diff --git a/img/music.png b/img/music.png new file mode 100644 index 0000000..80eac13 Binary files /dev/null and b/img/music.png differ diff --git a/week1.md b/week1.md index e69de29..45e6b5c 100644 --- a/week1.md +++ b/week1.md @@ -0,0 +1,13 @@ +(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. diff --git a/week2.md b/week2.md index e69de29..8071b07 100644 --- a/week2.md +++ b/week2.md @@ -0,0 +1,11 @@ +# 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. diff --git a/week3.md b/week3.md index e69de29..6c80c2b 100644 --- a/week3.md +++ b/week3.md @@ -0,0 +1,19 @@ +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. diff --git a/week4.md b/week4.md index e69de29..747bd50 100644 --- a/week4.md +++ b/week4.md @@ -0,0 +1,14 @@ +# 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. diff --git a/week5.md b/week5.md index e69de29..55fdaa7 100644 --- a/week5.md +++ b/week5.md @@ -0,0 +1,22 @@ +# 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) \ No newline at end of file diff --git a/week6.md b/week6.md index e69de29..f2e033f 100644 --- a/week6.md +++ b/week6.md @@ -0,0 +1,13 @@ +# 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) + diff --git a/week7.md b/week7.md index e69de29..cbf0d59 100644 --- a/week7.md +++ b/week7.md @@ -0,0 +1,11 @@ +# 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.