Analysis of Flight Variability: a Systematic Approach

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: 99%

Constructing Spaces and Times for Tactical Analysis in Football

Andrienko

Anzer

et al. 2021

Self Cite

“…The space-time cube (STC) [35] enables fast indexing and access by dividing the space-time space into uniformly sampled grids. Extended variants of the STC (e.g., Nanocubes [43]) are widely employed for supporting multivariate spatial-temporal data exploration [40,53], aggregation [3,11], and interactive analysis [6,8,39]. KNN-based queries refer to retrieving top K trajectories which have the minimum distance to given locations.…”

Section: Efficient Trajectory Queriesmentioning

confidence: 99%

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

Huang

Zhao

Chen

et al. 2019

Fig. 1. (a) The query condition specification view. (b) The relevance tree with the spatial keyword 'tourist attractions'. (c) The drop-down menu for changing the type of the input keyword. (d) The semantics view shows that the major region functional topic is 'residential related'. (e) The map view shows that the queried trajectories are mainly distributed in the northwest (named 'Jiangxin island'), the middle, and the east (named 'Wuhua building') of the city. (f) Most trajectories land the island from its east through ferry. (g) The region functional topics. (h) The rendering parameter widget. (i) The temporal graph view. (j) The detail result view of the queried trajectories. (k) Detail study of urban areas.Abstract-Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POIs and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.

“…Rational evaluation methods use deductive reasoning by relying on logically true premises. For example, the analysis of algorithms complexity as reported in [3,44] is rational. This method is used to evaluate the efficiency of an algorithm by determining the time required to execute its instructions.…”

Section: Epistemology Dimensionmentioning

confidence: 99%

The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics

Khayat

Karimzadeh

Ebert

et al. 2019

Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluation methods specialized for VA. In this paper, we present an analysis of evaluation methods that have been used to summatively evaluate VA solutions. We provide a survey and taxonomy of the evaluation methods that have appeared in the VAST literature in the past two years. We then analyze these methods in terms of validity and generalizability of their findings, as well as the feasibility of using them. We propose a new metric called summative quality to compare evaluation methods according to their ability to prove usefulness, and make recommendations for selecting evaluation methods based on their summative quality in the VA domain.