مقایسهٔ روش‌ها

	Cartogram Construction	Choropleth Classification
حوزه	Human Geography	Human Geography
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	2004	1967
پدیدآور	Cartogram tradition (diffusion method by Gastner & Newman; circular method by Dorling)	Thematic cartography tradition (class-interval methods synthesized by Slocum et al.; Jenks's optimal method)
نوع	Map transformation that rescales region area to represent a variable	Procedure for grouping data values into ordered classes for a choropleth map
منبع بنیادین	Gastner, M. T., & Newman, M. E. J. (2004). Diffusion-based method for producing density-equalizing maps. Proceedings of the National Academy of Sciences, 101(20), 7499–7504.	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
نام‌های دیگر	Value-by-Area Map, Area Cartogram, Density-Equalizing Map, Anamorphic Map	Class Interval Selection, Data Classification for Maps, Choropleth Class Breaks, Thematic Map Classification
مرتبط	4	4
خلاصه	A cartogram is a map in which the area of each region is rescaled so that it is proportional to some variable — population, votes, GDP — rather than to its true geographic size. The aim is to correct the visual bias of ordinary maps, where large but sparsely populated regions dominate the eye while small, populous ones nearly vanish, by making each region as big as the quantity it represents. Cartogram construction is the family of techniques that produce these value-by-area maps, ranging from contiguous density-equalizing diffusion to non-contiguous circle and rectangle methods, each balancing the accuracy of areas against the recognizability of shapes.	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.