A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Clustering Ontology-Based Metadata in the Semantic Web
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Ontological Engineering
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
NET-DBSCAN: clustering the nodes of a dynamic linear network
International Journal of Geographical Information Science
A kernel density estimation method for networks, its computational method and a GIS-based tool
International Journal of Geographical Information Science
Traffic Accidents Knowledge Management Based on Ontology
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 07
An ontology-based framework for geospatial clustering
International Journal of Geographical Information Science
Towards an ontology-based spatial clustering framework
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Network density estimation: analysis of point patterns over a network
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
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Road traffic accidents are a social and public challenge. Various spatial concentration detection methods have been proposed to discover the concentration patterns of traffic accidents. However, current methods treat each traffic accident location as a point without consideration of the severity level, and the final traffic accident risk map for the whole study area ignores the users' requirements. In this paper, we propose an ontology-based traffic accident risk mapping framework. In the framework, the ontology represents the domain knowledge related to the traffic accidents and supports the data retrieval based on users' requirements. A new spatial clustering method that takes into account the numbers and severity levels of accidents is proposed for risk mapping. To demonstrate the framework, a system prototype has been implemented. A case study in the city of Calgary is also discussed.