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.

There is always more to see when it comes to great charts and maps. Welcome to DataViz Weekly, where we put a spotlight on notable data visualizations as they come out around the web.

Here are the projects that made the cut this time:

• Tehran strike damage by land use — Bloomberg
• Six decades of population change in Europe — Correctiv
• Job stability across occupations in the United States — Nathan Yau
• Buildings by proximity to roads — Benjamin Lozes

Tehran Strike Damage by Land Use

U.S. and Israeli air strikes on Iran began in late February 2026, leaving significant damage across Tehran before a ceasefire took hold in early April. Military installations, government buildings, and residential and commercial areas sit side by side in the city’s dense urban fabric.

Bloomberg analyzed strike damage across Tehran through the lens of land use, cross-referencing damage analysis by Oregon State University researchers with land-use data from OpenStreetMap and Overture Maps. The piece opens with a choropleth map of the city coloring damage clusters by predominant category: civilian, commercial, industrial, military, and government. Grids of Voronoi treemaps then show the actual land-use mix within every cluster, making visible how sites classified as predominantly military or industrial typically contain civilian and commercial areas as well. Further on, a bubble map of Iran shows cumulative damaged area by location, with Tehran and Isfahan standing out as the most heavily affected.

👉 See the piece on Bloomberg, by Golnar Motevalli, Krishna Karra, Tom Fevrier, and Raeedah Wahid.

Six Decades of Population Change in Europe

Europe’s total population has grown steadily over recent decades. But that overall growth conceals sharp differences from place to place.

Correctiv built a scroll-driven visual story around a choropleth map drawn from new EU Joint Research Centre data covering population change at the local level since 1961. Municipalities across 32 European countries are colored green for growth and red for decline. The narrative steps through regional patterns in Spain, Greece, Germany, Bulgaria, and Lithuania, showing how rural depopulation and post-communist emigration played out differently across the continent. At the end, the map opens as a fully interactive tool — search for any town or village and see its population trajectory as a line chart spanning six decades.

👉 Check out the story on Correctiv, by Lilith Grull, Ada Homolova, Frida Thurm, Luc Martinon, Philipp Waack, and others.

Job Stability Across Occupations

Not all jobs are equal when it comes to how long people stick around. Some occupations hold workers for years. Others see a constant stream of new faces.

Nathan Yau provides a look at job tenure and turnover across occupations in the United States based on data from the Current Population Survey. A dumbbell chart lists occupations sorted from longest median tenure to shortest, with the 25th and 75th percentile ranges flanking each median point. A scatter plot then positions each occupation by median tenure against its interquartile range, grouping jobs into four zones: high turnover, heavier rotation with some long-term workers, more stable, and mixed workforce. Both charts are interactive: you can search for a specific occupation and see details on hover.

👉 Look at the post on FlowingData.

Buildings by Proximity to Roads

Buildings in dense cities can sit just a few feet from passing traffic. That proximity shapes the noise, air quality, and safety residents experience every day.

Benjamin Lozes built an interactive map using OpenStreetMap data that calculates the distance from every wall vertex of every building to the nearest road segment. Buildings are colored by a continuous gradient encoding that distance. At lower zoom levels, each structure appears as a single dot. Zoom in further and the map switches to full wall-segment rendering, coloring individual wall sections by their specific proximity. Currently available regions include Zurich, New York City, California, Colorado, Greece, Monaco, and Vietnam.

👉 Explore the map here.

Good data visualization keeps showing up across the web. We track it down and bring it together every Friday. So, more great charts and maps are coming next time — stay tuned:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on April 24, 2026.

More Great Charts and Maps to See — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Robust data visualization makes complex subjects easier to see and understand. DataViz Weekly is our ongoing series collecting the best examples we come across most recently.

Check out what stood out to us this week:

• Causes of death across countries — Our World in Data
• Cuba’s oil crisis — Reuters
• Family business succession wave — The Economist
• Three years of war in Sudan — Al Jazeera

Causes of Death Across Countries

Around 165,000 people die every day around the world. The causes behind those deaths look very different depending on where in the world you live.

Our World in Data published an interactive treemap built by Sophia Mersmann and Fiona Spooner using data from the IHME’s Global Burden of Disease study. Each rectangle represents a cause of death, sized proportionally to its share of total mortality. A color system divides causes into four broad groups: non-communicable diseases in blue, infectious diseases in red, injuries in green, and neonatal and maternal deaths in purple.

The visualization is available in an article by Hannah Ritchie that opens with a global view, then steps through the same format for low-income and high-income countries separately, making the structural differences between them immediately visible. The fully interactive version at the end lets you select a country or region, filter by year — with data spanning over four decades — and narrow by age group or sex.

👉 See the article on Our World in Data.

Cuba’s Oil Crisis

Cuba produces less than a third of the oil it needs and depends on imports for the rest. The loss of its two main suppliers has pushed the country into a severe energy crisis, with widespread blackouts affecting daily life.

Reuters built a data story around a stream graph tracing Cuba’s crude oil imports month by month from January 2025 through March 2026. Three bands represent Venezuela, Mexico, and other countries. The stream narrows dramatically as imports collapse, with annotated callouts marking key events: Venezuela’s final shipment in December 2025, Mexico’s exit in January 2026, and a small Russian delivery at the end of March amounting to just 7–10 days of supply under rationing.

Bar charts further into the piece show petroleum’s share of Cuba’s energy consumption compared to neighboring countries and the regional average, and break down oil use by sector.

👉 Check out the story on Reuters, by Ally J. Levine, Travis Hartman, Tiana McGee, and Marianna Parraga.

Family Business Succession Wave

Family firms account for around two-thirds of all businesses worldwide and a similar share of global GDP. Many are now approaching a generational handover at the same time, as founding owners across the West and Asia reach retirement age.

The Economist uses circle packing charts to show the composition of large listed companies across major markets by ownership type, plotting each company as a bubble sized by market capitalization. Red marks family firms, yellow marks the rest.

The piece opens with a single pack of bubbles. As you scroll, the visual steps through regional breakdowns for the U.S. and Europe, followed by individual Asian markets. Each view makes the varying density of family ownership immediately visible across markets.

