diff --git a/Mingjie Zeng - week1.md b/Mingjie Zeng - week1.md new file mode 100644 index 0000000..a82441a --- /dev/null +++ b/Mingjie Zeng - week1.md @@ -0,0 +1,23 @@ +#### Reflection 1 - 01/20/2022 +#### Mingjie Zeng (671222265) +#### Email:mzeng2@wpi.edu +---- + +For this week's reflection, I chose a picture from an article titled as Industries and Economies Leading the Robotics Revolution. + +The data visualization picture is shown as below. +The original article link is: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r1-pic1.jpeg) + +This graph shows the percentage of robotic shipments in each country at different years. + +I chose this graph to share as this week's reflection for the following reasons. + +First, this graph shows data on robot shipments in many dimensions, including years, countries, and specific percentages. Even in terms of time dimension, it contains both actual data and predicted data at the same time. + +Second, this chart looks like a simple bar chart, but each bar is actually a pie chart, so this chart is actually a combination of multiple pie charts. This approach is innovative and informative for me. + +Third and the most important one, this graph makes the pie chart more comparable. +As the example given in the first lecture, one of the characteristics of a pie chart is that it is not intuitive to compare the components, and it is not intuitive enough to compare two or more pie charts of similar composition with each other. +However, this chart converts each pie chart into a bar format, allowing for visual comparisons between the individual pie charts, and we can clearly see the trend of each part of the pie chart. The most interesting point is that this representation clearly shows the total share of certain components. For example, if we want to know the proportions of China, the US and Japan in the robot shipments industry, we can just focus on the three different green sections, which clearly shows this data. diff --git a/Mingjie Zeng - week2.md b/Mingjie Zeng - week2.md new file mode 100644 index 0000000..b37cacb --- /dev/null +++ b/Mingjie Zeng - week2.md @@ -0,0 +1,36 @@ +#### Reflection 2 - 01/24/2022 +#### Mingjie Zeng (671222265) +#### Email:mzeng2@wpi.edu +---- + +Here's the link of this week's reflection: http://histography.io/. + +This web page shows the interactive timeline that spans across 14 billion years of history, from the Big Bang to 2015. +The site draws historical events from Wikipedia and self-updates daily with new recorded events. +The interface allows for users to view between decades to millions of years. +The viewer can choose to watch a variety of events which have happened in a particular period or to target a specific event in time. +And in this page, every dot is a historic event from wikipedia. + +Here's the main page of this website: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r2-mainpage.jpg) + +The left column lists the categories and their number of historical events happened. +When you change from one category to another, the right area with dots with change and animate accordingly. +And when your cursor moves to a dot, the historical event and its picture corresponding to that dot will pop up, and you can click on that dot for more relevant information. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r2-animation.jpg) + +And we can change the time period of the event currently displayed on the main page by changing the selection of the timeline, and the animation effect of the dots will appear while the timeline is changed. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r2-timeline.jpg) + +When you check a new time period, new dots are created and the significant events in them are marked with popping-ups and pictures. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r2-dot.jpg) + +I chose this website for reflection because it's beautiful and with quite a lot of information. The animation with the dots is delicated playful and the interaction is quite enjoyable for users. +It's actually just a simple display of events with time but the effect makes it a wonderful art thing. These dots also have the effect of a bar chart, allowing a clear comparison of the number of events occurring each year. +That's such a clever use. + + diff --git a/Mingjie Zeng - week3.md b/Mingjie Zeng - week3.md new file mode 100644 index 0000000..a9411d7 --- /dev/null +++ b/Mingjie Zeng - week3.md @@ -0,0 +1,39 @@ +#### Reflection 3 - 01/31/2022 +#### Mingjie Zeng (671222265) +#### Email:mzeng2@wpi.edu +---- + +This week I found a special but simple example of visualizing data and here is the link of example: https://www.behance.net/gallery/99114047/Population-Density?tracking_source=search_projects_recommended%7Cdata%20visualization%20dataviz. + +The theme of this data visualization is a very common theme, the problem of the population. +But this visualization has a rather special entry point, not from the point of view of population size, but from the point of view of population density to represent some issues. + +In order to visualize the population density of each country and to facilitate a visual perception of the comparison, +the authors designed a uniform presentation format for the attributes necessary to be able to present population density, as shown in the following figureļ¼š +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r3-format.jpg) + +In this image, which illustrates what the symbols, dots and area of the circle represent. Different symbols are for different geographical area like Africe, Americas, Asia and Europe. +Every dot represents one person and the number of dots in the dark circle represents how many people there are in one square km of land area. +And for the pink circle, it represents the total population of this country. + +The following image is the main result of the data visualization. +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r3-main.jpg) + +What's interesting about this effect is that it completely eliminates the need for numerical representations in the presentation +and uses a visual format to give the readers a