week 7 reflection - Ryan Dillon

week1.md

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+https://pudding.cool/2022/11/upward-mobility/
+
+The visualization that I chose comes from a story on The Pudding regarding how the neighborhood you grow up in affects your economic success and mobility in the future. 
+The visualization compares the communities of Gardena and Fremont using circular bars representing percentiles in different categories (where a full semicircle represents 100%)
+I think that the method of using semi-circular bars to compare two different areas creates a really clear comparative visual, because it is very easy for the viewer
+to tell which of the bars is longer than the other. Additionally, the markings denoting the 0th, 25th, and 50th percentiles add even more clarity, and the viewer is able to
+use them as benchmarks for the length of the bars. The colors of each of the bars are clearly differentiated from each other, which makes reading the legend on the sides
+a lot easier. This visualization was also interactive, adding each of the bars one at a time as the user scrolls. Adding the opposing bars for each of the communities at 
+separate times also increased the impact that the visualization had, because the time that the bar took to reach its full size also correlated with the percentile of the bar.
+I think the choice to include both the data about the racial makeup of the city in addition to the median household incomes was an interesting choice. By including them all
+in one visualization it gives the reader the opportunity to misintepret something or become confused about how the two topics are related. This is especially evident in how the lengths of 
+the bars are very different, with both of the categories showing the racial makeup percentiles being much smaller than the household median incomes'. However, I do think that 
+having multiple visualizations to represent the differences in these communities would be more confusing than the singular one. Overall, I think that the visualization is 
+visually compelling and attractive, as well as does an effective job of communicating the data that the author intended.

week2.md

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+https://www.nytimes.com/interactive/2023/09/01/upshot/ncaa-college-realignment.html?searchResultPosition=1
+
+The visualization that I chose this week comes from the New York Times and depicts the changings in the last 60 years of NCAA football conference compositions. 
+My first thought was that this visualization does its best at conveying a large amount of data over a long period of time, meaning that it would be difficult 
+to condense this data visually even more and have it still make sense. However, it still is a little overwhelming to the viewer to have this amount/length of visual
+to take in, which is one of my first critiques. The change in color along with the shifting arrows marking a team's change in conference is very clear visually
+to the viewer, but having all of the rest of the lines in the same color gray make it difficult to read. It is still difficult despite the viewer's ability to hover over 
+a team's line and have it become darker. If I were to make a change, I might make each conference a different color and show the movement of a team to another conference
+by having the arrow along with a gradient transition between the colors of the conferences. Another aspect of this visualization that is a little unclear is that some teams
+have a abrupt start or end in the same color as a team that is changing conferences. It is a little unclear as to what this means - did the team disband? One thing I 
+found very useful about this visualization was the ability to highlight a specific team using a dropdown menu bar at the top. This was useful because it highlighted the 
+entirety of the team's history in a bright yellow, making it incredibly distinct from the rest of the visualization. Overall I believe that this visualization does an 
+adequate job of conveying the data it means to, but there are also several improvements and streamlines that could be made.

week3.md

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+https://pudding.cool/2023/05/country-radio/
+
+The visualization that I looked at this week showed the data surrounding back-to-back plays of country songs on the radio. Specifically, they were looking at 
+the gender, sexuality, and racial makeup of the artists whose songs were played. The visualization is composed of multiple columns representing different days
+throughout the calendar year, and each column is composed of horizontal bars whose color represents what song by certain artists were played back to back. One of 
+the reasons that I like this visualization is the high level of options for customizability that the user has. They can choose from multiple regional locations/radio
+stations to see the data for that station, as well as within each station, choose to see the data on back-to-back plays based on gender, race, or sexuality. While
+there is a lot of data being shown, the simplicity of the colored thin bars makes it very easy for the user to see at a glance the makeup of the station's back-to-back
+plays. I think that the charts of numbers being shown above the visualization makes it a little more confusing, as the numbers aren't always related to the configuration
+of the visualization that is being shown. I also do not really like that it is unclear what the columns represents (which are days at 1-2 week intervals throughout the
+year). I think that if they were to get rid of the number table and solely show the visualization with a bit more of a descriptive blurb, it would be more effective.
+Otherwise, I think the overall design with the columns and thin bars was an excellent choice to showcase the data in this project.

week4.md

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+https://flowingdata.com/2023/12/07/fields-of-study-and-the-jobs-of-young-people-with-higher-incomes/
+
+The visualization I chose this week is a tree map (or pair of tree maps rather) that show information on people that make over $100,000 dollars a year. The first
+graph shows their bachelor's degrees, and the second graph depicts their current jobs. I thought that this was a very tidy way to present all of this information, and 
+it seems very organized at first sight. I also enjoyed the color scheme, which contained various shades of green (where the darkness of the shade represents a higher number of people
+in that category, I think), and black outlines. The color scheme made the visualization non-distracting, so I could focus on different pieces of information without
+being drawn to another area of the graph. I think that this visualization would benefit from having some sort of legend next to it, so I could be able to tell what
+the shades of the color mean on what sort of scale. Also, the biggest portions of the second tree map say "all other jobs". While I appreciate that there are probably 
+a very large number of jobs that didn't fit into the other categories on the map, I might have appreciated a little insight into exactly what these "other" jobs are. I think
+that overall this visualization does a clean and succinct job of presenting the intended data, but I am left wondering a little more than I should be. Some additional information
+or visual aids showing additional aspects of the data could be very beneficial. Also, some of the text for the smaller sections inside the map is unreadable without zooming
+in, so I think the use of tooltips might be more helpful in this case than having tiny text.

