iiinova — where your digital self keeps evolving

Reposted by Surbhi Bhatia

VizChitra @vizchitra.bsky.social · 04/09/2026

What do data folks actually think about AI? 👾 We asked people of VizChitra. The takes: thoughtful, divided, evolving. We believe communities grow through conversation. If you’re curious about data, you’ll fit right in. Join the community ✨

001

Surbhi Bhatia @surbhaai.bsky.social · 14/09/2026

"The vibe coder knows without going, sees without looking, and accomplishes all without doing a thing." - @rickrubin.bsky.social in #TheWayofCode That every step in the data x design x development process is now more accessible is a huge unlock for some. [1/n]

110

Surbhi Bhatia @surbhaai.bsky.social · 23/08/2026

I am heading to my karmabhoomi and the city of films, with a deck full of data on bollywood, with @pintofview - mumbai! Bombay friends, would love to see you there! More about the talk here: urbanaut.app/spot/kuch-ku...

011

Surbhi Bhatia @surbhaai.bsky.social · 04/08/2026

A Paula Scher quote has been my phone wallpaper for months: "It took me a few seconds to draw it, but it took me 34 years to learn how to draw it in a few seconds." Watching @adolfux.bsky.social distil an idea into a few strokes in under 30 seconds was seeing the quote in practice. #VizChitra2026

000

Surbhi Bhatia @surbhaai.bsky.social · 13/07/2026

The number on our weather apps and the heat we actually live through in Indian cities are often degrees apart. On Chennai's hottest days, the app might read 36°C, but for three in five days a year, the coastal city feels hot enough to fall into severe heat stress territory. (1/n)

A calendar chart comparing recorded temperature and felt temperature across 2024 for Chennai. Twelve monthly calendars show days with severe heat (red squares) and warm nights (yellow dots). Recorded temperatures show 0 severe days and 136 warm nights, while felt temperatures show 217 severe days and 175 warm nights, highlighting how much hotter conditions feel than weather records suggest. From Panes of Heat, Vizchitra, Data, Otherwise, Climate Exhibition, Kashvi Bansal, Surbhi Bhatia and Parth Bisen.

160

Surbhi Bhatia @surbhaai.bsky.social · 11/07/2026

This was all charts, all art, and a whole lot of heart ♥️ #VizChitra2026

060

Surbhi Bhatia @surbhaai.bsky.social · 02/07/2026

The one thing in common between watching movies and working with data: they both have plots! Box office opens 4th of July. See you at @vizchitra.bsky.social!🍿 #Vizchitra2026

040

Reposted by Surbhi Bhatia

VizChitra @vizchitra.bsky.social · 30/06/2026

The weather app tells only 1/2 the story Panes of Heat by Kashvi Bansal, @surbhaai.bsky.social & Parth Bisen turns the gap between the heat we measure and feel into an architectural installation On view at Data, Otherwise, #VizChitra2026 📅 3 Jul, 3–8 PM 📍 BIC 🎟️ tickets.vizchitra.com

VizChitra 2026: Data, Otherwise exhibition announcement for a climate exhibition at Bangalore International Centre. Featuring Kashvi Bansal (Editorial Designer, The Ken), Surbhi Bhatia (Data Journalist), and Parth Bisen (Disassembler of Electronics), presenting Panes of Heat. The poster includes the text "Windows Into a Future Too Hot to Step Outside," the exhibition date and time (3 July 2026, 3:00–8:00 PM), and a QR code to get tickets.

031

Surbhi Bhatia @surbhaai.bsky.social · 29/06/2026

Kashvi / Parth / and I are bringing the heat (literally!!) to @vizchitra.bsky.social! Look out for Panes of Heat, our installation on the gap between the heat we record as temperature data and the heat we actually experience, at Data, Otherwise! July 3rd & 4th @ Bangalore International Centre.

