Сравнение на методи

	Modifiable Areal Unit Problem	Choropleth Classification
Област	Human Geography	Human Geography
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1984	1967
Създател	Stan Openshaw	Thematic cartography tradition (class-interval methods synthesized by Slocum et al.; Jenks's optimal method)
Тип	Source of bias and sensitivity in the analysis of spatially aggregated data	Procedure for grouping data values into ordered classes for a choropleth map
Основополагащ източник	Openshaw, S. (1984). The Modifiable Areal Unit Problem. Concepts and Techniques in Modern Geography No. 38. Geo Books, Norwich. ISBN: 9780860941347	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
Други названия	MAUP, Scale and Zoning Effect, Aggregation Problem	Class Interval Selection, Data Classification for Maps, Choropleth Class Breaks, Thematic Map Classification
Свързани	4	4
Резюме	The modifiable areal unit problem (MAUP) is the finding that statistical results computed on spatially aggregated data depend on the arbitrary choice of how space is divided into zones. Stan Openshaw's 1984 monograph crystallized the issue into two intertwined components — a scale effect, where results change as data are grouped into larger or smaller units, and a zoning effect, where results change when the boundaries are redrawn at a fixed scale. Because the units used in geography (census tracts, districts, grid cells) are almost always modifiable rather than natural, almost every aggregate spatial statistic is potentially an artefact of its zonation.	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.