Week 1 Reflection

week1.md

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+(Since I came into this course slightly late, I am going to be describing a visualization which is similar to one that I have already had experience with but is genuinely important to me.)
+
+My reflection this week is on this [election visualization](https://www.nytimes.com/interactive/2021/upshot/2020-election-map.html)
+from the New York Times. It visualizes the election results from the 2020 US presidential election, allowing the user to search by city, state or 
+address to get more information about results for the specific town. Mousing over the visualization will allow the user to view the election results
+for a particular county, the user can also zoom in to the map using the scroll wheel to get more detailed information. Each county and city's results are
+represented by a level of blue or red depending on which candidate won by what percentage of the vote in a particular region. The user can 
+also be presented an alternate view which shows the change in the results from 2016 as it relates to the 2020 election.
+
+This visualization is important to me personally since it is reminiscient of the visualizations that the New York Times has every election. I use
+their visualizations every election season and am fascinated how nice of a user experience they provide to keep up with the results
+in real time. This is one of the main inspirations that I have for taking this course since it provides a lot of information in an easy-to-use and
+accessible manner.

week2.md

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+# Reflection 2
+
+For this week I read a study called "Evidence for Area as the Primary Visual Cue in Pie Charts" by Robert Kosara ([link](https://ieeexplore.ieee.org/document/8933547)), which attempts to find out the way
+that poeple read pie charts from a glance. I read that there are three main ways of reading a value of a section of pie charts: by the angle of the section,
+the arc length porportional to the circumference of the circle, and the area of the piece. Traditionally, the angle of the section
+has been used due to the results of a previous study which became the consensus. However, Kosara hypothesises that this may not be accurate.
+Participants were shown a number of pie charts with different values, some as 2D representations, others as 3D representations with a tilt
+with varying angles to distort aspects of the pie chart and were asked to report their values. The results of all participants was compared against existing models
+and it was determined that area may be the most accurate measurement method through this model.
+
+This upcoming week, I want to try to investigate some more visualizations, but this study caught my eye and thought it was very interesting.

week3.md

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+This week I found a visualization from r/dataisbeautiful about the game Wordle. I have been obsessed with
+Wordle for the past few weeks so it was fun to see what people have made in terms of visualizations.
+
+Link: https://www.reddit.com/r/dataisbeautiful/comments/sgc5g6/oc_how_good_is_your_favorite_wordle_starter/
+
+The visualization looks at a subset of a ranking of different starting words in the game. From what I can tell
+after a cursory glance of the source provided in the citations, they use a bot to make guesses for particular words
+then ranks them based on how many guesses it took to guess all available words in the game, the word salet being the most popular
+word. The visualization provides a histogram for the number of guesses it took to get a valid solution. The author also 
+provides statistics for the average number of combinations after the first guess such as an average value of the score that you
+would get after the first guess.
+
+Personally I have a number of issues with the visualization just as someone reading it for the first time. First, I am not sure about the 
+meaning of the colored letters at the top of each word in this context, it's not clear whether or not this is some sort
+of histogram or an indication of ranking. Second, the fact that the author is using terms of statistics they came up with
+such as "Match Score" and "Avg. Remaining" and only provides the meaning of these terms in small text at the bottom of the visualization
+is very confusing. I would recommend the author to possibly document what each field means at the top of the visualization with
+an example. Finally, it would have been nice if the author had labeled their axes just so that at first glance I would have had
+an idea of what the chart was instead of guessing.

week4.md

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+# Reflection Week 4
+James Plante
+
+This week I found an [article](https://fivethirtyeight.com/features/groundhogs-do-not-make-good-meteorologists/) 
+by FiveThirtyEight about the inaccuracies of groundhogs in predicting whether or not there is going to be six more weeks of winter.
+It turns out (unsurprisingly) that most are not very accurate. The
+reason I found this interesting is that I found the article generally humorous and it provided some very vivid visualizations.
+
+The first visualization that I found interesting was an analysis of Punxsutawney Phil's (a groundhog in Pennsylvania) accuracy over time.
+The authors decided to go with a fan-like visualization with the instances of whether or not they predicted correctly with a percentage
+of how accurate they were in the middle. I thought this was a great visual way to show time series data while giving a way to visualize
+it categorically with no interaction whatsoever. They also provided a heatmap for the accuracy of different groundhogs based on region which
+I found somewhat humorous since some groundhogs do worse than random chance. Another interesting visualization that they did was their
+"bias" towards summer and winter which has a very interesting way of differentiating the different colors through texture.

week5.md

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+# Reflection Week 5
+James Plante  
+February 14, 2022
+
+This week, I tried to find an interactive visualization that was not from mainstream outlets that 
+would visualize a subject I was interested in. In doing so, I found this visualization by Susie Lu,
+John Akred, and Silicon Valley Data Science called "History of Rock in 100 Songs". This exploratory
+visualization allows users to explore the history of rock/metal music based on a curated list of 
+100 songs by the Guardian. This visualization interested me because I had taken a music course
+last year that covered the history of American pop music and knew a lot of the content on here.
+
+![image of the visualization](img/music.png)
+
+The main point of exploration that the user has is selecting a group either from a network visualization in the middle of the screen or a timeline on the side with a list of all of the bands in reverse chronological order by their start date. What I found interesting was that they had incorporated multiple
+datasets allowing them to visualize the influences and influencees of the artist as well as what songs
+were selected by the Guardian in terms of their relative valence (mood) and energy. I also liked
+their use of complementary colors for the network to make the highlighted elements stand out.
+While I found this  visualization very useful, I wish that there was an initial pop-up with the 
+legend on how to interpret the network visualization since while I could get the gist of what it was doing from my background knowledge, I didn't get the full picture on what was going on until I
+looked at the small "Show Legend" button in the top left corner of the screen.
+
+[link to visualization](https://svds.com/rockandroll/#slyandthefamilystone)

week6.md

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+# Reflection 6
+James Plante
+February 21, 2022
+
+This week, I decided to utilize my New York Times subscription and find an interesting interactive visualization from The Upshot. The article
+posits the question of how to best analyze the results of the Beijing Olympics: should we quantify which country "won" by how many medals
+each earned or how many gold medals they earned? The article provides an interactive visualization providing users a way to explore how assigning
+different weights to medal types effects the outcome of the ranking. The user can mouse over portions of one visualization using the mouse and see
+the overall ranking on the right as well as how other countries performed. I found this genuinely impressive since it gives the users a very
+interesting way to explore the data by just mousing over a visualization and doing a visual scan.
+
+[Link to article](https://www.nytimes.com/interactive/2022/02/07/upshot/which-country-leads-olympic-medal-count.html)
+

week7.md

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+# Reflection Week 7
+James Plante
+February 28, 2022
+
+This week, I found a visualization of a COVID dashboard by InformationIsBeautiful ([link](https://informationisbeautiful.net/visualizations/covid-19-coronavirus-infographic-datapack/)).
+It visualizes various statistics related to the COVID-19 pandemic and gives the user a somewhat interactive way of exploring them. While there are a lot of visualizations on this page,
+I mainly want to focus on a few aspects. The first is a chart showing countries' infeection, death, and vaccination rates. What caught my attention about these graphs is how much infomration can
+be packed into one chart: they detail both the measurements in terms of both absolute numbers and as a ratio per 1000 people represented by a small unit chart. I also appreciated their 
+use of complementary colors to help contrast the different fields. Another interesting aspect of the dashboard is the "Country Comparer" which gives a way for users to compare the 
+same data but in context to these values over time. I thought it was interesting from an HCI perspective to put the country fields on the top since the user sees the fields immediately after
+scanning. I also found exploring the bubble chart visualization describing the different treatments for COVID very interesting and informative.