Disease incidence is usually connected to biological factors such as genetics, eating habits, exercise and so on. But are there are other socioeconomic factors that inﬂuence disease incidence as well? This TED talk from Bill Davenhall inspired us to explore socioeconomic factors that may influence disease incidence.

To explore the connections of socioeconomic factors such as education levels, regional population, income level of the area where you live and the air pollution in terms of toxic levels, we developed a visualization tool called DiseaseTrends. DiseaseTrends allows the exploration of possible correlations between those socioeconomic factors with diabetes prevalence and cancer incidence rates across counties throughout the United States. A user can interactively explore these factors at a county, regional (user defined cluster of counties), state or national level.

When a user explicitly selects a county, we display 5 similar counties based on their socioeconomic factors. The motivation behind this feature is to allow users to identify similar counties that may have varying disease incidence rates, which may in turn lead to further exploration.

As mentioned above, a user can specify regions manually that cross state boundaries. A user defined circular cluster can be specified using Ctrl on PC, Cmd on Mac – then click and drag. Here the user has specified four regions. The maximum prevalence (in red) and the minimum (in green) across the selected region is highlighted in the panels below.

We use bullet graphs (pdf link) as introduced by Stephen Few to display the quartile distribution of a factor as well as the corresponding state and national average (faint and dark vertical bars).

Through this tool, we can easily see the now popular diabetes belt, as shown here

High incidence rates in Native Indian reservations such as Navajo County, Sioux County, Rolette County and Big Horn County too can be seen.

We would like to mention that DiseaseTrends does not imply any causation and can merely hint at possible associations. It is completely up to a researcher in the field of public policy / public health to further investigate the findings.

Bernice E. Rogowitz covered fundamentals in human perception and cognition, and discussed how they apply to visualization. She covered a huge array of topics, ranging from the pupil being partially responsible for our depth perception, all the way to color theory and how it relates directly to the biology of the human eye.

The presentation had a great flow, starting at a very high level to give everyone an idea of what questions they would be able to answer at the end. As the talk progressed, she covered detailed biological details of the human eye, and progressed to the intersection of perceptual issues and computer science.

In the biological portion, we learned that there are five layers of cells in the retina, each responsible for different tasks. Much of the interesting stuff happens at the very beginning (photoreceptor distribution) and then further into the process at the ganglion cells. She went over how lateral inhibition is caused by the spatial distribution of the photoreceptors connected to a single ganglion cell, and how this is the reason for several of the optical illusions we perceive. She did a great job of explaining the connections between biology and perceptual issues.

Cultural differences were also addressed. The eye movements we have are actually learned when we learn how to read. Cultures with different reading directions have substantially different reading directions.

The section on the Striate Cortex was especially interesting. This is the first time in the visual system that images from each eye are merged (the point where depth perception occurs). This section sends output to 60% of the brain! This is a huge amount, and makes the visual system incredibly important to the decision making process.

This tutorial had a huge quantity of useful information and was really well put together! She concluded with a great summary of four things to remember:

There are different response rates for different stimuli, how well do you want to convey magnitude information?

Color and luminance mechanisms have different spatial sensitivities.

Certain visual information is perceived “pre-attentively” such as color.

How the world is perceived depends on what the user is trying to accomplish.

These notes were transcribed by Lane Harrison (@laneharrison) and Drew Skau(@seeingstructure). They are both graduate students at UNCC. Thanks guys!! This is almost as good as being there.

The IEEE VAST Challenge this year consisted of three mini challenges and one “grand challenge”. For the uninitiated, the challenge datasets are designed to reflect real-world analytical challenges. This year’s challenges involved microblog+geospatial data, cyber security data, and a corpus of plain-text news documents.

While there is no “winner”, the challenge entries are given awards based on their respective strengths. Participants are also given detailed feedback from both visualization experts and actual analysts who work with such data. This feedback is one of the most rewarding aspects of the VAST Challenge. As we all know, it’s hard to get access to analysts.

One highlight is a geo-text visualization approach from the Universität Stuttgart. ContentLens is a novel integration of tag-cloud methods and geo-visualization and interaction techniques. Perhaps if you ask nicely, they’ll give another demo.

Another interesting approach come from Penn State, who leverage their GeoViz toolkit to analyze a large (10gb+) set of cyber security data. In fact, the security data took center stage in a panel of security and/or visualization practitioners.

Some key points in the panel suggested a need for smart collaboration in large-scale security analysis. Analysts can often unnecessarily repeat work or miss threats that has already been addressed by other analysts in their organization. Maybe we need an “Amazon- Recommendations” component for security analysis tools.

Finally, workshop participants heard about and discussed the possible future VAST Challenges. Can visual analytics handle a million-node network? Can this community design tools that enable consumers to make discoveries in the vast sea of data that surrounds them? Well, that’s up to you.

