• Free Form
  • Individual meetings to discuss your prototypes

Assignment

• Free Form
  • Complete your visualization and prepare to present it at our final class meeting in two weeks.

• Presentation
  • Kevin on Amanda Cox & The Upshot
• Free Form:
  • Review proposals as a group
  • Meet individually to look over progress

Assignment

• Free Form
  • Develop an initial prototype of your visualization
  • If your prototype is static, commit your mock-ups as a (potentially multipage) PDF called process/prototype.pdf
  • Whether you're building something screen based or simply using Excel & Illustrator to generate your visuals, place your most up-to-date data files in project/data and name your primary script project/sketch.js
  • Whatever form your project takes, the prototype must use real data; this is a first draft, not a pencil sketch! If your dataset is too large to visualize fully in one week, pull out a handful of representative cases and visualize those to show the range of outcomes.

• Presentation
  • Olivia on Forensic Architecture
• A Thousand Suns
  • Final crit
• Free Form
  • Lightning round: report on topics & data sources
  • Individual meetings & work in small groups

Assignment

• Free Form
  • Select one idea to develop further for your final project and create:
    1. a one-page proposal,
    2. three concepts with two sketches apiece,
    3. a spreadsheet with one or more tabs arranging your collected data into a form you can begin generating charts from.
  • See the “Sketches and Data” section of the assignment for details…

• Presentation
  • Judy on Hans Rosling
• A Thousand Suns: initial critique
• Free Form discussion: postponed to next week

Assignment

• A Thousand Suns
  • Complete your final version and commit your code and documentation in a folder called 3.mapping-quantities/final. If you have designed a static visualization, upload a PDF at the proper scale and trim its art-board to be full-bleed. Your description of the project’s data and visualization approach should be in a file called 3.mapping-quantities/final/README.md.
• Free Form
  • Refine your lists of 10 ideas and 5 data sources and be prepared to present them to the class next week.

The method of increasing lightness while shifting hue is a wonderful solution for visualizing the scale of data. However it is only appropriate for sequential datasets.  It is also interesting to see the colors used in divergent datas and the importance of catering towards color blind individuals.  Categorical data on the other hand is tricky to manage as a very controlled color palette is needed.

Forgetting the specific definition of these datasets for a second, we first explore the fascinating ideas behind color and the power it has on perception and audiences. Different colors can enhance storytelling, graphics and work to make the viewer more interested and more engaged with the content in front of them. Simmon works to explain and analyze how color palettes should be shifted situationally, playing with ideas of hierarchy, depth and data visualization.

Regarding the three types of datasets, the first is sequential datasets, which are  represented with shifts in hue, saturation, and lightness simultaneously. This type of representation is most apt in order to depict data that has variations continuously from high to low.

Divergent datasets are useful in order to focus on differences. The color palettes for this type of dataset rely on variations of hue, lightness and saturation, and work the best when two contrasting hues and a neutral middle color are used.

Qualitative datasets are used to categorize items or data. Similar to how the data is separated into different categories, the use of various colors for qualitative datasets mirror the differentiation of categories and data.

I must note that while I understand how color can affect mood, tone etc, and have aptly used it in other design and artistic endeavors in order to convey the feelings I want, I had never consciously considered how utilizing varieties of color schemes and palettes would be more apt depending on the data. Rather, I believe I was more concerned with color in order to simply provide clarity and ease of understanding, and thinking about color palettes subconsciously.

Sequential datasets are suitable for colors scales that are smooth and even. The lightness and saturation of the palette should be uniform. Since this type of datasets includes continuously varying data (ex: from low to high), the color scheme should also reflect the continuously changing quality.

Divergent datasets focus on measuring differences. The concept of divergent palettes is introduced, which is composed of two sequential palettes merging with a neutral central color. This color is usually white and gray.

Qualitative datasets categorize things. As data is separated into different categories and classes, the colors for qualitative datasets should be as diverse and distinct from each other as possible. Because people all perceive things differently, and also because of the simultaneous contrast effect, the maximum number of categories that can be displayed and recognized is probably no more than 12.

I also think it is a big challenge to design a graph with color that can be perceived by normal people and colorblind people at the same time. What will a qualitative dataset look like under this idea?

