Try R

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Today I tried R and completed Code School’s 7 chapters of exercises. Here’s some screenshots of the chapters below!

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Spatial Text Visualization Using Automatic Typographic Maps

While reading this paper I could not stop thinking about the San Francisco art styled typographic maps of the districts (which to be honest I kind of like). However, this paper was showing how there is a much more efficient way of producing a typographic map with a computer. Typographic maps merge text and spatial data and can be used for traffic density, crime rate, demographic data and more. Mostly became popular because of their high visual aesthetics. A particular visualization mentioned that became really popular was the common text visualization (a word cloud), but this paper listed a couple problems with such visualizations. I think the most memorable figure in the paper was the side by side comparison, which I thought was very effective. Yes, you could see some obvious differences, but not super ridiculous ones, so when the time it took gets taken into consideration the 2 weeks to 2-3 seconds is remarkable. Also I thought the scenario of a cop looking at a map with the highlighted specific area of interest in typographic style was interesting.

Geovisualization for Knowledge Construction and Decision – support

This paper stemmed from the idea that there is a lot of visual digital data being collected from geospatial referencing from vehicles, PDAs, cell phones, etc, that should be utilized in a visualization, a geovisualization to be exact. They describe geovisualization as the process for leveraging data resources to meet needs and with GIS it is also a field of research and practice that develops visual methods and tools for lots of applications. As one would assume geovisualization draws from both cartography and geography. They present four functions, which are: explore, analyze, synthesize, and present. There are three main applications for geovisualization: public health, environmental science, and crisis management. (The environmental science part interests me the most, because it seems like a field I could utilize my math major, and my minors in computer science and environmental science!) One really cool thing is that this paper referred to a paper written by a woman (the Viewpoints paper) and I think that’s a first from the papers we’ve read!

Colorbrewer.org: An Online Tool for Selecting Colour Schemes for Maps

This paper explains how to select color schemes by: number of data classes, the nature of their data, and the end-use environment (something I didn’t necessarily think of previously). Other things I learned from this paper include the idea that diverging schemes are always multi-hue sequences. We all know that nominal data has no order, but now I also know that it doesn’t make sense to pair it with light to dark color scheme for that exact reason. When working with the data class number there is a fine line between generalization and too many colors to differentiate. The more complex the spatial patterns, the harder it is to distinguish slightly different colors. I found it interesting that illustrator and photoshop use different color conversion algorithms. Something I learned was the difference between design and display mediums and how much paying attention to these are. After checking out http://colorbrewer2.org I found it very clear and useful. It will definitely be a resource of mine in the future!

The Evolution Of Thematic Cartography / A Research Methodology and Historical Review

This paper provided a very effective, clear basis for different thematic cartography and its evolution. It did a great job explaining and thoroughly describing the basics in hopes to provide a strong basis for research and hopefully aide people in finding new techniques for visual representations with thematic cartography. I felt like the historical content was really informative and cool! Also the figures provided in the paper were effective and clear. Overall, not super crazy ideas presented, but a very clear general basis to base further research. They divided the development corresponding to point, line, and area symbolization. These can be divided into four stages according to the qualitative-quantitative and physical-cultural distinctions.

Graphical Perception of Multiple Time Series

Original paper linked here. I liked the figure that opened their paper, it gave a visual representation of what the paper was going to be about before I could read the abstract. After reading this paper I can see the potential perks of stacked graphs (when the different stacks are sums of the whole, example: if you divided sales for the same company up into categories). As far as braided graphs, which their paper showed that people liked (given how they look in their paper, I’m not too sure I believe them). They seem pretty ugly, but I can see how they work. However, I agree with them that aesthetically they can make some changes to it. I took particular interest in the fact that they had a training session for the participants and a pilot study, which is ideal! I also like how they added that the questions/tasks asked increased in difficulty (something that is important that I noticed from my own work on user studies). Lastly, the paper notes that it really became about differing between shared and split spaces, which makes sense.

Evaluation of Alternative Glyph Designs for Time Series Data in a Small Multiple Setting

Original paper linked here. I think the clarity of this paper is something others strive for. All of my notes while reading it seemed to be praise: First off, I liked the term “screen real estate”. I thought Table 1 was especially nice because it even addressed the issues with each type of glyph. It was great how they incorporated many hypotheses all for different tasks. This along with practically every other part of the paper showed how thoroughly they not only researched their topic, but how strategically well they planned their research. In general, I love a table with some real statistics shown and they did a thorough job at including that. I think the Table 2 is the best type of table they could have used to show their results in an effective, but simple way, especially after around 2 pages of statistical summary. Lastly, I like how they introduced their participants section of the paper. Having thought about this for a research project before I think they did it in a nice, super informative, but concise way. I was curious if they had a tutorial for their participants to get their hands dirty with the glyphs before the evaluation and their techniques for that.

Exploratory Analysis of Time-series with ChronoLenses

The original paper linked here. Overall, I think this paper was very professional and did a really good job at showing the amount of research they did and incorporating it not only in the introduction section, but throughout the whole paper. Unfortunately, I felt like the paper was visually cluttered (even though they had a section on visual clutter) and therefore hard to follow. The figures got ahead of themselves, which caused a big distraction for me while reading this paper. Also I think they referred to figure 5 when they meant figure 6 when talking about the ALMA Observatory Usage Scenario. Besides those mistakes, I think the paper did a good job and if those things were fixed I think it would have been a great paper.

Visualizing Time-Series on Spirals

After reading this research paper by Marc Weber, Marc Alexa, and Wolfgang Müller I am still not sure how I feel about visualizing time-series on spirals. Though I definitely feel much more comfortable with the idea after reading this paper, I’m not sure how much I like it. I think maybe using it to get a feel for the periods of the data may help, but besides that it seemed difficult to pick out more information from the data, or the paper didn’t include helpful advice on how to do so. However, I did think the paper used lists or bullet points to effectively explain what the visualization had to support (section 3). After seeing the figure that included a helix in 3D I was very scared they were going to expect us to use that in the visualization to extract information, but was very glad that they didn’t expect us to use it for anything other than navigation and seemed to understand why it shouldn’t have been used for more. Also the fact that they used polar coordinates made me happy, I was going to be very disappointed if they didn’t (because polar coordinates are awesome). Unfortunately the paper seemed to have quite a few typos.