What are some alternatives to a boxplot? I am working on creating a website, which displays the census data for a user selected Polygons & would like to graphically show the distribution of various parameters (one graph per parameter).
The data usually has the following properties:


*

*The sample size tend to be large (say around 10,000 data points)

*The range in values tends to be quire large (for example, the minimum population can be less than 100 & the maximum can be something like 500,000)

*q1 usually is close to the minimum (say 200) while q2 & q3 will be within 10,000

*It doesn't look anything like a normal distribution


I am not a statistician and hence my description might not be exactly clear.
I would like to show this distribution on a graph, which will be seen by citizens (the layman, if you like).
I would have best liked to use a histogram, but it is not possible due to the large range of values, due to which making bins is not really easy & straight forward.
From what little I know about statistics,  a box plot is what is often used to show this kind of data, but I feel that for a layperson, deciphering the Box plot is not easy.
What are my options to show this data in an easy to understand manner?
 A: I'd suggest you persevere with histograms. They're much more widely understood than the alternatives. Use a log scale to cope with the large range of values. Here's an example I cooked up in a couple of minutes in Stata: 
I admit that the x-axis numerical labels weren't entirely straightforward or automatic, but as you're building a website I'm sure your programming skills are up to the challenge!
A: Here is a matlab function for plotting multiple histograms side-by-side in 2D as an alternative to box-plot. See the picture on the top. And here is another one
The density strip is another alternative to box-plot. It is a shaded monochrome strip whose darkness at a point is proportional to the probability density of the quantity at that point. This is an R implementation of the density strip 
A: I rather like violin plots myself, as this gives an idea of the shape of the distribution.  However if the large range of values is the issue, then maybe it would be best to plot the log of the data rather than the raw values, that would then make choosing the box sizes for histograms etc.  As the display is for laymen, don't mention logs and mark the axis 10, 100, 1000, 10000, 100000, 1000000 etc.
A: How about using quantiles? It is not necessary to present a graph then, only a table. For village census I think the users will be most interested how many there are villages of certain size, so giving for example deciles will tell them them information such as $x\%$ of all the villages are smaller than the certain number. For deciles $x=0,10,20,...,100$. You can graph this table with the percents on a x-axis and the deciles on the y-axis.
A: If you are targeting the general population (i.e. a non statistical-savvy audience) you should focus on eye-candy rather than statistical accuracy.
Forget about boxplots, let alone violin plots (I personally find them very difficult to read)! If you'd ask the average street man what a quantile is, you would mostly get some wide eyed silence... 
You should use barplots, bubble charts, maybe some pie charts (brrrr). Forget about error bars (although I would put SD in text somewhere where applicable).
Use colors, shapes, thick lines, 3D. You should make each chart unique and immediately easy to understand, even without having to read all the legends/axes etc.
Make a smart use of maps by coloring them.
Information is beautiful is a very good resource to get ideas. Look at this chart for instance: Caffeine and Calories: anyone can understand it, and it's pleasing to the eye.
And, of course, have a look at Edward Tufte's work.
A: A boxplot isn't that complicated. After all, you just need to compute the three quartiles, and the min and max which define the range; a subtlety arises when we want to draw the whiskers and various methods have been proposed. For instance, in a Tukey boxplot values outside 1.5 times the inter-quartile from the first or third quartile would be considered as outliers and displayed as simple points. See also Methods for Presenting Statistical Information: The Box Plot for a good overview, by Kristin Potter. The R software implements a slightly different rule but the source code is available if you want to study it (see the boxplot() and boxplot.stats() functions). However, it is not very useful when the interest is in identifying outliers from a very skewed distribution (but see, An adjusted boxplot for skewed distributions, by Hubert and Vandervieren, CSDA 2008 52(12)).
As far as online visualization is concerned, I would suggest taking a look at Protovis which is a plugin-free js toolbox for interactive web displays. The examples page has very illustrations of what can be achieved with it, in very few lines.
A: You might also want to have a look at beanplots. 

[Source]
Implemented in R package by Peter Kampstra.  
