Spatial Data Mining: Clustering of Hot Spots and Pattern Recognition

Spatial data mining is the extraction of implicit knowledge, spatial relations or other patterns not explicitly stored in spatial database. The focus of this paper is placed on the information derivation of spatial data. Geographical coordinates of hot spots in forest fire regions, which are extracted from the satellite images, are studied and used in the detection of likely fire points. Due to the saturation of spectral band, there are false alarms in the derived data set. We use clustering and Hugh transformation to determine regular patterns in the derived hotspots and classify them as false alarms on the assumption that fires usually do not spread in regular patterns such as in a straight line. This project demonstrates the application of spatial data mining to reduce false alarm from the set of hot spots derived from NOAA images.