Both of these try to solve the issue of showing multiple scalar maps on top of each such that correlations between the data are easier to appreciate.

Ware’s approach only deals with two of these at a time, and makes use of different visual channels to encode each scalar map. While this has the advantage of minimizing interference, the “resolution” of each visual channel is not the same (color channel has a much higher resolution than the “texton” channel). This means that one has to pick which one of the two fields is more important, which may not be an obvious choice. Additionally, because of this resolution issue, the textons have to be big enough, which may occlude some of the information underneath.

Hagh-Shenas approach is slightly different. The main idea here is to use random textures to illustrate the values of up to six variables across different regions. While this scales better than Ware’s approach, it does so at the cost of having discrete regions, rather than continuous ones. I also agree with Nate on this one, the random dots look extremely ugly, though I should say that the original incarnation of color weaving (used for labeling LIC images) looks a lot better

Ware’s QTonS idea is novel in that it addresses the issue of overlaying two distinct maps by using two distinct channels. However, it is not clear to me what the best way to encode such textons is. At least in the examples in the paper, the QTonS seem to run the risk of obscuring data hidden behind the pattern. They also could potentially distract the user from the underlying data mapping if an appropriate color scheme is not selected (i.e. one that does not complement the original color scales used in the mapping such that either popout phenomena dominate the user’s attention or that the QTonS are not readily visible in certain regions.)

Shenas’ paper appears to take a slightly different approach by offering an alternative to the traditional color blending approach of overlapping color regions by manipulating both color and pattern to convey multiple sets of information. While I have my reservations on the experimental design, the idea of randomly blending two colors does remove the obscurity concerns of textons and certainly feels more conventional with respect to traditional encoding principles. However, again if the color set selected does not have enough contrast, the “random noise” encoding could potentially be undetectable. Either way, the simplicity of this idea does make it a promising possible solution.

Both the papers have addressed two different, yet very important and practical problems. for which there are no clear solutions. Interestingly, both these papers don’t give any new idea, but they provide solutions which are although quite intuitive needs some nitty-gritty details.

I wish in the first paper, the author had used a simple vocabulary for Texton which unnecessarily gives an idea of a new concept, which is not the case. My concern with the texture elements is that they have potential to create “Visual Pollution” and therefore extreme care must be taken in designing and implementation. A generalization of this problem is hard and an efficient and successful implementation may depends on the problem and probably having two images side by side may be better in some cases.

If someone doesn’t read the second paper, then sooner or later, he/she will have to address all the problems, which this paper has addressed. Nice thing about this paper is that it provides some formalization of the problems and give mathematical models to solve them., but unfortunately, solutions of some of these problems are NP complete, and therefore, one has to go for approximation or some kind of heuristics.

It was hard to see blue or purple color spectrum in attributes blocks because blocks interfere with each other. I kinda failed to see some attributes independently and with integration. Nice thing about this visualization is you can choose to zoom-in, choose one attribute, and block sizes. I wonder a space between blocks might help not to mix colors.

Quantitative texton sequences did well showing bivariates. GR and HL spectrum were misleading, DOT is ok but dot sizes do not pop out and hinders color spectrum a bit.

Pixel oriented visualizations are all about saving space on the computer screen. The idea is to one piece of data into one color of one pixel. This paper focuses on how various things like location relative to other pixels and color can make a difference as well as giving examples of applications.

It seems that both techniques are about saving “space” in a way. Ware is using one map where he could use two in order to prevent users from having to glance back and forth between them. He is trying to make it easier to take in information at a glance, which is what it seems that pixel oriented techniques are about as well. Though that takes maximizing the amount of information to visual space to the extreme within computer screens.

Combing the two articles is like blending art and science. Ware is relying on the viewer’s ability to determine differences in shapes that follow a logical progression. These shapes are not necessarily generated by any kind of scientific process, but rather chosen based aesthetic principles. Kleim, however, takes a very methodical approach toward the end result. His methods provide a framework for standardizing the visualization of complex datasets which potentially allows comparisons to be made more effectively. As with any blending of art and science, experimentation and practice will be needed to determine the best approach for a given problem.

Daniel Keims paper is a very technical paper with the algorithms for basic pixel-oriented technologies, such as color mapping, pixel arrangement, shape design for subwindows, and ordering for dimension. I’m still struggling in the algorithms and might need some more time to digest them and bring out my own idea.

These readings provides enough motivation for understanding design choices involved in visualizing bivariate data on a map.The multi-dimensional nature of color makes it preferred choice for encoding bi variate data. Trumbo’s paper argues that choice of colors is an important one and should follow a set of principles e.g order, separation etc. The Colin’s paper take the issue of color selection one step further and stress the superiority of small size textures (Texons) as an alternative encoding scheme for one of the dimensions of data in bivariate maps.

Trumbo’s focus was entirely on effective use of colors and offered theoretical methods for picking colors for an overlay color approach. A more systematic evidence for the suitability of a particular color/texture scheme came from Colin’s paper.

The combined take away points from these readings can be (i) Choice of color sequence should following perceptual guidelines related to correctly specifying the order, separation in the data. (ii) The perceptual separation of small size texture is higher than colors (iii) When reading maps both integral and separate view is an important measure for evaluating design.

As a final note, I found one aspect of the 2-D keys, in Colin paper, contradictory to ‘rainbow maps are bad’. Is this particular key an example of bad color map?