手法を比較

	Areal Interpolation	Choropleth Classification
分野	Human Geography	Human Geography
系統	Process / pipeline	Process / pipeline
提唱年	1979	1967
提唱者	Waldo Tobler (pycnophylactic) and Michael Goodchild & Nina Lam (areal weighting)	Thematic cartography tradition (class-interval methods synthesized by Slocum et al.; Jenks's optimal method)
種類	Method for transferring attribute data between incompatible sets of areal units	Procedure for grouping data values into ordered classes for a choropleth map
原典	Tobler, W. R. (1979). Smooth pycnophylactic interpolation for geographical regions. Journal of the American Statistical Association, 74(367), 519–530.	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
別名	Cross-Areal Estimation, Zone-to-Zone Interpolation, Spatial Data Transfer	Class Interval Selection, Data Classification for Maps, Choropleth Class Breaks, Thematic Map Classification
関連	4	4
概要	Areal interpolation is the family of methods for transferring attribute data — populations, counts, rates — from one set of areal units (the source zones) onto a different, incompatible set (the target zones). The need arises constantly in geography because census tracts, postal zones, electoral districts, and grid cells rarely align, yet analysts must combine data reported on mismatched geographies. The methods range from simple area-proportional weighting through ancillary-informed dasymetric refinement to Waldo Tobler's 1979 volume-preserving pycnophylactic smoothing, each trading simplicity for accuracy.	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.