Promotional poster for VizChitra 2026 announcing participation in Data, Otherwise, the climate exhibition. Large text reads, "We will be at Data, Otherwise. Our work is on display at the climate exhibition at VizChitra." The poster includes ticket information (tickets.vizchitra.com), dates (3 July – Workshops & Exhibition; 4 July – Conference), and the venue (Bangalore International Centre). The lower half features warm orange illustrations of buildings, climate-themed icons, and a tilted behind-the-scenes photograph of three people assembling an installation by hand, highlighting the making process.

031

Surbhi Bhatia @surbhaai.bsky.social · 19/06/2026

Left a piece of my heart in Sinai, quite literally as per map coordinates, on this day last year.

Travels through the Suez Canal and Sinai Peninsula in June 2025

010

Surbhi Bhatia @surbhaai.bsky.social · 07/06/2026

Come fall in love (with data)… all over again! Tickets here: tickets.vizchitra.com

030

Surbhi Bhatia @surbhaai.bsky.social · 02/06/2026

Looking forward to speaking at Outlier 2026 alongside an incredible lineup exploring the intersection of AI and data visualization!

030

Surbhi Bhatia @surbhaai.bsky.social · 31/05/2026

Last month, I made thirty charts in thirty days for the #30DayChartChallenge. Wrote about what the process behind it demands of you: a forager's instinct, an athlete's discipline, a tailor's precision, a traffic controller's grip on attention. Full post here: surbhibhatia.com/vizardry/30-...

surbhibhatia.com

Thirty charts in thirty days

Notes from the #30DayChartChallenge and the process of making a chart every day of the month

1163

Surbhi Bhatia @surbhaai.bsky.social · 23/05/2026

A m-a-p s-p-a-m on palindromic place names in India! Saw @ansgarw.bsky.social post a map of places in Germany whose name is a palindrome, so rehashed my old #30DayMapChallenge entry on it.

A map of India titled “map spam” showing palindromic place names across the country. Hundreds of towns and villages with names that read the same forwards and backwards such as Naman, Rar, Mallam, and Nattam are marked with pink dots and connected labels. The map uses a minimal palette of magenta and cream, with state boundaries outlined in pink. Northern and central India have especially dense clusters of labels. Subtitle reads: “Palindromic place names in India.” Vertical side text credits Surbhi Bhatia, OpenStreetMap, and the #30DayMapChallenge 2022.

1121

Surbhi Bhatia @surbhaai.bsky.social · 18/05/2026

Somewhere between tales of horror and sales is panicking, some design thinking saved the day! Last week at @gitlab.com's The Other Interface, I spoke about shadow systems, which emerge as a spreadsheet here and a slack thread there in large-scale products. #ProductDesign #EnterpriseDesign

A collage of three presentation slides shown in a brick-walled conference room. The top slide reads “Tales of horror” beside a bright green hand graphic and tech icons. The middle slide is titled “Designing to Minimise the Need for Shadow Systems,” with details of a GitLab Bangalore event. The bottom slide displays the phrase “Sales is PANICKING” in large black and red text.

An AI drawn infographic titled “Minimizing Shadow Systems” explains why unofficial workflows persist and how UI/UX interventions can reduce them. The left side lists causes such as habit, query limits, data freshness gaps, flexibility needs, and AI reducing traceability. The right side highlights solutions like tagging accountability, self-service dashboards, and reducing off-system leakage, with sketches of dashboards, spreadsheets, AI tools, and workflow diagrams.

A projected slide titled “Designing to Minimise the Need for Shadow Systems.” The slide includes the event details “The Other Interface, GitLab Bangalore, May 2026” by Surbhi Bhatia from Open Network for Digital Commerce. Ceiling-mounted projector and lights are visible above.

030

Surbhi Bhatia @surbhaai.bsky.social · 14/05/2026

At enterprise scale, the whole is always bigger than the sum of its parts. I’ll be at @gitlab.com's event The Other Interface today, talking about how to minimise shadow data systems in large orgs through interface design: luma.com/wo9kofo5

A desktop monitor displays a presentation slide titled “Designing to minimise the need for shadow systems,” for Gitlab's event The Other Interface, in Bangalore, May 2026. The scene is a dimly lit workspace setup.

