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ST-TrajVis. (a) 2D Map (b) Space-Time Cube (c) Data Query Component (d) Data Enhancement Component.
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Huge amounts of movement data are nowadays being collected, as a consequence of the prevalence of mobile computing systems and location based services. While the research interest on the analysis of spatio-temporal data has also significantly increased, there are still several open challenges in areas such as interaction and information visualizati...
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... developed ST-TrajVis, a web application for the visual exploration of trajectory data, to obtain feedback for an initial analysis of how the users' interact with some of the existing techniques. An example screenshot of the application can be seen in Figure 1. ST-TrajVis is divided into four main interactive components, namely a 2D map, a spacetime cube, a data querying area, and a data enhancement area. ...
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... Several visualization techniques to analyze ST data, such as, human trajectories, cars or boats movements have been proposed [16]- [18] and a relevant challenge is to understand which type of visualization technique is more adequate to support the visualization tasks and analytical questions [11]. Trajectories of moving objects are typically represented on maps and space time cubes using solid lines, segmented lines or point symbols over the geographical representation, where the widths and/or colors may encode movement information or attributes [17], [19], [20]. ...
... Methods developed for the visual representation of spatio-temporal data are often either abstracting data to highlight certain characteristics, or only use two of the three axes in 3D visualisations for spatial dimensions. The space-time cube (STC) (Hägerstrand 1970) uses two axes for spatial dimensions and the third for the temporal dimension (Gonçalves et al. 2013;McArdle et al. 2014). The visual expressiveness of the STC is limited, and its applicability is heavily depending on the data domain and the research task (-Demšar et al. 2015;Kjellin et al. 2008;Lee et al. 2014). ...
The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement data sets. To explore and analyse these data, applications are required that are capable of handling the data while providing an environment that enables the analyst(s) to focus on the task of investigating the movement in the context of the geographic environment it occurred in. We present an extensible, open-source framework for collaborative analysis of geospatial–temporal movement data with a use case in collective behaviour analysis. The framework TEAMwISE supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote set-ups. The implementation can be deployed in a variety of immersive environments, for example, on a tiled display wall and mobile VR devices.
Graphic abstract
... Overall, the task performance with different spatiotemporal visualization techniques strongly depends on the tasks and data used [48]. Consequently, a newer group of studies does not study individual spatiotemporal visualizations, but allows users to select the ones which seem most appropriate for the current task [49] or combines them in a coordinated manner [44], [50]- [52]. Users more often turned to the STC in a complex dataset, and to a static map in a simpler data set [51]. ...
Spatiotemporal visualizations pose certain challenges for comprehension and reasoning processes. This specifically applies to casual users who want to gain an overview of the spatiotemporal origins of large cultural collections in a relatively short time. Based on a distributed cognition approach, we compare four visualization techniques (coordinated multiple views, color coding, animation and space-time cube representations), which offer different solutions to represent spatial and temporal information in an integrated fashion. We firstly assess the strengths and weaknesses of these techniques with regard to existing work. We then complement these findings with a close look at the reasoning processes of 18 casual users. In a mixed methods study, they explored a photo collection from the middle of the 20th century by means of all four spatiotemporal visualization techniques. We investigate and compare the effects of these techniques on processes of internalization, spatiotemporal reasoning and comprehension by analyzing the observed spatial, temporal and spatiotemporal insights and the degree of connectedness of these insights. Our results provide an in-depth look at how each visualization technique supports the construction of coherent internal representations and, together with data on cognitive load and subjective references, help to understand for which tasks and users they are suited.
... As such, the choice for an adequate visualization technique can be seen as an important, and sometimes controversial, challenge, since the results of previous studies suggest that each technique is more adequate than the other for specific types of visualization tasks (e.g., identify, compare, associate). Interestingly, despite some authors suggesting the interest of combining both techniques (Amini et al. 2015; Gonç alves et al. 2015), few studies have addressed this challenge, while those that did can be seen as somewhat focused in distinguishing the different components rather than exploring the actual consequences of their combination (Gonç alves et al. 2013; Kveladze et al. 2015). In this paper, we aim to address those issues. ...
