Efficient visibility determination in urban scenes considering terrain information
Spatiotemporal co-occurrences are the appearances of spatial and temporal overlap relationships among trajectory-based spatiotemporal instances with region-based geometric representations. Assessing the significance of spatiotemporal co-occurrences plays an important role in the spatiotemporal frequent pattern mining applications of moving region objects. Currently, a spatiotemporal version of the popular Jaccard measure is used for measuring the strength of spatiotemporal co-occurrences. We will demonstrate the shortcomings of the Jaccard (J) measure when it is used for assessing the significance of co-occurrences among spatiotemporal instances with highly different spatiotemporal evolution characteristics. We will present two extended novel measures (J+ and J*) that address the problems linked to the J measure. Our work includes algorithms for the significance measure calculations, the proofs and explanations about the key properties of measures, and a detailed experimental evaluation section. Our experiments include in-depth relevancy and running time analyses demonstrating the suitability of our proposed measures for spatiotemporal frequent pattern mining algorithms.