Q1: Describe how your dashboard answers the questions presented. You don't have to address every question directly, but should at a high level address the main questions.

A1: Graph 1 (The horizontal bar chart) draws out the number of international football games played between the years 2016 and 2020. A cleaned form of the football.csv data is iterated over and a count is incremented based on the "year" attribute. In order to cater to persons of varied disabilities, accessibility of the bar chart is addressed by displaying the count as a text label at the end of each bar.
An observation extracted from the displayed data is the evident lack of games in 2020 due to the corona-virus pandemic.

Graph 2 (The world map) plots markers, extracted from the football.csv dataset, on a country if it falls into the category of Top 10 teams in the world based on winning percentage. Winning percentage for a country is calculated by dividing the number of games won, by the number of games played by that country between the years 1872 to 2020. A threshold of a minimum of 500 games played has been put in place. Accessibility is improved by picking a contrasting color choice for the markers so that it can be easily seen by the colorblind. On hover over a marker, it expands to provide a clearer insight of the country in question.

Graph 3 (The word-bubble) is used to depict the best picks for the next FIFA Football World Cup based on winning percentage of teams in the world cups that took place in the years selected. By clicking on the checkboxes, a user is able to toggle the years to be taking into account for our prediction and a new svg is rendered. I threshold of a minimum of 5 world cup games is taken into account. In order to improve accessibility, font size is made large.

Q2: List 3 reasons why D3 was helpful and improved your visualization.

A2: D3's powerful visualization engine, and ability to read and crunch data efficiently, makes it a very useful library to use in order to provide insights. It's close coupling with HTML, javascript and SVG's make it easy to use prior knowledge on these topics in order to enhance the way data can be depicted. It works on most modern browsers too! The ability to quickly and efficiently manipulate the DOM makes it easy in fast rendering and debugging capabilities.

Q3: List 3 reasons why D3 would not be the best tool for creating a visualization.

A3: D3 is a complicated library to get a hang off, especially when compared with other available data visualizations libraries available in the market. Its learning curve is steep. D3 has a couple of data source limitations, but this disadvantage did not play a role in our dashboard. Strong data privacy is lacking in D3, and data that should not be shared might not be the best choice to work with D3. Creating a visualization in D3 is time consuming and not very easy to get right.

Q4: Evaluate the accessibility of your dashboard based on the readings in the “Before you begin” section. What kinds of users might find this dashboard accessible and who might have more difficulty? What additional actions might you take to make this dashboard more accessible to all audiences?

A4: The dashboard has been created with accessibility in mind and various methods have been implemented to ensure it passes accessibility requirements. In accordance with the article "A Comprehensive Guide to Accessible Data Visualization" written by Amanda Miller [1], users with visual impairments are assisted by the written text around each graph which gives a brief summary of what the graph represents. This text can be picked up by screen readers. Thresholds and other factors considered have been explicitly written out in paragraph tags. Counts of bar plots have been stated as text at the end of the plot. Color contrasts between the background, text and plots have been studied and adjusted accordingly in order to cater to those who are colorblind. However, people with severe colorblindness may find the plots tough to understand as there still remains a greater scope for improvements in design and user-experience. Also, technical limitations in the plotting and representation of the data may confuse a lot of people.

Q5: Reflect on the stages of your design and implementation process when you could have taken steps to make your dashboard more accessible to all audiences. What are some factors that kept you from taking these steps?

A5: While selecting the color scheme for the dashboard, if a greater time investment and research effort was put in trying to find a more contrasting scheme, the dashboard would have been more accessible to users with even severe forms of colorblindness. One of the factors that led to quick decisions having to be made is due to the steep learning curve I found for D3.js.