Graded Assignment-1 (May Term 2023) :- Visualization Critique

[Jimmi-Kr]

For the graded assignment 1, find a simple, stand-alone, static visualization and write a short critique on: How effective is it at what it aims to do? What works well and what doesn't? What could be better? (See Week 1, Part 7 lecture video for a briefing of the assignment)

Make your submission as a comment. It should contain:

• Title of the example with one image (add URL reference if it is online)
  • Short critique (could be in paragraphs or bullet points)
  • You can edit or update your comment up until the submission deadline but do not make multiple comments. If your GitHub username is not your actual name, include it in the comment/comment title.

Here are samples (sample 1,sample 2, Sample 3) of how this is to be submitted. Use examples that are not used in the samples.

[sayan10rakshit]

Top 50 Fast Food Chains in The US

Name: SAYAN RAKSHIT
Roll No: 21F1002696

Image source: https://www.reddit.com/r/dataisbeautiful/comments/13k5fho/oc_fast_food_chains_with_the_most_locations_in/?utm_source=share&utm_medium=android_app&utm_name=androidcss&utm_term=1&utm_content=share_button

What works

• The visualization is visually descriptive and informative
• The use of brand logos makes the information more visually appealing
• The different groups are easily distinguishable from each other
• The color palette and font family make the visualization readable

What doesn't work

• The size and placement of the group labels makes it difficult to interpret the nature of the groups at a glance
• The visualization looks cluttered
• No aggregate information is provided for each group
• The brand boxes are not proportional, making it difficult to compare the brands within and across groups
• The small brand boxes are difficult to read

What can be improved

• A legend could be added to show the color coding of the groups
• The brand boxes could be made proportional so that it is easier to compare the brands within and across groups
• Distribution of the brand outlets can be shown by a supporting geospatial visualization to make the visualization more informative
• A sunburst chart can be used as a supporting visualization to compare between the groups and where the brands lie within the groups

[h1m4n3hu]

Drugs World

Name: Himanshu Soni Roll No: 21f2000698

Source: https://www.informationisbeautiful.net/visualizations/drugs-world/

What works:

• The grouping of drugs is well-defined. One can easily differentiate which drug falls into which category, and which ones pertain to the property of more than one class.
• Subcategories are also defined in bold above the more diminutive associations inside the set diagram.
• Usage of every drug is labeled alongside, which makes it apprehensible.
• The chart stands self-explanatory to a novice in drugs and medicines.
• The common usage/meaning of a class is given in brackets, which makes it coherent to keep track of subclasses.
• The representation is not made dense for precise meaning to nodes and labels.

What doesn't work:

• A lot of white space takes up the visual. The set diagram, hence, could have been more extensive, taking up more space outside and making up more space inside of it.
• One must have a good knowledge of the classification of pharmaceuticals beforehand, making it even to other classes like Opioids, Centrals, and Narcotics- as they weigh similarly to other classes in the visual; otherwise the legend should mention why such comparison is taken.
• Text size could be bigger.
• The subcategorization inside the sets falls out of the desired area, making a vague assumption for classification. This again directs us to the sizing of the text, but can be undertaken size of the chart as size is minimal.
• Colour coding darkens in the middle region making it uneasy to read. Text is, hence, colored white, but only for one of the classes(Cannabis here), making it unclear for is it highlighted or differentiated.
• A legend should be made on the color chosen for each class, for example, on the basis of intensity, usage, or life of a drug class.

What could be better:

• The usage could have been close to the drug itself, making it clear to map.
• The size of the chart can be enhanced since the design is already annular and the text size, therefore, had to be reduced at times to fit in the desired area.
• Mapping should either be done for subcategories(not individual drugs) as it is now done for only some of the drugs, which makes it indecipherable for other drugs.
• Or if usage could have been mentioned as an attachment to every drug, a tree-based classification might have given it a more professional look.

[anant7k]

Visualizing Currencies’ Decline Against the U.S. Dollar

Anant Kumar BS, 21f1000683

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URL: https://elements.visualcapitalist.com/visualizing-currencies-decline-against-the-u-s-dollar/

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What works

• Visualization is very descriptive and easily understandable.
• Good choice of color encoding which helps in understanding and clearly portraying dominance of US dollar against other worldwide currencies.
• Supportive bullet points helps make visualization more infomative and provide interesting insights to the observer.
• Additional currency graphs helps in understanding economic/political impacts in a country through price fluctuations against dollar.
• The dollar index chart is demonstrated using bar chart which helps in easily understanding the relative growth and economic impacts on dollar across neighboring years.

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What does not work

• The bar chart does not portray an overall trend of the dollar index.
• The relative percentages in the bar graph could be often misleading to the users as it does not take into account the value of dollar index.
• The weightage of dollar index is heavily skewed due to currency Euro, so a major economic change in European union could heavily impact the dollar strength.
• Though the vizualization portrays value of US dollar with respect to different worldwide currencies, it does not help us understand any relationship among currency pairs.
• Time period used in bar graph visualization is from 1973 on a 3-year scale whereas in individual currency line charts, monthly scale is used from Jan 2021. It would be troublesome for an observer to correlate information provided in the dollar index bar chart to the individual currency charts for previous years.

