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CGT 270 Data Visualization Makeover Monday Name: Date: Max points: 100 Lab section: Time: ______ Show your work!!! Acquire Gather the information needed from the Makeover Monday web page to complete this section. Week: 44 Date: 10/28/2018 Year: 2018 Data: Gender gap in internet access Source Article/Visualization: What does gender gap in internet access look like? https://www.makeovermonday.co.uk/data/ Represent Critique Critique the visualization: what do you like about it, dislike about it, what do you plan to do differently? I like how the graph is color coded to show the difference between the positive and negative percentages. I don’t really like how the internet users % of households and the gender gap in internet access % difference data is not stacked, I think it would look nicer if it were stacked instead. In my revision, I will stacked the data so it’s easier to understand. Based on your knowledge of the Periodic Table of Visualization Methods (discussed in class), discuss which one of the 6 categories does the visualization you provided in the Represent stage falls in . Identify the method most closely related to the visualization in the Represent Stage and discuss the characteristics: overview, detail, detail AND overview, divergent thinking, convergent thinking. Refer to Readings to assist with categorizing the visualization. This visualization would fall under the Data Visualization category since it’s a representation of quantitative data. The chart showcases each the percentages of internet access and includes the number of internet users in % of households for each country. Readings: Fall 2023 – Makeover Monday Template_v4.0

CGT 270 Data Visualization Makeover Monday Paper: Periodic Table of Visualization Methods: https://designsojourn.com/wp-content/uploads/periodic_table.pdf Interactive Periodic Table of Visualization Methods https://www.visual-literacy.org/periodic_table/periodic_table.html Mine What question(s) are you attempting to answer? What is the gender gap in internet access between the countries provided in the dataset? Filter Show (display, list, make it visible) the filtered data. Country Internet users; % of households Gender gap in internet access; % difference Algeria 0.744 0.217 Argentina 0.759 -0.057 Australia 0.861 0.021 Austria 0.888 0.022 Azerbaijan 0.782 0.15 Bahrain 0.975 0.002 Bangladesh 0.068 0.556 Belgium 0.873 0.021 Botswana 0.457 0.323 Brazil 0.749 -0.032 Bulgaria 0.721 0 Burkina Faso 0.129 0.375 Burundi 0.003 0.4 Cambodia 0.4 0.32 Stakeholders  Who is your audience? What assumptions did you make? What visualization tool/software did you use? People who live in the mentioned countries. I assume the data is correct for the provided countries. I will be using Excel to build my visualization. What to submit: This document in PDF format only (if you do not know how to do this, see Lab 0 Exercise 1). Answer all of the questions. Save this document as: LastnameFirstInitial_CGT270F23_MM#_YYYY.pdf. Replace # with the Makeover Number (1, 2, 3, etc.), replace YYYY with the year the data is representing. Choose the best layout for your makeover visualization: Portrait or Landscape, Remove the page of the layout that you DO NOT choose. No blank pages! Fall 2023 – Makeover Monday Template_v4.0

CGT 270 Data Visualization Makeover Monday Refine (Makeover – Landscape view) Use an additional page if necessary. Remember, the purpose of visualization is “insight.” Take and include a screenshot of your visualization and include it below. Use Data Visualization Best Practices (see data visualization checklist). You MUST use more advanced chart types for your makeover. Chart types that are not allowed: bar (single or stacked), pie, line charts, scatter plots, no tables. My revised graph Fall 2023 – Makeover Monday Template_v4.0

CGT 270 Data Visualization Makeover Monday Resources Data Visualization Checklist: http://stephanieevergreen.com/wp-content/uploads/2016/10/DataVizChecklist_May2016.pdf How to give constructive criticism: https://personalexcellence.co/blog/constructive-criticism/ Sample Makeovers: https://www.makeovermonday.co.uk/gallery/ Grading Rubric Excellent Good Fair Needs Improvement Meets ALL or most of these: Makeover is esthetically pleasing (color, perception), best practices followed (insightful), Correct dataset downloaded; provided an interesting point of view of the data; critiqued previous makeover, critique is constructive (indicates one thing that is done well, and one thing that could be done differently, what will be done to improve the visualization), assumptions (more than one) are listed. [50 pts] Meets MOST of these: Makeover is esthetically pleasing (color, perception), best practices followed (insightful), Correct dataset downloaded; provided an interesting point of view of the data; critiqued previous makeover, critique is constructive (indicates one thing that is done well, and one thing that could be done differently, what will be done to improve the visualization), assumptions (more than one) are listed. [25 pts] Consistently meets SOME of these: Makeover is esthetically pleasing (color, perception), best practices followed (insightful), Correct dataset downloaded; provided an interesting point of view of the data; critiqued previous makeover, critique is constructive (indicates one thing that is done well, and one thing that could be done differently, what will be done to improve the visualization), assumptions (more than one) are listed. [12.5 pts] Little to no evidence of the understanding of the data visualization process. Lackluster makeover or no makeover. Little effort. [0 - 11 pts] Sketch included: hand drawn, data vis best practices evident. [25 pts] Sketch included: hand drawn, lacking data vis best practices. [12.5 pts] Sketch included, but was generated by computer [6.25 pts] No sketch included. [0 pts] More advanced chart types used [25 pts] More advanced chart types used, followed most best practices [12.5 pts] Basic chat types used in the makeover [6.25 pts] Little to no improvement in visual representation of the data [0 pts] Deductions Description Deduction File naming Convention not used -2 Did not remove unwanted/unused pages -3 Did not use data from the assigned year -5 Fall 2023 – Makeover Monday Template_v4.0