A conceptual framework and taxonomy of techniques for analyzing movement

Andrienko, Gennady; Andrienko, Natalia; Bak, Peter; Keim, Daniel A.; Kisilevich, Slava; Wrobel, Stefan

doi:10.1016/j.jvlc.2011.02.003

Cited by 123 publications

(83 citation statements)

References 24 publications

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“…For instance, the flocking patterns that are visualised in Figure 5 were determined interactively, and they were found to be representative for the data set and consistent with the findings that indicate that the average size of tropical cyclones varies between 330 and 660 km (Chavas and Emanuel 2010). The most common way to visualise the clustering results is by colouring the display elements representing the clustered items according to their cluster membership and involving graphical summarisation of clusters, such as generation of convex hulls (Andrienko et al 2011). We have used both the approaches for the task of flock verification; flocks are shown as cylinders where the size is proportional to the value of " parameter.…”

Section: Embedding the Algorithm Into A Visual Environmentsupporting

confidence: 69%

“…In order to verify those flocks, the visual interface should also be able to deal with the visualisation of these large data sets. Andrienko et al (2011) offer comprehensive review of the most common ways to visualise the trajectory data and spatio-temporal events. The established visualisation techniques are animated map and Space-Time Cube (STC) (Andrienko et al 2011).…”

Section: Embedding the Algorithm Into A Visual Environmentmentioning

confidence: 99%

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Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

Turdukulov¹,

Romero²,

Huisman³

et al. 2014

International Journal of Geographical Information Science

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The popularity of tracking devices continues to contribute to increasing volumes of spatio-temporal data about moving objects. Current approaches in analysing these data are unable to capture collective behaviour and correlations among moving objects. An example of these types of patterns is moving flocks. This article develops an improved algorithm for mining such patterns following a frequent pattern discovery approach, a well-known task in traditional data mining. It uses transaction-based data representation of trajectories to generate a database that facilitates the application of scalable and efficient frequent pattern mining algorithms. Results were compared with an existing method (Basic Flock Evaluation or BFE) and are demonstrated for both synthetic and real data sets with a large number of trajectories. The results illustrate a significant performance increase. Furthermore, the improved algorithm has been embedded into a visual environment that allows manipulation of input parameters and interactive recomputation of the resulting flocks. To illustrate the visual environment a data set containing 30 years of tropical cyclone tracks with 6 hourly observations is used. The example illustrates how the visual environment facilitates exploration and verification of flocks by changing the input parameters and instantly showing the spatio-temporal distribution of the resulting flocks in the Space-Time Cube and interactively selecting, querying and saving the resulting flocks for further analysis and verification.

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Section: Embedding the Algorithm Into A Visual Environmentsupporting

confidence: 69%

Section: Embedding the Algorithm Into A Visual Environmentmentioning

confidence: 99%

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

Turdukulov¹,

Romero²,

Huisman³

et al. 2014

International Journal of Geographical Information Science

View full text Add to dashboard Cite

show abstract

“…This paper presents work that draws on and extends the work of others, in particular Andrienko et al (2011) and Laube (2014), to develop a framework for the modelling of information on movement. We have carefully expressed the field/object and Eulerian/Lagrangian perspectives in formal terms, and have demonstrated sometimes quite subtle distinctions between these two dichotomies.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…This classification has contributed to the development of data mining and visualisation of movement. Andrienko et al (2011) proposed a conceptual framework for movement with atomic spatial, temporal, and object components. The paper mainly focused on an object perspective but their analysis also covered properties of locations (spatial and temporal).…”

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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“…Joh et al, 2002;Kisilevich et al, 2010;Andrienko et al, 2011;Vrotsou et al, 2011). However, these approaches usually rely on a movingwindow in time to construct trajectories.…”

Section: Trajectory Networkmentioning

confidence: 99%

Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics

Straumann

Çöltekin

Andrienko

2014

The Cartographic Journal

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We present a study that explores methodological steps towards (re)constructing collective narratives from the phototaking behaviour of two groups (foreign tourists and inhabitants of Switzerland) by analysing spatial and temporal patterns in user-contributed, georeferenced photographs of Zurich, Switzerland. We reason that the photographers typically capture a scene or a moment because they want to remember or share it, thus these scenes or moments are meaningful to them. Various scholars suggest that the human experience (i.e. this meaningfulness) is what separates a place from the mathematical descriptions of space. While this notion is well known in larger geographic literature, it is under-explored in cartographic research. We respond to this research gap and reconstruct static and dynamic patterns of photo-taking and -sharing behaviour to assist in capturing the implicit meaning in the studied locations. These locations may be meaningful to only a certain group of people in certain moments; therefore, studying group differences in spatial and temporal photo-taking patterns will help building a collective and comparative story about the studied place. In our study, we focus on experiences of foreign versus domestic visitors, and in the process, we examine the potential (and feasibility) of georeferenced photographs for extracting such collective narratives using qualitative and quantitative visual analytical methods.

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A conceptual framework and taxonomy of techniques for analyzing movement

Cited by 123 publications

References 24 publications

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

A framework for models of movement in geographic space

Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics

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