GeoDa 1.10 Released with New Local Multivariate Cluster Functionality

Analysts can now download the new version 1.10 of GeoDa, which contains new local cluster functionality that will be extended in future releases, as well as a lot of bug fixes and smaller enhancements.

The major new local cluster features include:

Local uni- and multivariate cluster maps (based on Geary's c; see new paper by Luc Anselin on extension of Geary's c to multivariate spatial association). In the maps below this is applied to the classic data set of "moral statistics" of France (Guerry, 1833) to show significant high and low spatial concentrations of literacy (left map) and significant associations of property crime and literacy (right map).


Some classic non-spatial multivariate cluster techniques, including principal component analysis, k-means, and hierarchical clustering that can be mapped (based on the C Clustering Library). Using the same data as in the example above, the maps below show local clusters of property crime, literacy, and suicide.


See a complete list of new features and bug fixes in GeoDa 1.10: