Reflections

week1.md

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+# Reflection 1
+
+Hilson Shrestha
+
+My reflections for this week
+
+## Population Percentage Visualization
+https://www.reddit.com/r/dataisbeautiful/comments/s4xrca/oc_population_of_each_state_as_a_percentage_of/
+
+
+This is an animated visualization of the percentage of the population of all the states in the US from 1900 to 2020. The Bubble area represents the percentage of the population. Within each bubble, there is an abbreviation of the name of the state and also the outline of the state. The bubbles have been positioned almost similar to the actual position of the state on the US map. However, it is not exact because the size of the bubble (population percentage) keeps changing over time.
+
+This visualization does a really good job of realizing how the population percentage changed over time. It is clear from the viz that the population of California is increasing while New York is decreasing. However, I find that there are a few problems with this visualization. 
+
+The outline of states is not effective. Since some of the states' population percentage is very low, its outline is very small and is hardly noticeable. Similarly, the name of the state is really hard to read. To make things worse, dark color scales represent a lower population percentage. So, the fact that the visualization uses dark text on top of the outline of the state makes the text even harder to read.

week2.md

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+# Reflection 2
+Hilson Shrestha
+
+My reflection this week: 
+## Mountains out of Molehills
+
+https://informationisbeautiful.net/visualizations/mountains-out-of-molehills/
+
+This visualization is a timeline of media inflamed fears. It show world's major fears and its frequency as mentioned in the media at any given time.
+When you click on an area, it shows the description of the fear in a box. The visualization also gives us the ability to switch from intensity scale (no. of news media mentions) to death scale.
+
+It is interesting to see how some frequencies of fears are mentioned in media are way more than the actual human deaths when one fear is compared with another fears. This visualization also tackles the problem of obstruction by layering different fears from bottom to the top based on frequency in each year.
+
+However, I find that there is a big drawback of this visualization. There is neither actual number of media mentions nor deaths displayed in the visualization. With different baselines, even if there was a scaling on y axis, it would have been challenge to decode the actual value.
+I think it would be better atleast to show the values in the description that appears when you click on a fear.

week3.md

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+# Reflection 3
+Hilson Shrestha
+
+My reflection this week: 
+## Plastic Waste Pollution data visualisation
+https://www.behance.net/gallery/106936329/Plastic-Waste-Pollution-data-visualisation
+
+
+This is a visualization of distribution of plastic waste generated by continents and countries in 2010.
+
+![Drag Racing](https://mir-s3-cdn-cf.behance.net/project_modules/fs/906be4106936329.5fabeafe9b5ba.jpg)
+
+
+This is a really interesting because it integrates multiple data in a single continuous visualization. On the left we have amount of plastic waste generated by continent. This distribution is then divided into plastic waste generated per country and arranged according to the amount generated. Then the same lines continue from being curve to a straight line to display the share of plastic being inadquately managed. On the far right, this visualization also includes GDP per capita for reference.
+
+Even though the lines from continent level to country level is overlapping, use of colors and opacity is properly done to tackle obstruction of lines.
+
+Currently, this visualization sorts countries by amount of waste generated. I think, it would also be interesting to see how this visualzation looks like when it is sorted by amount of recycled plactics.
+
+The second part of the visualization includes the weight of waste in each of the oceans. For scale, it cleverly quantifies the waste weight with the number of whales.

week4.md

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+# Reflection 4
+Hilson Shrestha
+
+My reflection this week: 
+https://www.precisionforcoviddata.org/
+
+## Community Vulnerability to COVID-19
+
+This is a visualization of US communities' vulnerability to COVID-19. It shows where the virus is spreading fastest and overlays information such as race, test sites, critical risk workers.
+
+The visualization begins by showing us a guide on how to use the system because some different controls and selections can be made. 
+![guide](img/reflection-4-guide.png)
+
+
+On the left, there is an area for category selection. And on the right is a map that displays a map associated with the selected category. We can use a search bar to look into a particular state or city. One cool thing about this visualization is the use of filters. For example, for any given selection, the user can filter out the states or counties by high or low vulnerability, or filter by race.
+
+Furthermore, there is a breakdown of vulnerability by population percentage.
+![white-vulnerability](img/reflection-4-white-vulnerability.png)
+
+![black-vulnerability](img/reflection-4-black-vulnerability.png)
+
+Another thing that I noticed here is how the data in the category selection reflects the region selected on the map. If we are viewing the map of the entire country, the data on the left summarizes the entire US population. But if we select a particular county without changing any filter, the data on the left reflects that selected county.
+
+I think a possible improvement for this visualization is by combining multiple filters at once. eg. I want to view areas with high socioeconomic status and low test sites.
+
+![filter](img/reflection-4-filter.png)

