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I thought the discussion around how "the purpose of visualization is insight not pictures" was really powerful and for me, sort of emerges as guiding principle on this topic. Really focusing on the purpose of sharing the data, or the story you are trying to share is essential to define as you consider the most effective way to display it. Cairo reminds us the importance of asking ourselves "Why am I publishing/creating this?"

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I really appreciated the discussion about accessibility. Since the concept of data visualization is inherently focused on the visual piece, it is so important that we also consider how we make our presentation of data accessible to individuals who might be visually impaired and also those who might not now how to engage or interpret what they are seeing. I learned that redundancy can actually be really useful and an expert annotation layer can really add important depth to the interpretation.

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The example Cairo shared regarding how 'homeless' was defined in data in the "At School Without a Roof" chart really got me thinking about the importance of being comprehensive in explaining the information that you're sharing, but at the same time, when it comes to visual displays, simplicity feels most easily understood to the reader. What are the best ways to achieve this balance? I know it depends on the purpose, and I know that additional information can be included in an explanation, but do we also have to anticipate that not all interpreters of the visuals will read the explanation?

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I immediately thought of how I can apply what I learned in this presentation as a teacher in the math classroom. I found this message closely aligned with the curriculum that I teach and the lens I use to teach mathematics. “Making good choices.” Isn’t that what we want students to do? As Alberto referenced, making good choices is making better decisions and that is critical thinking skills, one of the primary focus of high school education. High school mathematics curriculum is a discipline to teach students reasoning skills, this is closely related to the message of the presentation. Mathematical thinking is a way to understand data, and data visualization powerful way to present information and teach students how to develop insights to situations. I have dabbled in data science as a course in high school and find this perspective can be incorporated into all mathematic courses.

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I learned how to connect the work I do in my classroom more clearly to philosophy and how to explain and justify that connection. High school mathematics is a sandbox to develop reasoning skills. While it is hard to include moral reasoning in a public school curriculum, on can include discussions of ethics. Referencing the work from PPM715, the public education system is charged with promoting and supporting democracy. It is essential that young Americans are taught how to be productive citizens. I am eager to think about and share with my students this perspective on the philosophy of mathematics. Making sense of data is a way to make sense of the world through “words, written or spoken, images”...and I would add “numbers” expressing the world around us. In PPM715, we had a rigorous discussion about democracy and technology. In my work as a high school math teacher, connecting moral thinking to supporting democracy is a way to maintain professionalism, protecting the rights of students, while addressing threats to our democracy due to ethical (moral) practices.

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I wonder how I could learn to “explain” visual data to my students like Hans Rosling in his presentations. I would very much like to help my students make sense of data, this presentation provides justification for the value of data visualization. The questions I have is how I can actually do this with my students. As mentioned, students struggle with reading the most basic graphs, but the more interesting ones are also more challenging. We are encouraged to don’t just show, explain. I don’t know how to best explain this to high school students. I can show and tell, and is suggested - but that still is a gap between showing and explaining.

A specific question I have is why the image representing people will a gun licence was represented with a red dot. Why did the designer choose red? Blood red? Using the message of Alberto, why did the designer choose that color? Was the message to impress upon people the danger of all of their neighbors with a gun licensce? As mentioned, this did not necessarily mean that all of the people have guns in their home. That might be an important distinction. Alberto did not take that line of discussion as far as I would have liked. I would like to know what Alberto thought about that visualization.

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I'm sticking with circles...even though he discussed pie charts & how they are not always the best. In DuBois' book and in other visual considerations I am always drawn to circles. I've got to start in a place that makes sense to me & then branch out from there. I also took a lot of notes on all of the books & resources he shared. I'm happy to have a bit of a road map for where to go for more information.

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My question is a constellation of questions that I think will start to have greater meaning as I gain experience in this area. Right now, I'm at a very basic level of even starting to conceptualize making visual representations of data. Truthfully, I typically skip over data visuals and go right to the written explanation of the data which makes more sense to me. I'm grateful that we have "hands on learning" time for this.

