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+The dashboard I think good is the premier league dashboad https://www.premierleague.com/ .
+The most parts that impressed me is that I think the interaction is very smooth and once I move onto some signals, it will reflect quickly. Moreover, the stats dashboard is also the
+point that attracts me. For example, I need to check the game history between 2 specific teams, the dashboard will list all the stats info for me. Based on these information, I could
+bet a bit, which helps me win a lot! (It's just a hobby!). Moreover, it also lists some transfer information, helping me to keep in touch with the latest news. And we could compare
+whatever we want, like player vs player or team vs team. It will show the stack histogram to give me a direct feeling of the result.
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+This week I want to recommend the website https://www.fifaindex.com/ . It's a database for all the football players. we can show all the stats by comparing the players you want. For example,
+we could pick Halland and Ronaldo. And we also could get the players ranking to get the players you like in the game. The ranking is generated by the performance in the real world. And it's
+also the trend for the Ballon d'Or. And the gamer who likes playing FIFA are keen on using this database, especially the comparison function. That's the dashboard I want to introduce this week.
+
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+Today I want to introduce the DOTA2 built-in dashboard, it could reflct the trend of the individual game process. And we also could compare personal performance with the global player at your level. Many professional team including PSG.LGD, Liquid and OG use this dashboard to do data analysis and make new strategegy. In addition, if you buy the dota plus VIP, you will get the latest high winning rate hero combines to improve your gaming experience.
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+The dashboard I want to introduce is the https://covid19.who.int/ , Many of you guys may have already saw it. This dashboard is easy to use. Moreover, you also could choose the country you are interested in, leading to a new dashboard for the active and death trending charts. 
+
+The WHO coronavirus (COVID-19) dashboard presents official daily counts of COVID-19 cases, deaths and vaccine utilisation reported by countries, territories and areas. Through this dashboard, we aim to provide a frequently updated data visualization, data dissemination and data exploration resource, while linking users to other useful and informative resources.
+
+Also it offers huge data download links, we could donwload what we want to do more prediction about this disease.
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+https://www.nytimes.com/interactive/2021/03/02/climate/atlantic-ocean-climate-change.html
+
+This is quite a dive by Moises Velasquez-Manoff and Jeremy White for The New York Times. They look at the potential danger of melting ice from Greenland flowing into the Gulf Stream.
+
+An animated map of currents and temperature, reminiscent of NASA’s Perpetual Ocean from 2011, shows what’s going on underwater. The piece flies you through as you scroll with a familiar view as if you’re in space looking down.
+
+Keep reading though, and you’re taken underwater 800 feet below the surface. It’s like seeing the currents from a fish’s point of view.
+
+The transition of this 3D visualization is very cool to me. I wonder how did Moises Velasquez-Manoff and Jeremy White do it and how long did they take. BTW, the birdview visualization kind of looks like the Starry Night painted by Vincent Van Gogh. In addition, the idea of diving into the ocean is also very creative and impressive. One thing I've learned from this visualization is that design is very important. The first step of a good visualizatoin is to have a good design and catch others' eyes.
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+https://www.behance.net/gallery/81688575/Space-Junk-BBC-Science-Focus\ 5d1f26159e60e
+
+This visualization shows the different types of debris that have been created from stuff we've launched into space. The y-axis is linear showing the average distance you can find each given thing from earth, and the x-axis organizes by type. There is a lot of information in the viz, as this graphic is essentially trying to map out 5 dimensions at ounce: distance, mass, origin, time, and type. Because of this, the graphic might seem a bit cluttered at first. I think for the given number of dimensions, this graphic is actually pretty clean. Without interactivity, it's difficult to fit these things together. However, I think the design choices allow this to work.
+
+The circles represent the number of each object at said distance, while the lines leading up to them have a thickness based on their mass. Timelines are also provided at the bottoms of each of the object type x-labels. I really like the color scheme. The dark blue background works well with the space theme. I like the interesting color gradient for each of the circles. I like how the circles contain numbers (so sizes can be compared). I think the font choice works as well; i see it a lot in visualizations with map data.
