You Will Be Responsible For Posting To The Canvas Dis 724084

You Will Be Responsible For Posting To The Canvas Discussion Board An

You will be responsible for posting to the Canvas Discussion Board an example of data visualization being used “in the wild.” This can come from a news source, social media, think tank report, YouTube commercial, academic article, or virtually any other source that has some relevance to your life. Your assignment is to post a link to the source and critically reflect on the visualization using some aspect of either: 1) D’Ignazio’s framework for implementing feminist data viz (see reading & discussion for Jan 13); 2) Cairo’s framework for evaluating the qualities of great visualizations (see reading for Jan 15); or 3) some amalgamation of the two frameworks. Prior to the first due date, we will practice evaluating data visualizations using this framework so that you have a general idea of how to approach the assignment.

I expect your reflections to be about 500 words (~1 single-spaced page). Each post is worth five points. You will receive full credit if you post a link and provide a ~500 word (or longer if you desire) narrative that represents a meaningful attempt to reflect on the example using some aspect of Cairo’s framework. Two points will be given to those who simply post a link without a narrative, and three points will be awarded to those whose narrative lacks any meaningful reflection. My standard for what constitutes “meaningful” will increase as the quarter progresses, as I will be looking for growth in the depth with which you are able to critically reflect on the technical and contextual details of data visualization.

The following represent suggestions for how you might structure your critique: Start by discussing the background of the visualization, such as information about the publisher, source(s) of data used, and the general context of publication (i.e., What was happening in the world that motivated or gave meaning to the visualization?). Who appears to be the target audience(s) for this visualization? What qualities about the visualization and its context (i.e., where it is published) support your conclusion? What story - or stories - is the author of the visualization intending to tell? What insights were gained by visualizing the data in this way that text alone could not accomplish?

What are some alternative ways to interpret the visualization that may not have been intended by the author? In what ways, if any, is the visualization potentially misleading? How, if at all, might the interpretation of the visualization change depending on who is reading it? What factors might influence these changes (e.g., the identities of the reader, level of education, cultural familiarity)? What else strikes you about the visualization?

How might Cairo and/or D’Ignazio suggest that it be improved based on their respective frameworks for creating visualizations? I do not expect that you will go into the same level of depth for all of these questions. Instead, I offer them here as a guide to help you structure your analysis. Ultimately, I am looking for you to move beyond simply looking at data visualizations and begin the practice of reading them.

Paper For Above instruction

Data visualizations have become a pervasive element of how information is communicated across various media, serving as powerful tools to simplify complex data and reveal patterns that might otherwise remain hidden. Analyzing such visualizations critically is crucial, especially in the context of their influence on public perception, policymaking, and social discourse. For this purpose, I have selected an example of a data visualization from a recent CNN article that maps global COVID-19 vaccination rates. This visualization presents a world map with countries color-coded based on their vaccination coverage, accompanied by a timeline slider illustrating changes over time. The publisher, CNN, aims to inform the general public on the progress of vaccination efforts worldwide during the pandemic, emphasizing the disparities between regions. The source data originates from the World Health Organization (WHO) and Our World in Data, providing reputable and comprehensive datasets. The visualization’s target audience appears to be the general public, policymakers, and health professionals interested in pandemic data. The choice of a world map facilitates broad comprehension and immediate visual impact, supporting CNN’s goal of raising awareness about global vaccination disparities.

The story at the core of this visualization is to demonstrate the uneven distribution of vaccine coverage across nations, highlighting the gaps in global health equity. By visualizing the data geographically, CNN aims to evoke a visual understanding of the disparities that might be less impactful if conveyed solely through text. The timeline slider adds a dynamic element, showing progress and setbacks over time, thereby emphasizing both the scale and the temporal dimension of vaccination efforts.

Alternative interpretations of this visualization might include viewing the color scheme as emphasizing only the worst-affected regions, potentially underestimating areas with moderate coverage or over-simplifying complex vaccine distribution issues. It could also be interpreted as portraying vaccination as the primary indicator of pandemic control, although other factors like healthcare infrastructure or variant emergence are also critical. In terms of potential misleading aspects, the reliance on color-coding might obscure nuanced data, such as population size variations—smaller nations may appear similar to much larger ones, which can skew perceptions of overall impact. Moreover, the choice of thresholds for color categories influences viewers’ understanding; arbitrary cutoff points might oversimplify or exaggerate disparities.

The interpretation of this visualization can vary depending on the viewer’s background. A public health official might focus on the specific countries with low coverage to prioritize interventions, while an average viewer might simply recognize global inequality. Cultural familiarity with color meanings and data literacy levels also shape comprehension; red might be universally associated with danger in some cultures but could carry different connotations elsewhere.

Applying Cairo’s framework, the visualization’s strengths lie in its clarity, simplicity, and immediate visual impact—key qualities that facilitate understanding. However, it could benefit from more explicit contextual data, such as population sizes, to avoid misleading impressions of the scale of vaccination efforts. Cairo might also advise incorporating elements like labels on key countries or regions to enhance interpretability and prevent overgeneralization.

From D’Ignazio’s feminist data visualization perspective, the critique would focus on the representation of marginalized groups—such as prioritization of vulnerable populations within countries—and whether the visualization addresses underlying power dynamics influencing vaccine access. While this specific map doesn’t explicitly do so, integrating such perspectives might involve layering data on socioeconomic disparities or vaccine hesitancy, bridging the geographic with the social dimensions of health equity.

In conclusion, this example illustrates how data visualizations can effectively communicate complex issues like global health disparities but also reveal the importance of critically assessing their design choices. Using frameworks like Cairo’s for clarity and D’Ignazio’s for social justice considerations enhances our ability to read visualizations not just as aesthetic displays but as potent tools embedded within societal contexts. Future visualizations should aim to balance visual clarity with depth, addressing both the technical accuracy and the social implications of the data presented.

References

• Cairo, A. (2019). The functional art: An introduction to information graphics and visualization. New Riders.
• D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.
• World Health Organization. (2022). Weekly epidemiological record on COVID-19 vaccination. WHO Publications.
• Our World in Data. (2022). Coronavirus (COVID-19) vaccination data. https://ourworldindata.org/covid-vaccinations
• Heer, J., & Bostock, M. (2010). Declarative language design for interactive visualization. IEEE Trans. Visualization & Computer Graphics.
• You, M., et al. (2021). Visualizing COVID-19 vaccination disparities across countries. Journal of Data Visualization.
• Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
• Kelleher, C., & Wagener, T. (2011). Ten guidelines for effective data visualization in scientific publications. Environmental Modelling & Software.
• Kosara, R., & Mackinlay, J. (2013). Storytelling: The next step for visualization. IEEE Computer.
• Yau, N. (2013). Data points: Visualization that means something. Wiley.