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What can be improved

• The industry standard is using a Time-Price Line/Candlestick chart which often portrays additional information including value trends, volatility.
• We can use an index which have equal share of weightage from different worldwide currencies to reduce the skewness towards Euro.
• To display relationships amongst different worldwide curreny pairs, we can use a shared standard scale which helps the oberserver to understand the currency price fluctuations.
• In order to fit more data in the individual currency charts, we can switch to higher time frames and display yearly instead of monthly scale.
• We can add additional information about important political/economic events like the dot-com bubble, lehman brother collapse, covid-19 across years which would help an oberserver visualize the timeframe more interactively.

[savindraiitm]

Mercator projection

Name: Savindra Singh Shekhawat Roll No: 21f1003973

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The Mercator projection (/mərˈkeɪtər/) is a cylindrical map projection presented by Flemish geographer and cartographer Gerardus Mercator in 1569.

I chose this as i believe we use this map in daily life (Google maps, textbooks, articles etc) but many of us don't know it's actually showing countries with misleading sizes.

Article URL: Wikipedia

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Img Source: Visual Capitalist

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What works

It is the standard map projection for navigation because it is unique in representing north as up and south as down everywhere while preserving local directions and shapes. The map is thereby conformal.

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What doesn't work:

The Mercator projection inflates the size of objects away from the equator. This inflation is very small near the equator but accelerates with increasing latitude to become infinite at the poles. As a result, landmasses such as Greenland, Antarctica, Canada and Russia appear far larger than they actually are relative to landmasses near the equator, such as Central Africa.

Examples of size distortion

• Greenland appears the same size as Africa, when in reality Africa's area is 14 times as large.

• Greenland's real area is comparable to the Democratic Republic of the Congo's alone.

• Africa appears to be roughly the same size as South America, when in reality Africa is over one and a half times as large.

• Alaska appears to be the same size as Australia, although Australia is actually 4.5 times as large.

• Alaska also takes as much area on the map as Brazil, whereas Brazil's area is nearly 5 times that of Alaska.

• Madagascar and Great Britain look about the same size, while Madagascar is actually more than twice as large as Great Britain.

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Visit thetruesize.com for interactive map to know true sizes better.

[Khushiin]

TOPIC: Renewable Energy and Batter Installations in the U.S. in 2023. STUDENT: Khushee A Namdeo, 21f3001500 URL: https://www.visualcapitalist.com/mapped-renewable-energy-and-battery-installations-in-the-u-s-in-2023/

CRITIQUE:

Positives (What works well): 1) The different sources of renewable energy are color-coded with different colors each. This makes it visually appealing. 2) The relative size of bars provides some amount of comparison of renewable energy available in each country. 3) The picture is a representation of more than one type of renewable energy, hence providing us with more information on each country. 4) The arrangement of the different countries is according to their actual position in an actual map, which makes it more informative.

Negatives (What doesn't work well): 1) If I want to make comparisons between each source of energy production in different countries, then it becomes very difficult to do so. 2) The countries having very less amount of renewable energy have quite a flat surface to be seen in the pictorial representation and hence difficult to compare again. 3) The significance of the interruption of the picture of clouds in between the pictorial representation of a few countries is unclear. 4) A few data are labeled while others are not. The significance of labeling the ones which are labeled and the significance of not labeling the others are again not clear. 5) It is unclear if we have to compare the height, the area, or the volume of the bars representing the amount of renewable energy in each of the countries. 6) There might be countries which have good amount of certain source of renewable energy but have poor amount of some other renewable energy. This is not well specified in the representation and hence doesn’t provide us with a wholesome comparison in each country and the available renewable energies in each country. 7) It is difficult to find the %difference in the amount of renewable energy in the different countries mentioned.

How effective was the representation?

-> Though the representation is able to convey the basic idea behind the availability of different renewable resources in the different countries, it is only decently informative for an ordinary man who doesn't have much depth of knowledge in this area. But for someone who has the depth of knowledge in this area, this representation seems to be very basic. Thus, the data storytelling should have been better.

What could have been better?

1) For better clarity, we can represent each type of renewable energy separately for each country. 2) We can avoid a 3D representation of data and use a histogram/ pie chart for comparisons. 3) A bubble kind of representation for each type of renewable source of energy would have been a better way to represent the data.

[iSarthakGautam]

CO2 Emissions

Name: Sarthak Gautam Roll No: 21f1000864

Introduction:

The visualisation under review presents the topic of carbon dioxide (CO2) emissions and their impact on global climate change. It aims to highlight the responsibility of regions, countries, and individuals in reducing emissions. Link to article: CO2 Emissions.

The visualisation can be found at the following URL: Image.

Critique:

What works well:

• Proportional box size: Each country's box size accurately represents its cumulative CO2 emissions, enabling easy visual comparison.
• Grouping by continent: Countries are grouped and color-coded by continent, providing a clear organizational structure.
• Intuitive understanding: The use of proportionality allows users to rank major countries based on their CO2 emissions effectively.
• Data sources: The visualisation mentions the data sources used, ensuring transparency and credibility.
• EU-28 information: The inclusion of additional information about EU-28, a group comprising 28 European Union countries, reduces clutter and enhances clarity.
• Precise CO2 amount: The visualisation displays the exact amount of CO2 emissions in tonnes, facilitating accurate comprehension.
• Consistent legends: The legends are appropriately matched with the corresponding boxes using the same colour scheme.
• Time-frame context: The introduction provides a brief explanation of the time frame covered in the visualisation.
• Effective colour contrast: The colour contrast in the visualisation is visually pleasing and aids in data comprehension.

Effectiveness of the representation:

The current representation of CO2 emissions effectively conveys the basic idea. However, for individuals with a deeper understanding of the subject, the visualisation falls short in providing meaningful insights. To enhance its effectiveness, a more robust data storytelling approach is recommended. Techniques such as incorporating a pie chart or other interactive elements can provide a more comprehensive and engaging exploration of the data.

