Why aren't bivariate histogram matrices as common as scatter plot matrices Bivariate histogram matrices give me intuition which categorical variables are related to one another. For example:

I can easily see which variables are related to one another. Indeed, this graphical device isn't brought up as much as scatter plot matrices. Why not? One point of scatter plot matrices is to infer linear correlations between variables in our dataset.
Scatter plot matrices are only useful for continuous variables. So - bivariate histogram matrices seem to be the best bet for categorical variables. 
Are there any disadvantages to this approach? Or do you folks use a different technique for visualizing associations between categorical variables?
 A: The reasons that come to my mind:


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*Such plots are unreadable for color-blind people.

*There are multiple different color palettes used for plotting -- it is not always instantly obvious what colors indicate "high" and what colors indicate "low" values. Moreover, my experience suggests it is very subjective what palettes are "intuitive" for different people.

*If you plot multiple such plots on a single page they easily become totally unreadable, while with scatter plots at least some amount of readability is preserved (example below).





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*It is much more complicated to choose appropriate color palette and appropriate scaling for numbers-to-colors mapping (so not to end up with unreadable blob) -- often it needs multiple tries until finding the right scaling. 

*Choosing sizes of the bins is even more crucial than in simple histograms, because you can easily end up with colorful "white noise" for small bins, or just a few big rectangles that resemble rather abstract art, than say anything about your data.

*If you want to publish it, often you need to provide the plots in black-and-white, or grayscale, so such plots can be risky. If you didn't know about such editorial policy, you could easily end up with an almost-uniformly gray rectangle instead of your beautiful, colorful plot.

*Plot that looks good on your screen, does not have to look good on another monitor, with different settings.

*There is limited number of colors that we can distinguish, so only a limited variability of data can be appropriately visualized. This is nicely illustrated on the picture from medium.com site:



