Assignment 1: Visualization Critique

[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 title.

[pragun445]

Name - Pragun Aggarwal

Title of the Example - Where News Audiences Fit on the Political Spectrum

Short Critique -

1. The static visualisation is very effective in telling the user whether the audience is more consistently liberal or conservative on a plus 10 and a minus 10 point scale. Each source of news has its points on the graph and depicts the information very neatly and properly.

2. The graph very well depicts the data points with each source of the news and conservative or liberal way of its audience on a plus 10 and minus 10 point scale. What doesn't work well is that its not a very accurate depiction of the information as the labels are not present in the graph. Also it is a very vague form of information which just shows the basic data in a very raw format. In depth details cannot be extracted from the graph and a new visualisation will be needed for that.

3. Data labels could be added on the graph. Also another axis can be added to represent more complex information for the graph.

[datababa1]

Name - Apratim Chandra Singh

Title - 2019 had the most women candidate (URL - https://www.bbc.com/news/world-asia-india-48366944)

The graph puts forth the point of showing representation of women in politics in India since 1960. It is effective in communicating the overall trends of female participation from 1960 to 2019. It also substantiates and explains the steep decline in the graph by providing a cause for the same.

The graph could've displayed better by incorporating women participation as % of total participation as well. This way it could've oriented readers to the context of the issue. The other aspect that could've been improved is that instead of mentioning the arrow giving the reason, a straight line at 1996 point could've been drawn effectively narrating a before and after story in the graph. The value of the initial value could've also been provided since the end value is given thus maintaining a continuity and also let the user know the growth in absolute value too.

[ranjan8manish]

Name : Manish Ranjan Title: Stopping religious intermarriage is a high priority for Hindus, Muslims and others in India Link: https://www.pewresearch.org/religion/2021/06/29/religion-in-india-tolerance-and-segregation/pf_06-29-21_india-00-3/

Critique: The horizontal bar graph shows what percentage of Indian adults say that it is very important to stop men/women in their community from marrying outside their religion. It clearly depicts the percentage distribution of such people across different religions. If the graph also showed percentage of people who are against intra-religion marriages as well then it would have clearly shown that these people actually don't want marriages outside their caste/community and hence as a result they are against the inter-religion marriages. So point of discussion would be how these people how could be convinced to go for intra-religion marriages. Then only it would help in promoting idea of inter-religion marriage as well.

[sandheepgopinath]

Critique :

The above graph represents the wind energy generated by different states in the USA. The length of the blue bar for each state represents the amount of wind energy generated. It also shows the amount of energy generated from other sources. The numbers at the end of the bar also show a clear indication of the percentage of energy generated by wind energy in the state compared to the total energy generated.

However, this representation could be improved by adding the same data into a geographical representation that has terrains. Details from the terrain such as proximity to the sea, presence of hills or mountains, etc. This will help to get a better understanding of where the windmills are situated, and if the positions are impacting the energy generation.

[sarthakvarora]

The Dichotomy of Iraq

When I think of Visualizations and the kind of impact they have, this example comes to mind. Similar to how in the metro example we defy the geometric route and distances in between, if we ignore the convention of histogram direction and chronology here, one can have two different narratives from the same chart. In one diagram a good picture is painted that the number of deaths in Iraq is reducing (here, the chronology of the chart is primary), while the other flips the narrative on its head (pun intended) and makes the viewer forget the chronology and focus rather on the sheer volume of deaths in Iraq over the years (here, the peaks/volume of the chart is primary)

One thing which stands out especially for me is the color scheme and how flipping a conventionally upward directing histogram, one can portray the jarring imagery of a “bloody” toll in the form of blood dripping.

Similar to all histograms it has a comparison of length. Certain events which led to a change in numbers are signified in the chart. The other visualizations show the distribution split over localities in Iraq, biggest reasons for deaths, etc. While the visualizations cover all the aspects of what they aspire to convey, here are some negative critiques I found:

• The chart tries to hide the coalition deaths of the Iraqi security forces, by pushing them outside the donut chart, with no highlight and to the side.
• For “The biggest killers”, while they smartly show an intersection between executions and small arms gunfire, the value of intersection is not gauged easily. Also the order of the bubbles is not signifying anything, and is rather confusing.
• Similar to our discussion in class, the fatalities by area are not talking about the population densities of the areas. Also using colors based on ranges instead of a gradient ends up showing that there is a significant difference between 99 and 106 while there is no significant difference between 0 and 99.

