborhoods were 4.5 times more likely to not have facilities than high-income areas (95% CI=2.87-7.12). Parks were more equitably distributed. Most resources located in recreational facilities required a fee and were less dense in minority and low-income areas. Those located inside parks were usually free to use, sports-related, and denser in poor and minority neighborhoods.Conclusions: Recreational facilities and the resources they offer are not equitably distributed. The presence of parks in poor and minority areas suggest that improving the types and quality of resources in parks could be an important strategy to increase physical activity and reduce racial/ethnic and socioeconomic disparities. [ABSTRACT FROM AUTHOR]