Kirk (2016) tells us that data adjustments affects what dat…
Kirk (2016) tells us that data adjustments affects what data is displayed and presentation adjustments affects how the data is displayed. Each of the adjustments involve specific features. Data adjustments include: Framing, Navigating, Animating, Sequencing, and Contributing. Presentation adjustments include: Focusing, Annotating, and Orientating.
Answer
Kirk (2016) provides valuable insights into the impact of data adjustments and presentation adjustments on the visualization of data. These adjustments play a crucial role in how data is both displayed and perceived. It is important to note that data adjustments influence what data is presented, while presentation adjustments determine how the data is presented. In this paper, we will delve deeper into these adjustments and explore their specific features.
Data adjustments encompass a range of techniques that help in shaping the data that is ultimately displayed in a visualization. These techniques include framing, navigating, animating, sequencing, and contributing. Each of these adjustments imparts a distinct effect on the data presentation and contributes to the overall understanding and interpretation of the visualized information.
Framing is a data adjustment technique that involves selecting a specific subset of data to be displayed within the visualization. By narrowing down the focus to relevant data, important insights can be drawn and visual clutter can be minimized.
Navigating refers to the process of allowing users to interactively explore the visualization by providing various means for maneuvering through the data. This adjustment facilitates user engagement and comprehension by empowering them to navigate through the data in a more interactive and intuitive manner.
Animating involves the use of motion and dynamic elements to visualize changes over time or to illustrate relationships between different data points. This adjustment helps in highlighting temporal patterns, trends, and correlations, giving viewers a richer understanding of the data.
Sequencing involves arranging the data in a particular order to highlight specific aspects. By controlling the sequence in which data is presented, certain patterns or relationships can be emphasized, leading to more effective communication of information.
Contributing adjustment focuses on enabling users to actively participate in the visualization process by allowing them to add their own data or insights to the visualization. This interactive feature enhances collaboration and promotes a deeper understanding of the data by encouraging users to contribute their unique perspectives.
On the other hand, presentation adjustments focus on how the data is visually presented to the users. These adjustments have a direct impact on the clarity, emphasis, and interpretation of the visualized information.
Focusing is a presentation adjustment that involves directing the viewer’s attention to specific elements within the visualization. By emphasizing certain data points, relationships, or patterns, focusing helps in guiding the viewer’s perception and understanding of the data.
Annotating is another presentation adjustment that involves adding textual or graphical annotations to the visualization to provide additional information or context. This adjustment enhances the interpretability and relevance of the visualized data by providing explanatory notes or highlighting key findings.
Lastly, orientating adjustment pertains to the arrangement and positioning of elements within the visualization. This adjustment includes aspects like layout, color, scale, and visual hierarchy, which collectively influence the overall visual appeal and clarity of the visualization.
In conclusion, Kirk’s framework provides a comprehensive understanding of the impact of data adjustments and presentation adjustments on data visualization. Data adjustments influence what data is displayed, while presentation adjustments determine how the data is displayed. Each of these adjustments incorporates specific features that contribute to the effective communication and comprehension of visualized data. By considering and implementing these adjustments, visualization designers can create more insightful and informative visual representations of data.