(Img Source: Link)

[tanyaahuja147]

https://www.weforum.org/agenda/2020/09/covid-19-deaths-global-killers-comparison/ The color coding does not tell us anything. Some diseases did not exist earlier like AIDS so this is not a fair comparision The graph does not tell me if the deaths due to various diseases has decreased over time or not, Because as science and medicine improve and get better, deaths might decrease. Also, Global population is increasing, due to which diseases and deaths will also increase. If not time then the percent of death to population. Ex, population in year 1990 is 1 crore and deaths are 1 lakh, population today is 2 crore deaths are still 1 lakh then it is better now than before.

[rishav-gupta]

Name: Rishav Gupta Title: Dietary preferences across the country

The world believes that India is a predominantly vegetarian country. However, a nationwide survey, conducted by the Office of Registrar General & Census Commissioner, reveals otherwise. The above graph shows the distribution of vegetarians versus non-vegetarians.

Critique:

• They have used concentric circles to depict the data. This is problematic because we are unsure what information is encoded in the circles. Is the information encoded in the radius or the area?
• The numbers are very inconveniently placed in some places. This is even true for some of the state name labels placed on the visualization.
• The numbers are mentioned in too many significant digits. It would have been better if these were rounded up.

[sahilshaheen]

2021 GDP Breakdown by Country

1. The shapes used to represent countries hampers our ability to compare and contrast the GDPs between countries. If the data was represented by a pie chart, we would use the angles of the sectors to easily compare the GDPs. (In this case where we have a lot of countries to compare, I think a tree map would be even better.)

2. From the captions "together, the US and China account for 42%", "only 18 countries have a share > 1%", the narrative intent is evident as illustrating that most of the GDP is concentrated in the hands of a few countries. It would have been better if the 18 GDP-rich countries had been grouped together to match the narrative. The colour coding would continue to provide secondary information on breakdown by geography.

[sehaj1001]

Name: Sehajpreet Kaur Title: Distribution of messages shared on Whatsapp in India by topic in light of rising nationalism (BBC)

Critique: While the graph succeeds in giving the viewer an approximate sense of the topic-wise distribution of the Whatsapp messages being shared, I felt that choosing to represent the contribution of a topic as the area of a box did not convey the alarming rates effectively (which only become evident when one reads the exact number at the top). I could not infer the area of each box precisely and was only left with an overarching sense of the biggest contributors. Furthermore, I feel that sub-dividing the topics into parts with the same nomenclature ('other') is quite misleading, especially because one's immediate attention goes to the graph first and the legend at the top later. I also felt that 29.9% was an extremely precise number to mention, especially for a graph that relies on the viewer's perception of area which only works in approximates. Perhaps a simple bar chart could have been more efficient.

[lohkna007]

Name - Gaurav Lohkna

Source: https://www.livemint.com/news/india/india-s-workforce-is-masculinising-rapidly-1560150389726.html

Critique:

1) Boundaries are not consistent with all the intervals, there is a boundary for one particular interval. What is supposed to be understood by that? Is it highlighting that specific region or that interval? It is ambiguous.

2) The intervals are not regular and due to this, the complete graph can be something different and make a lot of difference if we change any one interval so the intervals should be regular.

3) The scale is not even continuous apart from being irregular, so comparing different states would be difficult.

[abhishekanandiimr]

Name: Abhishek Anand

1. This Stream Graph depicts changes in covid cases over time in various countries and continents. It's worth noting that the stream of a few countries/continents begins and ends at different times.

2. The height of each individual stream shape indicates how that stream's value has changed over time. The duration of the stream is indicated by the length of the stream form. Colors are frequently used to either provide more information about a category's quantitative value (via a sliding scale of hues) or to distinguish between distinct categories.

3. The stacked area chart becomes cluttered and difficult to read if we add too many categories at once.

4. Another limitation is that streamgraphs don’t support negative values. That’s because there are no clear zero baselines to differentiate positive from negative.

5. The scale cannot be read at a glance because there is no y-axis. Using Interactive popups and annotations can help improve it a little.