An interactive section then displays all eight markets at once. Hovering over any bubble identifies the company. A toggle switches between relative and actual size, shifting the picture from ownership share to raw market weight.

👉 Look at the piece on The Economist.

Three Years of War in Sudan

War broke out in Sudan on April 15, 2023, between the Sudanese Armed Forces and the paramilitary Rapid Support Forces. Three years on, more than 14 million people have been displaced and over 50,000 deaths recorded.

Al Jazeera built a scrollytelling piece anchored by a persistent dot map showing all 13,400+ recorded attacks across the country, color-coded by actor: green for the Sudanese Army, red for the RSF, and yellow for others. As the narrative scrolls through 10 of Sudan’s most heavily attacked regions, additional visuals appear on the left.

A dot map opens the piece with attack counts by region. A line chart traces the timeline of attacks by each party from 2023 through early 2026. Choropleth maps show how territorial control shifted — from the RSF’s early dominance across Darfur and the capital region to the army’s recovery of the east and center by April 2026.

👉 Explore the story on Al Jazeera, by Mohamed A. Hussein and Amr Alkazaz.

This is what good data visualization looks like in practice — charts and maps that make complex subjects genuinely easier to understand. Check back next week for more great examples:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on April 17, 2026.

How Data Visualization Opens Up Complex Subjects — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Good data graphics keep turning up online, and we never stop looking. DataViz Weekly is our regular roundup of the latest examples we think are worth your attention.

Take a look at our new picks:

• Early leaves and blooms across the United States — The Washington Post
• D.C. cherry blossom shifts — Sara Staedicke
• Satellites crowding Earth’s orbit — The Guardian
• Every building in the Netherlands by year built — Bert Spaan

Early Leaves and Blooms Across the U.S.

Plants leaf out and flower in response to accumulated warmth, not the calendar. In 2026, an exceptionally warm March pushed that timing well ahead of schedule across much of the United States.

The Washington Post tracked where spring arrived early and where it ran behind, using data from the USA National Phenology Network. A city-level chart opens the piece: select a location to find out when leaves appeared and flowers bloomed this year against the historical average and range. Maps of the contiguous United States follow to put the geographical pattern in full view. First for leaves, then for blooms, pink marks areas ahead of schedule, green marks areas behind, with some cities annotated and new records flagged. A third map shows where March temperatures themselves broke records.

👉 See the piece on The Washington Post, by Ben Noll, John Muyskens, and Naema Ahmed.

D.C. Cherry Blossom Shifts

Washington, D.C.’s cherry blossoms draw over a million visitors each spring. This year, peak bloom arrived on March 26, which is three days ahead of the National Park Service forecast and even earlier than every other major prediction.

Sara Staedicke examines the biology of the 2026 season and the long-term shift in bloom timing for the U.S. capital. As you scroll, a timeline of cherry tree development appears year by year since 2006, tracking progression through six bloom stages from green buds to peak bloom. Daily temperature data then fills in behind the bars, making the link between warm or cold spells and the pace of development visible directly.

Line and dot charts then zoom in on 2026 — first how temperatures compared to the 30-year average, then how the actual peak bloom date stacked up against forecasts from five organizations. A scatter plot takes the long view, plotting every NPS-recorded peak bloom date since 1921 with a 10-year moving average showing the steady drift toward earlier flowering. The final visual maps cherry tree locations around the Tidal Basin and across the city.

👉 Look at the story on DC in Bloom.

Satellites Crowding Earth’s Orbit

Since 1957, the number of objects in Earth’s orbit has grown from one to more than 30,000. The rise has accelerated sharply with commercial mega-constellations adding thousands of satellites in recent years.

The Guardian looks at how Earth’s orbit became this crowded and what a collision chain reaction could mean for the future of space. First, dots flying around an animated globe show how satellite counts have accumulated over time. A unit chart then plots satellite launches per year from 1957 to the present, with each dot representing about ten satellites. The same dots are then reorganized into a series of proportional circle comparisons, breaking down what is up there: decayed versus still in orbit, government versus private launches, Starlink versus everything else, active satellites versus space junk, and objects by country.

👉 Check out the article on The Guardian, by Frederick O’Brien, Ashley Kirk, and Oliver Holmes.

Every Building in the Netherlands by Year Built

The Netherlands maintains a national registry of every address and building in the country, including when each was constructed. The dataset currently covers more than 11 million structures.

Bert Spaan mapped it all. Each building is colored by year of construction, with hues of red, yellow, and blue shifting across the centuries of the built environment. Clicking any building brings up its registry ID, construction year, and links to OpenStreetMap, Google Street View, and Allmaps.

It’s a new version of his own map created in 2013 and last updated in 2015, at that time covering under 10 million buildings. This latest version has a toggle layer highlighting the roughly 1.5 million buildings added since. There is also a city-guessing game based on building patterns.

👉 Explore the map on Bert Spaan’s website.

Good visualization makes data easier to understand. This week’s four projects do that across very different ground. We will be back with more examples of data graphics worth exploring in Data Visualization Weekly — stay tuned.

Bonus: We recently published a step-by-step tutorial on how to create a JavaScript vertical area chart. It uses 80+ years of U.S. presidential job approval/disapproval ratings (Gallup) as a practical example, visualized as two mirrored area series running top to bottom. Check it out.

Originally published at https://www.anychart.com on April 10, 2026.

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

Traditionally, charts that visualize data over time are horizontal. But sometimes a vertical layout is a better fit. In this tutorial, you will learn how to create an interactive vertical area chart using JavaScript step by step.

The practical example uses monthly approval and disapproval ratings of American presidents from 1941 to 2025, according to Gallup polls. The final chart shows over 80 years of public support and opposition across U.S. administrations as two mirrored area series running top to bottom.

The result will look like this:

What Is a Vertical Area Chart?

A vertical area chart is a type of data visualization that rotates a standard area chart 90 degrees. The time or category axis runs vertically, and the value axis runs horizontally. The filled area between the series line and the baseline still communicates magnitude and change — the orientation just shifts so that time flows top to bottom instead of left to right.

This layout works well in a few specific situations: when the timeline is long and a horizontal chart would compress the data too much, when category labels are long and hard to fit on a horizontal axis, or when the chart is embedded in a vertically scrolling page and a wide horizontal layout would interrupt the reading flow. It is also a natural choice when the main story is the balance between two opposing series.