• Presentation
  • Summer on the Washington Post graphics dept.
• Reading #2: Subtleties of Color
  • To actually use your newfound understanding of color, start looking into using chroma.js in your sketches
    • Note the use of the .hex() method to convert from chroma’s color representation to p5’s on line 23 of this example
  • If the chroma.js library is too heavyweight for your needs, take a look at my brewer palette generator and see if you find it easier to use. Consult this catalog to find the name of the palette you want.
• A Thousand Suns
  • Review your sketches merging an external data set with the testing timeline
  • Pick one direction to develop for next week

Assignment

• A Thousand Suns
  • Develop your initial visualization and commit your code and documentation in the folder called 3.mapping-quantities/project. If you have designed a static visualization, upload a PDF at the proper scale and trim its art-board to be full-bleed.
  • Fine tune the text and typography surrounding your diagram in order two provide ‘three reads’ in terms of information from headline to body text to legends & labels (similar to the three visual reads we explored in the first assignment).
  • Include a brief (just a few sentences) explanation of your project’s data and how it is presented in 3.mapping-quantities/project/README.md
• Free Form
  • Spend an hour brainstorming ten ideas for your final project. Focus on data that seems interesting to you for reasons you might not be able to articulate, then start posing questions that you could potentially answer with that data (either alone or in combination with other information).
  • Describe each idea in a sentence or three (ideally ending with a concrete, testable question) in 4.final-project/process/ideas.md (you’ll need to run make update to pull down the template for this file first)
  • From your ten ideas, find five data sources on the web and document them in the file 4.final-project/process/datasources.md using the format demonstrated with the USGS example at the top of the file (and be sure to delete this once you've completed your list).

In that sense, it seems to be necessary to avoid the usage of green, as the general blend of color seems to accentuate this problem even more so than other colors. Color palettes that have both a progression in lightness as well as a shift in hue seem to be the best solution as the two ends of the color spectrum, as a result, vary so drastically from one another that the middle increments have a more distinguishable step up from the previous.

On the otherhand, it is interesting that for Divergent Data, because the focus is more on the two ends rather than all values, the full palette tends to want more of the eye catching variances to avoid the center range. Categorical Data focuses on distinct hues for each value, with the reading stating that the maximum number is around 12, or possibly even fewer. To go over this estimation would mean that certain colors may start to resemble another specific color on the spectrum, which may cause confusion in perceiving the chart if it is a large scatter of colors.

Based on the three different types of data––sequential, divergent, and qualitative, the three basic aspects of colors, hue, saturation, and lightness, are to be manipulated separately or collectively to fit the dataset. Sequential data is best displayed with a palette that varies in both lightness and saturation, with the supplementary addition of shift in hue; divergent data is best displayed with bifurcated palettes with a neutral central color; qualitative data should be represented by a set of easily distinguishable colors, or colors that vary sharply in hue, saturation, or lightness. In general, palettes appropriate for its content and distinguishable to human perceptions are the perfect palettes.

In addition to the fundamentals, there are some subtle aspects of color design for data visualization. Considering the perceptual norms of colors, the color palettes should be tailored for each dataset based on intuitions, layering, complementary datasets, breakpoints, separations, and hierarchies.

Sequential datasets require simultaneous shifts in hue, saturation, and lightness because they vary continuously from high to low.

Divergent datasets make use of continuous linear color palettes that rely on variations of hue, lightness, and saturation. Two contrasting hues with a neutral middle color is the best for this dataset.

Qualitative data are best to be represented by various colors since the increased amount of data requires a higher level of differentiation.

After reading the series of articles I am left with questions pertaining to how we can make color accessible for blind people? Especially since color is an integral component to understanding the information presented. Also, I wonder what determines if use of a color is qualitative or quantitative. Qualitative data uses color to separate areas into categories. Therefore, it is is best to have very different colors in the set to avoid confusion. However, I am curious how colors that are too similar can possibly affect the integrity of the data. It seems that color choice is important in providing a viewer with clarity.

One interesting point in the beginning of the blog is the inaccuracy of human's perception of color with the computer generated gradient. It points out that among the 3 main variables, lightness is the most common and easiest one to spot. There're 3 main color palette put in use, sequential data palette, divergent color palette, and categorical color palette. The one thing I haven't noticed before is the accessibility of the colors. When color blindness is taken into consideration, the choice of accessible color palette can be come very challenging.

At the end of the blog, we've also get a sense of color's meaning is more or less cultural and relies heavily on context (the data set). When the color can intuitively communicate the data's information (eg. green as plants, grey as deserted lands, blue as water), it's highly recommended to follow such rules to help the general readers understand the data more easily.