Movement Patterns in Spatio‐temporal Data

Gudmundsson, Joachim; Laube, Patrick; Wolle, Thomas

doi:10.1007/978-0-387-35973-1_823

Cited by 58 publications

(34 citation statements)

References 11 publications

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“…Gudmundsson et al (2008), noted that the combination of an orthogonal temporal axis with two spatial dimensions, together with a global positioning system (GPS), or other tracking technologies, such as video, or a wireless local area network, provide potentially very effective tools for analysis of 3D movement patterns. Data analysis may reveal significant information about the flock as a whole.…”

Section: Tracking Animal Movementsmentioning

confidence: 99%

“…This could include bottlenecks and frequently-used corridors, which show indications of flock behaviour or random grazing (Djordjevic et al, 2008). In addition, monitoring may show evidence of flocking size (number), meeting places, or periodic patterns and frequented locations, as shown schematically in Figure 1 (see Kreveld et al, 2007;Benkert et al, 2008;Djordjevic et al, 2008;Gudmundsson et al, 2008). In many applications, the terms 'meeting places' and 'frequent locations' can be replaced by 'gathering areas'.…”

Section: Tracking Animal Movementsmentioning

confidence: 99%

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A Geo-Visual Analytics Approach to Biological Shepherding: Modelling Animal Movements and Impacts

Benke

Sheth

Betteridge

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The lamb industry in Victoria is a significant component of the state economy with annual exports in the vicinity of $1 billion. GPS and visualisation tools can be used to monitor grazing animal movements at the farm scale and observe interactions with the environment. Modelling the spatial-temporal movements of grazing animals in response to environmental conditions provides input for the design of paddocks with the aim of improving management procedures, animal performance and animal welfare. The term 'biological shepherding' is associated with the re-design of environmental conditions and the analysis of responses from grazing animals. The combination of biological shepherding with geo-visual analytics (geo-spatial data analysis with visualisation) provides a framework for improving landscape design and supports research in grazing behaviour in variable landscapes, heat stress avoidance behaviour during summer months, and modelling excreta distributions (with respect to nitrogen emissions and nitrogen return for fertilising the paddock). Nitrogen losses due to excreta are mainly in the form of gaseous emissions to the atmosphere and leaching into the groundwater. In this study, background and context are provided in the case of biological shepherding and tracking animal movements. Examples are provided of recent applications in regional Australia and New Zealand. Based on experimental data and computer simulation, and using data visualisation and feature extraction, it was demonstrated that livestock excreta are not always randomly located, but concentrated around localised gathering points, sometimes separated by the nature of the excretion. Farmers require information on the nitrogen losses in order to reduce emissions to meet local and international nitrogen leaching and greenhouse gas targets and to improve the efficiency of nutrient management.

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Section: Tracking Animal Movementsmentioning

confidence: 99%

Section: Tracking Animal Movementsmentioning

confidence: 99%

A Geo-Visual Analytics Approach to Biological Shepherding: Modelling Animal Movements and Impacts

Benke

Sheth

Betteridge

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Various data mining techniques have been used to detect generic movement patterns [2]. In 2004, Laube et al [12] defined a collection of spatiotemporal patterns based on direction of movement and location, e.g.…”

Section: Mining Movement Patternsmentioning

confidence: 99%

“…They are the spatiotemporal "trace" left behind by the behavior of moving entities [2]. Examples of movement patterns include flocking as in a "mob" of sheep [3], leading and following found in group dynamics [4,5], or converging and diverging of pedestrians in crowding scenarios [6,7].…”

Section: Introductionmentioning

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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“…The trajectory adopts an essentially objectbased, Lagrangian perspective on movement, with its focus on entities and their varying locations. In fact, this perspective underlies the overwhelming majority of research on movement within GI science (see, for example, Gudmundsson et al 2008, Soleymani et al 2014, Laube et al 2005, Wolfson et al 1998, Van der Weghe et al 2006.…”

Section: Introductionmentioning

confidence: 99%

A framework for models of movement in geographic space

Wang

Duckham

Worboys

2015

International Journal of Geographical Information Science

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This paper concerns the theoretical foundations of movement informatics. We discuss general frameworks in which models of spatial movement may be developed. In particular, the paper considers the object-field and LagrangianEulerian dichotomies, and the SNAP/SPAN ontologies of the dynamic world, and classifies the variety of informatic structures according to these frameworks. A major challenge is transitioning between paradigms. Usually data is captured with respect to one paradigm but can usefully be represented in another. We discuss this process in formal terms and then describe experiments that we performed to show feasibility. It emerges that observational granularity plays a crucial role in these transitions.

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Movement Patterns in Spatio‐temporal Data

Cited by 58 publications

References 11 publications

A Geo-Visual Analytics Approach to Biological Shepherding: Modelling Animal Movements and Impacts

A Geo-Visual Analytics Approach to Biological Shepherding: Modelling Animal Movements and Impacts

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

A framework for models of movement in geographic space

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