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Abstract: Flickr presents an abundance of geotagged photos for data mining. Particularly, we propose the concept of extracting spatio-temporal meta data from Flickr photos, combining a collection of such photos together results in a spatio-temporal entity movement trail, a trajectory describing an individual's movements. Using these spatio-temporal Flickr photographer trajectories we aim to extract valuable tourist information about where people are going, what time they are going there, and where they are likely to go next. In order to achieve this goal we present our novel spatio-temporal trajectory regions-of-interest mining and sequential pattern mining framework. It is different from previous work since it forms regions-of-interest taking into consideration both space and time simultaneously, and thus produces higher-quality sequential patterns. We test our framework's ability to uncover interesting patterns for the tourism sciences industry by performing experiments using a large dataset of Queensland photo taker movements for the year 2012. Experimental results validate the usefulness of our approach at finding new, information rich spatio-temporal tourist patterns from this dataset, especially in comparison with the 2D approaches shown in the literature. [Copyright &y& Elsevier]

We focus on the problem of using contiguous SPM to extract succinct, redundancy controlled patterns from large vehicle trajectories. Although there exist several techniques to reduce the contiguous sequential pattern output such as closed and max SPM, they still produce massive redundant pattern outputs when the input sequence database is sufficiently large and homogeneous — as is often the case for vehicle trajectories. Therefore, in this work we propose DC-SPAN: a distinct contiguous SPM algorithm. DC-SPAN mines a set of sequential patterns where the maximum redundancy of the pattern output is controlled by a user-specified parameter. Through various experiments using real world trajectory datasets we show DC-SPAN effectively controls the redundancy of the pattern output with trade-offs in pattern distinctness. Additionally, our experiments also indicate that DC-SPAN efficiently computes these patterns, incurring only a marginal running time cost over existing state-of-the-art contiguous SPM approaches. Lastly, due to the less redundant and more succinct pattern output we also briefly explore visualisation as a useful technique to interpret the discovered vehicle routes. [ABSTRACT FROM AUTHOR]

Highlights: [•] Propose an arbitrary shape detection regions-of-interest mining algorithm. [•] Propose a sequential trajectory pattern mining framework for Flickr geo-tagged photos. [•] First time analyzing geo-tagged photos using sequential trajectory pattern mining. [•] Experiments supporting the applicability, and effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]