020

Surbhi Bhatia @surbhaai.bsky.social · 06/05/2026

Very exciting! The 2023 SDG Atlas was something I kept going back to for research and trainings. Keen to explore this new edition!

030

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 29/04/2026

#Day30: Global Health Data Exchange India’s health risk profile is shifting. Risks from unsafe water, poor sanitation, and undernutrition have declined. New threats such as air pollution, heat, and conditions like high blood pressure and sugar have climbed the ranks. #30DayChartChallenge #dataviz

A two-panel bump chart tracking health risk factors in India from 1990 to 2023, ranked by impact on disability-adjusted life years (DALYs). The left panel (in red) shows rising risks, with particulate matter pollution becoming the top risk, followed by high systolic blood pressure, high fasting plasma glucose, smoking, and high LDL cholesterol. High temperature also rises in rank over time. The right panel (in green) shows declining risks, led by improvements in child growth failure, low birth weight and short gestation, unsafe water, sanitation, and lack of handwashing facilities. Iron deficiency, vitamin A deficiency, and suboptimal breastfeeding also fall significantly in rank. Made for #30DayChartChallenge Day 30 prompt: Global Health Data Exchange

063

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 29/04/2026

#Day29: Monochrome Spending ₹100+ on a protein bar? Here's unpacking what you're paying for across popular options. #30DayChartChallenge #dataviz

292

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 29/04/2026

#Day28: Modeling None of the three largest emitting nations are on track, or even pledging to meet the 1.5°C climate target of the Paris agreement, show projections. #30DayChartChallenge #dataviz

Slope chart titled “Off track for the Paris Promise” comparing 2023 emissions with 2030 projections for China, India, and the United States against the 1.5°C goal based on the Paris Agreement. For China, India, and the United States, projections for 2030 emissions under current policies and NDC pledges remain well above levels required for 1.5°C alignment. China’s emissions stay near current levels, far from the steeper cuts needed. India’s emissions rise under its pledge, instead of declining as required. The United States shows declines, but not enough to meet either its fair share or domestic 1.5°C pathways. Across all three, the gap between pledged action and required reductions remains large. Made for #30DayChartChallenge Day 28: Modeling

173

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 29/04/2026

#Day27: Animation Ship traffic through the Strait of Hormuz comes to a halt and has yet to recover. A prolonged disruption to one of the world’s busiest oil routes amid the US-Iran war. #30DayChartChallenge #dataviz

123

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 28/04/2026

#Day26: Trend Iced matcha, hojicha latte.. Japan's green teas are the trend in cafes around the world. But at home, both consumption and production are falling, while exports rise to match the growing demand abroad. #30DayChartChallenge #dataviz

A three-panel chart titled “Trying to Match(a) Up” shows trends in Japan’s green tea sector. The first chart shows both domestic production and consumption declining from 2004 to 2024, with consumption falling below production in recent years. The second chart shows Japan’s green tea trade shifting from a net importer in the early 2000s to a growing net exporter by 2024. The third chart shows global Google search interest rising sharply for matcha, hojicha, and konacha, especially after 2020, while interest in sencha peaks earlier and then declines. Made for #30DayChartChallenge Day 26 prompt: Trend.

193

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 27/04/2026

#Day25: Space A space race between countries to reach the Earth’s orbit, and how often the attempts succeed. #30DayChartChallenge #dataviz

A dark, space-themed chart titled “A changing balance of space power” shows the number of orbital launches by country from 1957 to 2026. Each country has its own horizontal strip of vertical bars by year, coloured blue (success) going up and yellow (partial failure), and red (failure) going down per year. The USSR/Russia dominates early decades followed by a decline. The United States shows steady activity with a sharp rise in recent years. China’s launches increase rapidly after the 2000s, becoming one of the leading contributors. Europe (ESA), Japan, and India show moderate but growing activity. Smaller contributors like Iran, Israel, North Korea, and others appear sporadically. Totals for each country are listed alongside their names. Made for #30DayChartChallenge Day 25 prompt: Space

