1. Spatial Data Mining
Definition: Spatial data mining is the process of discovering interesting patterns from large spatial datasets; it organizes by location what is interesting.
Challenges:
Autocorrelation
Space is continuous
Complex spatial data types
Regional knowledge
Large dataset sizes and many possible patterns
Importance of maps as summaries …
One other area of focus is spatial data mining. Spatial data mining centers on finding interesting patterns in spatial datasets. Spatial data have several unique characteristics, such as the autocorrelation, the continuous nature of space, complex spatial data types and the importance of regional knowledge. Spatial data mining techniques have to address these challenges.
Contributor: Christoph F. Eick

2. In the last 4 years, our research group developed spatial data mining methodologies, algorithms and tools. One of our main contributions is a region discovery framework. The framework provides search engine type capabilities to scientists to “find interesting places in spatial datasets”. A second contribution is the development of a family spatial clustering algorithms with plug-in fitness functions. Plug-in fitness functions enable scientists to describe the characteristics of clusters they are interested in. A third contribution are co-location and correlation mining frameworks. The figure on the upper left depicts a data mining result concerning co-location patterns between deep and shallow ice on Mars. The area in red indicate regions on Mars in which deep and shallow ice are co-located, and the areas in blue indicate regions where deep and shallow ice are anti-co-located. Finally, more recently, we started some new research centering on change analysis in spatial datasets.

3. UH’s niche in this area
We develop spatial data mining methodologies, algorithms and tools that address the following problems:
Regional pattern discovery—finding interesting places and their associated patterns in spatial datasets
Spatial clustering algorithms with plug-in fitness functions
Co-location and correlation mining
Change analysis in spatial datasets
Hotspot discovery
In summary, our research group develops tools that “organize by location what is interesting” based on a given interestingness perspective. Namely, we provide tools for spatial clustering, co-location and correlation mining, hotspot discovery, and change analysis in spatial data. Moreover, we develop methodologies and tools that extract regional knowledge from spatial datasets.
Contributor: Christoph F. Eick