[m-saiteja]

Name: Sai Teja Muliki Title : America's Food Delivery App Market Source: https://www.visualcapitalist.com/doordash-is-dominating-the-u-s-food-delivery-market/

Critique: Above image clearly shows how the market for applications like Doordash, Ubereats and others have evolved over the period of 3 years from 2018 to 2021.

• It is hard to infer numerically how much the market share of each company has increased or decreased.
• An overall estimate of the entire American Food delivery app market would have given a better chance for the audience to understand the scale at which the graph is plotted.
• Also, it is difficult to infer the factors that contributed to the growth of Doordash while other apps either remained constant or plunged in their market share.

[aravindbhaskar41]

Name: Aravind Bhaskar

Title : Climate change preparedness of US cities

Critique:

1. In the above data visualization a semi-circle of the varying radius is used to depict the level of public concern but it gets misleading with the area/ size of the semi-circle. This is the classic example of distance/level being depicted with area
2. The dotted semi-circle below showing the no of disaster declaration is also misleading how to correlate area with a number
3. The x-axis represents the level of preparedness clearly

[Ramsai9ch]

Name : RAMSAI REDDY CHAMAKURA Title : Tendulkar's Rise And Fall - Runs Scored And How He Was Dismissed

The bar chart tried to show runs scored by Sachin in each year, how he dismissed, runs against each opposition, and when he won the awards.

• They have given color coding for each type of dismissal, but it is unclear how they showed it in the bar chart. By the kind of chart, they have chosen visualizing this metric will be difficult.
• Runs by the opposition pie chart are also ambiguous, and we can’t infer data from it. It is also hard to interpret the relation between a given pie chart and a bar chart. Because there are some colors in the bar chart which were not there in the pie chart
• They have nicely shown the runs scored in each year and awards won because we can observe a correlation between runs scored and awards he won(he won most of the awards after a good year with the bat).

[Lakshna1295]

Name : Lakshimi Naraayani Source: https://www.visualcapitalist.com/the-companies-that-defined-2021/

The above image depicts the top 6 companies which greatly influenced 2021. Critique: The following are the limitations of the above visualization:

1. The data has no defined scaling.
2. There is an overlap between the different plots, which does not give a clear understanding of the initial value of any company as there is no scaling factor.
3. Only the major events have been highlighted and the spikes are not clearly defined.
4. The spikes for Robinhood and coinbase have labels that could be misinterpreted for Tesla and Pfizer.

[pawanreddy-u]

https://www.thehindu.com/elections/tamil-nadu-assembly/lypimm/article53570867.ece/alternates/FREE_435/vbk-tn-trend-1230hrsJPG

The given image captures the election results of Tamil Nadu in 2021. Critique: Looking at the left geographic graph, one can’t say which team has actually got the majority. There is also no information about the names of constituencies. There is no title explaining what the graphs and tabular data are about There is no info about the margin of votes by which a particular candidate has won the election in a particular constituency (vote share).

[Apoorva2908]

Name : Apoorva Shekhawat

1. The Y axis of a bar graph should start at zero. Makes it seem like Indian women are one-fifth the size of Latvian women or something.

2. The figures scaled on the X-axis as well which adds no information.

3. The bar chart would have been better than the figures.

[josephbenofficial]

UKRAINE REFUGEE CRISIS

Submitted by Joseph Ben

Short Critique:

This visualization shows the exodus of Ukrainian refugees into neighbouring countries. The map uses immigration data from the UN Refugee Agency (UNHCR). Some great aspects of this visualization include:

How the size of the circles and number of dots is able to capture the number of refugees. The directions and the areas from where Russian troops are entering and Ukrainian refugees are fleeing. A clear idea of the location of Ukraine and its neighbouring countries, is useful for those who are not familiar with the layout of Europe. The timeline of the refugee crisis. Giving a brief summary of what is happening to those living in ignorance of this war. Some cons of this visualization:

They have got the spelling of Chornobyl/Chernobyl wrong. The outlines of the countries are not very visible. It is difficult to understand which country is which. Poland is written on the Baltic sea. Moldova's dots overlap with Romania's and Romania's on Croatia.

[taniadaw]

Title : MLS Players salary in 2013

Critique: This graph is a bar chart depicting the salaries of MLS players. It contains two graphs- the main graph depicts base salary of players while the smaller graph depicts estimated salary cap costs of the 20 highest paid players on each club.

