Assignment 1: Visualization Critique

info-design-lab / Plaksha_Dataviz

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Assignment 1: Visualization Critique #11

Open venkatrajam opened 2 years ago

venkatrajam commented 2 years ago

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.

Refer to the last TLP batch's submissions for the assignment and try to come up with new examples.

PratProduct commented 2 years ago

Name: Pratyay Prakhar Title: Dominant themes in the books nominated for the Booker’s Prize in 2012 Source: BOOKER PRIZE 2012 INFOGRAPHIC | Delayed Gratification (slow-journalism.com)

Background: Delayed Gratification, a slow journalism magazine, created this infographic to show the dominant themes in the books nominated for the Booker’s Prize in 2012.

Critique: Positives: • The visualisation meets the intention of showing the most popular themes by being specific and accurate. • The number of themes used in each book can be ascertained easily. • It looks pretty. Negatives: • The mesh of lines which does not help much with any insights is grabbing the attention. • It is required to zoom in to view some data of interest such as viewing the themes and the names of the books. Thus, the visualisation is not full-scale. • The visualisation takes more than 5 seconds to understand and is not that intuitive. The connection between the longlist and the final theme is tough to understand. A simple bar chart which shows the popular themes would have been more helpful. • The annotation ‘What makes a prize-winning novel?’ is slightly misleading because it ignores all the other parameters other than the theme of the books. A suitable title should be used as well. • The colour coding and the ordering used for the themes are completely arbitrary. The number of books in which a particular theme is used has no bearing on the order of arrangement and is random. The colour code used for both ‘East London’ (a place) and ‘Corruption and Theft’ is shades of blue although both are unrelated. • Some very specific themes like ‘An escaped tiger’ are shown which could have been ignored to make the visualisation look less cluttered.

Aayush4396 commented 2 years ago

Name - Aayush Kumar Gupta Title - Global net human causing greenhouse gases emissions (1990-2019)

About: The above graph shows global net anthropogenic GHG emissions from 1990-2021 and for each year it is bifurcated with respect to the sources of emission(like carbon dioxide from fossil fuels, land use, methane, fluorinated gas, etc). With this graph, one can infer that emissions have increased in all the GHG sources throughout the time period.

Critique- a. Positives: -> The graph shows both the relative and absolute GHG emission sources for the decade years (1990, 2000, 2010, 2019) by giving the percentages as well as the total amount of emissions. -> If a viewer wants the absolute value of the emission of a source, then the person can get that as total emission and individual percentages are given.

b. Negatives: -> The addition of percentage emissions is not 100% for the years 1990 and 2019. -> The notion of the area is depicted but for each year we are concerned only with the percentage which is given by the length of that source vertically. -> The individual percentage of each source is given for that year, but if we see a percentage change of a particular source (let's say carbon dioxide (blue)), we are unable to infer how much the percentage increase year on year. -> There is a graph given that doesn't let the viewer understand what it is about (between the main graph and the legend, something of a whisker's plot) -> The graph could have annotations or some extra graphical representations on what are the threshold values for the GHG emissions. -> One addition that could have been made was that either continent-wise or the top 10 worst countries by emissions can be shown on a separate graph.

Classwork: 19/10/2022

Data types used:

Nominal and ordered data types with regard to the GHG emission source.
Interval data (quantitative) ie. time given in decades (x-axis) is a continuous type of data.
Quantitative data present on the y-axis ie. the GHG emissions
Percent data is also quantitative ratio data, the one on each decade year which is a discrete type of data.

Visual property | graphical element:

Points x,y as GHG emissions vs time.
Area is used to represent the GHG emissions, but the data type is one-dimensional continuous over time (GHG emissions only) and should be represented discretely.
Colour is used for each source separately.
Each source's GHG emissions are connected with a line year-wise.
Labels are nominal data.

Image source: https://news.un.org/en/story/2022/04/1115452

abdksyed commented 2 years ago

Name: Syed Abdul Khader Title: Global Economic Inequality Source: https://ourworldindata.org/global-economic-inequality

About: The chart depicts the estimate of the distribution of daily incomes across different continents over the past two centuries.

Shortcomings:

First major concern is no y-axis label. The height/hump of each continent is not clear as to what it means.
- Recommendation - Have a y-label explaining what the height represents
If the y-axis represents population distribution over annual income, is Asia Pacific(in red) cumulative over other continents?
- Recommendation - Maybe avoid stacking distribution on top of each other.
Constant International Poverty Line over two centuries.
- Recommendation - Unclear understanding on the "international $ in 2011 price"
No Defined Start, End and Mean on the distribution.
- Recommendation - For each continent having, a start, end and mean $ value will help in comparison.
Each colour at the ends of the distribution is mixed.
- Recommendation - Having a black solid boundary between distributions will help to differentiate different continents.
Redundant x-axis label, across all three graphs
- Recommendation - Since the x-label (Daily income per capita) is common for all three, using one at the end will suffice.
Unnecessary hyphen in "North - and South America" (UI Copy from HCI)

Positives

Took into consideration the inflation from 1800 to 2015
Also Purchasing Power Parity between different continents was considered when considering the graph along the same currency.
Log axis along the x-axis to display a very wide range, which is helpful with sparse data towards the end.
Consistent colour coding and good choice of colours, which contrast with each other.

Mapping of Data Properties to Visual Properties

There are four continents and the data type falls under nominal, Unique colour is used to represent each category - Correctly Depicted
The daily income is a ratio data type, which is plotted on the x-axis.
Since the x-axis is continuous, the plot is the distribution which is continuous - Correctly Depicted
The data of three years (1800,1975,2015) is Interval and used as a separate graph

Beastwham commented 2 years ago

Name: Prashant Kapoor Title: The space race is dominated by new contenders (The Economist)

Background: The Economist created this graphic to show how the space market has changed today. In the past, only developed economies' armed forces or space agencies could launch satellites. Today it's done by companies and governments of developing economies. The infographic shows how many launches happen today and by which entities.

Critique:

Length indicates how many satellite launches happened during a particular time interval. This concept is similar to bar graphs, and multiple such lines combined together make area another element of the graph which is what draws the viewer's attention. By looking at the area, one can see how satellite launches by a particular entity, say US or USSR, have changed over time.
There are different colours for different entities. They haven't made it too complex by choosing only the major entities to represent. This lowers cognitive load.
Accidents are clearly marked and labelled, these indicate turning points in the history of satellite launches
Colouring of japan and other countries seems inconsistent. You cannot say from where the lines start, do they start from where the lines of Russia end or do they start from the bottom and lines for Russia are covering them. Same goes for China.
The space shuttles part, darker coloured region in the US graph isn't entirely clear. Even after reading the article I still don't fully understand what it means.
Section for commercial launch providers can be put on the side for better comparison with state launch providers section
It's a very rough graph and cannot be used to represent numbers, however, it is effective for storytelling. General population does not need to get overburdened with numbers.
Comparing Arianespace or Russian Companies with Other US companies, ULA or SpaceX is difficult. It's not clear.

Pragyawasan commented 2 years ago

Title: India on the Move Source: www.routesonline.com

Purpose: The India on the Move infographic aims to show the rapid growth that India has seen in the aviation sector from 2017 to 2018.

Pros:

A lot of information is well-packed into one infographic
The use of Indian flag colours is very aesthetic
It is smartly designed; the wheel of the plane is used as a pie chart and mountains in the background are also used as infographics

Cons:

The heading says ‘Airports – Challenge of rapid capacity growth’, however, the entire infographic does not mention any challenge.
The graph on the top (frequency and growth) is using circles to show different airports, however, it doesn’t tell if it is the area of the circle that has to be considered or the diameter. For instance, Bangalore is at 100,000 and Delhi is at ~250,000 but Delhi doesn’t look 2.5 times the size of Bangalore. Also, airport short forms are used without any full forms given anywhere, everyone might not know all of them – I didn’t know PNQ. I would suggest adding a list of airport short forms with their respective city names at the bottom of the infographic.
The mountains are depicting certain infographics but the sizes and colours aren’t intuitive. For instance, 11% is lighter than 13% and 19% is lighter than both 11% and 13%. The 13% mountain looks larger than the 19% mountain and the 75% mountain doesn’t look size appropriate. I would suggest sizing and colouring the mountains according to their percentages, for instance, the 75% mountain could be made as the darkest and the largest mountain at the farthest point from the plane.
The pie chart in the middle uses Indian flag colours for the top two countries, however, the rest of them are just shades of grey. Three of them even have the same colour and hence one cannot make out just by looking at the chart which country is represented by which pie slice, it can only be understood after looking at the percentage distribution. I would suggest using tones of orange and green to represent all countries and using the respective country colours to write their names as it has been done for the top two countries.
The horizontal bar graph says ‘Growth vs Sept 2017’, however, it is not mentioned with which period is the growth of 2017 being compared to. Only after reading the middle part can one understand that the comparison is with 2018. Also, it doesn’t mention why only the three airlines are being used here.
The box chart showing different airlines at the bottom is not clear at all. It shows a distinction between legacy and low-cost but it is difficult to understand what the size of each box is trying to depict. The bottom right boxes just say ‘Legacy’ and ‘LCC’ with no explanation. Also, the legend used for this graph has a different green than the one actually used.
The infographic has used the term ‘LCC’ in the bottom part and nowhere can you find the full form. One can only guess that it stands for ‘Low-cost’ something.
The pie chart at the bottom has also used Indian flag colours but the green is the same as the background and hence the pie chart isn’t standing out.

Visual properties:

The graph at the top right corner used the shape circle to show different airport sizes and their growth over the years. They have used the colour white for all circles and in case of an overlap between 2 they have reduced brightness of the circle behind. I feel this doesn't work so well as the 2 circles then seem to be related.
At top left they have used triangles to depict mountain infographics. They have used different levels of brightness for each mountain but I feel the brightness should have been in an ascending/descending order so that it more intuitive.
For the pie chart in the middle, after the first 2 colours they have used greys with different brightness, but again it is not ordered and all greys seem to be related which isn't correct.
At the bottom, they have used a pie chart in which one colour is the same as the background colour which makes it difficult to visualise properly.

vivek6311 commented 2 years ago

Vivek Kumar, TLP-23

Title:- Ranked: The World’s Most Surveilled Cities Link of graph: - https://www.visualcapitalist.com/ranked-the-worlds-most-surveilled-cities/

Background:- The article by Visual Capitalist mentions the number of cameras in each city based on estimated figures for India. For China, it simply divides the total number of cameras by the total population which may not be a good estimate. Secondly, It does not mention anything about its impact.