+
+I might add a weight map to the graphic for the circles. The n the size of the circles represents is based on area, not radius. The y-axis scales linearly on the other hand, but has distinct labels at major, yet seemingly arbitrary points. This might be able to work if they gave labels to what is interesting about those heights. All of the different groupings have the same set of y values. This is kind of a strange way to do it because some of the values are 0, and we don't have a sense of the actual gradation of satellites.  
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+http://vis.stanford.edu/files/2013-SemanticColor-EuroVis.pdf
+
+I found this paper on matching semantics with colors. We might, for instance, associate blue with sky or money with green, and the question of how to algorithmically match these to better generate content specific visualization designs is the focus of this paper. To validate their algorithmic approach, the researchers had participants look at different visualizations and have their response time recorded. One motivational observation from previous studies is that people take longer to name words when they are colored in a conflicting way. For instance, if fire were to be colored blue or grass as red (note as opposites).
+
+They had two experiments. The general goal of both of them was to take a set of colors (20 of the tableau preset), and figure out how to best categorically assign them to a set of labels based on people's response times and accuraces of answering a set of questions about a colored bar plot. They first identified a set of related images to the semantic labels, then applied a series of filtering algorithms in order to attain histograms of each color's relative appearence in corresponding images. These color frequencies were then applied towards their experiment.
+
+The goal of their first experiment was to validate whether colors matching the semantics actually mattered. They found that colors chosen by data viz experts + algorithm improved participant responses. They also found the experts did a better job than the algorithms at choosing semantic-resonant colors for concrete categories. They found that question type also influenced how much the color improved, suggesting that time spent thinking about the answer was more important than how resonant the colors were. Interestingly, the algorithm performed worse than the expert on concrete (fruit/vege) labels but about the same as expert on iconic (drink/company). This could be because the relative variance in color along the images they scraped was lower for a company logo.
+
+I think this is a very interesting focus of research. I would be interested in learning more about what the tradeoffs might be of choosing one color over another to represent a label. For instance, besides response time, can we more accutately intuit a data type of it has one color versus another? If a secondary color is used, is this any better than using an unrelated color or worse (for instance brown or green for banana vs blue)? More broadly, I would also be interested in seeing whether or not certain variable types affect other areas of perception. For instance, if the label is apple vs house, how might we anticipate the relative discrepincies in quantity.
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+https://www.reddit.com/r/dataisbeautiful/comments/m1vjvs/maps_of_the_world_with_different_sea_and_lake/
+
+For this week's reflection, I chose a visualization that displays six maps of the world with each map showing a varying sea level. The first three show the world if the sea level was 100 m, 500 m, and 1000 m lower and the last three show the world if the sea level was 100 m, 500 m, and 1000 m higher. I thought it was interesting because due to climate change, the oceans are rising and this might be our reality in the not so distant future. I thought it was kind of interesting to see which regions would be covered by ocean first.
+
+I thought the visualizations themselves were cool but I think it would have they also would have made a great dynamic visualization because it would have been really cool to see the oceans rising on one single map. It would have been really interesting to see that progression happening. Otherwise, I thought the visualizations were very interesting and informative.
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+https://www.reddit.com/r/dataisbeautiful/comments/mkinm8/rent_prices_versus_household_income_in_major_us/ Interactive Version: https://themeasureofaplan.com/rent-prices-versus-income/?v=min
+
+I chose a visualization this week from the reddit page r/DataIsBeautiful. It's a chart that plots the average income to the average monthly rent for approximately 20-30 cities in the US, including New York City, San Francisco, Los Angeles, and Boston. The size of each point is mapped to the population size of each city and the color of each point is mapped to the region where the city is located, e.g the Northeast, Midwest, West and South. I thought this visualization would be interesting to see because I am going to be graduating soon and will be looking for a job and place to live so information like monthly rent and income will be a huge consideration when that time comes.
+
+I was a little surprised to see that monthly rent looked to be, for the most part, correlated to the average income for that city. I thought that there would be a few more outliers. There were only a few major outliers like San Francisco, Los Angeles, Miami, and San Jose. But the behavior of the outliers did track with my expectations because the monthly income in most of these cities was lower than the corresponding monthly rent predicted by the trendline. The only city whose monthly rent was lower was San Jose.
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