How to Build a JavaScript Vertical Area Chart

Building an interactive vertical area chart with JavaScript involves four steps: creating the HTML page, loading the library, preparing the data, and writing the visualization code.

1. Create an HTML Page

Start with a minimal HTML file containing a <div> element that will hold the chart. The #container div is set to fill the full browser window here, but you can replace the percentage values with fixed pixel dimensions if the chart should occupy only part of a page.

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>JavaScript Vertical Area 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>

Now that the HTML page is in place, let’s add the charting library.

2. Include the JavaScript Files

In this tutorial, we will be using the AnyChart JavaScript charting library. The vertical area chart type is available in its anychart-base.min.js module. Load it from the AnyChart CDN by adding a <script> tag 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 vertical area charts -->
  <script src="https://cdn.anychart.com/releases/8.14.1/js/anychart-base.min.js"></script>
</head>
<body>
  <div id="container"></div>
  <!-- chart code goes here -->
  <script>
  </script>
</body>

With the library loaded, the next step is to prepare the data.

3. Prepare the Data

The chart uses U.S. presidential job approval data from the American Presidency Project at UC Santa Barbara, based on Gallup polling results covering 16 presidents from Franklin D. Roosevelt to Donald Trump’s second term. Monthly averages of approval and disapproval percentages were computed across all available polls for each month.

The dataset contains 910 monthly data points. Each row holds a month label, the average approval percentage, and the average disapproval percentage stored as a negative number. Storing disapproval as negative creates the mirrored effect around the zero baseline — the technique that gives this chart its distinctive shape. Here is a sample of the data:

Each row is a three-element array: the month label string, the approval value, and the disapproval value, and it looks like this in the code:

// each entry: [month label, approval %, disapproval as negative %]
// the full dataset has 910 rows; the complete version is in the Playground link below
const rawData = [
  ["Jul 1941", 67, -24],
  ["Aug 1941", 67, -24],
  ["Sep 1941", 70, -24],
  // ... 907 more monthly entries ...
  ["Dec 2025", 36, -59]
];

4. Write the JS Code for the Chart

All the JavaScript goes inside an anychart.onDocumentReady() wrapper - a function that AnyChart calls as soon as the page has fully loaded. This guarantees that the chart container <div> exists in the DOM before the chart tries to render into it.

anychart.onDocumentReady(function () {

  // all chart code goes here

});

Add the Data

The rawData array from Step 3 is the first thing to place inside anychart.onDocumentReady().

The time axis will use a date/time scale, which requires JavaScript Date objects rather than strings like "Jul 1941". Each row is therefore needs to be converted before passing it into the chart.

MONTH_IDX is a small lookup object that maps three-letter month abbreviations to their zero-based index - January = 0, December = 11 - matching the Date constructor.

// map month abbreviations to their zero-based JS Date index
const MONTH_IDX = {
  Jan:0, Feb:1, Mar:2, Apr:3, May:4,  Jun:5,
  Jul:6, Aug:7, Sep:8, Oct:9, Nov:10, Dec:11
};

// convert each "MMM YYYY" string to a Date object, keep the approval and disapproval values
const data = rawData.map(function(row) {
  const parts = row[0].split(" ");
  return [new Date(parseInt(parts[1]), MONTH_IDX[parts[0]], 1), row[1], row[2]];
});

Create a Data Set and Map the Two Series

AnyChart uses a data set as a single source that can feed multiple series at once. We load all 910 rows into one with anychart.data.set(), then create two mappings from it. approvalMap reads column 1 as the series value; disapprovalMap reads column 2. Both share column 0 - the Date object - as their x position.

// load the data into an AnyChart data set
const ds = anychart.data.set(data);

// map approval (column 1) and disapproval (column 2) as separate series
// both share column 0 (the Date object) as their x position
const approvalMap    = ds.mapAs({x: 0, value: 1});
const disapprovalMap = ds.mapAs({x: 0, value: 2});

Create the Chart

One call creates the chart. anychart.verticalArea() returns a vertical area chart instance - a standard area chart rotated 90 degrees, with the time axis running vertically and the value axis running horizontally. Everything else - series, scales, visual settings - attaches to this object.

// create the vertical area chart
const chart = anychart.verticalArea();

Add the Two Area Series

Each series gets its own chart.area() call, bound to one of the data mappings created above. In addition, connectMissingPoints(true) keeps the line continuous across months where no poll was conducted - without it, the chart would show gaps in the data.

// approval series: positive values extend to the right of the zero line
const approvalSeries = chart.area(approvalMap);
approvalSeries.name("Approval");
approvalSeries.connectMissingPoints(true); // bridge months with no poll data

// disapproval series: negative values extend to the left of the zero line
const disapprovalSeries = chart.area(disapprovalMap);
disapprovalSeries.name("Disapproval");
disapprovalSeries.connectMissingPoints(true);

Configure the Scales

The chart has two scales to configure: the x-scale for the vertical time axis and the y-scale for the horizontal value axis.

The default x-scale places ticks at data-point positions, which is not useful here. We replace it with a date/time scale that puts one tick at the start of every calendar year. inverted(true) flips the direction: 1941 at the top, 2025 at the bottom - the natural reading order for a historical timeline.

// replace the default scale with a datetime scale for proper yearly tick marks
const xScale = anychart.scales.dateTime();
xScale.ticks().interval("year", 1); // one tick at the start of every calendar year
xScale.inverted(true);              // 1941 at top, 2025 at bottom
chart.xScale(xScale);

For the y-scale, we fix the range at −100 to 100. This keeps both sides symmetrical and prevents clipping even the most extreme values in the dataset.

// set the horizontal value axis to run symmetrically from -100 to 100
chart.yScale().minimum(-100);
chart.yScale().maximum(100);

Final Steps

A few more calls finish the setup before rendering.

First, chart.title() sets a descriptive title above the chart.

// add a descriptive title above the chart
chart.title("U.S. Presidential Approval Ratings (1941–2025)");

Second, chart.legend(true) enables the legend so the viewer knows which color is approval and which is disapproval.

