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Kernel estimation as a basic tool for geomorphological data analysis
Author(s) -
Cox Nicholas J.
Publication year - 2007
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.1518
Subject(s) - kernel density estimation , event (particle physics) , probability density function , kernel (algebra) , geology , flood myth , convolution (computer science) , histogram , raw data , set (abstract data type) , computer science , mathematics , statistics , artificial intelligence , geography , archaeology , image (mathematics) , quantum mechanics , combinatorics , estimator , artificial neural network , programming language , physics
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. Copyright © 2007 John Wiley & Sons, Ltd.