Steve Drucker from Microsoft Research talked about his rich interactive narrative player work. The tool uses XML to set up a queue of keyframes with audio narration. It supports audio narration and multimedia embedding.

Wesley Willet talked about supporting ad-hoc storytelling in social media. He raised several technical issues, including the need for interactive visualization state encoding in URLs. This would allow people to link to a specific state in the visualization, and share that state over social media.

Jerome Cukier talked about adding personal connections to visualizations to help audience engagement. He also discussed the importance of providing interaction that lets users feel like they are exploring the dataset and coming to their own conclusions. A significant take- away quote Jerome had is “Trust is not really an exact science.”

Sunah Suh framed a discussion about the impact of culture on visualization and visualization on culture. She discussed the multiple forms of literacy beyond just natural language, addressing issues of statistical literacy as well as visual. She raised the point that visualization does not just reflect and rely on societal norms, it also reinforces them. One example she gave was the pink and blue color coding in Baby Name Voyager reinforcing a binary gender concept.

The takeaways from the first half of the session dealt with technical issues of platforms and tools to support narratives, as well as some of the social issues going on in narrative visualizations. Both of these are important issues as narrative visualization finds its place in culture. The necessary tools must be developed to help narrative visualization become ubiquitous, but also there are social issues to be addressed as people come to terms with the new media.

These notes were transcribed by Lane Harrison (@laneharrison) and Drew Skau(@seeingstructure). They are both graduate students at UNCC. Thanks guys!! There were other excellent speakers who presented, but Drew and Lane were presenting in an adjoining session.

The annual IEEE Visualization, IEEE Information Visualization and IEEE Visual Analytics Science and Technology conferences – together known as IEEE Visweek will be held in Providence, RI from October 23rd to October 28th.The detailed conference program is spectacular and can be downloaded here.Some of the new events this year are under the Professional’s Compasscategory. It includes a Blind date lunch (where one can meet some researcher they have never met and learn about each others research), Meet the Editors (where one can meet editors from the top graphics and visualization journals), Lunch with the Leaders session (an opportunity to meet famous researchers in the field) and Meet the faculty/postdoc candidates (especially geared towards individuals looking for a postdoctoral position or a faculty position). I think this is an excellent idea and hope that the event is a hit at the conference.I am also eagerly looking forward towards the two collocated symposia – IEEE Biological Data Visualization (popularly known as biovis) and IEEE LDAV (Large data analysis and visualization). Their excellent programs are out and I’d encourage you to take a look at them.

The telling stories with data workshop too looks great and will be a continuation of the great tutorial held by the same group last year. I am eagerly looking forward to it.

Apart from this are the excellent papers that will be presented at the conference. I shall write another post about the ones I am particularly looking forward to. With so many exciting events going on, it almost seems like a crime to have all of them happening in the span of a few days.

I shall definitely be blogging about the event as much as I can. You can also follow me on twitter, which will have more real time tweets than the blog which will distil a days worth of information into a post.

Let me know if you are going to be around and I’ll be happy to talk to you.

Data visualization is being used for detecting fraud, especially with respect to wire and credit card transactions. Work done at the Charlotte Visualization Center at UNC Charlotte provides some interesting insights into fraud detection. This work was conducted in collaboration with the Bank of America.In the following paper they highlight four visualization techniques that allow for fraud detection.

Heatmap: A heatmap depicting the relationship between accounts and transactions.

Search by example: Find accounts with transactions/activity similar to the current account being monitored.

Strings and beads: A line graph based visualization that shows critical events as ‘beads’ on the graph. The use of a log scale for the y-axis is a neat idea and probably allows for improved exploration.

Keyword graph: A graph visualization showing keyword similarity This paper was based on previous work done by the same group titled Wirevis. I would encourage interested readers in reading the original paper as well as the previous paper (Wirevis).

Lately, I have been collecting links to videos of talks related to Data Visualization. I found multiple talks for some people and so have categorized them accordingly. I have also tried to provide some context to the individual/group.

I think the first TED talk by Hans Rosling (@hansrosling) got a lot of media attention and made people sit up and appreciate the power of ‘narrative visualization’. He almost make it look like a sport with him serving as the role of a commentator. The title on TED’s website for the talk is “the best stats you’ve ever seen“. I am not sure about that, but it is a very entertaining talk.

Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate.

Tom Wujec is a fellow at Autodesk. His talk on 3 ways the brain creates meaning provides an amazing insight into our brain. He addresses issues related to why data visualization works and how the brain visualizes data.

Nicholas Christakis presents a very fascinating talk where he used social data visualization to explore the influence of social networks – “The hidden influence of social networks.” In his talk he says that spreading of obesity is due to your social network. Smoking and even divorce can be linked to the company you keep.