// show the series legend
chart.legend(true);

Finally, chart.container() names the <div> the chart should render into, and chart.draw() triggers the render because nothing appears on the page until this call runs.

// point the chart at the container div and render it
chart.container("container");
chart.draw();

Full Code and Result

Here is the complete, runnable HTML code with all the pieces assembled. The data array is abbreviated below — the full 910-row dataset is available in the Playground link below.

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>JavaScript Vertical Area 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>
</head>
<body>
  <div id="container"></div>
  <script>
    anychart.onDocumentReady(function () {
      // monthly approval/disapproval data, 1941–2025
      // source: American Presidency Project, UC Santa Barbara
      const rawData = [
        ["Jul 1941", 67, -24],
        // ... full dataset in the Playground link below
        ["Dec 2025", 36, -59]
      ];
      // convert "MMM YYYY" labels to Date objects for the datetime scale
      const MONTH_IDX = {
        Jan:0, Feb:1, Mar:2, Apr:3, May:4,  Jun:5,
        Jul:6, Aug:7, Sep:8, Oct:9, Nov:10, Dec:11
      };
      const data = rawData.map(function(row) {
        const parts = row[0].split(" ");
        return [new Date(parseInt(parts[1]), MONTH_IDX[parts[0]], 1), row[1], row[2]];
      });
      // load into a data set and create two series mappings
      const ds = anychart.data.set(data);
      const approvalMap    = ds.mapAs({x: 0, value: 1});
      const disapprovalMap = ds.mapAs({x: 0, value: 2});
      // create a vertical area chart
      const chart = anychart.verticalArea();
      // approval and disapproval area series
      const approvalSeries = chart.area(approvalMap);
      approvalSeries.name("Approval");
      approvalSeries.connectMissingPoints(true);
      const disapprovalSeries = chart.area(disapprovalMap);
      disapprovalSeries.name("Disapproval");
      disapprovalSeries.connectMissingPoints(true);
      // datetime x-scale: yearly ticks, oldest at top
      const xScale = anychart.scales.dateTime();
      xScale.ticks().interval("year", 1);
      xScale.inverted(true);
      chart.xScale(xScale);
      // y-scale: symmetrical range around zero
      chart.yScale().minimum(-100);
      chart.yScale().maximum(100);
      // title, legend, and render
      chart.title("U.S. Presidential Approval Ratings (1941–2025)");
      chart.legend(true);
      chart.container("container");
      chart.draw();
    });
  </script>
</body>
</html>

That’s it! A basic JavaScript vertical area chart is ready, showing U.S. presidential approval and disapproval ratings since 1941 according to Gallup. Take a look at it below or open it on AnyChart Playground.

How to Customize a JavaScript Vertical Area Chart

Now let’s make some changes to the chart’s design and behavior. The five customizations below improve readability and add contextual information to the vertical area chart built in the previous part of the tutorial.

A. Smooth the Curves with Spline Area

The plain area() series connects data points with straight line segments, producing a jagged silhouette. Switching to splineArea() fits a smooth curve through the same points. Over 910 monthly values, the smoothed version reveals the broad trends without visual noise from minor month-to-month fluctuations.

Replace chart.area() with chart.splineArea() for both series:

// splineArea draws smooth interpolated curves instead of angular segments
const approvalSeries    = chart.splineArea(approvalMap);
const disapprovalSeries = chart.splineArea(disapprovalMap);

B. Set Series Colors

Green and red carry an intuitive meaning for approval and disapproval data. Use the fill() method to set the area color and opacity, and stroke() to set the outline color and thickness.

// green fill and outline for the approval series
approvalSeries.normal().fill("#27ae60", 0.5);
approvalSeries.normal().stroke("#27ae60", 1.5);

// red fill and outline for the disapproval series
disapprovalSeries.normal().fill("#e74c3c", 0.5);
disapprovalSeries.normal().stroke("#e74c3c", 1.5);

C. Format the Value Axis

Disapproval values are stored as negative numbers, so the horizontal axis would normally label the left side “-75”, “-50”, and so on. Displaying both sides as absolute values — “75%”, “50%” — makes the chart symmetrical and easier to read. The Math.abs() function in the label format() callback handles the conversion. While here, grid lines at every 25 percentage points and an explanatory axis title add further clarity.

// display absolute values with % sign on both sides of the axis
chart.yAxis().labels().format(function() {
  return Math.abs(this.value) + "%";
});

// label the axis to indicate which direction means approval and which means disapproval
chart.yAxis().title("← Disapproval  |  Approval →");

// add vertical grid lines at ±25, ±50, ±75, and 0
chart.yScale().ticks().interval(25);
chart.yGrid(true);
chart.yGrid().stroke({color: "#dddddd", thickness: 0.5});

D. Highlight the Zero Baseline

The zero line — where approval equals disapproval — is the most important reference point in the chart. A lineMarker at value 0 draws a prominent vertical line across the plot, making the boundary between net-positive and net-negative approval immediately visible.

// draw a distinct line at zero — the boundary between net approval and net disapproval
const zeroLine = chart.lineMarker(0);
zeroLine.value(0);
zeroLine.stroke({color: "#444444", thickness: 2});

E. Add a Contextual Tooltip

By default, the tooltip shows the raw timestamp and series value. We can make it more informative by displaying the president’s name and party in the title, the calendar month in the body, and the correct percentage label for each series. This can be done through a lookup table with a function to work with it, followed by the tooltip configuration itself.

Build the President Lookup Table and Function

The lookup table is an array of objects, one per president, each holding the name, party abbreviation, and the start and end dates of their term. JavaScript Date objects use zero-based month numbers, so January = 0, April = 3, August = 7, and so on.

// each entry: president name, party, and term start and end as Date objects
const presidents = [
  {name: "Franklin D. Roosevelt", party: "D", from: new Date(1941,6,1), to: new Date(1945,3,1)},
  {name: "Harry S. Truman",       party: "D", from: new Date(1945,3,1), to: new Date(1953,0,1)},
  {name: "Dwight D. Eisenhower",  party: "R", from: new Date(1953,0,1), to: new Date(1961,0,1)},
  {name: "John F. Kennedy",       party: "D", from: new Date(1961,0,1), to: new Date(1963,10,1)},
  {name: "Lyndon B. Johnson",     party: "D", from: new Date(1963,10,1),to: new Date(1969,0,1)},
  {name: "Richard Nixon",         party: "R", from: new Date(1969,0,1), to: new Date(1974,7,1)},
  {name: "Gerald Ford",           party: "R", from: new Date(1974,7,1), to: new Date(1977,0,1)},
  {name: "Jimmy Carter",          party: "D", from: new Date(1977,0,1), to: new Date(1981,0,1)},
  {name: "Ronald Reagan",         party: "R", from: new Date(1981,0,1), to: new Date(1989,0,1)},
  {name: "George H.W. Bush",      party: "R", from: new Date(1989,0,1), to: new Date(1993,0,1)},
  {name: "Bill Clinton",          party: "D", from: new Date(1993,0,1), to: new Date(2001,0,1)},
  {name: "George W. Bush",        party: "R", from: new Date(2001,0,1), to: new Date(2009,0,1)},
  {name: "Barack Obama",          party: "D", from: new Date(2009,0,1), to: new Date(2017,0,1)},
  {name: "Donald Trump",          party: "R", from: new Date(2017,0,1), to: new Date(2021,0,1)},
  {name: "Joe Biden",             party: "D", from: new Date(2021,0,1), to: new Date(2025,0,1)},
  {name: "Donald Trump",          party: "R", from: new Date(2025,0,1), to: new Date(2029,0,1)}
];

Finding the president in office on any given date requires a function that searches the lookup table by date range. getPresident() takes a timestamp, walks through the table, and returns the matching president object. If no entry covers the date, it returns null.

// find the president in office on a given date (passed as a timestamp)
function getPresident(ts) {
  const d = new Date(ts);
  for (const p of presidents) {
    if (d >= p.from && d < p.to) return p;
  }
  return null;
}

Configure the Tooltip

Two settings merge the series data and enable HTML formatting. displayMode("union") displays both series in a single tooltip, so the viewer sees approval and disapproval for the same month side by side. useHtml(true) enables HTML markup in the tooltip body, which lets us use <br/> to put values on separate lines.

// merge both series into one tooltip and allow HTML inside it
chart.tooltip().displayMode("union");
chart.tooltip().useHtml(true);

The tooltip title should name the president in office at the hovered date. titleFormat is a callback that runs once per hovered point and returns this tooltip title string. Inside it, this.x holds the timestamp of the hovered month - passed directly to getPresident() to retrieve the president's name and party.

// tooltip title: look up the president in office on the hovered date
chart.tooltip().titleFormat(function() {
  const p = getPresident(this.x);
  return p ? (p.name + " (" + p.party + ")") : "";
});

The tooltip body should show the calendar month and the series value. format runs once per series and returns that series' line in the tooltip body. A short month-name array rebuilds the date label from the timestamp. For the disapproval series, Math.abs() converts the stored negative value back to a readable positive percentage.

// month name array for formatting the date label in the tooltip body
const MON = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"];

// tooltip body: show the month, then the value for each series
chart.tooltip().format(function() {
  const d = new Date(this.x);
  const label = MON[d.getMonth()] + " " + d.getFullYear();
  if (this.seriesName === "Approval") {
    return label + "<br/>Approval: " + this.value + "%";
  }
  return "Disapproval: " + Math.abs(this.value) + "%";
});

Final Result

Below is the complete interactive vertical area chart with all customizations applied — smoothed curves, custom colors, formatted axis, zero baseline, and a contextual tooltip showing the president in office for any hovered month. Feel free to explore the full code and play with it further on AnyChart Playground.

Conclusion

This tutorial covered building an interactive JavaScript vertical area chart that maps more than eight decades of public opinion data in a single view. The vertical orientation, mirrored series, and contextual tooltip make it easy to compare how approval and disapproval moved together across U.S. administrations.

Browse the gallery for more vertical chart examples. To build a horizontal area chart, see the area chart tutorial. Beyond that, explore other JavaScript charting tutorials, the supported chart types, the documentation, and the API reference.

Questions? Ask in the comments or contact the Support Team.

Originally published at https://www.anychart.com on April 9, 2026.

Data visualization has two core purposes: explanation and exploration. In DataViz Weekly, you can see how they naturally end up combining.

Check out some of the most interesting data visualization examples we’ve found around the web lately, where charts and maps do their job in a compelling way:

• China’s rise in AI research talent — The Economist
• Shadow fleet capitalizing on the Iran war — The Financial Times
• Vietnam’s rise as a U.S. electronics supplier — Bloomberg
• Food self-sufficiency by country — Not-Ship

China’s Rise in AI Research Talent

The United States has long been home to the largest share of the world’s top AI researchers. That is changing, as both the training and the retention of elite talent shift toward China.

The Economist tracked the education and career paths of researchers who presented papers at NeurIPS 2025, the world’s leading AI conference. A bump chart opens the piece, ranking countries by their number of top AI researchers from 2016 to 2025, with China jumping to first place last year. Next comes a scrollytelling section with a Sankey diagram as the centerpiece, showing how top AI researchers’ countries of origin changed across three stages: undergraduate, postgraduate, and work. It starts with the 2019 picture and transitions to 2025 as you scroll, revealing how the U.S. and China shares moved in opposite directions. Further down, bar charts dig deeper into researcher populations and career trends across countries.

👉 Check out the piece in The Economist.

Shadow Fleet Capitalizing on Iran War

When war with Iran broke out in late February 2026, a fleet of tankers operating outside Western insurance and regulatory systems moved to center stage. Commonly known as the shadow fleet, these ships have long transported sanctioned crude from Russia, Iran, and Venezuela, and as conventional traffic stalled in the Gulf, demand for their services surged.

The Financial Times investigates how the shadow fleet has navigated and capitalized on the Iran war, using a series of visualizations. Three stacked area charts, visible above, track where sanctions-hit oil from Russia, Iran, and Venezuela has flowed since the mid-2010s, showing how Western buyers gradually exited as China and India absorbed the bulk. A dot-and- connector map built on satellite and ship-tracking data shows individual tankers switching off their signals or spoofing GPS positions near Kharg Island, Iran’s main export terminal, around the start of the conflict. Further into the piece, additional visuals, including line and stepped area charts, cover the fleet’s operations and evasion tactics in more detail.

👉 See the story on the Financial Times, by Lucy Rodgers, Nassos Stylianou, Alice Hancock, Chris Cook, Irene de la Torre Arenas, and Sam Learner.

Vietnam’s Rise as U.S. Electronics Supplier

When the Trump administration imposed sweeping tariffs on April 2, 2025, China faced the steepest levies. One stated goal was reducing U.S. dependence on Chinese-made goods and bringing manufacturing back to America.

Bloomberg analyzed shipment-level customs data to examine what actually shifted in the year since. A pictogram chart opens the piece, showing Vietnam overtook China as the leading source in 2025 compared to 2024, with an annotation callout that the shift was driven largely by manufacturers like Foxconn and BYD expanding from China into Vietnam. A Sankey diagram then maps Foxconn’s Vietnamese factory — components flowing in from China, South Korea, and Taiwan at the top, finished MacBooks, iPads, and motherboards flowing out to export destinations at the bottom. Further graphics reveal more about the shift, including a diverging bar chart showing how China’s losses in U.S. electronics imports were largely offset by gains from others, mostly Vietnam and India.

👉 Take a look at the article on Bloomberg, by Andy Lin, Nguyen Xuan Quynh, Spe Chen, and Claire Jiao.

Food Self-Sufficiency by Country

No country runs its food supply in isolation. Most rely on global trade to fill gaps between what their land produces and what their population needs.

Amanda Shendruk built a data story examining how self-sufficient 187 countries are across seven food groups, based on research published in Nature Food. A choropleth map for each food group shows what share of domestic need each country can meet through its own production. The picture varies sharply by category. For meat, 121 of 187 countries produce a surplus. For fish and vegetables, more than half cannot cover their own needs. Only Guyana currently qualifies across all seven food groups. A final choropleth map shifts the question to potential, showing how much agricultural land each country would need for a fully home-grown diet.

👉 Explore the story on Amanda Shendruk’s Not-Ship.

AI research talent, oil markets, global supply chains, food security — four very different subjects, each brought into sharper focus by thoughtful data visualization. We will be back next week with more examples of great charts and maps in action:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on April 3, 2026.

Compelling Fresh Examples of Data Visualization in Action — 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 has stories to tell. Visualization helps them reach us. Continuing our regular DataViz Weekly feature, we’re happy to share new examples of how that works in the real world.

Here’s what caught our attention these days:

• Strait of Hormuz oil and gas flows — The New York Times
• Rural hospital crisis in the United States — Reuters
• Arrests of immigrant parents of U.S. citizen children — ProPublica
• U.S. state-to-state migration flows — Will Sigal

Strait of Hormuz Oil and Gas Flows

The Strait of Hormuz connects the Persian Gulf to the rest of the world. Before fighting began in late February 2026, around 80 oil and gas tankers passed through it daily, carrying roughly a quarter of the world’s seaborne oil and a fifth of its gas.

The New York Times visualized the supply chain that conflict has since upended using a Sankey diagram. It opens with origin ports and producing countries — Iraq, Saudi Arabia, Qatar, the UAE, and others — sized by their share of energy exported through the strait. Flows converge at the strait itself, showing the full combined volume, then branch outward to destination regions. Asia receives roughly 80% of the total. Annotations mark disruptions, and callouts on the receiving end describe specific consequences country by country: gasoline price increases in the United States, factory shutdowns in Bangladesh, fuel shortfalls forcing changes in India and Southeast Asia, and rising costs across Europe.

👉 See the piece on The New York Times, by Lazaro Gamio, Blacki Migliozzi, and River Akira Davis.

Rural Hospital Crisis in U.S.

More than 40 percent of rural hospitals in the United States operate at a loss. Hundreds are considered vulnerable to closure, and demographic and policy pressures are compounding the strain.

Reuters built a data story grounding the crisis in numbers, presented through a variety of visualizations. A cartogram, pictured above, uses bubbles to show the share of rural hospitals at risk of closure by state. The concentration across the South and Midwest immediately clear. Using more charts and maps, the article also looks at the health profile of rural communities, the gap in services and revenue between rural and metro hospitals, and how Medicaid enrollment has shifted across administrations and shaped the landscape. Kansas runs through it as a concrete example.

👉 Take a look at the story on Reuters, by Sarah Slobin.

Arrests of Immigrant Parents of U.S. Citizen Children

New data shows that immigrant parents of at least 11,000 U.S. citizen children were arrested and detained in the first seven months of Trump’s second term. That is roughly double the rate under the previous administration.

ProPublica analyzed an exclusive ICE dataset covering arrests from late 2021 through mid-2025. A stepped area chart tracks monthly arrests of parents of U.S.-born children, distinguishing the Biden and Trump periods. The rise after January 2025 is immediately visible. A Sankey diagram, pictured above, then traces what happened to arrested mothers during equivalent seven-month windows under each administration: from arrest into detention, then splitting into those released, remaining in custody, or deported.

👉 Check out the article on ProPublica, by Jeff Ernsthausen, Mario Ariza, McKenzie Funk, Mica Rosenberg, and Gabriel Sandoval, with graphics by Chris Alcantara and data reporting contribution by Al Shaw.

U.S. State-to-State Migration Flows

Millions of Americans move between states every year. The patterns of where people go — and in what volume — shift over time.

Will Sigal built an animated flow map based on U.S. Census data, representing internal migration corridors as continuous streams of moving dots from origin to destination. Dot density and band width are volume-weighted: busy corridors produce denser, more active flows, while quieter ones run sparse. A playback control steps through the data year by year, revealing how patterns shift over time. You can also control what gets visible on the map, such as selecting a specific state, switching between inflows, outflows, or both, or highlighting a top corridor.

👉 Explore the map here.

The data in each of these projects had a story to tell: energy flows, healthcare economics, enforcement numbers, and migration patterns. The visualization is what made it possible to hear it.

We will be back with more great examples next week — stay tuned:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on March 27, 2026.

When Data Makes the Story — 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 is everywhere, but what it means is rarely obvious on its own. DataViz Weekly is back with four projects that illustrate how good visuals help close that gap.

