Data Vases: Plots for Visualizing Multiple Time Series

Data Vases: Plots for Visualizing Multiple Time Series Sidharth Thakur and Theresa-Marie Rhyne, Senior Member, IEEE A BSTRACT We present a two-dimensional approach we call Data Vases that yields a compact pictorial display of a large number of numeric values varying over time. Our method is based on an intuitive and flexible but less widely-used display technique called a “kite diagram.” We show how our interactive two-dimensional method, while not limited to timedependent problems, effectively uses shape and color for investigating temporal data. In the future we hope to demonstrate that our methods can be extended to three dimensions for visualizing time-dependent data on cartographic maps. K EYWORDS Time series visualization, kite diagrams, glyph-based representation. I NDEX T ERMS H.5.2 [Information Interfaces and Representation]: User InterfacesScreen Design; I.3.8 [Computing Methodologies]: Computer Graphics-Applications 1

I NTRODUCTION

In this work we address challenges associated with the graphical representation and exploration of multiple time-dependent quantities. Our motivation is to support visual analysis of data such as census records that can have a large number of interesting correlations and trends in the temporal domain. Some specific challenges and issues addressed in our work are: • Displaying several time-dependent or time-varying quantities simultaneously without causing overplotting. • Developing effective graphical representations that exploit a human user’s visual and cognitive abilities to detect interesting changes in time and quickly get overviews of data having multiple time-varying quantities. • Census records and many other temporal data often contain a geospatial context such as cartographic maps. A challenge is how to expose patterns in the temporal and spatial domains while maintaining the ability to inspect several time-varying quantities. Our method creates what we call Data Vases: interesting and intuitive graphical patterns of time-varying data. Data vases are helpful in many analysis tasks such as quick comparison of global and local time-varying patterns across many data sets, identification of outliers, and exploration of data with multiple levels of temporal granularity. 2

BACKGROUND

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R ELATED W ORK

Many sophisticated solutions have been developed to display multiple time series data that provide both context and details of the data. A representative example of one of the standard approaches is ThemeRiver [2], in which the time-varying quantities are displayed as smooth areafilled and layered profiles, and which generates aesthetically pleasing “currents.” However, this approach is often limited to the display of primarily non-negative data. While another similar method called • Sidharth Thakur is with The Renaissance Computing Institute, Raleigh, North Carolina (USA), E-mail: [email protected]. • Theresa-Marie Rhyne is with The Renaissance Computing Institute and North Carolina State University, Raleigh, North Carolina (USA), E-mail: [email protected] and [email protected].

Fig. 1: Illustration of the steps involved in the creation of a kite diagram of a simple data series shown in step 1. In the case of multiple data series separate kite diagrams may be generated for comparison. Horizon Graphs [1] allows plotting of negative data, it sacrifices the ability to compare overall profiles of the time-varying quantities. To address some of these challenges we propose a method that exploits an intuitive two-dimensional method called kite diagrams [3]. Kite diagrams utilize simple graphical shapes that enable an observer to visually inspect and compare up to a few data series. Figure 1 illustrates the steps involved in the generation of a kite diagram of a simple data series (shown in step 1). An attractive feature of kite diagrams is that they may be used for representing data that have a temporal domain as well as multi-variate data that do not involve time. Kite diagrams are commonly used for plotting simple statistical data. To extend kite diagrams to the visualization of more complex data, some limitations need to be overcome. For example, kite diagrams are suitable for displaying positive values only. Another limitation is that comparing multiple series in static kite charts is difficult and more interactive exploratory tools are needed. 3 Data Vases: D ISPLAY OF M ULTIPLE T IME S ERIES We present an approach for visualizing multiple time series that combines kite diagrams and some standard effective guidelines for laying out dense information in two dimensions. Our methods exploit many salient perceptual organizing principles of graphical representations [6] such as bilateral symmetry, closure of shapes, and distinction between figure and ground to create information-rich and interesting glyphs (or graphical widgets) of the data. Figure 2 shows data vases corresponding to the crimes reported in the hundred counties in North Carolina (USA). An individual data vase shape represents the profile of a single time series and is plotted along a vertical temporal axis with time increasing from the bottom towards the top. As in the kite diagrams, the absolute values of the data variable are encoded by the width of the graphical shapes. The resulting shapes look like profiles of flower vases; we therefore use the term data vases to refer to this representation of time series data. The temporal axis may be drawn horizontal to accommodate a larger number of time steps relative to the other data dimensions. We next discuss different aspects of data representations and exploratory features of our visualization approach based on data vases. 3.1 Interpolated Versus Discrete Profiles of Data Vases The data vases shown in Figure 2 are constructed using profiles of line graphs, which employ a linear interpolation between consecutive time steps to represent a continuous data series. However, in many data, such as census surveys, the quantities may not vary linearly between each time step. We therefore utilize profiles of bar graphs or histograms to generate “discrete” representations of the data.

