As of today, data analysis focuses primarily on the findings to be made inside the data and concentrates less on how those findings relate to the domain of investigation. Contemporary visualization as a field of research shows a strong tendency to adopt this data-centrism. Despite their decisive influence on the analysis result, qualitative aspects of the analysis process such as the structure, soundness, and complexity of the applied reasoning strategy are rarely discussed explicitly. We argue that if the purpose of visualization is the provision of domain insight rather than the depiction of data analysis results, a holistic perspective requires a qualitative component to to be added to the discussion of quantitative and human factors. To support this point, we demonstrate how considerations of qualitative factors in visual analysis can be applied to obtain explanations and possible solutions for a number of practical limitations inherent to the data-centric perspective on analysis. Based on this discussion of what we call qualitative visual analysis, we develop an inside-outside principle of nested levels of context that can serve as a conceptual basis for the development of visualization systems that optimally support the emergence of insight during analysis.

Abstract In order to discuss the kinds of reasoning a visualization supports and the conclusions that can be drawn within the analysis context, a theoretical framework is needed that enables a formal treatment of the reasoning process. Such a model needs to encompass three stages of the visualization pipeline: encoding, decoding and interpretation. The encoding details how data are transformed into a visualization and what can be seen in the visualization. The decoding explains how humans construct graphical contexts inside the depicted visualization and how they interpret them assigning meaning to displayed structures according to a formal reasoning strategy. In the presented model, we adapt and combine theories for the different steps into a unified formal framework such that the analysis process is modelled as an assignment of meaning to displayed structures according to a formal reasoning strategy. Additionally, we propose the ConceptGraph, a combined graph‐based representation of the finite‐state transducers resulting from the three stages, that can be used to formalize and understand the reasoning process. We apply the new model to several visualization types and investigate reasoning strategies for various tasks.