Satellite Imagery on Visualizing.JP

Teal before, yellow after: the 2,312 mosques in the FT's "How China is tearing down Islam"

Wed, 04 Nov 2026 00:00:00 +0900

The Financial Times (FT) examined 2,312 mosques with Islamic architecture in Google Earth satellite imagery and counted 1,714 that had been modified. It fixed teal (#35e0cb) for before and yellow (#ffec1a) for after, and uses these two colors for the dots on the national map and for the before-and-after switches between satellite photos.

The piece is “How China is tearing down Islam.” It was published on 27 November 2023 as an FT Visual Investigations project, and it won a Sigma Awards 2024 prize (according to its entry page on the Sigma Awards entries site). Below, I read it in the order a reader scrolls, based on the body text and source code (HTML, CSS, JavaScript) of the project page, the Flourish data for the regional chart, and the Sigma Awards application.

It starts with one mosque in Beijing

When you open the piece, a photograph of a single mosque fills the screen. It is the Doudian Mosque on the outskirts of Beijing. The text describes it as one of the most magnificent mosques in northern China.

A photograph of the white Doudian Mosque, with a dome and two minarets, taken from directly above the front

Source: Financial Times (© The Financial Times Limited 2023) / How China is tearing down Islam

In the next step, a second image, whose alt text reads “Mosque, outlined,” fades in over three seconds. Then, as you scroll, drone footage plays. It was shot by Marine Zambrano. The text goes on:

But earlier this year, its minarets were removed, its domes replaced with pagoda-style cones and its Arab-style arches squared off.

Summary: earlier that year the minarets were removed, the domes were replaced with pagoda-style cones, and the arches were squared off.

From here, the screen moves from a single building to a map of all China. At first only the area of Xinjiang is shaded dark blue-green, and yellow dots appear in it. In the next step, dots spread across the country. The map data contains 1,714 dots. In descending order of dot count: Xinjiang 387, Gansu 355, Ningxia 268, Henan 205, Yunnan 146, Qinghai 118.

After the map come satellite photos. First four large tiles appear, switching one by one at one-second intervals from before to after. Then a full-screen grid of 70 before-and-after satellite pairs scrolls automatically. The images date from January 2017 to March 2023.

The sequence runs from one photo of one building, to a national map, to 70 pairs of satellite photos. It can be read as a structure that first shows what happened to one building, then shows how many places the same thing happened.

From 4,450 locations to 1,714 mosques

The numbers are given in the methodology paragraph at the end of the article.

We built a dataset of 4,450 reported locations of mosques across China by combining search results from Baidu Maps, Google Maps, and OpenStreetMap, as well as mosques in Xinjiang from the Australian Strategic Policy Institute. We inspected each location using Google Earth satellite imagery, identifying mosques and comparing changes with their architecture over time.

Summary: the team combined search results from Baidu Maps, Google Maps and OpenStreetMap with the Xinjiang mosques from the Australian Strategic Policy Institute (ASPI) into a list of 4,450 locations. It then checked each location in Google Earth satellite imagery, confirmed whether it was a mosque, and compared how the building changed over time.

The result is in the next paragraph.

In total, we located 2,312 mosques with Islamic architecture. Of these, 1,714 (74.3 per cent) had Arabic-style features removed between 2018 and 2023.

Summary: the team located 2,312 mosques with Islamic architecture, and in 1,714 of them (74.3%) Arabic-style features had been removed between 2018 and 2023.

Lined up by stage, the numbers look like this.

Stage	Count	Wording in the text
Candidate locations	4,450	reported locations of mosques
Confirmed as Islamic architecture	2,312	mosques with Islamic architecture
Modified or removed, 2018–2023	1,714 (74.3%)	had Arabic-style features removed

What was judged is minarets, domes, and other exterior Arabic-style features. The methodology also states its limits. The mosques examined are only a portion of those in China as a whole. In some regions the most recent satellite imagery dates from 2020, so buildings modified after that were not counted. It therefore presents 1,714 as probably an underestimate.

