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

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.

► Propose a framework for qualitative spatial representation and reasoning.
► Develop a statistical method to uniquely reason directional relations between points.
► Compare the accuracy of four direction reasoning methods.
► Construct the composition tables for all four direction reasoning methods.