2114

Surbhi Bhatia @surbhaai.bsky.social · 25/04/2026

#Day24: South China Morning Post Theme Day इনடఇಅ/India, a living mosaic of diverse languages. Inspired by: multimedia.scmp.com/culture/arti... @scmpgraphics.bsky.social #30DayChartChallenge #dataviz

0132

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 25/04/2026

#Day23: Seasons Weather to visit, or whether to wait? A cheat sheet for the best time to visit Indian cities. Data from last year, does not capture current conditions of this mad April heat in Bangalore! #30DayChartChallenge #dataviz

A stacked bar-heatmap style chart titled “Weather to visit, or whether to wait?” compares the best time to visit major Indian cities across months from January to December. Each row represents a city: Delhi, Mumbai, Chennai, Bengaluru, Pune, Kolkata, Hyderabad, Jaipur, Ahmedabad, and Guwahati, and each cell is color-coded by conditions: yellow for “ideal time,” orange for “too hot,” blue for “too rainy,” and grey for “polluted” (with Delhi also showing “too cold + polluted” in January).
Made for #30DayChartChallenge Day 23 prompt: Seasons

183

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 22/04/2026

#Day22: New Tool But.. do I have a chart for you anyway? Here's a better visual answer to how much of a country lives in its capital? #30DayChartChallenge #dataviz

A grid-style visualization titled “Capital concentration” shows the share of each country’s population living in its capital city (in %). Each country is represented by a small tile containing a simplified map outline, the capital city marked with a dot, and a percentage label. The size of the dot increases with higher concentration.

Countries are arranged from lowest to highest share. At the low end (under 1–2%) are countries like Myanmar, Malaysia, Tanzania, Nigeria, Brazil, and the United States, indicating highly distributed populations. Most countries cluster between 5% and 25%, including India (~2.1%), the United Kingdom (~14.8%), Bangladesh (~19.6%), and Japan (~26.7%).

At the high end, small or city-state-like countries dominate: Qatar (~70.7%), Kuwait (~84%), Singapore (~84.8%), and Bahrain (~89.5%), where a large majority lives in the capital. The visual highlights how capital cities dominate population distribution in smaller nations, while larger countries tend to be more dispersed. Source: European Commission Joint Research Centre via Our World in Data (2020). Made for #30DayChartChallenge Day 22 prompt: New Tool

183

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 21/04/2026

#Day21: Historical India’s aviation industry has a long history, and a longer list of airlines now history. After GoAir and Vistara, only a handful of passenger airlines remain: Air India/Express, SpiceJet, IndiGo, Akasa Air. A story of many takeoffs and even more endings. #30DayChartChallenge

A horizontal timeline titled “Up in the Air: A Timeline of India’s Active and Defunct Airlines” shows the lifespan of Indian airlines from the 1920s to 2025. Airlines are represented as horizontal lines spanning their years of operation, with green lines indicating active airlines and grey lines with red endpoints indicating defunct ones. Categories include passenger (national and regional), cargo, and charter, distinguished by shades of green.

At the top, a few long-running active airlines stand out, especially Air India (spanning over 90 years), alongside others like IndiGo, SpiceJet, Vistara, and newer entrants such as Akasa Air. The density of active airlines increases after the 1990s, reflecting liberalisation and growth in the aviation sector.

Below, a large cluster of defunct airlines appears, particularly concentrated between the 1990s and 2010s, with many short-lived carriers lasting only a few years. Earlier decades (1930s–1970s) show fewer airlines with longer durations, while the post-1990 era shows frequent entries and exits.

Overall, the chart highlights the rapid churn in India’s aviation industry, with many airlines failing within a short time, contrasted by a small number of long-standing carriers.

142

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 20/04/2026

#Day20: Global change How do countries power themselves? Over the last two decades, the world’s electricity mix has been slowly turning green, but not everywhere. Data: @ourworldindata.org #30DayChartChallenge #dataviz #energymix

A tilemap of the world showing how electricity production sources have changed from 2000 to 2024. Each tile contains an area chart indicating the share of fossil fuels (grey), nuclear (yellow), and renewables (green). Across most regions, especially Europe, parts of Latin America, and Africa, renewable energy use has grown. In contrast, several countries in Asia and the Middle East remain dominated by grey, indicating continued dependence on fossil fuels. Nuclear energy (yellow) is visible in a smaller set of countries, notably in Europe and East Asia. Overall, the map highlights a global shift toward renewables, though progress is uneven across regions.

284

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 19/04/2026

#Day19: Evolution Tried a chart about the evolution of charts! Centuries of trying to understand the world through data and graphics visualised. Data: www.datavis.ca/milestones/ by @datavisfriendly.bsky.social #30DayChartChallenge #dataviz @infobeautiful.bsky.social @datavizsociety.bsky.social

A horizontal timeline chart titled “Viz-ibly, we’ve come a long way: The evolution of maps, graphs and data visualisation,” spanning from 6000 BC to the 2000s. Events are grouped into four rows: Cartography, Statistics & Graphics, Technology, and Other.

In Cartography, milestones are marked such as first known map (6200 BC), first world map (550 BC), latitude/longitude (90 AD), cylindrical projection (1569), first modern atlas (1570), choropleth maps (1819, 1826), London Underground map (1933), GIS (1960), and early weather and disease maps.

In Statistics & Graphics, markers include early star charts and Earth measurements (240–134 BC), planetary diagrams (950), first data graph (1644), bar and line graphs (1786), pie chart (1801), mortality curves (1828), age pyramid (1874), and later methods like Chernoff faces, treemaps, parallel coordinates, ISOTYPE, Grammar of Graphics, and Gapminder.

In Technology, markers include parchment (170 BC), paper (105 AD), movable type (1453), graph paper (1794), lithography (1798), first photograph (1827), first digital computer (1944), Fortran (1957), and S language (1978).

In Other, milestones include color system diagrams (1758), periodic table (1869), and fisheye view (1981).

1137

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 18/04/2026

#Day18: Unicef @unicef.org With hospitals bombed, food and medicines in short supply and and limited access to essentials, the war on Palestine has undone decades of progress in child survival. #30DayChartChallenge #dataviz

Line chart showing under-five mortality in Palestine from 1975 to 2024 (deaths per 1,000 live births). The rate falls steadily from 109 in 1975 to 14 in 2022, indicating decades of improvement in child survival. In 2023, the trend reverses sharply, rising to 42, and remains high at 38 in 2024. Earlier years are shown in green to emphasize the long decline, while recent years are highlighted in red to show the sudden increase. Made for #30DayChartChallenge2026 Day 18 prompt: Unicef

285

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 17/04/2026

#Day17: Remake If Greek sounds like nonsense to English speakers, what sounds like nonsense to the Greek? (Answer: Chinese) Remaking an old chart I built on RAWGraphs, back when I didn’t realise messing with reading direction is a design sin: x.com/surbhaai/sta... #30DayChartChallenge #dataviz

A sankey diagram titled “What do we utter for utter nonsense?” mapping phrases meaning “it sounds like X to me” to different languages. On the left are expressions for unintelligible speech (e.g., “Chinese,” “Greek,” “Spanish,” “Farsi,” “Arabic,” “Hebrew,” “Turkish,” as well as terms like “gibberish,” “martian,” and “hieroglyphics”). Coloured flows connect these phrases to languages listed on the right, showing which languages are commonly used by speakers to signal confusion. The thickest flow is from “Chinese,” indicating it is the most widely used reference for incomprehensible speech across many languages, while others like “Greek” and “Spanish” appear with smaller, more regionally clustered connections. Chart made with RAWgraphs in 2019.

A multi-panel circular chord diagram, remake of the previous sankey chart, showing how different languages describe what is incomprehensible to them. Each chart pairs a language (who hears it, in blue) with what they say they hear (in red). Examples include English speakers saying “It sounds Greek to me,” Greeks referring to Chinese, and other languages pointing to Spanish, Farsi, or generic “gibberish” and “alien” speech. The flows between segments illustrate which languages are perceived as unintelligible by others, highlighting cultural patterns in how people describe confusion. Made for #30DayChartChallenge Day 17 prompt: Remake.

172

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 17/04/2026

#Day16: Causation When's the last time you made an ATM run? As UPI payments grow, ATM cash withdrawals have fallen, a big shift in how India pays. #30DayChartChallenge #Dataviz

A panel of bar charts titled ATMs Withdrawing showing two time series from June 2021 to February 2026. The top panel shows UPI transaction volume rising steeply from around 28,000 lakh in mid-2021 to over 200,000 lakh by early 2026. The bottom panel shows ATM cash withdrawal volume declining gradually from around 5,500 lakh to roughly 4,200 lakh over the same period, with October spikes each year indicating seasonal Diwali peaks. Together the charts illustrate as digital UPI payments grew, ATM cash withdrawals have fallen. Data from RBI Payment Systems Indicator. Made for #30DayChartChallenge Day 16 prompt: Causation.

192

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 16/04/2026

#Day15: Correlation Do longer constitutions correlate with rules that are more complex to follow? Looks like more words relate with lower rule of law. Made with @datawrapper.de: www.datawrapper.de/_/u8KX1/?v=10 #30DayChartChallenge #dataviz

Scatter plot comparing the length of constitutions (in number of words, x-axis) against the Rule of Law Index for 2025 (y-axis) for countries across all continents. The dots are diamond shapes, sized by word count. The correlation is -0.16, suggesting longer constitutions are very weakly associated with lower rule of law. Countries with shorter constitutions like Denmark, Norway, and Finland score highest on rule of law. India stands out as an outlier: the longest constitution (~150K words) with a mid-range rule of law score of ~0.49. Venezuela has a long constitution and one of the lowest rule of law scores. Source: World Justice Project and Wikipedia. Made fo #30DayChartChallenge 2026, Day 15 prompt: Correlation.

173

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 16/04/2026

#Day14: Trade Chhattisgarh living up to its title as the rice bowl of India. #30DayChartChallenge #dataviz #minimal

A tilemap of India showing the share of rice in each state's total exports for April 2024–March 2025. Each state is represented as a square divided into dark green (rice exports) and light green (other exports). Chhattisgarh has the largest rice share, followed by Punjab, Haryana, Bihar, West Bengal, and Andhra Pradesh. Source: Niryat portal. Made for #30DayChartChallenge 2026, Day 14 prompt: Trade

182

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 13/04/2026

#Day13: Ecosystems A public transport ecosystem in motion. Elevated.. underground.. or ground level? Where the metro runs is not random, there’s more to unpack here in how land-use and density shape these choices. #30DayChartChallenge #dataviz

A four-panel map comparing metro systems in Delhi, Bengaluru, Mumbai, and Kolkata, illustrating whether lines run elevated, at grade, or underground. Stations are color-coded: blue for elevated, pink for at-grade, and orange for underground, with grey indicating unavailable data. Central business districts are highlighted in teal. Across cities, underground sections cluster in dense urban cores, while elevated tracks extend into suburban and peripheral areas. Made for #30DayChartChallenge Day 13 prompt: Ecosystems.

