Multivariate Maps (3+Variables)

Mulitvariate mapping is a very efficient and cheap way to display data. It requires a little more work but you are able to display tons of data.

In 1973, Herman Chernoff introduced a visualization technique to illustrate trends in multidimensional data. His Chernoff Faces were especially effective because they related the data to facial features, something which we are used to differentiating between. Different data dimensions were mapped to different facial features.

Chernoff faces

Display multivariate data in the shape of a human face. The individual parts, such as eyes, ears, mouth and nose represent values of the variables by their shape, size, placement and orientation. The idea behind using faces is that humans easily recognize faces and notice small changes without difficulty. Chernoff faces handle each variable differently. Because the features of the faces vary in perceived importance, the way in which variables are mapped to the features should be carefully chosen.

They use facial features to represent trends in the values of the data, not the specific values themselves. While this is clearly a limitation, knowledge of the trends in the data could help to determine which sections of the data were of particular interest.
Here, the faces are described by a few facial characteristic parameters:

Eye Size
Pupil Size
Eye Slant
Darkness of Hair/face
Size of Mouth
Position of Eyebrows

http://kspark.kaist.ac.kr/Human%20Engineering.files/Chernoff/Chernoff%20Faces.htm

Here is a Cheroff Face map showing death penalty execution.

http://aaronrb204.blogspot.com/

Here is another website that I thought was pretty cool because it shows the Chernoff Faces in 3D:

http://filer.case.edu/~dbh10/eecs466/CFaces.png


Another example of multivariate maps that people display data for is climate maps. Here I found a map that is showing how certain parts of the United States effects the vegetation growth. They are using multiple colors to display their information.

http://images.google.com/imgres?imgurl=http://gis.esri.com/library/userconf/proc98/proceed/TO350/PAP333/P33329.GIF&imgrefurl=http://gis.esri.com/library/userconf/proc98/proceed/TO350/PAP333/P333.HTM&h=511&w=764&sz=168&hl=en&start=2&um=1&tbnid=6hmkzGq6kYm05M:&tbnh=95&tbnw=142&prev=/images%3Fq%3D%2Bmultivariate%2Bmaps%26um%3D1%26hl%3Den%26sa%3DG

This is the way that the national map of multivariate vegetation patterns appears under the new RGB color scheme. The individual clusters essentially merge with neighbors, and the map changes into a spectrum of color gradients which reflect the dominant suites of variables affecting vegetation growth in each region of the country. The red Southwest is dominated by physiographic factors like slopes and elevation. The blue Northeast is dominated by thermal factors. The green Southeast has rather poor soils, on the whole. The upper Midwest is very light blue because of the cold continental winter. The Pacific Northwest and the Central California valley are light green - fairly favorable conditions for plants.

WORLD MAP OF KOPPEN-GEIGER CLIMATE CLASSIFICATION

Here is a multivariate map that is showing the main climates, the precipitation, and the temperature. This is my favorite map because I feel this is a very clear map that is able to be read with ease:

http://images.google.com/imgres?imgurl=http://squ1.org/files/wiki/climate/koeppen-map-large.gif&imgrefurl=http://squ1.org/wiki/Climate_Classifications&h=790&w=1200&sz=134&hl=en&start=1&tbnid=94J1fNAfDFBfsM:&tbnh=99&tbnw=150&prev=/images%3Fq%3Dhttp://koeppen-geiger.vu-wien.ac.at/%26hl%3Den

As you can see from these maps, multivariate maps give map makers the ability to display a lot of information on a map. You are able to take a variety of objects and place them all on the same map. Yes, they are a little harder to interpret if you don’t know what your looking for but it gives a detailed representation of data at a cheaper price.