Areas for improvement:

• Colour selection: The choice of colours can be refined to accurately represent CO2 emissions. Red, traditionally associated with high emissions, should be allocated to the highest polluting continent or country. Green, representing environmentally friendly practices, should be assigned to the least polluting entity.
• Readability of small countries: The readability of small country labels within their respective boxes can be improved to ensure clarity.
• Addressing blank spaces: The visualisation should address the presence of empty or small boxes within continents, which can be visually distracting or confusing.
• Placement of Europe: Considering that Europe is the largest contributor of CO2 emissions, its placement in the visualisation should be reconsidered to reflect this fact accurately.
• Additional context: The visualisation would benefit from including information about common CO2-emitting assets such as aviation, shipping, and other industries to provide a more comprehensive understanding.
• Enhanced comparison: It is currently cumbersome to visually compare two countries. Incorporating alternative visual elements, such as a pie chart, would facilitate more effective comparisons.

Conclusion:

In conclusion, while the visualisation effectively conveys the concept of CO2 emissions, there are several areas that can be improved to enhance its clarity, readability, and context. By refining colour selection, addressing readability and empty spaces, providing additional context, and incorporating interactive elements, the visualisation can become more informative and engaging for users seeking a deeper understanding of CO2 emissions.

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[sejalanandIITM]

Ranked: Top 10 Most Valuable Airline Brands Since 2013

https://www.visualcapitalist.com/cp/ranked-top-10-most-valuable-airline-brands/

The visualization titled "Ranked: Top 10 Most Valuable Airline Brands Since 2013" presents the most valuable airline brands from North America, Europe, Asia and Middle East.

How effective is it at what it aims to do?

By looking at an overall perspective, the visualization is somewhat effective in detailing the most valuable brands every year from 2013 to 2022. One cannot comment on the most valuable brands across all the years. Only by looking at the section of a particular year, the top 2-3 can be quickly figured out.

What works well?

• The infographic provides a clear demarcation of the different years from 2013 to 2022.
• The growth or decline the brand values of different airlines companies as a whole in different years is clearly visible.
• The impact of COVID-19 is also clearly visible through the graph.

What doesn’t work well?

• Logos have been used repeatedly which adds to the clutter in the infographic.
• The numbers are very small while the logos are unnessarily larger.
• The legend of regions is not very apparent and does not add much to the readability.
• The colours of the legend are not approriately chosen with respect to the background that hinders its clear visibility.

What could be better?

• The infographic could use some simpler coding for brands instead of using logos several times.
• The coding for brands could be smaller and the numbers can be a litter bigger in size.
• The legend should be clearly visible and labelled to describe what it wants to convey. Either the background color or the color of the legend should be changed to make it more visible and effective.

[ghost]

The Rise and Rise of A.I.: Large Language Models (LLMs)

Name: Prateek Ganguli Roll: 21f1004044

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URL: https://informationisbeautiful.net/visualizations/the-rise-of-generative-ai-large-language-models-llms-like-chatgpt/

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Pros (What works)

• Colour has been used to categorize LLMs by organization, making it easy to visually filter by organization.
• Proper choice of using bubble sizes to convey number of parameters used in the LLM.
• Shows a cubic-fit dotted line for easy visualization of the trend with respect to time.
• Additional horizontal dotted line at 175 bn parameter mark helps distinguish between really large LLMs and medium sized LLMs.

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Cons (What doesn't work)

• Difficult to tell the actual number of parameters used per LLM as bubble sizes are subjective to scale.
• The cubic fit trend-line appears to weigh all LLMs equally, making a large number of small LLMs skew the results in comparison to the major LLMs.
• Some of the extremely small LLMs are clustered under a single label, making it difficult to know which label applies to which LLM for closely spaced clusters.
• Scaling of the axes is not mentioned anywhere, making the 175 bn parameter dotted line incomparable with any known point.

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Suggestions for improvement

• Relax the scaling of both axes to reduce crowding of points.
• Label the large bubbles with the parameter marks, so that we have a visual scale of reference.
• Label the y-axis with major-ticks and minor-ticks to quantify the parmeter numbers.
• Filter out LLM bubbles that are smaller than some threshold to make the clustered labels easier to tell apart.
• Use weighted means for plotting the cubic-fit trend line, so that a lot of small LLMs do not skew the trend with respect to the major LLMs.

[blackpearl006]

Name : Ninad Aithal Roll no: 21f1006030

Global Carbon Footprint

url : https://www.core77.com/posts/19153/footspotting-global-carbon-footprint-infographic-19153

Pros:

• The visualization creatively uses a human foot to depict the carbon footprint of countries, which can evoke an emotional response and make the issue more relatable to viewers.
• The countries are organized and color-coded by continent, providing a clear and intuitive visual structure that aids understanding and comparison.

Cons:

• One drawback of this infographic is the absence of a clear source or year of data. It's important to provide this information to ensure transparency and enable viewers to assess the relevance and reliability of the data presented.
• There is an unidentified blob between China, Japan, and Kazakhstan, which can be confusing and detract from the overall clarity of the visualization.
• The use of a scale is not apparent in the infographic, making it difficult to interpret the relative carbon emissions accurately. It would be beneficial to specify whether a normal or logarithmic scale is used to provide better context for comparison.
• Comparing countries as large as some continents with smaller countries like Luxembourg may not accurately represent the reality of carbon emissions. Consider using alternative visual strategies to ensure a more accurate portrayal of relative emissions.
• The infographic lacks a clear distinction in colors between North America and Europe, making it challenging to differentiate between the two regions at a glance.

