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	Ecological Fallacy Analysis	Choropleth Classification
Ämnesområde	Human Geography	Human Geography
Familj	Process / pipeline	Process / pipeline
Ursprungsår	1950	1967
Upphovsperson	William S. Robinson	Thematic cartography tradition (class-interval methods synthesized by Slocum et al.; Jenks's optimal method)
Typ	Diagnosis and correction of bias when inferring individual relationships from aggregate data	Procedure for grouping data values into ordered classes for a choropleth map
Ursprungskälla	Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15(3), 351–357.	Slocum, T. A., McMaster, R. B., Kessler, F. C., & Howard, H. H. (2009). Thematic Cartography and Geovisualization (3rd ed.). Pearson Prentice Hall, Upper Saddle River, NJ. ISBN: 9780132298346
Alias	Ecological Inference, Ecological Bias Analysis, Aggregation Bias Analysis	Class Interval Selection, Data Classification for Maps, Choropleth Class Breaks, Thematic Map Classification
Närliggande	4	4
Sammanfattning	The 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.	Choropleth classification is the cartographic procedure of grouping the values of a quantitative variable into a small number of ordered classes so that areas can be shaded on a thematic map. Because a continuous distribution must be reduced to a handful of colour categories, the choice of how many classes to use and where to place the break values strongly shapes the map's message — the same data can look uniform or sharply divided depending on the scheme. Standard methods include equal interval, quantile, Jenks natural breaks, standard deviation, and head/tail breaks, each making different assumptions about what pattern the map should reveal.