Data Visualization Unit 2

   Fisheye View.  

A fisheye view is a type of visual distortion used in data visualization and computer graphics to emphasize certain areas of interest while simultaneously providing an overview of a larger dataset or scene. This technique is inspired by the optical distortion of fisheye lenses used in photography, where objects near the center of the field of view appear magnified while those farther away are compressed.

In data visualization, fisheye views are often used to address the challenge of displaying large and complex datasets, where it's essential to maintain context while focusing on specific details. Here are some key characteristics and applications of fisheye views:

1. Distortion: Fisheye views apply distortion to the data visualization, with the center of the view showing a magnified or detailed portion of the data while the outer areas exhibit a compressed or abstract representation. This allows users to maintain awareness of the entire dataset while exploring details.

2. Focus and Context: Fisheye views provide a balance between focusing on specific data points or regions (the "focus") and understanding the overall structure or context of the data (the "context"). Users can interactively zoom in to explore details and zoom out to see the broader picture.

3. Navigation: Fisheye views are often used for interactive navigation in large graphs, maps, and diagrams. Users can click or hover over a specific area to expand it for closer examination and then easily return to the overview.

4. Information Hierarchies: Fisheye views are useful for displaying hierarchical data structures, such as tree maps and nested diagrams. Users can expand and collapse nodes in the hierarchy while maintaining an understanding of the overall structure.

5. User Interaction: Interactive fisheye views are commonly employed in user interfaces to improve usability. For example, in file explorers, the fisheye effect can be applied to directory structures, allowing users to see the contents of folders without leaving the current view.

6. Network Visualization: Fisheye views are valuable for visualizing complex networks, such as social networks or computer networks. They enable users to zoom in on specific nodes or connections while preserving the overall network topology.

7. Scientific Visualization: Fisheye views can be used in scientific data visualization to explore large datasets, such as climate models or molecular structures. Researchers can zoom in on specific data points or regions of interest while maintaining awareness of the broader dataset.

8. Information Dashboards: In dashboards with multiple charts and widgets, fisheye views can be applied to individual components to allow users to explore data in more detail without overwhelming the entire dashboard.

Fisheye views are particularly effective when combined with interactive features like mouse or touch input, allowing users to control the degree of distortion and focus as they explore complex datasets. This approach strikes a balance between providing a detailed view of data and ensuring users do not lose sight of the bigger picture.

Non-Linear Magnification

Non-linear magnification in data visualization (DV) refers to the use of magnification or scaling factors that are not constant across the entire visualization but vary non-linearly based on certain criteria or conditions. This technique is employed to emphasize specific data points or regions of interest in a non-uniform manner, allowing for more effective representation and exploration of data. Here are some key aspects and applications of non-linear magnification in DV:

1. Conditional Magnification: Non-linear magnification can be applied conditionally based on certain rules or criteria. For example, data points that meet specific thresholds or significance levels may be magnified more than others, making it easier for users to identify critical data.

2. Data Clustering: In some cases, non-linear magnification is used to cluster data points or areas with similar characteristics. Clusters may be magnified differently to highlight variations within and between clusters.

3. Zooming and Interaction: Non-linear magnification can be integrated into interactive zooming features, allowing users to focus on specific regions of interest with varying levels of magnification. This is particularly useful for exploring large datasets.

4. Heatmaps and Color Scales: Heatmaps often employ non-linear magnification of color scales to highlight areas with higher or lower values. This technique is used in various domains, such as geographical mapping and gene expression analysis.

5. Dynamic Magnification: In dynamic visualizations, non-linear magnification can change over time or in response to user interactions. For instance, when visualizing stock market data, the magnification might increase during periods of high volatility.

6. Tree Maps: Non-linear magnification is commonly used in tree maps, a type of visualization that represents hierarchical data structures. In a tree map, larger rectangles may represent higher-level categories, while smaller rectangles within them represent subcategories, and the level of detail can vary significantly.

7. Focus+Context Techniques: Focus+context techniques often employ non-linear magnification to create a central focus area with a higher level of detail, surrounded by less detailed context information.

8. Scale Breaks: Non-linear magnification is used to create scale breaks in certain types of charts or graphs. This helps to highlight important data ranges while compressing less relevant portions of the data.

9. Density-Based Visualization: In density-based visualizations, non-linear magnification can be used to emphasize regions with higher data density, making it easier to spot clusters or patterns.

10. Visual Storytelling: Non-linear magnification is a valuable tool in visual storytelling, where it can be used to emphasize key plot points or highlight specific aspects of a narrative.

Non-linear magnification in DV can significantly enhance the effectiveness of visualizations by guiding users' attention to the most important and informative elements while de-emphasizing less critical details. It provides a flexible means of focusing on specific data features, which is especially useful when dealing with complex and heterogeneous datasets.

This concludes our unit 1 and unit 2 of Data Viz.

With this , I hereby state that the I or the institute does not disclaim any copyright, we have used all the educational materials only for studies, purely academic purposes, 

We are not responsible for any unauthorized , unfair means used for by others.

Snehal Moghe

Faculty, CSE Department

Medi-Caps University Indore (MP) India

snehal[dot]moghe[at]medicaps[dot]ac[dot]in

snehalmindore[@]gmail[dot]com