Suggestions:

• Considering the urgency of the climate crisis, it might be more appropriate to use an alarming color, such as red, for regions like North America and Europe, while using green for countries with lower emissions, such as some African countries. This color scheme can help emphasize the varying degrees of carbon footprint across different regions.
• Providing a comprehensive key or legend within the infographic would enhance understanding and ensure that viewers can interpret the colors, symbols, and representations accurately.
• It would be beneficial to specify the year of the data, as this particular infographic represents data from 2011.

[upatil98]

Which countries directly import the most Russian gas

Name: Uday Patil Roll: 21f1003481

URL: https://www.aljazeera.com/wp-content/uploads/2022/03/INTERACTIVE-Which-countries-directly-import-the-most-Russian-gas_.png

Pros (What works)

• Countries are grouped and colour coded by continent.

• y axis clearly shows a country's share of Russian gas imports.

  Cons (What doesn't work)

• Alphabets are chosen as x-axis variables.
• Although some countries do import 100% of the gas from Russia but they aren't necessarily dependent on it as plot is trying to suggest.
• Some countries which do import gas from Russia are missing.
• Doesn't show the population of a country as more population would generally lead to more gas consumption.
• Hard to find a trend and get a general idea which country actually imports more gas.

• No label for y-axis.

  Suggestions for improvement

  • Use a numeric value like volume of gas imported will help visualize a trend.
  • Take into account the population of the country
  • Most Countries who's import is 100% from Russia might be because of their vicinity to Russia, so It's better that they are colour coded differently as East European countries.

[deep87we]

Name :Deepanshu Mahajan Roll no:21f1006962

URL:[https://en.wikipedia.org/wiki/Bubble_chart]

Pros 👍

• Visual representation of three variables like property crime ,violent crime and state: Bubble charts allow the visualization of three different variables simultaneously. In addition to the x-axis and y-axis variables. This can provide a more comprehensive understanding of data relationships.
• Easy identification of patterns and trends: The use of bubbles helps to identify patterns and trends in the data quickly. By observing the positioning, size, and color of the bubbles, patterns or clusters can be easily recognized.

Cons

• Too much information on one chart : Including too much information ruins the point of data visualization in the first place. The purpose of data visualization is to allow the audience to easily digest the information and this graph does the opposite of that.
• Difficulty in precise data comparison: While bubble charts allow for visual comparison, it can be challenging to precisely compare the values of individual data points. Determining exact differences in bubble size or precise positioning on the chart may require additional effort.
• Subjectivity in bubble size interpretation: The interpretation of bubble sizes can be subjective and may vary among different individuals. The perception of size may differ, leading to potential misinterpretations or biased conclusions.

Suggestion for improvements :

1. Reduce clutter: If the bubble chart becomes visually cluttered with too many overlapping bubbles, consider using techniques to declutter the chart. This can include adjusting bubble sizes, using transparency or color-coding to differentiate overlapping bubbles, or grouping similar data points together.2.
2. Highlight important data points: If there are specific data points that require emphasis, consider highlighting them using different colors, shapes, or sizes. This draws attention to key information and helps users focus on important insights.

[Vishvam10]

Name : Vishvam Sundararajan S Roll No : 21f1005939

URL : https://github.com/bsc-iitm/Data-Visualization-Design-CS4001/assets/78094956/8392f69e-c05a-450c-960a-75ed2512256b

(Kindly ignore the popup in the visualization. It is due to mouse hover 😅)

What works

• Visualling appealing and pleasing
• Information is well organized
• Using the size of the bubbles to indicate the relative revenue and profit is clever and simple
• Proper scale and legend is given
• The color palette is well chosen

What doesn't work

• Does not mention for what year the data has been collected (One has to look closely in the bottom-left corner to figure something out)
• The font kerning is not done properly and hence looks a bit weird when wrapped
• The visualization looks a bit cluttered

What can be improved

• Font should have be kerned properly
• Title could have been bigger
• Some amount of spacing in between the bubbles (kind of like a force field) would reduce the clutteredness

[Prahlad19]

Ranked: The World’s Biggest Steel Producers, by Country

Name: Prahlad Singhania Roll No: 21f1006059

URL: https://www.visualcapitalist.com/biggest-steel-producers-country/

What works:

• Data is augmented very well throughout the visualization wherever necessary. • Addition of descriptive notes in certain part of visualization makes it more comprehensive and understandable • Visualization is formatted very well. Firstly it shows the current scenario of steel production in various country and then it illustrates how the current leading steel producer enhance their journey from scratch.

What doesn’t work:

• Pie chart is not appropriate in this scenario. It is suited only when we quantify something in proportion instead of marking in numbers. • The unit of steel is not constant throughout the graph. • The size of slices is inconsistent with respect to holding capacity • While illustrating the journey of current leading steel producer, it fails to mark prior leading steel producer ad its proportion.

What can be improved:

• Bar graph would have been the most appropriate in this visualization. • The unit of steel production should either be in terms of mt or bt throughout the visualization. • It should also mark the leading countries and their proportion while illustrating the journey of current leading steel producer.