Critique

The graph mentions Delhi, India’s largest city but does not mention in what terms it is the largest city like area, population, economy etc.
On the other hand, the graph mentions that Chinese cities are highly surveillance but does not mention which cities.
It seems that information is factually incorrect as the graph mentions surveillance based on the number of cameras used but does not mention who uses this camera government or the people. If private people use it for their personal use, then it may not be counted as surveillance.
The graph mentions that Delhi has the highest crime rate but does not give a reason for the highest number of cameras in Indore or Hyderabad.
In my opinion number of cameras per square meter (Area) would give better representation as it will tell how frequently you will come under some camera rather than population.
In my opinion the graph does not represent anything significant except for numbers of cameras like the correlation or causal relation between surveillance and other indicators like crime, freedom etc.
The graphical representation does not look very well and is not symmetric.

Visual Properties Position:- Not defined Length- Number of CCTVs represented in figures and through data Size- Varies on number of cameras/ Not symmetric Shape- Pie Chart and also a numerical representation Texture- CCTV as numbers Density - No. of cameras per thousand people. However, I think the number of areas per Sq. KM would be a better representation. Volume- Not used Line Types- Not defined

kirubanath commented 2 years ago

Kirubananth Sankar Title: The Most Surveiled Cities in the world Source: Visual Capitalist

Worlds-Most-Surveilled-Cities_MAIN (1)

Overview:

This Graphic attempts to show the most surveilled cities in the world. Each camera box in the graph represents one camera per 1000 people.

Pros:

The overall information is easy to grasp
It is very visually appealing with its minimalistic design.

Critique:

The use of the area is highly misleading as the information here is just the number of cameras. But they made the lines of cameras go radially outward, giving the impression that the cities of India have many times more surveillance compared to other cities.
If we compare Moscow and Singapore, there is only a difference of one, but visually it looks like Singapore is doing much worse.
Having surveillance cameras is not necessarily a bad thing. But this entire graphic influences that idea on people with the choice of color and wordings like 'do you ever feel like you are being watched?'. Rather than presenting accurate information, this entire graphic feels like propaganda.
As soon as we see the graphic, the first few things that catch our eyes are the title, then the word China and then Delhi, India. If we just go by the title, it would feel like Delhi is the worst-performing city. But that is actually not the case, again misleading the readers.
Finally, the graphic doesn't even show China's cities. Although it is mentioned in the text, it is still misleading to casual readers. Immediately after looking at the graphic, the first information that screams out is that India is doing the worst, which is not the case.

Suggestions:

They should use a bar graph instead of this circular one, if they want to communicate the information accurately
If they did the above step, they can show the estimation for china also in the same graph, thereby reducing the confusion with regard to India being the highest.
They should remove phrases which just cause alarm rather than to accurately ommunicate information like the first subheading. People should be provided with information and then allowed to make their own judgements, not be influenced subconsciously. Infact here it just feels like they are doing the very thing they themselves hate which is authoritarianism, by influencing people with misinformation.

BhanuN1997 commented 2 years ago

Name: Bhanu Nalluri Title: The World’s Most Surveilled Cities Link: https://www.visualcapitalist.com/ranked-the-worlds-most-surveilled-cities/

Brief Description:

This visualization shows the top 10 cities in the world by the number of cameras installed per 1000 people. It excludes Chinese cities due to limited transparency in reporting by the Chinese government (as per the report).

A few Good things:

Design aspects: The data is presented in a clean and minimalistic format. The red fonts for headings and key points in the Vis. totally catches the user’s attention from the rest of the text. Color Psychology: The red color font subconsciously brings in the user’s danger-bearing emotions. 'This truly raises privacy concerns for readers' Supplementary Information: The Vis. highlights the reasons behind the spike in cameras for incongruous cities. Eg. Delhi, LA

Critique:

Date: The Vis. and the report didn’t mention the date of the statistics. Incomplete description of the numbers: The article and Vis. failed to explain whether the government owns the surveillance cameras, non-government entities, or both. Inference behind numbers: Some cities may attract a large number of tourists and businesspeople. Just going by the population size may not paint an accurate picture of the numbers. We cannot compare Singapore with Baghdad. Singapore relatively attracts a lot of tourists compared to St. Petersburg and Baghdad.
Pie chart data labels size: The breadth and length of the data labels of the pie chart are inconsistent w.r.t to per capital numbers. For example, the area of Singapore’s data label (18) is relatively larger than Russia’s data label (17) Unnecessary Elements: The large crowd in the middle of the vis. adds no value for the reader Political and crime rate: Adding political and crime rate sensitiveness index/visuals to the Vis. will provide more details about the dynamics of the cities

Screenshot 2022-10-17 201748

29pranav commented 2 years ago

Name: Pranav Satheesan Title: America's top 50 Fast Food Chains Image Source: https://www.visualcapitalist.com/ranked-the-most-popular-fast-food-brands-in-america/

Description of the chart:

This graphic uses data from a report on America’s top 50 fast food chains by Quick Service Restaurant (QSR) Magazine. The popular brands are sized by their 2021 systemwide sales and broken down into six broad categories: Burger, Chicken, Snack, Pizza, Sandwich, and Global

Positives:

• Colours and visual representation is eye catchy. • Good visual representation – Logos are used instead of names as people associate with logos more. • Different colour schemes for categories helps to identify different types of food items.

Negatives:

• The encoded information seems ambiguous. The user is unsure of whether the encoding is done based on radius or the area of these circles. • The information depicted by smaller circles are not even visible without zooming into the chart. • Ranking is done based on the sales, but comparing two QSRs in two different sectors is like comparing apples with oranges. Ranking must be done inside each sector.

Suggestions:

• Region wise revenue of outlets could’ve been compared and added to the chart. • Sector wise revenue of outlets could’ve been compared.

Data Types:

• Sales in 2021 is continuous quantitative data. • Rank of a restaurant is ordinal data. • Names and logos of restaurant used is nominal data.

Visual Representation:

• Here the actual logos of fast food chains is saturated. • The categories are represented by bright colours. • The shapes of every data point is circular and the size is consistent with the sales of the food outlet

ada-nai commented 2 years ago

Name: Adarsh Nair Title: USA COVID cases visualization

Image Source: The New York Times

Description:

The above visualization was published by the New York Times depicting the number of COVID cases recorded in the USA during the COVID19 pandemic.

Critique:

What works:

The spiral graph is unique and eye-catching.
The distribution of cases is given month-wise, the time frame is equally distributed into twelve sectors.
The visualization gives an indication of the deviation of COVID cases from the 7-day average throughout the period.
The spiral nature of the graph indicates a cyclicity to the peaks and troughs of number of cases, and it is better understood using this choice of visualization

What does not work:

The month-wise timeline for the year progresses in the clockwise direction and the year-wise timeline is depicted in the outwards direction. This can be confusing as the mental model is to observe time-series data in a linear scale in the month-year format.
The number of cases during a particular period is depicted by the thickness of the curve, but it is not an ideal representation and can be considered as a Ponzo illusion i.e. the number of cases can seem to be greater for the later periods in the visualization.
The estimated thickness for a band is mentioned (increasing thickness from 0 to 150K cases), but it is difficult to estimate the count for an individual period.
The visualization is not ideal for a long range of time periods as the newer time-frames will diminish the earlier ones.

Suggestion(s):

There should be a mechanism to quantify the number of cases for a particular period in the visualization.
The plot of values should have been on only one side, to better visually estimate the number of cases for a certain period.

Data Types:

The 7-day average value is quantitative and continuous, and is depicted using a line representation.
The time variable is an interval data type and is depicted using the spiral nature of the chart.
The number of cases is a quantitative variable and is depicted using the violin-plot like representation. It indicates deviation from 7-day average and is appropriate.

Mapping visual properties to data properties

Position: The position is inidicated by co-ordinates in the spiral plane.
Length: The count of values (cases) is defined by thickness of the plot for a particular period.
Color: Only a single color is used for demonstration. While any color could be used, more information could be encoded in the diagram with the help of color (saturation) to indicate the number of COVID cases for a particular period.
Orientation: The points closer to the center are from an earlier period. So, orientation closer to the center indicates ordinal value of time.

pujan2000 commented 2 years ago

Name - Pujan Chitalia Title - The World's Most Populous Countries Image Source - https://www.visualcapitalist.com/wp-content/uploads/2022/10/most-populous-countries-main-1.jpg

most-populous-countries-main-1

Aim- The visualization is trying to show the population trend of the 10 most populous countries and also predict the population for the year 2023.

Critique-

Negative- • The representation is done with wavy edges which is unnecessary. • It can not be understood whether to compare the area, length or the breadth of the wavy rectangular shape. • The population numbers are only mentioned in the start and at the end so we can not see how the population progresses over the years. • The colours used to represent the population of the country is random. Country flag colours could be used instead for better representation and recall. • There is a mis-match between the colour used in the chart and the flag colours used at the bottom for India, Nigeria and Germany. • If we observe a single country data for example Russia, the yellow wave starts at 132M and ends at 144M so we can see an increase in the population but the yellow wave has moved backwards which might indicate that the population has decreased over the years

Positive- • The chart looks visually appealing and is somewhat clear in conveying the message of the population trend. • The facts mentioned below and on the left sides are important and give better insights regarding the data.

Mapping Data Properties to Visual Properties- • The country data is nominal data. There are around 13 countries and different colours are used to represent different countries. This causes a little confusion in the minds of the viewers as colour shades tend to match. • Time period is interval data.

Kunalbhardwajj commented 2 years ago

Name - Kunal Bhardwaj Title - Visualizing the Relationship Between Cancer and Lifespan Link: https://www.visualcapitalist.com/the-relationship-between-cancer-and-lifespan/

Aim: This visualization is trying to represent the relationship between cancer and lifespan. Researchers at The Wellcome Sanger Institute recently found that the mutation rates are closely associated with species’ lifespan and through this visualization, the idea is to understand the relationship between cancer and aging on the basis of this research.

Effectiveness: The visualization is very much able to provide relevant information and also different plots and curves are used to give a better display and to reduce cognitive load.

Pros:

Different graphs are used to make the information elaborative.
Well-written texts along with images to clear the scope of misunderstanding.
Colors are used appropriately and kept less in number, giving it a minimalistic appearance.
The use of body shapes for different mammals makes the visualization intuitive and provides a better representation.
Consistency among the ratios of different body shapes while plotting the curve between mutation rate and lifespan.

 Cons:

Although pictorial representations of different species are used but in the graph where there is a curve plotted for representing the relationship between mutation rate and lifespan, shapes are overlapping making it somewhat confusing for the reader to correctly understand the correlation for all the species.
The triangular area representation used for comparing variations between sizes, lifespans and the total number of mutations is not completely serving the purpose and takes up larger space at the same time.
Although the representation is quite elaborative in terms of the context but the choice of bigger figures makes it very long to convey less information.

Suggestions:

Instead of using overlapping figures for different species, codes or dots of different colors can be used for clear representation.
For comparisons between sizes, lifespans, and the total number of mutations for different species in place of triangular area representation, a multiple bar graph can be used with three columns where one column would be for size, one for lifespan, and one for variations so that it would convey the same information in less space.

Visual Aspects of Data:

Based on data semantics and data behaviour, as the data is reflecting relationship among different species of mammals and their mortality rates due to cancer, to represent different species their body shapes are used which make the visuals quite intuitive.
Now based on data type, in figure 2 where mortality rate is represented and different species are ordered based on their sizes i.e Ordinal categorical data is used and to represent the same visuals of body shapes make it easy to understand.
In figure 3 where mutation rate in different species is plotted against lifespan of those species so the data is Nominal categorical data here but in the representation being used here different body shapes are overlapping with each other which makes it somewhat difficult for readers to understand the exact position on curve for different species, instead of using this, different symbols could be used with proper colour coding so that there won't be any overlapping and species could be represented distinctively.

ShashankPrasad0506 commented 2 years ago

Name: Shashank Prasad

Title: Child Labor is on the decline Source: https://www.ilo.org/wcmsp5/groups/public/@dgreports/@dcomm/documents/publication/wcms_575499.pdf

![Child labor is on the decline](https://cdn.vox-cdn.com/thumbor/FJsOhtJeJPkB9sQ3_1CGEeY6CkY=/0x0:6986x4815/1120x0/filters:focal(0x0:6986x4815):format(webp):no_upscale()/cdn.vox-cdn.com/uploads/chorus_asset/file/13153631/childlabor.png)

The visualization aims to show a decline in child labor worldwide over the years 2000 to 2016.

Pros: 1) The graph is minimalistic and the legend, additional information provided does not clutter the graph. 2) Both the absolute numbers and percentage are mentioned which is easier for the viewer to decipher.

Cons: 1) There is inconsistency in the representation of area of green spheres when compared to the area of orange spheres. For eg, In the year 2000 data, green sphere and orange sphere are overlapping, which might mean that they are common children in both the two spheres but they are different and should not overlap. 2) Hazardous work is represented in green color whereas it could have been in red color as red would align more with hazardous. 3) There is approximately 40% reduction in child labor from 2000 to 2016 but is not intuitively visible from the graph and seems like a 6% decrease. 4) Adding the numbers of children in Child labor and number of children in Hazardous work will give the total number of children in employment but can be confusing due to the legend named as "Child Labor". The first legend can be renamed as "Child labor in Non-hazardous work". 5) The reduction in the number of children in child labor amounted to 16 million for the 2012 to 2016 period, just one-third of the 47 million reduction recorded during 2008 to 2012, but the graph spacing and placing of the spheres are not aligning with the mental model as it looks like there is more decrease from 2012 to 2016.

Suggestion: A Stacked Bar chart would have been a better way to depict the data.

Suvrojyoti-Paul commented 2 years ago

Suvrojyoti Paul

Title- Importers of Russian Fossil Fuels

This graph is a bar chart depicting the largest importers of Russian fossil fuels since the war.

Critique-

Positives: • The visualization successfully manages to show the largest importers of fossil fuels from Russia. • The Chart legend looks very neat and handy. • The visualization looks very colorful which helps in figuring out the chart. • The annotations are present which again gives the user a lot of important information.

Negatives: • Zooming is required to read all the texts on the chart. • The bars are not telling us the total quantity of fossil fuel each country is buying. • The graph is also not telling us the percentage of each type of fossil fuels the countries are importing. • The bars are horizontal which can be little hard to read. • The X axis (shows dollars) is present in both at the top and at the bottom.

Image Source- https://www.visualcapitalist.com/whos-still-buying-fossil-fuels-from-russia/

HimanshuBhardwaj2398 commented 2 years ago

Submitted By: Himanshu Bhardwaj

Title: Netflix vs Disney: Who’s Winning the Streaming War?

Source: https://www.visualcapitalist.com/cp/netflix-versus-disney-subscribers

Visualization Description:

The visualization aims to compare the performance of two OTT platforms: Netflix and Disney, it has been done by looking at the paid subscriptions over the years for both platforms.

What works?

1.) The visualization effectively conveys the growth of both the platforms over time in terms of number of paid subscribers. 2.) Color coding has been chosen in accordance with the brand colors, which makes the visualization more intuitive 3.) Different branches of Disney+ are taken into consideration, which helps in understanding their individual contribution

What does not work/ Suggestions?

1.) The cause of sudden jumps/declines in subscriptions has been linked with release of various shows at the corresponding times but the effect of COVID is not incorporated (which could have led to huge shift in subscriptions) 2.) Timeline for some important events has been pointed using arrows, it can be improved by incorporation of dots on the plot along with arrows pointing towards the dot. It will depict the timeline in a better fashion. 3.) There is no information of subscription prices, which can help to know the monetary share of market that each platform holds. 4.) Colors of Disney+ logo and its corresponding number of subscribers does not match. 5.) A more prominent color should be chosen for labelling Y axis, the current color appears quite dull. 6.) The plot is based on absolute value of subscriptions. In my opinion, a bar plot of YOY(year over year) growth for last few years can be incorporated to represent the current growth rate better.
7.) Including Disney’s sports streaming platform’s (ESPN+) subscriptions while comparing with Netflix which is predominantly films and series streaming platform; can appear counter intuitive as it compares completely different services. 8.) Incorporation of information related to "number of new titles being added" and correlating that with growth can also help us to know about the respective efforts/strategies and their impact for both platforms.

Comments on Graphical elements : Format -- Data Characteristics : Visual Property/Graphical Mark

1.) Nominal data( Netflix, Disney(Sub Categories-> Disney+, Hulu, ESPN) : Colors, corresponding with Brand colors are used which makes the plot easier/intuitive

2.) Discrete Values of Subscriptions : Connection, Orientation (Connection of lines is used to represent the progression of number of subscribers, which represents the change in respective subscribers )

AyushiNM commented 2 years ago

Name - Ayushi Mittal Title - India's sex ratio at birth has been moving toward balance in recent years.

PR_2022 08 23_india-sex-ration_00-01

This visualization displays India’s sex ratio at birth over the years 1960 to 2020. The natural sex ratio at birth is about 105 boys per 100 girls. It shows how after the introduction of pre-natal testing and ultrasound, there was a sharp increase in the number of male births per 100 female births, which suggests sex-selective abortions. It could also be noticed how this ratio has normalized again over the last decade as pre-natal testing became illegal and the government started the “Save the Girl Child campaign”.

Data Visualization Critique -

Positives

This visualization has done a great job at communicating its objective of displaying how India’s sex ratio at birth has been moving toward balance in recent years.

The data has been displayed over the years 1960 to 2020.

The choice of range for the sex ratio at birth is appropriate because we are displaying the number of boys per 100 girls and the ratio doesn't seem to go under 100.

The data points clearly display the number of male births per 100 females.

The baseline for the natural sex ratio (105) is displayed, making it easier for the reader to understand the issue.

The y-axis lines make it easier to observe the transition.

The line chart makes it easier to observe the male bias and the sex-selective abortion transition over the years.

Another great this about this visualization is how the key events (Introduction to pre-natal testing, legalization of abortion, availability of ultrasound, the advent of the “Save the Girl Child” campaign) are marked, making it easy for the readers to understand how these events had a major impact on India’s sex ratio at birth.

Negatives

The baseline set at 105 doesn't make a lot of sense based on this graph. They should have included more history to establish 105 as the baseline, maybe from independence or before.
We can also observe that the graph shows data over decades (10 years) gap, except for 2015, which is inconsistent.
The thickness of the lines takes focus away from the circles which represent the sex ratio and are the key feature of this graph.
Key events are not marked using proper shapes.
The ban on “Prenatal sex determination (1994)”, which is an important event in the context of this story is not displayed in this visualization.
When the data is displayed in terms of ratio, it is not communicating the seriousness of the issue well because to an uninformed reader, 110 may not seem much different from 105 as the research claims return to normality at 108. However, if the area had been used to display this information, it would have made a greater impact on the user.
Could have used color coding to depict various levels of sex ratios (to mark the seriousness).
This visualization is trying to establish causality between specific government policies and the normalization of the sex ratio, which is slightly misleading as there are many factors involved like education, urbanization, access to quality healthcare, globalization, etc.

Suggestions

The range of years could have started sooner, maybe after independence (1947).
It would help to introduce a geographical and community factor to this data to identify the regions that have seen the most improvement and the ones that still need work.
Use of proper color schemes to display various levels of sex ratio.
Use of some other shape like stars or squares to mark key events would have been appropriate.
The thickness of lines could be less, and the lines could be dotted, because the main focus is on the sex ratio for particular years, and the lines just show the transition.
The size of the circles could be bigger since they're the main focus of this visualization, and they could be solid colored rather than empty.

tirthatilakpani commented 2 years ago

Data Visualisation A_1.pdf Name- Tirtha Tilak Pani Update- Class Exercise based on this assignment Assignment_1+ Class Exercise.pdf

subandwho commented 2 years ago

Name- Subandhu Title - The rise of gaming revenue visualized.

Link: https://www.visualcapitalist.com/wp-content/uploads/2020/11/history-of-gaming-by-revenue-share-full-size.html Disclaimer: The rise of Video games and entertainment has been unparalleled. In a few decades, video gaming has grown to the extent that streaming giants such as Netflix and Amazon Prime video fear online gaming as a bigger competitor than cinema films. The given infographics tracks the rise of the video game industry and where it is headed.

Critique:

Cons:

The graph does a poor job at the division of game genres using the legend. The legend used for handheld devices misses the viewers eye often, since it is depicted using a darker hue as compared to the vibrant and engaging color schemes used to represent other gaming domains.
The graph is not uniform in terms of the revenue it wishes to convey. The size occupied would appear to be a depiction of the revenue however the size appears to convey no real meaning as similar areas seem to overlap in different categories .
It is unclear what the shifts of the graph( the peaks and the troughs) denote. The viewer might assume they represent crashes in the genre of games but that assumption is challenged as there are several random peaks which paint a confusing image of the information being portrayed.
The graph does an unconvincing job at explaining the rise and parallel growth of the mobile and PC gaming industry. While an exponential curve depicts mobile games have risen. PC games appear to be crashing down which is contradictory to the text information provided.
Major events in video game industry such as the rise of Twitch and peak due to “Battle Royale” video games is also unclear. They are placed as timeline events and their impact on the infographic’s true motive is not visible.
The graph uses far too many legends and tries classifying multiple sub categories. This causes few genres to be left out due to lack of relevant data.
There is no clear metric on which event caused the growth in revenue. The lack of a scale which would assist in interpreting cause and affect is clearly visible.

Pros:

The infographic outlines almost every event that has had an impact on video games and their market. One can use this graph to trace events to present day.
The use of gaming icons as placeholders is a clever and appealing manner of representing major events creatively.
The present market share of major gaming industries is well portrayed. Moreover the years provided adjacent to each event allows one to compare different industries at a given point in time.

Suggested Changes:

The legend must collect overlapping categories such as HandHeld and Mobile games together, similarly Cloud and PC gaming fall within the same ambit and must be categorized together.
Revenue generated by each industry can be interestingly conveyed using stacked up icons on the y axis instead of a cumulative sum provided at the end.
Major events must be highlighted and their impact on the revenue be depicted via strong peaks and/or steep rises.

Final verdict:

This infographic is engaging and appealing to look at and does convey important information about the history of Video Games in general. However, it misses out on several opportunities to convey what the title promises, “The Rise of Gaming Revenue”. It would best suit the title “Console Wars”

Edit: 19-10-2022

Visual Property: The infographic represents all required categories with distinguishable legend, the use of images also conveys major events in the timeline presented.
The data used (revenue of sales) is continuous, cumulative data divided into sub categories. Further the years depicted as placeholders are discrete data points,.
The discrete data is well represented, however the cumulative revenue represented does not convey information well, a cumulative area chart with the ordinal categories represented by colors and revenue generated by years on x axis and revenue on the y axis.
The orientation which appears to be rising from a single point shows as if all data points originate at the start, hile that is not the case, a different orientation could be used.

itsnitiz commented 2 years ago

millionaire-migration-2022_main

Submitted by Nitish Mallick Image Source: https://www.visualcapitalist.com/migration-of-millionaires-worldwide-2022/

Title: Mapping the Migration of the World’s Millionaires

Background: This graphic maps the migration of high-net-worth individuals (HNWIs)—people with a net worth of over US$1 million showing where rich people are flocking, and where they’re fleeing over top ten countries.

Critique:

Easy in interpreting. Colour matchup for influx and outflux with the use of annotations (+. -) is one such example.
The size and area of the arrow suggest which country has the highest migrations i.e. outflux and influx. For e.g Russia and China with red arrows shows a high no of outflux. But in the latter, the green arrow which is influx doesn’t show the same. If I try to find the country with the highest no influx, it is difficult to find.
All the annotations from the map image are legible. Legibility as we discussed in class is first and foremost for any visualization.
The only con that I feel is no representation of the variety of countries from which migration is coming from to a country and vice versa. It could have been accommodated by the use of flags under the arrow. Legend of the flags with countries could have been given.
Another thing that could have been done is to represent the flags with the map surface. Only colored arrows (red/green) could have been drawn with no migrations to different associated countries. The information would have been richer and also appealing.

Data Types and Encoding:

Country names which are nominal data have been encoded with flags and map which comes under textures/symbols.
The figures Positive and negative are discrete figures which have been encoded in map color saturation (density) and also use of line with density. Here I think lines were not required to encode discrete data.

monikrish2698 commented 2 years ago

Name: Monish Krishnan Title: Refugees and Asylum Seekers (Ethiopia) : Link

Image-to_critique_2

The info chart aims to uncover the influx of refugees and asylum seekers to Ethiopia from neighboring countries. Unrestricted to numbers and percentages on the geovisualization, the static visualization also breaks down the information by gender, nationality, trend analysis, and arrivals by location (2018). To me, the visualization managed to concisely display vast details in a series of different types of charts.

Some elements that drew my attention to the visualization:

The refugee centers and camps on the map are distanced by their real geographical locations.
A map of the African subcontinent on the top right, with Ethiopia highlighted, will help end-users understand the geography of the refugee situation.
Retaining the borders of the neighboring countries allows users to comprehend the geographical trends across different centers.

I found a few details that diminished the effectiveness of the visualization:

The dotted lines representing the border of Ethiopia give an odd look to the map.
The bubbles on the geovisualization representing the proportion of asylum seekers received in different refugee camps across Ethiopia are misleading. It could be easily misinterpreted as pie charts. Even if the bubbles were pie charts, the details of other nationalities in the pie charts are missing.
There is inconsistency in the horizontal bar graphs in the second row of the visualization. On the left, only proportions are shown. And on the right, both ratios and absolute numbers are shown.
The “Age/Gender Breakdown” graph individually represented the youth group and included them in the adolescent and adult groups.
The “Age/Gender Breakdown” graph is Inconsistent with the usage of nouns and adjectives in the labels (Adults vs. Elderly).
The absolute numbers in “Breakdown by Nationality” and “New arrivals trends by location | 2018” are inconsistent. The former is colored with the colors used in the geovisualization, and the latter has numbers. It also labels the horizontal bars with the respective locations (with color).
The “Arrivals | 12 months trends” line graph shows. There is no explanation for this drop in the visualization.

The visualization could have been made better by focusing on the following details:

Removing redundant details (absolute numbers and proportions, consistency in information representation, showing the sum of absolute numbers from graph)
The labels can be ordered to avoid any overlap.
Distinct colors can be used to represent the locations (Grey color for neighboring countries and also for Sudanese refugee representation).
The information can be condensed further by displaying relevant information (for example, adding the proportion of refugees by nationality and by camp location (on the map).
Minimize the number of legends used on the maps.

Date: 19-10-2022 / Classwork

Considering the data was available at individual level and has been aggregated for visualization purposes, the data types can be identified in the following types:

Nominal: Nationalities, localities (within Ethiopia), Refugee camps and UNHCR offices, classification of individuals (Infants, Children, etc.)
Ordinal: Labels used to represent the population [Infants, Children, Adolescents, Elderly]
Quantitative: Absolute numbers and proportions representing the trends, binning of ages to concisely represent the refugee population, monthly trends. Data elements:
Arrivals number plotted as a line chart with time in x-axis and absolute numbers in y-axis. The trend is unclear with a line chart because it simply connects the dots (could have been a dot plot)
Age/Gender breakdown – Bars representing the proportion of population and the sizes represent the proportion
In the map where different bubbles different the proportion of refugees received in different refugee stations and camps
Distinct colours used to represent different nationalities. no two colours share the same shade. Easy to differentiate.
To represent different refugee camps and UNHCR offices, the map uses multiple icons and symbols but similar kind and colours are used to represent locations of same category.
The locations of the offices and maps are plotted based on their geographical distances.

Harsss commented 2 years ago

Name: Harsh Bindal Title: Total Copies in Circulation evolution by volume in Japan Source: http://comicdata.blog.fc2.com/

DVassignment1

Background: This graph shows data about the sold copies of japan's most popular anime source's material copy.

Critique:

In this image they just wrote "physical copies sold in Japan" but they should also mention what they are talking about. Is it just manga or novels and special volumes too
From vol 1 to 5, the line of One piece is above Naruto, but from vol 5 to 6, the line of Naruto is above One Piece and then after One Piece, which shouldn't be. They should be consistent.
Don't just write the volume after the animation starts, they should also make some markers on the graph to express this.
Instead of using random colors on the chart, they can use some specific colors to help people associate with those anime. For example, red for one piece (which is fine), orange for Naruto, dark blue for Jujutsu Kaisen, green for Boku No Hero Academia, and so on.
they are comparing old animes like one piece and naruto to newer anime (like jiu-jitsu Kaisen) which makes it a bit biased because now people can read online copies which is not the case with old anime, so they had to buy it back then.
The graphics they use for Naruto are from Boruto and which is not included in this data, so they should not use them.
They should mention the anime manga they chose before the latest or last volume, and the anime manga they chose before the 28th volume. For example, Naruto has 72 volumes, including only 28 volumes, and includes all the volumes of Jujutsu Kaisen.
On the Y axis, they could write it as 10M instead of the full number.

Visual Elements

X-axis- Volume number (Ordinal Data)
Y-axis- No. of copies (Quantible Data)
Encoding: Data is discrete so the line is inappropriate.
position (x,y) | Points (it is appropriate but they hadn't used it)
Length |Line (Should not be used as data is not continuous)
Color| Use hue for different categorical data
Shape| Should use shape to mark the important position where the anime starts
Labels| Use labels for nominal data

ghost commented 2 years ago

Name: Savya Sachi Pandey Title: Mapping the percentage of people not able to afford a healthy diet across the World

Image Source: https://www.visualcapitalist.com/mapped-the-3-billion-people-cant-afford-a-healthy-diet/

Aim: a healthy diet is one that meets daily energy needs as well as requirements within the food and dietary guidelines created by the country, therefore this visualization aims at demonstrating the number of people who are not able to afford a healthy diet across the world.

Critiques

Positives:

The visualization meets the intention of showing the percentage of people not able to afford a healthy diet.
The colour used to differentiate is helping in defining various countries and makes the mind mapping easy.
The mapping of percentage is clearly visible.
The smaller countries have been depicted by magnifying lens which can be read easily. (eg: Norway, Finland, etc.)

