Auto-Carto 2005 Research Symposium
Las Vegas, Nevada
DOI of Published Version
Multivariate choropleth maps are often used to compare patterns of different spatial variables. This approach can be implemented by simultaneously drawing a series of choropleth maps, with each representing a particular variable. In this paper, we develop an evolutionary algorithm that can be used to generate a set of classifications that allow a user to explore the spatial patterns of multiple choropleth maps in terms of their visual correlation. Synthetic and census data are used to demonstrate the effectiveness of our approach. We also discuss the role of our approach in an interactive mapping environment and its implication for spatial data mining.
multivariate choropleth map, optimization, spatial visualization
Copyright © 2005 Ningchuan Xiao and Marc P. Armstrong