• Too much data on the chart leads to poor readability.
• Boxes are vertical and horizontal both which makes it difficult to read.
• The scale of the variables requires zooming in significantly to read the data.
• A number of completely different variables are included in a single chart, which makes it complicated for audience to understand.
• The chart is overall confusing due to the variety of colors, abbreviations, orientation of the data present.

[akash-chowdhary]

Name: Akash Chowdhary Source: https://inc42.com/features/healthtech-in-the-spotlight-with-4-unicorns-and-2-2-bn-in-investment-healthtech-funding-shoots-up/

Critique:

• This graph aims to showcase the number of health tech startups funded and their cumulative funding amounts since 2014.
• The reference marking seems to be incorrect. For 2014, there are two data points: 28 and 144. The red bar (144) is supposed to denote the deal count but it denoted the funding amount here. The black line (28) is supposed to denote the funding amount but it denoted the number of deals.
• Two Y-axes (funding & deals) with one x-axis (year) were not intuitive to look at.
• The reference label for funding talks about Funding Amount ($ Bn) but it is plotted in Funding Amount ($ Mn)
• The plot aims to bring out the point that there was a 4.8x increase in funding in 2021 but it does not say in reference to which year. Also the lengths are not scaled properly when we compare 2020 vs 2021.

[LogeshKG]

Name: Logesh Kumar G

30 Years of Gun Manufacturing in America

source: https://www.visualcapitalist.com/30-years-of-gun-manufacturing-in-america/

Though the visualization clearly shows the top 5 gun manufacturing states in the USA through the number of manufacturing licenses, it has not taken population density into account. Licenses correlated to population density and the area of each state would have given better data representation

Production and selling of illegal/unlicensed firearms are not taken into account which accounts for a fair share of firearms in the USA

The top 5 states in terms of licenses owned could have also been backed by the total number of firearms owned by the population from these states. Easier to identify whether the production serves the local population or across the USA.

Though we've data about total firearms produced annually, it is not clearly evident where the distribution of these firearms is at maximum, w.r.t the top three manufacturers, in addition to production numbers, representing percentage share by each of the three manufacturers in the top 5 states would've helped to visualize who are the prominent players geographically.

Details about gun laws in the top 5 states could've helped to identify if the lenient is promoting gun production

Colours could've been more distinctive in representing the annual production of firearms.

[rishi456187]

Link to data (TLP Master time table):

https://plakshauniversity1-my.sharepoint.com/:x:/r/personal/adarsh_a_plaksha_edu_in/Documents/TLP%20Master%20timetable%202021-22.xlsx?d=w650a084b127f4c508f9b54b8250c5464&csf=1&web=1&e=3rdcrA

critique,

1. wrong format chosen
2. Block size has no relation with time.
3. 5 pm is coming after 6 pm
4. No color coding

[jaisal1497]

Title: Football Passes Visualizing. 880k passes in 890 matches Name: Jaisal Chaudhary Thoughts:

The visualization aims to paint a picture of how passes are made in football matches. The dataset considers about 23 seasons of football leagues ranging from world cups to champions league.

1. At first glance it looks like the visualization aims to represent the actual passes in a match. However, it is unidirectional i.e. Goal post A to Goal Post B and not vice-versa.
2. The passes visualized are in a vertical direction, it does not consider back passes, cross-passes (from side to side) and only passes from the goalkeeper. This is misleading in my opinion as the visualization is made on a football pitch
3. In that case, the information 'Distance from the net' becomes irrelevant as it does not offer us any insight. As per the creator: This viz aggregates all passes on a grid with a 1-meter step. It means all distances and passes on a square meter of the football pitch represent by a line with average length and direction.
4. The number of passes according to the visualization is 880k in 890 matches, which averages to about 1000 passes in each match which seems to be highly unlikely.

Source: https://preview.redd.it/q0qyg4v3w4761.gif?format=mp4&s=926edad758c8cd3e7349ad693c39cb242de4c647

[SmrutiShirodkar]

Name: Smruti Shirodkar

Title: Which Countries Trust Their Government, and Which Ones Don’t?

