摘要：Geographic information systems have proven instrumental in assessing environmental impacts on individual and community health, but numerous methodological challenges are associated with analyses of highly localized phenomena in which spatially misaligned data are used. In a case study based on child care facility and traffic data for the Los Angeles metropolitan area, we assessed the extent of facility misclassification with spatially unreconciled data from 3 different governmental agencies in an attempt to identify child care centers in which young children are at risk from high concentrations of toxic vehicle-exhaust pollutants. Relative to geographically corrected data, unreconciled information produced a modest bias in terms of aggregated number of facilities at risk and a substantial number of false positives and negatives. GEOGRAPHIC INFORMATION systems (GIS) have proven instrumental in assessing environmental impacts on individual and community health. 1 – 13 Recent studies have begun to systematically address technological limitations associated with GIS by enhancing accuracy of positional, attribute, and temporal data; by tracking demographics and disease as geographic boundaries change over time; by identifying the best household and area measures of socioeconomic status; and by determining appropriate scales for studying links between environmental exposures and health outcomes. 14 – 25 Improved data and advances in techniques have enabled epidemiological and atmospheric researchers to apply GIS to highly localized problems, but such analyses present numerous methodological challenges, especially when data of different pedigrees are not collocated at small geographic scales. We assessed the impact of using geographically unreconciled traffic volume data along with census-based street data in a case study of child care centers whose locations near major roadways could put young children at risk from high concentrations of toxic vehicle exhaust pollutants. Recent epidemiological evidence indicates a heightened prevalence of respiratory morbidity and mortality among people living near high-traffic roadways, and childhood cancer, brain cancer, leukemia, and preterm and low-weight births have been positively associated with traffic density among those living near such roadways. 26 – 31 Although other environmental risk factors may be present in high-traffic areas, air pollution studies point to the significance of high concentrations of vehicle-generated pollutants such as carbon monoxide and ultrafine particles. Typically, pollutants decline exponentially to near background levels within as little as 150 m of major roadways, with the greatest decrease occurring within 50 m. 32 – 34 Because dispersed monitoring stations are insufficient to determine pollutant concentrations at nonadjacent locations, and given the expense of directly measuring pollutants at multiple sites, researchers conducting epidemiological and distributional studies have used traffic volume line data and census-based line data to approximate exposure to vehicle-related pollutants. 30 , 35 – 38 However, this method can result in exposure misclassifications if these data sets are not precisely “aligned” with each other in GIS analyses. (Such discrepancies are not uncommon in health-related research, especially when data from different sources are used. Detailed statistics on misalignments in this study are described later.) Such geographic misalignments can result from the underlying data source, data cleaning processes, or the original intended scale of the data. We examined the effects of reassigning attribute data from 1 geographic data source to census-based line data on estimated exposure levels of facilities geo-coded via census-based line data. A more general issue not broached in this article is the question of “georeferencing,” or determination of spatial accuracy relative to the earth. Previous studies have addressed misalignment problems associated with geographic data sets in different ways. One approach is to increase buffer areas beyond the ideal criterion distance to avoid false negatives, but this method can produce false positives. 37 , 38 Wilhelm and Ritz addressed such misalignments by transferring traffic count values from the original traffic line geography to census-based line segments—a method similar to that described here—but only for a select set of neighborhoods. 30 Green et al. 35 and Houston et al. 37 did not correct misalignments but assumed that discrepancies between traffic volume and census-based geographies are randomly distributed and do not produce spatial biases. 35 Although the value of spatially aligned data was recognized in these studies, none of them included systematic comparisons of results from reconciled and unreconciled data sets. We evaluated the impact of reassigning traffic counts to a census-based geography for Los Angeles County, which is home to 9.5 million people and covers approximately 12 300 km2. Our evaluation took the form of a case study designed to identify licensed child care facilities close to major roadways with high traffic volumes. Assessments were made at both the policy level (“What is the prevalence of the problem?”) and the programmatic level (“Which facilities are affected?”). Results suggested that use of reconciled data provided valuable methodological enhancements in terms of identification of “at-risk” centers.
