A new edition of DataViz Weekly brings together four recent projects that stood out to us. They cover different subjects, formats, and storytelling approaches, but each shows how carefully designed charts and maps can make data easier to read.

The lineup includes:

• Argentina’s squad for the 2026 FIFA World Cup — La Nación
• Heat across the World Cup host cities — Bloomberg
• Super El Niño on the way — BBC
• Oldest first names in the United States — Data Stuff

Argentina’s 2026 World Cup Squad

The 2026 World Cup has just begun, and Argentina enters it as the defending champion. The team won the 2022 tournament in Qatar. Lionel Scaloni has now named the squad tasked with trying to do it again.

La Nación compared the 2026 squad with the 2022 champions through two scatter plots, both built on Opta performance data and national team appearance records. The first plots each player by age along the horizontal axis and caps for the national team up the vertical, sorting them into zones from young to veteran. Yellow marks the Qatar squad, blue marks the 2026 group, and a dark ring flags the players named to both.

The view opens on the 2022 squad. Dashed lines then connect each returning player to their 2026 position as you scroll, before the chart settles on the current group. Many who reached Qatar with fewer than 15 caps now arrive with more than 40.

The second scatter swaps in a player rating against the competitiveness of each player’s club. The returning players travel the same scroll path from their 2022 standing to their 2026 one. Of the 26 players named, 17 also featured in Qatar, the most of any Argentine squad returning to defend the title.

Check out the article on La Nación, by Leandro Contento with Matías Conde, Pablo Loscri, Maria Rodríguez Alcobendas, and Andrés Eliceche.

Heat Across World Cup Host Cities

The same tournament also raises another question: what kind of heat will teams face as they move from city to city? The 2026 World Cup is spread across 16 host cities in the United States, Canada, and Mexico. It runs through the summer, which forecasters expect to be especially hot.

Bloomberg measured the heat each team faces using wet-bulb globe temperature, a figure that combines heat and humidity. A scroll-driven map of the host region sets the scene, its grid cells shaded by the ten-year median reading at each location, with the 16 stadiums marked. As you move through it, team flags drop onto their venues, and dashed lines trace each team’s route across the continent.

Tunisia and France come out with the most heat-exposed schedules. Uzbekistan lands coolest despite traveling through hot cities, because several of its matches fall in air-conditioned stadiums.

A ranked dot plot then lays out every team in turn, with a dot for each of its three group-stage games and a marker for the average, sorted from the hottest schedule down. Threshold lines mark where cooling breaks or postponement would come into play. Two more charts follow. A lollipop chart places the projected heat of the 2026 final against every final back to 1950. A bracket of the knockout rounds colors each match by its potential heat.

See the piece on Bloomberg, by Emma Court, Elena Mejía, David Ingold, and Joe Wertz.

Super El Niño on Its Way

From there, the heat story widens beyond football. El Niño is a recurring warming of the surface waters of the tropical Pacific. It reshapes rainfall and wind patterns across much of the world. Forecasters have warned that a new one is forming and could be among the strongest on record.

The BBC tracked the pattern’s emergence through a series of maps and line charts. The first is an animated globe centered on the Pacific, its sea surface shaded against a recent baseline, blue for cooler and orange for warmer. Three steps move through the months. In December, the waters are cool, with no El Niño present. The central Pacific warms by early spring. By April, the warming has taken firm hold, and the main monitoring region stays boxed throughout.

An area chart then traces an index of Pacific temperatures back to 1950, red where it crosses into El Niño and blue where it drops into La Niña, with a marker on the right for the forecast range later in 2026. A world map marks the regions a strong El Niño tends to leave wetter or drier, annotated with effects from a suppressed monsoon to heightened wildfire risk. Finally, a line chart sets monthly global temperatures against a pre-industrial baseline, coloring the El Niño and La Niña years and running a trend line through the long-term rise.

Explore the story on the BBC, by Mark Poynting, Erwan Rivault, Becky Dale, and Jess Carr.

Oldest Names in the U.S.

For a change of pace, the final project turns from climate and sport to something more personal. Baby names rise and fall in popularity over the decades. A name near the top of the charts in one generation can be almost unheard of a few generations later.

Erin Davis used census records, historical baby name data, and actuarial life tables to estimate the age distribution of people alive in the United States today under each name. The piece centers on a grid of small histograms, one per name, each showing the share of people in each age bracket, with the average marked. Myrtle leads the set, its bars piling up in the 80s and 90s.

A pair of scatter plots follows, both plotting a name’s average age against how far it has fallen from its peak. The first rings the most endangered names. The second sorts names from dated and dusty to newer and trendy, with examples labeled in each corner. An interactive histogram closes the piece, with a dropdown to pull up the age profile of any name.

Take a look at the post on Data Stuff, Erin’s blog.

Each project in this edition shows how charts and maps can turn data into something easier to compare, explore, and understand. That is what we keep looking for in DataViz Weekly: recent data graphics that bring the story in the data into clearer view. We will be back next Friday with more examples of data visualization done well:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on June 12, 2026.

Fresh Examples of Data Graphics Done Well — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Where does the volume go? It’s the question Sankey diagrams answer best, making any flow legible at a glance with proportional-width bands between stages.

Qlik has had a native Sankey for years that handles simple flows well, but reality often goes further. Meet our Sankey Chart extension for Qlik Sense, built for the flows that have outgrown it.

Why a New Sankey

A Sankey diagram visualizes flow as columns of nodes connected by curved bands. Nodes are categories at each stage. Bands carry value, with width scaled to the size of the flow. Read left to right, you see how a starting volume distributes through stages to outcomes.

Qlik’s native Sankey is the right call for shorter flow journeys, covering the basics:

• up to five stages
• source-only selection on flow click
• source-or-target link coloring

When those limits start to bite, our Sankey Chart extension takes it further:

• up to ten stages
• full flow-click selection
• richer visual context

Where It Fits

Anywhere a value flows through stages and the question is where the volume goes:

• Budget allocation and cost analysis:
  How money flows from business units through departments to expense categories
• Supply chain analysis:
  Product flow from factories through distribution centers to retail locations
• Customer journey mapping:
  User paths from acquisition channels through product features to conversion or churn
• Energy and resource flows:
  Energy conversion from sources through generation stages to end uses
• Website traffic analysis:
  Visitor paths from landing pages through site sections to exit or conversion
• Conversion funnels and marketing attribution:
  Multi-step conversion paths or attribution across touchpoints

What Our Sankey Does Well

Up to 10 stages

Our Sankey Chart accepts up to 10 stages (dimensions), giving headroom for the deeper hierarchies and longer journeys that real-world flows often have.

