1.) Turning the Data Around

This article’s general sentiment to “consider the human” resonates strongly with me. The idea that data visualization should benefit the community or people in which the data is taken from seems only right. Reading this article, also makes me reflect on all the past data visualization projects I have done. I can’t help but wonder if our ‘New Yorkers who Clean Up and New Yorkers who Complain’ piece helped sanitation workers in any way – did it give back to them? Did any of my previous ‘Data + Art’ projects do that? I would like to think that it helped shed light on the fact that garbage collection is a dangerous job, but did it? Did it benefit the workers? How would I improve upon my previous data projects using this mindset of ‘turning the data around’? How would they feel if they saw the data visualization? Would they feel like their issues were being heard? Would they feel like they had been part of a study they didn’t agree to be a part of? Or worse, would they feel uncomfortable seeing this? I cringe thinking about it, but it’s very possible that it made them uncomfortable and not heard. It would have been better to get to know sanitation workers, to get to know the people first – not just make a data visualization. Good to be self-critical. It’s helpful reading this article and keeping in mind the community in which the data is taken from. I’d also like to know more ways to empower the people whom data is collected from other than putting it in a public setting.

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2.) Chapter Two: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

A great saying the article repeated was “data visceralization not visualization.” Designing for viscerlizations requires a much more holistic embodiment from the person. As the article mentions multiple times, it’s important to keep in mind that objective, rational “truths” are impossible. The argument to keep data “objective” is faulty in that everyone’s “truth” is different. We live in a subjective, non-binary world full of many unique truths. Another great point is the idea that novel representations of data are much more memorable than a typical bar graph. Even the “chart junk”, which are illustrated data visualizations, are more unique and therefore more effective. It was helpful to read this and remember that when making my own data visualizations it is important to a.) show not tell, b.) think of unique forms to represent this data, c.) convey the emotions of the dataset, and d.) design for “data viscerlizations” which require a “holistic conception of the viewer” and treats viewers as more than just a pair of eyes.