The purpose of this article is to explore the potential of visualization for corporate knowledge management. The employed methodology consists of a taxonomy of visualization formats that are embedded in a conceptual framework to guide the application of visualization in knowledge management according to the type of knowledge that is visualized, the knowledge management objective, the target group, and the application situation. This conceptual framework is illustrated through real-life examples. Our findings show that there is much room for knowledge management applications based on visualization beyond the mere referencing of experts or documents through knowledge maps. The research implications thus consist of experimenting actively with new forms of visual knowledge representation and evaluating their benefits or potential drawbacks rigorously. In terms of practical implications, the authors encourage managers to look beyond simple diagrammatic representations of knowledge and explore alternative visual languages, such as visual metaphors or graphic narratives. The originality and value of this paper consists of two elements: first, the systematic, descriptive and prescriptive approach towards visualization in knowledge management, and second the innovative examples of how to harness the power of visualization in knowledge management
This paper focuses on an aspect which has been neglected, but which is decisive: The transfer of knowledge to different stakeholders; especially the transfer of insights derived from information visualization tools. In knowledge management the transfer of knowledge is a core process, which can be improved by using our innate abilities to process visual representations. The potential of visualizations are manifold. But business managers miss a holistic framework on the use of visualization methods for information exploration and communication tasks. This paper analyzes how architects use visualizations to amplify cognition and to transfer knowledge. It introduces a mediating framework that brings together isolated research directions and defines the new research focus knowledge visualization. Knowledge visualization examines the use of visualizations for the transfer of knowledge between at least two peoples. We found that the new focus is decisive and has implications for researchers in information visualization and knowledge management.
Abstract. This article presents synergies between the research areas information visualization and knowledge visualization from a knowledge management and a communication science perspective. It presents a first theoretical framework and a model for the new field of knowledge visualization. It describes guidelines and principles derived from our professional practice and previous research on how architects successfully use complementary visualizations to transfer and create knowledge among individuals from different social, cultural, and educational backgrounds. The findings and insights are important for researchers and practitioners in the fields of information visualization, knowledge visualization, knowledge management, information design, media didactics, instructional psychology, and communication sciences.
Urban forms at human-scale, i.e., urban environments that individuals can sense (e.g., sight, smell, and touch) in their daily lives, can provide unprecedented insights on a variety of applications, such as urban planning and environment auditing. The analysis of urban forms can help planners develop high-quality urban spaces through evidence-based design. However, such analysis is complex because of the involvement of spatial, multi-scale (i.e., city, region, and street), and multivariate (e.g., greenery and sky ratios) natures of urban forms. In addition, current methods either lack quantitative measurements or are limited to a small area. The primary contribution of this work is the design of StreetVizor, an interactive visual analytics system that helps planners leverage their domain knowledge in exploring human-scale urban forms based on street view images. Our system presents two-stage visual exploration: 1) an AOI Explorer for the visual comparison of spatial distributions and quantitative measurements in two areas-of-interest (AOIs) at city- and region-scales; 2) and a Street Explorer with a novel parallel coordinate plot for the exploration of the fine-grained details of the urban forms at the street-scale. We integrate visualization techniques with machine learning models to facilitate the detection of street view patterns. We illustrate the applicability of our approach with case studies on the real-world datasets of four cities, i.e., Hong Kong, Singapore, Greater London and New York City. Interviews with domain experts demonstrate the effectiveness of our system in facilitating various analytical tasks.
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