Visual exploration of movement and event data with interactive time masks

Anzer

et al. 2021

Self Cite

A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new time-homomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections. As similar patterns may occur at different spatial locations, we also propose constructing new spatial reference systems for aligning and matching movements irrespective of their absolute locations. The approach was tested in application to tracking data from two Bundesliga games of the 2018/2019 season. It enabled detection of interesting and meaningful general patterns of team behaviors in three classes of situations defined by football experts. The experts found the approach and the underlying concepts worth implementing in tools for football analysts.

Section: Relevant Visual Analytics Approaches Beyond Footballmentioning

confidence: 91%

Constructing Spaces and Times for Tactical Analysis in Football

Anzer

et al. 2021

Self Cite

“…Filtering operations can be applied sequentially to results of previous operations. Temporal filtering can be based on linear [1] or cyclic [27,28] models of time or on selection of time intervals satisfying interactively specified query conditions [15].…”

Section: Interactive Filtering Of Movement Datamentioning

confidence: 99%

“…Different types of interactive filtering applicable to spatio-temporal data have been described elsewhere [6,15]. Among them, the filter type called 'time mask' [15] is suitable for supporting analysis with regard to the global context (section 3.3). The filter selects data from time intervals satisfying specified query conditions.…”

Section: Dynamic Aggregation and Filteringmentioning

confidence: 99%

Analysis of Flight Variability: a Systematic Approach

García

Scarlatti

2019

Self Cite

In movement data analysis, there exists a problem of comparing multiple trajectories of moving objects to common or distinct reference trajectories. We introduce a general conceptual framework for comparative analysis of trajectories and an analytical procedure, which consists of (1) finding corresponding points in pairs of trajectories, (2) computation of pairwise difference measures, and (3) interactive visual analysis of the distributions of the differences with respect to space, time, set of moving objects, trajectory structures, and spatio-temporal context. We propose a combination of visualisation, interaction, and data transformation techniques supporting the analysis and demonstrate the use of our approach for solving a challenging problem from the aviation domain.

“…Paper [13] proposes a taxonomy of attributes that can be derived from the spatial positions alone or in combination with data describing the spatial, temporal, or spatio-temporal context of these positions. Filtering can also be done according to a selection of time intervals satisfying interactively specified query conditions [16], which can be set based on aggregated properties of the overall movement, occurrences of events, or values of any time-dependent attributes, such as weather parameters. Trajectory parts that occurred during the time intervals for which query conditions hold are treated as active and the remaining parts as inactive.…”

Section: Interactive Visualization and Filtering Of Trajectoriesmentioning

confidence: 99%

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Fuchs

et al. 2018

Self Cite

Fig. 1. Different approach routes to London airports are represented in a 3D view by central trajectories of density-based clusters of relevant parts of flight trajectories. The images show the routes that were used in two days with different wind parameters.Abstract-Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.