CiteRivers: visual analytics of citation patterns

Note:

This visual tool is aim to help explore the citation network of the given publications (conference proceeding). It shows the citation word cloud, trend, diversity, author and publisher venue.

Points:

• Not clear of the scale of stream panel and the relation with spectral clustering. The benefit of using clustering techniques to show the publication in a river style is not clear. 
• An across stream citation analysis would be useful, i.e. to select more than one cell of the river. 
• The word meaning in the word cloud may be varied. E.g. the network is with multiple meaning across different research, even in the same domain. 
• The user case showed the citation pattern of given IEEE publications, but lack of the discussion of the found pattern. This may be the key value to the target users. 
• A user case that may be interesting: The given year publications are major based on which year's work? This could be a influence index for the past works (also the scholar). 

VIS15 preview: CiteRivers: Visual Analytics of Citation Patterns from VGTCommunity on Vimeo.


Reference:

1. Heimerl, Florian, et al. "CiteRivers: visual analytics of citation patterns." IEEE transactions on visualization and computer graphics 22.1 (2016): 190-199.

A visual analytics agenda

Note

This paper points out the potential research directions for visual analytics.

• let user obtain deep insight, assessment, planning and decision making. 
• let user see, explore and understand large amounts of information simultaneously
• convert all types of conflicting and dynamic data in ways that support visualization and analysis.
• communicate the information in the appropriate context to a variety of audiences. 

The science of analytical reasoning, take a crisis event as example.

• understanding historical and current situations. 
• identifying possible alternative future scenarios
• monitoring current events to identify both expected and unexpected events. 
• determining indicators of the intent of an action or an individual.
• support the decision maker in times of crisis. 

visual representations and interaction technologies

• facilitate understanding of massive and continually growing collections of data of multiple types. 
• provide frameworks for analyzing spatial and temporal data
• support the understanding of uncertain, incomplete, and misleading information. 
• provide user and task-adaptable guided representations that enable full situation awareness while supporting development of detailed actions. 
• support multiple levels of data and information abstraction, including integration of different types of information into a single representation. 

Data representations and transformations

• transforming data into new scalable representations that faithfully represent the underlying data's relevant content. 
• synthesize different types of information from different sources into a unified data representation, so users can focus on the data's meaning in the context of other relevant data
• develop methods and principles for representing data quality, reliability and certainty, measure through-out the data transformation and analysis process. 

Reference

1. Thomas, James J., and Kristin A. Cook. "A visual analytics agenda." IEEE computer graphics and applications 26.1 (2006): 10-13.

Effectively Communicating Numbers & Tapping the power of visual perception

Note

A good introduction white paper about how to present the quantitative information for business. It is a good basic reading material [1]. The data visualization is used to enable the "visual perception" of the user, "human visual system is a pattern seeker of enormous power and subtlety". In other hand, if the data present in different way, the user may not be able to catch the invisible patterns, which makes the less effective communication.

The human eye is catching light and translate them into color and thoughts [2]. The light shines on the fovea area would be highlighting to catch more attention. The other parts of retina may with less detail, but the capable and ready to catch any point of changes, e.g. something moving or pop-up. Besides, the human brand is with long and short term memory. The short term memory is processing and discarding the received information, like a RAM in computer. The speed is quick but with very limited capacity. The long term memory requires more time to organize but can last longer for later use, like a hard drive. It would be crucial to design the visualization follow the nature of human brand preference.

There are two kind of attention of visual perception, pre-attentive and attentive. The pre-attentive is processing very quick and parallel, like pop up in your eye. For instance, a serial number with highlighted target number. The highlighted number would jump out the serial of number for the user to recognize. The preventative attribute can only be accurately to encode number in 2D locations, e.g. 2D scatter plots. More than 2D would turn the display into attentive process, which requires more time and serial process effort. One exception is to use the colored points for categorical distinguish. Hence, a 2D scatter plot with color categorical may be the best use case for user to understand the data.

Reference

1. Few, Stephen, and Perceptual Edge Principal. "Effectively Communicating Numbers." Principal Perceptual Edge. White Paper. Downloaded from (2005).
2. Few, Stephen. "Tapping the power of visual perception." Visual Business Intelligence Newsletter (2004).

Google+ ripples: A native visualization of information flow

Note

A nested circles style to present the temporal pattern of re-sharing. The sharing action is structure as tree-map. The nested circle helps to highlight the cluster in each branch. This paper discusses the design factors included: social media sharing pattern, rendering, interaction and animation. I think it would be a useful way to tell the story about the temporal, social network trends. The display is bright for the user to understand the whole picture of the certain topic or post to spread.

