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Week4-Amos-Roche #74

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dd12ef1
Adding the week1 reflection - Amos ROche
Jan 20, 2022
ca6445c
adding the week 2 reflection
Jan 24, 2022
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amosr2002 Feb 7, 2022
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9 changes: 9 additions & 0 deletions week1.md
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![miro](https://miro.medium.com/max/1050/1*7eB6_Sdc4nbu5_cg5yWRkw.png)

I chose to include an image of a shot chart of Giannis Antetokounmpo during the 2018-2019 Nba Season. This shot chart is interesting because, previously, scouts would look at film and shooting percentages to determine how to guard the player more effectively. With this chart, generated by the data points, nba scouts can simply look at the shot chart to see which spots the player excells more at. As we can see in the shot chart, Giannis Antetokounmpo shoots very well in the paint area (shots near the basket) and he does not take as many shots around the 3-point line and midrange. But if Giannis does take 3-pointers they are mainly from the top of the 3-point line.

These shot charts contains a variety of information that the coach/scout can use. For example, since Giannis's threepoint/midrange percentage is not too good, this can allow the defense to build schemes to try to stop giannis from getting near the basket and make him shoot more midrange shots and shots from the wing areas. This shot chart can also help a player focus on particular shots. Since giannis likes shooting from the top of the key 3-pointer, Giannis and the staff can make him practice those three-pointers more.

Shot charts can also be obtained for certain stretches, just not a player's career or particular season. Say if Giannis has a good January shooting three pointers, the defense can put more pressure guarding the three-pointer, and put less pressure near the basket.

Shot charts and other basketball visualizations will continue to be used and analyzed so coaches/scouts can make clearer and more informed decisions on offensive/defensive schemes
12 changes: 12 additions & 0 deletions week2.md
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![population](https://dpbnri2zg3lc2.cloudfront.net/en/wp-content/uploads/old-blog-uploads/europe-1km-density-exports-1.png)

![population1](https://1.bp.blogspot.com/-FRl_80wfZDw/Xqno1RJ5g0I/AAAAAAAAFX8/sKpUw9SpyFUDyXXpfhRCi4_rEeuERhNiACLcBGAsYHQ/s320/europe_1km_density_exports_4.png)

![population2](https://1.bp.blogspot.com/-O0W9mqkr6F0/Xqnork_w9jI/AAAAAAAAFXs/DL6tX5ngi6IxLYoZTpQG59m9jdRAozPwgCLcBGAsYHQ/s320/europe_1km_density_exports_3.png)

This visualization represents the population density in europe. The areas where in the map where it looks more crowded with the larger bar heights represents the areas with high population density. Instead of using a simple 2D map with highlighted areas, this 3D map provides a more creative design element as it provides an extremely aesthetic data map while providing the main functionality of effectively displaying the most population dense locations in Europe. The author that created this visualization stated that this map was created to allow the audience to understand the patterns in the data but to also provide us with a eye-catching visualization. He also stated this visualization was meant to be more experimental and aesthetic than simple just analytical.

It was also very interesting that the author of this map tried creating different perspectives of the data. Difference density locations can appear a little different in each perspective. So including all the different perspectives can allow the audience to grasp the true trends of the information.

I think visualizations like these will be very vital and important, especially in the coming future. This is because, in presentations, visualizations like these 3D maps will allow the audience to be drawn to the aesthetic of the map which may lead the audience to grasp the concept and trends in the data more easily. Many times, when data visualizations don't interactively and aesthetically prove the point in the data, the main ideas don't stick in the audience's mind.

5 changes: 5 additions & 0 deletions week4.md
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![miro](img/Taxonomy_of_ideas-3.png)

This is a visualization of a chart and words to describe ideas are clustered according to their categories. For example, incredible, brilliant, great, smart, fantastic, etc. are clustered together. But this visualization used different coloring and text fonts to show how different the words are. For example, "dumb" and the "worst" are much different words when it comes to describing ideas. "The worst" shows a poor conceptual structure and shows the idea is dysfunctional. But dumb is more of a loose idea not as dysfunctional as "the worst" idea.

I think this is a very interesting data visualization because usually when you see words bunched together in different clusters you think the words are all meant to interpret the same. But since the visualization included labels on the axises, such as dysfunctional/functional, this allows the audience to grasp the differences in these idea descriptive words.