Mining Knowledge in Geographical Data

Huge amounts of data have been stored in databases, data warehouses, geographic information systems, and other information repositories , and this data is still growing rapidly 4]. Companies are building data warehouses capable of storing hundreds of terabytes of data related to natural resource exploration; astronomical databases are measured in terabytes in size; and it is expected that the NASA Earth Observing System will transmit about 50 giga-bytes of image data per hour. This huge amount of data has posed great challenges to traditional data analysis methods for information and knowledge extraction. Data mining, or knowledge discovery in databases (KDD), has been emerging as a new research eld and a new technology for discovery of interesting, implicit, and previously unknown knowledge from large databases 4]. Data mining represents the connuence of several research elds, including machine learning, database systems , data visualization, statistics, and information theory. Besides many studies of knowledge discovery in relational and transaction databases, spatial data mining, which refers to the extraction of implicit knowledge, spatial relationships , or other patterns not explicitly stored in spatial databases, has attracted attention in recent research 2, 6, 7, 8, 10, 12]. Spatial data has many features distinguishing it from relational databases. It carries topologi-cal and/or distance information, usually organized by sophisticated, multidimensional spatial indexing structures, accessed by spatial data access methods, and often requiring spatial reasoning, geometric computation, and spatial knowledge representation techniques. Thus, spatial data mining demands an integration of data mining with spatial database technologies. A crucial challenge to spatial data mining is the exploration of eecient spatial data mining techniques due to the huge amount of spatial data and the complexity of spatial data types and spatial access methods. Spatial data mining can be used for browsing spatial databases, understanding spatial data, discovering spatial relationships and relationships between spatial and nonspatial data, reorganizing spatial databases, constructing spatial knowledge-bases, optimizing spatial queries, etc. It is expected to have wide applications in geographic information systems, remote sensing , image database exploration, medical imaging , navigation, and many other areas where spatial data is used. In this article, a short overview is provided to summarize recent studies on spatial data mining , including spatial data mining techniques, their strengths and weaknesses, how and when to apply them, and what are the challenges yet to be faced. Statistics was used as the most common approach for analyzing spatial data. Statistical …