An extend reading of the nested circle of [2]. The paper models the exploratory search tasks as a radar plot. The user can drag the interested item into the plot to filter the result. In [1], the figure helps to show the social media sharing pattern as circles, however, in [2], from a different perspective, to help the user to filter the result. The two scenario may mutually relevant.

Reference

1. Viégas, Fernanda, et al. "Google+ ripples: A native visualization of information flow." Proceedings of the 22nd international conference on World Wide Web. ACM, 2013.
2. Kangasrääsiö, Antti, et al. "Interactive Modeling of Concept Drift and Errors in Relevance Feedback." arXiv preprint arXiv:1603.02609 (2016).

The structure of the information visualization design space

Note:

This paper provided a framework to organize and structure the visualization plots. It considers the following features:

1. Data Type: Nominal, Ordinal, Quantitative, Intrinsically Spatial, Geographical, Set mapped to itself
2. Function for recording data: filter,sorting,multidimensional scaling,interactive input ot a function
3. Recorded Data Type: same as Data Type
4. Control Processing : tx (text)
5. Mark Type: point,line,surface,area,size
6. Retinal properties: color, size, connection, enclosure
7. Position in space time: position in space time, N (Nominal) O (Ordered) Q (Quantitative)
8. View transformation: ::=nb (hyperbolic mapping)
9. Widget: slider, radio buttons

For example: Multi-Dimensional Tables

Points: 1) many of the visualization is not web-based. Is there any particular reason to use web standard? 2) if the web-based visualization, what is the framework different? e.g. the web-based application may using more mouse gesture to click, scale and hover. Or, with help of useful libraries like D3.js, how does it influences the implementation of data visualization? 3) the design space for non-web-based applications are more open and less limitation, but accessibility is weak to share and collaborative.

Worth to read more: [2], [3], [4] for the web-based space of data visualization.

Reference:

1. Card, Stuart K., and Jock Mackinlay. "The structure of the information visualization design space." Information Visualization, 1997. Proceedings., IEEE Symposium on. IEEE, 1997.
2. Figueiras, Ana. "A Typology for Data Visualization on the Web." IV 13 (2013): 351-358.
3. Turetken, Ozgur, and Ramesh Sharda. "Visualization of web spaces: state of the art and future directions." ACM SIGMIS Database 38.3 (2007): 51-81.
4. Brath, Richard, and Ebad Banissi. "Using Typography to Expand the Design Space of Data Visualization." She Ji: The Journal of Design, Economics, and Innovation 2.1 (2016): 59-87.

A Tour through the Visualization Zoo

Note

This paper introduced the basic figure plots for data visualization. The mentioned schemes included:

• Time Series Data: Index Chart

• Time Series Data: Stacked Graph

• Time Series Data: Small Multiples

• Statistical Distribution: Horizon Graph

• Statistical Distribution: Stem-and-Leaf Plot

• Statistical Distribution: Q-Q Plots

• Statistical Distribution: Scatter Plot

• Statistical Distribution: Parallel Coordinates

• Maps: Flow Map

• Maps: Choropleth Map

• Hierarchies: Node-Link

• Adjacency Diagrams: Lcicle Tree Layout

• Adjacency Diagrams:Enclosure Diagrams

• Network: Treemap

• Network: Nested Circles

• Network: Force-directed Layout

• Arc Diagram

• Matrix View

Reference

1. Jeffrey, Heer, Bostock Michael, and Ogievetsky VADIM. "A Tour through the Visualization Zoo." Communications of the ACM 53.6 (2010): 56-67.

High-dimensional data visualization

Note:

This paper introduced the basic figure plots to display the multi-dimensional data. The mentioned schemes included:

• Mosaic Plots

This plot is good for categorical data display, for the user to compare the different between features. But it requires the user to pay attention to multiple directions (top/bottom, left/right), which makes it harder to follow, less user perception. Besides, this plot provides a quick overview categorically, but for ordinal and interval variables.

• Trellis Displays

Nice to provide a comparison between variables, not suitable for temporal data and categorical data. Besides, many of the cells may repeating or empty.

• Parallel Coordinate Plots

Nice to show the temporal data, requires the skill to solve the overplotting, scaling and sorting problems.

• Projection Pursuit and the Grand Tour

Not easy for the human brand to process a 3D plot, but it shows the dynamic between the dimension projection. For instance, using a scatterplot with 3 dimensions, let the user explore the pattern across dimensions, is one type of grand tour.

Summary

A summary with the functionality of exploration and presentation included the interactivity of each plot. However, I think the Trellis may also provide interactively, e.g. this demo. 

Reference

1. Theus, Martin. "High-dimensional data visualization." Handbook of data visualization. Springer Berlin Heidelberg, 2008. 151-178.