Botao Reflection6

Reflection4.md

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+CS 573 - Data Visualization
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+Botao Han
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+Reflection 2
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+Source: https://www.reddit.com/r/dataisbeautiful/comments/skdz61/temperature_change_distribution_over_the_world/
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+This week found a chart that is both interesting and kind of sad. It's a chart that shows different parts of the world's temperatures compared to the average temperature from 1961 to 1990. We can see that the temperature increases globally from 1-celsius degree to around 2-celsius degrees. 
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+I found a point that needs to draw our attention the most. Even though the ocean and land temperature are increasing rapidly, from a global perspective, The average temperature in the arctic increased almost shockingly 2-celsius degrees, which is twice as land and ocean average increase from 1961 to 1990. even though it's not shown in the chart, I can see that at around the year 1990, the average temperature of the Arctic started to rise sharply. I assume it's because of the increase in industrial development. The good thing is that as technology develops, the concept of environmental protection is becoming more and more popular. I hope someday the global average temperature will not increase anymore.

Reflection6.md

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+CS 573 - Data Visualization
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+Botao Han
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+Reflection 6
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+Source: https://www.reddit.com/r/dataisbeautiful/comments/swg5el/oc_comparing_play_time_vs_number_of_players_for/
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+This week I found a interesting post on dataisbeautiful Reddit forum. It's a chart that Comparing relationship Play Time and Number of Players for Popular BoardGameGeek Categories.
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+I am not much a board game lover. But I found some interesting properties in this graph. For each game, there is quite a high probability of a 0 hour playtime. Some people guess that the creator of this graph use some kind of smoothing tool to make the graph more elegant. The author later proved that the assumption is right, the underlying probability densities are estimated with kernal density estimators which don't obey boundary constraints. 

reflection1.md

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+CS 573 - Data Visualization
+
+Botao Han
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+Reflection 1
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+Source: https://www.reddit.com/r/dataisbeautiful/comments/s7la7d/oc_student_covid_cases_in_our_local_school/
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+This week I read a post on Reddit. A person posted a bar chart that described the overall cases in their local school district. 
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+This is a straightforward bar chart with numbers of cases on the left side and the dates on the bottom. Some may say this is a poorly organized chart, but I think this is precisely the chart that satisfied the most common need. This chart showed a sharp increase in cases from July 11th, 2021, to January 22nd, 2022. This is an excellent chart because it provides the information a group of people needs to know, the college student around the country. With the aggravated pandemic, a new semester after the winter break has begun. With more and more students coming back to school, some people from hometowns may get infected and infectious to other students. Some students may not realize the problem and come to class in person even though some classes offer an online option. This chart could be an excellent example of how severe the problem is and get less contact as possible.
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+Even though this chart did its job showing the severity of the pandemic, it still has some flaws. The static could have a tag on top of the data to present the exact numbers of cases. Also, there could be labels for the x and y-axis to make the data more intuitive.
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+Still, I think this is a great chart showing the information people really need. Nowadays, many charts are beautiful and eye-catching but ignore the real purpose of visualized data, showing information more intuitively.

reflection2.md

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+CS 573 - Data Visualization
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+Botao Han
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+Reflection 2
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+Source: https://www.reddit.com/r/dataisbeautiful/comments/s8r040/oc_domestic_abuse_hotline_calls_in_connecticut/
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+This week I found a bar chart when I was discovering the data visualization community in Reddit. This is a chart that shows the trends of numbers of domestic abuse hotline calls in Connecticut throughout time from Jan 2020 to the end of 2021.
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+We can notice that the author of the chart highlighted several important time periods. Before the First public COVID lockdown, the number of calls is around 1300 per month. The number skyrocketed as the Lockdown began. We can make a posit that this data group has a positive relationship with the severity of the pandemic. The peak of the graph is around a half month after the COVID death peak. I assume that the public COVID lockdown made people panic and cause more domestic abuse, and the number of hotline calls went peak as the COVID deaths went peak because people are most anxious about the pandemic. The
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+Even though it's not covered in this graph, I can assume that as the Omicron came there may have been a significant increase in hotline calls because the Omicron is more infectious.