Jenks natural breaks optimization

The Jenks optimization method, also called the Jenks natural breaks classification method, is a data clustering method designed to determine the best arrangement of values into different classes. This is done by seeking to minimize each class’s average deviation from the class mean, while maximizing each class’s deviation from the means of the other groups. In other words, the method seeks to reduce the variance within classes and maximize the variance between classes.

The Jenks optimization method is directly related to Otsu's Method and Fisher's Discriminant Analysis.

George Frederick Jenks was a 20th Century American cartographer. Graduating with his Ph.D. in agricultural geography from Syracuse University in 1947, Jenks began his career under the tutelage of Richard Harrison, cartographer for TIME and Fortune magazine. He joined the faculty of the University of Kansas in 1949 and began to build the cartography program. During his 37-year tenure at KU, Jenks developed the Cartography program into one of three programs renowned for their graduate education in the field; the others being the University of Wisconsin and the University of Washington. Much of his time was spent developing and promoting improved cartographic training techniques and programs. He also spent significant time investigating three-dimensional maps, eye-movement research, thematic map communication, and geostatistics.

Jenks was a cartographer by profession. His work with statistics grew out of a desire to make choropleth maps more visually accurate for the viewer. In his paper, The Data Model Concept in Statistical Mapping, he claims that by visualizing data in a three dimensional model cartographers could devise a “systematic and rational method for preparing choroplethic maps”. Jenks used the analogy of a “blanket of error” to describe the need to use elements other than the mean to generalize data. The three dimensional models were created to help Jenks visualize the difference between data classes. His aim was to generalize the data using as few planes as possible and maintain a constant “blanket of error”.

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