Knowledge discovery and reasoning in geospatial applications

Abstract

In the last decade and following the big advent in remote sensing, terabytes of geographic data have been collected on a daily basis. However, the wealth of geographic data cannot be fully realized when information implicit in data is difficult to discern. This confronts researchers with an urgent need for new methods and tools for automatically transform geographic data into information and, furthermore, synthesize geographic knowledge. New approaches in geographic representation, query processing, spatial analysis, and data visualization [1, 2] are thus necessary. Geo-referenced data sets include geographical objects, locations or administrative sub-divisions of a region. The geographical location and extension of these objects define implicit relationships of spatial neighborhood.Knowledge Discovery (KD) in databases is a process that aims at the discovery of relationships within data sets. Data Mining, the focus of this chapter, is the central step of this process. It corresponds to the application of algorithms for identifying patterns within data. The Data Mining algorithms have to take this spatial neighborhood into account when looking for associations among data. They must evaluate if the geographic component has any influence in the patterns that can be identified or if it is responsible for a pattern. Most of the geographical attributes normally found in organizational databases (e.g., addresses) correspond to a type of spatial information, namely qualitative, which can be described using indirect positioning systems. In systems of spatial referencing using geographic identifiers, a position is referenced with respect to a real world location defined by a real world object. This object represents a location that is identified by a geographic identifier (more details can be found in Chapter Spatial Techniques). These geographic identifiers are very common in organizational databases, and they allow the integration of the spatial component associated with them in the process of knowledge discovery. © 2010 Springer-Verlag Berlin Heidelberg.