Three to five stages is still the sweet spot for visual readability. The extra capacity is now there whenever your data warrants it.

Full flow-click selection

Click a link and the chart selects both source and target dimension values at once. It acts as a flow-level filter that narrows the entire Qlik model to a specific flow path in a single click.

Node clicks add multi-select within a dimension. A confirm/cancel toolbar handles applying or aborting the selection, and selections survive Qlik repaints.

Rich visual context

Gradient links blend from source node color to target node color, so you see where a flow originates and where it lands. Alternative coloring modes (by source, by target, or single color) cover dashboards that prefer simpler treatment.

Node coloring offers three modes — by dimension level, unique per node, or single color, with full support for master dimension colors.

Level headers above each column label the dimension represented, useful on charts with four or more stages where the meaning of each column might not be obvious from node names alone.

Tooltips break down each flow in detail. Hover a node and see its income, outcome, dropoff, and share of the level total. Hover a link and see source → target, the link’s share of total flow, share of the source node’s output, and share of the target node’s input.

Three sliders fine-tune layout density: node width, node spacing, and link curvature.

Native to Qlik

Our Sankey Chart fits Qlik’s standard integration set: master dimension colors, calculation conditions, story snapshots, image and data exports, and locale-aware number formatting.

See the Demo App

We built a free demo app to showcase the new Sankey chart against a global supply chain — flow from source regions through materials and product lines to end markets. Multi-stage enough to stress the chart’s interactions, snappy enough to click through without waiting.

Easiest to open in your browser, but the QVF is downloadable if you’d rather load it into your own Qlik environment:
➡️ Explore the Sankey Chart demo app

Try the flow-level selection in particular: click a link between two stages and watch the entire sheet filter to that path.

Get Started

The Sankey Chart extension is available now:
➡️ Download the extension — free trial included

Install takes just a few minutes on any Qlik Sense environment:

• On Desktop, drop the extracted folder into your Extensions directory.
• On Enterprise, import the archive through Qlik Management Console.
• On Cloud, upload through the Management Console (with a qlik.anychart.com entry in your Content Security Policy).

The installation guide in the Sankey Chart documentation walks through each part in detail.

Or if you’d rather see it against your own data with our team first:
➡️ Book a guided live demo

Share Feedback

Releasing v1.0 is the start, not the goal. We already have a nice roadmap in mind, but the next version that’s truly worth shipping is the one shaped by the real apps you’re building with Qlik.

If you try our Sankey on your data and something doesn’t work as well as you’d like — a stage layout you wish it had, a coloring mode that’s missing, a flow interaction you need — let us know. Your feedback determines what the next version will bring.

The Sankey Chart joins our family of Extensions for Qlik Sense, alongside Spreadsheets, Decomposition Tree, Gantt Chart, Sunburst Chart, Circular Dendrogram, and more. See where it fits in your Qlik dashboards.

Originally published at https://qlik.anychart.com on June 9, 2026.

A good chart gives numbers a shape the eye can follow. DataViz Weekly is our regular roundup of data visualization projects from around the web that exemplify that power.

Check out our selection for this edition:

• Growing registration outside both major parties across the U.S. — USAFacts
• SpaceX IPO against top global listings — Bloomberg
• California’s largest fortunes and their taxes — NYT Opinion
• 2026 Ebola outbreak in Congo — NBC News

Growing Registration Outside Both Major Parties

When Americans register to vote in many states, they record a party affiliation. The choice is not limited to the two major parties. A voter can register with a minor party or decline to affiliate with any party at all.

The Viz Lab at USAFacts used ternary scatter plots to visualize the latest shifts in voter affiliation. Each triangle places a state’s counties between three corners, one for Democrats, one for Republicans, and one for voters who are unaffiliated or with a minor party. Every county appears as a diamond sized by its registered voters and pulled toward the corner that holds most of them, shaded from blue to red by which major party is ahead.

As registration shifts year by year, each diamond drifts and leaves a trail, so a long trail marks a large move and its direction marks where the county is heading. A year slider with a play button runs the animation, and a search box highlights any county.

In the Colorado chart, for example, most trails climb almost straight toward the Other or Unaffiliated corner. Separate ternary charts follow for North Carolina and Kentucky, where the trails lean toward the Republican corner instead, and an interactive version opens the same view to 27 states and Washington, D.C.

The piece also carries a set of proportional symbol maps, namely arrow maps. Up and down arrows on each county mark the percentage point change in the share registered with the Democratic party, with the Republican party, and outside either one.

👉 Explore the project on USAFacts.

SpaceX IPO Versus Top Global Listings

SpaceX is preparing to go public after more than two decades as a private company. Much of its value rests on its Starlink satellite internet business.

Bloomberg set SpaceX against the 100 largest global stock market listings since 2000 through a bubble chart that builds up as you scroll. The horizontal axis runs across the years and the vertical axis measures offer size. The listings first appear as dots along the baseline, then rise to their offer size, with the largest labeled, among them Visa, AIA, Alibaba, and Aramco. Each point then expands into a bubble scaled to the market capitalization reached after listing, where Aramco stands out at $2.4 trillion.

The bubbles next take on color by sector, and a dotted outline marks the companies ultimately owned by sovereign states. At the end, SpaceX enters at the top right as a single large bubble far above the rest, with a roughly $75 billion offer size and a target valuation above $2 trillion.

A few more charts round out the piece. They place SpaceX next to the Magnificent Seven before and after their own listings, track the valuations of other large private companies, and plot price to sales against revenue across a set of firms.

👉 Check out the story on Bloomberg, by Demetrios Pogkas, Jennah Haque, and Kiel Porter.

California’s Ultrawealthy and Their Taxes

A very small number of Californians hold an enormous share of the state’s private wealth. Those fortunes have grown many times over across the past four decades.

Economists Emmanuel Saez and Gabriel Zucman open their guest essay in The New York Times Opinion section with an area chart tracing the combined wealth of the top 0.0002% of Californians in today’s dollars. The graph climbs from $22 billion in 1982 to $1.6 trillion in May 2026, with the dot-com peak and the postpandemic dip marked along the way. The essay then steps year by year through California’s richest individuals from 2005 to the present.

The next section turns to a set of icebergs, one for each of Mark Zuckerberg, Larry Page, Sergey Brin, and Jensen Huang, as the richest Californians nowadays. A guide first explains how to read them. The small tip above the waterline is the income that was taxed, the band at the surface is retained corporate profits, and the large mass below is additional stock gains. The effective tax rate is labeled at each level and falls from the tip to the base. For each person, the figures break a wealth increase of more than $150 billion into those three parts, with the underwater portion dwarfing the visible peak.

👉 Look at the article on The New York Times, by Emmanuel Saez and Gabriel Zucman, with graphics by Gus Wezerek.

2026 Ebola Outbreak in Congo

An Ebola outbreak is spreading in eastern Democratic Republic of the Congo. The World Health Organization declared a public health emergency in mid-May 2026. The cases involve the Bundibugyo species, a rare form that has caused only a handful of recorded outbreaks.

NBC News set the current outbreak against past ones with a line chart. The horizontal axis counts the days since the WHO declaration, and the vertical axis counts cumulative cases. The 2026 Congo outbreak appears in orange. A solid line tracks confirmed cases, which reach 378 within the first weeks, while a dashed line tracks suspected cases and climbs almost vertically past 900 before the WHO revised that count down in early June.

Faded gray lines trace several earlier outbreaks, including the West Africa epidemic of 2014 to 2016, the largest on record. The country’s own 2012 Bundibugyo outbreak, in the same orange as the current one, sits low and flat near the bottom, already passed within the first weeks. The steepness does the work here, setting the pace of the 2026 outbreak against the earlier ones in a single view.

Beyond this chart, the piece maps where the outbreak is spreading and uses further graphics to explain how the virus is transmitted and what it does to the body.

👉 See the piece on NBC News, by Jane Weaver, Jiachuan Wu, and Javier Zarracina.

Four subjects, four different ways of putting the numbers in plain sight through thoughtful visualization. Another set of compelling charts and visual stories lands here next Friday:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Stay tuned.

Originally published at https://www.anychart.com on June 5, 2026.

Charts That Frame Numbers Clearly — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Every week brings more charts, maps, and visual stories into view. In DataViz Weekly, we pick out recent data visualization work that feels worth a closer look, whether for the subject, the design choices, or the way the data is brought into focus.

Here is what we are featuring this time:

• 70 years of Eurovision lyrics — Giuseppe Sollazzo
• California politics beyond left and right — San Francisco Chronicle
• The Japanese yen under pressure — Bloomberg
• Britain’s second city debate — YouGov

70 Years of Eurovision Lyrics

The Eurovision Song Contest has been staged almost every year since 1956. Its entries span dozens of languages and seven decades of changing styles.

Giuseppe Sollazzo classified all 1,795 songs performed between 1956 and 2025 into ten lyrical themes, from love and joy to rebellion and war, and built the piece around a dot plot. Each dot is one song. The columns run by year from left to right, and inside each column, the songs are ordered by result, with the winner at the top.

As the reader scrolls, the dots hold their positions and only change color. They turn first by theme. Love in red fills most of the chart, while empowerment rises from the 2010s. The dots then recolor by language, tracing how English spread, pulled back under the contest’s national-language rules, and returned to dominance after 1999.

A run of line charts measures each theme’s share of songs per year, including a love-against-empowerment pair whose gap closes over time. A set of word-frequency line charts then follows single words across the decades, plotting pairs such as man against woman and war against peace.

The piece ends with an interactive visualization. Three tabs let the reader search any song, plot one word against another, and split the field by language.

👉 Explore the project on puntofisso.net, by Giuseppe Sollazzo.

California Politics Beyond Left and Right

Political maps usually sort communities along one line, from left to right. That framing can miss differences that do not fall neatly on the spectrum.

The San Francisco Chronicle analyzed how California precincts voted on 65 ballot measures between 2016 and 2024, then used those patterns to place each precinct in a two-dimensional space.

The centerpiece is a scrollytelling scatter plot. It opens as a single horizontal spread of 10,600 dots, one per precinct, arranged left to right by partisanship, with the pile thickest in the middle. A second axis then lifts the dots into a full scatter plot, adding a vertical populist-to-technocratic dimension. Three corners take shape. Two are on the left, one populist and one technocratic, and the third is on the right.

The same scatter then fills with color as the precincts are grouped into six clusters, each given a name by the Chronicle, from Left Coast to Staunch Conservatives.

An interactive version closes the piece. A dropdown switches between any of the 65 propositions and recolors every dot on a diverging orange-to-teal scale from no to yes, showing which coalitions formed on each measure.

👉 See the story on the San Francisco Chronicle, by Aseem Shukla and Nami Sumida.

Japanese Yen Under Pressure

The Japanese yen has lost ground against the U.S. dollar over the past year. In late April 2026, Japan stepped into the currency market to slow the decline. A persistent gap between low interest rates at home and higher returns available abroad has been one of the main pressures.

Bloomberg traced the year through a vertical line chart that runs down the page rather than across it. Time flows from top to bottom, from May 2025 to May 2026, while the horizontal axis carries the exchange rate, a weaker yen to the left and a stronger yen to the right. The line drifts leftward as the currency loses value, reaching the 160-per-dollar level that prompted the intervention.

As the reader scrolls, annotated callouts attach to points along the line. They mark Bank of Japan rate decisions, leadership changes in Tokyo, the late-April move into the market, etc. The most recent reading sits at the bottom.

👉 Check out the piece on Bloomberg, by John Cheng and Christopher Udemans.

Britain’s Second City Debate

London is Britain’s capital and by far its largest city. Which city counts as second has no official answer. Manchester and Birmingham are the usual contenders, with a few other cities staking an occasional claim.

YouGov surveyed more than 56,000 Britons and mapped the answers across the country. Displayed above is a choropleth map of Great Britain colored by each area’s most common response. Hue identifies the city, so Manchester reads red, Birmingham purple, and Edinburgh green, while the shade deepens with the size of the local majority. Hatching marks the places where the top two answers sit within five points of each other.

Birmingham’s support concentrates around the West Midlands, Manchester’s spreads more broadly, and Scotland leans toward Edinburgh and Glasgow.

Small-multiple choropleth maps then break the question out city by city, first for the three leading contenders and then for outside candidates such as Cardiff, Liverpool, and Newcastle, each shaded by the share who chose it. Bar and column charts cover the rest of the findings, including how strong a case the public gives each city, how the answer divides by age, and which factors people weigh most.

👉 Look at the article on YouGov.

That is it for this week’s selection of recent data visualization work worth a closer look. We will be back next Friday with more charts, maps, and visual stories in:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on May 29, 2026.

