Visual analytics tools for analysis of movement data

Andrienko, Gennady; Wrobel, Stefan

doi:10.1145/1345448.1345455

Cited by 226 publications

(155 citation statements)

References 19 publications

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“…Other approaches involve proposing exploratory analysis frameworks (Andrienko, Andrienko, & Wrobel, 2007;Yan, Chakraborty, Parent, Spaccapietra, & Aberer, 2011), which can then be used to identify temporal and / or spatial threshold values for segmenting trajectories into stops and moves. However, this approach can be thought of complementary to the "stop and moves" one, thus inheriting its shortcomings.…”

Section: Human Geographymentioning

confidence: 99%

Transportation mode detection – an in-depth review of applicability and reliability

2016

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The wide adoption of location-enabled devices, together with the acceptance of services that leverage (personal) data as payment, allows scientists to push through some of the previous barriers imposed by data insufficiency and privacy skepticism. The research problems whose study require hard-to-obtain data (e.g., transportation mode detection, service contextualization, etc.) have now become more accessible to scientists because of the availability of data collecting outlets. One such problem is the detection of a user's transportation mode. Different fields have approached the problem of transportation mode detection with different aims: Location Based Services is a field that focuses on understanding the transportation mode in real-time, Transportation Science is a field that focuses on measuring the daily travel patterns of individuals or groups of individuals, and Human Geography is a field that focuses on enriching a trajectory by adding domain-specific semantics. While different fields providing solutions to the same problem could be viewed as a positive outcome, it is difficult to compare these solutions because the reported performance indicators depend on the type of approach and its aim (e.g., the real-time availability of Location Based Services requires the performance to be computed on each classified location). The contributions of this paper are three fold. First, the paper reviews the critical aspects that are desired by each field of research when providing solutions to the transportation mode detection problem. Second, it proposes three dimensions that separate three branches of science based on their main interest. Finally, it identifies important gaps in research and future directions,i.e., proposing: widely accepted error measures meaningful for all disciplines, methods robust to new datasets and a benchmark dataset for performance validation.

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Section: Human Geographymentioning

confidence: 99%

Transportation mode detection – an in-depth review of applicability and reliability

2016

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“…A STC is suitable for simultaneously visualising a relatively low number of trajectories. When that number increases, the STC suffers from occlusion and cluttering, making it difficult to identify trends [16]. Usually aggregation, for example, using kernel densities [17] or generating clusters of the most salient patterns [18] can resolve this.…”

Section: Trajectory Representation and Visualisationmentioning

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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“…In [2], behavior patterns are learned from GPSdata. Data mining tasks are performed by human experts using visual analytics methods.…”

Section: Motivationmentioning

confidence: 99%

Perception and probabilistic anchoring for dynamic world state logging

Blodow

Jain

Márton

et al. 2010

2010 10th IEEE-RAS International Conference on Humanoid Robots

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Abstract-Knowing precisely where objects are located enables a robot to perform its tasks both more efficiently and more reliably. To acquire the respective knowledge and to effectively use it as a resource, a robot has to go through the world with "open eyes". Specifically, it has to become environment-aware by keeping track of where objects of interest are located and explicitly represent their geometrical properties. In this paper, we propose to equip robots with a perception system that passively monitors the environment using a 3D data acquisition system, identifying objects that might become the subject of future manipulation tasks. Our system encompasses a 3D semantic mapping and reconstruction pipeline and a storage and data merging unit for perceived information that provides on-demand modeling and comparison capabilities. Based on probabilistic logical models, we address the important perceptual subtask of object identity resolution, i.e. inferring which observations refer to which entities in the real world (perceptual anchoring). Our system can be used as a bootstrapping system for the generation of object-centric knowledge and can, in this way, be used as a mid-level perception system that enables activity recognition, scene recognition and high-level planning.

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Visual analytics tools for analysis of movement data

Cited by 226 publications

References 19 publications

Transportation mode detection – an in-depth review of applicability and reliability

Transportation mode detection – an in-depth review of applicability and reliability

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

Perception and probabilistic anchoring for dynamic world state logging

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