Spatial Data Mining Software
Flow Mapping with Graph Partitioning and Regionalization

Flow Mapping with Graph Partitioning and Regionalization is an integrated software tool to explore flow patterns in large spatial interaction data. It involves two packages: (1) GraphRECAP, which uses spatially constrained graph partitioning to find a hierarchy of natural regions defined by spatial interactions; and (2) FlowMap, which visualize flows based on the discovered regions and related attributes.
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.
SOMVIS: Multivariate Mapping and Visualization

SomVis is an integrated software tool that is able to: (1) perform multivariate analysis, dimensional reduction, and data reduction (summarizing a large number of input data items in a moderate number of clusters) with the Self-Organizing Map (SOM); (2) encode theSOM result with a systematically designed color scheme; (3) visualize the multivariate patterns with a modified Parallel Coordinate Plot (PCP) display and a geographic map (GeoMap); and (4) support human interactions to explore and examine patterns.
VIS-STAMP: A Visualization System for Space-Time and Multivariate Patterns

VIS-STAMP is a geo-visual analytic software package that couples computational, visual, and cartographic methods for exploring and understanding spatio-temporal and multivariate data. It can help analysts investigate complex patterns across multivariate, spatial, and temporal dimensions via clustering, sorting, and visualization. Specifically, VIS-STAMP builds on a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices , and a geographic small multiple display.
POLO: Program for Optimal Linear Ordering

Linear ordering is to transform a set of data objects to a one dimensional order, which preserves multivariate or graph patterns as much possible. Linear ordering is widely used to discover patterns. When applied in geographical analysis, a linear ordering can be used to facilitate the detection of space-time (Guo et al. 2006), space-multivariate, and spatial interaction patterns (Guo 2007).