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.
Grids are commonly used for presenting spatial data. However, they have not been previously used for analyzing GPS trajectories. Instead, slower and more complicated algorithms based on individual point-pair comparison have been used. We demonstrate how a grid representation can be used to compute four different route measures: novelty, noteworthiness, similarity and inclusion. The measures may be used in several applications such as identifying taxi fraud, automatically updating GPS navigation software, optimizing traffic and identifying commuting patterns. We compare our proposed route similarity measure, C-SIM, to 8 popular alternatives including Edit Distance on Real sequence (EDR) and Frechet distance. The proposed measure is simple to implement and we give a fast, linear time algorithm for the task. It works well under noise, changes in sampling rate and point shifting. We demonstrate that using the grid, a route similarity ranking can be computed in real-time on the Mopsi2014 route dataset which consists of over 6,000 routes. This ranking is an extension of the most similar route search and contains an ordered list of all similar routes from the database. The real-time search is due to indexing the cell database and comes at the cost of spending 80% more memory space for the index. The methods are implemented inside the Mopsi (http://cs.uef.fi/mopsi) route module.