Spatial Knowledge for Disaster Identification

Recent developments in information technology have enabled collection and processing of vast amounts of personal data, business data and spatial data. It has been widely recognized that spatial data analysis capabilities have not kept up with the need for analyzing the increasingly large volumes of geographic data of various themes that are currently being collected and archived. On one hand, such a wealth of data holds great opportunities for geographers, environmental scientists, public health researchers, and others to address urgent and sophisticated geographic problems, e.g., global change, epidemics etc,. Our study is carried out on the way to provide the mission-goal strategy (requirements) to predict the disaster. The co-location rules of spatial data mining are proved to be appropriate to design nuggets for disaster identification and a framework has been suggested. Principal Component Analysis is a statistical method for identifying patterns.