Visualization Critique (2022)

[venkatrajam]

For the first assignment, 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?

You comment 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 anytime after you post, but do not make multiple comments. If your github username is not your actual name, include it in the comment/comment title.

[shweta-ratanpura]

Shweta Ratanpura M.Des Interaction Design 216330016

URL: https://www.behance.net/gallery/82752197/The-Menstrual-Cycle-Scientific-American

PROS

1. The topic of menstruation is still a taboo in most regions around the world - putting it in such an elegant manner may help people approach the topic more comfortably. The illustrations are aesthetic yet realistically detailed.
2. The visualisation successfully covers multiple layers of information related to the menstrual processes in the body, while still maintaining legibility.
3. The cyclic nature of ovulation as well as the linear information for hormone production have been combined really well to show the correlation between them.
4. The use of colours is appropriate to the topic - a majorly monochromatic colour palette creates a soothing narrative.

CONS

1. Although the supporting text does mention that many women may experience varied cycles, the visual does not account for menstrual cycles lasting less or more than 28 days
2. One of the main aspects of the cycle is the fertilisation window, which has not been highlighted appropriately. Similarly, the days of menses should also be emphasised upon better.
3. The legend for IU/L is equidistant, however one tends to look at the area rather than the length considering the leaf like graphics for representation of LH and FSH.
4. Also, the leaf graphics are organic in nature, thus making it very hard to grasp or compare the data in terms of length or area both.

[Ishitasharmax]

Much to lose for U.S. farmers - Data Visualization

Ishita Sharma 20U130030 Bdes, 3rd Year

Critique

Pros:

• Covers multiple categories of information by including exports by state and their value, the types of goods exported, and markets purchasing said goods.
• The data is color encoded based on the type of goods sold, which works well for differentiating between the goods and making it simple on the eyes.
• The same order of products (from soybeans to other products) is followed in each category making it easier to interpret and follow.
• The use of icons to depict the state, type of good, and country makes the infographic more accessible to people where language could be a barrier.
• Accurate findings by billions have been displayed per category under the label, making it easier and exact when trying to figure out which countries buy the highest exports and their comparison to the rest of the world.

Cons:

• The color of the states, countries, and "other product" icons is the same, even though there is no direct relation. This makes the data slightly confusing.
• The objective of the infographic is not clearly represented until the supporting paragraph is read, explaining how trade tariffs against the top 4 countries(Canada, China, Mexico, and EU) that buy the majority of the exports will affect the farmers. Rather, the value of the export goods and their process from origin state to exported country is shown.
• The division by state is not required for the objective to come through. It is excess information and unnecessarily clutters the data visualization.
• The colors of corn and tree nuts are very similar and the overlaps cause confusion in following the data.

[mdawkhar]

The $100 Trillion Global Economy (Global GDP 2022) B.des, 20U130007

URL:https://www.visualcapitalist.com/100-trillion-global-economy/

PROS

• Countries are organized into continents and color coded for visual appeal.

CONS

• The arrangement of nations seems random to me because it is not geographically accurate. However, each country is encircled by its neighboring nations. It is still difficult to locate if the person does not know the map by heart.
• Country's flag? Is it even needed? All flags are not known to most people and is not required if the country's name is visible.
• Visibility issue? Less GDP countries are represented by only their flags and are also small in size, with no GDP data.
• The countries with higher GDPs could have been positioned in the middle of their respective continent to make comparisons easier.
• Calculating the area of a polygon is difficult. The corners of the forms do not even meet in one point (for eg, Taiwan), makes determining the area much more difficult.
• Comparing countries is challenging. Because of the area map, discrimination and ranking are not possible.
• Less GDP countries could have been shown in combined format.
• The color of the Middle East and Oceania differs from that of their respective representing areas.
• The background image of the sky and stars is misleading.
• The 2021 visualization appears to be clear and minimalistic in case of color scheme. (https://www.visualcapitalist.com/visualizing-the-94-trillion-world-economy-in-one-chart/)

[Bishal2288]

Bishal Goswami PhD, Batch: Spring Sem 2021 214134002

The Missing Migrants Map, 2015 Created by: Valerio Pellegrini and Michele Mauri, based on data from Missing Migrants Project

URL: https://www.behance.net/gallery/34680727/THE-MISSING-MIGRANTS-MAP-Corriere-della-Sera

What works:

1. The quick instruction to read the visualisation in the very beginning is a good feature as it orients the readers.
2. The choice of dot density map is a wise choice as it highlights the gravity of the situation. Hence, the visualisation creates a sense of urgency which is required as a response towards this crisis.
3. The colours opted for the visualisation are uniform, which makes it pleasing for the eyes.
4. Grouping the regions as ‘A’ and ‘B’ and corresponding additional information panels are easy to comprehend for further information.
5. These information panels containing crisp one-liner stories and links on the worst migrant journeys meeting with accidents is a really helpful feature for people to become aware on such accidents.
6. Other additional information in the form of graphs/texts, which is also very useful, include: a) Number of deaths and missing people in the worst incidents from Central and Eastern Mediterranean, b) A separate note on the migration incidents on Europe, which mostly take place on landmass while crossing borders, c) Comparison between missing and dead migrants, and d) Categorisation of migrants as per gender, region of origin and the routes they take.

What doesn’t work/could be improved:

1. The worst incidents marked by numbers in the groups ‘A’ and ‘B’ are not serially placed, which makes it difficult to find a missing number elsewhere on the map.
2. It was much later realised that the numbers correspond with the date of the incident, starting from the oldest incident to the newest. A note regarding this in the instruction panel in the beginning would have been useful. Also, perhaps chronological order in numbering was not necessary at all.
3. The borders of the countries on the map are in red, which makes it difficult to notice some dots (also in red) on the landmass. Some other colour could have been better.
4. Because of the chosen font, number ‘1’ looks like ‘I’. Since, the visualisation has both numbers and letters, it briefly led to doubt.

[AmishaNair]

Storm Warnings

Number of billion-dollar weather and climate disasters in the U.S.

Amisha Nair B.Des, 20U130006

URL: https://nicolasrapp.com/studio/portfolio/storm-warnings/

Critique:

Things that work:

• The graph makes it easy to see that the number of disasters has increased enormously, especially since 2010 (mainly severe storms).
• Sense of flow is established with the wavy lines, ie, direction of reading is evident.

Things that don’t work:

• The data and title are quantitative (no. of events) but the method of representation makes it impossible to actually see the number of events. What stands out is the number of an event with respect to another. For eg, in the year 2015, there were mostly severe storms, then floods, then winter storms, then wildfires, and so on.
• Some annotation of the actual number of events could be employed.
• The graph is attempting to convey no. of events (length of the line), but brings area into play. This is often misleading.
• The position of an event moves up and down based one the frequency of events below it. For eg, no. of severe storms was the same in the year 2014 and 2015, but it looks like 2015 had more floods. Similarly, the no of floods is the same in the years 2006 and 2008, but it looks like there were more floods in ’06 compared to ’08.
• What is represented on the vertical year line? The same data set for the year can be placed at the top and bottom of the vertical line and have the same meaning.
• What does the fluctuation in between the years represent?
• There is also some disconnect with the grave subject matter because of the bubblegum-pop colours.
• The graphic that explains how it should be read is confusing – brings area into play instead of length.
• The arcs on the corners are used for continuity, to bring the data to the next row. However, it causes a lot of visual discord and clutter – mainly caused by the overlapping of elements. This could also make it harder for the viewer to understand that its just a connector, and the value is the same on both sections of the year.

Things that can be improved:

• The brief paragraph mentions quite a lot on the economic effect of these storms and the cumulative losses incurred, but the actual dollar amounts are not a parameter for the visualisation.
• Colour coding could be improved – blue for floods, red for wildfires etc.

[joe-jatin]

Jatin Anand B.des, 20U130017

URL: https://www.columnfivemedia.com/wp-content/uploads/2021/04/good-infographic-the-almighty-dollar-_-mapping-distribution-of-income-by-religious-belief-1.jpeg Mapping distribution of income by religious belief in USA Critique:

What works

1. Even though the graphs are not lined next to each other parallelly, it is still easy to compare.
2. Even though it is not hinted anywhere that the graph shows the percentage of total population of each group, it is fairly understandable.
3. The graph of each group of people has a uniform width even though it is radially arranged therefore it is not confusing.

What doesn’t work

1. The purpose of the ripple-like concentric pattern below in the middle is not understandable to any degree.
2. It does not show the actual population of each group and hence can’t be compared numerically.
3. The graphical relationship between the illustrations and the corresponding names of every group of people (religiously or nationally connected) breaks at national average since there is no illustration relating to it.
4. It is poorly evident through current illustrations or text(very small) that the population under-study is of America.
5. The size and arrangements of the contents causes the vital information present in the paragraph under the main heading to be poorly visible.

[Abhinavbansal20]

Title: Visualizing Crime in India Student: Abhinav Bansal, M.Des (216330003)

Link: https://www.behance.net/gallery/45298165/Data-Visualization-Visualizing-crime-in-India

Positives:

1. An excellent feature of the visualization is that it relates with the geographical location of the states. This makes it easy to visualize the dominance of crimes in the Northern belt of the country.
2. Factors such as the position in hierarchy (Depicted by arrows) are very well presented.
3. A lot of relatable data, such as literacy and population is presented. Hence, the crime rate is not seen in isolation, thus giving it more credibility.
4. Converse to point 5 of improvements, the prominence in thickness highlights the most concerning crime-factor of the individual states.

Improvements:

1. The measuring index is Circumference. However, the visualization makes it seem that area-based values are presented. The Murder Value (Red) is visually greater than total crimes (Blue)
2. Total number of crimes per state not mentioned, which would make the visualization richer.
3. Value based scale to quantify data is missing. Something like the radius, or a graphic scale could have been added.
4. The vertical bar representing 'National Statewise Population Average' is difficult to identify and locate. Probably a different colour coding could have been used in that horizontal bar. In addition, since the national average would be constant, there was no need to add it with individual population bars. A scaled legend could have been made.
5. Since there is no weightage given to the type of crime, the different thickness of individual circles could have been avoided. This would also bring in more clarity. Currently, some of the crimes are not visible.
6. Since the visualization focuses on a comparative representation, a linear visualization showing the Crime-Rate per Population could have been added. Although an attempt for the same has been made, it is not very evident.

[minhaskm]

US Government Budget - 2016

Student: K M Minhas, 20U130013, B.des

Link - https://obamawhitehouse.archives.gov/interactive-budget

Pros

• They kept it simple and it gives more transparency as everything adds up to a whole.

• Colour coding helps in differentiating each program. Different shades of the same color are used to represent the subcategories of each program area.

• It helps in comparing how much of the budget is allotted to each program area.

• Being interactive (hovering the mouse) helps in giving more data on how much is allotted.

• Sector with the largest allotted amount is placed at the top left and the amount (and hence the area) gets reduced as we go towards the lower right. Making it somewhat follows the golden ratio, visually.

Cons

• It gives the information on how much is allotted only by hovering the mouse, hence it cannot be used for print media.

• It doesn't show how the tax dollar is divided within each program for all the programs.

• Visualization becomes cluttered towards the smaller program areas.

• Minor program areas cannot be represented due to lack of space.

• Comparison between smaller program areas is difficult.

[shivanginegii]

Shivangi Negi M.Des Interaction Design 216330018

Title: Where does all the world's food go? URL: https://informationisbeautiful.net/visualizations/global-food-supply-where-does-all-the-worlds-food-go/

Pros:

• By representing Where does all the world's food go, right from the kind of food categories to the consumption related statistics, large set of information is being conveyed in a manner which is convenient to read and interpret. It is mostly achieved by efficient structuring and placement. But was all this data even needed, when the purpose was to just represent "where the food is being consumed"
• Division of information is well done. Legends are clear and concise. The parallel set chart, efficiently leads the division of food categories, although colour representation could have been more distinct. It is convenient to compare the information presented, by representing it in a table format, using width, quantity and colour to compare across all categories.
• The colour of food categories along with their representation in icons makes it easier to differentiate & identify the data
• Almost every category is well described using words and numerical values.

Cons:

• Primary focus is placed onto the categories of food that is produced, rather than where the food is actually going. The use of colours vs. greyscale, and its placement on the info graph, makes this difference even more prominent.
• The space below ‘biofuels, seems to be underutilized.
• A large chunk of “ whole grains” from How much do we make, is connected to animal feed, but it isn't evident due to the missing connectivity like the connectivity with locations.
• The description of “Amount per person per year” along with the representation, isn’t making it clear whether it is the - consumed amount or recommended.
• Since the quantity of food produced is in million tonnes per year, representing the quantity of food consumed per country should have been represented.
• Colour Coding has alot of errors and could have been improved. Like, the colour of “milk & dairy” is similar to who gets the food. The change in greys from one location to another , in contrast with the multiple types of colours used to represent food categories, makes it appear like gradation, hence is misrepresented. Different categories of food could have been represented in colours that are more related to them.
• The dotted dividing lines in grey from “ who gets the food” to what is being overconsumed or consumed is very similar to the colour or “location” and “ milk and dairy”, hence it is causing a clash.
• The colour of regions highlighted on the map is not similar to the colour in “ who gets the food”, hence a disconnect is created.
• The colour in “overconsumed” & “under-consumed” is very similar to that of the location so it is creating confusion.

[Juii28]

‘Best Picture winners at Oscar from 1927 to 2014

Jui Sonawane 20u130028 B.des 3rd yr

URL here

What works?

• The data represents awards for ‘Best Pictures’ at Oscar from 1927 to 2014.
• It includes symbolic representation of categories like Drama, Comedy, Action, Adventure, Musical, Biography, Crime, and Western which are a total of 8 Categories.
• The profit percentage is expressed in terms of the circle's area, making it easier to compare the most profited and least profited films.
• It also includes the Number of nominations for a particular year, user ratings for the movie, and duration.
• Representing large amounts of different data, brilliantly as one visualization.
• Visuals: The color scheme used is contrasting, font size and color make it easier to read and differentiate.

What doesn’t work/ what can be improved?

• Cannot tell the category which received the most or least awards (i.e Drama category or Action?). Incomparable.
• The number of nominations mentioned can be color-coded from Red to yellow, which helps us to understand which film received the highest number of nominations.
• Even though Film duration is mentioned, it's visually difficult to tell the longest and shortest film, so as a solution one can add subdivisions in the range 80-120.
• User Ratings at first glance seem very identical.
• The circles overlap in certain spaces, which might mislead the user that the revenue profit is shared.
• The data visualization is static and cannot be used for any kind of dynamic data.
• It is hard to obtain any kind of rankings and derive ratios by comparing two movies

[zuhaasif]

Visualizing Stolen paintings Zuha Asif P 216330014 M.Des Interaction Design

URL: https://www.behance.net/gallery/110024405/Stolen-paintings

Negatives

• Both the X and Y axis shows year of theft, which makes it confusing
• Simple data is represented in a more complex manner
• The bend line makes it confusing to plot the exact year of theft
• The dotted lines connecting thefts of same museum is confusing
• The legend being placed down and numbering of paintings shown up makes the visual hierarchy complicated
• At certain areas the bends tend to overlap causing confusion
• At a glance both blue and black look similar
• The centuries of paintings given at the bottom is often confused with year of theft. Instead it could have been the period of painting.(Renaissance, Baroque)

Positives

• Clearly shows thefts has increased recently
• The visualization is unique, creates interest in viewers

[aliviachaudhuri]

Diversity in Tech URL: https://informationisbeautiful.net/visualizations/diversity-in-tech/

Alivia Chaudhuri 216330017 IxD

This visualisation titled “Diversity in Tech” is meant to represent the gender diversity of global tech companies in the year 2017. Concept & Design: David McCandless // v4.0 // June 2018 Code & Design: Fabio Bergamaschi Research: Miriam Quick, Stephanie Tomasevic Dataset: bit.ly/KIB_DiversityTech

What works:

• The employee breakdown of key technology companies is a question worth investigating because there are a lot of underrepresented groups in the STEM field.
• The visualisation can convince certain companies to relook at their hiring strategy for better diversity and inclusivity as compared to other companies in the tech field.
• Easy comparison - The length of the bars can easily be compared at a glance as they share a common vertical baseline.
• The data shown is quite simply represented and easy to understand.
• This visualization can be helpful for a potential employee who is looking to work in a more diverse company. It has an element of storytelling - how some ethnicities and gender are better represented in a few companies than in others.
• Font sizes are legible and comfortable to read on screen.