[afnan-ahmad]

The Next Billion Internet Users

Critique by: Afnan Ahmad | 21F1003730

Source: https://www.visualcapitalist.com/the-next-billion-internet-users-worldwide | Carmen A. (August 2020)

What works

• The visualization is able to convey the message with sufficient clarity.
• The colors and fonts used in the visualization are good enough to maintain readability.
• The circles drawn over the various geographical regions on the map are proportionate in area to the number associated with that region. This helps in getting a sense of scale, and to some extent in comparing the numbers between the regions.
• Additional text that is associated with some regions, through a connecting line, delivers a little more context without overlapping and impacting readability.

What doesn't work well

• It may not be apparent immediately, however, since the numbers are not given relative to (as a percentage of) a region's population, directly comparing between two and more regions might lead to a wrong conclusion.
• Apart from the map itself, most of the visualization consists of numbers and text only, therefore it is not necessary to have a legend for this visualization. However, for the flags that are shown on the map for two North European countries, having 96% and 98% internet access respectively, it would be better if their names are also mentioned in the visualization. (Sweden and Norway, respectively)
• Although it is a typographical error rather than a visualization error, the small compass that is drawn on the right side, is missing a W.

What could be improved

• Percentage of the number of unconnected people relative to a region's population could also be added alongside the actual numbers. This will help in comparing between two or more regions on a standard scale.

[dipak-patil-iitm]

The Rise and Rise of A.I. Large Language Models (LLMs) _{& their associated bots like ChatGPT}

Critique by: Dipak Patil (21f1004451) Source: https://informationisbeautiful.net/visualizations/the-rise-of-generative-ai-large-language-models-llms-like-chatgpt/

What works:

• The graph is well-designed and easy to understand.
• It provides a clear overview of LLMs' rapid growth and insight into which company is working on these models.
• It clearly shows data is trained on how many parameters.
• Data is up-to-date as new models emerge, showing the creators are committed to keeping the information accurate.
• It has the potential to raise awareness of LLMs and their potential benefits.

What doesn't work:

• The graph does not provide any information about the accuracy or performance of LLMs. This is a significant limitation, as it makes it difficult to assess the true potential of these models.
• The creators could have industries/domains in which these models are used like they are Chatbots, Image Generation, etc.
• It would have been great if the graph could have shown the number of active users who are using the models

What could be improved

• Accuracy/performance of models can be added inside the visualization to compare models

[VarnikaRB]

LGBTQ+ representation on TV

Critique by: Varnika Bagaria | 21f1007039

Source: Data Visualization

What works

• Data is augmented very well throughout the visualization wherever necessary.
• The colors and fonts used in here are contrasting hence is clearly visible
• The legend used helps is distinguishing the different categories at different levels
• The graph is easy to understand
• The visualization is formatted well as it distinguishes each layer based on certain criteria:
  • LGBTQ category
  • Type of drama

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What does not work

• There are too much emphasis on small categories. Some categories could have been merged and termed as others which could have provided a significant understanding.
• There are no measurement specified which makes it difficult to understand the magnitude by which it differs
• So many levels of categories is making it difficult to analyse. On looking this visualization seems nice but it is difficult to come to a specific conclusion

What can be improved

• Instead of fitting all details in a single graph. It could have been split into multiple graphs.
• Percentages can be used to compare the the categories
• Instead of one doughnut graph multiple pie chart could be used
• This graph should have also taken watching percentage of such shows to make it more effective

[abulaman8]

Operation "RED OCTOBER"

submission by: S Abulaman (21f1000506)

Red October impact data viz by Kaspersky Labs

What's Good

• Symbols used although not common are well documented to convey what they represent
• Good choice of visual using a map to display the effect of the attacks in various countries

What's Bad

• Red everywhere, hides the actual impact of the attack on each country
• Entire countries are colored, implying a greater effect , when in reality it was most probably a few organizations that were attacked

Ways to Improve

• Use a green to red spectrum to show the effect of attack on each country
• color only those regions that were attacked instead of the whole country

[PavanReddy28]

Top 2000 songs ever made

Name : Pavan Kumar Reddy Yannam ID: 21F1005053 Source: https://www.visualcinnamon.com/portfolio/top2000/

Figure 1:

What Works

• Visualisation is visually descriptive and informative
• Use of records (circular units) to depict songs is visually appealing to target audience (music lovers).
• Circular shaped units were effectively used:
  • To depict rank in top 2000 using size (radius) of circular units
  • To depict highest rank in weekly ranking using a Black-white color palette
  • To help recognise famous singers/artists of songs using VIBGYOR color palette for circumference along with the associated legend. Thus, infographic are used effectively.
• "Year of release" axis helps understand the year the song belongs to, ie, timeline of songs.
• Visualisation clearly shows the trend of Dutch people liking more 70s and 80s songs, based on the width at left half of time axis.

What doesn't work

• Data is not effectively augmented.
• Too much information is cramped in the plot (Cluttered plot).
• Very less information of songs outside top 100 is depicted in the figure.

What could be better

• Use a different representation like marginal histograms to represent number of songs in a year instead of using dense cluster of small circular units. Provides a cleaner visualisation.
• Align and augment the legend of top 10 songs more effectively, by providing the content at one place. Infographics can also be used to make the visualisation more effective.

[Varun-Sood-IIT]

Topic Name: "Which Countries are Granted the Most Patents?"

• Name = Varun Sood
• Roll No. = 21F1003382

Image Source: Click Here for "Which Countries are Granted the Most Patents?"

Image Source: Click Here for "Which Countries are Granted the Most Patents?"