152

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 12/04/2026

#Day12: Flowing Data Inspired by @flowingdata.com's time-use charts: flowingdata.com/2025/11/04/s..., here's a look at how men and women in India spend their day. Data: Time Use Explorer by #DiagramChasing / @aman.bh & vivek matthew. #30DayChartChallenge #dataviz

Small-multiple charts titled “A Day in the Life of Men and Women in India", comparing how men and women in India spend their day. The visualization shows the share of survey respondents engaged in different activities across a 24-hour day, comparing men (teal) and women (beige). Women spend significantly more time on unpaid domestic work, including food preparation, household activities, household maintenance, and caregiving, with peaks in the morning and evening. Men spend more time in paid work, travel, leisure, sports, and consumer purchases. Based on data from India’s National Time-Use Survey 2024. Made for #30DayChartChallenge Day 12 prompt: Flowing Data

1132

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 11/04/2026

#Day11: Physical Remember when you had to burn a CD to share a song? Now that feeling is burned into memory. Data: public.tableau.com/shared/4Z36J... #30DayChartChallenge #dataviz

Two real compact discs (CDs), doubling up as donut charts to show how the share of CD's in music revenue in the US fell from 89.4% in 1999 to just 3.1% in 2024. Made for #30DayChartChallenge Day 11 prompt: Physical.

1153

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 10/04/2026

#Day10: Pop culture Are #bollywood movies becoming longer? The average runtime hasn’t changed much, but more films cross the 3-hour mark than before. #30DayChartChallenge #dataviz #PictureAbhiBaakiHai

A chart showing the runtimes of the top 10 highest-grossing Bollywood films for each year from 1951 to 2025. Each year displays a vertical spread of dots representing individual films. The y-axis ranges from 1 hour to 4.5 hours, indicating runtime, with shorter films shown in purple and longer films in orange. Most films cluster between 2 and 3 hours. Five-year averages are marked and labeled to highlight broader trends. Select outliers, both above 3 hours and below 2 hours, are annotated. Made for the #30DayChartChallenge, Day 10 prompt: Pop Culture.

1124

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 09/04/2026

#Day9: Wealth What does it take to become a billionaire? A @piie.com paper titled The Origins of the Superrich unpacks how the world's richest made (or inherited) their fortunes. #30DayChartChallenge #dataviz

A horizontal stacked bar chart titled “The Birth Lottery: How the world’s billionaires made their fortunes.” It shows the share of billionaires in selected countries by source of wealth: inherited versus self-made. Inherited wealth dominates in countries such as South Korea (74%), Switzerland (73%), Germany (65%), and Sweden (63%). In contrast, self-made billionaires account for the vast majority in Russia (100%), China (98%), the United Kingdom (94%), and Indonesia (90%). The United States (71%), Canada (72%), Australia (72%), and India (66%) also show strong self-made majorities, while France (51% inherited, 49% self-made) and Brazil (48% inherited, 52% self-made) are more evenly balanced. The data is sourced from Freund and Oliver (2016), The Origins of the Superrich, published by the Peterson Institute for International Economics, with figures as of 2014. Made for #30DayChartChallenge Day 9 prompt: Wealth

182

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 08/04/2026

#Day8: Circular A decade of bad air. Air quality in India’s major cities has stayed consistently poor, often reaching hazardous levels, with no signs of improvement year after year. #30DayChartChallenge #dataviz #airpollution #cleanair

A circular heatmap showing daily air quality patterns across major Indian cities over a decade. Each ring represents a year, and each segment represents a day. Colors range from blue (cleaner air) to red (more polluted air). Made for #30DayChartChallenge Day 8 prompt: Circular

1224

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 07/04/2026

#Day7: Multiscale A look at ~20 years of government securities yields. Who lends to the Indian government and at what rates, reflects how investors price risk and uncertainty. #30DayChartChallenge #dataviz

A three-panel chart titled "The long and short of India's borrowing costs," covering data from Jan 2007 to March 2026. The top panel shows 10-year and 1-year Indian government bond yields, with major global and domestic events annotated. The middle panel shows the term spread between long and short-term rates in basis points. The bottom stacked bar chart shows who lends to the Indian government, such as commercial banks, insurance cos, RBI. Source: Reserve Bank of India and Investing.com. Made for #30DayChartChallenge Day 7 prompt: Multiscale

182

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 06/04/2026

#Day6: Reporters Without Borders Press conferences held or attended by the current and former prime minister. #30DayChartChallenge #dataviz

A calendar-style chart showing press conferences organised or attended by former prime minister of India Manmohan Singh and current prime minister Narendra Modi. Years are arranged in columns and days of the month in rows, and days on which press conferences were held are marked, coloured based on whether the press meet happened in India, abroad or en route on a flight.

