What inspires me most about this project is the way the designer cleverly combines visual forms with information. The branching structures not only represent differences in bird-call frequencies, but also resemble trees and other natural forms found in birds’ habitats. This creates a direct visual connection between the data and its context, helping the audience understand the subject more intuitively. At the same time, the data guide alongside the visualization provides more detailed information, allowing viewers to explore and understand the data represented by each visual element without making the main graphic overly complicated.

The overall visual structure makes differences in frequency easy to identify while maintaining a clear and visually appealing composition. This project also made me realize how extensively visual forms can be developed from the characteristics and content of the data itself, as well as the context in which it exists. This approach not only makes the information easier to understand, but also creates a stronger connection between the visual language and the subject being communicated.

The whole website works as a continuous and coherent research report rather than a collection of separate visualizations. It even begins with a short story as an introduction before gradually leading the audience into the data and different topics. This narrative approach makes the process of exploring the information feel more connected and engaging. It also helped me gain a deeper understanding of the role of storytelling in data visualization, and how a narrative structure can guide the audience through complex information in a more natural and understandable way.

For me, this makes the visualization less effective because the design looks interesting, but the information are not very easy to understand. I think if the designer make the outer information more simpler will help the audience understand the data more quickly and easily.

For me, this is a good example of how data visualization can become an interactive social experience, rather than just a way to present information.

I think this data visualisation is particularly successful. It shows how art can be used to communicate information that is often missing or difficult to represent.

The main theme is diseases and the emotions arising from facing diseases. It’s not only visually clear but also uses a more suitable visual language to explore how data connects to personal emotion. When I look at this artwork, those red points make me feel that I am a part of Cooper’s story. It transforms abstract data into a more personal and emotional experience. Moreover, I can feel the pain and hurt she goes through. There are some strong connections between visual image, theme and texts.

In the end, even though it is not a scientific representation of data at all, it provide a powerful way to paints personal journey for a patient who fights with disease

What inspires me is you might not fully grasp it at first glance, but it sparks curiosity. We can see a lot of colors, symbols, and hand-drawn lines.And through that they build their own system language. Instead of using traditional charts, they collect data from everyday experiences and transform it into personal and meaningful visualisations. I also like how the audience needs to spend some time decoding the work. This makes the process of reading data more interactive, rather than simply giving us information directly. Of course, it also depends on the subject matter; not every theme can be expressed in this kind of language—sometimes a more direct approach is needed—but since this is a personal and emotional piece, I feel it is very fitting.

For me, this shows that data visualisation can be both informative and emotional. As the two authors stated “we argue we can use data to become more humane and to connect with ourselves and others at a deeper level.”

I consider its effectiveness to be relatively low because it packs a large amount of information into a very small space. While the Mickey Mouse shape is immediately eye-catching, the lack of a clear reading order or hierarchy makes the data difficult to read.

Of course, we can also identify its most important companies by their colors.but in my view, this can be misleading—suggesting that the central elements are the most significant, when in reality, but it not the truth.While the Mickey shape creates strong visual recognition and makes the theme more memorable, it seems to prioritize visual impact over the clarity of information and the lack of guidance.For me, this is an interesting example of how a strong graphic concept can sometimes make data more difficult to understand.Perhaps more guidance or differentiation could make the hierarchy clearer?

This picture is "good-looking and narrative", but "difficult to read, difficult to compare, difficult to analyze accurately". It is more like a storytelling infographic than an efficient data analysis chart.

In the data visualization, the information hierarchy of this chart is relatively clear, and the visual logic is relatively unified. Each country has three circles with the same structure in the chart, so you can quickly see the land reuse structure of different countries. The three colors are fixed and correspond to three categories, which reduces the cost of understanding. There is no cross-fusion of a large number of lines, which is more convenient for people to read and extract information from it.

The chart compresses the complex data of multiple countries&three land reuse methods into a unified and easy to recognize visual structure, which is especially suitable for observing pattern differences between different countries.

This chart is not only beautiful, but also well-organized, easy to read, and hierarchical. People can quickly grasp the data information that the chart wants to convey, so I think it is a good data visualization chart.

It integrates data from 2019 and 2020 within the same framework. Labelled percentage growth allows audiences to clearly understand both overall scale and changing trends. Black, white and orange colours are used for information categorisation rather than just decorative purposes. Most importantly, it provides a clear entry point for reading. A prominent title presents the core subject at a glance. The centre outward layout naturally guides the eye from the general overview to individual platforms. Supplementary text on the side explains events and background behind the statistics. By establishing this thoughtful visual hierarchy, it enables quick data comparison and helps audiences comprehend the information efficiently.

I chose it because it handles "visual encoding" brilliantly. Position is used to build an ordered coordinate system, color and shape distinguish fresh from rotten apples, and length and density hint at increasing chaos. It doesn't rely on piling up charts to present data, but uses visual metaphors to make "entropy increase" instantly understandable. This approach of wrapping scientific rationality in artistic sensibility is both aesthetically pleasing and easy to grasp, which perfectly hits my sweet spot for data visualization.