Source: https://www.visualcapitalist.com/global-trust-in-government-2022/

Thoughts:

1. The negative and positive scale is not very clear at the first glance
2. While the visualisation aptly captured the trends, it fails to provide a legible understanding (unless you spend ~10 mins reading it)
3. The circular highlights that capture the low trust, high trust and public services vs govt. plays an important role to display the trend weightage however, it is quite confusing in first glance and doesn't emphasise on the information well.
4. The criteria for this clustering is not quite clear, is it the points lying close to each other or something else?
5. What are the factors influencing the scores?
6. Does adding the GDP and per capita income to the chart helped understanding the relationship between people's trust on their govt?
7. The goal of the graph and what should one infer from it is unclear by just looking at the graph.

[yasirulhadi]

Name: Yasir ul Hadi Source: https://www.vox.com/2014/8/20/6040435/als-ice-bucket-challenge-and-why-we-give-to-charity-donate

Thoughts

It shows money raised from popular fundraisers and the number of related disease deaths.

• First, You have to realise that each colour is associated with a different cause — It looks like that the top circles represent deaths and dollars for the first cause and similarly for the second one
• Second, even once you’ve realised what is being displayed, and how, you have to go disease by disease to see what is going on; there’s no way to understand the whole pattern at once
• Third, other than pink for breast cancer and maybe red for AIDS none of the colour mappings are standardised in any sense, so you have to keep referring back to the legend at the top
• No clarification on how deaths are correlated with the money raised

[01supriya]

Title: Global Data Hub on Human Trafficking Name: Supriya Kumar Mishra Source: https://www.migrationdataportal.org/themes/human-trafficking

Thoughts

• The graph depicts global human trafficking statistics. The colored circle provides no information or statistics about the countries.

• There is no information on the map concerning the orange and blue dots, making it difficult for the reader to deduce anything from the image.

• While the text mentions the color-coding of the circles, it would have been better if the author had mentioned it in the diagram.

• The percentage of humans trafficked who are forced into prostitution, forced labour, or other forms of exploitation is presented in the graphic, although the size of the graph makes this unclear.

[uneetkumarsingh]

source: https://qz.com/296941/interactive-graphic-every-active-satellite-orbiting-earth/

The animated visualisation captures number of satellites in different orbits and labels them, inter-alia, country-wise and orbit height wise.

Comments

1. Visualisation is multi page and therefore difficulty to create a complete mental picture of satellites in all orbits.
2. Given that the dissemination of visualisation is happening for general audience, adding too many details(what rocket they were launched on) overwhelms the reader.
3. The area of bubbles representing different metrics of satellite misleads as they creates an impression that outer space is dramatically over crowded. This is created because of absence of scaling/normalisation with respect to size of earth(used in starting of page itself)
4. Chart is overlayed with text which gives an impression of clutter.

[sarthak78]

Name: Sarthak Gupta Title: visualizing user activities and metadata generated through various trackers

1. The graph helps in visualizing how different activities from the start of the day are tracked by different tech players in the market which leads to targeted marketing to the users.
2. It also shows how data collected from different activities are interrelated and influence the search patterns for other content
3. Graph is easy to understand and uncover many hidden insights due to its minimalistic design and appealing color themes.
4. if one clearly sees, the dots represent the type of trackers depicted based on the search, which organizations' trackers get activated and how many trackers share location and unique id, and how a user can control their privacy

Reference: https://www.visualcinnamon.com/portfolio/new-york-times-digital-trackers/

[blessondavis]

Title: Diminishing Financial Returns of higher education

Observations:

1. It fails to show the cost of not going to school in the first place. The cost of not going to college will be higher as they tend to earn even less.
2. Education is a one time expense compared to the income which is per annum. Even pay tends to increase over time which is not captured here. Inflation could also have been shown here as it is significant over a large period of time.
3. It has failed to show the stream (science, arts, etc). These vary a lot when they are compared against each other.
4. The graph fails to show other relevant data like improvement in living standards, or increase in scholarship options for the college.

[Apharna]

Title: How investing in girls' education could return billions in GDP Name: Apharna M L Source:

1. World Economic Forum
2. THE CASE FOR HOLISTIC INVESTMENT IN GIRLS (https://ir.citi.com/1lC5yCxLduM4bYUlBpFC79dMTp0GjGGFL7Dh1ZVt72YDIT4sT29hPBSCP3jggXWiHMSTIz3gSIc%3D )

Data Visualization:

Background: An article on the World Economic Forum webpage, this article is based on the research work done by a research team of women from Citi GPS and Plan international. The article and the research paper both intend to advocate for active investment in girls' education by elucidating the direct impact the investment in girls' education could have on the GDP growth of developing countries.