What doesn't works/could have been made better:

• 23 tech companies have been listed and compared, but the criteria for choosing these particular companies are not mentioned/clear.
• The length of each bar keeps changing horizontally across. Every time one looks at a percentage they have to rescane what the complete 100% looks like.
• Gender has many spectrums. A graph talking about diversity should have included other genders like transgender too.
• There is no way to see overlapping data, eg, Percentage of Latino males, percentage of black females etc. (Data for such visualisation has not been recorded in the dataset, which could have given richer insights.)
• Categories like - ‘multi’ and ‘other’ do not make sense and are not clarified with an index.
• A group of data at the bottom in grey shows gender and race diversity in the top 50 US companies, US congress and Fortune 500 CEOs. This has multiple issues: Comparing with the top 50 US tech companies would have made more sense than comparing with the overall top 50 US companies. The type and position of employees have not been discussed. It could be possible that a company has a lot of female receptionists (pink-collar jobs) but not enough female engineers. Comparing CEO positions of Fortune 500 companies feels irrelevant since there are various kinds of companies in the top 500 apart from tech.
• The colour used to represent white individuals is very similar to the colour for males. It unintentionally looks like a continuation of the previous data (% of males)
• Nowhere is it mentioned that all data is in percentages.
• The usage of grey colour and separate chunking of that data is not very intuitive enough for comparing with the whole.
• The colour scheme is not relevant to either diversity or the tech field. It is harmonious but could have utilised a more polychromatic palette or existing colour notation like the multiple hues of the pride flag.

[romapandya49]

Kun Faya Kun visualised

Roma Pandya, 20u130001, BDes 3rd year.

Link: https://www.instagram.com/p/CeJSHi9sCw-/?igshid=YmMyMTA2M2Y=

The design shows graphic visualization of the song, Kun Faya Kun, showing various information points such as the lyrics, language, singer, instrumental/lyrical, duration and density of words.

Pros:

1. The distinction in singers is easy to make since contrasting primary colours are used for each.
2. All the lyrics are written on the graphic, so that people can sing along to it - reminding the viewer of the enjoyment the song brings.
3. It uses placement of the dots (above or below rectangle) to indicate language (Urdu or Arabic) instead of writing the lyrics in those languages. The lyrics are written in English and that makes sure that even if the reader cannot read either of the languages they can still sing the lyrics if they can read English.
4. One can understand the pace of the song to a certain extent from the word density.
5. At one point the colour of one singer blends into another, beautifully showing the way their voices combine.
6. Gives the intervals between lyrics which is found only as 'instrumental' in brackets in lyrics you find online.

Cons:

1. The colours chosen are very bright and saturated, and the overall style doesn't reflect the peaceful feeling of the song.
2. The temporal encoding isn't entirely accurate, according to the visual the song is 6:20 minutes long whereas it's actually 7:50 minutes long.
3. It lacks information regarding the instrumental parts, such as type of instrument, vocal instrumental, etc.
4. The visual is meant for people who can read English which limits the audience.
5. The harsh boxes and the style goes against the representation of a peaceful, calming song.

[HarshSPatil]

Harshavardhan Patil, 20u130025 B.des share of deaths attributed to indoor air pollution in Africa link- https://github.com/gilbertfontana/TidyTuesday/blob/main/Week15/tidytuesday_week_15.png

What works

• the dots make an abstraction of the shape of continent of Africa which gives a visual clue to the location.
• colours/size representing the varying degrees of percentages of deaths.

What doesn't work

• No signfier suggesting the visualization is about death.
• it does not take into account the total number of deaths.
• additional data could be represented such as total population and total number of deaths to give more context to the data.
• the size scale and colour coding both represent the same data which could be used for multiple sets of data eg.- colour for share of deaths attributed to indoor pollution and size to the total number of deaths.
• data is capped at 20 % and it is in increments of 5 so accurate representation is not present.

[A-b-h-i-s-h-e-k-D-u-t-t-a]

World's Biggest Data Breaches & Hacks

Abhishek Dutta 20U130009

Link - https://informationisbeautiful.net/visualizations/worlds-biggest-data-breaches-hacks/ formationisbeautiful.net/visualizations/worlds-biggest-data-breaches-hacks/

Pros

• We can derive some useful information from this representation that there was a boom in data breaches and hacks around and after 2015
• It succeeds in showing exaggeration by the fact that there are circles which look like mere dots.

Cons

• At first glance, the Yellow Circles might look like some anomaly since a complementary has been used to highlight them (which ends up isolating those because of the contrasting colours). But in fact, some Circles are yellow merely because they have an interesting story. This could have been executed in a better way by either using a shade of base colour or just giving an inside stroke (to retain the area).

• The data in some places seem too exaggerated (like the dots are soo small that they get lost in the clutter at times). Usage of spheres (i.e. volume) instead of circles could have slightly lessened the size disparity. (Since for the same volume, the difference in radii of circles is lesser)

• There seems to be the use of an overall gradient running from the bottom to the top which one could most likely assume signifies time, but that doesn't make sense because the Y-axis is already showing time. (So what is it for?)

• Colour coding could have been put to use by assigning different colours to the multiple app categories which would enable one to derive a lot more information.

• The X-axis has not been used at all. (like it could have been used for app categorization or hacking method which would have reduced the filters whilst providing extra information like which hacking/breaching method was used the most)

• Since it's an interactive Visualization, it shows details of the event while on hover. But the problem is that even on hover the colour remains the same while the size increases to show more info which leads to momentary confusion.

