Data and information visualization

Summary

Data and information visualization (data viz or info viz) is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data (exploratory visualization). When intended for the general public (mass communication) to convey a concise version of known, specific information in a clear and engaging manner (presentational or explanatory visualization),

Official source

https://en.wikipedia.org/wiki/Data_and_information_visualization

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Interactive visualization for resource allocation tasks

EPFL2000

This article explores how readers recognize their personal identities represented through data visualizations. The recognition is investigated starting from three definitions captured by the philosopher Paul Ricoeur: the identification with the visualization, the recognition of someone in the visualization, and the mutual recognition that happens between readers. Whereas these notions were initially applied to study the role of the book reader, two further concepts complete the shift to data visualization: the digital identity stays for the present-day passport of human actions and the promise is the intimate reflection that projects readers towards their own future. This article reflects on the delicate meaning of digital identity and the way of representing it according to this structure: From Personal Identity to Media is a historical introduction to self-recognition, Data Visualization for Representing Identities moves the focus to visual representation, and The Course of Recognition breaks the self-recognition in through the five concepts above just before the Conclusion.

2019

Self-recognition is an intimate act performed by people. Inspired by Paul Ricoeur, we reflect upon the action of self-recognition, especially when data visualization represents the observer itself. Along the article, the reader is invited to think about this specific relationship through concepts like the personal identity stored in information systems, the truthfulness at the core of self-recognition, and the mutual-recognition among community members. In the context of highly interdisciplinary research, we unveil two protagonists in data visualization: the designer and the observer - the designer as the creator and the observer as the viewer of a visualization. This article deals with some theoretical aspects behind data visualization, a discipline more complex than normally expected. We believe that data visualization deserves a conceptual framework, and this investigation pursues this intention. For this reason, we look at the designer as not just a technician in the visualization production, but as a contemporary ethnologist - the designer as a professional working in a social environment to comprehend the context and formulate a specific inquiry with the help of appropriate visual languages.

2016