Abstract
Abstract. Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multi-scale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatio-temporal relations of movement events from taxi trajectory data.
Highlights
Individual or collective movement events contain potentially meaningful information about the spatiotemporal evolution of movers, and they reflect urban transportation patterns(Wang. 2016)
We develop a prototype interactive interface to visually explore spatio-temporal relations among movement events and
If an analyst chooses one day as the time interval and the relations are grouped by week, the analyst will find the weekly periodicity in these relations. These characteristics reflect the fact that the spatio-temporal relations of movement events depend on the spatial and temporal scales that are set by the analyst
Summary
Individual or collective movement events contain potentially meaningful information about the spatiotemporal evolution of movers, and they reflect urban transportation patterns(Wang. 2016). Determining the spatiotemporal relations in these multi-scale and multi-semantic movement datasets is especially difficult(Wibisono, 2016) Facing these challenges, many scientists and researchers are interested in exploring movement data through visual analytics technologies[4]. Many scientists and researchers are interested in exploring movement data through visual analytics technologies[4] These methods combine visual interactive techniques with intelligent data analysis methods to facilitate the process of obtaining meaningful insights and actionable findings from large spatial-temporal datasets. Even though many studies have been done, and several papers have proposed some visual exploration methods or frameworks to extract movement patterns (such as TrajGraph(Xiaoke, 2016) and TrajRank(Lu, 2015)), methods for the visual exploration of spatio-temporal relations among movement events and contexts are still absent, and the representation of the multi-scale and multi-semantic characteristics of these datasets is not complete.
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