[ghost]

Title: The 1,234 satellites orbiting the earth

Mithun Murali, M.des IxD

URL: https://www.scmp.com/infographics/article/1670384/infographic-satellites-network

Pros:

1. The title and the subhead instructions help the reader to understand how to interpret this visual representation.
2. A straightforward and thematically appropriate black and white color theme is used which invokes the feeling of outer space; synonymous with the common perception of outer space.
3. The quantitative data is organized and sequential, which also helps in easily tracing back to the root or the origin of each data branch.
4. Clear representation of data using circles in orbit matching with the context. Relevant data are provided near each circle even though it may look cluttered on the first gaze.

Cons:

1. The scale of the 'concentric arc' is not easy to understand. There is no uniformity in the arcs' representation. The arc is not adding any value and hence it can be removed since the corresponding numerical data is already present near each circle.

[tanushkasarang]

Title: A Grand Taxonomy of Shakespearean Insults Student: Tanushka Sarang 20U130027

Infographic Designer: Charley Chartwell

What works well:

1. Title is included in the infographic; there is no scope for confusion.
2. The value of this infographic won't change overtime.
3. It demands engagement of the viewer to understand the visual.
4. Details have been incorporated well; the name of the play has been included for every Shakespearean insult.
5. Colours used are soothing and apt, they represent the quotes displayed are of the past (early modern English).
6. The infographic can be sold as a print, framed and hung on a wall, there is a commercial side to it.

What doesn't work:

1. Only a limited subgroups can be included, for it to remain economical.
2. Colours used in the subgroups are vague. E.g.: red for bodily qualities, brown for food/drink, yellow for types of individuals.
3. To read the quotes and enjoy the infographic, one has to zoom in many times for it to be legible.
4. The interlinking of certain quotes through opposite groups across the page, leads to clutter.
5. The connected lines have to be placed in such a way that it doesn't overlap any other text, space is wasted because of this.
6. There's no clear understanding of the number of Shakespearean plays used, one has to find them individually.

[ApoorvAnurag]

Apoorv Anurag 216330001 IxD M.Des 2nd year

Vegetables pounds per year per capita This chart shows the data for fresh vegetables (as opposed to canned or frozen). By pounds per day per capita, potatoes made up about half of all fresh vegetables in 1970. In 2019, that rate was about half, while overall vegetable consumption increased spread out through more varieties. The visualization is made by Nathan Yau, who has a PhD in Statistics from University of California, Los Angeles (UCLA).

Reference- https://flowingdata.com/2021/06/08/seeing-how-much-we-ate-over-the-years/

Pros-

1. Colors are semantically assigned to the vegetable's color.
2. The visualization tells when and at what time approximately the consumption of any vegetable crossed the consumption of another. This can mean a lot about people's behavioral changes or any social causes that led to it.
3. Vegetables on the top get ample space compared to the vegetables at the bottom, which tells that Potatoes are consumed much more than vegetables at the bottom of the diagram.
4. The data visualizer considered the factor of population change over the century. Hence it is good to consider consumption per capita per day. It gives a uniformity over the ages.

Cons-

1. At once, it isn't evident if we should compare the height of each vegetable's graph or the area.
2. It is confusing if the consumption is visualized by the height of each colored portion of the vegetable or the length from the top of the colored part to the bottom of the graph.
3. It is hard to see the visualizations in the middle and compare their consumption in a vertical direction. This may be because of the lack of grid lines.
4. It does not give information about exactly how much the consumption of vegetables in between 1970 to 2019. The vegetables at the bottom are also not annotated by their consumption.
5. It isn't easy to trace the name of the vegetable in between the graph. I must follow up the colored strip of that vegetable from either end to get their names.

[Naman-Verma-gg]

The science behind the most popular infographics ( https://www.siegemedia.com/creation/infographics )

I was on the internet looking around things about what made a good infographic when I found this beautiful one and I couldn't resist the urge to critique and talk about it here.

Pros

• This Infographic has an exceptionally well flow and this flow tells the story that it intends to quite prominently, effectively and beautifully.
• The idea and the topic is captured so prominently and beautifully in the top red box with the white text in different fonts with a bar graph in the background and it seems like the perfect way to capture attention.
• The gray box below it that transforms the whole into a dialogue box effectively establishes the flow with the little notch below it to push you into the rest of the infographic
• In the industries section, the graph in the background changes its opacity ever so slightly to remain unnoticed YET noticeable enough to give a clear distinction between the rows to your eyes without feeling out of place.
• The horizontal bar graphs are all encoded with its percentage data well with max being the max value any of the bars go.
• The simple colour coding of gray and red is easy on the eyes and red in contrast to gray attracts just enough attention to not be overwhelming.
• The dot plot about average word count is easy to interpret, clear and the red line serves as a reference for the most populated height quite effectively.
• The dot plot has the exact numbers written on their plots which eliminate the inaccuracy that might have been the case due to using large circular icons.
• The representation of the colours used as ink levels in colour droppers is a brilliant idea that makes things interesting enough and still makes it possible to be encoded accurately to the percentages of its data.
• The colour here also breaks the monotonicity of the previous colour scheme and reignites interest also very subtly establishing without any flashy division elements the fact that a new section starts here.
• The next section the graph background returns, the sizes of the circle seem to be encoded to the percentage, they are side by side and 2d thus easy to compare and relate to.
• Extra text boxes explaining terms are a nice touch.

Cons

• The four icons in the beginning seem to be breaking the theme that the section is set in as two icons are red and grayscaled while the other two are in colours making the colour coding there confusing and its need and aim there questionable.
• The industries bar graph do not have a prominently described upper limit and it is confusing to understand the scale of the graphs with respect to the percentages as in most cases the percentages don't add up to a hundred and the fact that the upper limit is 40% based on the single bar in the linkedIn section is very confusing and hard to infer.
• Similarly, in the colour section, the scale and the max limit of the percentages with respect to the ink levels is not clear.
• The circles talking about using a color wheel scheme and the ones using not are not encoded to the percentage which could have been done.
• The size of the next set circles encoded to the percentage of use but parts of it stick out, thus not maintaining a hard constant edge, making it confusing to make out which one is should be used.
• The text boxes below only explain two colour wheels (the most popular ones ofc) but it still feels like two were favored since the next percentages even though were half of the previous one, realistically speaking were just a few percentages away
• The percentages in most cases throughout the infographic, including the last one, don't add up to a 100 making it ever so confusing about the what the stats are and how they are conveyed and portrayed here.