What works

• The steady decrease in the circle size proportionate to the steady decrease in the percentages is almost satisfying to look at.
• The use of Uniform non-clashing primary colors with a dull-white background avoids too much strain on the eyes.
• It is immediately clear on how to interpret the chart and what is the natural direction of the dataflow.
• A quick summary is provided in the front and center of the visualization which helps a bit.
• I suppose this chart does tell a story which is helpful for patent industry people?
  • Because, for the laymen people it isn't really that interesting ;)

What doesn't work

• At first glance it seems that China is on steroids taking away EVERYTHING in EVERYTHING instead of just the overall 38%.
• The blue/yellow stars seems needlessly frivolous until one scrolls ALL the way down to realise their true purpose.
• The country-wise depiction is effectively useless after the "Control" category point shown in the spiral.
• I literally do not see any other country name besides "China", "Japan", "USA" and "South Korea".
  • Surely, that isn't misleading in any sense at all... is it? (FYI: Sarcasm being used here :)

What could be improved

• Many of the "categories" could have been grouped into their "industries" thus reducing the overall content cluttering.
  • "Tele-communications" and "Digital-communications" could have been grouped under "Communications"
  • "Medical Technologies" and "Pharmaceutical Tech." could have been grouped under "Medical Care"
• A simple pie-chart of top 6 countries in each category would have been much more informative.
• Camel case could be used instead of all capitals as it would surprisingly reduce cluttering.
  • Overall, there ain't much, as the maker had a formatting idea & stuck with it :(

[21f1004666]

Governance of different countries across the world

Name: Andiboyina Mourya Chakradhar Nagesh Roll no: 21f1004666

What works

• Aesthetically pleasing
• Use of background and font makes the visual readable.
• Interesting use of flower petals to denote each category and mentioning which petal represents which category

What doesn't work

• All the countries are grouped by each other randomly relative to their position on the world map, which makes it harder to identify smaller countries.
• Bad use of colors to represent the situation of a country. Viewers can only differentiate very few colors in the image.
• Some shapes didn't get labeled, making it even harder for viewers to identify labeled countries.
• The visual tells that the size of each country is represented by GDP per Capita (the bigger the GDP per Capita the bigger the size) but it doesn't mention a scale clearly.
• The layers of each petal are represented in years but due to the choice of colors and sizes, all the lines in the petal appear homogenous.

What can be improved

• The shapes with labels can be represented on a world map.
  • It makes labeling easier for the designer.
  • For viewers, representing those shapes on a map makes it easier to comprehend the visual
• It is better to use solid colors with scale (from bad to good).
• Could reduce the number of layers in each petal from 10 to 5.
• A GDP per capita scale should be represented on the visual to represent the size of the shape.

[SrivinaySridhar]

France’s Defensive map in the 2018/19 Football World Cup season

Name: Srivinay Sridhar Roll number: 21f1006569

Link: https://statsbomb.com/articles/soccer/frances-defense-steals-the-world-cup-show/

What works

• A simple and effective way of representing the defensive actions made by the team.
• The separation of zones makes it easier to visualise the actions in those zones respectively.
• A heat map like colours of zones helps to understand the concentration of these actions in various zones.
• Aggregates of the actions are shown to the bottom and right of the map to add more value to the visualisation.
• Comparison of the aggregates with the average using a line graph with contrasting colours give us a benchmark for how the team is performing compared to other teams.
• The plotting of the individual defensive actions on the map improves the visual appeal as well as provides more detail to the visualisation.

What does not work

• The distinction between the zones is not very apparent as there is only a thin border between them.
• The zones are not labelled and it is difficult to identify them when trying to talk or explain something related to a specific zone.
• The contrast between the field markings of the map (like the centre circle, penalty spot, etc.) and the markings of the individual defensive actions is lost because they are both indicated in white colour which may lead to confusions and loss of information.
• Defensive actions are more meaningful when there is knowledge about the possession of the team. We are unaware if the low/high defensive actions is because the team has kept possession or let the opponents hold possession.

Ways to improve

• Make the borders between zones more prominent to be able to distinguish them easier.
• Label the zones to be able to use them in conversation or text.
• Use a more contrasting colour for the field markings.
• Add information about the general possession percentage of the team along with the average of the other teams to give more perspective of the defensive actions.
• The separation of zones could have been improved if the team’s formation was incorporated helping us understand how the players are performing in defensive actions.

[S-D-P]

Name: Siddhi Dhirajkumar Pandirkar RollNo: 21f1001177

Visualization Link Article Link

Pros

1. Effectiveness: The polar chart effectively presents the relationship between education, child marriage, and underage pregnancy for 36 different states and union territories of India. It allows for a quick visual comparison of the prevalence of child marriage and underage pregnancy among women with at least 10 years of education across different regions.
2. Design Elements: The polar chart is visually engaging and allows users to explore the data for different regions easily. The chart is very simple and minimalistic, which leads the viewer directly to the point. The labels and legends are clear and properly aligned, making it easy to identify and understand the variables being represented.
3. Insightful Analysis: The visualization highlights the correlations between education and the prevalence of child marriage and underage pregnancy. It enables viewers to identify regions with higher or lower rates of child marriage and underage pregnancy among women with at least 10 years of education, leading to the insights about the impact of education on these issues.