very graphic representation of the results. + +The advantage of this approach is that one can clearly see at a glance which country is the most densely populated, +and which country has the largest actual population and the regional division of that country. +This way of presenting the data is very efficient and because it has its own rules, it can be quickly applied to more country population density data or similar analysis of other data types. + +For the theme itself, on the one hand it can facilitate a direct comparison of population density, as in this picture, we can clearly see that Bangladesh has the highest population density: +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r3-density%20comp.jpg) + +On the other hand, it is also possible to make a direct judgment about the total population, +as in the following figure, we can easily determine that the country with the largest total population is India: +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r3-population%20comp.jpg) + +This kind of population density presentation is very design and artistic, +and at the same time can fully meet people's needs for this data. +I think it is a worthy way to learn data visualization, and can also expand people's ideas for data visualization presentation. + diff --git a/Mingjie Zeng - week4.md b/Mingjie Zeng - week4.md new file mode 100644 index 0000000..798a89c --- /dev/null +++ b/Mingjie Zeng - week4.md @@ -0,0 +1,66 @@ +#### Reflection 4 - 02/06/2022 +#### Mingjie Zeng (671222265) +#### Email:mzeng2@wpi.edu +---- + +This week I'm sharing a very interesting data visualization that shows the distribution of blocks along layers in the game Minecraft: https://public.tableau.com/app/profile/nir.smilga/viz/MinecraftBlocksDistperLayer_16438944312130/MinecraftBlocksDistbyLayer?publish=yes. + +The data comes #GamesNightViz, https://github.com/0xTiger/blockheights Extracted from 15 Regions, Minecraft Version 1.15.1. + +This is main page picture of this special game data visualization: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-main.jpg) + +The whole project is showing the distribution of the different kinds of blocks per layer of Minecraft. +The total real number and type of blocks in the game are counted at the top of the page, as well as the number and type of blocks shown in this model. +The number of blocks and the corresponding types of blocks in the area shown on the page, as well as the number of layers in which these blocks are distributed, are also counted. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-blocks.jpg) + +On the left side of the page, the detailed type and distribution of blocks in the current area is displayed, +each showing the proportion of currently visible blocks to all blocks in this category. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-dist.jpg) + +And when the user hovers over a block, it shows the proportion of the currently visible blocks in this category and the layers in which the blocks in this category are distributed in the current area. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-dist2.jpg) + +Further, if this category of blocks is clicked, this category of blocks is highlighted in the distribution map of the main page. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-highlight.jpg) + +When the user's mouse hovers over a block, the type of the current block and the number of layers it is on are displayed. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-hover.jpg) + +There is also a map window on the page that allows users to adjust the extent of the area displayed on the main page. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-map.jpg) + +In addition, this model shows the types and distribution of rare blocks. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-rare.jpg) + +Moreover, when users click on one of these blocks, it will highlight the block on the main page of distribution. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r4-rare_highlight.jpg) + +I chose this page as this week's reflection for several reasons. + +The first reason is that this page uses many different forms of data visualization, +including bar charts, highlighting, and different display flags for different blocks. + +The second reason is that the theme of this data visualization is interesting and graphic, +using the original style of the game to make statistics about the unique block elements of this game, which is very ornamental. + +The third reason is that this model is very interactive regarding the data, +such as the ability to adjust which part of the data is displayed on the main page, the ability to highlight a specific category of data individually, etc. + +Another reason is the large amount of data presented in this model, +which is a bit long in terms of rendering time, but the final result is still worth learning. + + + + + diff --git a/Mingjie Zeng - week5.md b/Mingjie Zeng - week5.md new file mode 100644 index 0000000..7f4d839 --- /dev/null +++ b/Mingjie Zeng - week5.md @@ -0,0 +1,63 @@ +#### Reflection 5 - 02/13/2022 +#### Mingjie Zeng (671222265) +#### Email:mzeng2@wpi.edu +---- + +This week's reflection comes from The Pudding: https://pudding.cool/2017/05/song-repetition/. This is an investigation of the repetition of lyrics in pop songs. + +The author analyze the repetitiveness of a dataset of 15,000 songs that charted on the Billboard Hot 100 between 1958 and 2017 based on the hypothesis that proposed by a computer scientist Donald Knuth about The Complexity of Songs. +It's not easy to translate the feeling of repetitive into a numerical feature. Even if there are the same number of repeated words in two versed of the same length, the repetition of these two verses may not be the same. +So the author use the Lempel-Ziv algorithm to compress the repeated sequences to see the proportion of repeated parts in the lyrics. +Through this way, the author get the distribution chart of compressibility of 15,000 songs from 1958 to 2017, excluding 20 outliners: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-main.jpg) + +The percentages on the x-axis are not