Fig. 2: Evolution of the kite-diagram-based data vases for displaying the number of crimes reported from 1980 to 2005 in North Carolina’s (USA) 100 counties. Some important features of the representation have been highlighted in the figure. 3.2 Color Coding The symmetric shapes of data vases are limited to conveying the absolute values of the variables. We exploit different color coding schemes to enhance the data vase charts to improve differentiation of the data values and to represent additional information. For example, different color palettes can be used to represent positive and negative data values. The color coding of data vases provide other enhancements over kite diagrams such as the representation and exploration of more complex data. For example, the data vases can be color-coded using a segmented color palette instead of a continuous palette; this enables the identification of time periods for the different regions when the migration rates changed significantly. Other color coding schemes that are sensitive to statistical properties in the data can be combined with the data vase technique to create additional useful representations of the time-varying data [5]. 3.3 Sorting Options Other interesting and informative representations of the timedependent quantities are possible by sorting the data vases using some other criteria. The data vases can be ordered according to some statistical properties (e.g. mean) for investigating interesting associations and patterns in other data dimensions as well. 3.4 Data Vases and Data Filtering Data filtering is an indispensable analytic tool in any type of visualization for investigating dense data. Some standard data filtering methods include user-driven dynamic queries that employ straightforward graphical widgets like sliders and range selectors [4]. These and other data filtering tools can be used effectively with data vases for exploring time-varying data and to answer some analytic questions, for example, when certain data values of interest appear in the data. For example, the data vases can be filtered to show either the negative or the positive migration rates. 3.5 Exploration of Different Levels of Granularity An important characteristic in some time-varying data is the different levels of granularity of the temporal domain : for example, variable values may be recorded over different resolutions of time such as days, months, or years. Data vases can be used for representing the different temporal granularities in the data using some straightforward strategies; for example, • Data vases can be created using averages over the coarser time scales to show summaries of the data series, and • Interactive techniques can be used for exploring the data values corresponding to the finer resolutions in the given temporal range(s); for example, exploring monthly rates for a given year. Figure 3 shows two views of a data set having two levels of granularity. The data values in the chart correspond to average unemployment rates for yearly time intervals (background) and the rates for monthly time intervals (foreground). In the chart in the background the average for the year 1999 has been replaced by monthly rates. This

Fig. 3: Charts showing vases corresponding to different levels of temporal granularity in data pertaining to unemployment rates in different counties in North Carolina. Unemployment rates averaged over each year (background); monthly unemployment rates (foreground). ability to collapse and expand different levels of granularity nicely affords the exploration of the data with multiple levels of details. 4 C ONCLUSION We have highlighted a visualization technique for creating engaging and informative displays of multiple time series. Our method is based on an intuitive two-dimensional graphical plot entitled “kite diagrams,” and involves the creation of interesting graphical shapes that we call “data vases” to represent time-varying numeric data. We have presented several example visualizations and discussed various standard graphical techniques (e.g. color coding) and exploration tools (e.g. dynamic data filtering) that can be used with our method. A future extension of our technique will deal with the representation and exploration of temporal data on cartographic maps. ACKNOWLEDGEMENTS This work was conducted at the Renaissance Computing Institute’s Engagement Facility at North Carolina State University. We thank many members of the North Carolina State University community who provided comments, feedback, and data sets to evolve this work. R EFERENCES [1] S. Few. Time on the horizon. Online at - http://www. perceptualedge.com/ articles/visual business intelligence/time on the horizon.pdf, Jun-2008. [2] S. Havre, B. Hetzler, and L. Nowell. Themeriver (tm): In search of trends, patterns, and relationships, 1999. [3] C. R. C. Sheppard. Species and community changes along environmental and pollution gradients. Marine Pollution Bulletin, 30(8):504 – 514, 1995. [4] B. Shneiderman. Dynamic queries for visual information seeking. IEEE Softw., 11(6):70–77, 1994. [5] C. Tominski, G. Fuchs, and H. Schumann. Task-driven color coding. In Procs. of the 2008 12th Intl. Conference Information Visualization, pages 373–380, Washington, DC, USA, 2008. IEEE Computer Society. [6] C. Ware. Information Visualization: Perception for Design. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2004.