The article is not built from satellite imagery alone. The text also brings in photos taken on the ground and interviews with residents, a local government’s renovation drawings in Sichuan, and an image of a planning document from Xining. In its Sigma application, the team lists Drone, Scraping, QGIS, Google Sheets, OpenStreetMap, Node.js and others among the technologies used.

A renovation elevation of the prayer hall of the Maoxian Chengguan Mosque (茂县城关清真寺). Dashed lines mark the position of the dome, and a multi-tiered Chinese-style roof is drawn above it

Source: Renovation drawing for Maoxian (a local government document), Financial Times (© The Financial Times Limited 2023) / How China is tearing down Islam

Teal for before, yellow for after

The page’s stylesheet repeats two colors. Teal (#35e0cb) is before the modification, and yellow (#ffec1a) is after. This can be read as the one rule that ties the data to its appearance.

Near the top of the text, the two words “before” and “after” appear in colored bold. “Before” is teal and “after” is yellow. On the satellite tiles, the border and the date label at upper right are teal for before and yellow for after. The dots on the map of China are also small yellow squares.

Where the text discusses Doudian in detail, two satellite images sit side by side. The project page labels them 2017 and 2023 (the images themselves carry no year). The 2017 image is tinted teal and the 2023 image yellow. Around the building’s perimeter runs a line made of a solid black line overlaid with a white dashed line.

Two satellite images of the Doudian Mosque. On the page, the left one, labeled 2017, is tinted teal and the right one, labeled 2023, is tinted yellow; a solid black line overlaid with a white dashed line traces the building’s perimeter

Source: Financial Times (© The Financial Times Limited 2023) / How China is tearing down Islam (the two images placed side by side by the author)

The table below summarizes how the colors are used on the page.

Presentation (location)	Data	Mapping to appearance
Opening photo and drone footage	One mosque, Doudian, before and after	After the before photo, an image with the alt text “Mosque, outlined” fades in over 3 seconds. Drone footage advances as you scroll
Map of China	Locations of the 1,714 modified mosques	Small yellow squares (thin black outline). Xinjiang first, then the whole country. Each dot appears with a delay of 0 to 750 ms
Colored words in the text	Before / after	“Before” in teal bold; “after” and “demolitions and modifications” in yellow bold
Four large satellite tiles	Four before-and-after satellite pairs	Switch from before to after every second. The before image blurs and disappears. Border and date label: teal for before, yellow for after
Satellite photo grid	70 before-and-after satellite pairs	The grid scrolls automatically, and each tile that enters the screen switches from before to after
Before-and-after per mosque	Doudian, Najiaying and Weizhou, before and after	Satellite images and ground photos placed side by side before and after, each with its year
Maoxian renovation drawing	Government drawings and documents	In the text, “dome” is highlighted in teal and “pagoda-style structure” with a yellow background
Regional scatter plot (Flourish)	31 provinces and regions	x = Muslim population share (log scale), y = modification rate, circle area = number of mosques with Islamic architecture, fill yellow

The map dots are yellow, which can be read as meaning the dots are buildings “after.” The 1,714 modified mosques line up on the map as yellow dots. On the satellite tiles, the same yellow goes on the frame of the after image. Whether you read the text, look at the map, or compare the photos, yellow appears to be built to mean after.

The two-color switch also helps with comparing photos. In the Doudian satellite images, the roof shape seen from above the dome differs between 2017 and 2023. Because the colors differ, you can tell which image is the before without reading the date.

The regional scatter plot, and totals that do not match

Later in the text, a scatter plot made with Flourish is embedded. Its title is “Mosque modifications were more systematic in areas with larger Muslim populations.” Each circle is one province or region. The horizontal axis is the Muslim population share (log scale), and the vertical axis is the share of mosques with Islamic architecture that were modified. Circle size represents the number of mosques with Islamic architecture examined.

In the embedded data, the figures for some regions are as follows.

