Search

Highlights • Propose a framework to find stop episodes from spatio-temporal trajectories. • Incorporate relevant real places from OpenStreetMap for place matching. • Propose a place matching algorithm that overcomes the place ambiguity issue. • Provide experimental results supporting the effectiveness of proposed method. • Provide a case study demonstrating the validity of proposed framework. Abstract This paper introduces a place-matching pattern mining approach that detects place-matching patterns from raw spatio-temporal GPS trajectories using real-world places from OpenStreetMap. The approach begins by annotating raw trajectory recordings as either stopping or moving. It then groups contiguously stopping entries into so-called stop episodes; each of which is then associated with a number of potential stop place candidates from the real-world place repository OpenStreetMap. As each stop episode may have multiple place candidates, the proposed approach uses a Hidden Markov Model to probabilistically match each sequence of stop episodes to its most likely sequence of visited real-world places. The result of this stop episode formulation and place-matching is that the original trajectories are transformed into a discrete, greatly simplified, and more semantically meaningful sequence of place visitations. This format enables the last step of our approach where frequent itemsets and sequential patterns are extracted using traditional approaches. Experimental results with real and synthetic datasets demonstrate our approach's running time performance, robustness to GPS noise, dataset compression, and matching accuracy. Additionally, a case study using human trajectories from the real-world Geolife dataset reveals many interesting and seemingly real patterns. These findings suggest the general validity and applicability of our approach as a place-matching trajectory data mining approach. [ABSTRACT FROM AUTHOR]

We present an extensible, generic, spatio-temporal trajectory simplification framework that modularises trajectory simplification into the stages of normalising, ranking, and reduction. We combine a range of ranking strategies and scoring heuristics – some from the literature and some new – into our framework modules and create a variety of spatio-temporal trajectory simplification methods. These trajectory simplification methods are experimented upon using real world and synthetic datasets, measuring running time, geometric displacement, and region-of-interest visitation. The results indicate that our proposed framework creates a number of efficient and effective spatio-temporal trajectory simplification methods. [ABSTRACT FROM PUBLISHER]

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]