Key data points in the article:

1. Making sure all girls are finishing secondary education by 2030 could boost the gross domestic product (GDP) of developing countries by 10% on average over the next decade.
2. Every $1 spent on girls' rights and education would generate a $2.80 return.

Commentary on the data visualization and its use in the article: The use of this particular visualization from the many other visualizations and illustrations in the research paper is a testament to the importance of the philosophy of "simplicity for impact". This seemingly simple line graph boats of the impressive feat of mapping the GDP increase that investment in girls' education could result in.

However, upon critical inspection, it is to be noted that despite fitting the bill adequately, the visualization could be better in that it appears shoddy with 8 lines ( plots for 8 different countries) adjusted on a cramped scale on the Y-axis. And the design choice of having all the lines be various shades of blue in the graph makes it harder to differentiate or retain country-specific information for the readers. The initial affordance upon first glance is to assume that the mapping is between GDP numbers and the year, however, the graph is actually a denomination of the "percentage increase to GDP" which without the adequate knowledge of the current GDP values of the 8 countries listed does not provide the complete picture of economic growth resulted or it's relevancy.

[04kaushal]

Source - > https://carnegieendowment.org/2019/09/05/dawn-of-india-s-fourth-party-system-pub-79759 Topic -> Distribution of Vote Share for majority parties in Lok Sabha Election

Critique -

1. Very high level representation and does not elaborate the proportion.
2. Representation is below state level, in such a scenario it is difficult to find out which seat had winning candidate / party
3. Overall difficult to interpret and a different graph type will required altogether to conclude or convey a story
4. While the number of others has increased during 2014 representation, simply denoting it by others will not convey anything
5. A stacked bar representation break down by seat will convey better story, figure will be in percentage.

[krishna151]

Title: The percentage increase in pollution in Indian and Chinese Cities from 1998 to 2016 Name: Krishna Rajagopal Source: https://images.app.goo.gl/Snmz9FYv1b56qg7x6

In this particular graph, Bloomberg is not just representing polluted cities in India and China. The percentage increase in these cities from 1998 to 2016 is also being represented.

There are 20 cities in total with 9 Chinese cities and 11 Indian cities. Both are differentiated with ash and white colors, respectively.

The first thing wrong about his chart is how the labels are placed – far away from the graph. Although this is due to the inclusion of the -% and all but one city has a positive increase in pollution, it links the bars somewhat abstruse.

The designer employs a progressive pattern in arranging the cities. However, there are no start and endpoints. Also, you can’t point out just how much of a percentage increase in population was recorded for each year.

Finally, the use of an all-black background is always not ideal when preparing a data visualization.

[kesarwani-shivam]

Title : Plastic Waste Pollution Name: Shivam Kesarwani Source: https://www.behance.net/gallery/106936329/Plastic-Waste-Pollution-data-visualisation

Critique-

The visualization attempts to picture share of different continents and their countries to the plastic waste generated annually.

Few of the things that can be misleading:

1. Inappropriate linear scalling

   • The amount of waste generated by a country should be directly proportional to the corresponding length on the vertical axis in the bar chart. This proportion is not maintained as all the less contributing countries appear to have same share.

     • In the bottom bar chart picturing the plastic in different oceans, the heights are not scalled in proportion to the coresponding number. 642 is approximately four times of 154 but the heights don't project so.

2. Use of Area to depict Linear relation.

   • The circles to depict GDP are very cluttered. The better way could be to order the countries on the basis of their GDPs inplace of the plastic generated. Since information about the volume of plastic generated is already contained in the vertical axis.
   • The area of the 2-D pipes for each country is misleading.

Apart from this, the visualization is very crowded. An interactive interface to zoom in around mouse pointer can possibly solve this.