Province / region	Muslim population share	Mosques with Islamic architecture	Modification rate
Xinjiang	56.51%	539	71.8%
Ningxia	35.08%	294	91.16%
Qinghai	18.43%	146	78.77%
Gansu	8.15%	434	81.8%
Henan	0.96%	212	96.7%
Liaoning	0.53%	43	9.3%

The text also says more than 90% in Ningxia and more than 80% in Gansu. The sources are given as “2020 Chinese Census” and “FT Research.”

Adding up the 31 regions in this Flourish data gives 4,440 locations examined and 2,310 with Islamic architecture. These differ from the text’s 4,450 and 2,312 by 10 and 2 respectively. The piece does not say why they differ.

This is a point to watch when following the numbers. The national figures (4,450 / 2,312 / 1,714) are the methodology figures in the text, while the regional figures are from the scatter plot data. When citing both in the same article, it is safer to make clear which set a number comes from.

How third parties saw the piece

The Sigma Awards judges cite both how the data was built and how the page was put together.

The team behind “How China is tearing down Islam” built a database of 4,450 buildings and manually reviewed each one of them for accuracy. … This entry successfully combined photos, videos, data visualization and coherence page design to tell a powerful story.

Summary: the team built a database of 4,450 buildings and manually checked each one. It combined photos, video, data visualization and a coherent page design to tell the story. The judges also note that many of the reporters in China worked at personal risk.

The Guardian cited the FT’s analysis in its article of 25 May 2024. It wrote that last year’s FT analysis showed that three quarters of more than 2,300 mosques had been modified or removed since 2018. It also reports on what later happened to the mosques in Najiaying and Shadian.

The anthropologist Ruslan Yusupov made a different point in an essay for the Political Theology Network in March 2026. On the Chinese internet, he notes, even those who celebrate Sinicization use “before-and-after” paired images.

when the narratives about Sinicization emerged online, they did so through a powerful medium of visual juxtaposition called “before-and-after” images.

Summary: when narratives about Sinicization emerged online, they did so through a powerful medium of visual juxtaposition, “before-and-after” images. Yusupov argues that paired images keep the “before” as something lacking. Yusupov is also one of the people interviewed in the FT’s piece.

The three assessments make different points. The judges look at how the piece was made, The Guardian at the numbers, and Yusupov at the before-and-after form itself. Yusupov’s point can be read as showing that the before-and-after form the FT used is just as available to the opposing side.

Takeaways

What a visualization practitioner can take from this piece is to narrow the color convention down to one and keep to it to the end.

The FT’s color mapping can be read as narrowed to a single rule: before = teal, after = yellow. It carries this through the words in the text, the tinting of the satellite photos, the borders and date labels of the tiles, the dots on the map, and the highlights on the drawing. It can be read as built so that it works without a legend.

This method is useful when you set different kinds of material side by side in one article. Photos, maps and drawings are each made with different tools. If you decide on one color convention first, then even when each kind of material looks different, they read as parts of the same article.

The way of counting is also worth taking. The FT put the narrowing in numbers in the text: from 4,450 candidates to 2,312, then to 1,714. It also wrote the limits in the same paragraph. In an article that presents counts, stating where the counting began and what was not counted, next to the numbers, lets readers check them.

Finally, there is Yusupov’s point. The before-and-after form is powerful, but either side can use it. Placing before and after images side by side does not by itself convey what was lost. In the FT’s piece, the residents’ words and the government documents can be read as carrying that part.

References

Primary sources

Financial Times, “How China is tearing down Islam,” 2023-11-27. Project page
Stylesheets of the piece: styles.a686d242.css, ChinaMap.58928fe1.css
Scripts of the piece: index.page.client.a5b40edf.js, chunk-de8e8e48.js (map of China), chunk-74e2fd96.js (satellite tile grid)
Flourish, “China Mosques - Scatter” (visualisation 15487273). Embed
Sigma Awards 2024, “How China is tearing down Islam” (original application text and judges’ comments). Sigma Awards entries
Sam Joiner, post on X, 2023-11-27. Thread Reader
Human Rights Watch, “China: Mosques Shuttered, Razed, Altered in Muslim Areas,” 2023-11-22 (an earlier report cited in the text of the piece). HRW

