Spatial Data Mining
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
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
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.
Bei Problemen oder Vorschlägen wenden Sie sich bitte an: