Redesigning Reuters Data Stories (2024)

[venkatrajam]

For this assignment, we'll use data stories from Reuters Graphics.

Select a story that you like, study it carefully and redesign it. Specifically I want you to focus on understanding the data that powers the story, and how it is visually encoded to tell the intended story. Document your design process, capturing the following:

• What is the story the author is trying to tell?
• What the data he/she is using to tell the story? Describe its details -- type of data, extent of the data, dimensions of the data, gaps in the data, what data is essential and what is irrelevant.
• How is it encoded, problems with it and how you attempted to improve it.

You may choose to expand or curtail the scope of the data used in the story, or add an additional dataset to tell the story better. But do not deviate from the main intent of the original story. In other words, it is a redesign exercise, and hence I do not want you tell a different, unrelated story.

While you should provide a link to the original story, it might be useful to capture and display inline, appropriate parts of the original visualization, and your own design iterations to produce a coherent documentation.

For reference, take a look at what the previous batches (2019, 2020, 2021, 2022, 2023) did with this assignment. They worked on The Hindu Data Point articles which have become rather less challenging and increasingly harder to access.

[Sydney1306]

Nitya Vyas, 23M2248 https://www.reuters.com/graphics/MCCORMICK-VANILLA/010092KE3B8/

A Scarce Flavour

Updates on the link: https://sydney1306.github.io/datavizvanilla/

[kimayaitkarkar]

Kimaya Itkarkar 22b3635 Vaping crisis in the US Existing Design: https://www.reuters.com/graphics/HEALTH-VAPING/0100B4RB2H6/ Link to redesigned article: https://usavapingcrisis.my.canva.site/

[vijayanand-25]

Vijayanand Banahatti (23D1322)

Cricket’s mammoth Indian Premier League By Sumanta Sen - Published May 22, 2024 https://www.reuters.com/graphics/CRICKET-IPL/klvynexrypg/

=========================== Phase 1 - Issues in existing visualisation ==========================-

============================ Phase 2 - Redesign thougths and reasoning ===========================- Viz-1 and Viz-2 needs to be combined as Viz-1 space could be used better by conncting with Viz-2, Also, Viz-2 is just a bar graph of a few full member profit numbers, so we can use the data of cricket board revenue for all ICC full members to make it complete (this data is available). We can also show the population of ICC full member country which is one of the reason for higher revenue (which is from broadcasting rights which is directly related to viewership).

Viz-3 shows the IPL brand value over the years but this data is showing the numbers over years. I thought of using internet penetartion after launching digital India inititive to show the possible reason for increase in IPL brand value after 2016.

Viz-4 and Viz-5 has same data shown in different format (so needs to show the imapct of IPL). The rise of IPL in short duration compared to other league needs to be highlighted. I thought of using the league age and current broadcasting rights value to show the growth direction. The growth direction steepness OR flatness shows the imapct it has over time.

The Viz-6 and Viz-7 shows the playing window of league and ICC matches in a separte way but its all connected so, planning to use only one vizualization. I wanted to make this vizualization circular as its year long data and repetation happning every year. Planning to use clock anology and cricket ground is circular !

The Viz-8 shown men and women league launch years for 4 countries. And i want to add more details about the number of teams in the league and the delay % in lauching women league compared to mens league.

The Viz-9 shows the ownership of teams by IPL team owners in other league. In this visualization there are missing details about the number of teams in the given league, how many are owned by IPL team owners. How many other league IPL team owners do not own etc. So trying to show the owner at the center and other teams revolving around them. (i also thought of circle like network graph but then the connection overlapt is on one side and confusing.

============================ Phase 3 - Redesigned page with new visualization (No text is changed as some image may need modification to text ) ===========================- Original story: https://www.reuters.com/graphics/CRICKET-IPL/klvynexrypg/ Redesigned story (only visualization): https://imaginative-operator-614138.framer.app/getting-started

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Tools used: Flourish Studio, Datawrapper , MS Excel, Figma

[TejalK22]

Tejal Kalgutkar 24M2525

THE STORY

Changing Tides By Jackie Gu, Published March 30, 2023 The first international agreement to protect the world's oceans aims to create “international parks” in the high seas.

The story is about a new international treaty Biodiversity Beyond National Jurisdiction treaty (BBNJ) that aims to protect the high seas—parts of the ocean that don’t belong to any one country. It highlights the importance of this agreement, which is meant to save marine life and stop the damage caused by overfishing, pollution, and climate change. The BBNJ treaty is a big deal because it’s the first time countries have come together to protect such a large part of the ocean, which makes up two-thirds of the planet.

DATA BEHIND THE STORY

Redesigned

Exclusive economic zones (EEZs) The Red List Index of species survival for animals in different habitats Marine Protected Areas

[HarshAgrawal04]

Harsh Agrawal 22B3629

Cricket’s mammoth - Indian Premier League

Redesign

Combined the above two charts to make a new viz

[Anumeha7]

Anumeha Patoria 23m2244 https://www.reuters.com/graphics/CLIMATE-UN/EMISSIONS/jnvwexaryvw/

Updated Prototype - https://anumeha7.github.io/datavisualization/

[yashrawat458]

Yashwant Rawat 23M2254 AI Rally Tracker - (https://www.indiatoday.in/interactive/immersive/ai-rally-tracker-pm-modi-rahul-gandhi-lok-sabha-election-speech/)

This article talks about all the rallies that Narendra Modi and Rahul Gandhi did for Lok Sabha Election 2024. The article lists the rally in a chronological order with an AI generated word cloud to show the most prominent words.

Data collection - Rally List / Phase of election

(Progress) Phases of election on Indian Map -

(Final)

High Res. files - Click Here

[TanmayKuwalekar]

23M2245 Tanmay Kuwalekar

In a flash, a changed world

https://www.reuters.com/graphics/WW2-ANNIVERSARY/HIROSHIMA/rlgpdnqljpo/

The article offers an overview of the Hiroshima atomic bombing and its role in raising global awareness of nuclear disarmament. It traces nuclear tests conducted from 1945 to 2019, providing data on which superpowers performed these tests, the number of tests per year, their intensity, and related treaties signed by nations. Key milestones in the history of nuclear testing are also highlighted, emphasizing international efforts to control and reduce the use of nuclear weapons.

Rationale for Redesign

The existing visualization effectively shows test intensity but makes tracking the timeline difficult due to its fragmented, scrollable format. The redesign adopts a sunburst visualization approach, offering a comprehensive view of the timeline and highlighting the nuclear activity of eight key nations.

Redesign Approach

The sunburst format visually represents how nuclear tests expand outward from the center, mimicking the spread of a bomb's impact. This design enables users to see the entire timeline at once while focusing on key information, such as the year-specific tests and the contribution of nuclear-powered nations.

Redesign

Dark Background

Light background

High Res images Figma Prototype: https://www.figma.com/proto/sqAJlBY9CXti9dno3qPBgN/Data-Viz?page-id=228%3A44000&node-id=228-44010&node-type=frame&viewport=984%2C773%2C0.24&t=bbKgapB8gUTvHYEJ-1&scaling=min-zoom&content-scaling=fixed Flourish: https://public.flourish.studio/visualisation/19505391/

Data sourced from following sites: https://www.armscontrol.org/factsheets/nuclear-testing-tally https://ourworldindata.org/grapher/number-of-nuclear-weapons-tests https://www.statista.com/statistics/264435/number-of-nuclear-warheads-worldwide/#:~:text=Number%20of%20nuclear%20warheads%20worldwide%202024&text=There%20were%20approximately%2012%2C100%20nuclear,the%20United%20States%20and%20Russia.

[gouraang-gune]

Gouraang Gune - 22B3632

Vaping Crisis in the US Potential for Redesign

1. Use a traditional US map or a cartogram instead of this simplified version in order to give viewers more clarity.
2. Use a different colour scheme for cases and deaths.

Add interactions to show how the weekly cases progress over the year.

1. Experiment with different grouping.
2. Try to show in only one graph.

1. Not sure if it's because of my internet connection, but this scrollytelling animation seems to glitch often.
2. Try to show in an isometric breakdown to make it look more interesting.

Using US map with numbers and colours instead of state names would be more effective.

Redesign

This map is interactive, which solves the major problem of viewing state wise data while also maintaining the simplicity of a hex map. Interactive nature of the map also gets rid of the need for a legend.

Original data did not have a visualisation in the first place.

Despite being a proper percentage tally, which adds up to 100%, the original visualisation was represented in a non intuitive way.

Instead of showing multiple ages (8th, 10th and 12th grade) I chose to only focus on 12th grade, which allowed me to effectively show the variation in trends, particularly how vaping of Marijuana has overtaken vaping of flavouring.

Most of the data visualised in this section of the article is outdated and hence unnecessary. However, I managed to find data regarding the TYPES of taxes imposed by various US States on E-Cigarettes. I chose to encode this data using a cool colour scheme instead of the warm colour scheme which the rest of the article follows, as this is talking of measures used by State Governments to reduce usage of E-Cigarettes by imposing taxes and thus, using a cool colour scheme resulted in a more semantically accurate visual encoding.

The redesigned article has been linked above.

[Adith3608]

Adith M Sajeev - 22b3625 How a deadly bat virus found new ways to infect people

https://github.com/user-attachments/assets/354300cf-1e3f-4cb8-8b5d-9140c4c9d1ae

[evana23m2242]

How children starve

Original Article : https://www.reuters.com/graphics/ISRAEL-PALESTINIANS/GAZA-HUNGER/myvmakwxrvr/

What is the article about ? The Reuters article focuses on the growing food insecurity and malnutrition crisis in the world . It highlights how years of conflict, blockades, and economic collapse have pushed a significant portion of population into hunger and severe food shortages. The story is told through the lens of the ongoing Israeli-Palestinian conflict.

What the Reader Should Understand:

• The scale and severity of the hunger and malnutrition crisis.
• The long-term effects of the conflicts on basic human survival, particularly in terms of food and health.
• The urgent need for international intervention and aid to address the growing humanitarian crisis in the region.
• The human cost of the political and economic blockades, especially on vulnerable populations like children.

Types of Data Visualized

• Food Insecurity Levels: Categorization of the population by their levels of food insecurity (mild, moderate, severe), potentially shown as stacked area charts or segmented visualizations.
• Child Malnutrition Rates: Qualitative Data on the effects on children in Gaza suffering from malnutrition over time.

Data Source : https://www.ipcinfo.org/ipcinfo-website/ipc-dashboard/

Redesigned Visualisation - Quantitative Data interactive prototype : https://public.flourish.studio/visualisation/19503924/

Watch video : https://github.com/user-attachments/assets/e159b4eb-1f0c-4e9c-ba33-c405c50f853c

Redesigned Visualisation - Qualitative Data interactive prototype : https://www.figma.com/proto/Sc6MuypYoP8c0aX7RIzHO3/Dataviz?node-id=185-133&t=gUMLTHK1VXNMI1Qm-1

Watch video : https://github.com/user-attachments/assets/4bd1eb29-7afe-4feb-8906-62a59031ddb3

[saikatb1swas]

The Unstoppable Pop of Taylor Swift idea of a better visualisation (rough)

(Redesign): Prototype here Not Complete, WIP

[nachiketnanoty]

https://www.reuters.com/graphics/world-coronavirus-tracker-and-maps/countries-and-territories/india/

https://www.reuters.com/graphics/HEALTH-CORONAVIRUS-USA/0100B5K8423/

https://www.reuters.com/graphics/USA-ELECTION/MIGRATION-DEPORTATIONS/akpeoeoerpr/

[AnkitaThakur1309]

The hottest year: How heat fueled extreme weather in 2023

Original: https://www.reuters.com/graphics/CLIMATE-UN/WEATHER-EXTREMES/zdvxrmeakvx/

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Redesign: Video: https://github.com/user-attachments/assets/33815e4d-5bef-42ed-a888-bd91a68fba78

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Redesign: Video: https://github.com/user-attachments/assets/a9fcf786-9221-4632-b446-0b1f3f0ee744

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Redesigned Link to the page with redesigned visualization

[hayat-tamboli]

Hayat Tamboli - 23M2249 A data visualization of Bollywood movie data https://man-shar.github.io/BollyBlues/bolly.html

Link to prototype: https://data-vis-ten.vercel.app/

[antibodhi]

Sidharth Goutham 23m2252

India election results 2024 https://www.reuters.com/graphics/INDIA-ELECTION/zgponbzllvd/

Prototype - https://sites.google.com/view/election-dataviz/home

[KeSaanu]

Kyawsaanu Mog | 22B3609

Biden is now deporting more people than Trump

Understanding the story - The article highlights how both President Joe Biden and former President Donald Trump have pledged to return more migrants to their home countries, albeit with differing approaches. It emphasizes that Biden has now surpassed Trump in the number of deportations, particularly in the fiscal years 2023 and 2024

Data Used:

Numerical Data: The article provides figures on the total number of deportations conducted by Customs and Border Protection and Immigration and Customs Enforcement.

Time Series Data: This data spans from fiscal year 2009 to 2024, showing trends in deportations over time.

Categorical Data: The data is categorized by the type of deportations, such as those conducted by different agencies.

The Issues with the current visualization - While gathering data to use for the new design I realised that the data used is either wrong or incomplete as the graph here does not take account into title 42 which is a reason behind a lot of deportation during and post covid. The article is trying to represent that in a lot more immigrants have been deported in Biden's term. But when we look at the data with Title42 in mind we realise that though most of the expulsion during 2021-22 was due to Title 42 which was during Biden's term yes, but the provision itself was applied during Trump's term (it is to state that the provision was also continued during Biden Administration). Though all of this is written in the article, this all is not mentioned anywhere in the data visualization which can be misleading. Another thing was that they differentiated which agency has done how much deportation. I do not think it matters in the long run which agency is deporting, what stands is that immigrants are been deported

Redesign -

[subirust]

Subir Mondal https://www.reuters.com/graphics/JAPAN-YEN/EXPLAINER/xmvjnxjmbvr/

JPYUSD vs NIKKEI 225 Index

Japan import vs JPYUSD

Japan export vs JPYUSD

data source: https://tradingeconomics.com/ method of data extraction https://www.graphreader.com/

[yashkaranjavkar]

Yash Karanjavkar | 23M2250

ISRO Missions 1975-2022

Original: https://www.isro.gov.in/Timeline.html Inspiration from: https://www.reuters.com/graphics/NORTHKOREA-MISSILES/TESTING/byvrllmjmve/

Link to prototoye: https://sites.google.com/iitb.ac.in/vizro?usp=sharing

[Khushboo768]

Khushboo Kumari | 23m2251

The Collapse of Insects https://www.reuters.com/graphics/GLOBAL-ENVIRONMENT/INSECT-APOCALYPSE/egpbykdxjvq/

Watch/ Download Video: https://drive.google.com/file/d/1Z_a6X1apHvjTcNYrYT5BW2YXZI2qZ2LO/view?usp=drive_link Interactive Prototype: https://public.flourish.studio/story/2619256/ Interactive Prototype Option 2: Discovered V/S Accessed Species

Data Sources:

1. UNEP
2. Xerces Society for Invertebrate Conservation
3. Animal biodiversity: An introduction to higher-level classification and taxonomic richness.

[M4Y4NK-V3RM4]

Mayank_verma - 22B3608

Original article Two airlines dominate India's growing market

Process and outcome

[ArjunC-1]

Arjun Chawla-22b3624

Ebola Epidemic-https://www.reuters.com/graphics/HEALTH-EBOLA-CONGO/010081G62NR/

.

Through this visualisation, the designer has attempted to communicate 2 distinct dimensions of the virus spread:

1. Reproduction number i.e. the number of people each patient goes on to infect (how fast the virus spreads)
2. Mortality rate i.e. percentage of infected population that die

In an attempt to emphasise how devastating Ebola is, the prominent visualisation is mortality rate. This however does not take in to account the rate of spread of the virus. The sample size used is 250, which makes it harder for people to read (167/250 vs. 67/100)

This can be improved through a more intuitive visualisation which takes into account the speed of the spread, which would give a more balanced view of the epidemic. . . .

This is the “ring vaccine strategy” in which the vaccine is administered only to the people most at risk- the people (directly or indirectly) in contact with a patient.

While it does effectively communicate how the strategy is meant to stop the spread of the virus, it also make it seem like the people vaccinated are only those who have been in direct contact with the patient, which is not the case. In reality it also includes people such as healthcare and frontline workers, extended family members and neighbours.

This visualisation can be improved by using imagery/iconography for households, families, neighbours, hospitals etc, which would reduce its overall read time. . . .

Redesign: The point this visualisation is trying to convey is that mapping the spread of the virus is extremely challenging due to its spread-zone having heavy overlap with conflict areas.

The original visualisation uses the area of a circle to denote the number of conflicts at a place. The large overlapping circles obstruct borders on the map and make it very difficult for readers to see the ebola zone itself. It also misleads readers into thinking that it denotes the zone of conflict, even covering a lot of area that is conflict-free (such as Rwanda).

In the redesign, the ordinal colours (red, orange, yellow) form a kind if a heat map, accurately denote the points of conflict and their intensity. Readers can also clearly see the ebola zone as well as its overlap with conflict points.

[manyawww]

Manya Wahi 22b3618

How Trump has transformed refugee admissions https://www.reuters.com/graphics/USA-IMMIGRATION-REFUGEES/010080770F0/

[namit900]

Namit Tirkey 23M2256

The annual spikes in popularity of Christmas Songs focusing on Mariah Carey's "All I Want for Christmas Is You.": https://www.reuters.com/graphics/CHRISTMAS-SEASON/MUSIC/byvrrzgjlve/

[Yuva306]

Yuva Gharat 22b3612

Tokyo Olympics 2020 Athletics https://www.reuters.com/graphics/OLYMPICS-2020/EXPLAINER/rlgvdyxxlpo/

Tokyo Olympics 2020 Fencing https://www.reuters.com/graphics/OLYMPICS-2020/EXPLAINER/xlbpgawjgpq/

[pujapdas]

Puja Das M.Des. By Research, 24M2526

[Risn7]

Aman Singh 22b3633 https://www.reuters.com/graphics/AFGHANISTAN-QUAKE/jnpwwbxyqpw/ Story Intent The article from Reuters aims to illustrate the devastation caused by the earthquake in Afghanistan, focusing primarily on its human and geographical impact. The author tells a story of both tragedy and response, using the event as a lens to expose broader issues of fragility in a country already grappling with significant hardships. The visuals are used to support this narrative by providing data on casualties, geographical damage, and the aid provided.

Data Analysis The data used includes: Geospatial data – to pinpoint the epicenter and affected regions. Casualty statistics – focusing on the number of deaths and injuries. Comparative data – showing historical earthquakes and their impacts. Relief efforts – detailing countries that provided aid and the level of international response. The data types span across quantitative (casualties, aid relief amounts) and qualitative dimensions (geographical details, descriptions of relief efforts). The essential data includes casualty numbers, earthquake magnitude, and relief details, while some comparative historical data on previous earthquakes, though interesting, could be streamlined.

Visual Encoding Currently, the visual story combines geospatial maps with overlays of the affected areas, comparative bar charts of past disasters, and a timeline of relief efforts. Problems include: Cluttered visuals: There’s a lot of information in one place, making it harder to focus. Color use: The color scheme could be improved for better contrast and clarity. Lack of interaction: It would be helpful to include more interactive elements, such as hovering over regions to get precise data.

Proposed Redesign Simplify the map: Focus on the most severely affected areas and use a contrasting color palette to differentiate between impacted regions. Use clear, bold markers for major epicenters and relief sites. timeline: A timeline showing the earthquake and relief efforts would help users track developments over time more effectively. Add interactivity to data: Allow users to hovering data over the map of different elements, such as casualty numbers or relief efforts, for detailed pop-ups explaining more nuanced aspects like the demographics of those affected. Infographics for historical comparisons: Instead of complex bar charts, use simple, intuitive icons or graphs to compare this earthquake with others. This would reduce cognitive load while still communicating historical context.

The data i will be working on-

Redesigned I chose a 3D map for a more intuitive representation, highlighting earthquake-affected areas with different colors to indicate varying intensities. I also improved the clarity and visibility of the legend, positioning it prominently in the bottom left corner. Additionally, I created a simplified map of Afghanistan that marks the earthquake zones for enhanced understanding.

I have modified the border lines for improved visibility and combined all elements into a cohesive design. Additionally, I added a legend at the bottom with color specifications to enhance the clarity and overall comprehension of the data visualization.

I have streamlined the content by removing unnecessary points and adjusted the Y-axis for clarity. Additionally, I eliminated the grey death circle and changed the color of the main circle to enhance visibility and make the data visualization more engaging and easier to understand.

In this visualization, my goal was to create a more engaging representation that clearly illustrates the occurrence and intensity of earthquakes. I utilized red color and bolded the border lines to enhance the clarity of geographical structures. By incorporating opacity, I ensured that when circles overlap, they appear darker, allowing you to easily see how many times an area has experienced earthquakes. Additionally, I modified the tectonic plate boundaries for better clarity and understanding.

[rudhsy]

Anirudh A 22B3630

Trump's Major Legal Woes

https://www.reuters.com/graphics/USA-TRUMP/INVESTIGATIONS/gkplwboyyvb/

The graphic intends to outline former U.S. President Donald Trump’s numerous legal battles. This timeline visually illustrates the various investigations and legal charges Trump faces across federal and state levels. It highlights the intersection and progression of these legal challenges, showcasing when key milestones occurred in each investigation and when charges or other legal actions were taken

Type of Data:

• Legal events (e.g., probes, indictments, trials) are represented in categories.
• Time-based data showing when each legal issue or charge was initiated and progressed.
• Some investigations branch into sub-investigations or overlap with others.

Dimensions of Data:

• Legal Case Timeline (from March 2019 to May 2024) with around 15 different legal cases and investigations, each tracked through significant events (e.g., subpoenas, indictments, court rulings).

Data Gaps:

• Explanations of events are simple and lacks further details.

Essential Data:- All major legal cases and investigations, the timeline of key events, and who initiated the probes.

Irrelevant Data:- Detailed descriptions of the legal procedures might overwhelm the visualization. Details about entities that are not directly involved in Trump’s legal cases like analysts, would be unnecessary.

Encoding Methods:

• Different cases are represented as color-coded lines running vertically, intersecting at key dates.
• Each major legal investigation has a distinct color.
• Each node is explained by a short text description placed near the timeline.

Problems with Current Encoding:

• The overlapping cases and events make the timeline visually dense and hard to follow, especially as some cases span multiple years and intersects.
• The colors used, the long lines used for representation of cases and the nodes where the cases are marked gives it a transit map vibe.
• Too much textual information is packed into the nodes.
• The graph is static, making it interactive might reduce the clutter

Proposed Redesigns

• Separate Timeline Layers: Break down the investigations into separate horizontal rows rather than intersecting on a single vertical line. This separation would make each investigation easier to follow without overlap.
• Clickable Nodes or Pop-ups: Replace the lengthy text blocks next to the timeline nodes with simple symbols or icons. Clicking these icons could reveal more detailed information in a pop-up or tooltip format, helping to reduce clutter.
• Instead of relying solely on colors, introduce different shapes or patterns for the lines to differentiate between investigations (e.g., solid lines, dashed lines).
• Introduce a filter option that lets users toggle on/off different investigations so they can focus on a subset at a time.
• Include a sidebar or footnote section with expandable explanations. This could provide context on legal terms (e.g., “What is an indictment?”), how these cases interrelate, and their significance without overcrowding the main visual.

[Bairipawan]

Pawan Kumar 23m2253 A visual guide to Eurovision https://www.reuters.com/graphics/MUSIC-EUROVISION/dwpkdykkzvm/ Redesigned Visual process and prototype Redesigning Reuters Data Stories (2024).pdf

[aakankshasinghhh]

Aakanksha Singh 22b3621

Selected Article: USA Markets

[des-gaurav]

Gaurav Bisht - 22b3605

Selected Article: https://www.reuters.com/graphics/USA-SPELLINGBEE/GRAPHIC/klvykoogzvg/

Storyline of the Visualization

The author is tracing the evolution of word difficulty in the Scripps National Spelling Bee, focusing on how winning words have become progressively challenging over time. The storyline explores the complexity of winning words, connecting their difficulty to the event’s prestige and the contestants’ skill levels. The intended takeaway is an appreciation for the increasing sophistication of contestants and the expanding difficulty of the English language words they are asked to spell.

Data Source and Type

Type of Data: The visualization uses historical competition data from the Scripps National Spelling Bee, highlighting:

1. Winning words: specific words contestants spelled correctly to win. 2. Yearly records: each year, starting from the competition's inception, is documented with one winner per year. 3. Word characteristics: this includes the word’s origin, length, and perceived difficulty. 4. Extent of Data: The timeline spans multiple decades, offering a historical view of word difficulty trends over time. The data covers key attributes of each winning word but lacks other potential insights, such as contestants' ages, backgrounds, or educational impacts.

Data Dimensions and Gaps Dimensions: Year (temporal dimension): indicates the chronology. Winning Word (categorical text data): each year’s unique winning word. Word Difficulty (ordinal/quantitative): inferred from complexity but not quantified in this visualization. Word Origin (categorical): indicates if a word is from Latin, French, etc.

Opportunities for the Redesign

1. Doesn't visually indicate why certain words are harder to spell (or understand from pronunciation).
2. The history of Spelling bee is not presented in a linear and easy to understand visual way, could have been a horizontal timeline.
3. Missing Difficulty Quantification: No metric exists to measure difficulty, leaving users to infer it visually based on word length or origin.
4. Limited Context on Word Choice: There’s no explanation of why certain years had easier or harder words, which could illuminate factors like judge decisions or evolving linguistic trends.

Essential vs. Irrelevant Data

1. Essential Data: Year, winning word, word difficulty, and word origin are crucial to understanding the progression in word difficulty.
2. Irrelevant Data: Extraneous details such as non-winning words or competition day statistics don’t add value to the story.

[prjwl-k]

Prajwal Kulkarni - 23M2247

[Prateekpal41366]

Prateek Pal_ 22b3617

Title- Understanding Independents

Age, Race and Education Independent voters skew younger than both Republicans and Democrats. Of this group, 26% belong to Generation Z, born between 1997 and 2012, and most were not old enough to vote in the 2020 election. Another 36% are Millennials, or members of Generation Y, born between 1981 and 1996.

Design

The problem with this data visualization is that it’s difficult to compare the different party types and their respective voter demographics. This can be improved by using a radar chart. Additionally, a slope graph could be used to display the college vs. non-college data, making trends more immediately visible.

Redesign

Working and Living The age of the Gen Z cohort also means many are just beginning to enter the workforce, though 41% are not working at all compared with 37% of Republicans and 35% of Democrats.

Design

The problem with this data visualization remains the same: it is difficult to compare and not easily understandable. The first data viz with human illustrations is not clear and i think it is not correct and hence can not be redesigned.

Redesign

Top of Mind Asked what will sway them most when choosing a candidate in November’s election, more Republicans and Democrats are decisive about which issues are most important to them than their independent counterparts.

Design

Redesign

Seventy-five percent of independents say their personal economic situation is totally important to how they vote, compared with 87% of Democrats and 93% of Republican voters.

What is clear among Independents, however, is that while voting may be a new experience for many of them, it’s possible many didn’t vote in 2020 because they lacked the motivation to cast a ballot.

Design

Redesign