Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects

Dodge, Somayeh; Weibel, Robert; Forootan, Ehsan

doi:10.1016/j.compenvurbsys.2009.07.008

Cited by 169 publications

(171 citation statements)

References 29 publications

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“…For example, a pertinent property of eye movement is the presence of instantaneous jumps (saccades) over relatively long distances (Dodge et al 2008(Dodge et al , 2009. Intermediate points between start and end positions of a saccade are not meaningful: it cannot be assumed that there exists a straight or curved line between two fixation positions such that the eye focus travels along it attending all intermediate points.…”

Section:  Spatial Propertiesmentioning

confidence: 99%

Understanding movement data quality

Andrienko

Fuchs

2016

Journal of Location Based Services

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Understanding of data quality is essential for choosing suitable analysis methods and interpreting their results. Investigation of quality of movement data, due to their spatio-temporal nature, requires consideration from multiple perspectives at different scales. We review the key properties of movement data and, on their basis, create a typology of possible data quality problems and suggest approaches to identifying these types of problems. Permanent

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Section:  Spatial Propertiesmentioning

confidence: 99%

Understanding movement data quality

Andrienko

Fuchs

2016

Journal of Location Based Services

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“…This approach has also been extended to consider places where entities move slowly as significant [28]. Additional attributes of movement (speed, acceleration, duration and direction) are also used to determine similarity [1].…”

Section: Trajectory Similaritymentioning

confidence: 99%

“…For the purposes of data representation and modeling, such moving objects are considered as entities, whose position and/or geometric attributes change over time [1]. Despite the diversity of sources of movement data, fundamentally the same process is captured: how objects move through the basic framework of the physical world, defined by geographic space and time.…”

Section: Introductionmentioning

confidence: 99%

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

McArdle

Demšar

Spek

et al. 2013

Web and Wireless Geographical Information Systems

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Abstract. Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

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“…Trajectory segmentation: The classic idea of trajectory segmentation [2], [5], [16] is to obtain segments where movement characteristics inside each segment are similar. Movement characteristics might be attributes of the points of the trajectory such as speed, direction, or curviness.…”

Section: Related Workmentioning

confidence: 99%

Traveling Salesman in Reverse: Conditional Markov Entropy for Trajectory Segmentation

Kafsi

Grossglauser

Thiran

2015

2015 IEEE International Conference on Data Mining

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Abstract-We are interested in inferring the set of waypoints (or intermediate destinations) of a mobility trajectory in the absence of timing information. We find that, by mining a dataset of real mobility traces, computing the entropy of conditional Markov trajectory enables us to uncover waypoints, even though no timing information nor absolute geographic location is provided. We build on this observation and design an efficient algorithm for trajectory segmentation. Our empirical evaluation demonstrates that the entropy-based heuristic used by our segmentation algorithm outperforms alternative approaches as it is 43% more accurate than a geometric approach and 20% more accurate than path-stretch based approach. We further explore the link between trajectory entropy, mobility predictability and the nature of intermediate locations using a route choice model on real city maps.

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Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects

Cited by 169 publications

References 29 publications

Understanding movement data quality

Understanding movement data quality

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

Traveling Salesman in Reverse: Conditional Markov Entropy for Trajectory Segmentation

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