Spatial Data Mining

Spatial Data Mining

Objective

The main difference between data mining in relational DBS and in spatial DBS is that attributes of the neighbors of some object of interest may have an influence on the object and therefore have to be considered as well. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, new techniques are required for effective and efficient data mining.

Techniques

Database Primitives for Spatial Data Mining
We have developed a set of database primitives for mining in spatial databases which are sufficient to express most of the algorithms for spatial data mining and which can be efficiently supported by a DBMS. We believe that the use of these database primitives will enable the integration of spatial data mining with existing DBMS’s and will speed-up the development of new spatial data mining algorithms. The database primitives are based on the concepts of neighborhood graphs and neighborhood paths.

Efficient DBMS Support
Effective filters allow to restrict the search to such neighborhood paths “leading away” from a starting object. Neighborhood indices materialize certain neighborhood graphs to support efficient processing of the database primitives by a DBMS. The database primitives have been implemented on top of the DBMS Illustra and are being ported to Informix Universal Server.

Algorithms for Spatial Data Mining
New algorithms for spatial characterization and spatial trend analysis were developed. For spatial characterization it is important that class membership of a database object is not only determined by its non-spatial attributes but also by the attributes of objects in its neighborhood. In spatial trend analysis, patterns of change of some non-spatial attributes in the neighborhood of a database object are determined.

Applications

Spatial Trend Detection in GIS
Spatial trends describe a regular change of non-spatial attributes when moving away from certain start objects. Global and local trends can be distinguished. To detect and explain such spatial trends, e.g. with respect to the economic power, is an important issue in economic geography.

Spatial Characterization of Interesting Regions
Another important task of economic geography is to characterize certain target regions such as areas with a high percentage of retirees. Spatial characterization does not only consider the attributes of the target regions but also neighboring regions and their properties.

Publications

List of papers

Team

Homepages: DBS Institut LMU

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	Database Primitives for Spatial Data Mining We have developed a set of database primitives for mining in spatial databases which are sufficient to express most of the algorithms for spatial data mining and which can be efficiently supported by a DBMS. We believe that the use of these database primitives will enable the integration of spatial data mining with existing DBMS’s and will speed-up the development of new spatial data mining algorithms. The database primitives are based on the concepts of neighborhood graphs and neighborhood paths.
	Efficient DBMS Support Effective filters allow to restrict the search to such neighborhood paths “leading away” from a starting object. Neighborhood indices materialize certain neighborhood graphs to support efficient processing of the database primitives by a DBMS. The database primitives have been implemented on top of the DBMS Illustra and are being ported to Informix Universal Server.
	Algorithms for Spatial Data Mining New algorithms for spatial characterization and spatial trend analysis were developed. For spatial characterization it is important that class membership of a database object is not only determined by its non-spatial attributes but also by the attributes of objects in its neighborhood. In spatial trend analysis, patterns of change of some non-spatial attributes in the neighborhood of a database object are determined.

	Spatial Trend Detection in GIS Spatial trends describe a regular change of non-spatial attributes when moving away from certain start objects. Global and local trends can be distinguished. To detect and explain such spatial trends, e.g. with respect to the economic power, is an important issue in economic geography.
	Spatial Characterization of Interesting Regions Another important task of economic geography is to characterize certain target regions such as areas with a high percentage of retirees. Spatial characterization does not only consider the attributes of the target regions but also neighboring regions and their properties.