Visual analytics techniques for exploration of spatiotemporal data

• Main Author: Ferreira, Nivan
• Language: EN
• Published: Polytechnic Institute of New York University 2016
• Subjects: Computer science
• Online Access: http://pqdtopen.proquest.com/#viewpdf?dispub=10043943

<p> Spatial and temporal interactions are central and fundamental in pretty much all activities in our world and society. Every day, people and goods travel around the world at different speeds and scales; migratory animals engage in long-distance travels that demonstrate the biological integration around the globe; weather phenomena, like typhoons and hurricanes, form and move around the Earth and may have large social-economic impact. In all these examples, proper understanding of the underlying phenomena can produce insights with the potential to shape the future development in those domains. </p><p> The rapid development of acquisition technology and the popularization of GPS enabled mobile devices as resulted in spatiotemporal data being produced at massive rates. These create opportunities for data-driven analysis that can highly influence decision making in a diverse set of domains. In order to take advantage of all these data and realize their potential, it is crucial to be able to extract knowledge from them. Interactive visualization systems are acknowledged to be important tools in this scenario: it leverages the human cognitive system and the power of interactive graphic tools to enable quick hypothesis testing and exploration. However, the volume and inherent complexity of spatiotemporal data makes designing such systems a difficult problem. In fact, such complex data collections pose challenges in both managing the data for interactive exploration as well as in designing visual metaphors that enable effective for data exploration. Also, such visual metaphors are limited by constraints imposed by the display and data dimensions, often resulting in extremely cluttered visualizations that are hard to interpret. While, filtering and aggregation strategies are often applied to eliminate clutter, they might hide interesting patterns. Therefore, purely visual/interaction methods need to be complemented with techniques that help in the process of pattern discovery. This dissertation presents novel visual analytics contributions for the analysis of spatiotemporal data to attack the challenges aforementioned. Visual analytics combine interactive visualization with efficient pattern mining techniques to enable analysts to explore large amounts of complex data. The first contribution is the design of the TaxiVis visual exploration system. This system couples together a novel visual query model with an efficient custom-built data layer. These two components enable easy query composition via visual methods as well as interactive query response times. TaxiVis also makes use of coordinated views and rendering strategies to generate informative visual summaries for query results even when those are large. </p><p> The remaining of the contributions in this thesis consists of two pattern mining techniques that help in the navigation through the data via pattern discovery. These two techniques have the goal of enhancing the analytical power of tools such as TaxiVis. Furthermore, these techniques have in common the use of concepts and techniques widely applied in scientific visualization and computer graphics. This approach allows us to have novel perspectives on the problems of finding patterns in spatiotemporal data that, to the best of our knowledge, have not been considered in the machine learning and data mining fields. The first technique consists of a topology-based technique whose main objective is to help users to find the ``needle in the hay stack'', i.e., guide users towards interesting slices (spatiotemporal regions) of the data. We call this process event guided exploration. The overall idea behind this technique is to treat topological features of time-varying scalar functions derived from spatiotemporal data as treated as events. Via visual exploration of the collection of extreme points extracted over time, important events of the data can be found with relatively a small amount of work by the user. The second pattern mining technique consists of a novel model based clustering technique designed for trajectory datasets. This technique, called Vector Field K-Means, models trajectories as streamlines of vector fields. One important feature of this modeling strategy is that it tries to avoid overlapping trajectories to have discrepant directions at their intersections. Clustering is achieved by using the spatial component of trajectories to fit a collection of vector fields to the given trajectories. This technique achieves richness and expressivity of features, simplicity of implementation and analysis, and computational efficiency. Furthermore, the obtained vector fields serve as a visual summary of the movement patterns in each cluster. Finally, Vector Field K-Means can be naturally generalized to also consider trajectories with attributes. This is achieved by using a different modeling strategy based on scalar fields, which we call Attribute Field K-Means.</p>