(Update: The source is available for download at bottom of the entry.)
I must admit I always felt a little uncomfortable generating or referencing histograms. The enormous bias that might be introduced by changing the segment/bin size always nags in the back of my mind. True, thoughtfully constructed histograms with a fairly large sample set can be very illuminating, but the reader must trust the visualization architect completely or rebuild the graph from source data and verify that the parameters were well-chosen.
For those who have the same concerns will I outline a set of visualization techniques that are less susceptible to accidental/intentional bias. These visualizations are simple to implement, very spatially compact, easy to understand, and applicable to not only value distributions within a set but also representing clustering of events in time.
Enough talk, take a look at an example visualization:
Continue on for for an explanation and all the details…