Negatives:

The country name is not labeled correctly, it would be difficult for people to read and understand the country by simply reading the country code.
Corrupt or black money is not taken into account while calculating the average income of households.
The magnifying lens criteria is somewhat overriding the details around it. It could’ve been better if the lens was shifted a little upwards making the whole visualization more appealing.
Not legible enough, looking at first glance.
While comparing the data across countries, it may give wrong results for the countries with high retail/food inflation during that time, which may seem that the particular country has more people being unable to afford a healthy diet.
Due to unorganised distribution of income across the country it becomes difficult to calculate the average household income as it would increase the overall income and hence would not show the actual figure.
The data of total dependent members of the household is not taken into account, as there can be as large and as small no. of people in the family and only one person can be earning.
Continent wise comparison would've made the insights clearer.
Colour contrast in Africa is not clear and is a bit mixed up.

Mapping Data Properties to Visual Properties:

The percentage of people not able to afford a healthy diet in each country is Quantitative, Ratio data and the countries name in themselves is the categorical data.
The adjacent countries are differentiated from each other through a thick line of length equal to its border.
Different countries are depicted with different bright colours.
The shape is depicted in terms of unique symbols i.e. the actual shape of the country, and to depict the small countries, a lens shape is used. Also the semi circle shape is used to depict the total number of people who cannot afford a healthy diet in a region/continent.
The density of the colour is more in the African region and colour contrast with density is not much differentiable.

AdarshGowda03 commented 2 years ago

Name: Adarsh Gowda Title: The World’s Most Populous Countries (1973–2023) Image Source: https://www.visualcapitalist.com/most-populous-countries-over-50-years/

ezgif-2-521ec5e3d5

Aim: The visualization aims to show how the top 10 most populous countries have changed over the past 50 years.

Critiques

Positives:

The visualization has used area to show how the population of each country has changed over time, allowing us to easily visualize how the population of each country has changed over time.
The colour coding used to represent each countries makes it quite distinguishable for the viewers.
All the top ten most populous countries in 1973 may not be in the top ten most populous countries by the end of 2023, so to show the top ten most populous countries in 2023, the graph has also included countries like Nigeria and Mexico, which were 11th and 13th in 1973 and are expected to jump to the 6th and 10th position in 2023, respectively.
The visualization is informative because it explains why China's population growth rate has been lower than India's population growth rate over the last 50 years. (One-child policy).

Negatives:

The image of the people depicted in the visualization does not contribute to the graph's goal. The visualization could have included images of people with higher densities in areas with high population growth.
When the graph of a country is crossing another's then the area of the graph decreases which directly indicates the rapid decrease in the population but there is no factor explaining the sudden decrease in the population of the country.
Initially the total population has been mentioned as 4 billion but towards the end they have not given the total predicted population of 2023, they could've mentioned the total 2023 predicted population.
Towards the end in the visualization, they have mentioned as "2023P" which doesn't define anything and has lost legibility, instead they could've mentioned "2023 Predicted" as they are predicting the population of 2023.
They have used random colours to depict the countries instead they could've used the respective country's flag colour which could've been easier to visualize.
In the visualization, a bifurcation by a line is clearly visible every 10 years, which does not provide any information about the percentage change in the population, instead before each bifurcation, they could have mentioned the population count and the relative population change percentage over the last 10 years, which could have provided more information about how the population has changed over a 10-year bifurcation.

Mapping of Data Properties to Visual Properties:

1) Data is continuous and Nominal 2) Area comparison 3) Colour code is used to differentiate 4) Line graph could have better as it is depicting percentage change

anchitna commented 2 years ago

Name - Anchit Narayan Title - General election 2019: How Remainers and Leavers plan to vote

About: Brexit triggered the general election, but is it still the most important issue for voters? This chart shows how people respond now when asked which are the most important issues facing the country, and how this compares with the position at the beginning of the election campaign four weeks ago.

Critiques:

Positives:

1) The chart compares 6 different parameters affecting the election in one frame. 2) The vertical axes has percentages marked clearly.

Negatives:

1) In the charts compared in then & now, there seems to be no change. 2) The chart doesn't show how many people were surveyed for exit polls 4 weeks ago and now. 3) It is difficult to compare then & now. 4) Colour coding used for representing opinion poll organisation(panelbase) has a colour used by a political party(Liberal Democrats). 5) There doesn't seem to be any significance for this comparison from the voters perspective. 6) A comparison chart should convey a narrative, but this chart fails to do so gracefully, user doesn't understand by looking at the chart why are we comparing 4 weeks ago and now in time space.

Suggestions:

1) The colour coding should be changed to rectify mis-representation. 2) The spacing between then and now should be increased. 3) The chart should actually show some comparison.

Mapping of data properties to visual properties:

1) Three different exit poll conducting company for which the data type falls under nominal data. 2) Six different issues for voting preferences are depicted in the chart which is a nominal data. 3) The horizantal axis has time series shown as 4 weeks ago and now which is interval data. 4) The vertical axis has percentage shown as 4 equal intervals, which depicts a ratio data.

gaurav639 commented 2 years ago

Name: Gaurav Singh Title: How Does the COVID Delta Variant Compare?

covid-delta-comparison-infographic

Background: This visualization compares COVID-19 and COVID-19 Delta Variant on the basis of spread rate, viral load, virus detectable, infectious period, risk of hospitalization and also give some information about other COVID-19 variants.

Critique:

Pros:

Only 3-4 colors are used in the entire visualization which gives a better look and makes mind mapping easy.
By looking at the corona virus image beneath heading, it is clear that visualization is about COVID-19.
Visualizing the spread rate by showing dotted line between human images is very attractive.
Location wise "variant origin" mapping is elegantly shown on globe
Visualization of vaccine in the end is a nice add on towards educating people to get vaccinated.

Cons:

Risk of hospitalization is only shown for the delta variant and not for the left one.
In the right side of "spread rate" category, the white lines use to show the spread are very light and are not visible properly, making it hard to understand it on the go.
On the "Variant Origin Timeline", the color of "Variants Under Monitoring"(Eta, lota, Kappa) and "Variants of Interest"(Mu, Lambda) is same, so you can't make any difference among these categories.
On the figure of the globe, only information about "Alpha" variant is given and all other left uninformed which is causing inconsistency.

AdityaPandey0901 commented 2 years ago

Name: Aditya Pandey

Source for Analysis: Visualizing China’s Dominance in the Solar Panel Supply Chain, Visual Capitalist Author: Niccolo Conte Date: August 30th 2022 Link: China's Dominance in Solar Panel Supply Chain

China_Solar

Visual Elements | Data Element

1) Symbols/Icons (5 distinct) | Manufacturing Phases [Categorical] -The encoding here makes sense.

2) Solar Modules (4 spatially separate rectanges) | Manufacturing Phases [Categorical] -Same as above

3) Rectangular Area (x*y) | Manufacturing Capacity share [Ratio Data] -Area here is slightly discretized (one can see small rectangular chunks that create discrepancies in the smaller regions/rectangles). Market share however is continuous. Mismatch of visualization, pie chart type things or aherence to proportional area would have been better.

4) Color (Shades of blue) | Regions (China, APAC, Europe, etc.) [Categorical] -This makes sense, the shades of blue are distinct enough to not give the impression of continuity.

Commentary on the Visualization

The above viz is designed to show the worldwide manufacturing capacity of solar PV panels throughout the world, supporting the titular argument for Chinese dominance in this domain’s supply chain. Capacity share at the steps of PV cell manufacturing is shown in 4 area graphs representing modules on a panel, with a set of concise explanations at the top.

As a whole, it is quite persuasive: The choice of an area graph means that Chinese solar is visually dominating the area on each graph, and China is also spelled out in a larger font. Moreover, color is also at play, as Chinese market share is shown in the exact shade of blue in most conventional solar modules.

However, there are a few downsides:

- Manufacturing Capacity vs Controlling the Supply Chain: The image uses ‘Capacity’ ie. the total potential output at each manufacturing stage to make arguments for supply chain control. This however is slightly: the utilization of this output and the actual flow of demand to/goods from China would really be what “controls the supply chain”.

-The area proportions can be misleading, especially when looking at all other (not China) manufacturing regions. Three instances in which this misleads a viewer are below

1) Asia Pacific for example is grouped together, but in many places India also appears as a separate square, making it unclear whether it is grouped in with that blurb (The wafers part for example doesn’t include India but does include Asia Pacific, surely India should also have some role there?).

2) Area proportions on the boxes aren’t entirely accurate either: In the modules section for example, the combination of ‘India’ (2.8%) and ‘Rest of the World’ (1.9%) takes up an equal amount of space as North America (2.4%) and Europe (2.8%). This is visually more due to the inclination on the panel.

3) The design choice to have the Chinese blurb always be in the center of the panel, while other countries are at the edges, can be disorienting for the viewer. I as a viewer expect consistency of placement throughout the graphs (ex: having other regions consistently on the left or the right), although I understand the choice being used for the larger goal/ to make the Chinese argument.

-The ‘Manufacturing Process’ depicts five stages for manufacturing, while the larger share of capacity viz depicts only 4 out of the 5. This makes it unclear to the viewer as to whether that stage is being shown at all (and if not why?), and if it is, where is it being encapsulated under (is it counted under polysilicon most likely? Or wafers?).

Stuti-018 commented 2 years ago

Name: Stuti Pasricha Title: Safe Skies - Despite a recent tragedy, air flights are getting safer Source: https://www.economist.com/graphic-detail/2014/03/11/safe-skies

This graphic intends to depict how over the period, flights have become much safer. It shows the number of casualties that occurred by accidents, hijacking, and bombing over the bar chart and the number of passengers using the line graph by year.

Critique: • The graph clearly depicts the decrease in accidents with an increase in passengers carried over time. It is easy to understand for anyone. • Too many bars give the graph the appearance of being overly congested. Multiple years could’ve been binned together, resulting in a lesser number of bars. • The ‘Aircraft passenger carried’ blue line could have been represented on a different graph with categories like male, female, and children. • Instead of stacking accidents, hijacking and bombing bars one above the other, they could have been placed adjacently. This would’ve made the comparison between the number of hijackings in one year vs the next easier. • Instead of using shades of the same colour, different colours would have given more contrast to the graph.

Class exercise (19-10-2022) • Position- On X-axis we have years of transformation and on Y-axis we have 2 types of values. On left we have Number of casualties with maximum of 3,000 and on right we have number of passengers travelled through aircraft in billion with maximum of 3billion. • Length- Equally thick bars but the quantity is too much which is creating the graph overly congested. • Colour Hue- Type of casualties are not Ordinal to change the hue. More work can be done on colour combination. They should have the same brightness, but different contrast placed beside each other. • Shape- instead of stacked, placing beside each other might have been a better choice.

malhotra-bhavini11 commented 2 years ago

data+v5

Figure 1. Title: LGBTQ+ representation on Television Source: https://madelinestanislav.com/data-visualization

The goal of the graph as shown in Figure 1 is to illustrate the degree/aspects of LGBTQ+ representation within the entertainment and media subsector of Television. Additionally, the graphical representation of the data helps to identify patterns and trends present and observable between different metrics on which it can be measured. By doing so, it aims to inform others The graph was created in order to bring about more deliberate and diverse choices in terms of writing, casting and overall actions which would increase inclusivity.

This visualization shows various features used to evaluate LGBTQ+ representation such as genre, network, type of representation, whether the casted actor is LGBTQ+, and the number of seasons the program has run. These features are plotted to determine in what quantity they exist and their relationality. Furthermore, the visualization also depicts the average quantification for the corresponding categories.

It can best be utilized by individuals within the entertainment and media industry, members of LGBTQ+ community, and those who are pop-culture or television connoisseurs. It can help in identifying where exactly change would be most needed, such as a particular genre or television network to bring about awareness and consequently, alter the current system.

This visualization depicts five data variables: genre, network, type of representation, whether the casted actor is LGBTQ+, and the number of seasons the program has run, and the encodings used are color, width, length and connectivity respectively.

The visualization made by the author is simple and comprehensible. Anyone who will have a quick look at it can understand it quite easily without much effort and time. The legend is made and placed carefully to avoid confusion. Moreover, the data exists in the form of a shape not points, so it is, as discussed in today’s lecture, less allostatic load on the viewer’s mind.

The visualization is effective except for 2 areas: the color and the size. Due to that many features, the size of the graph, in other words, the number of layers is very large and difficult to understand and may result in the viewer feeling as if they’ve been overloaded with information. Additionally, the colors in the graph were a touch too complementary. This might also require a lot of cognitive effort from the viewers. To better improve this visualization, the author could use a different color scheme so that the different categories are more distinguishable. Another option would be to also provide a gradient to represent quantity. For example, the color used to represent the weak can be a bit darker as the lighter shade merges with the background. Instead of using the area for the number of occurrences, the author could have used the different shapes to depict 4 different occurrences.

When I came across this particular visualization, the structure reminded me of the Polar Area Diagram, also known as Wind Rose plot innovated by Florence Nightingale that was covered during the first lecture of this Data Visualization course. I felt that despite its flaws, the fact that it was able to invoke a new sense of data literacy and understanding, to effectively persuade people, unite them under one vision and bring about change, shows that there is scope for data visualization to achieve the same in other domains.

On that note, the visualization shares some similarities to the disadvantages and problems visible in Nightingale's Wind Rose plot. That visualization as well as this one is similar to a pie chart but more intricate. While the slices of the polar diagram have equal angles, in pie charts the arc length of each slice is proportional to the quantity it represents, which is clearly observable in the innermost 2 layers, from the proportional difference between Netflix and another network such as the CW. The arc length of the slice in the 2 most innermost layers represents the quantity. From then onwards, moving outwards, we can observe that each slice has equal angles like the Wind Rose plot.

One of the disadvantages of this original, particular visualization is that it is very specialized and can only really be used to show amount, direction and hue. Also, a disadvantage of the Wind Rose plot is that subtle differences are hard to see, and that’s why they are best used when trying to demonstrate trends rather than delivering exact numbers. If a viewer wanted to see 2 features such as the percentage of queer actors playing queer roles on each network, that wouldn’t be possible with the visualization as it is now.

Meanwhile, some of the advantages of the chosen visualization is that firstly, it is visually impactful, the diagram is able to display the causal relationship between the network and various measure of LGBTQ+ very clearly since it’s easy to distinguish the difference in length, arc length and color than the angles in each sector as seen in traditional pie charts. Also, another advantage is that we can compare multiple features simultaneously. The author of the visualization was clearly able to show the difference between shows of different lengths on different networks, as that was extremely noticeable while studying Fig 1,

One recommendation I would make is to incorporate multiple datasets over a dimension of time. In this way, the author would be able to use the graphical representation to show a definite change if any, which could aid in proving the conclusions obtained from the graph, the same way that Nightingale was able to depict the various causes of death during the Crimean War over time. As she saw a definite decline in deaths from infectious disease, perhaps a correlation between the validity of these metrics as a benchmark for LGBTQ+ representation in media would be proven.

One possible interpretation of the discussed visualization I regarded was a radar chart. My reasoning was that perhaps an addition of a radar chart with the implementation of some heuristics such as could be applied to sort the variables into relative positions that reveal distinct correlations, trade-offs, and a multitude of other comparative measures. Also given the data in the original figure, it would be a good fit since a radar chart is meant for multinomial data. In this way, we could explore how certain features/data types interact with each and their relationality while currently we are restricted to the relationality between the hypothesis and the data types. Since radar graphs are also used in quality improvement to display performance metrics of computer programs, I believed it would be fitting since this is also a type of quality improvement (sentiment-wise, that is). A couple of limitations upon adding or implementing a radar chart is that in order to more easily compare spokes of different lengths, concentric circles can be added, acting similar to grid lines.

Fig 2. Title: Basis for recommended radar chart

Shiv907 commented 2 years ago

Title: A global look at wealth and happiness Name: Shivanshu Gupta Image Source: https://www.visualcapitalist.com/relationship-between-wealth-and-happiness-by-country/ Background: The graph aims to visualize the relationship between wealth and happiness, by country.

Positives: 1) The color coding is simple and beautiful. 2) Bottom left of the graph is dark grey because the presence of grey countries is more. The top left corner is more yellowish because of the presence of more yellow countries.

Negatives: 1) The graph fails to tell any story. 2) Happiness score (Y-axis) starts from 2.0 whereas Wealth per adult (X-axis) starts from 0 which shows inconsistency in scale. Also further down the graph distance between the $50k and $100k is less than 0$ and $500, which is wrong. 3) Graph captures the political scenario of Lebanon but fails to mention Ukraine Russia war. Moreover, it shows a war-torn country like Ukraine happier than many peaceful countries. 4) The graph only plots data points but fails to show any correlation between money and happiness. 5) The graph captures the political situation of Lebanon but fails to do the same for other countries. 6) Size of the many countries looks very similar to each other although their population sizes are different.

Suggestions: 1) There was no need to comment on Lebanon's political situation in the visualization. 2) Visualization could have used a more evenly distributed scale on X-axis. 3) As the report takes the data from 2022, it should have highlighted Ukraine Russia war as a significant event.

Classwork: 19 Oct 2022 Position: X, Y axis -- Both are Quantitative Data Length: NA Size: Area represents the population Color: Different color coding for countries belonging to a different continents. Shape: NA Orientation: NA Density: NA Texture: NA Volume: NA Connection: NA

promwonka commented 2 years ago

Long-haul-flights-Economist

Name: Pramod Krishna Title: Falling ticket prices for longer flights Source: https://flowingdata.com/2018/12/10/falling-ticket-prices-for-longer-flights/

Aim: Using protractor-like design, they used angles to represent the percentage change in ticket prices and the radius to represent the distance of a flight. It shows how the cost of long-haul flights has changed in recent years.

Description: The protractor features two lines. The blue-coloured lines are for 'trans-Atlantic' flights while the ash-coloured lines are for other flights. There are three axes with two being 'Distance in Km' and the last one being 'change in the price of economy-class tickets'

Cons:

At a look the graph looks very hard to read, it takes some time to understand what they have tried to depict.(Cognitive load is high)
Some lines are overlapping and it's hard to differentiate (Amsterdam- Frankfurt, London Frankfurt)
It's hard to find the percentage change in ticket prices as some lines don't reach till the end of the protractor.
The choice of colour for 'other flights' is not so visible, it blends with the background giving the impression, it's not important compared to 'trans-Atlantic' flights.

Pros:

The graph crunches a lot of data in a rather new and innovative way.
It's easier to find one fact, the prices have fallen since 2014, - they have clearly depicted it by segregating the graph into two parts (more expensive, less expensive)
They have compared equivalent quarters to make the graph, that's a good thing. We don't have to search for seasonal data.

Suggestions:

Use of brighter colors for both types of flights would be better.
A scaled-up version of the graph as a scatter plot would also look good - with distance in one axis and percentage change in price on the other.

Edits:

Color: Only transatlantic is being highlighted, not 'other flights' data , it should also be colored. Connection/Enclosure : The graph doesn't help in identifying in percentage change in the price, as it doesn't go until the markings are there.

Nominal data : Name of flight(route) Quantitative data: percentage change in price, distance of flight

anav99 commented 2 years ago

Name : Anav Bhatia Title : Best Selling Video Game Consoles of All time The visualization helps us to gain an idea of the sales of different gaming consoles by different companies in different regions.

Untitled Positives

A whole list of consoles is covered
The region, company and platform have been clearly divided.
The sales figures are clearly indicated and the length of the black line is decreasing as the sale reduces.

Negatives

The colour is not a good indication of the different companies. It is a random selection of colours.
The flow of the coloured lines is not very clear. It appears as if the lines are randomly drawn and there is no clear pattern.
It has also been mentioned that Sony's second console is the most successful platform in gaming history but it is not clear in what terms the success has been measured.
There is no consideration to the duration of the console sale.

Suggestions

Rest of the world is a very broad term and some other regions can be included.
Year of maximum sale for the consoles can also be included.
There is no need for random lines.
The description at the bottom can be removed.
The duration of the console sale can be included.

SonicShotGaming commented 2 years ago

Name: Priyam Shah Title: Visualized: The World’s Population at 8 Billion

Aim of Visualization: The aim of the visualization is to depict the population of various countries throughout the world, where the countries are grouped according to region. This would make it easy to visualise how big certain parts of the world are, with respect to others.

Critique:

Positives:

The graph is very clean and easy to read
You can tell by just a glance if the population of country is bigger than another country or not
Having flags within the graph can help recognise countries without requiring to read the names of the country
Different regions of the world have been chunked together making it easy to find countries that are close to each other

Negatives:

The shapes used in the graph to represent countries is very abstract and not uniform
The size of the chunks of countries are not drawn to scale with respect to their populations
There is no reasoning behind the colour scheme used in the graph
There could be more information present on the graph, there is a lot of empty space.