Secondary sources

Sigma Awards 2024 judges’ comments. Sigma Awards entries
Amy Hawkins, Elena Morresi, “Last major Arabic-style mosque in China loses its domes,” The Guardian, 2024-05-25. The Guardian (checked via a reprint on Yahoo News UK)
Ruslan Yusupov, “The Montage of Privation: Islam and the Architecture of Sinicization in China,” Political Theology Network, 2026-03-10. Political Theology Network

Total Area, Per Capita, Share of Land: Country Rankings Reshuffle in Green to Grey

Wed, 04 Nov 2026 00:00:00 +0900

Green to Grey shows the natural land and cropland that turned into buildings and roads across Europe between 2018 and 2023, country by country, in three ways: total area, per capita, and share of national territory. Norway ranks 11th by total area, 1st per capita, and 26th by share of land. Depending on what you divide by, the ranking of countries reshuffles. The three metrics are three identical grey bar charts that you switch between with buttons; they are not placed side by side in a single figure.

The work is a cross-border investigation published on October 1, 2025. It was led by the Dutch nonprofit Arena for Journalism in Europe and the Norwegian public broadcaster NRK, with scientists from the Norwegian Institute for Nature Research (NINA) and news organizations from several countries. It is a winner of the Sigma Awards 2026. Below, I read it in the order a reader sees it, drawing on the main story, the methodology page, the data behind the country charts, an interview with the creators, and a later academic paper.

Repeating “green to grey” in color

The main story opens with three sets of satellite photos: a coastal forest in Greece that became a five-star hotel, farmland in the UK that became an Amazon logistics hub, and a pine forest in Germany that became a Tesla factory. A 2018 Pléiades image and a 2023 Pléiades Neo image are stacked, and as you scroll the 2023 image fades in. It is a scroll-linked crossfade, not a before-and-after slider.

The text is split into two lines. The earlier state (“In Greece, a forest on the coast…”) sits on a green background, and the later state (“became a five-star hotel.”) sits on a grey one. The site’s colors, in charts, maps and buttons alike, come from just two families: green #39b54a and grey #5d676b. The title’s “green to grey” becomes the color scheme of the whole page.

The investigation started from a domestic Norwegian investigation that NRK published in January 2024. In 2024, Arena invited NRK, NINA and news organizations from other countries to a meeting in Bologna. The team began work in January 2025 and published in October 2025. The main story says it involved “41 journalists and scientists from 11 countries.”

The encoding of each component is as follows.

Component	Data	Mapping to the visual
Three opening satellite photo pairs	The same places in 2018 and 2023	Scroll amount = opacity of the 2023 image (0 to 1)
Dot map	Random sample of constructions larger than 10,000 m²	Position = place, color = grey (construction), land = pale green. Dot size is unrelated to the data
Counter of area lost	1,500 km² per year converted to seconds	Seconds elapsed since loading × 47.5 m²
Three country bar charts	Estimates for 30 countries	Length = value, all the same grey. Buttons switch to another chart
Wetlands of İzmir	Satellite images from 2018 and 2023	Drag-to-compare before-and-after slider

The dot map shows spread, not location

The first figure in the main story is a map of Europe on which grey dots are placed. Its caption reads “Random sample of constructions larger than 10,000m² identified by our methodology.” The text says each dot is one place where nature or cropland was lost.

About 16,000 dots are drawn at first, and once the map enters the screen, 100 more are added every 100 milliseconds, for about 29,000 in total. Each dot’s radius is picked at random from three values: 0.5, 1.0 and 1.5. The size of a dot does not represent the area of the construction.

In an interview with the Royal Geographical Society (RGS), the creators Zeynep Şentek and Léopold Salzenstein explain the design of the map as follows.

We specifically did not include a zoom feature on the map. This was to emphasise the overall of scale of nature loss within Europe rather than focusing on detailed data for local areas.

In short, they deliberately left out a zoom feature because they wanted to show the scale of loss across Europe rather than detailed local data. They also say that the dots are added as the page loads in order to show that the loss is still going on. At the end of the map sits a counter of the area lost since you started reading. The note beside it reads “(47.5 m² per second, 4.1 km² each day).”

The academic paper that came later draws the same land take in a different form. It is a map that aggregates the data into a 50 km grid and colors natural land and cropland areas with a bivariate palette.

A map dividing Europe into 50 km grid cells and coloring the land take of natural land and cropland with a bivariate palette, together with aerial photos of two places where cropland and forest turned into buildings

The paper’s map lets you read where and how much was lost through color intensity. The work’s dot map gives up on letting you read quantity and hands that role to the motion of dots accumulating. Even with the same data, the journalistic figure seems to have chosen to convey spread and continuity before quantity.

Three denominators, three bar charts

The country comparison is in the “No country spared” section of the story. Above a Datawrapper horizontal bar chart sit three buttons: “Total area (km²),” “By population (m²/person/year)” and “By country size (%).” Pressing one switches to a different Datawrapper chart. Only the selected button is filled in green.

Three identical grey horizontal bar charts. From the left, Turkey is at the top by total area, Norway per capita, and the Netherlands by share of land

Source: Arena for Journalism in Europe, NRK et al., Green to Grey (three Datawrapper charts placed side by side by the author)

The three charts share the same bar color and ordering; only the denominator differs. Here are the rankings of four countries, taken from the values in the published charts.

Country	Total area (km², 2018-2023)	Per capita (m²/person/year)	Share of land (%)
Norway	207 (11th)	6.26 (1st)	0.06 (26th)
Turkey	1,860 (1st)	3.55 (10th)	0.24 (6th)
Netherlands	192 (12th)	1.77 (23rd)	0.46 (1st)
UK	604 (5th)	1.47 (26th)	0.25 (4th)

Countries with equal values are counted in the order they appear in the chart. Norway’s 0.06% is tied with Latvia and Ukraine, and Turkey’s 0.24% with Ireland.

The text also puts all three views into words. By total area, Turkey leads with 1,860 km², followed by Poland. Adjusted for the size of the country, the Netherlands and Belgium come first. Adjusted for population, the Nordic countries fare worse.

But if we adjust the figures based on population size, Scandinavian countries fare worse. Norway’s construction translates to roughly six square metres of nature and cropland every year for each resident. In Switzerland, per capita losses are ten times less.

In short, divided by population the Nordic countries look worse: in Norway it is roughly 6 m² per resident each year, and in Switzerland a tenth of that.

The units of the three charts are not aligned. Total area is a six-year sum, while per capita is “per year.” Share of land corresponds to total area divided by the country’s area. For the Netherlands, 192 km² divided by about 41,500 km² gives 0.46%, a cumulative figure over six years (my own check). The definition of share of land is not written in the chart.

From their headlines, The Black Sea (“Turkey Leads In European Nature Loss”) seems to stand on the total-area metric, and NRK (“Nordmenn bygger ned mest natur”) on the per-capita metric. The setup can be read as letting every news organization pick the denominator that makes its own country stand out.

Method and uncertainty placed beneath the numbers

The bars in the country charts carry only point estimates. The uncertainty is gathered on a separate methodology page, signed by Salzenstein and NINA’s Zander Venter.

The input is Dynamic World, from Google and the World Resources Institute, which converts 10 m resolution Sentinel-2 imagery into per-pixel land cover probabilities. For each pixel, a regression line is fitted to the 2018-2023 trend of the “probability of being built up,” and a pixel counts as land take when the slope exceeds 0.05. About 84 billion pixels and nearly 185,000 images were processed.

The area is not obtained by counting pixels. People verified the extracted points, and the estimate corrected for the resulting errors is given with a 95% confidence interval. Across EEA-39 it comes to about 9,000 km², or 1,500 km² per year on average.

Forty journalists and fact-checkers manually verified more than 10,000 locations across Europe, a process that took more than 500 hours in total.

