From 43d90f6cf185e1b3b569f8f905e1c64b302527a1 Mon Sep 17 00:00:00 2001 From: bkirsh99 Date: Tue, 9 Jun 2026 14:01:23 -0400 Subject: [PATCH] asignment 2 uploaded --- 02_activities/assignments/assignment_2.md | 46 ++++++++++++++++------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/02_activities/assignments/assignment_2.md b/02_activities/assignments/assignment_2.md index 7bb5b2df7..81bc420bd 100644 --- a/02_activities/assignments/assignment_2.md +++ b/02_activities/assignments/assignment_2.md @@ -10,25 +10,43 @@ - For each visualization (good and bad): - Explain (with reference to material covered up to date, along with readings and other scholarly sources, as needed) why you classified that visualization the way you did. ``` - Your answer... - - - - - - + + 1. Good Visualization Example: “Meningitis and Neonatal Sepsis” Dashboard by the Meningitis Research Foundation (https://www.vizforsocialgood.com/portfolio/meningitis-research-foundation) + + I classified this visualization as good because it communicates a large amount of important health information in a clear and organized way. The dashboard combines maps, bar charts, summary statistics, and comparison visuals to help viewers understand the global impact of meningitis and neonatal sepsis. + One reason this visualization works well is that it matches its purpose and audience. The goal is to inform viewers about the global burden of these diseases and encourage awareness. During lectures, we discussed how visualizations should be designed around purpose, audience, and medium. This dashboard works well because it is easy for both researchers and the general public to understand. + Another reason is that the visualization manages cognitive load effectively. Even though it contains many pieces of information, the layout is divided into clear sections. Important statistics are highlighted using larger text and contrasting colors, helping guide the viewer’s attention step-by-step instead of overwhelming them. + The visualization also uses Gestalt principles effectively. Similar colors group related information together, while proximity helps viewers connect charts and labels that belong to the same topic. This improves readability and helps viewers process the information more quickly. + This dashboard also has a strong perceived factual basis. During class, we discussed how some visualizations appear more trustworthy because of their clean structure, labeling, and use of sources. This visualization clearly cites organizations such as WHO and IHME, which increases credibility and trust in the information being shown. + In addition, the visualization demonstrates strong provenance rhetoric because the source of the data and the purpose of the dashboard are clearly communicated. Rather than focusing on decoration, the design supports understanding and interpretation of the data. + Finally, the visualization balances aesthetics and clarity well. The color palette is consistent and professional, helping the dashboard remain visually appealing without distracting from the information itself. + + 2. Bad Visualization Example: “2024 Health Data” Circular Health Dashboard by Big Mountain Studio (https://dribbble.com/shots/25828065-2024-Health-Data) + + I classified this visualization as bad because it prioritizes appearance over clear communication. Even though the design is visually attractive, it is difficult to quickly understand the actual data and relationships being shown. + One major issue is cognitive load. During class, we discussed how unnecessary visual complexity can make information harder to process. This visualization contains many overlapping circular layers, colors, dots, bars, and shapes that compete for attention at the same time. Viewers must spend a large amount of effort trying to understand what each ring represents before they can even interpret the data itself. + Another issue is that the visualization does not match its purpose very well. If the goal is to help viewers compare health trends over time, the circular layout makes this difficult. It is much easier for humans to compare values using straight lines and aligned positions than curved radial shapes. A simple dashboard with line graphs and bar charts would allow viewers to understand trends much faster. + The visualization also connects to several Gestalt principles discussed during class. While similarity and proximity are used through repeated colors and grouped shapes, the design overloads the viewer with too many visual groups at once. Instead of guiding attention clearly, the chart feels crowded and confusing. + This visualization also affects perceived factualness and rhetoric. The clean colors and modern design make the dashboard appear scientific and trustworthy, even though the actual communication of the data is weak. During lectures, we discussed how visualizations are rhetorical objects and are never completely neutral. In this case, the design focuses heavily on aesthetics and emotional appeal rather than clarity and interpretation. + Finally, the provenance rhetoric is fairly weak because the design emphasizes style more than explanation. While some labels are included, the viewer is not clearly guided through how the data was collected or how different metrics should be interpreted. ``` - How could this data visualization have been improved? ``` - Your answer... - - - - - - + 1. Good Visualization Example: “Meningitis and Neonatal Sepsis” Dashboard by the Meningitis Research Foundation (https://www.vizforsocialgood.com/portfolio/meningitis-research-foundation) + + One improvement would be reducing the amount of text shown in some sections because large blocks of text may discourage viewers from reading all the information. + Another improvement would be increasing contrast in certain areas to improve accessibility and readability for users with visual impairments. + The visualization could also include short interactive explanations or tooltips to help viewers better understand some of the medical terms and metrics being displayed. + + 2. Bad Visualization Example: “2024 Health Data” Circular Health Dashboard by Big Mountain Studio (https://dribbble.com/shots/25828065-2024-Health-Data) + + The biggest improvement would be simplifying the layout. Replacing the circular dashboard with smaller line graphs and bar charts would make trends easier to compare and interpret. + Another improvement would be reducing the number of visual elements shown at once. Fewer colors, shapes, and overlapping layers would lower cognitive load and improve readability. + The visualization could also improve by adding clearer labels and explanations so viewers immediately understand what each section represents. + Finally, designing the chart more around audience understanding instead of visual appearance would create a clearer and more effective visualization. + ``` - Word count should not exceed (as a maximum) 500 words for each visualization (i.e. 300 words for your good example and 500 for your bad example)