Recommendations:

The tiles used to represent countries could be uniform shapes so that it is easy for the viewer to compare different tiles
There should be a colour scheme implemented into the chart so that it is even more intuitive for a viewer to derive something from the visualization
More information could be present either in the same graph, or in separate supporting graphs (Gender, Age, etc…)
The tiles that are given could also be drawn relative in size so it is easier to compare with other countries

Tejasvi337 commented 2 years ago

Name Tejasvi Kumar Singh Title Interest Rate Hikes vs Inflation rate by country Image source https://www.visualcapitalist.com/wp-content/uploads/2022/06/interest-rate-hikes-vs-inflation-1.jpg

About The visualisation aims to map the response of central banks of different developed countries to inflation faced by them. With countries opening up after covid-19, economic activities started increasing. However, the supply side of goods was still not up to its pre-Covid levels. As a result, inflation slowly crept in. Furthermore, Russia’s invasion of Ukraine fueled energy and crisis, worsening the economic situation.

The figure depicts how the central reacted (by raising the interest rate) to inflation. The data presented spans from Jan 2022 to Jun 2022

Pros: The visualisation incorporates within itself some details about the jargon used e.g. information about the policy rate. It makes the visualisation comprehensible even to people not having domain knowledge.

Cons: Two visual representations are employed to signify the passing of time. The first one is the increase in the flag size and the second one is using the arrow

The quality of the arrow lines is bringing down the entire appeal of the graph. When the arrow lines are vertical or horizontal they look good but tilting breaks them into discrete units

Although the visualization aims to convey the response of central banks to tame inflation it fails to capture how aggressively they are reacting. Was it a steady increase over the periods or was it just a one-time increase in the policy rate? Through the text (near UK and US flags) it tries to integrate that information but fails to incorporate it visually

The colour theme the designer opted for the charts could have been better. Since blue and red are the most common colours on countries’ flags, the designer should not have selected these for the background and the outlines.

Recommendations Neutral complimentary colour to make it more visually appealing

The arrow line could have been broken to signify the number of rate hike operations performed. Say the Bank of England hiked the rate 5 times, we could have 5 arrowed lines, with the tail of the next line on the head of the previous one

Mapping data properties to visual properties

In the graph, the major data features are country, policy rate (before & after) and inflation (before & after). The plot encoded the label (country), which is a nominal data type, of each point using that country's flag. Since there are only 10 data labels it does not create an issue. The authors of the graph went for the scatter plot to visualize the data which is appropriate given the nature of the data (policy rate and inflation are each quantitative data type).

kishlaykumar1995 commented 2 years ago

Name - Kishlay Kumar Title - The Inflation Factor: How Rising Food and Energy Prices Impact the Economy Source - https://elements.visualcapitalist.com/wp-content/uploads/2022/08/VCE-How-Rising-Energy-and-Food-Prices-Affect-the-World-Economy_Aug-30.jpeg

VCE-How-Rising-Energy-and-Food-Prices-Affect-the-World-Economy_Aug-30

Background Since Russia’s invasion of Ukraine, the effects of energy supply disruptions are cascading across everything from food prices to electricity to consumer sentiment.

In response to soaring prices, many OECD countries are tapping into their strategic petroleum reserves. In fact, since March, the U.S. has sold a record one million barrels of oil per day from these reserves. This, among other factors, has led gasoline prices to fall more recently—yet deficits could follow into 2023, causing prices to increase.

With data from the World Bank, the above infographic charts energy shocks over the last half century and what this means for the global economy looking ahead.

Critique Positives

The use of images to represent the challenges faced by the oil industry today aligns well with the mental model of the user.
The flow of the visualisation(inflation rates followed by causes followed by ripple effects) effectively conveys the message in the article.
The use of minimal and consistent colors is aesthetically pleasing and reduces the load on the mind.

Negatives

Global Growth Scenarios in ripple effects uses a mix of shape and color coding which is both unnecessary and confusing at times. Recommendation: Use either color or shape to encode a single piece of information.
Also, Global Growth Scenarios in ripple effects doesn't mention if the 4 different lines represent the cumulative effect of all factors combined or of each factor individually. Recommendation: The above information can be mentioned somewhere beside the legend.
Rising Global Inflation bar chart in ripple effects doesn't give any information about how many countries are being evaluated under each category. Recommendation: The above information can be mentioned alongwith the legend.
Rising Food Insecurity and Social Unrest graph in the ripple effects section shows overlap between the different regions(in the Venn Digaram) even though their values are independent of each other and is somewhat misleading.
Rising Food Insecurity and Social Unrest graph only shows the number of people facing food insecurity but doesn't tie it to social unrest in any way.

Mapping of visual data elements

The first graph represents 3 types of data -
1. The data on the Y-axis is continuous percentage data of monthly inflation and has been plotted using a line chart and an area chart.
2. There are 2 categories which have been represented using using different colours.
3. The data on the X-axis is interval data which is the years through which the inflation percentages have been plotted.

All the above elements seem to be correctly encoded.

Rising global inflation section in Ripple Effects has 2 categories separated by colour with continuous data of percentage of countries exceeding inflation targets plotted on Y axis against discrete interval year data on the X axis(even though it acts as individual categories in this graph to make the bar chart possible).
The slower global growth section in Ripple Effects shows continuous percentage growth data with 4 categories represented using 4 different lines with separate colours plotted along the X axis against year on Y axis similar to previous graph

vanshu2k commented 2 years ago

Name: Vanshu Saini Title: Countries that voted in favor, against favor, and abstained from suspending Russia from the UN’s Human Rights Council 21321

Source: https://www.visualcapitalist.com/russia-has-been-suspended-from-the-un-human-rights-council/

Background:

93 countries voted in favor of suspending Russia from the UN’s Human Rights Council, including all NATO member countries
24 countries voted against the resolution, including; China, Iran, and North Korea

Positives:

It highlights some of the countries statements why they are not in favor of suspending or why they choose to abstain from Russia from the UN’s Human Rights Council.
The visualization is simply clean and aesthetic to the eyes.
It is able to depict the whole context of which countries are in favor of Russia, who are not, and who are choosing to remain to abstain for the moment.
It has all countries flags that have the right to vote, neatly packed up in an image.

Negatives:

The box that contains all the NATO countries is not bold enough to catch the eye.
Other countries that voted in favor of suspending Russia from the UN’s Human Rights Council or chose to remain abstained have highlighted a few countries while pointing an arrow to them which doesn’t signify anything related to the suspension.
It contains all the countries flags in a circle shape in which names are given to only those which are on the extreme left or right which has space left and it shows randomness in the visualization.
The font color that is used for depicting the numbers in contrast to the text is not bright enough for the user to get their attention.
It is hard to differentiate which countries are in favor of Russia, which are not, and which are choosing to remain abstained by seeing each countries flag individually without names.

Suggestions:

The box which has NATO members could have been bold and brighter.
The text color could have been done better by using bright white color fonts in the number & using little lighter ones on texts.
It could have contained a color notation of two main words “Suspend” and “Against” as Green and Red for more clarity between half of the visualization.

Visual Properties:

• Circle has been used to represent nations. • To distinguish each data, the flag of the nation has been put in every circle • There are four different blocks into which the countries have been divided with their vote. • Numerical values in front of the block represent the number of votes for that particular block. • Color coding is used only between the main title and the rest of the graph to distinguish them. • Labeling of all the nations is not done throughout the visualization.

vrk7 commented 2 years ago

Name: Vysakh R K Title: The Minority Majority - A diverse America, shaped by immigration Link: Foreign-born share in the US Source of the image: Twitter

ImmigrationGraph2LN-superJumbo

The above graph shows the immigrant's population share over the years. From the graph, the following can be inferred:

Congress removed country-based immigration quotas, resulting in a significant rise in the immigrant population of the U.S.
In light of the release and removal of country-based immigration quotas, certain countries like Canada, Ireland, and Germany had fewer immigrants in the U.S.
Another title that matches is – “Do quotas affect diversity”?

Critique: Positive thoughts which struck me while going through the graph:

Clearly shows, based on what factor the immigrant population raised.
Very evident from the number of foreign nationals, from where immigrated people are high to the US (Europe).
Clearly mentioned the reason why the unexpected dip and rise in the slope of the graph.

The following are the few details that I consider to be backside for the static graph given above:

The graph hasn’t shown what the Y-axis represents, which will cause huge confusion among the observers.
What do the lines in the coloured section indicates? This is creating greater confusion.
The graph hasn’t mentioned the closeness among the free line in the graph, does it mention any new parameter or another factor that tells the metric of the graph?
The graph hasn’t explained the type of immigration that the dataset has considered, legal or illegal. This factor is very important as during the time duration of the 1930s and 1970s, the amount of illegal immigration was at its peak. Thus, without giving proper distinguishing, the data cannot be visualized to a better extent.
It hasn’t mentioned what set of populations makes up the sample size of the data under consideration.
It’s shown the Y-axis shows the percentage of some data. Even if logically thinking, the Y-axis is considered to be the percentage of people, still the question of what percentage of people constitute 1 percent of the population?
What does “OTHER” in the graph signifies? Is it just Australia or all others which are missing?
Where is the distinction between Germany and Ireland in the figure? Is it just Germany and Ireland contributing or are there other countries as well, which provide some sort of contribution?

My suggestions for rectifying some of the backsides mentioned above:

The legends need to be taken for, like what do X and Y axis specifies.
Explain the need for the free hand-drawn lines in the colored region in the graph specifies. If this would have been done, a great lot of confusion could have been reverted.
The sample size of the population should have been given clearly as that means a lot when doing the statistical study of any dataset. Only by providing this information, a proper generalization can be made.
Clear distinction between the type of immigrant is to be given (illegal or legal).
Provide proper annotations, for example, what “OTHER” signifies in the graph.

Edits: Date - 19/10/2022 Classwork

Seeing the graph given above, the data types can be categorized into the following:

Nominal data: Continents (all except Australia)
Quantitative: Absolute percentage of immigrants who came from various parts of the world to the US has been made visible.
Ordinal data: The legends are used clearly to distinguish the various continents in the graph.