I felt the the story telling aspect of this infographic was phenomenal and it thus stuck with me.

[AdarshPrakash01]

Adarsh prakash 20u130016 Bdes, 3rd year

CRITIQUE https://en.wikipedia.org/wiki/Water_footprint#/media/File:World_Water_Footprint.png PROS • The infographic covers a wide variety of subjects related to water footprint • The colour coding is trying to make a statement or tell a story that some countries are clearly suffering due to lack of water • The size and length related information is also easy to understand CONS • It doesn’t cover the topic of how much water is used for a specific product or use. It just conveys that water is being used. • The colour coding is not linear in the darkness of the colour used so it makes it harder to read. • Instead of green they could have used warmer colors like red, which gives more contrast and brings more attention to it

[ankitgit-hub]

https://www.nytimes.com/2022/01/06/opinion/omicron-covid-us.html

Cons-

The colours and the shape used give the visualisation an appearance of worm, which is inappropriate when the topic is Covid 19.

Choice of a spiral though not unusual to represent this kind of data, can be unfamiliar to the general audience.

Hard to tell estimate the no of cases using the width of the spiral

Pros -

It’s an attention grabbing visualisation, which very well could be it’s intended purpose since it accompanies an opinion piece.

Representing timelines with a spiral is economical and useful when real estate is of a concern (esp. on mobile)

Using a spiral makes it easy to see seasonality and correlation

[Tejaswinipundge]

Image source: https://www.behance.net/gallery/110024405/Stolen-paintings

Stolen paintings : The data visualizes details of 40 stolen paintings such as artist, Time period, museum from which it got stolen. etc. since 1900.

Pros :

• A Lot of information is nicely arranged in a economic way.
• Due to two different color used user can distinguish between ‘Never return’ and ‘got return’ Parameters.
• The information effectively organized across four axes and every axes represent a set of qualitative information.
• The identity of icons is explained very well.

Cons:

• The visualization is difficult to understand without icons and text content. Every time user needs to trace back the text content to verify what the element is about.
• Though information is effectively arranged in a small space, it is creating a lot of cognitive load.
• Shapes used are not semantically appropriate.
• There is no as such comparison of the data elements
• No proper visual hierarchy is seen.

[rubayatahmed]

Rubayat Ahmed 216330013 IxD, M.Des

A data visualization exploring over a century of extinction. https://www.behance.net/gallery/67151247/Lessons-from-those-weve-lost-Data-Visualization lessons_from_those_we_ve_lost_normal_res.pdf

What I liked :

1. The circular graph created by 10 concentric circles and radii, is very useful while navigating threat categories of species.
2. Proper instructions are given about how to read the visualization.
3. Mentioning scientific names under each species might be very useful for people who have interest.
4. Creating contrast between two consecutive years helps in easy navigation.

Could have been better :

1. The analogy of depicting 120 species in a circular manner similar to a clock is good but there are few points I noticed which might have been considered regarding this.

• Firstly, there is no marking from where to start reading.
• Secondly, there could be markings in particular intervals, similar to a clock. To give viewers enough stoppages to navigate through the timeline.

5. Types of animals i.e. Amphibians, Birds, Fish, Mammals, Reptiles are shown as alphabets only, which is difficult to identify when looked from distance and doesn't give the statistics of each types. Color coding/iconography could have solve the problem.
6. Same years could have been grouped together, which would have help to understand year wise statistics.
7. Color coding of of threat categories could have been made symbolic.
8. Not all the species have all the threats, Most of them have 3/4 known threat categories, which keeps other concentric circles empty. Besides that the threat categories are color coded, so maintaining two features(color coding & concentric circle) to show one parameter is ambiguous.
9. There are some hotspots where multiple impactful threats showed up to multiple species during the same time. But the visualization doesn't give any information or hint on that.

Overall the visualization gives individual species information quite well but it somewhat fails to show the patterns or co-relations between different factors.

[IndubhusanRoy]

Title: Visualizing the commercialization of space Name: Indubhusan Roy Roll no: 216330002 M.Des, 2nd Year, Interaction Design URL: https://datainnovation.org/2019/06/visualizing-the-commercialization-of-space/

https://datainnovation.org/2019/06/visualizing-the-commercialization-of-space/

The above visualization was published in the Wall street Journal in a series of data visualizations demonstrating the commercialization of outer space.

Critique: Pros The visualization clearly displays the growth in commercialization over the years and also tries to provide an overview of the orbital locations and positions represented by the different nations.

Cons Though the graphic used as a background shows an overview of orbital positions it does not provide any significant information and also makes the graphic look overwhelming

The colors used vary from grey to a highlighting cyan in a black background. This gives more prominence to the activity of the cyan colored country and the grey counterparts are very less visible to almost negligible because of the choice of the black background.

The colors used for USSR and China are very close in the color wheel and are therefore difficult to distinguish when plotted together in higher numbers

When the contribution by 'other' countries are such a high number in some strands it might be necessary to show who these countries are to provide a better idea of upcoming countries who are advancing in this area.