Featured today:

• 25 years of egg prices — John Rush
• Jobs most at risk from AI — The Washington Post
• Bahía Blanca flood reconstruction — LA NACION
• Global data center expansion — Environmental Reporting Collective

25 Years of Egg Prices

Some people collect receipts. John Rush has been scanning every one of his since 2001 — never typing in a single price, just keeping the images, waiting for the technology to catch up.

This year it did. He extracted 25 years of egg purchase data using two AI coding agents and presented the results on his blog. A dot chart tracks price per egg across the full span, with tabs switching between nominal and inflation-adjusted prices, and one more for cumulative spend. An animated dot map traces every purchase location year by year across the Pacific Northwest. A line chart measures year-over-year price swings under the label “Personal Egg-flation.”

A calendar chart then lays out every single purchase week by week from 2001 to 2026, the full archive visible at once. The rest of the piece documents how the pipeline was built — what broke, what replaced it, and how each problem got fixed.

🔗 Look at the post on John Rush’s website.

Jobs Most at Risk from AI

Which workers are most exposed to AI, and which ones will find it hardest to adapt if their jobs change? Researchers at GovAI and the Brookings Institution analyzed more than 350 occupations across both dimensions.

The Washington Post built an interactive bubble-based scatter plot, placing each occupation on two axes: AI exposure running horizontally from least to most, and adaptability running vertically. Bubble size reflects the number of workers in each occupation. A search box lets you locate specific job titles directly on the chart.

A second view of the same chart colors each bubble by the gender makeup of the occupation, making the concentration of female-dominated roles in the high-exposure, low-adaptability corner immediately visible.

🔗 See the article on The Washington Post, by Kevin Schaul and Shira Ovide.

Bahía Blanca Flood Reconstruction

On March 7, 2025, catastrophic flooding struck Bahía Blanca, Argentina, leaving 18 people dead and more than 1,400 temporarily displaced. A combination of extreme rainfall and structural drainage failures turned the city’s streets into channels.

LA NACION reconstructed the disaster in a scroll-driven piece built on data from the National University of the South and the National Water Institute. The elevation profile pictured above uses an animated water fill to show the flood’s movement across the city’s terrain — how water built up on the steep upper slope and poured into the flat urbanized plain below, where the near-zero gradient trapped it. The piece also includes a rain-styled column chart tracking hourly precipitation across the day and a map with embedded video footage showing water levels across different parts of the city.

🔗 Check out the story on LA NACION, by Matías Avramow, Pablo Loscri, and Gabriel Podestá.

Global Data Center Expansion

Data centers are proliferating rapidly around the world, driven by demand for AI computing power. The energy, water, and land they consume have put them in direct conflict with the communities where they land.

Dirty Data, a collaborative investigation by the Environmental Reporting Collective, published an interactive world map tracking this expansion, compiled by journalists working across Asia, Latin America, and Europe. Each data center appears as a point on the map, with a toggle to switch to a hex grid view that aggregates density. A color-by selector changes what the map encodes: operational status, facility type, capacity, water use, or investment level. Country and status filters let you narrow the view.

Developed by Yan Naung Oak, the map anchors a series of reported stories covering communities displaced, water supplies strained, and legal challenges brought by residents against governments and developers.

🔗 Explore the map on the Dirty Data website.

A personal archive, a labor market model, a flood record, and global infrastructure — this week’s picks are as different as data projects get. What they share is a commitment to making the data they’re built on easier to engage with directly, thanks to good visualizations.

More real-world examples of charts and maps next week in DataViz Weekly:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on March 20, 2026.

From Personal Grocery Receipts to Global Data Centers — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.

Looking for data visualization done well? You’re in the right place. In DataViz Weekly, we feature recent work that shows how charts and maps can make complex data easier to grasp.

This edition’s lineup:

• Gender views in Europe — Teresa Talò
• Freiburg’s urban forest — Stefan Reifenberg
• Road risk across the United States — Mark Sanborn
• Party positions in Baden-Württemberg — DIE ZEIT

Gender Views in Europe

Attitudes toward gender roles still differ widely across Europe. Men and women within the same country do not always see them the same way either.

Teresa Talò shows those differences in two interactive views built from European Values Study 2017 data. The project opens with a choropleth map that colors each country on a scale from more egalitarian to more traditional based on responses to statements about work, family, and the roles of men and women. The default setting combines all seven questions into a single score. A dropdown lets you switch to individual statements and see how the geographic pattern changes.

Further down, a dumbbell chart breaks the comparison down by country. Each row has two dots, one for men and one for women, linked by a line to highlight the distance between them. A dashed vertical line marks the European average. With countries sorted from less traditional to more traditional, the chart works as both a ranking and a compact view of gender gaps across the region.

👉 Look at the project here.

Freiburg’s Urban Forest

Cities maintain thousands of trees, but those numbers rarely come to life as data. Freiburg im Breisgau, one of the greenest cities in Germany, keeps a public registry of every tree it maintains.

Stefan Reifenberg turned that registry into a scroll-driven visual story. It opens with all 45,005 trees collapsed into a single large circle, then breaks that mass apart into a bubble chart showing the 21 genera, sized by count. The next chart subdivides each genus into individual species, forming a circle packing chart. Hovering reveals names, counts, and average height, trunk, and crown measurements.

The story then shifts to trunk circumference as a proxy for age. A dot plot arranges trees into concentric rings, mirroring the cross-section of a real trunk, with the youngest trees at the center and the oldest at the outer edge. A later view isolates only the trees with the thickest trunks, Freiburg’s oldest specimens. Further sections cover height distribution and a small-multiples panel of radar charts summarizing tree statistics across the city’s 27 districts.

The piece closes with a fully navigable 3D map placing every tree at its real coordinates.

👉 Explore the story here.

Road Risk Across America

Thousands of people die on American roads every year. The risk is not evenly distributed. It varies by state, road type, speed limit, and metro area.

TripRisk uses federal crash and traffic data to map those differences across 8.7 million U.S. road segments. A dedicated road risk analysis page presents the national picture through a map of road segments colored by fatality rate, a ranked bar chart of all 50 states, a choropleth map of state-level risk, breakdowns by road type and speed limit, and a metro area ranking.

