9 Spatial Analysis and Mining

This chapter describes the Oracle Spatial features that enable the use of spatial data in data mining applications.

Note:

To use the features described in this chapter, you must understand the main concepts and techniques explained in the documentation for Oracle Data Mining, a component of the Oracle Advanced Analytics Option.

For reference information about spatial analysis and mining functions and procedures in the SDO_SAM package, see SDO_SAM Package (Spatial Analysis and Mining).

Note:

SDO_SAM subprograms are supported for two-dimensional geometries only. They are not supported for three-dimensional geometries.

• Spatial Information and Data Mining Applications
  Oracle Data Mining allows automatic discovery of knowledge from a database. Its techniques include discovering hidden associations between different data attributes, classification of data based on some samples, and clustering to identify intrinsic patterns. Spatial data can be materialized for inclusion in data mining applications.
• Spatial Binning for Detection of Regional Patterns
  Spatial binning (spatial discretization) discretizes the location values into a small number of groups associated with geographical areas.
• Materializing Spatial Correlation
  Spatial correlation (or, neighborhood influence) refers to the phenomenon of the location of a specific object in an area affecting some nonspatial attribute of the object. For example, the value (nonspatial attribute) of a house at a given address (geocoded to give a spatial attribute) is largely determined by the value of other houses in the neighborhood.
• Colocation Mining
  Colocation is the presence of two or more spatial objects at the same location or at significantly close distances from each other. Colocation patterns can indicate interesting associations among spatial data objects with respect to their nonspatial attributes.
• Spatial Clustering
  Spatial clustering returns cluster geometries for a layer of data. An example of spatial clustering is the clustering of crime location data.
• Location Prospecting
  Location prospecting can be performed by using thematic layers to compute aggregates for a layer, and choosing the locations that have the maximum values for computed aggregates.