Showing the number of satellite launched by a specific country may not be a direct representation of space commercialization by the nation as different satellites have very specific purposes

[JaanhaviSP]

Title: Satellites in space by Nadieh Bremer URL: https://www.visualcinnamon.com/portfolio/space-wars/

Pros: • The representation of the magnitude of the number of satellites is shown effectively; this makes us visualize the number of satellites that are in space • This data viz covers most of the details like Category, class, mass and launch date very effectively • The colour coding and classifying of each category by symbols has been done brilliantly, and the explanation for each colour and symbol is clearly mentioned as well • The necessary information like how to read the chart, Orbit distance and range and written clearly

Cons: • It is a bit overwhelming in the beginning, even though we get a grasp of what is happening • The launch date is differentiated by the difference in the shades of colours which is not very evident in the data viz • The colours (category) seemed to have clustered together for easy understanding while the symbols (class) do not, which does not give a holistic picture of the "class." • Orbits are illustrated very well, but is there a reason for the undefined shape? Could it be an accurate representation of orbits in space? If yes, is it a piece of the necessary information?
• The direction of the arrows to indicate "defence" is random and seems misleading. Does the direction of the arrows get to do anything with the direction of the satellite?

[SanikaD19]

Title: The Billion Dollar O Gram Name: Sanika Deshpande Roll no: 216330011 M.Des, 2nd Year, Interaction Design URL: https://informationisbeautiful.net/visualizations/the-billion-dollar-gram/

Pros The data is composed in a whole which makes it easier to identify each of the proportions compared to the whole data visualization The colors are also chosen as per the importance of the category. User will focus more on the black tab at the first glance

Cons To make it fit in a whole, they have rearranged the boxes as per the space and not as per category. If they would have been as per category it would have been easier to compare the percentage Color coding has gradation which is mostly as per the amount but is not very clear. It looks like they have with a lot of effort made the information fit in the rectangle, no specific hierarchy of placement is followed Few very smaller units inside the main units doesn’t provide exact detailed information All the amounts are at an individual level, not able to picturise the total amount

[Pratham-IDC]

Football World Cup results throughout History 20U130024 URL - https://www.behance.net/gallery/145722157/SPORT-World-Cup-Results-Throughout-History?tracking_source=search_projects%7Cdata%20visualization%20infographic

Pros

1. The team names are arranged alphabetically in circular order makes easy to look for particular teams data.
2. Symmetry is playing good role and the lines are making navigation easier from stage to stage.

Cons

1. The random colors given to the bubbles does not indicate anything only their size shows amount of finishes and it is Slightly making it confusing.

What can be Improved

1. Bubbles can be colored in a monotone scheme to reduce confusion.

[amitkumarram95]

Amit Kumar Ram Roll no. 216330005

Title : Keep it underground Link to visualization : https://www.behance.net/gallery/100153201/Fossil-Fuels-Dataviz

Context : The visualization shown above is breathtaking, to say the least. At the core, it is a simple yet profound representation of the amount of carbon dioxide in the atmosphere, how that has increased over time, and how fossil fuel consumption is responsible. The lines, gradually growing denser as the years progress, project a bleak future, albeit in a sublime manner.

Positives

• It seems that the purpose of this visualization is to make people sit and stare at it, mesmerized, even before they stumble upon the information that it is trying to convey. It is more simulative than it is informative - and that effect is achieved without doubt.
• At the heart of it, it is a uniquely simple visualization that just shows two metrics - atmospheric CO2 concentration (in ppm) over time, and fossil fuel emissions (in GtC/yr) over time. Only three values are used for each of these metrics -- six values in total, and yet it manages to convey a story.
• This visualization expertly plays with density, and that in itself is enough to convey the sharp rise in atmospheric CO2 that we should expect by year 2100. The data provided almost acts as second fiddle to the majestic, cosmic waves.
• The treatment is very artistic, which works very well for a simulative visualization.
• I love the fact that there exists a central point of convergence, where the metaphorical sky and earth meet, data about fossil fuels below and atmospheric CO2 above.
• The mood of this visualization is very "out of this world" - cosmic and primordial, in a way that it makes us realize that the problem of increasing CO2 emissions - is a problem that is bigger than us.

Negatives

• This visualization has a calming feel to it, almost as if it's a beautiful thing that's happening. This is strange considering the bleak subject matter, one that should evoke action, and to some extent - anger.
• At first glance, it is not clear whether the horizontal lines next to PPM values such as 284, 416, 1231 are merely pointing to the corresponding year/ time period for these values, or if the length of these lines signify the specific value. On closer inspection, it becomes clear that these lines are merely pointers, since the line next to 284 is way shorter than the line next to 416.
• Even though I have spoken about how this visualization places emphasis on communicating a feeling, it is imperative to note that the information written on it is not very readable. Using terms like ppm and GtC next to the values - makes it very difficult for a person from a non-scientific background to make complete sense of the data.
• Do the dots sprinkled across the visualization add anything, other than aesthetic value? It also seems that the different lines used in the visualization end in dots. So are these dots some kind of datapoints along the y-axis? Although this doesn't make much sense as - intensity of lines is used as a way to represent datapoints, and the different values are written in a descending manner as we go up the y-axis.
• The connection established between fossil fuel emissions and atmospheric CO2 concentration, although meaningful, is misleading in a way. Is all atmospheric CO2 a result of fossil fuel consumption only? This visualization makes it seem that way by ignoring other factors that may come into play.

[pavanxs]

Major Streaming Services Compared.

Pavan Sai 20U130031

Pros

• Data is Simple and Easy to understand. it properly shows how each music streaming service is performing.
• A good Decision to represent different colour's in different companies.
• Using different shapes to represent different types of data.
• Showing the size of data in units instead of length or area or other forms. as others are quite restrictive and this is simple to represent in small area.
• And numbers are written beside the visual data helps users know the precise data.