The Road Safety Explorer tool lets you zoom into any part of the country and load the visible road network colored by risk, with crash sites overlaid as point markers. A separate route planner uses those same segment-level scores, along with weather and daylight conditions, to compare fastest and safest routes between addresses.

👉 Check out TripRisk: review the Analysis and try the Explorer and Route Planner tools, by Mark Sanborn.

Party Positions in Baden-Württemberg

Party positions can shift from one level to another. In Germany, state branches do not always line up with the national party line or with their counterparts in other states.

DIE ZEIT uses a scatter plot to show where the parties in Baden-Württemberg’s 2026 state election stand. The horizontal axis runs from economically left to economically right. The vertical axis runs from socially progressive to socially conservative.

Each party appears as a labeled dot, first showing Baden-Württemberg positions alone, then, as you scroll, compared with federal parties and with party branches in Rhineland-Palatinate. A later sequence adds a time dimension, connecting points to trace party trajectories across election cycles since the early 1990s.

👉 See the article on DIE ZEIT, by Paul Blickle and Michael Schlieben.

Gender attitudes, urban trees, road danger, and political positioning. Four very different subjects, one common thread: good charts and maps give complex data a shape people can follow. Stay tuned for more data viz done well in Data Visualization Weekly.

➡ DataViz Weekly on AnyChart Blog
➡ DataViz Weekly on Medium

Originally published at https://www.anychart.com on March 13, 2026.

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

The web keeps producing data visualization work across all kinds of subjects and formats. We keep tracking it, and DataViz Weekly is where the most interesting projects we come across get their spotlight.

See our latest picks:

• Iran crisis and its impact — Reuters
• Pokémon taxonomy tree — The Straits Times
• 250 years of U.S. immigration — The Economist
• Terrain of human happiness — The Pudding

Iran Crisis and Its Impact

On February 28, the United States and Israel launched air strikes on Iran. The conflict sent immediate shockwaves across the region and beyond.

Reuters compiled its ongoing visual coverage of the Iran crisis into a single, continuously updated page. It currently opens with an animated map built on ship-tracking data showing a near-standstill in vessel movement across the Strait of Hormuz and surrounding waters, a chokepoint for roughly a fifth of the world’s oil and LNG supply. A locator inset orients viewers to the wider Gulf region.

Further sections include more maps, such as those providing an at-a-glance view of air traffic disruptions (see the animation above) and damage assessments of key Iranian government and military sites. There are also diagrams of Iran’s power structure annotated to show which senior figures have been killed, a chart tracking U.S. public opinion on the strikes, and more.

➡️ See the reporting on Reuters, by Sudev Kiyada, Clare Trainor Farley, Ally J. Levine, Travis Hartman, Adolfo Arranz, Han Huang, Mariano Zafra, and others.

Pokémon Taxonomy Tree

Pokémon has drawn on real-world biology, mythology, and science since its debut in the 1990s. This year marks three decades of the franchise, and the roster has grown to over 1,300 characters spanning animals, plants, ancient creatures, and beings from other dimensions.

The Straits Times built a radial tree diagram placing all Pokémon released through 2025 within a custom taxonomy modeled on biological classification. Arceus sits at the center as the in-universe creator, with branches extending outward through up to four nested tiers. Those are groupings like Animals, Vertebrates, Amphibians, and Frogs. Each Pokémon appears as a colored dot at the outermost ring. A horizontal strip above the sidebar acts as a scrollable index, letting you step through every entry in sequence.

Clicking a dot or using the search bar opens a detail panel on the right. The panel shows the Pokémon’s real-world inspiration, its evolution chain, weight, height relative to a human figure, and a short description. Category filters, including Animals, Plants, Fungi, Objects, Sprites, Artificial, Extraterrestrials, and more, let you focus on specific branches of the tree.

️️➡️ Explore the interactive on The Straits Times, by Ang Qing, Billy Ker, Ng Weng Chi, Lee Yu Hui, Shannon Teoh, and Youjin Shin.

250 Years of U.S. Immigration

Immigration has defined American society since the country’s founding. Periods of openness have repeatedly given way to restriction, and the origins of newcomers have changed dramatically with each wave.

The Economist traces this history across several data visuals. An interactive choropleth map shows the birthplace of the largest immigrant population in each state, with five snapshots from 1850 to 2024 showing how the dominant origins shifted over time. An area chart covers the foreign-born share of the U.S. population since 1850. Two spline charts, with individual survey readings shown as dots behind the trend lines, track public opinion on immigration levels since 1965 and attitudes by party affiliation since 2001.

➡️ Check out the article on The Economist.

Terrain of Human Happiness

What makes people happy varies widely by life stage, personality, circumstance, and how much control a person has over the moment. Researchers have long tried to categorize that terrain, but happiness resists tidy classification.

Alvin Chang mapped over 100,000 crowdsourced happy moments onto a navigable fantasy-style landscape. Immediate pleasures sit in the north, long-term ones in the south, self-directed ones to the east, and passive moments to the west. The landmass is divided into labeled continents: Family, Children, Love, Friends and Social, Career and Work, Leisure, Hobbies and Creation, and more. Countries and sub-regions are nested inside.

Each moment is represented by a drawn person icon. Clicking one opens a card with the contributor’s age, gender, country, marital status, and their happy moment in their own words. A minimap in the corner, laid out as a quadrant chart, keeps the two axes visible as the map is zoomed and panned.

At the end of the guided narrative, a filter panel opens up the full dataset for free exploration by location, age, sex, parental and marital status, and keyword search.

➡️ Look at the project on The Pudding, by Alvin Chang.

Iran crisis, Pokémon taxonomy, two and a half centuries of U.S. immigration, and a landscape of happy moments — this week’s selections show just how many directions data visualization can take. See you next Friday with more in DataViz Weekly:

👉 DataViz Weekly on AnyChart Blog
👉 DataViz Weekly on Medium

Originally published at https://www.anychart.com on March 6, 2026.

Visualizing Data: Iran Crisis, Pokémon Taxonomy, U.S. Immigration, Human Happiness — DataViz Weekly was originally published in Data Visualization Weekly on Medium, where people are continuing the conversation by highlighting and responding to this story.