An Instrument for Evaluating the Quality of Data Visualizations

Visualizing data in tables usually is not the best way to help users understanding large amounts of data. Instead, data visualization in graphical format, such as charts, has been used to explore available data. A good data visualization is defined as a well-designed presentation of interesting data, aiming to communicate ideas with clarity, precision and efficiency. Nowadays, considering the open data movement, many open data portals offer different types of data visualization. However, when browsing some of these portals, we can find many bad data visualizations, frequently ambiguous, confusing and unusable. Hence, it is essential to have an instrument capable of analyzing the quality of data visualizations, helping the designer to use the full capacity of a data visualization to provide a more efficient resource information to the users. This work aims to present an instrument that integrate a set of heuristics to assess the quality of data visualizations. Such heuristics were chosen due to have been previously proposed in several works in literature, and proved successful. To evaluate the proposed instrument, we conducted an experiment with a group composed by computer science graduate students. We analyzed the results using Cohen's Kappa and Any-two agreement statistical tests, which indicated that the instrument is adequate.