I would like to plot the following per zone (30 zones total) data on a printable/non interactive map:
How would you display the above 6 layers effectively on one map?
I would say you can't include all that data on one map and have it make any sense. I'd recommend you think along the lines of Tufte's principle of small multiples, having multiple smaller maps of the same area, each using a different variable. Example: http://www.juiceanalytics.com/writing/better-know-visualization-small-multiples/
Even then, you have the problem that you're using a bunch of different units, so you need a bunch of keys. Another way to view the data (but not in a map) would be to use a table with all the values, colored (ie - different colors for below average, average, above average)
Would also recommend you look at the census atlas for more map ideas: http://www.census.gov/population/www/cen2000/censusatlas/
Might help to reflect more on what message you're trying to communicate, exactly (not just what data you have).
It is not possible to show efficiently so much data on a single map. Two possibilities:
Produce 6 maps,
Analyse your data to classify your regions, and display the result of the classification. A principal component analysis can help to determine the most important correlations within your variable. This method has been used to produce this synthetic map:
from these ones:
I agree that small multiples are probably a good way to approach this problem. To supplement the map I would also suggest a scatterplot matrix of your variables, which would identify bivariate correlations. While you lose the geographic aspect of your data, it is much simpler to visualize the relationships between variables in a scatterplot than it is to compare two maps (even side by side).
If you still want some sort of spatial trends captured, you could include spatial statistics (such as local Moran's I) between the distributions and/or the original variables.
Edit: I've come across recently some work revisiting the Moral statistics published by Andre-Michel Guerry (originally in 1883) that has the goal of visualizing multi-variate relationships in space. The implementations of those authors are very similar to what has been suggested in this thread, small multiples, principal components analysis, scatter plot matrices, and within polygon diagrams. Attached are some pictures from A.-M. Guerry’s Moral Statistics of France : Challenges for Multivariable Spatial Analysis by: Michael Friendly Statistical Science, Vol. 22, No. 3. (August 2007), pp. 368-399 (The PDF is free). Also another article (Dray and Jombart, 2010) analyzes the same data and has some source code in R to make said plots.
One picture is a scatterplot matrix, the other is what is called a star diagram (which is just a different way to represent bar charts like Pablo suggested).
Here is a wonderful example of small multiples posted on Andrew Gelman's (and company blog) Statistical Modeling, Causal Inference, and Social Science. The map is of voter support for school vouchers by state, conditional on income and various race and religion categories. White non-evangelicals really don't like school vouchers! (If you go to the actual blog though it appears in the 2004 survey data their is more support for school vouchers among that group).
To choose between solutions presented here, you could provide two key informations :
Solutions cited here may have different efficiency according to purpose and public.
I'd like to generalize the answer of Julien (one synthetic map by way of a PCA) by citing the technique of the matrix diagonalization, described by J. Bertin. Its usefull when one seek after a synthese of all the information, rather than a complete data presentation.
In brief, it consists in representing each variable with an histogram, sort an stack the histograms in such a way that the values (the map zones) are aligned in a diagonal fashion, to obtain a typology :
That's a lot of information and it's a fact that a single map combining all of them in a thematic way would result in a useless presentation because of visual pollution. In other hand there are 30 zones, so, many maps for each zone would result in pollution too.
My solution: Choose which one is the most important information, let's say 'household income', then zone the map in some categories of income. And finally for each income spot, plot a bar chat with the other 5 attribute.
With that map can make some comparisons like, for example: "High income areas always shows large number of workers and an average age of more than 21 years".
Look at the example...
Perhaps some of these ideas could help?
Assuming you have six dimensions:
1: Choropleth: Household income example 0
2, 3, and 4: Symbols: Representing number of people as dots, which can allow you to see the background: example 1, example 2 using grayscale for workers/non-workers and a different color scheme to show age
5: 3D: Using population density as terrain example 3
6: (I can't think of a 6th way!)
Is it redundant to show 'Number of households', 'Population density' and 'Number of people'?
I would be skeptical if a map with this complexity would be clear to anyone aside from you. If I was presenting it I would show each element separately first, and then add it on so the audience can understand the steps.
One alternative way (if you don't have room for a radar graph for each zone, could be to create a 'glyph' representing this information example 4, fig 10.28. I think these are usually hard to understand, and not easy to design clearly, but the linked example could be used in this case.
Another thought I had, would be to extrude the polygons to the same height for each polygon, and then use a section of the height to represent these parameters. Similar to making a bar chart for each area, but where each section is layered on top at similar intervals. This would need to be viewed from 3D which would mean some of it would be obscured.
It is a challenging task. My answer is to go with a multivariate map. Check this map out. The map will look busy if you show all the variables on a one map. Make sure you select appropriate color scheme if you choose to go with a multivariate map.
One degree of simplification would be to express one item, such as population density, through a cartogram, i.e. distort the area of each unit so that it is in proportion to population:
(source: amherst.edu)
The main drawback is that the viewer must be able to recognize the distortion of the zones from their "usual" shapes.
More info here: http://gis.amherstma.gov/data/SpringNearc2009/Session4Cartograms.pdf