Cons

1. The chosen polar chart may not effectively convey the intended message about the relationship between education, child marriage, and underage pregnancy. It could benefit from a different chart type that offers clearer representation and easier interpretation of the data.
2. One potential improvement could be the use of a line chart, where each parameter (education, underage pregnancy, child marriage) is represented by a separate line. This would allow for a more direct comparison between the variables and provide a clearer understanding of the relationship.
3. Alternatively, a combination of a bar chart for education and line charts for underage pregnancy and child marriage could be considered. This would enable the viewer to easily grasp the impact of education on reducing child marriage and underage pregnancy.
4. However, it is important to acknowledge that implementing these suggestions may require more horizontal space to accommodate all 36 regions effectively. The trade-off between data representation and available space should be carefully considered to ensure the visualization remains clear and informative.

[viboognesh]

HIV Prevalence Map Critique

What Works:

1. The data graphic is clear and easy to understand. The colours are easy to distinguish, and the labels are clear and concise.
2. The data graphic is visually appealing. The colours are used effectively to create a sense of urgency, and the overall design is eye-catching.
3. The data graphic is relevant to the WHO's mission. The WHO is an organisation that works to improve global health, and this data graphic highlights a significant public health problem.

What Doesn't Work:

1. The data graphic does not provide enough context. It would be helpful to know the total number of people living with HIV in the world and the number of people who have died from AIDS.
2. The data graphic does not provide any analysis. It would be helpful to know why the HIV prevalence rate is so high in sub-Saharan Africa and what is being done to address the problem.
3. The data graphic does not include any calls to action. It would be helpful to know what people can do to help reduce the spread of HIV.

What Can Be Improved:

1. The data graphic could be improved by providing more context. This could be done by adding the total number of people living with HIV in the world and the number of people who have died from AIDS.
2. The data graphic could be improved by providing some analysis. This could be done by explaining why the HIV prevalence rate is so high in sub-Saharan Africa and what is being done to address the problem.
3. Including some calls to action could improve the data graphic. This could be done by suggesting ways that people can help reduce the spread of HIV.

[21f1005173]

Name : M.S.Srinivass Roll Number : 21f1005173

Source : https://elements.visualcapitalist.com/where-are-clean-energy-technologies-manufactured/

Pros :

• Its very easy to understand the visualization
• The choice of colors used in the visual. Different groups of countries are easily distinguishable in the graphic
• The data is represented well. Different types of clean energy technologies are grouped separately and inside each group there is a graph used for each different part that is manufactured.
• A common x axis is used correctly for all the horizontal bars, and is labeled correctly. its easy to visualize how much a group of countries contribute to the manufacturing of the parts due to the size of the bars
• The year for which the data holds true is mentioned clearly

Cons :

• The source is written in a very small font
• The fact that Asia Pacific doesn't include China for this graphic is written in a very small font and could potentially be missed
• Global capacity could be explained better. From my understanding the graph aims to let the viewer know where the technologies are being manufactured and how much of the total each group of countries manufacture, and that China is manufacturing most of it. Its hard to understand how global capacity ties into this
• The units used in Global capacity should be expanded
• The visual claims that China's average is 65% and there's a dotted line cutting through all the horizontal bars. I think this part could be done better in a separate pie chart which shows the total of all groups of countries

[priyanka-maz]

Major Music Streaming Services Compared

Name : Priyanka Mazumdar Roll No. : 21f1000367

Image Source: Visualization Link , Article Link

Pros

• % free users is depicted using fractions of circle, which is an effective way to visually represent the proportion or distribution of a specific metric.
• The metric value written beside each visualization of a column, helps with reducing confusion in to comparing similar numbers between companies.
• average artist revenue per play is shown in a sorted order, highlighting the relation between this metric and the main topic of the article.

Cons

• Lack of Contrast in the colour scheme, may confuse the viewers and reduce the ability to differentiate between adjacent companies.
• The lines joining average artist revenue per day and total users(millions) can be misleading as one may not be able to follow un-uniform lines and it may lead to data-point confusion.
• Fails to convey the central idea that is Major Music Streaming Services Compared as it is visually smaller, so viewers may deem it as less important. The article name Money Too Tight To Mention comes off as the central idea due to such sizing.
• Colour theme used is not inclusive in nature, as many colours used here may be difficult to interpret for visually impaired people.
• The values in plays needed to earn min. wage are not uniform (thousand, million) thus it can pose challenges in accurately representing the data.
• annual loss per year uses area to represent values, which may lead to perceptual inaccuracy as we are representing money, a linear value in a 2-dimensional format.

Suggestions for Improvement

• The colour theme may be changed, especially the contrast between the black background and colours on the blue palette.
• Instead of using curved lines, only horizontal and vertical lines may be used to reduce visual complexity without reducing throughput of information.

[mb1AtGithub]

Name : Manisha Bapat Roll no: 21f1000449

Critique on visualization published by Simon Scarr https://www.scmp.com/infographics/article/1284683/iraqs-bloody-toll

Pros:

1. Colour : Red which matched the title about deaths count.
2. Inverted axes : The bars point down, giving the effect of blood dripping down, very evocative.
3. Axes labels well set to help viewers see, realize and read downwards.
4. Important events in the timeline have been marked explicitly
5. The complete count mentioned within the bar graph, with different colours for coalition deaths and civilian deaths
6. Blood drops below the chart show the different sub-counts for civilian deaths
7. Separate charts giving details about coalition deaths.

Cons:

1. Not a very familiar way of plotting- and thus reading graphs for many viewers.
2. Some readers might take time to understand the inverted axes, and read it.
3. A lot of information packed in a one page visualization.

Conclusion: The visualization serves the purpose it was created for.