In short, 40 journalists and fact-checkers checked over 10,000 locations by hand, taking more than 500 hours in total. The AI’s judgments were hidden from them, and points were assigned regardless of country. The methodology page also has a figure comparing pixel counts with corrected values and a figure of accuracy by country.

The paper also draws per-capita values by country, with 95% confidence intervals.

Three panels showing per-capita land take by country as point estimates with horizontal 95% confidence intervals, in the order total, natural land, cropland

Source: Venter et al., Nature Communications (2026), Fig. 2 (CC BY 4.0)

The main story shows easy-to-read point estimates, while the methodology page and the paper show estimates with ranges. This can be read as reconciling readability of the numbers with ease of verification by separating them into layers.

At the same time, the sources disagree on some numbers. For each figure I use, here is how the sources compare.

Item	Figures by source
Participating countries	Main story: “11 countries.” Counting the home countries of the two lead organizations and the 10 participating outlets gives 12
Countries analyzed	The methodology page lists 30 countries but also mentions a stratum of “28 countries.” The paper has 29 countries, Datawrapper 30 (including Ukraine)
Number of stories	About: “51 stories in 11 languages”; application: “51 stories published in 10 languages”; Arena’s award announcement and The Spark: “47 publications in 10 languages”
Verified points	Main story and methodology: “more than 10,000”; paper: “9,893”
How clear-sky months were chosen	Methodology: “four-month period”; paper: “three consecutive months”
Ratio to earlier estimates	Main story: “up to one and a half times faster”; paper’s abstract: “nearly twice as large”
Norway’s share of land	The CSV distributed for the chart says 0.03% (which would rank 29th). The share-of-land chart rewrote this value to 0.06% in an edit on October 1, 2025

The last row is something I confirmed from the edit history that remains on the Datawrapper chart.

How third parties saw the work

On the Sigma Awards listing site, the work is labeled “2026 · Winner · Large.” The judges’ comment cites the clarity of the presentation.

What’s more, the presentation is intuitive and married with strong storytelling and on-the-ground reporting. Sometimes it can be hard to see environmental impacts over time. This project made it easy.

In short, the presentation is intuitive and tied to storytelling and on-the-ground reporting; environmental impacts over time are hard to see, and this project made it easy.

On April 13, 2026, a paper was published in Nature Communications. Its authors include people from NINA, NRK and Arena, so the part that counts as third-party is the peer review. On per-capita values, the paper says Norway and Estonia are the largest and Switzerland and Spain the most efficient. It also introduces a fourth denominator, land consumption per change in population. Among countries whose population fell, Hungary is highest at “250 m2 land take per citizen lost.”

The British commentary magazine Country Squire Magazine (March 11, 2026), citing the UK edition of the Guardian, wrote that the UK is “fifth-worst in Europe.” It then explained this as a ranking relative to the size of the country. In the shared data, however, the UK is 5th by total area, 4th by share of land, and 26th per capita. This looks like a mix-up of denominators. I have not been able to check the Guardian’s text. It can be read as an example of a ranking traveling apart from its denominator.

In a feature by Greece’s iMEdD Lab (March 10, 2026), Şentek says that in many cases where they re-checked, the AI was right and the human was wrong. In the range I have searched, I have not found any article that criticizes the method itself.

How to use it

What you can take from this work is not to settle on a single denominator. Whether you choose total area, per capita or share of land, each is a claim. Green to Grey makes all three into charts of the same form, and the text also puts all three into words.

However, the three charts are not lined up in a single figure. Unless the reader presses the buttons, the reshuffling does not come into view. If you want to show the reshuffling of ranks itself, you could also choose a figure that links the three ranks with lines. This work seems to have left the reshuffling to the words of the text.

The dot map removed meaning from dot size. If you do not use size, it is safer to make sizes uniform or to note that size is not used. Here the random radii leave room for misreading quantity.

Uncertainty can be placed in a separate layer. The main story relies on point estimates, and the methodology page and the paper show the ranges. Finally, check that the table you distribute matches the chart on screen. Norway’s 0.03% versus 0.06% was something I could not have known until I saw the edit history. The more a work depends on choosing a denominator, the more you need to check the values of the denominator itself.

