REDCAP: Regionalization with Constrained Clustering and Partitioning
Regionalization is to divide a large set of spatial objects into a number of spatially contiguous regions while optimizing an objective function, which is normally a homogeneity (or heterogeneity) measure of the derived regions. This software package includes a family of four hierarchical regionalization methods, which are based on three agglomerative clustering approaches, including the single linkage, average linkage (ALK), complete linkage (CLK), and the WARD's method. The Ward's, Full-Order-ALK, and the Full-Orde-CLK methods all can produce significantly better results than existing approaches. For a specific application context, it is recommended that these three methods are applied and compared.
- (2011). "Automatic Region Building for Spatial Analysis", Transactions in GIS, 15(s1), pages 29-45. [Journal Link]
- Guo, D. (2008). "Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (REDCAP)".International Journal of Geographical Information Science. 22(7), pp. 801-823.
- (2009). "Greedy optimization for contiguity constrained hierarchical clustering", The 4th International Workshop on Spatial and Spatiotemporal Data Mining, IEEE International Conference on Data Mining, pp. 591-596.
- Guo, D., M. Gahegan, A.M. MacEachren, and B. Zhou. "Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach".Cartography and Geographic Information Science,Vol. 32, No. 2, 2005, pp. 113-132.
- Guo, D., J. Chen, A. M. MacEachren, and K. Liao (2006), "A Visualization System for Spatio-Temporal and Multivariate Patterns (VIS-STAMP)", IEEE Transactions on Visualization and Computer Graphics, 12(6), pp. 1461-1474.
For the software, please contact email@example.com. Java is needed to run the following software. You can verify if Java is already installed on your computer at this link: http://java.com/en/download/installed.jsp.