week5.md

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+# Week 5 Reflections
+
+My reflection this week: 
+https://www.reddit.com/r/dataisbeautiful/comments/sriztv/oc_an_updated_version_of_my_animation_that_shows/
+
+## Hours of daylight throughout the year
+
+This is a simple animated visualization of how the duration of sunlight varies across the latitudes. It is built using python and matplotlib. Relating to Munzner's tasks abstraction, this probably falls under "enjoyment" purpose.
+
+![viz](./img/reflection-5.png)
+
+A slowed-down version of this visualization is available here https://imgur.com/a/9UoguN5
+
+In this animated visualization, the x-axis shows the month of the year and the y-axis shows the hours of daylight per day. The variable being changed is the latitude. It changes from -90 degrees to 90 degrees (representing south pole and north pole respectively).
+
+This visualization looks so weird given that the line goes from being curved to completely flat. However, if we take the earth's tilt of 23.5 degrees from the y-axis into account, clearly explains the phenomenon.
+
+The reason I chose this visualization for reflection, even though is so simple, is that I had seen some of these timelapse videos of the sun in the North pole regions and it always looks cool.
+
+https://www.youtube.com/watch?v=ndlQNicOeso
+
+https://www.youtube.com/watch?v=m4NJHI3dwTU
+
+An alternative to this visualization in my opinion is to use latitude on one of the axes and varying month as a variable to animate.

week6.md

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+# Week 6 Reflections
+
+My reflection for this week:
+https://graphicprototype.net/icebergs/
+
+This is a visualization of 7000 **icebergs around the coast of Antarctica**. The data were collected in 1997.
+
+![](img/reflection-6.png)
+
+This visualization has outlined the map of Antarctica with white dots to represent the position of icebergs around the coast. However, just displaying the position and size using circles would be very difficult to identify large or small icebergs given the massive size of Antarctica. So, this visualization draws lines radially outward from the position of the icebergs and represents the size of the iceberg not just with the radius of the circle but also the distance of the line from the circular baseline. A logarithmic scale is used here because iceberg sizes vary a lot.
+
+![](img/reflection-6-size.png)
+
+For scale comparison, they have a legend with an approximate size of cities or places. This gives us a sense of how huge the iceberges actually.
+
+This visualization has also labeled the nearby seas around Antarctica. We cannot put an arrow representing North since every direction we move will point to the north from here. However, I feel that adding the nearby countries name would give more sense of position on the map because most people are not familiar with the names of the sea around Antarctica. 
+
+I think it would be really interesting how the icebergs have changed over the years. And a possible exploration of this visualization could be a use of timelapse over the years. With global warming, do we see a decrease in the density of the icebergs and their sizes?

week7.md

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+# Week 7 Reflection
+
+My reflection for this week:
+https://graphics.reuters.com/TONGA-VOLCANO/LIGHTNING/zgpomjdbypd/index.html
+
+## The perfect storm: Tongo Eruption
+![](./img/reflection-7.png)
+
+This is a visualization of all the lightning strikes during the Tongo volcanic eruption. It is basically a heatmap with timeline at the bottom. Heatmap is overlayed on top of the map of Tongo region. Lightlning strikes in a small region is represented by by a square dot. Color of the square dot represents the number of lightning strikes in that particular area. The timeline at the bottom also has line chart representing the number of strikes at that time.
+
+This visualization shows the massive scale of the eruption. The website also has charts of other volcanic events for comparison.
+
+One thing I really like about the visualization is the use of timeline with heatmap. Initially, the heat is empty with just the outline of the countries. Then the dots show up for current lightning activity at a particular time. Then the color fades to represent a past time.