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Ecological Fallacy Analysis×Spatial Dissimilarity Index×
분야Human GeographyHuman Geography
계열Process / pipelineProcess / pipeline
기원 연도19501993
창시자William S. RobinsonRichard Morrill & David Wong
유형Diagnosis and correction of bias when inferring individual relationships from aggregate dataBoundary-aware index of residential segregation between two groups
원전Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15(3), 351–357. DOI ↗Wong, D. W. S. (1993). Spatial indices of segregation. Urban Studies, 30(3), 559–572. DOI ↗
별칭Ecological Inference, Ecological Bias Analysis, Aggregation Bias AnalysisSpatial Index of Dissimilarity, Adjusted Dissimilarity Index, Boundary-Adjusted Dissimilarity, Spatial Segregation Index
관련44
요약The ecological fallacy is the error of inferring relationships among individuals from correlations measured on groups, and ecological fallacy analysis is the practice of detecting, decomposing, and correcting that bias. William Robinson's 1950 paper demonstrated the danger starkly: the correlation between literacy and immigrant status across U.S. states was strongly positive at the aggregate level yet negative at the individual level. The work shows that an association observed between area averages can be inflated, attenuated, or reversed relative to the underlying individual association, so aggregate evidence cannot be read directly as evidence about people.The spatial dissimilarity index is a boundary-aware measure of residential segregation that corrects the classic index of dissimilarity for the fact that areal units are not isolated boxes but neighbours that share borders. Developed by Richard Morrill in 1991 and refined by David Wong in 1993, it discounts the aspatial index by the degree to which adjacent units differ in group composition, so that two groups clustered into separate but neighbouring areas are recorded as less segregated than two groups locked into a checkerboard. It directly addresses the long-standing checkerboard problem that the aspatial Duncan index cannot see.
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