Map style, label, and visibility rules, especially those aimed at differentiating "important" classes of features from "minor" ones, can be derived from statistical functions performed on feature attributes. If the source data classification scheme is not already strong in prioritizing features how we want to view them, then style patterns may emerge from calculations over an assortment of counts, sums, averages, and other measurements.
We will begin with a quick examination of popular open source web and desktop mapping engines -- do their configuration capabilities include formal constructs for deriving rules from statistics? Or must the developer arrive at "this looks right" through trial and error?
We'll extend the discussion to specific data distribution patterns that can be exploited for styling. We're accustomed to setting line styles, symbol and font sizes, colors, and visibility at different scales. The bell curve resulting from a query may point us to where we make the scale breaks, or toward how much color or size contrast to employ in order to make the best presentation from the particular data we are displaying. Perhaps we can arrange our queries, thereby grouping our features a certain way, to aim for an "ideal" curve that is already known to produce pleasing results.
A simple set of query tools for streamlining style assists from statistics will be used to create a few examples from troublesome data.