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Constrained free space diagrams: a tool for trajectory analysis
- Int. J. of Geogr. Inform. Sci
"... Abstract. We propose a new and powerful tool for the analysis of trajectories, which in particular allows for more temporally aware analyses. Time plays an important role in the analysis of moving object data. For many applications it is neither sufficient to only compare objects at exactly the same ..."
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Cited by 14 (7 self)
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Abstract. We propose a new and powerful tool for the analysis of trajectories, which in particular allows for more temporally aware analyses. Time plays an important role in the analysis of moving object data. For many applications it is neither sufficient to only compare objects at exactly the same times, nor to consider only the geometry of the trajectories. We show how to leverage between these two approaches by extending a tool from curve analysis, the free space diagram. Our approach also allows to take further attributes of the objects like speed or direction into account. We demonstrate the usefulness of the new tool by applying it to the problem of detecting single file movement. A single file is a set of moving entities, which are following each other, one behind the other. This is the first algorithm for detecting this movement pattern. For this application, we analyse and demonstrate the performance of our tool both theoretically and experimentally. 1
Computing similarity between a pair of trajectories
, 2013
"... With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of identifying common patterns between pairs or among groups of tr ..."
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Cited by 3 (1 self)
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With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of identifying common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of identifying similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity — both local and global — between a pair of trajectories to distinguish between similar and dissimilar portions. Our model is robust under noise and outliers, it does not make any assumptions on the sampling rates on either trajectory, and it works even if they are partially observed. Additionally, the model also yields a scalar similarity score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We also present efficient algorithms for computing the similarity under our measures; the worst-case running time is quadratic in the number of sample points. Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing with it with earlier approaches, and illustrating many issues that arise in trajectory data. Our experiments show that our approach is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling. 1
MoveBank Track Annotation Project: Linking Animal Movement Data with the Environment to Discover the Impact of Environmental Change in Animal migration Abstract
"... The behavior of animals is very much influenced by their surrounding environment. With the advances in positioning and sensor technologies, it is now possible to capture data of animal locations as well as their surrounding environmental information, at previously unseen spatial and temporal granula ..."
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Cited by 1 (1 self)
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The behavior of animals is very much influenced by their surrounding environment. With the advances in positioning and sensor technologies, it is now possible to capture data of animal locations as well as their surrounding environmental information, at previously unseen spatial and temporal granularities. As a consequence, research interest in developing computational methods for the analysis of movement has increased significantly over the past few years. Yet, the link between movement data and the environmental variables has been largely ignored in existing exploratory tools, as well as in previous studies of movement behavior of animals. The MoveBank environmental data annotation project expands an open portal of animal tracking data and enriches it with automated access to environmental variables, as well as effective computational methods to study and process movement and environment data. The aim is to facilitate the investigation and develop a new understanding of spatiotemporal patterns of animal movement in response to a changing environment. The outcomes will contribute to a better modeling, understanding, and ultimately prediction of the behavioral changes of animals in response to global change. 1.
Finding frequent sub-trajectories with time constraints
- In KDD UrbComp. ACM, 2013
"... ABSTRACT With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. Trajectory pattern mining has received a lot of attention in recent years. Frequent sub-trajectories, in particular, might contain v ..."
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ABSTRACT With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. Trajectory pattern mining has received a lot of attention in recent years. Frequent sub-trajectories, in particular, might contain very usable knowledge. In this paper, we define a new trajectory pattern called frequent sub-trajectories with time constraints (FSTTC) that requires not only the same continuous location sequence but also the similar staying time in each location. We present a two-phase approach to find FSTTCs based on suffix tree. Firstly, we select the spatial information from the trajectories and generate location sequences. Then the suffix tree is adopted to mine out the frequent location sequences. Secondly, we cluster all subtrajectories with the same frequent location sequence with respect to the staying time using modified DBSCAN algorithm to find the densest clusters. Accordingly, the frequent sub-trajectories with time constraints, represented by the clusters, are identified. Experimental results show that our approach is efficient and can find useful and interesting information from the spatio-temporal trajectories.
Algorithms for Hotspot Computation on Trajectory Data
"... ABSTRACT We study one of the basic tasks in moving object analysis, namely the location of hotspots. A hotspot is a (small) region in which an entity spends a significant amount of time. Finding such regions is useful in many applications, for example in segmentation, clustering, and locating popul ..."