References

Primary sources

Zeynep Sentek, Jelena Prtoric, Hazel Sheffield, Leopold Salzenstein, “Green to Grey: How Europe is squandering the little nature it has left,” Arena for Journalism in Europe et al., 2025-10-01. https://greentogrey.eu/
Léopold Salzenstein, Zander Venter, “Data Methodology,” Green to Grey. https://greentogrey.eu/methodology/
Green to Grey, “About.” https://greentogrey.eu/about/
Green to Grey, “Publications.” https://greentogrey.eu/publications/
Country bar charts (Datawrapper): total area https://datawrapper.dwcdn.net/p3y79/5/ (data https://datawrapper.dwcdn.net/p3y79/5/dataset.csv), per capita https://datawrapper.dwcdn.net/D5ozx/7/, share of land https://datawrapper.dwcdn.net/2bLXf/5/
Dot map data: https://greentogrey.eu/data/spots-data-static.csv, https://greentogrey.eu/data/spots-data-animated.csv
Site JS / CSS: https://greentogrey.eu/_next/static/chunks/143-d8b84dbd4ceb054a.js, https://greentogrey.eu/_next/static/css/bb6b167e4a7bb8eb.css
Sigma Awards 2026 entry page (original application text and Japanese translation): https://sigma-awards.data-visualization.jp/entries/2026-s-0003/
Royal Geographical Society, “Green to Grey” (interview with the creators). https://www.rgs.org/about-us/what-is-geography/geovisualisation/green-to-grey/
Arena for Journalism in Europe, “‘First of its kind’: Behind the scenes of Green to Grey’s groundbreaking data investigation,” 2025-10-03. https://journalismarena.eu/first-of-its-kind-behind-the-scenes-of-green-to-greys-groundbreaking-data-investigation/
Arena for Journalism in Europe, “Arena’s groundbreaking data investigation, Green to Grey, wins Sigma Award.” https://journalismarena.eu/arenas-groundbreaking-data-investigation-green-to-grey-wins-sigma-award/
The Spark (TBIJ), “‘Green to Grey’ captured Europe’s changing landscape.” https://the-spark-tbij.beehiiv.com/p/green-to-grey-captured-europe-s-changing-landscape
NRK, “Europa fra grønt til grått: Nordmenn bygger ned mest natur, viser europeisk kartlegging.” https://www.nrk.no/dokumentar/xl/europa-fra-gront-til-gratt_-nordmenn-bygger-ned-mest-natur_-viser-europeisk-kartlegging-1.17577504
NRK, “NRK avslører: 44.000 inngrep i norsk natur på fem år,” 2024-01-05. https://www.nrk.no/dokumentar/xl/nrk-avslorer_-44.000-inngrep-i-norsk-natur-pa-fem-ar-1.16573560
Venter, Czúcz, Kumano-Ensby, Salzenstein, Simensen, Solvang, Støstad, “Europe’s land take and the loss of nature and cropland to artificial surfaces,” Nature Communications, 2026-04-13. https://www.nature.com/articles/s41467-026-71931-w (doi:10.1038/s41467-026-71931-w, CC BY 4.0. Its authors overlap with the work’s creators)

Secondary sources

The Sigma Awards, “From Toxic Supply Chains in Africa to Baltic Sabotage to Stolen Children in Syria: 10 Data Projects Win 2026 Sigma Awards” (judges’ comment). https://www.sigmaawards.org/from-toxic-supply-chains-in-africa-to-baltic-sabotage-to-stolen-children-in-syria-10-data-projects-win-2026-sigma-awards/
Nature Communications (as a peer-reviewed paper). https://www.nature.com/articles/s41467-026-71931-w
“Britain’s Disappearing Countryside,” Country Squire Magazine, 2026-03-11. https://countrysquire.co.uk/2026/03/11/britains-disappearing-countryside
Athina Thanasi, “Green to Gray: The data, the mountains, and the politics of land,” iMEdD Lab, 2026-03-10. https://lab.imedd.org/en/?p=49962