[SrijanShukla]

Name: Srijan Shukla Roll No.: 21f1000671

ESPN CricInfo Cities with the best batting talentESPN CricInfo Cities with the best batting talent source: https://www.espncricinfo.com/story/which-top-cricket-city-would-win-the-world-cup-1196522

The written texts associated with the graphics need to be read in order to understand the meaning of the visualization. The color combination of red and blue used in the visualization is considered unfriendly to the eyes. The visualization does not fit neatly into any specific visual data visualization type, but it is closest to a bar chart. Instead of using bars, the designer used different shapes to represent each city, which makes it difficult to grasp the differences between them. In a typical bar chart, the height of each bar represents a difference, but in this visualization, the shapes' size, height, or width does not convey any meaningful difference. Despite Mumbai having the lowest average, it is visually portrayed as the tallest shape, further complicating the understanding of the data.

[abhishekVH]

Forbe's Top 50 Sports Franchices By Longetivity And Success

Name : Abhishek Kumar Gupta Roll Number : 21f1007086

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Image link

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Pros

1. Effective use of visual elements to convey information.
2. Engages and captures the attention of the audience.
3. Enables comparisons and trends analysis.
4. Visually appealing.

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Cons

1. Overemphasis on aesthetics at the expense of clarity and accuracy.
2. Lack of context or inadequate labeling, making it challenging to understand the data's significance
3. The top heading is confusing as it talks about players while the data shows different sports franchises.
4. The heading talks about 50 different franchises while we can see only few colors.

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Suggestions

1. The labelling could be better
2. More information is needed on what this visualization actually is.

[Shreyays]

Name: Shreya Y Roll number: 21f1002768

Link: https://www.behance.net/gallery/152938069/1901-2021-data-visualization?moduleId=871466079&action=moodboard

The attached chart intends to present statistics of the Nobel Prize recipients from 1901 to 2021.

What works:

Concise representation: It presents the complete picture of nobel prize winners in chemistry at a glance. Usage of scale: Uses appropriately scaled visual elements to represent metrics such as age and experience. Relevant information presented: The chart does not contain any redundant information.

What does not work:

Use of a single colour scheme : The use of a single colour makes it difficult to distinguish among the various statistics being presented. For instance, male and female data points are not clearly visible. Too many data points: The chart contains statistics of nobel laureates across 100 years, each of which is presented as a single data point. Lacks granularity: While the chart represents each prize winner, it being a static chart does not provide detailed information about each person. Vague title: The title is 'Statistics of Nobel Prizes in Chemistry 1901-2021, however there is no information at a glance on what kind of statistics are being represented.

What could be done better?

Context: Providing a year-wise summary chart as a contextual starter for the current chart will help the reader get top down understanding of the data. Grouping of the data points: Grouping data points by age, year, sub-domain etc. could provide a chart which is interpretable effortlessly. Labelled data: The data points could be labelled for better and quicker readability.

[harshadpaikrao]

Global EV Production 2022. Name : Harshad Shahu Paikrao Roll no. 21f1002085

image link : https://www.visualcapitalist.com/wp-content/uploads/2023/04/EV-Production-by-Brand-2022_Main.jpg

What works:

1. Ranks are clearly mentioned.
2. Captures the number of units manufactured as well as the growth rate.
3. Manufactures are grouped by countries which gives good idea about major EV manufacturing countries.
4. Legend in the right top corner help reading the graph.

What Doesn't Work:

1. Every circle represents two data points : Number of units and Growth rate. Here the size of circle corresponds to data and color corresponds to growth rate. One realizes this only after careful observation, initially the colors don't make any sense.
2. Size of circles in not proportionate to the data (number of units). e.g. Tesla production is almost 2.25 times that of gm. But the sizes of respective circle could not convey that.
3. Choice of colors for the growth rate is not impactful. e.g. the cluster of German manufactures appears to be almost the same shade, however the growth rate ranges from 0 to 39%.
4. Brand name are not mentioned anywhere only logos are used. This may not be helpful in case of new or less popular brand.

Suggestions for Improvement:

1. Better choice of colors and separate display for number of units and growth rate.

Conclusion : Overall the Visualization serves the purpose of showing the ranking but does not represent the data in correct proportion.

[faridkhan5]

Apple Q4 FY22 Income Statement Name: Farid Khan Roll no: 21f1002045

What works?

1. The diversification of revenue streams gives a clear and all those thin pipes becoming a large pipe gives a good understanding of how all segments contribute to the revenue
2. The thickness of the stream gives a good idea about the amount of money contributed
3. The choice of colors, where green color which symbolizes a successful operation used for profits and red used for all expenses, clearly distinguishes the profits from the expenditure

What doesn't work?

1. The streams look very cluttered, as it is difficult to track which stream belongs to which segment
2. The services segment needs to be placed further away from the products segment, as it looks like the logos are parts of the products segment
3. The choice of color for the revenue segment is very dull which gives the impression that revenue is not an important aspect
4. Few factors are bigger in text size in comparison but the amount contributed by them is lesser than the other factor which gives a false perception that they are more important

What can be done?

1. There needs to be a direction displayed to indicate the flow of money, as it is not understandable in the first glance as to where the money is coming from
2. The segment can be mentioned in the stream itself, making it easier to keep track of which stream belongs to which segment.
3. The color of the revenue stream can be changed to yellow since red and green together become yellow and this will indicate that they are then further separated
4. The size of the block of a factor should be proportional to the money accumulated by that particular factor eg; Revenue is more than the products segment, so the size of the revenue block could be bigger such that it shows that it contains more money
5. The text size of factors can also be adjusted such that they are proportional to the amount of money contributed by them