手法を比較

	Geodemographic Classification	Choropleth Classification
分野	Human Geography	Human Geography
系統	Process / pipeline	Process / pipeline
提唱年	2005	1967
提唱者	Richard Webber (and the geodemographics tradition synthesized by Harris, Sleight & Webber)	Thematic cartography tradition (class-interval methods synthesized by Slocum et al.; Jenks's optimal method)
種類	Pipeline that clusters small areas into interpretable neighbourhood types	Procedure for grouping data values into ordered classes for a choropleth map
原典	Harris, R., Sleight, P., & Webber, R. (2005). Geodemographics, GIS and Neighbourhood Targeting. John Wiley & Sons, Chichester. ISBN: 9780470864135	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	Neighbourhood Classification, Area Classification, Geodemographic Segmentation, Neighbourhood Typology	Class Interval Selection, Data Classification for Maps, Choropleth Class Breaks, Thematic Map Classification
関連	4	4
概要	Geodemographic classification is the process of grouping small geographic areas into a set of distinctive neighbourhood types according to the demographic, socioeconomic, and housing characteristics of the people who live there. It rests on the principle that 'birds of a feather flock together' — that residents of a neighbourhood tend to resemble one another and differ from those elsewhere — and turns dozens of census variables into a single, interpretable label for every area. Commercial systems such as Mosaic and ACORN and open classifications such as the UK Output Area Classification are all built this way, and the approach was consolidated as a discipline by Harris, Sleight and Webber in 2005.	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.