Cons

• Though Data is Simply represented, It takes time to access the data due to the line mapping.
• These lines visually make it hard to map the data to the company.
• Misrepresentation of the No.of users based on length.
• People may misunderstand as the companies are based on price and it would be true for total users.
• In annual loss per user its a bit hard to compare visual data without the numbers.

[AadishGeorge]

Aadish George 20u130036 Link - [https://drones.pitchinteractive.com/] Positives

1. If we scroll on those stacked bars, it shows a detailed data table showing the deaths that happened on the same day, making it more informative.
2. Since the visualization is made using horizontal lines/ bars, it is kind of easy to track and differentiate between at least the first category: children, , also eliminating the use of necessity of a Y-axis marking.

Negatives

1.Here, the lines which represent each drone strike are cluttered so much creating so much conclusion. Also some of them come from left while some from right, but it does not effectively convey any meaning, if any, adding on to the visual clutter it creates. 2.The difference between color codes given for children and civilians as well as high profile and others are very narrow creating that it is almost not differentiable.Plus the distinction between each year given is also very hard to see. 3.Also the stacked bar visualization is hard to compare. 4.If we scroll on that stacked bars, it show a detailed data table showing the deaths happened on the same day. But since what comes first in the stack is children, the deaths are not arranged according to the date, making it seem mixed.

[PalakKatiyar09]

Palak Katiyar 20U130008

Link: https://www.behance.net/gallery/95352539/It-Fell-from-the-Sky

What Works:

1. The visualisation on the map lets us see how much each area (country) has been affected
2. The graph shows how the number of meteorites have increased oover time very effectively
3. The names written next to the biggest strikes help in getting more knowledge
4. Mentioning of the main strike in the graph provides some basic knowledge to make the correlation regarding important events
5. Visually, the colour scheme is reminiscent of meteorites and it looks homogenous

What does not Work:

1. It is difficult to make the correlation between the afected areas and the time period
2. Making the bubbles based on the weight gives the incorrect impression that that much area was affected
3. It is not clear that in the areas, which were the exact spots that were affected
4. the reason behind increase in number of meteors is difficult to gauge from the visualisation
5. The years are not evenly mentioned in the graph, this can be cinfusing for viewers

[Anngarime]

Annapurna Garimella 216330010 M.Des. Interaction design

### Satellites in space by Nadieh Bremer for Scientific American

https://www.visualcinnamon.com/portfolio/space-wars/

Positives :

• The visualisation covers a vast spread and variety of information. It does a very good job of encapsulating the different categories, classes and other important attributes of the different satellites in space.
• After the context is established through the write up and the legend section, the visualisation becomes extremely easy to navigate, despite the visual noise.
• Appropriate use of bold & contrasting colours, chunking and categorising of information - are key to encapsulating a large body of information and presenting it in a palatable manner.

Negatives :

• Since the visualisation tries to include a lot of information, it might appear intimidating at first glance - due to high visual noise.
• According to the legend - shades of colour are used to indicate launch dates. However, this variation is not evident in the visualisation, and is not very discernible.
• It is difficult to compare the number of satellites owned by different countries through the area covered by the dots due to their varying sizes and numbers. For example - US vs Europe in Medium Earth Orbit, it is difficult to infer who has more satellites. While US seems to occupy larger area with larger dots, Europe seems to have a large number of smaller dots. Thus, mentioning the number of satellites owned by each country within a specific category and belt would help.
• Legibility of satellite class in smaller dots is extremely less
• Representation Information such as satellites owned by UK, HEOs etc., should ideally be included with the legend
• The relevance of the direction of arrows representing Defense satellites is unclear
• It is uncertain if the placement of dots within an orbit is intentional. For example, what happens when you interchange the positions of two dots within the same orbit and category?
• The non-linear orbit line gives a wrong conceptual image of satellite altitudes. Eg, Western Europe’s Medium Earth Orbit satellites appear to be at a similar elevation as US’s Low Earth Orbit Satellites
• The motive & criteria behind selectively naming a few satellites is unclear
• The Satellite image in the right bottom is unnecessary

[hitansh1299]

Hitansh Shah 21F1001178 https://visme.co/blog/wp-content/uploads/2021/08/Plastic-Waste-Pollution-scaled.jpg This visualisation explores which countries in 2010 produced the largest amount of plastic waste and what percentage of this was inadequately managed, but more importantly how does this management correlate to the nation’s GDP per capita?

On the left side, we see the distribution of total plastic waste generation by continent, leading into segregation by country, measured in ascending order of tonnes per year. This takes account of per capita waste generation and population size.

This leads into the estimated total percentage of this waste that was inadequately disposed of, including disposal in dumps or open, uncontrolled landfills; meaning that the material is not fully contained and can be lost to the surrounding environment. This makes it at a risk of leakage to the natural environment into oceans via waterways, winds and tides.

Finally, these findings are presented with the caveat of the individual country’s GDP per capita, which provides a basic insight into national wealth and infrastructure, both of which could have a significant bearing on waste management.

What Works:

• Beautiful Visualization in terms of color palette
• Visually appealing
• Compared large incomprehensible numbers with better analogs (Blue Whales)
• Gives a clearer picture of accountability for the plastic waste problem
• Countries color coded by continent to for ease of understanding

What Doesn't/Could be better:

• Informationally Sparse
• Visually cluttered because of representation for each country
• The Did You Knows could be places better as they add to the visual density
• The bold highlights for countries with 0% plastic mismanagement is barely visible, could possibly have used something more striking perhaps a different color outline
• The choice of representation of plastic waste in terms of blue whales seems a little arbitrary