Week6Reflections-azzhang3-Aaron-Zhang

week1.md

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+Week 1 Reflection
+Link: https://www.reddit.com/r/dataisbeautiful/comments/194xg2z/united_states_welcomes_over_one_million/
+
+This graph/data shows the percentage and the amount of international students from different countries that got accepted into
+a US Institution. I believe this data is interesting because it shows the diversity of the US and how many students are
+from each country. The data is presented in a unique way that somewhat represents a pie chart. The graph labels the 
+different regions of the world,  which includes 3 regions of Asia, Northern America, Europe, Oceania, Africa, etc., 
+each having their own section and colors. Within each section, they also split it up further into different countries. Then
+they show the percentages and the amount of students within each country.
+
+The graph does a great job at capturing the attention of the audience. The colors are bright and vibrant, and the graph is 
+unique in terms of its shape and design. The size of each section also corresponds to the percentages and the amount
+of students, which helps the audience easily view and understand the data. While the colors of each section are great, 
+I think the graph can do a better job at labeling each section, and assigning a color to the text as well. For example,
+while the section and text for Asia is purple, the text for number of students and the different regions of Asia are 
+black. I believe a better color scheme would be to have different shades of purple for the different regions of Asia 
+and have that correspond to the section as well. For example, Southeast Asia would have a different purple than South and 
+Central Asia, and East Asia. In addition, better consistency with the colors and text would also enhance the visualizations
+of the graph.
+
+

week2.md

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+Week 2 Relflection X Link: https://x.com/DataVizSociety/status/1126180096115560448?s=20
+
+Article Link: https://medium.com/nightingale/so-you-want-to-make-a-map-58c7f55f6b20
+
+This graph/data shows the percentage of poverty (proportion of employed population below the international poverty line 
+of US$1.90 per day) throughout the world. I believe this data is interesting as it displays the map in a 3d prespective. 
+It uses an isometric (parallel) projection which means that scale is equal in all directions across the entire 3D view. 
+The height of the countries are dependent on the poverty rate, the higher the total is, the higher the elevation it is for the 
+country. The elevation and poverty total ranges from 0 to 70,000,000. In addition, the map also provides colors, that somewhat 
+represents a heat map. The color measures the percentage of poverty within the country. They used 5 different colors to from 
+light tan to dark red, each presenting a range, from < 8% to > 54%.
+
+The graph does a great job at capturing the attention of the audience. The colors are strategically used in similar way to a heat map,
+but instead, the darker and redder regions are the ones with a higher percentage of poverty within the country/area. The graph also
+makes it really easy to read with the different elevations representing the total number of people in poverty within the country.
+Even, at a glance it is easy to see which countries have the highest total number and which countries has the highest percentage
+of poverty. In addition, the legends are clear and readers can easily identify it and determine what the graph represents. One 
+change I would make is probably change the text of the countries' name from light tan to dark than. This would make it easier
+for readers to identify which country it is that they are looking at. Overall, this is a great visualization and a greta way to
+display poverty totals and rates throughout the world. 

week3.md

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+Week 3 Reflection
+Link: https://www.reddit.com/r/dataisbeautiful/comments/19aace7/chinese_population_shrinks_for_second_time/
+
+This graph/data shows the total population and annual population growth rate in China. This data was interesting to me
+as it showed that the population of China decreasing and shrinks for a second year in a row. The graph is presented in 
+a unqiue way, using both a bar chart to show the total population and a line graph to show the annual growth rate. In addition,
+the colors were really well thought out, and fits with the theme/data that its displaying. The colors red and yellow represent
+that of the Chinese flag, which I believe helps the reader immediately understand and see what the graph is about. Also, I liked
+how they showed the data and population numbers for 2023, as this gives the audience just enough information to understand
+what the trend is like in recent years.
+
+While, the graph is great, there are some improvements that could be made. Firstly, it could be confusing to the audience how the 
+left side of the graph goes by decades (1970, 1980, 1990, 2000, etc) and the right side goes by years (2020, 2021, 2022, 2023). The
+audience could easily overlook that, and that could misrepresent the data. In addition, its missing years from 2010 to 2020. Leaving
+those years out could be done on purpose to construe the data and push a narrative. However, in my opinion, leaving information like 
+this out could give the data and what its trying to represent less credibility, as there are missing holes in the data. With that
+said I think the overall graph/data is great, the graph is visually nice, and its also simple, easy to understand, and straight
+to the point. 