... Previous research has shown at least two ways of combining 2D Maps and STCs (Figures 1c and d). The first consists of displaying both techniques simultaneously [4]. The second consists of switching and/or transforming one visualization into another [1,3]. ...
Two dimensional static maps and three dimensional space-time cubes are among the most studied techniques to visualize human movement data. Previous research suggests that both techniques are useful in different types of tasks. However, the analysis of trajectory data may not be focused in just one type of task, motivating further studies to quantify the advantages of combining both types of techniques. This paper describes our work-in-progress addressing this issue, proposing the combination of 2D maps and 3D space-time cubes for human trajectory visualization, and overviewing possible metrics for its evaluation.
... From the four categories that we previously identified, static maps are the ones in which temporal information may be the most easily overlooked. To prevent that, time can be represented similarly to a thematic attribute, using visual variables, like transparency (Booker et al. 2007) or colour (Gonçalves et al. 2013). Another alternative consists in the use of additional symbols, like textual timestamps, or cross-lines displayed orthogonally to the trajectory so that closer lines represent slower moving objects (Kjellin et al. 2010). ...
With the prevalence of mobile computing systems and location based services, large amounts of spatio-temporal data are nowadays being collected, representing the mobility of people performing various activities. However, despite the increasing interest in the exploration of these data, there are still open challenges in various application contexts, e.g. related to visualisation and human–computer interaction. In order to support the extraction of useful and relevant information from the spatio-temporal and the thematic properties associated with human trajectories, it is crucial to develop and study adequate interactive visualisation techniques. In addition to the properties of the visualisations themselves, it is important to take into consideration the types of information present within the data and, more importantly, the types of tasks that a user might need to consider in order to achieve a given goal. The understanding of these factors may, in turn, simplify the development and the assessment of a given interactive visualisation. In this paper, we present and analyse the most relevant concepts associated to these topics. In particular, our analysis addresses the main properties associated with (human) trajectory data, the main types of visualisation tasks/objectives that the users may require in order to analyse that data and the high-level classes of techniques for visualising trajectory data. In addition, this paper also presents an overview on a user study, conducted in function of this analysis, to compare two classes of visualisation techniques, namely static maps and space-time cubes, regarding their adequacy in helping users completing basic visualisation tasks.
... In previous work (Gonçalves et al. 2013), we studied the effectiveness of combining static maps and space-time cubes for the visualization of human trajectory data, by non-expert users. Despite the positive results, we concluded that it was necessary to conduct further comparative studies between the two techniques. ...
... In fact, our previous research goes in agreement with these considerations [25]. Following the previously mentioned taxonomy, and through the use of several processing data techniques, we developed and tested ST-TrajVis, an application for the visualization of trajectory data. ...
As a consequence of the prevalence of mobile computing and location based services, huge amounts of movement data are nowadays being collected. While the research interest on the analysis of trajectory data has also significantly increased, there are still several open challenges in areas related to geographic information systems. Despite the existence of several techniques for the visualization of movement data, it is still unclear how usable and useful these techniques are, how can they be improved, and in which tasks they should be used. In this paper, we highlight the current limitations in the visual exploration of trajectory data, and present the ongoing research aiming to address those issues. For that, we propose the development of taxonomies addressing visualization tasks, techniques, and data, based on empirical data, through systematic comparative usability studies, and present an overview of the current results.
The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement datasets. These datasets require applications that are capable of handling them whilst providing an environment that enables the analyst(s) to focus on the task of analysing the movement in the context of the geographic environment it occurred in. We present a framework for collaborative analysis of geospatial-temporal movement data with a use-case in collective behavior analysis. It supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote setups. The implementation can be deployed in a variety of immersive environments, e.g. on a tiled display wall or mobile VR devices.