New Data Visualization Work to Explore — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

There’s a chart we’ve been wanting to add to the Qlik toolkit for a while. The one that shows you what a hierarchy actually looks like — not packed by value like a treemap, not stacked in rings like a sunburst, but as the parent-child tree it really is.

Meet the Circular Dendrogram extension for Qlik Sense.

Why a Radial Tree

A dendrogram is a node-link tree: every parent-child relationship is drawn as an explicit edge. The radial form fits wide, deep hierarchies onto one screen, with branches fanning out from a single center to leaves on the perimeter.

Each top-level branch gets its own color from a categorical palette, and descendants inherit it, so the eye picks up structure instantly. You read the chart by following lines, not by comparing areas — which makes it the right pick when the question is about shape rather than magnitude.

Qlik Sense’s built-in charts cover adjacent cases — the Org Chart for rectangular, top-down hierarchies, the Network Chart for general node-graphs — but neither renders a hierarchy as a radial tree. The Circular Dendrogram is built specifically for that layout.

Where It Fits

Anywhere you have nested categories you want to read as a tree:

• Organizational charts:
  Divisions, departments, services, and individual staff in one view
• Product or service taxonomies:
  Categories down to individual SKUs
• Account hierarchies and cost-center breakdowns:
  Parent companies down to line items
• File systems and folder structures:
  Optionally sized by file size or file count
• Biological or scientific classification:
  Kingdom → phylum → class → order → family → genus

What It Does Well

Click to trace a path

Selecting a leaf or inner node brightens its ancestor chain and dims the rest, so you see your pick in the context of the full tree.

Confirm a single-leaf selection and the chart re-renders in chain mode: a clean linear path from root to leaf with every level labeled.

Confirm a multi-leaf selection that shares an upstream ancestor, and the chart shows the shared stem plus a radial fan from the divergence point.

The visualization stays readable as the hypercube narrows. Because this is a Qlik chart, your selection cross-filters every other chart on the sheet.

Handles messy, long-tailed hierarchies

Real data rarely has clean structure.

Top-N grouping collapses small branches into a synthetic Others wedge, recursively at each level or just at the top.

A label density slider keeps perimeter labels from colliding.

Arc extent options render the chart as a full circle (360°), a three-quarter arc (270°), or a semi-circle (180°) for narrow cells.

An optional center node hosts chart-wide totals and doubles as a clear-selection button.

Native to Qlik

Story snapshots, image and data exports, and locale-aware number formatting all work out of the box. Your measure’s qNumFormat and the tenant's locale separators are honored everywhere. A measure formatted as $#,##0.00 looks the same in tooltips, perimeter labels, and the center tooltip. No surprises.

See It in Action

We built a free demo app to showcase the chart against a fictional hospital:

• 8 divisions,
• 42 departments,
• 84 services, and
• 214 staff across clinical and non-clinical roles.

The dataset is hierarchical enough to stress the chart’s interactions and small enough to keep the demo snappy.

Open it in your browser (no Qlik login required), or download the QVF file:
➡️ Explore the Circular Dendrogram demo app →

Get Started

The Circular Dendrogram extension is available now:
➡️ ️️Download the extension — free trial included →

After downloading, it installs quickly on any Qlik Sense environment:

• Qlik Sense Desktop:
  Extract the archive into Documents\Qlik\Sense\Extensions\
• Qlik Sense Enterprise on Windows:
  Import the archive via Qlik Management Console → Extensions
• Qlik Cloud (SaaS):
  Upload via the Management Console; add qlik.anychart.com to your Content Security Policy

Step-by-step instructions for each environment are in the installation guide, part of the Circular Dendrogram documentation that contains full configuration details.

Want a walkthrough against your own data?
➡️ Book a guided live demo with our team →

One Ask

This is v1.0. We have a list of where we’d take it next, but the most useful version of v2.0 is the one that solves the dashboards you’re actually building.

If you try the chart on your data and something doesn’t quite work — a layout you wish it had, a measure mode that would help, an interaction that feels off — tell us. That’s the feedback we build from.

The Circular Dendrogram joins AnyChart’s broader family of Extensions for Qlik Sense, alongside Spreadsheets, Decomposition Tree, Gantt Chart, Sunburst Chart, and more.

See where it fits in your Qlik dashboards.

Originally published at https://qlik.anychart.com on May 28, 2026.

We have come across several interesting data maps over the past few days, so this edition of DataViz Weekly leans spatial. The four projects we selected show how maps can make location, scale, and geographic patterns easier to explore. Today’s selection:

• 146 million U.S. jobs by sector — Kyle Walker
• Sahel violence and Nigeria as its hotspot — The Guardian
• Rio de Janeiro’s sister cities — Georgios Karamanis
• Tobacco smoking endgame — Amanda Shendruk

146M U.S. Jobs by Sector

Roughly 146 million people work in the United States across a wide range of industries. Where those jobs sit on the map varies sharply by sector.

Kyle Walker mapped the United States by workplace location using the U.S. Census Bureau’s LODES dataset. The result is a zoomable dot map on a dark base, with each dot encoding jobs in one of 20 sectors. Sector colors group naturally. Oranges and reds cover primary industries and manufacturing. Teals handle trade and transportation. Blues mark finance and professional services. Greens cover education and healthcare, while purples are reserved for arts and hospitality.

The jobs-per-dot count scales continuously with zoom and updates live in the lower-left corner. At the national scale, each dot can stand for thousands of jobs. As you zoom in, that count falls, eventually reaching one job per dot at the closest levels. Broad urban clusters then resolve into the finer fabric of business districts, industrial parks, and retail corridors.

👉 Explore the map on Kyle Walker’s website.

Sahel Violence and Nigeria

Terrorism-related deaths in the Sahel have climbed sharply over the past two decades. Nigeria, the region’s most populous country, has become a particularly violent hotspot.

The Guardian drew on data from Acled and the Global Terrorism Index 2026 for a multi-part piece. A stacked area chart opens the story by showing the Sahel’s share of global terrorism deaths. It rises from under 1% in 2007 to roughly half over the past three years. Hexagonal binned maps then animate year by year from 2020 to 2025. They show political violence incidents across the region climbing from about 5,800 to over 15,000 before easing last year.

The centerpiece is a bubble map of violence involving non-state actors. Each bubble is placed at an incident site, sized by fatalities, and colored by the armed group involved. The view begins at the regional scale, picking out clusters in Sudan and the triangle where Burkina Faso, Mali, and Niger meet. It then zooms into Nigeria to walk through bandit violence in the northwest, herder and farmer clashes across western states, attacks around Kainji, and the long-running Boko Haram and ISWAP insurgency in the northeast.

