I have several hundred years of church baptisms that will be searched by people wanting to find the baptisms of their ancestors. I want to call attention to periods in the records in which the number of baptisms is particulary sparse when compared to other periods, because sparse periods may indicate poor record keeping rather than an actual decrease in the number of baptisms. The baptisms were performed fairly often (every few days for busy churches), but not absolutely regularly, so the baptism dates are distributed somewhat randomly, and any number of baptisms could be performed on any given day.
One simple approach is to simply check for gaps of a specified length between baptism dates, but this would miss sparse periods in which baptisms were performed but much less often than usual. Perhaps the least biased approach is to graph the entire data set and let people decide for themselves what periods qualify as sparse, but this method has its own drawbacks associated with presenting the data to users, who would need to interact with the plot in order to scan the huge amount of data in it. I don't want to deal with that unless forced to do so.
What kinds of statistical methods are used in situations like this? Can anyone provide the name of an algorithm that has been designed for more or less this kind of problem, or describe an appropriate means of analysis? I'm willing to write code (Python) if necessary.