week4.md

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+Week 4 Reflection:
+Link: https://www.reddit.com/media?url=https%3A%2F%2Fi.redd.it%2Fvisualising-japans-daily-earthquakes-2011-2021-v0-fipflil42jwb1.png%3Fs%3Db32464486a8cc43116d0d5de9f97229921e04a49
+
+This data shows Japan's daily earthquakes from 2011-2021. This data was interesting to me as it plotted every earthquake in Japan, and plotted the size based on the magnitude of the earthquake. The graph is unique in its way of presenting this information. It is sort of like a scatter plot, but instead of plotting it on the x and y, it is plotted around a circle, like polar coordinates. At 0 degrees its the month of January, 90 degrees is April, 180 degrees is June, and 270 degrees is October. In addition, I believe the colors were really well thought out, standing out from each other. From the data we can see some of the notable earthquakes in Japan including the largest, 2011 Tōhoku earthquake and tsunami. 
+
+While the graph is done really well, I believe that adding a legend would enhance the readability of the graph. The country of Japan in the middle of the circle is a great touch and ties everything together. As a reader, I can immediately understand or get a general idea of what the graph is about just based on the map of Japan in the middle. Also, adding some labels to some of the bigger and more notable earthquakes and where it is on the graph would also be really beneficial. It would help give a timeline, and creates story about some of the notable recent earthquakes in Japan. Overall, I think this graph is great and visually appealing. Along with that, it presents a unique way to make a scatterplot. 

week5.md

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+Week 5 Reflection:
+Link: https://overflowdata.com/demographic-traits/transportation/commute-county-22/
+
+This data shows the commuting times across different counties in the U.S, and the data was collected back in 2022. The data is represented like a heat map, using different shades of gray, ranging from white to black. That being the darker it is, the longer the average commute is. The graph is also very interactive, which I thought was really helpful in analysing this data set. There is a dropdown to show the top counties and a box and whisker plot which helps so the distribution of the data. In addition, hovering over any point on the box and whisker, or the top counties plot/chart highlights the county on the map. This is extremely helpful, as it gives you an idea of where the county is and maybe make a connection to why the commute might be longer or shorter. From the box and whisker plot we can also see some of the outliers, notably, Mora County in New Mexico. Users can also focus onto a state by selecting it in the drop down, which enhances the user experience. Hovering over a county on the map also gives you the name and the average commute time.
+
+Overall, this graph is done very well. It is clean, simplistic, visually appealing and offers a lot of additional features that enhances the user experience. One way to enhance the graph would be to adding timeline functionality/animation showing the gradual change of average commute times over the span of a couple years. Especially, with the use of the heat map, I am interested to see if there is any significant changes over these past few years.
+

week6.md

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+Week 6 Reflection:
+Link: https://www.reddit.com/r/dataisbeautiful/comments/1aun28g/oc_median_consumer_internet_download_speeds_from/
+
+This data shows the medium consumer broadband download speed of countries around the world throughout the years. The data is taken from 2012-2023, with countries including the U.S., China, Singapore, Chile, etc. The data is represented in a line graph with Speed (mbps) on the Y-axis and time (years) on the X-axis. At the far right of the graph, it also displayed the flag of the country and the speed of their internet. I thought that was a good addition to the graph as it provided some visuals. The graph is really simple, just using categorical data and colors to represent the different countries. I thought the graph was interesting as it shows the development of these countries over recent years, especially in the technology industry. The graph shows that all the countries are developing, with some faster than other. It also shows that countries like the U.S. have remained as one of the top developing countries. Additionally, countries like Singapore, China, and Chile had made massive strides in recent years.
+
+Overall, I think this graph is really well done. It is simple, clean, and easy to read. For improvements, I would first move the legend to the right side, as I feel that it is really cluttered at the top. In addition, I think the colors can correspond to the countries a little better, like using the countries flag colors for example. Also the right side of the graph is a little cluttered with all the flags, so I would consider making it so it displays the flags when you hover over a country's line and not constantly be visible. Overall, this graph is done really well. 
+