week5.md

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+https://public.tableau.com/app/profile/lomska/viz/NobelPrizeAwardedWomenB2VB/nobelwomen?_gl=1*194xan1*_ga*MjA3MDIzNjA4MS4xNzA1MTc5Njk0*_ga_8YLN0SNXVS*MTcwNzc1NzE2NS40LjEuMTcwNzc1NzE2OS4wLjAuMA..
+
+The visualization I chose this week comes from Tableau and is a visualization made by Tanya Lomskaya on their public software. It shows the data for women that were awarded
+Nobel Prizes in each of the categories from 1901-present day. The thing that drew me to this visualization was the circular format that the data was presented in. At first, I 
+was not sure the reasoning behind this design decision, but I really enjoy that at the end of this circular timeline there are bars that align with each of the categories
+showing the total number of women who had been awarded this prize. I really like the color scheme that this visualization uses, and I feel that the colors work well
+on the black background. A big feature of this visualization is the use of tooltips to give further information on each female Nobel Prize winner, presenting information
+such as country of origin, year of birth, what year and category they won the prize in, and a quote for the reasoning behind their award. I really appreciated this and 
+felt like it added a lot to the visualization. However, I do not like that when you mouse over the dots representing male or group awardees, the dot highlights as if it was 
+a tooltip, but does not give any information. I think it would be beneficial to make sure you can only mouse over and highlight female awardees. Additionally, the tooltips
+also extend to the bars showing the sum of awardees, which I really don't think is necessary, given that there are already numbers at the top of each bar showing the total. 
+I also think that the years at the cardinal points of the circle as well as showing the locations of World Wars I and II give good context for the timeline, but I think there 
+should be some consistency there. For example, there are years at the north, south, and west points on the circle, however the east point only says World War II. World War
+I is located somewhere in the northeastern quadrant. This makes the visualization less visually consistent, which is something I think could be improved.

week6.md

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+https://www.usatoday.com/story/graphics/2024/02/15/caitlin-clark-shot-charts-iowa-basketball/72550715007/
+
+The visualization I chose this week was inspired by Caitlin Clark defeating the all-time NCAA D1 women's basketball record for points scored. This visualization utilizes 
+circles with varying shades of red and sizes to indicate the shots that she took during her freshman year vs her senior year. A darker red indicates that she was more accurate
+in making shots than the D1 average, while a circle bounded by gray or black indicates levels below the D1 average. First, I thought it was interesting that they chose to 
+use the D1 shooting average rather than her own shooting average. However this works well to compare her to the rest of the field, especially in the case where she is such
+a standout shooter (#1 all time!). I am not sure why they were using shades of red to fill the circles that show a higher shooting average, and circles under the shooting 
+average were all white, but instead had different color borders, with shades nearing black to indicate the lowest shooting percentages. The size of the circle indicates the 
+number of shots that she took from that location, with a bigger circle indicating a higher number of shots. I liked this metric because it was very easy to tell visually 
+how big the circles were in comparison to one another. The two metrics used in conjunction with one another do a great job to show where she shot most accurately, the most amount of 
+times. Additionally, the commentary provided at the bottom and the reorganizaiton of the data points into groups really drive home the point that the visualization is trying to make,
+that between her freshman and senior year, Clark is more selective with her shots, but is also more accurate with her baskets. Also, the difference in location of the shots
+is striking, because the visualization clearly shows that she takes noticeably less shots from the mid-range now, instead choosing to shoot mostly from either under the basket
+or from beyond the three-point line, which are much more accurate shots. 

week7.md

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+https://pudding.cool/2022/02/women-in-headlines/
+
+This visualization comes from an article in The Pudding about the frequency of different words used in headlines for articles surrounding women. For the purposes 
+of this reflection, I looked at the last two visualizations in the article as you scroll down. The first of these is a chart showing the frequency of different words 
+compared across different countries, such as India, the US, South Africa, and the UK. I found this interesting first because of the differences and similarities that exist 
+across the countries. I think this graph does a good job of showing that due to the use of tooltips, as well as different colors showing different themes that the words
+have been categorized into. As you mouse over a particular bar representing a word, the graph grays out any other word, allowing you to see the rank of the specific word
+across all of the countries, which I think is very striking visually. The second visualization is a stacked bar chart showing the most common words and their frequencies
+organized by theme. I enjoy this chart because it also grays out the surrounding words when you mouse over a particular section, but also due to the tooltips. You can see
+a concrete number of how many times this particular word has been used since 2005, which I feel like is a needed metric, since the prior visualization didn't contain
+any sort of number or scale for the shown frequencies. As a final word, I really enjoy the written analysis that accompanies this last visualization, that "for every 
+occurrence of an empowering word, we read two words of crime and violence."