evenly spaced. The author use a logarithmic scale with the property that, for any given song, a song that compresses half as small is a fixed distance away. For example, the distance between 20% and 60% is the same as between 98% and 99%. + +And when the 20 outliners songs are included in this distribution, the chart would be like this: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-main2.jpg) + +To achieve this goal, the author also visualize the process of lyrics compression. When the algorithm scans the lyrics to find chunks of text that exactly match earlier parts and after finding the repetition, the authoe replace the later one with a marker pointing back to the occurrence of the first time: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-compression1.jpg) + +And here is the computation of the redution size: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-compression2.jpg) + +And here is the whole prograss of the compression of texts: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-process.gif) + +And the author also makes a list of the most repetitive songs among decades: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-most.jpg) + +And finally, the author makes all repetition of popular music by year a folding line Chart to observe the trend: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-whole.jpg) + +In addition to the above, the author considers the repetition rate of lyrics by different authors, as well as comparing the repetition rate of lyrics by artists from different periods. +This distribution also shows the phenomenon that the repetition rate of the lyrics gradually increases over time. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-artist.jpg) +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-artist2.jpg) + +An even more interactive and interesting point is that users can view the repetition rate of lyrics of different songs by specific artists. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r5-artist3.jpg) + +---- + +I choose this investigation for several reasons: +- The topic is interesting creative, lots of people may be curious about this but may not get down to it. +- It's very nice about the idea of translating the repetition of lyrics into the compression rate of the lyrics, making this a comparision feature. +- The way of presenting the compression procedure is fun and intuitive. +- The use of a logarithmic scale with the propotion of the songs compressed is clever and flexible. +- This project is also very interactive, as users can view the repetition rate of lyrics of different artists in different periods, and can view the repetition rate of lyrics of their favorite artists separately, and the display is also very straightforward and easy to understand. + + + + + diff --git a/Mingjie Zeng - week6.md b/Mingjie Zeng - week6.md new file mode 100644 index 0000000..f9e37d1 --- /dev/null +++ b/Mingjie Zeng - week6.md @@ -0,0 +1,54 @@ +#### Reflection 6 - 02/20/2022 +#### Mingjie Zeng (671222265) +#### Email:mzeng2@wpi.edu +---- + +This week's reflection is about the world's biggest iceberg's disaster journey: https://graphics.reuters.com/CLIMATE-CHANGE/ICEBERG/yzdvxjrbzvx/index.html. + +The world's biggest iceberg named A68a has been on a slow journey toward cataclysm. The mass is now moving straight toward a remote south Atlantic island and scientists say a collision could cause a local, environmental catastrophe. + +Here is the visualization about the path of the icebergs: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r6-main.jpg) + +The blue paths are the paths of "iceberg alley", a northeast route traveled by chunks of ice that break from the peninsula. +And the gray arrow shows the current ocean travel. +And the yellow path represents the path A68a has taken and it's not common. + + +The scientists expect the icebergs to break apart in the ocean with all the wave action and turbulence but A68a doesn't perform this way. +It's now headed straight for South Georgia Island, making a collision almost inevitable. + +And in this article, the author compares the size of A68a with the 66 concountries and territories smaller than the A68a iceberg. + +Here's the straight forward visualization about the size comparision: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r6-comp.jpg) + +With the size of the seperate square, we can easily have a feeling about the size of this iceberg and the magnitude of the disaster it would cause. + +And here's another visualization about the size information additional with the shape information: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r6-comp2.jpg) + +With the specific shape image of the iceberg and other well-known islands, there is a clear comparision in size. +A68a is very similar in shape to Jamaica, almost as long as the U.S. territory Puerto Rico, and dwarfs China's Hong Kong Island as well as Sourtheast Asian city state of Singapore. + +For the environmental catastrophe part, to analyze the influence, the authors mark where the penguins live: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r6-penguins.jpg) + +---- + +Here are some points that made me feel this visualization is worthy to share. +- First, this visualization is very useful and helpful in a real and practical scenario. All these visualizations is to help to analyze the possible coming troublesome the iceberg A68a would cause. I think this is the real contribution of data visualization. +- We can see the comparision between the visualization of squre and the map in the feature of island size, seeing that every kind of visualization has its advantages and underlines. +- The use of map and path are vivid and straight forward, and they use map in different ways, one for the route that the iceberg travels and the other for the distribution of penguins, inspirating the flexibility in the way we visualize the data. + + + + + + + + diff --git a/Mingjie Zeng - week7.md b/Mingjie Zeng - week7.md new file mode 100644 index 0000000..18af8da --- /dev/null +++ b/Mingjie Zeng - week7.md @@ -0,0 +1,72 @@ +#### Reflection 7 - 02/27/2022 +#### Mingjie Zeng (671222265) +#### Email:mzeng2@wpi.edu +---- + +This is so cool a reflection this week, you can't miss it: https://flagstories.co/. There are many more ways of the data analysis about nation flags than you can imagine. + +It's a project named flag stories, with all 23 aspects of analyzing a flag, including the most used flag elements, the flag tetris, the flag charts, the number of changes about flags about Asia & Oceania, Africa, America, Europe, +the dominating flag layouts, the most dominating flag colours of the world, the most used flag colours by continents, the similar flags, the flag proportions, how old are national flags, +the continent flags, the complexity of flags, world history in flags, how flags are combined, flag families, what do flag colours symbolise, what do flag colours say about a country, +when two flags become one, when is it national flag day, the most used symbols in flags, the most used main colors, the most used color combinations and number of colours in flags. + +In addition to the diversity of content for flag analysis, the presentation of data is also very diverse, including ordinary bar charts, also there are a variety of more artistic bar charts. Besides these varieties of bar charts, there are stacked bar charts, treemap, map, bubble charts, and other innovative representations. + +Let's choose some parts as an examples. + +First, let's see a creative bar charts: + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-bar1.jpg) + +In this part, each flag is splited into elements and these elements are sorted by shape. After quantifying them and stacking them +into bar charts, we can see the most used flag elements. Interestingly, the graphics keep the shape of the original on the flag as much as possible, making this bar graph fun. + +And here comes another beautiful design. All the flag elements are splitted into separate parts and then sorted by colors and stackeed together. +This not only shows the percentage of colors in the flags, but also still manages to retain the unique elements. I think this part is very artistic and creative. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-stack1.jpg) + +Sometimes common data displays can have a richer meaning when they represent specific content. Flags look so much more alike when leaving out layouts and simply visulize them as stacked bar charts according to color proportion. +It is amazing that so many countries have the same composition of colors, they only differ in proportion. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-stack2.jpg) + +Then there is another very nice bar chart, using the flag connected as a bar chart is a novel form, but also can visualize the change of the flag, is a very functional visualization. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-bar2.jpg) + +In the section on color usage statistics, another presentation treemap is used. Clearly we can see the primary colours. The authors also counted the use of different colors by region. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-treemap1.jpg) + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-treemap2.jpg) + +And here's some interesting comparision about the flags. Some flags have some relations with others, like rotate and mirror. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-comp1.jpg) + +Here are also some statistics for regional flags, including color composition, flag layout and the use of special symbols. This kind of visualization is very clear and informative. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-comb1.jpg) + +There is also the map visualization for clarifying the flag families. This simply tell where these kind of flags come from and the geographical connectivity. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-map.jpg) + +Also, colors have many meanings, this chart I think is a combination of bubble chart and bar chart, on the one hand, lists the amount of different colors used, on the other hand, lists the percentage of different meanings of the same color. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-color.jpg) + +And here is another special one, transforming the calendar into a pie-chart-like formation to show the national flag day, which is clear and creative. + +![image](https://github.com/JasmineZZZ9/reflections/blob/master/pics/r7-calendar.jpg) + +---- + +Here's the reasons why I like this visualization a lot: +- The use of different kind of visualization is very creative and artistic. And from my perspective, there are a lot of good combination of different kinds visulizations, such as the combination of bubble chart and bar chart. +- The simple visualization can be used in a more different and interesting way. For example, in a simple bar chart, it can be combined with vary attributes like the color, the symbols and even the complexity, presenting the numeric proportion as well as the addition info. +- The content itself is fun enough. I can never imagine that the nation flags have so many interesting facts and the layouts and colors and the symbols have so much info to dig out. This is a very inspiration example of data visualization. + + + diff --git a/pics/r1-pic1.jpeg b/pics/r1-pic1.jpeg new file mode 100644 index 0000000..3bca8fe Binary files /dev/null and b/pics/r1-pic1.jpeg differ diff --git a/pics/r2-animation.jpg b/pics/r2-animation.jpg new file mode 100644 index 0000000..ea609c9 Binary files /dev/null and b/pics/r2-animation.jpg differ diff --git a/pics/r2-dot.jpg b/pics/r2-dot.jpg new file mode 100644 index 0000000..aaeb0ba Binary files /dev/null and b/pics/r2-dot.jpg differ diff --git a/pics/r2-mainpage.jpg b/pics/r2-mainpage.jpg new file mode 100644 index 0000000..a3eb521 Binary files /dev/null and b/pics/r2-mainpage.jpg differ diff --git a/pics/r2-timeline.jpg b/pics/r2-timeline.jpg new file mode 100644 index 0000000..34b3d99 Binary files /dev/null and b/pics/r2-timeline.jpg differ diff --git a/pics/r3-density comp.jpg b/pics/r3-density comp.jpg new file mode 100644 index 0000000..76c7ce0 Binary files /dev/null and b/pics/r3-density comp.jpg 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