I chose it precisely to talk about the trap of "form over function." It packages simple population growth data into an overly abstract installation art piece. At first glance, viewers think "how cool," but at second glance, they realize they can't remember any specific information. The core of information visualization is to make complex data clear and easy to understand, not to use beautiful graphics to create reading barriers. It wins in aesthetics but loses the readability it should have most—that is where it fails.

In my opinion,I think Plateful Insights is an effective data visualisation work,because it presents global nutrition data in both physical and graphic forms. Six china plates represent different world regions. The size of each plate shows the extra calories available per person per day, while the chart on the surface shows the proportions of fats, proteins and carbohydrates. I find this is intuitive because the form of a plate is directly connected to the topic of food. The background visualisation also makes the project clearer. It uses different colourful dots and concentric circles to organise individual countries according to their level of calorie access. This allows viewers to first compare the regions through the physical plates and then explore more detailed country-level information in the background. They work together well, making complex nutrition data easier to compare, understand and remember.

This visualisation contains a large amount of information, but it does not communicate much beyond the original data. It mainly categorises the intangible cultural heritage into different groups, shows the number of items in each category, and then lists the individual items around the outside. However, there is little further analysis or interpretation of the data. The visualisation does not clearly reveal meaningful patterns, comparisons, trends, or relationships. The excessive connecting lines and densely arranged text also create visual clutter, making the information difficult to read. Although the different colours help distinguish categories, the overall structure is still complicated and requires considerable effort to understand. In this sense, the visualisation prioritises displaying a large amount of data over communicating useful insights. A more effective design could simplify the information and highlight key findings or comparisons rather than simply presenting the original dataset in a more decorative form

The concept is very clever, and it taught me some important ways to think about building an information visualization. For example, how do you choose the chart’s main organizing structure? This subject could be approached from many angles—events, works, people, and so on—but all of them are connected by time. A timeline can bring different people, events, and things together for comparison. Choosing it as the structure is especially effective, and makes the visualization’s most important information immediately clear.

In fact, even at the sketching stage, the chart’s information was clearly mapped out and divided into sections. This reminded me once again that the heart of data visualization is communicating information and working with data.

Another detail I really like is the use of custom symbols. The chart distills each painter’s personal style into a simple, flat graphic, then captures it in a distinctive icon. I find that especially delightful.

The work uses the symbolically rich Chinese character “人”, meaning “people”. Simple lines and colour choices make the final outcome minimal, intuitive and vivid.

Besides, this work encourages me to reflect on how much information is held within human bodily movement. I can see someone’s personal identity in the way they draw, how they speak, and even how they walk. Furthermore, the project incorporates video components. It combines footage of participants walking with their corresponding “人” symbols to animate these graphic marks. So these symbols break away from the two dimensional plane and move with human like posture.

For me, this represents my ideal data visualisation. It is playful yet straightforward. The data graphics capture my attention at first sight, and they remain engaging upon closer inspection. Its participatory quality allows everyone to take part in building the visualisation.

Ronghui Xia

I like how it builds a clear workflow: object selection, property identification, data matrix, binary translation and weaving. For me, this physical form makes data storytelling more personal. Instead of flat computer graphics, the colourful woven textures can evoke emotion and connect audiences to the real-life stories behind each item. I think it expands what data visualisation can be, proving that data does not only live on screen.

And many details are overly simplified. For example, the porcelain-making process in the central vase is presented only with basic graphics, numbers and short text. After reading this chart, I only gain basic, encyclopedia-style facts. It fails to spark curiosity for deeper exploration, and it does not invite audience engagement.

From this work, I have drawn a lesson for my own future design: I should avoid simply laying out raw information. Instead, I need to reflect on datasets, identify core focal points, use quantitative data, and build comparisons and analysis to communicate a clear story or argument. I should also explore ways to create audience interaction and let viewers participate within the design.

Ronghui Xia

It also transforms abstract numerical scales into spatial scales. By visualizing missile launches as concentric rings encircling North Korea, it converts abstract figures into an intuitive, contextualized spatial experience.

By organizing information across different scales, it creates a hierarchy of interpretation ranging from macro-historical contexts to specific military technologies. Through the filtering, categorisation, spatialisation and visual encoding of data, it establishes a framework for understanding complex geopolitical issues.

2. ‘Modes of Expression Through Movement’ (Figure 2) is ineffective; it is merely visual decoration in the form of a chart that answers no questions. The concentric circle grid lacks any scales, units or numerical labels, and the deepest purplish-red at the centre does not represent the maximum data value, but rather a visual illusion created by the superimposition of seven layers of transparency.

First, I believe the diagram is visually crowded and overwhelming. There are too many curved flowing lines packed together, and it is hard for viewers to quickly follow one single thread without getting confused. The labels are small and scattered around the circular layout, which makes text tricky to read.

Also, I think the colour coding could be clearer. While colours stand for different categories, some hues look quite similar. It is not easy to tell categories apart at a glance.

Another issue I notice is that it lacks clear quantitative comparison. The width of the lines represents page views, but I believe audiences cannot easily judge the exact difference in data volume just by eye. There is no simple scale or reference bar to help readers estimate values.

Lastly, I think the circular flow layout is beautiful but not intuitive. It prioritises artistic aesthetics over fast data reading. If viewers want to get specific information quickly, this design slows down understanding.