Multivariate map: Difference between revisions

Revision as of 22:14, 8 November 2020

Revision as of 22:29, 8 November 2020

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[[File:2016 US Presidential Election Pie Charts.png|thumb|right|300px|A multivariate symbol map of the 2016 U.S. presidential election, using a combination proportional and chart symbol]]

[[File:Dot map black hispanic.png|thumb|left|A bivariate dot density map showing the distribution of the African American (blue) and Latino (red) populations in the contiguous United States in 2010.]]

Multivariate thematic maps can be a very effective tool for discovering intricate geographic patterns in complex data, but some have been spectacular failures.<ref name="gistbok" /> The technique works best when the variables happen to have a clear geographic pattern, such as a high degree of [[spatial autocorrelation]], so that there are large regions of similar appearance with gradual changes between them, or a generally strong correlation between the two variables. If there is no clear pattern, the map can become an overwhelming mix of random symbols. It

A issecond alsoproblem occurs when the symbols do not harmonize well. In keeping with [[Gestalt psychology]], a multivariate map will work best when map readers can isolate patterns in each variable independently, as well as comparing them to each other. This occurs when the [[Map symbol | map symbols]] follow the gestalt [[principles of grouping]]. Conversely, it is possible to select thematic symbol strategies that are effective on their own, but do not work together well, such as a proportional point symbol that obscures the choropleth map underneath, or a bivariate choropleth map using base colors that create unintuitive mixed colors.

A third issue arises when a map, or even a single symbol, is overloaded with too many variables that cannot be efficiently interpreted.<ref name="torguson">{{cite book |last1=Dent |first1=Borden D. |last2=Torguson |first2=Jeffrey S. |last3=Hodler |first3=Thomas W. |title=Cartography: Thematic Map Design |date=2009 |publisher=McGraw-Hill |isbn=978-0-07-294382-5 |page=147}}</ref> Chernoff faces have often been criticized for this effect.

Thus, many multivariate maps turn out to be technically impressive, but practically unusable.<ref name="nelson1996"/> This means that the cartographer must be able to critically evaluate whether a multivariate map she has designed is actually effective. It has also been suggested that in some cases, a map might not be the best tool for studying a particular multivariate dataset, and other analytical methods may be more enlightening, such as [[cluster analysis]].<ref name="slocum2009" />{{rp|344})}

==See also==