Data Visualization & Information Aesthetics

What I took to be the ‘message’ of the pieceThe coming era of rich data and ubiquitous interfaces disrupts the way information is conventionally conveyed. As a result, I think one of the critical issues in the field of information is how to build better connections between people and information…

One of the last sections on "Problems of Honesty and Good Judgment" resonated with my experience in the working world. Oftentimes, misleading or difficult to understand graphs are included for the exact purpose of misleading or misdirecting an analyst away from topics that companies do not want people like myself…

Reading Response of the article http://socviz.co/lookatdata.html When I saw such an article detailing how to visualize data in science, I can't help but wonder, do we really need such a dogma about design? How does data visualization balance between science and art? If these are two…

How to visualize data ? There are absolute no methods to technically critique a method of visualization. However, after visualizing data for centuries now; we can discuss the rule of thumb which makes the data more accessible and readable. The author of the article  attempts to explain the idea of these…

Healy’s chapter on ‘badness’ focuses on the graphic choices that visualizers make when presenting data. He describes the three types of graphical issues: aesthetic, substantive, and perceptual, and explores how each can lead to misinterpretation at best and manipulation at worst. Healy also warns that addressing one or two…

Kierann Healy’s chapter »Looking at Data« is a helpful and profound base which helps to familiarize with the fundamentals of Data Visualization. Especially Healy’s clear structure and classification into 3 categories is an important support to get an overview; from which one can dive deeper into more details…

What you see is not always what you get. I must admit, Kieran Healy is a decent salesman. We begin the chapter “Look at data” at the top of his sales funnel with the exclamation that some data visualizations are better than others -- and before long we’re deep…

The author begins by providing sound advice for creating data visualizations. Specific points that I took away include: one of the first considerations is determining who the audience is - what may work for a group of experts will likely be different than what would work for a general audience.…

I appreciate the language and tone used throughout Healey’s introductory chapter from ‘Data Visualization for Social Science’. Each discussion topic is well defined and supported with references while not hindering the reader by the use of overtly technical language. The separation of aesthetic (bad taste), substantive (bad data) and…

Petit Response 1: Before reading Look at data by Kieran Healy, I was getting curious about how ”colors” in data visualization were to set to function where the change of which is often executed in codes or provided as a pre-defined color palette these days. Personally, 1.3.1 Edges,…

In Kieran Healy’s article, he discussed three obvious problems of data visualization tend to be aesthetic, substantive, and perceptual. An understandable graph always can decode dataset, visualize each variable rationally, and build a well communication to people. The “Monstrous Costs” by Nigel Holmes is an eye-catching graph in graphic…

Taste changes， the criteria for good and bad design also changed as well. So when comparing with bad data and bad design, bad design is worse. Speaking of the Data junk issues. The author here claimed that “Viewers do not find them more easily interpretable, but they do remember them…

The visualization of data is for making interpretation more intuitive. However, this is not the only reason for the method. More often than that, the difficulty of decoding data that the visualization possesses in it could vary, such as an academic data analysis which is not geared to the general…

The author uses the example of Holme’s “Monstrous Costs” chart as an embellished chart with junk that is memorable but not easier to interpret. I disagree with her statement. I think she forgets to mention the aspect of relatability and interpretation. A chart like the “Monstrous Cost” using a…

I have always wondered about the role human perception plays in a user synthesizing data visualizations. Up to what point does subjectiveness overpower objectiveness in the human brain? I think that there can never be a definitive answer with data visualization, but there is a certain level of foundational visual…

The introductory chapter of Data Visualization for Social Science by Kieran Healy points out three essential values for visualizing data: aesthetics, data quality, and human perception. The importance of aesthetics and data quality look very obvious for me, but I did not realize why perception would cause bad visualization until…

The Chapter “Looking at data” seems to live up to its goal to discuss why some visualizations are better than others. It refers to practical theories and effectively illustrates how we perceive data-related graphics through the use of examples. The chapter seems effective in extending the concepts of visual perception…

In this chapter Kieran Healy has grouped bad visualization figures mainly into three categories: aesthetic (visual), substantive (contents) and perceptual (psychological perception). He points out that for most visually bad visualizations, it’s not as efficient as other because of duplicated labels and pointless effects. I strongly agree with this…

The introductory chapter of Data Visualization for Social Science by Kieran Healy highlights visual, statistical and physiological elements that affect poor form in data visualization. Healy defines these three pillars as aesthetic, substantive and perceptual, and “…while often found together, [they] are distinct from one another” (Healy, 2018).  Aesthetic missteps…