Multivariate map: Difference between revisions

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** A ''bivariate [[choropleth map]]'' is the most common type of correlated symbol. Contrasting but not complimentary colors are generally used, so that their combination is intuitively recognized as "between" the two original colors, such as red+blue=purple.<ref name="trumbo1981" /> They have been found to be more easily used if the map includes a carefully designed legend and an explanation of the technique.<ref>{{cite journal |last1=Olson |first1=Judy M. |title=Spectrally encoded two-variable maps |journal=Annals of the Association of American Geographers |date=1981 |volume=71 |issue=2 |pages=259-276}}</ref> A common legend strategy is a two dimensional matrix, divided into smaller boxes where each box represents a unique relationship of the variables.

** A ''multivariate [[Dot distribution map | dot density map]]'' mixes dots of different colors in each district, typically representing separate subgroups of the overall population.<ref name="jenks1953">{{cite journal |last1=Jenks |first1=George F. |title="Pointillism" as a Cartographic Technique |journal=The Professional Geographer |date=1953 |volume=5 |issue=5 |pages=4--6 |doi=10.1111/j.0033-0124.1953.055_4.x}}</ref>

* A ''multivariate symbol map'' represents two or more variables in the same thematic map layer, using distinct [[visual variables]] for each variable.<ref name="slocum2009" />{{rp|337}}<ref name="nelson1996"/> For example, a layer of cities might be symbolized with circles of [[Proportional symbol map|proportional size]] representing its total population, and the hue of each circle representing the predominant source type of its electric power, akin to a nominal [[choropleth map]].

** A ''chart map'' represents each geographic feature with a [[Chart | statistical chart]], often a [[pie chart]] or [[bar chart]], which can include a number of variables. Each chart is usually drawn proportionally to a total, making it a multivariate symbol.

** ''[[Chernoff face | Chernoff faces]]'' have occasionally been used in maps since the 1970s, generally in an experimental situation.<ref>{{cite journal |last1=Wainer |first1=H. |title=Graphic Experiment in Display of Nine Variables Uses Faces to Show Multiple Properties of States |journal=Newsletter of the Bureau of Social Sciences Research |date=1979 |volume=13 |pages=2-3}}</ref><ref>{{cite journal |last1=Nelson |first1=Elisabeth S. |title=The Face Symbol: Research Issues and Cartographic Potential |journal=Cartographica |date=2007 |volume=42 |issue=1 |page=53}}</ref> This technique constructs a complex point symbol that looks like a face, with various facial features distorted to represent various variables, in an attempt to leverage the innate human experience of interpreting meaning from facial expressions. Experimental results have generally been mixed, and the technique has never gained wide popularity.<ref name="nelson1996">Nelson, E.S., and P. Gilmartin. 1996. ‘‘An Evaluation of Multivariate, Quantitative Point Symbols for Maps.’’ In ''Cartographic Design: Theoretical and Practical Perspectives'', ed. C.H. Wood, and C.P. Keller. Chichester, UK: Wiley. 199–210.</ref>

[[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.]]

The technique works best when the geographyvariables ofhappen theto variablehave hasa clear geographic pattern, such as a high degree of [[spatial autocorrelation]], so that there are large regions of similar colorsappearance with gradual changes between them;, otherwiseor a generally strong correlation between the two variables. If there is no clear pattern, the map can look like a confusing mix of random symbols. It is also 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 unintuivitive mixed colors. 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 multivariate patterns, and other analytical methods may be more enlightening, such as [[cluster analysis]].<ref name="slocum2009" />{{rp|331}344})

In general, bivariate maps are one of the alternatives to the simple univariate choropleth maps, although they are sometimes extremely difficult to understand the distribution of a single variable. Because conventional bivariate maps use two arbitrarily assigned color schemes and generate random color combinations for overlapping sections and users have to refer to the arbitrary legend all the time. Therefore, a very prominent and clear legend is needed so that both the distribution of single variable and the relationship between the two variables could be shown on the bivariate map.

==See also==