172

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 05/04/2026

#Day5: Experimental How your sambar tastes.. depends where you are. Comparing how often different ingredients appear in sambar recipes of South Indian states. Inspired by one of my all-time favourite charts by @davidawaldron.bsky.social: bsky.app/profile/juwe... #30DayChartChallenge #dataviz

A chart on different ingredients that go into making sambar and how frequently they're found in recipes from different south indian states - Tamil Nadu, Kerala, Karnataka, Andhra Pradesh and Maharashtra.

Made for #30DayChartChallenge Day 5 prompt: Experimental.

173

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 04/04/2026

#Day4: Slope For #bollywood, no original ideas is no problem. Copy-pasting South Indian films is an easy formula that pays, even if remakes never match up. Link: surbhi-bh.github.io/bollywood-re... #30DayChartChallenge #dataviz

212

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 03/04/2026

#Day3: Mosaic Homegrown or imported? Where do electric cars sold in select emerging markets come from? data: @iea.org #30DayChartChallenge #dataviz

A marimekko chart of electric cars sold in selected nations: Brazil, Türkiye, India, Thailand, Viet Nam, Indonesia, Malaysia, Mexico, Uzbekistan, Colombia, Costa Rica, South Africa, by source of manufacturing: Made in china vs imported from other nations vs locally produced. Chart shows Vietnam and India are quite self-reliant for electric cars, while other nations are heavily dependent on imports from China. Made for #30DayChartChallenge2026 day 3 prompt: Mosaic.

1134

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 02/04/2026

#Day2: Pictogram Or as I want to call it.. Our Warli in Data. #30DayChartChallenge #WarliArt #dataviz

1182

Reposted by Surbhi Bhatia

Surbhi Bhatia @surbhaai.bsky.social · 02/04/2026

#Day1: Part-to-whole As phones get bigger, my choices have become smaller. A phone that perfectly fits human hands has become hard to find. #30DayChartChallenge #dataviz New to bluesky. This post from yesterday got deleted by mistake oooops.. ouch... me sad.😭

A chart of phones I’ve owned from 2006 to today, showing a steady increase in overall size and screen-to-body ratio, from about 16% on the Nokia 2626 to 86% on my current iPhone, making newer phones harder to fit in my hand or pocket.

2153

Surbhi Bhatia @surbhaai.bsky.social · 25/04/2026

eep, that excludes others note was for myself while doing manual edits, forgot to move it out. Person: 0, Manual Figma Editing: 1, i guess #30DayChartChallenge

061

Surbhi Bhatia @surbhaai.bsky.social · 20/04/2026

Cleaned up #Day19: Evolution of #30DayChartChallenge a bit more to arrange label-markers chronologically #dataviz @datavizsociety.bsky.social

A horizontal timeline titled “Viz-ibly, we’ve come a long way: The evolution of maps, graphs and data visualisation,” spanning from 6000 BC to the 2000s. Events are grouped into four rows: Cartography, Statistics & Graphics, Technology, and Other.

Cartography includes markers such as the earliest known map (6200 BC), early world maps, latitude/longitude, projections, atlases, choropleth maps, the London Underground map, and GIS. Statistics & Graphics shows markers from early star charts and the first data graphs to bar, line, and pie charts, and later methods like treemaps, parallel coordinates, ISOTYPE, Grammar of Graphics, and Gapminder. Technology traces markers from parchment and paper to printing, graph paper, photography, early computers, and languages like Fortran and S. The “Other” row includes markers such as color systems, the periodic table, and fisheye views.

051

Reposted by Surbhi Bhatia

Michael Friendly @datavisfriendly.bsky.social · 20/04/2026

Nice! This kind of reverse log scale is the way to go in visualizing history over such a long time span.

161