Data Type:

Distinct colour has been used to distinguish various continents which seems to be a very convenient way to differentiate between themselves. This is highly helpful than using the same colour of different contrast.
The continents are stacked based on the percentage of population share in the US.
The continuous graph represents the population share of various immigrants in the US population.
The thickness of the plot of various colour (signifying the area), tells us the highest percentage of immigrants in that particular year.

Tamanna134 commented 2 years ago

Name: Tamanna Sahu Title: The 3 Billion People Who Can’t Afford a Healthy Diet

Screenshot 2022-10-17 225223

Background of the image:
This figure depicts the percentage of the population of various countries which can’t afford a healthy diet.

Pros of visualizing the data this way: The color coding shows the intensity to which a country’s population suffers in affording a healthy diet. By looking at map with color coding, we can observe which color is concentrated at which place.

Cons: It’s hard to go to each country’s map and then reading the data for it. It might happen that someone is not good at atlas. It’s also not that effective reading numbers this way. It would have been better to visualize this data by comparing data using bar graphs. Moreover, area covered by various countries is different letting many small countries go unnoticed.
The other drawback related to this data visualization is that criterion for each country’s household income is different along with food-based dietary guidelines. So there is no uniformity related to guidelines.

Visual Properties: -> Position of points is not presented instead the percentage of population deprived has been depicted on map with the nation. The bar graph comparing nations would have been better with nations as labels on x-axis and percentage on y-axis. -> The geometric surface which has been used is same as the mapping of any nation on the atlas. -> There is no particular defined uniform shape for every data as each nation has different shape and area. -> Color coding has been used to differentiate intensity of data. -> The darkness of color increases with percentage. -> Color of particular nation is uniform throughout in that area.

GaneshBShelke commented 2 years ago

Ganesh Shelke

Title – Ukraine’s Top Trading Partners and Products

This pie chart represents Ukraine’s top 10 trading partners and the top products they trade with other countries.

Critique –

Positives:

The visualization uses only two colors (import and export) which makes it easier to focus on key parameters.
There is a clear separation between top countries on one side and top products on the other side in visualization.
The percentage of imports and export (amounting to 100%) is mentioned for top partners.
Annotations make it easier to relate to that particular parameter.

Negatives:

The visualization does not show us the percentage of imports and exports with top countries individually as this would have given clear idea of how much the particular country trades with Ukraine in its total trade.
The pie area makes it difficult to comprehend when we compare the trade between two countries China and Germany. (It seems trade with China is much more than with Germany which in reality is just double that of with Germany.)
The symbols used to give key information are not properly visible.
The Ukraine map is not required as it is not giving any useful information and is just adding to the space.
The part inside the inner circle is redundant and could be removed.

Suggestions:

Some more prominent colors for annotations would make it easier to read the text.

Image source - https://www.visualcapitalist.com/visualizing-ukraines-top-trading-partners-and-products/

Visual Properties:

Position: The countries and products are separated properly on two sides of the visualization.

Length: Easy to compare the thickness of a particular country’s trade with that of other countries.

Area: Area for China’s trade seems misleading as compared to that of other countries.

Color: Only two colors are used to show imports and exports respectively which lessens the cognitive load.

Shape: Only one kind of shape (pie).

asheesh4545 commented 2 years ago

Name: Asheesh Kumar Singh Title: A view on Dispair Link: Link

dispair

Background : This Visualization has been created by Sonja Kuijpers and considers the data points about the different ways in which people committed suicides in Netherlands in 2017. Each category of suicide is being represented by a different element of nature such as cloud, trees, buildings, waves and underwater grasses.

Positives :

The Visualization has been done in a way that relates to a personal story and insights about sensitive data such as suicidal data. Visualizing such unfortunate data about people using natural symbols like stars, grass and clouds does complete justice to it.
It smartly retains data about the disturbing ways in which people might have committed suicide by representing those categories with stars and underwater grasses. The author refrained themselves from explicitly mentioning the undesirable ways of suicide which could have been disturbing to a specific audience.

Negatives :

At the first glance, the color contrast between the clouds and the atmosphere is hardly distinguishable. Also, the symbol of drowning (suicides caused by drowning) in the form of waves is hardly visible due to the fact that the colour of water and waves being the same.
One important piece of information which is explicitly not mentioned in the visualization but the author mentioned it explicitly in the article is that the size of all the symbols used here are relatively proportional to the age of that person who committed suicide.
In addition to that, the area above the buildings i.e. the bluish sky along with the purple area that consists of the clouds consumes a major section\height area of the entire visualization. Due to which the data points represented in the form of stars are hard to notice and can be often ignored.
Though this sensitive data has been captured quite brilliantly, at times, it is quite difficult to get an idea about the number of different ways in which people lost their lives.

Mapping :

Position : No axis Data Type : Quantitative(Discrete) and Categorical Color : The colors used perfectly fit in the scenery in order to represent a story behind the visualization. Shape : The icons / labels given on the right side are descriptive enough and easy to understand.

walia-muskaan007 commented 2 years ago

Name - Muskaan Walia Title - Arms Transfers: U.S. and Russia’s Biggest Trading Partners Image Source - https://www.visualcapitalist.com/cp/arms-transfers-u-s-and-russias-biggest-trading-partners/

Biggest-U

The above visualization shows the top 50 countries that imported arms from the United States and Russia between 2011 and 2021.

Critique -

Pros -

The visualization is successful in showing the biggest exporters of arms in the world - the proportion of the United States is higher is visible.
The proportion provided to each country is visible, with Saudi Arabia importing the highest number from US and India importing the highest numbers from Russia.
The icon of the flag beside the country name is somewhat helpful.

Cons -

The countries where Russia and US exported their arms are not depicted well and need to be more visible.
The countries importing the arms are not evenly spread. The country exporting the weapons are also misaligned. The width of the connecting bar depicting which country has a huge number of exports is not conveying the relevant data.
- Suggestion - It would have been better if both countries (US and Russia) would be centre aligned with the rest of the information spread evenly across the chart. Also, if the size of the flag with the greater number of exports would be bigger, it would have been easy to interpret.
The colour palette used to differentiate between different continents is a bit confusing, as the warmest colour seems to have gone home with the largest deal.
- Suggestion - It could have been better if different colours from different palettes could have been used (Eg: rainbow sequence).
At the first glance, it seems like an arms transfer graph with the arms transfer taking place within the country itself, and the list on both ends of the page appears to be the states of the two countries.
On zooming in, we come across some common countries which could have been avoided, which seem to occupy a lot of space.
- Suggestion - The common arms importers of the USA and Russia can be aligned in the middle in the area of intersection between the two countries.
The graph shows only the legal transfers taking place.
The width of each line does represent the size of the arms transfer, but it still does not represent the number of arms transferred. Also, representing the countries that are importing arms from their respective continents does not convey relevant information.
- Suggestion - Color coordinating the countries which are importing arms based on the number would have been easier to comprehend. (Eg: Maroon being the number of exports greater than worth 50 million dollars).

Data for Visualization - Assignment on 19/10/2022

Visual Property - The graphical elements used in the graph are really fine. The representation of each country with its flag is easy to interpret.
This is a categorical discrete representation of the rams transfer, hence there is no x-y axis representation.
The width of the lines represents the cardinal number of arms transferred but there is no numerical representation of how many arms were transferred. If the color coding could be used to signify the range for assets transfer.
The color coordination to illustrate which country belongs to which mainland is not at all relevant, the graph would have made understandable of the texture (hue) and density could be used to represent the range of fortune.

SumanthSonnathi commented 2 years ago

Name: Sumanth Sonnathi Title : Mumbai Dabbawallas Link: https://books.openedition.org/obp/1330

Dabbawallas

Description: Mumbai (also known as Bombay), the city known as the commercial capital of India always teeming with traffic. Public busses, cars etc., moving at a snail’s pace makes commuting a tough job which has the huge impact on food delivery. But still dabbawallas deliver the lunchboxes on time through the winding streets of the city and a solid history over hundred and thirty years.

Critique: -> Graph clearly indicates the increase in number of customers and dabbawallas over the years and capturing data for more than a century helps in clear analysis. -> There was a decrease in the number of customers in 1980’s due to the Bombay’s progressive social and economic reconfiguration. -> It would have been even better if the graph would have appropriate color , the % change and income details of dabbawallas.

Mapping Data properties to Visual properties:

Position: X-Axis represent the year appropriately but Y-Axis is used twice to represent the no. of Customers and Dabbawallas Length: Lines are of the same thickness Size: Graph size is compact. Enlarged graph would represent data in a better way. Color: Color seems to be odd and looks similar Shape: Shape is common and appropriate Density: Color saturation is low and not appropriate

ShivayNagpal commented 2 years ago

Name: Shivay Nagpal Title: To the Moon & Back: National Toilet Paper Usage Visualized Across the Universe Source: https://www.qssupplies.co.uk/world-toilet-paper-consumed-visualised.html

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Background: QS Bathroom supplies, a British wholesale and retail bathroom accessories company, published this infographic to help visualise the scale of toilet paper usage per country. The infographic uses distance between celestial bodies to put into perspective the amount of consumption, and further comments on the amount of trees required per country for the task of creating the paper.

Critique:

Positives:

The infographic uses flags on top of each country's header. This helps in lowering the cognitive load of the reader as they can identify the countries without reading their names.
The paper like bar plots aid the visualising process and make it more intuitive.
The value of each country's consumption is given in both metric and imperial units, making the infographic's user base more universal.
The graphics of the celestial bodies preserve the colours of their real-life counterparts, helping the infographic align with the reader's mental model.

Negatives:

The extremely tall height of the infographic makes it hard for the reader to absorb information without zooming and scrolling. A logarithmic distance scale would have been able to convey the same information without making the graphic so tall.
The infographic does not provide the distances to celestial bodies data in metric units, unlike the individual bar plots themselves. This is a inconsistency that might confuse a reader who understands only metric units.
The graph roughly resembles a country population plot (without a few outlier countries like India who do not use toilet paper as much) and does not really provide a lot of information on the usage trends per country. Additionally providing per capita consumption would have been helpful in putting into perspective the consumer behaviours from different countries. for eg, visualising consumption per capita per day might be easier for a reader to comprehend and learn about country-wise consumption. This data could be provided in the free space on the lower section of the infographic.

Mapping Visual properties to data properties: The length of toilet paper used (data type is quantitative) is represented by the height of bars. The nations (data type is nominal) is represented symbolically by using flags.