Kernel estimation, based on the convolution of a probability density function with a set of magnitudes or event dates, provides tuneable smooth pictures of probability density functions and event intensity functions. Such pictures are in several respects superior to those provided by histograms, box plots, cumulative distributions or raw plots. They permit examination of broad features and fine structure, are readily produced with modest computational effort and are essentially free of artefacts arising from binning. Examples are given using data on cirque lengths, limestone pavements, glacier areas and dated flood deposits. The technique deserves widespread use in geomorphology and allied sciences.
Cox, N. (2007). Kernel estimation as a basic tool for geomorphological data analysis. Earth Surface Processes and Landforms, 32(12), 1902-1912. https://doi.org/10.1002/esp.1518