Computing longest duration flocks in trajectory data

Gudmundsson, Joachim; Kreveld, Marc van

doi:10.1145/1183471.1183479

Cited by 224 publications

(197 citation statements)

References 14 publications

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“…Later Gudmundsson et al [21] considered the same problems and extended the algorithmic results by primarily focusing on approximation algorithms -'Any exact values of m and r hardly have a special significance -20 caribou meeting in a circle with radius 50 meters form as interesting a pattern as 19 caribou meeting in a circle with radius 51 meters.' Benkert et al [5] and Gudmundsson and van Kreveld [20] only recently revisited the flock pattern and gave a more generic definition that bases purely on the geometric arrangement of the moving entities and thus excludes the need of an analytical space as with the initial definition of the patterns [36,39].…”

Section: Promising Patternsmentioning

confidence: 99%

Reporting Leaders and Followers among Trajectories of Moving Point Objects

et al. 2007

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Abstract. Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatio-temporal movement patterns in large tracking data sets. We present a natural definition of the pattern 'one object is leading others', which is based on behavioural patterns discussed in the behavioural ecology literature. Such leadership patterns can be characterised by a minimum time length for which they have to exist and by a minimum number of entities involved in the pattern. Furthermore, we distinguish two models (discrete and continuous) of the time axis for which patterns can start and end. For all variants of these leadership patterns, we describe algorithms for their detection, given the trajectories of a group of moving entities. A theoretical analysis as well as experiments show that these algorithms efficiently report leadership patterns.

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Section: Promising Patternsmentioning

confidence: 99%

Reporting Leaders and Followers among Trajectories of Moving Point Objects

et al. 2007

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“…This approach results in efficient approximation algorithms for finding such flock patterns of a fixed length and a fixed number of flock entities, where the radius is approximated within a factor of 2. For the same definition of flock, Gudmundsson and van Kreveld [14] showed that for any radius approximation with factor smaller than 2, computing the longest duration flock and the largest subset flock is NP-hard to compute and even NP-hard to approximate within a factor of |T | 1− and |A| 1− , respectively, where T is the set of all discrete time steps, A is the set of all sensor nodes, and > 0. As another such movement pattern, Andersson et al [15] gave a generic definition of the pattern leadership and discussed how such leadership patterns can be computed from a group of moving entities.…”

Section: Mining Movement Patternsmentioning

confidence: 99%

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

Laube

Duckham

Wolle

2008

Geographic Information Science

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Abstract. Movement patterns, like flocking and converging, leading and following, are examples of high-level process knowledge derived from lowlevel trajectory data. Conventional techniques for the detection of movement patterns rely on centralized "omniscient" computing systems that have global access to the trajectories of mobile entities. However, in decentralized spatial information processing systems, exemplified by wireless sensor networks, individual processing units may only have access to local information about other individuals in their immediate spatial vicinity. Where the individuals in such decentralized systems are mobile, there is a need to be able to detect movement patterns using collaboration between individuals, each of which possess only partial knowledge of the global system state. This paper presents an algorithm for decentralized detection of the movement pattern flock, with applications to mobile wireless sensor networks. The algorithm's reliability is evaluated through testing on simulated trajectories emerging from unconstrained random movement and correlated random walk.

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“…Recently, algorithms to find flocks have been proposed to identify groups of entities that travel together for an extended period of time [10]. Formally, given the trajectories of a set of n entities, a time interval I of at least k consecutive snapshots, and a distance r, a flock f (m, k, r) is a set of at least m entities such that for every snapshot t in time interval I, there is a disk of radius r that contains all the m entities [10].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, for each flock in the C 50 100 flocks, any sub-interval of the I will result in a flock, leading to C 50 100 × (1000 − 500 + 1) flocks. Although algorithms for discovering longest flocks have been proposed [10], the problem of combinatorial explosion related to entities has not been addressed; (3) Flocks move and evolve over time. Because of these limitations, there is a need to discover how flocks interact with each other over time throughout the observations.…”

Section: Introductionmentioning

confidence: 99%

“…Geographic data mining approaches have been proposed to detect generic spatial-temporal patterns such as flock, leadership, convergence, and encounter in geospatial data [15,3,16]. A method to calculate the longest duration of flocks and meetings in spatial-temporal data has been proposed [10]. Definition of moving clusters and efficient algorithms to identify them have been proposed [14].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Herds and Their Evolvements from Trajectory Data

Huang

Chen

Dong

2008

Geographic Information Science

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Abstract. A trajectory is the time-stamped path of a moving entity through space. Given a set of trajectories, this paper proposes new conceptual definitions for a spatio-temporal pattern named Herd and four types of herd evolvements: expand, join, shrink, and leave based on the definition of a related term flock. Herd evolvements are identified through measurements of Precision, Recall, and F-score. A graph-based representation, Herd Interaction Graph, or Herding, for herd evolvements is described and an algorithm to generate the graph is proposed and implemented in a Geographic Information System (GIS) environment. A data generator to simulate herd movements and their interactions is proposed and implemented as well. The results suggest that herds and their interactions can be effectively modeled through the proposed measurements and the herd interaction graph from trajectory data.

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Computing longest duration flocks in trajectory data

Cited by 224 publications

References 14 publications

Reporting Leaders and Followers among Trajectories of Moving Point Objects

Reporting Leaders and Followers among Trajectories of Moving Point Objects

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

Modeling Herds and Their Evolvements from Trajectory Data

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