👉 See the article on The Guardian, by Eromo Egbejule, Antonio Voce, Ashley Kirk, Alex Olorenshaw, and Stefania Orlando.

Rio de Janeiro Sister Cities

Sister city agreements are formal partnerships between two cities. They are intended to support exchange and cooperation across borders. Rio de Janeiro has signed more of them than any other city in the world.

Georgios Karamanis turned Rio de Janeiro’s 93 sister city agreements, using data from Wikidata compiled by Ahmad Barclay for the Twin Cities Explorer project into a scroll-driven connector map. The first view places every twinned city in the world as a faint dot on a dark world background. The view then narrows to Rio, where each partner city appears as a yellow dot connected by a curved great-circle line.

As you scroll, the narrative steps through the network continent by continent. Niterói sits just across Guanabara Bay. Asunción and Buenos Aires anchor the southern lines. Europe holds 26 partners, including 14 in Portugal. Africa adds 15 connections, from Praia and Luanda to Lagos and Maputo. North America contributes 12, from Havana to Atlanta. The Asian arc stretches east through Beijing, Seoul, and Busan to Kobe, 18,717 kilometers from Rio. A summary at the end tallies 93 partner cities across five continents and 776,848 kilometers of lines in total.

👉 Check out the story here.

Tobacco Smoking Endgame

The U.K. has passed a generational smoking ban. From 2027, tobacco cannot be sold to anyone born in 2009 or later, with the legal purchase age rising one year per year. It is the second country to enact such a law, after the Maldives passed its own six months earlier.

Amanda Shendruk’s piece on Not-Ship covers the case behind the U.K. ban and where the rest of the world stands on ending smoking. A global choropleth map shows tobacco endgame readiness at a glance, drawn from a 2024 Lancet Global Health study. Countries fall into four categories. Dark blue marks endgame ready, light blue almost ready, light orange more action needed, and orange early epidemic stage. The 28 endgame-ready countries cluster across Africa, Latin America, and South Asia rather than the wealthier West.

Supporting visuals include a bar chart ranking smoking as the third highest global risk factor for death. A one-dimensional dot plot shows percentage change in smoking rates by country between 2005 and 2025.

👉 Look at the piece on Not-Ship.

Data maps run through this week’s selection in different forms, from dots and bubbles to connectors and choropleth maps. Each approach to visualize location-related data helps frame a different kind of question. We will be back next Friday with more good data visualization work from around the web:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Interesting Data Maps From Around the Web — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

A step line chart displays data as a continuous staircase, making it ideal for visualizing values that change at specific moments and hold constant in between. This tutorial walks through building one with JavaScript using ten years of U.S. Federal Funds Rate data, from a blank HTML page to a fully interactive step line chart ready to embed in any web page or application.

Here’s what the final chart will look like:

What Is a Step Line Chart?

A step line chart (also called a stepped line chart) is a chart type where data points are connected by horizontal segments and vertical transitions rather than diagonal lines, creating a staircase-shaped series. Each horizontal segment represents a value held constant over time; each vertical line marks the exact moment and magnitude of a change.

Step line charts are the right choice for data that shifts at specific decision points and stays flat in between: interest rate targets, price tiers, software version numbers, configuration values. Both the timing and the direction of each change are immediately readable from the staircase shape. The closest alternative is a jump line chart, which leaves a visible gap between segments instead of connecting them, emphasizing the discrete nature of each value over the transition path.

Building a JavaScript Step Line Chart

Building an interactive JavaScript-based step line chart involves four steps: creating the HTML page, loading the necessary JS files, preparing the data, and writing the chart code.

1. Create an HTML Page

The chart needs a home, so let’s begin with a minimal HTML file with a <div> that it will render into. The #container div fills the full browser window here, giving the ten-year timeline enough horizontal space. For a partial-page embed, replace the values with whatever percentage or pixel dimensions suit your layout.

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>JavaScript Step Line Chart</title>
  <style>
    /* make the page and container fill the full browser window */
    html, body, #container {
      width: 100%;
      height: 100%;
      margin: 0;
      padding: 0;
    }
  </style>
</head>
<body>
  <!-- the chart will render inside this div -->
  <div id="container"></div>
</body>
</html>

With the page set up, let’s load the necessary files.

2. Include the JavaScript Files

This tutorial uses AnyChart’s JavaScript charting library. The step line chart is part of the anychart-base.min.js module, which covers all basic Cartesian chart types. In addition, the data will be loaded from a CSV file, so anychart-data-adapter.min.js is also needed - it handles loading external data files.

Add both using the <script> tags in the <head> section, then add an empty <script> block in the <body> where the chart code will go.

<head>
  ...
  <!-- load the AnyChart base module, which includes the step line chart -->
  <script src="https://cdn.anychart.com/releases/8.14.1/js/anychart-base.min.js"></script>
  <!-- load the data adapter module, which enables loading external CSV files -->
  <script src="https://cdn.anychart.com/releases/8.14.1/js/anychart-data-adapter.min.js"></script>
</head>
<body>
  <div id="container"></div>
  <!-- chart code goes here -->
  <script>
  </script>
</body>

The scripts are in place — now for the data.

3. Prepare the Data

The data visualized in this tutorial comes from FRED, Federal Reserve Bank of St. Louis and covers ten years of the Federal Funds Rate, from May 2016 to May 2026. The Fed doesn’t set a single rate; it sets a target range. When headlines say “the Fed raised rates to 5.5%”, that’s the upper end — 5.25%-5.50% in that case. This chart tracks exactly that number, the upper limit of the target range.

The data is available for download as a CSV directly from the FRED series page. The file has two columns: observation_date (an ISO date string) and DFEDTARU (the upper target rate as a decimal), with one row per calendar day — 3,653 observations in total in our case. The first few rows look like this:

observation_date,DFEDTARU
2016-05-06,0.5
2016-05-07,0.5
2016-05-08,0.5
2016-05-09,0.5

With the file hosted and accessible, the AnyChart data adapter can load it directly — no manual parsing or data transformation needed.

