Evaluation of alternative glyph designs for time series data in a small multiple setting

Abstract

We present the results of a controlled experiment to investigate the performance of different temporal glyph designs in a small multiple setting. Analyzing many time series at once is a common yet difficult task in many domains, for example in network monitoring. Several visualization techniques have, thus, been proposed in the literature. Among these, iconic displays or glyphs are an appropriate choice because of their expressiveness and effective use of screen space. Through a controlled experiment, we compare the performance of four glyphs that use different combinations of visual variables to encode two properties of temporal data: a) the position of a data point in time and b) the quantitative value of this data point. Our results show that depending on tasks and data density, the chosen glyphs performed differently. Line Glyphs are generally a good choice for peak and trend detection tasks but radial encodings are more effective for reading values at specific temporal locations. From our qualitative analysis we also contribute implications for designing temporal glyphs for small multiple settings.

Star Glyph Insets for Overview Preservation of Multivariate Data

“…Results from quantitative experiments suggest to use circular designs rather than linear ones, because they facilitate the detection of single dimensions [12]. The ranking of visual variables by Cleveland and McGill suggest to use position or length for displaying the data value rather than color.…”

Glyph-based video visualization on Google Map for surveillance in smart cities

“…Semantic information is extracted from many traffic surveillance videos that are also linked to Google Map for 3D association. Meanwhile, glyph is individually recognizable and offers multifield visualization [29,30]. Well-developed glyph-based [31] visualization technique is proposed that can enable effective and efficient visual communication and information encoding.…”

Investigating Time Series Visualisations to Improve the User Experience

“…The aforementioned trend of focusing more on visual representation than on interaction is also predominant in graphical perception of time series visualisations, which is a well-studied area in visual analytics. A common practice in quantitative graphical perception studies of time series visualisations [4,10,14,16,21] is to conduct them in a static setting, i.e., not allowing the users to interact with the time series visualisations, thereby limiting our knowledge of the user experience. In addition, the overall coverage of different visual representations of time series data is lacking.…”

“…In addition, the overall coverage of different visual representations of time series data is lacking. Several previous studies [4,10,14,16,21] have compared the effectiveness of time series visualisations within and across the categories of positional (e.g., Figure 1(a&b)) and colour visual encodings (e.g., Figure 1(c&d)). However, to the best of our knowledge, there has been no study that compares the effectiveness of time series visualisations that use area visual encoding (e.g., Figure 1(e&f)) with their positional and colour counterparts.…”