[arnav-tlf]

Source : https://www.vox.com/2015/6/23/8832311/war-casualties-600-years

Title : 600 Years of War and Peace Name : Arnav Sharma

Critic's thoughts :

When the chart was published back in 2015, the creator intended to relay to the audience, how the world was in a moment of relative peace with death rate continously & significantly decreasing in the past 5 decades, especially military casualties. He also tries to justify the peaks in death rates by specifying major wars in those periods. This is consistent with the data throughout making the correlation very convincing. However, since the data is in continuum and there are gaps between two key events when the death 'growth/decline' rate is nearly constant, it leaves one wondering about the reasons of persisting death rates between two major wars.

Circles representing conflict & circle areas representing deaths can be intutively percieved but unspecified scale can be confusing. Similarly, increasing variation & circles representing minor events can be represented using colors other than red.

[devashreepatel]

Title - Bank credit card growth in India Name - Devashree Patel

Critique-

1. Graphics is way too “long”. takes too much time to understand.

2. The stash of money on the left adds no value to the graph and is distracting.

3. The area of the semicircles interpreting the values is difficult to perceive.

4. Bar graph could have been used instead of this to represent the values clearly.

5. It is difficult to compare the semicircles while trying to understand the difference between two values.

[Harleen8-Bagga]

https://www.businesstoday.in/latest/economy-politics/story/india-unemployment-rate-rises-to-77-in-december-cmie-242161-2020-01-02

Title: Unemployment rate(%) in India(2019) Name: Harleen Kaur Bagga Critique -

1. On every peak and decline, no figures are stated. It's becoming increasingly difficult to determine how much the unemployment rate increased or decreased between the current month and the previous month
   2. While I can clearly see the lag or increase in the unemployment rate in the first six months. By glancing at the graph, I have only a rudimentary understanding of the numerical variance in the unemployment rate across quarters or the first two quarters.
   3. It would have been great if they had put a bar chart beside to provide a measurable idea of the unemployment rate drop or increase.

[JazBern]

Title : Orbital launches per year Name : Jasmine Bernard

The chart below shows the number of manned and unmanned orbital launches by year. After dipping in the early 2000s, the number of launches is increasing due to a growing number of private launches (e.g. SpaceX).

Critique: The graph seems to be very cluttered because of too many bars. A line graph could have been better to the following reasons: (i) intent is show the increase in launches after the dip in early 2000s. (ii) x-axis is not a category (iii) chart is to represent 60 years of data.

[Aviral0]

Short Critique by AVIRAL JAIN:

Title: Offensive and Defensive Impact of 2019 NBA Free Agents

In professional sports, a free agent is a player who is eligible to sign with other clubs or franchises; i.e., not under contract to any specific team. The term is also used in reference to a player who is under contract at present but who is allowed to solicit offers from other teams. This graph depicts the impact of various free agents both in defence and offence and the salary they are drawing.

• 3 features of NBA players (Defensive impact, offensive impact and the salary) represented in 2D graph very effectively
• One can get good insights as to what players are undervalued and overvalued
• This graph can be referred when NBA team managers decide which players to buy during the player transfer window
• This graph can help undervalued players negotiate higher salaries
• The bubble size are slightly hard to compare, it is hard to fathom the delta from a bigger bubble to a smaller bubble
• A legend could be provided stating the maximum and the minimum salary, that can give a mental scale to compare the bubble size
• Different bubble colour could have made this graph more stark and interesting, players along the diagonal could have even given a different bubble colour (green) or overvalued players could be given a different bubble colour (deep red)
• Not sure what negative offensive and negative defensive number means here, it is contradicting intuition (player having negative defensive impact is still considered of good value, D’Angelo Russell)

[Santoshsrini]

Title: Talks of AI and ethics in on the rise Name: R. Santosh Srinivas

https://www.cbinsights.com/research/artificial-intelligence-ethics/

The graphs attempts to show that the talks of AI and ethics is on the rise. It does so by plotting a line graph of the mentions of AI and ethics in the quarterly news from the year 2014 - 2018.

Critique:

1.It is effective in validating its hypothesis as it uses the suitable plotting technique of an increasing line graph.

2. The heading of the graph could be made more clearer as one might doubt if AI and ethics were both included in the same news coverage or is it separate news coverage although the intention was of the former. An alternative would be "AI-ethics" instead of AI and ethics.
3. If one were to find the exact numbers of news mentions from this graph it would be next to impossible. The scale on both the X and Y axis could be improved to include this aim too.
4. If one were to validate this hypothesis with an alternative method, it could be to use a bar graph with the exact numbers of news mention on the Y axis bar with the years on the X axis.