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ABSTRACT We study one of the basic tasks in moving object analysis, namely the location of hotspots. A hotspot is a (small) region in which an entity spends a significant amount of time. Finding such regions is useful in many applications, for example in segmentation, clustering, and locating popular places. We may be interested in locating a minimum size hotspot in which the entity spends a fixed amount of time, or locating a fixed size hotspot maximizing the time that the entity spends inside it. Furthermore, we can consider the total time, or the longest contiguous time the entity spends in the hotspot. We solve all four versions of the problem. For a square hotspot, we can solve the contiguoustime versions in O(n log n) time, where n is the number of trajectory vertices. The algorithms for the total-time versions are roughly quadratic. Finding a hotspot containing relatively the most time, compared to its size, takes O(n 3 ) time. Even though we focus on a single moving entity, our algorithms immediately extend to multiple entities. Finally, we consider hotspots of different shape.
RESEARCH ARTICLE Exploring Dance Movement Data Using Sequence Alignment Methods
"... Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving obje ..."
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Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clus-tering method. The applicability of this approach is tested using movement data from samba and tango dancers.
RESEARCH ARTICLE Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways
"... Diverse classes of proteins function through large-scale conformational changes and vari-ous sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimen-sional space, it has been difficult to qu ..."
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Diverse classes of proteins function through large-scale conformational changes and vari-ous sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimen-sional space, it has been difficult to quantitatively compare multiple paths, a necessary pre-requisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by ther-mal fluctuations using a toy model system. Using, as an example, the closed-to-open transi-tions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range
goodrich(at)ics.uci.edu
"... rt(at)cs.brown.edu We give efficient data-oblivious algorithms for several fundamental geometric problems that are relevant to geographic information systems, including planar convex hulls and all-nearest neighbors. Our methods are “data-oblivious ” in that they don’t perform any data-dependent oper ..."
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rt(at)cs.brown.edu We give efficient data-oblivious algorithms for several fundamental geometric problems that are relevant to geographic information systems, including planar convex hulls and all-nearest neighbors. Our methods are “data-oblivious ” in that they don’t perform any data-dependent operations, with the exception of operations performed inside low-level blackbox circuits having a constant number of inputs and outputs. Thus, an adversary who observes the control flow of one of our algorithms, but who cannot see the inputs and outputs to the blackbox circuits, cannot learn anything about the input or output. This behavior makes our methods applicable to secure multiparty computation (SMC) protocols for geographic data used in location-based services. In SMC protocols, multiple parties wish to perform a computation on their combined data without revealing individual data to the other parties. For instance, our methods can be used to solve a problem posed by Du and Atallah, where Alice has a set, A, of m private points in the plane, Bob has another set, B, of n private points in the plane, and Alice and Bob want to jointly compute the convex hull of A∪B without disclosing any more information than what can be derived from the answer. In particular, neither Alice nor Bob want to reveal any of their respective points that are in the interior of the convex hull of A ∪B.
JOURNAL OF SPATIAL INFORMATION SCIENCE Number 9 (2014), pp. 101–124 doi:10.5311/JOSIS.2014.9.179 RESEARCH ARTICLE Similarity of trajectories taking into account geographic context∗
"... Abstract: The movements of animals, people, and vehicles are embedded in a geographic context. This context influences the movement and may cause the formation of certain behavioral responses. Thus, it is essential to include context parameters in the study of movement and the development of movemen ..."
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Abstract: The movements of animals, people, and vehicles are embedded in a geographic context. This context influences the movement and may cause the formation of certain behavioral responses. Thus, it is essential to include context parameters in the study of movement and the development of movement pattern analytics. Advances in sensor tech-nologies and positioning devices provide valuable data not only of moving agents but also of the circumstances embedding the movement in space and time. Developing knowl-edge discovery methods to investigate the relation between movement and its surround-ing context is a major challenge in movement analysis today. In this paper we show how to integrate geographic context into the similarity analysis of movement data. For this, we discuss models for geographic context of movement data. Based on this we develop simple but efficient context-aware similarity measures for movement trajectories, which combine a spatial and a contextual distance. These are based on well-known similarity measures for trajectories, such as the Hausdorff, Fréchet, or equal time distance. We validate our approach by applying these measures to movement data of hurricanes and albatross.