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I so appreciated Alberto's focus on moral philosophy and the tenant of "Do No Harm." For me it was a great juxtaposition with the conversation we had last night about the evils (my word) of social media undermining democracy. In terms of data visualization, everything is new learning & I was grateful that the content was easy to digest while still being meaningful and helping me to put some of the guideline to immediate use.

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It sounds simplistic, but the best takeaway from this presentation is a greater awareness of the power of data visualization, and making a commitment to move forward ethically with my data, as well as advocating and educating others that may not understand or appreciate the power of data and the importance of ensuring all data is presented in an ethical and appropriate way. This presentation also underscores the responsibility of the researcher to ensure they have sufficient knowledge and expertise about data visualization in order to be an effective researcher and data ambassador.

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I came away with a new appreciation for my role as a researcher in creating data visualizations in a moral and ethical way. I found it interesting that Dr. Cairo advocated for the importance of understanding the implications of data visualization decisions, and that morality should always come into play when creating a data visualization. I am heartened to know that this is a priority because there is so much misinformation out there, where data is manipulated in a way that supports specific agendas and ideas, versus laying out the data and reporting it truthfully, regardless of whether or not the data supports any specific agenda and idea.

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Dr. Cairo spent some time talking about the importance of determining the appropriateness of data visualizations in specific situations, like the graphic shown in the news about gun owners after the Newtown School shooting. Since it can be a somewhat subjective interpretation of what is and is not appropriate to share via data visualization, is there a checklist or a core set of questions that researchers should ask themselves before creating a data visualization to act as a sort of check and balance to ensure that one doesn’t unintentionally create an inappropriate data visualization?

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Cairo made me think about how I can make my own visuals more accessible. Both to the visually impaired but also to those not familiar with the type of graphic and the nature of the data I am presenting. I also think that this will influence my thinking about my audience so I'm considering both my typical audience but also those who may not usually view my data or data like it.

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One thing that I learned was that it is good practice to created long form captions for charts and other data visualizations. This will make the graphics accessible for the visually impaired and also teach people how to read and interpret the data visualizations.

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What are the ways we can increase literacy related to reading data visualization graphics?

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Cairo discusses the use of data visualizations for private and public purposes. I primarily create visualizations for the public but seldom use them to advance my own understanding of an issue. I am going to think about ways I can use visualizations to help me make sense of data, which I think will have the benefit of leading to more questions and a deeper overall analysis.

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I thought Cairo’s explanation of a “why visualization” was really important. He talked about the necessity of integrating moral philosophy into visualizations to help the creator think about ethical implications and how to integrate inclusion, equality, and equity into visualizations.

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In the beginning, Cairo discusses the need for people to be informed chart readers. How do we get more people to be informed chart readers and critical consumers of data presented visually?

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Alberto champions the use of data annotations to better help the audience understand the data that is being visualized. This is a useful tool that I can utilize on any future data visualizations by providing additional context and information on the dataset.

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During the presentation, Professor Cairo highlights how impactful participatory data visualization can be for an audience. In my professional work, I have found this to be very true. An interactive data visualization can be an engaging and useful tool in understanding a complex data set. The USDA Food Access Research Atlas, and the ConnectME Authority Broadband Availability map, are great examples of participatory data designs.

USDA: https://www.ers.usda.gov/data-products/food-access-research-atlas/go-to-the-atlas/

ConnectME: https://maps.sewall.com/connectme/public/

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The Food Access Research Atlas (formerly the Food Desert Locator) is a mapping tool that provides a spatial overview of food access indicators by census tract, allowing users to investigate multiple indicators of food store access.

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Alberto identifies that the purpose and the audience should be the most important considerations when developing a data visualization. He also states that the way the data is sorted should depend on the questions that the creator wants to answer. What if one of these elements is unclear to the creator? As in, they are evaluating data without a clear purpose, audience, questions to answer, or all three. What suggestions could Alberto provide for a more ambiguous situation? Or perhaps, a situation where the purpose/audience is identified by an outside stakeholder? Is data best understood organically without competing interests and influence? How do we, as researchers, start with data and then determine the most impactful use for it?