4. Write the JS Code for the Chart

The entire chart script goes inside anychart.onDocumentReady(), which fires only after the page has fully loaded and the #container div is in the DOM. This is also where the data loading kicks off.

anychart.onDocumentReady(function () {
  // ... all the following JS chart code goes here
});

4.1. Load and Parse the CSV

The chart code can’t run until the file has arrived, so everything goes inside the callback that anychart.data.loadCsvFile() fires when it does. The callback receives the raw CSV text — anychart.data.set() turns it into a structured data set (ignoreFirstRow: true drops the header row), and mapAs() tells AnyChart which column maps to which field: column 0 (observation_date) becomes the x value, column 1 (DFEDTARU) becomes the series value.

anychart.data.loadCsvFile(
  "https://raw.githubusercontent.com/andreykh1985/anychart-data/main/DFEDTARU.csv",
  function (data) {

    // parse the CSV; ignoreFirstRow skips the "observation_date,DFEDTARU" header
    var dataSet = anychart.data.set(data, {ignoreFirstRow: true});

    // map column 0 (observation_date) to x, column 1 (DFEDTARU) to value
    var mapping = dataSet.mapAs({x: 0, value: 1});

    // ... chart code goes here

  }
);

4.2. Create the Chart and Series

With the data mapped, creating the chart takes three lines: a step line chart instance, a date-time x-scale to position each day at its correct location on the time axis, and a series bound to the loaded data.

var chart = anychart.stepLine();
chart.xScale(anychart.scales.dateTime());
const series = chart.stepLine(mapping);

4.3.Finish and Render

Set a descriptive title, assign the container, and call draw() to render the resulting step line chart. The chart doesn't appear on the page until that last call runs.

chart.title("U.S. Federal Funds Rate (2016–2026)");
chart.container("container");
chart.draw();

Full Code and Result

Here is the complete, runnable HTML with all the pieces assembled.

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>JavaScript Step Line Chart</title>
  <style>
    html, body, #container {
      width: 100%;
      height: 100%;
      margin: 0;
      padding: 0;
    }
  </style>
  <script src="https://cdn.anychart.com/releases/8.14.1/js/anychart-base.min.js"></script>
  <script src="https://cdn.anychart.com/releases/8.14.1/js/anychart-data-adapter.min.js"></script>
</head>
<body>
  <div id="container"></div>
  <script>
    anychart.onDocumentReady(function () {
      anychart.data.loadCsvFile(
        "https://raw.githubusercontent.com/andreykh1985/anychart-data/main/DFEDTARU.csv",
        function (data) {
          var dataSet = anychart.data.set(data, {ignoreFirstRow: true});
          var mapping = dataSet.mapAs({x: 0, value: 1});
          var chart = anychart.stepLine();
          chart.xScale(anychart.scales.dateTime());
          const series = chart.stepLine(mapping);
          chart.title("U.S. Federal Funds Rate (2016–2026)");
          chart.container("container");
          chart.draw();
        }
      );
    });
  </script>
</body>
</html>

That’s it! A basic JavaScript step line chart is ready. The long flat segment at 0.25% represents the two years the rate was held at near-zero following the March 2020 emergency cut. The eleven consecutive hikes from 2022 to 2023 form a steep ascending staircase up to 5.50%. The subsequent cuts in 2024 and 2025 descend in steps on the right side of the chart.

See it embedded below and open it on AnyChart Playground to explore and play with the full code.

How to Customize a JavaScript Step Line Chart

The basic chart shows the rate history, but a few targeted changes can make it significantly more readable. The four customizations below address the line visibility, tooltip content, axis labeling, and navigation across the full ten-year range.

A. Style the Series Stroke

The default stroke is thin and uses AnyChart’s automatic palette color. A heavier line with a specific color will make the staircase stand out more clearly.

series.stroke("#1976d2", 3);

B. Format the Tooltip

By default, the tooltip title shows the date in a format like “2023 Apr 16”, and the body shows “Series 0: 5.25” — a generic series name and a bare number with no unit. Let’s replace both: the title gets a cleaner date format, and the body gets an explicit label with a percentage sign.

chart.tooltip().titleFormat(function () {
  return anychart.format.dateTime(this.x, "MMM d, yyyy");
});
chart.tooltip().format(function () {
  return "Upper limit: " + this.value + "%";
});

The titleFormat() and format() methods each accept a callback; inside them, this.x is the timestamp of the hovered point and this.value is the rate.

C. Format the Axes

Axis customization often makes a bigger difference than it may seem.

The date-time scale auto-picks a tick interval based on available width — with ten years of data, it defaults to every three years. Setting the interval to 1 forces one tick per year:

chart.xScale().ticks().interval(1);

The y-axis also needs attention — bare numbers like “1.75” carry no unit on their own. Adding a “%” suffix and an axis title makes the scale immediately readable:

chart.yAxis().labels().format("{%Value}%");
chart.yAxis().title("Target Range Upper Limit");

By default, the y-scale sets its range automatically based on the data in view. That works fine in many cases, but you can override it whenever a specific baseline or ceiling makes more sense. Here, locking the range between 0 and 6% gives the rate history a consistent frame. And it will pay off in the next step too: once the scroller is in place, zooming in won’t cause the axis to jump.

chart.yScale().minimum(0);
chart.yScale().maximum(6);

D. Add a Scroller

Ten years of daily data in a single view makes individual FOMC decisions hard to isolate. Let’s add a scroller so readers can zoom into any period they want to examine — the eleven consecutive hikes from 2022 to 2023, the emergency cuts of March 2020, the slow normalization through 2016–2018 — without losing sight of the full ten-year range. Enable it with xScroller(); it adds a narrow strip below the chart with draggable range handles:

chart.xScroller().enabled(true);

Final Result

Below is the complete interactive JavaScript step line chart with all customizations applied — custom stroke, formatted tooltip, year-by-year axis labels, and a scroller. Check it out and open it on AnyChart Playground where you can play with the code, add your own data, and so on.

Conclusion

This tutorial covered building an interactive JavaScript step line chart from scratch using ten years of real Federal Reserve rate data loaded directly from a CSV file. Along the way, the chart received a date-time scale to represent duration correctly, stroke styling with interactive states, a formatted tooltip, year-by-year axis labels, and a scroller for timeline navigation.

For further exploration, see the step line chart documentation. For related chart types, check out the line chart tutorial and browse the line chart examples in the gallery.

Have questions or ran into something unexpected? Leave a comment or reach out to the AnyChart Support Team.

Originally published at https://www.anychart.com on May 19, 2026.

A new Friday, a new round of DataViz Weekly. We are here to share a small selection of data visualization projects that pulled us in lately and we believe will do the same for you.

