Spatial Decision Tree-Application to Traffic Risk Analysis
نویسندگان
چکیده
Spatial data mining fulfills real needs of many geomatic applications. It allows taking advantage of the growing availability of geographically referenced data and their potential richness. This includes the risk analysis linked to a territory such as epidemic risk or traffic accident risk in the road network. This work deals with the method of decision tree for spatial data classification. This method differs from conventional decision trees by taking account spatial relationships in addition to other object attributes. Our approach consists in materializing those spatial relationships (originally implicit) leading to treat them as normal attributes. Then, any conventional method or tool allowing building decision tree could be applied providing naturally a spatial decision tree. Compared to existent approaches, this one is more flexible because no specific algorithm is imposed. Moreover, it considers the organization in several thematic layers that is characteristic of geographical data by distinguishing the intra theme and the inter theme relations (such as the road section contiguity or the proximity between road sections and schools). This method has been tested in the framework of traffic risk analysis.
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