Visualizing Sixteen Years of Data About Portuguese Leisure-Cultural Activities

Visualizing Sixteen Years of Data About Portuguese Leisure-Cultural Activities

Clarissa Rodrigues (Information Systems Department, engageLab, University of Minho, Guimarães, Portugal) and Elizabeth Carvalho (Business and Information Technology Research Center (BITREC), University Atlântica, Barcarena, Portugal)
DOI: 10.4018/jcicg.2012070102


This paper describes an interactive data visualization application that aims to show how the Portuguese people spent culturally their leisure time between 1994 and 2009. The leisure trend is displayed to the end-user through the use of different visualization techniques and visual cues. The authors developed the visual representations based on the use of simple and regular visual shapes that could be easily combined, interpreted, memorized and used. To better evaluate their results, the authors tested their prototype against a preselected group of subjects.
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According to Peters (2007), images are especially powerful whenever it is difficult to describe the depicted information by words or numbers. In the visualization research area, there are two categories to discern: the scientific visualization and the information visualization. The main difference between them is that the first one focuses on physical data such as molecules, the human body and data from natural phenomena, for instance, while the second one focuses on abstract, non-physical data such as text, hierarchies and statistical data (Mackinlay, 2000). The most important aspect though is that they share visual techniques and perspectives (Rhyne, 2003).

One of the greatest challenges when dealing with information or scientific visualization is that we do not have any rule of a thumb or sufficiently stable methodology that should be followed, in order to produce a 100% acceptable solution. Instead we have to consider several guidelines that suggest the best way to be followed according to data characteristics, visualization goals and potential end-users profiles.

Furthermore, to create a visualization requires a number of complex and not straightforward judgments. One must determine which questions to ask, to identify the appropriate data, and to select effective visual encodings to map data values, using graphical features such as position, size, shape, and color (Heer et al., 2010). The challenge is that for any given dataset the number of visual encodings—and thus the space of possible visualization designs—is extremely large.

There are taxonomical studies on interaction in visualization, e.g. (Tweedie, 1997; Ward & Yang, 2004; Yi et al., 2007), and taxonomy proposals for specific classes of techniques and applications, e.g. (Ellis & Dix, 2007; Elmqvist & Tsigas, 2008). Brodlie (1993) proposed a notation for symbolic labeling visualization methods. Card and Mackinlay (1997) depicted a descriptive structure for visualization. Duke et al (2005) presented an argument to bring taxonomy and ontology together. Other important examples are given by Bertin (1981; 1983), Cleveland and McGill (1984) and Wilkinson (1999).

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