See what is in this edition:

See what is in this edition:

• Winners and losers on Polymarket — The Washington Post
• Patterns in English similes — The Pudding
• Big Tech lobbying in Germany — Zentrum für Digitalrechte und Demokratie
• Atlas of global progress — The World Bank

Polymarket Winners and Losers

Polymarket lets users buy and sell shares tied to whether real-world events will happen, from elections to award show winners. The platform’s user base has grown into the millions over the past three years.

The Washington Post built a click-through interactive examining how winnings and losses are distributed across that user base. The heatmap chart above plots nearly all Polymarket users by net winnings on the horizontal axis and total trades on the vertical, with color encoding the number of users in each bin. The near-vertical spike at zero is where most accounts cluster, just to the loss side of break-even. Subsequent steps zoom the chart out to its full extent, exposing how roughly 1,200 users sit far above the rest. One of them is highlighted as an apparently automated account that placed over a million Oscar-related trades and netted three million dollars.

Earlier steps build context. A back-to-back bar chart breaks 1.7 million losing accounts and 765,000 winning ones into dollar buckets, layered with the total sums lost and won. Other steps cover how prediction market prices imply probabilities and how Polymarket’s loss rates compare with U.K. online gambling.

👉 See the article on The Washington Post, by Jeremy B. Merrill and Leslie Shapiro.

Patterns in English Similes

Similes are everywhere in English writing. The phrases writers reach for tend to repeat, settling into patterns and clichés.

Russell Samora at The Pudding pulled 200,000 similes in the “as ___ as ___” form from a large corpus of novels, covering the 500 most frequent adjectives. The piece opens with a fill-in-the-blank prompt for “my mouth has gone as dry as ___”. A column chart reveals the dominant completions for “dry”, with bone, desert, and dust leading a long tail of rarer choices. The visualization above is a grid of small multiple column charts, each one covering a single adjective and ranking its top 20 noun pairings. The contour of each chart tells you something. A long flat profile points to an adjective with real variety. A tall first bar dwarfing the rest signals a fixed pairing.

A bubble chart further down inverts the question and ranks nouns by versatility, applying Simpson’s diversity index to each one. Specialists cluster at one end, generalists at the other. The clearest specialist is the cucumber, locked to “cool” so tightly that two random “cool as a ___” similes will match on the noun nine times out of ten. At the other extreme, the same noun shows up among the top picks for more than a dozen different adjectives.

👉 Check out the project on The Pudding, by Russell Samora, with design and illustration by Shelly Tan.

Big Tech Lobbying in Germany

Big Tech firms invest substantial resources in shaping legislation across Europe. In Germany, their influence flows through layered networks of industry associations, lobbying agencies, and topic- or party-aligned clubs that connect them to lawmakers.

The Center for Digital Rights and Democracy (Zentrum für Digitalrechte und Demokratie) released an interactive network diagram of these connections, drawing on entries in the German Lobby Register. Each organization appears as a bubble sized by its 2024 lobbying budget and colored by type. Red marks Big Tech firms, blue industry associations, green lobbying agencies, and yellow other clubs and aligned networks. Arrows show directed relationships, where an arrow from A to B means A is a member or client of B. Hovering over a bubble highlights its connections, and clicking opens a panel with registry details and listed contacts.

An intro overlay walks readers through what the project covers before they reach the graph, and stays accessible from the sidebar. The focus is direct lobbying of the federal government and parliament. Adjacent channels of influence, including EU institutions, courts, public sector procurement, and media outreach, sit outside the current scope.

👉 Explore the visual at lobbylandkarte.digitalrechte.de, by Joris Leander Kanowski.

Atlas of Global Progress

The world has seen broad gains across most areas of human development over the past 75 years. Recent years have brought a slowdown across many of the same dimensions.

The World Bank’s Atlas of Global Development 2026 examines where countries stand and how fast they are moving across five themes: people, prosperity, planet, infrastructure, and digital. The project pulls together more than 121,000 data points spanning 75 years and 200 economies into 12 scroll-driven stories and 95 visualizations.

The chart above plots the global average speed of progress across six indicators from 1950 to 2024. Each indicator appears as a faint line with a shaded range area band around it, and a bolder line traces the cross-indicator average. The Y-axis runs through ordinal labels from Reversal at the bottom up through Standstill, Slow, Typical, and Fast. The bolder line peaks just before 2010 and slides down to its lowest point on record.

The themes that follow build out the picture story by story.

👉 Discover the Atlas on Data360, with visuals designed and developed by Jan Willem Tulp, Christian Laesser, Maarten Lambrechts, Ændra Rininsland, and Alice Thudt.

Prediction markets, English prose, lobbying networks, global development. Four very different subjects, each opened up by thoughtful visualization. See you soon with more great examples of charting and mapping in action, in our weekly series:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on May 15, 2026.

Four Recent Data Visualization Projects That Held Our Interest — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Data visualization does not take weeks off. We track charts and maps as they appear across the web and bring the most compelling finds together in DataViz Weekly.

Our picks for this edition:

• AI-generated content on the web — Jonas Dolezal, Sawood Alam, Mark Graham, and Maty Bohacek
• Divorce rates by job — Nathan Yau
• Global press freedom at a 25-year low — Reporters Without Borders
• Italy’s pet boom — Il Sole 24 Ore

Rise of AI-Generated Content

The share of AI-generated content on the internet has grown rapidly since late 2022. Public debate about its effects on factual accuracy, diversity of ideas, and writing quality has intensified alongside that growth.

Researchers from Imperial College London, the Internet Archive, and Stanford University tracked how much of the web’s newly published content is AI-generated and what effects it has on online discourse. The chart above uses dots, trend lines, and confidence bands to show how the share of AI-generated websites climbed from virtually nothing before late 2022 to roughly 35% by mid-2025.

The full project examines six commonly feared negative effects of AI content, such as truth decay, stylistic monoculture, and shrinking diversity of ideas. For each, scatter plots and column charts present the quantitative findings alongside survey responses from U.S. adults. A concluding pair of horizontal bar charts places the statistical correlation for each effect alongside the public agreement rate, showing where evidence and perception diverge.

🔗 See the project at ai-on-the-internet.github.io, by Jonas Dolezal, Sawood Alam, Mark Graham, and Maty Bohacek.

Divorce Rates by Job

Not every marriage survives a lifetime. In the United States, roughly one in three ends in divorce, and the numbers look very different depending on what people do for a living.

Nathan Yau represented divorce rates across hundreds of occupations as an interactive beeswarm chart, positioning each job vertically by its rate and sizing the bubbles by number of workers. Orange marks occupations above the overall average; teal marks those below. A search box lets you locate any specific job.

