Semantic and qualitative spatial reasoning based road network modeling

Abstract

Road Network Modeling is a fundamental issue for urban computing and uses massive primitive geospatial data retrieved from Geographic Information Database. The modeling for road network is extremely complicated because of the scalable and reticular relations between the roads in the city. In this paper, we propose an approach of qualitative spatial relation and semantic web based predication for road network modeling, and define five spatial relation predicates according to the notions in point-set topology for better representing the spatial relation between roads. The roads and junctions in road network are modeled as standardized well-known text literals, and deterministic spatial realtions are calculated by spatial relation reasoning. Then, all road network elements and their relations are stored as RDF triples into LarKC, a platform for scalable semantic data processing and reasoning. In this paper, we show that the triplized road network data stored in semantic web repository is very convenient for spatial information quering and junction type calculation.