Policy and programmatic importance of spatial alignment of data sources.
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.
Ong
Paul
P
UCLA School of Public Affairs, 405 Hilgard Ave, Los Angeles, CA 90095, USA. pmong@ucla.edu
Graham
Matthew
M
Houston
Douglas
D
D001336:Automobiles / Q000706:statistics & numerical data*; D015982:Bias; D002648:Child; D002656:Child Day Care Centers / Q000706:statistics & numerical data*; D040362:Geographic Information Systems; D006291:Health Policy; D006801:Humans; D012107:Research Design; D001335:Vehicle Emissions / Q000009:adverse effects
D001335:Vehicle Emissions