Qualitative spatial representation and reasoning for data integration of ocean observing systems

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dc.contributor.author Li, Longzhuang
dc.contributor.author Guardiola, Jose
dc.contributor.author Liu, Yonghuai
dc.date.accessioned 2011-11-08T16:15:37Z
dc.date.available 2011-11-08T16:15:37Z
dc.date.issued 2011-11
dc.identifier.citation Li , L , Guardiola , J & Liu , Y 2011 , ' Qualitative spatial representation and reasoning for data integration of ocean observing systems ' Computers, Environment and Urban Systems , vol 35 , no. 6 , pp. 474-484 . en
dc.identifier.issn 0198-9715
dc.identifier.other PURE: 173293
dc.identifier.other dspace: 2160/7691
dc.identifier.uri http://hdl.handle.net/2160/7691
dc.description.abstract Spatial features are important properties with respect to data integration in many areas such as ocean observational information and environmental decision making. In order to address the needs of these applications, we have to represent and reason about the spatial relevance of various data sources to facilitate retrieval and integration of data. In this paper, using the in situ ocean observing stations in the Gulf of Mexico as an example we develop a statistical method, the semi-circular method, based on the semi-circular normal distribution to uniquely reason directional relations between indirectly connected points in addition to adopt the state-of-the-art qualitative spatial representation and reasoning techniques to represent partonomic, distance, and topological relations. In the experiment, the performance of the semi-circular method is compared with three existing methods, and the experimental results show that the statistic-based semi-circular method obtains the overall adjusted correct ratio of 88.1% by combining qualitative distance and directional relations, which achieves the comparable accuracy as and is slightly better than the probabilistic-based heuristic method. en
dc.format.extent 11 en
dc.language.iso eng
dc.relation.ispartof Computers, Environment and Urban Systems en
dc.title Qualitative spatial representation and reasoning for data integration of ocean observing systems en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.compenvurbsys.2011.04.002
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en
dc.description.status Peer reviewed en


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