A temporal focus + context visualization model for handling valid-time spatial information

Abstract

Spatiotemporal databases provide effective means to represent, manage and query information evolving over time. However, the visualization of record sets that result from spatiotemporal queries through traditional visualization techniques can be of difficult interpretation or may lack the ability to meaningfully display several instants at the same time. We propose a Temporal Focus + Context visualization model to overcome issues from such techniques resorting to concepts from Information Visualization. In this model, Focus + Context is applied to time rather than, as more typically, to attributes or space, and allows large amounts of data from distinct periods of time and from several record sets to be compressed onto one. Underlying the proposed visualization technique is the calculation of a temporal degree of interest (TDOI) for each record driven by specific analysis, exploration or presentation goals and based on the record valid time attribute, as well as on user-defined temporal visualization requirements. In the mapping stage of the visualization pipeline, the TDOI for a record is used to control graphical properties, such as transparency and color. More complex rendering properties, such as sketch drawing edges or other non-photorealistic enhancement techniques, can also be used to convey the temporal aspects of data, replacing the original graphical features of the record data. By enhancing or dimming the representation of a data item, according to the corresponding degree of interest, it is possible to meaningfully compress information about distinct temporal states of data onto the same visualization display. The model has been applied to several test scenarios and proved appropriate and useful for a wide range of domains that require the display, exploration and analysis of spatial information discretely evolving over time.

Visualizing time-related data in biology, a review

“…Flat representations of time often exhibit a high level of data compression into one image and possibly unrealistic co-localization of entities that exist at different time points. In animations, on the other hand, transitions are sometimes hard to observe given the short time span, and sequential time points cannot be compared [ 35 ]. The current approaches to visualizing time in biology often aim for a partial reconciliation between the two modes, as described in the following section.…”

Multi-agent system approach for improved real-time visual summaries of geographical data streams

“…An existing visualisation approach involves the direct representation of each record in a set of data to enable the analyst (Carvalho et al, 2008), to extract and lay out remarkable patterns and to interact with them. However, these techniques may not be effective when applied to very large and complex datasets, which are increasingly common.…”

Interactive discovery of sequential patterns in time series of wind data

“…Moreover, the design of our environment followed the Focus + Context principle where Context delivers the full extent of spatio-temporal dataset (patterns), and Focus is a part of the dataset that has been temporally focused on details (Card et al 1999, Carvalho et al 2008. The next subsections describe in detail the three visual representations.…”