Using the k-Means Clustering Algorithm to Classify Features for Choropleth Maps

Authors

Mark Polczynski, Marquette UniversityFollow
Michael Polczynski, Georgetown UniversityFollow

Document Type

Article

Language

eng

Format of Original

7 p.

Publication Date

Spring 2014

Publisher

University of Toronto Press

Source Publication

Cartographica

Source ISSN

0317-7173

Original Item ID

DOI: 10.3138/carto.49.1.1517

Abstract

Common methods for classifying choropleth map features typically form classes based on a single feature attribute. This technical note reviews the use of the k-means clustering algorithm to perform feature classification using multiple feature attributes. The k-means clustering algorithm is described and compared to other common classification methods, and two examples of choropleth maps prepared using k-means clustering are provided.

Comments

Accepted version. Cartographica, Vol. 49, No. 1 (Spring 2014): 69-75. DOI. © 2014 University of Toronto Press. Used with permission.

Recommended Citation

Polczynski, Mark and Polczynski, Michael, "Using the k-Means Clustering Algorithm to Classify Features for Choropleth Maps" (2014). Electrical and Computer Engineering Faculty Research and Publications. 98.
https://epublications.marquette.edu/electric_fac/98