Further down, the same data breaks into small multiples of beeswarm charts by job category, making the spread within each field visible at a glance.

🔗 Check out the post on FlowingData.

Global Press Freedom at a 25-Year Low

Press freedom has been declining globally for years. This year it reached its lowest recorded level in a quarter century.

Reporters Without Borders published their 2026 World Press Freedom Index, and the choropleth map that traditionally accompanies it has never been this red. Five indicator filters covering political, economic, legal, social, and security dimensions let you shift between different aspects of press freedom, and hovering or clicking any country shows its score alongside last year’s figure for comparison.

The index also comes with a dedicated scroll-driven visual story this year. It uses dot and line charts to trace average scores worldwide and by region, and cartograms to show press freedom relative to population. Individual country spotlights include the steepest fall — Niger — and the biggest improvement.

🔗 Explore the map at rsf.org and the visual story at infog-index.rsf.org.

Italy’s Pet Boom

Italy has one of the lowest birth rates in Europe, and its pet population has been rising steadily. The total number of pets in the country now exceeds its human population.

Il Sole 24 Ore’s Lab24 built a scroll-driven data story exploring the demographic and economic dimensions of this shift. It opens with an alluvial diagram tracking the decline in Italy’s child population alongside the rise in cat and dog ownership from 2014 to 2024.

Further into the piece, the economics of Italy’s pet boom come into focus. A range of bar, line, and pictogram charts covers topics including the cost of owning a dog versus raising a child, the pet food market compared against baby food, and more.

🔗 Look at the story on Il Sole 24 Ore’s Lab24, by Massimo De Laurentiis, with data visualization by Alice Calvi and Luca Galimberti.

The subjects change from edition to edition. The quality bar does not. More examples of data visualization at work in the real world — next time:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on May 8, 2026.

Visualizing Data on AI Content, Press Freedom, Pet Boom, Divorce | DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Every week, countless data visuals appear across all domains and formats. Every Friday, we curate those we found most interesting, sharing them as examples of data visualization work in practice.

Glad to feature this time in DataViz Weekly:

• British voter intent by demographic — The Economist
• America’s electrical grid under strain — The New York Times
• Disappearance of iceberg A23a — The European Correspondent
• 2025 year in music — Chartmetric

British Voter Intent by Demographic

Britain’s two-party political system is fragmenting. Conservatives and Labour have dominated Westminster for a century, but Reform UK and the Green Party are now competing seriously for voters on both flanks.

The Economist built a statistical model of the British electorate based on nearly 40,000 survey responses from More In Common, updated weekly. Users select from eight demographic characteristics: sex, age, ethnicity, region, education, employment, housing tenure, and urban or rural setting. The combinations yield 275,000 possible voter profiles.

The first visual is a vertical stepped line chart. Each demographic selection appears as a row, with color-coded step-lines for Labour, Conservative, Liberal Democrats, Reform UK, Green, and Other showing how that choice pulls each party’s probability away from the national average. Final percentages for the assembled profile appear at the bottom, with 2024 estimates alongside for comparison.

A polar chart with an ordinal scale then plots the profile as a bold outlined polygon on six party-labeled axes. Thousands of faint overlapping shapes fill the same space, representing every other demographic combination in the dataset. Clicking any shape reveals the characteristics behind that profile.

👉 See the article on The Economist.

America’s Electrical Grid Under Strain

The United States electrical grid was built more than a century ago and has seen limited fundamental upgrades since. Electricity prices have risen sharply in recent years while demand, flat for over a decade, is growing again.

Robinson Meyer’s essay in The New York Times Opinion section, with graphics by Sara Chodosh, opens with a scrollytelling sequence built around a map of U.S. transmission lines. As the narrative advances, planned data center locations appear across the network, showing where new demand is concentrating.

Line charts then trace retail electricity prices by sector from 2000 to 2025, and track changes in electricity sales across residential, commercial, and industrial customers. Two interactive stacked area charts with alluvial effect follow — one for residential consumption, one for commercial. Each tracks how electricity use by activity has shifted over time, with historical data extending into projections through 2050. Hovering highlights any individual use category across the full timeline.

Additional graphics include a dot chart comparing state-level changes in electricity load and price, a stepped line chart of utility spending shifting from generation toward distribution and transmission, and a cartogram mapping natural gas price risk by state.

👉 Check out the essay on The New York Times, by Robinson Meyer and Sara Chodosh.

Disappearance of Iceberg A23a

Iceberg A23a broke off from an Antarctic ice shelf 40 years ago as the largest iceberg on record. After remaining grounded for three decades, it began drifting north in 2020 and by early 2026 had broken apart almost entirely.

The European Correspondent tracks A23a’s disintegration through two charts by Meike Eijsberg that together give the iceberg’s size a concrete human scale. The first is a small multiples grid of proportional squares placing A23a’s footprint at two moments — 3,500 km² in January 2025 and 141 km² in March 2026 — alongside the areas of 30 European capital cities, from London down to Brussels.

The second is a stepped area chart tracking A23a’s area month by month from mid-2024 through early 2026. The descent is gradual at first, then near-vertical in late 2025 as the iceberg entered warmer waters, with annotations marking the points where it shrank below the size of London and then Madrid.

👉 Look at the piece on The European Correspondent, by Ida Ovesson and Meike Eijsberg.

2025 Year in Music

Each year, the global music industry produces millions of new releases and reshapes itself around new platforms, markets, and listening habits. 2025 brought another wave of change.

Chartmetric’s annual Year in Music report, designed by Beyond Words Studio, takes it on across nine themes: top artists and tracks, genres, live events, media syncs, brand affinities, songwriters, streaming, social media, and global trends. The piece is a scroll-driven interactive where each section stands on its own and can be navigated independently. A scrollytelling intro animates key platform statistics and a career stage breakdown before handing off to the main report.

The visual range is wide. Across the nine sections, the report draws on circle packing, Sankey diagrams, bubble charts, quadrant charts, Venn diagrams, bar charts, and more to cover a year’s worth of the music industry from every angle.

👉 Explore the full report on Chartmetric, designed by Beyond Words Studio.

Voter demographics, power infrastructure, a vanishing iceberg, a year of global music — data is everywhere, and visualization helps us understand it better. Next Friday, DataViz Weekly returns with another selection of charts and maps worth your attention. Stay tuned:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on May 1, 2026.

Fresh Data Visuals That Caught Our Attention — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.