[utkg26]

Title- The Death Spiral

Name- Utkarsh Garg

The Chart compares the Average Number of Covid Deaths and Its Increase or Decrease per Day for various countries.

Short Critique:

• The illustrator has tried to depict a timeline of events in a spiral tracking the changes in deaths per day.
• There is an information overload in the graph with 8 different colours for countries and date milestones depicted as a point on the spiral
• Various other information is also displayed along with the spiral indicating the events that day. Due to the closeness of the date data points, there is difficultly to follow and interpret dates for any particular country
• The dates for each spiral are different and are not a point for comparison
• 0-200 Range of Avg. Number of Death (Y-axis) not interpretable
• The no correlation or insight that is coming out on comparisons among various countries
• The x-axis is in absolute value but gives an indication that it is a percentage change. It uses smoothed data – a moving average from the day before to the day after each date shown
• The chart compares the absolute number of deaths without taking into account the population or population densities of the depicted countries

[arunimamor]

Claim/Title: Global warming had stopped (British Mail)

Short Critique :

1. The graph is showing air temperatures. This is a very poor measure of global warming as much of the heat ends up in the oceans.
2. This is a very short term graph. We can’t make good predictions or conclusions based on a little data.
3. Failed to show the temperatures from the industrial revolution which would have shown a much clearer picture of the situation.

[Riikon]

Title: The World Cup's Big Guns Name: Arun Sar

https://www.espncricinfo.com/story/_/id/27143430/kane-williamson-hand-steadies-new-zealand-ship

Critique: The total sum of all the percentages mentioned here isn't 100% as the graph would lead one to believe. It can't be hundred since the data is the percentage of runs scored by the top scorer of their respective teams. Therefore using piechart for visualizing such data doesn't make any sense since the data is neither mutually exclusive nor collectively exhaustive. Using a bar chart would have been more suitable in this situation as it would have given us a comparison between the percentage of runs scored by the top scorers of different teams. That would probably show how dependent a team is on their own top scorer.

[saivenkatrammallela]

Title: Top 50 Website Critique: 1) Ranks could have been better represented by a table(at least for the first 10 entities) 2) Website specific information and facts took away from consistency. The chart was comparing different entities. Adding additional information about specific entities that is not consistent across the board creates confusion and information overload. 3) The circular structure of the chart doesn't convey any additional information. A different structure that allowed text to be more legible and clustered companies based on their ownership or a bar chart that is ordered based on entities ranks with colours representing ownership would have been better in this case.

[subratsaxena]

Name: Subrat Saxena Source: The Guardian The goal of this infographic is to compare poverty with academmic success. The left side of the map shows the local authorities as small clusters throughout it. While on the right side we have constituencies. The findings hasn't been grouped correctly. Also the color scale selection is not good at all. The user is not able to interpret whether the statements are true or false. The values indicated in the key are contradicting in one sense. It is not the most obvious thing to differentiate which makes this infographic even worse to interpret.

[BRupani]

Title: Continent wise life satisfaction vs life expectancy Name: Bhawna Rupani

Critique: This visualization has used scatter plots for plotting different continents on the basis of colour coding displaying different countries of the continents.

1. The inconsistency of the size of spheres is unable to justify the goal of this infographic which is displaying the life expectancy and satisfaction for the given duration.
2. The graphic is very cluttered which is a pain to the eye to understand it.
3. The user is unable to derive detailed insights as the approach is non simplistic.
4. There are many circles which are very small and hence without any name tags/labels which is again a major drawback.
5. Different shades of green, blue look the same when the size of spheres becomes very small and user is unable to distinguish between different coutries.
6. A vertically stacked bar chart would have done a better job here.

[udays2]

Name: Udaya Bhaskar Vaddi Title: Analysis of Anime TV Shows Source: https://github.com/OneCodeMan/GinData

Critique: This visualization infers the data of the anime show and the fan count and overall rating of respective anime shows along with the no. of episodes. Some of the points I have noticed are:

1. The circles are overlapping which makes it hard to notice many other circles.
2. The smaller circles go unnoticed.
3. A legend is not mentioned, which makes it unclear if the color has some importance or is being used usually.
4. Instead of using a bubble, it would have been much better to use bar plot instead.