Scrollytelling on Visualizing.JP

On a Day with a High of 36°C, the Heat Index Reached 69°C: The Hindu's 'Chennai's heat index'

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

The Hindu calculated a heat index from the readings of sensors carried by four workers. The piece gives the maximum temperature in Chennai, in southern India, on June 9, 2025 as 36°C. On the same day, the heat index for a street-food cook and a fisherman who went out to sea reached as high as 69°C. The sensors were Arduino Nano boards fitted with a temperature and humidity sensor, and the heat index was calculated with the formula of the U.S. National Weather Service (NWS).

The piece is a standalone interactive page published on July 12, 2025 by the Data team of The Hindu, an English-language daily in India. The page’s headline is “TAKING ON THE HEAT,” and its entry name in the Sigma Awards is “Chennai’s heat index: How heat stressed are you?” Below, I read it in the order a visitor sees the page, following the text and code of the piece and its Sigma Awards entry.

On June 9, the high was 36°C

When you open the page, the large red-orange letters “HEAT” appear over a black-and-white photograph of a person drinking water. Below them is a single lead sentence.

The opening of the piece: the headline “TAKING ON THE HEAT” and a lead sentence saying the maximum on June 9 was 36°C, laid over a black-and-white photograph

Source: The Hindu Data team / The Hindu, “Heat index: how heat-stressed are you?”

“The maximum temperature on June 9 in Chennai was 36°C. But, high humidity and the kind of work they did compounded the heat stress for many.”

In short, the high was 36°C, but humidity and the kind of work made the heat stress heavier for many people. With this first line, the article can be read as fixing the maximum temperature as the thing everything else will be compared against.

Next come cards for four people, each with a portrait. A card gives the person’s name, occupation, the highest heat index of the day with its time, and a quote from the person.

Person	Occupation / place	Maximum written on the card
Sathya	Tiffin cook (a street-food cook) at Parry’s Corner in the city center	69°C at 11:30 a.m.
Anand (age 33)	Fisherman who goes out to the Bay of Bengal from Marina Beach	69°C at sea at 12:56 p.m.
Prakash	Factory worker polishing stainless-steel utensils in Korukkupet	About 66°C at 3:10 p.m.
Leon (a pseudonym)	Auto-rickshaw driver	56.8°C at 2:20 p.m.

All four numbers on the cards are more than 20°C above 36°C.

Handmade sensors and the NWS formula

The method is described in the body text after the cards.

“The Hindu Data team used Arduino Nano devices with temperature and humidity sensors to continuously record time-stamped data on an on-device SD card. We handed these devices out to four people from different parts of Chennai.”

The team recorded time-stamped data continuously on the device’s SD card and handed the devices to four people working in different parts of Chennai. The piece says the recording day was June 9, 2025, that there was light rain at 4 p.m., and that this was a few weeks after the city’s heat peak.

How the heat index was calculated is explained in a note at the end of the page. The team used a formula from the U.S. National Weather Service (the article writes “U.S. National Weather Service (NOAA)”) that takes air temperature and relative humidity. The India Meteorological Department (IMD) provisionally divides the value into four levels: below 35°C, 36 to 45°C, 46 to 55°C, and above 55°C. The body text gives an example: 35°C at 85% relative humidity gives a heat index of 60°C. Recalculating it with the NWS formula gives 60.0°C, which matches.

The piece does not hide gaps and outliers in its data. The body text says that there are periods when the devices were switched off by mistake and the data is missing, and that the team filled those periods in through interviews. Sathya’s chart carries a note that unusually high values appeared because the device was placed near the stove. Anand switched his device off at night, and the piece says the nighttime heat index could not be calculated.

Three numbers that change as you scroll

Each of the four people gets one scene that follows their day. A square illustration and three numbers are pinned to the left third of the screen. The numbers are “TIME,” “TEMP,” and “HEAT INDEX.” Paragraphs of text flow through the right two thirds, and when a paragraph reaches the middle of the screen, the illustration and numbers switch.

On a black background, an illustration of a woman wiping her face with the end of her sari, with three numbers, 11:30 A.M., 48.3°C and 69°C, and a paragraph of text on the right

Source: Soumyadip Sinha (Illustrations in the piece’s credits) / The Hindu, “Heat index: how heat-stressed are you?”

The figure above is the scene where Sathya sets down her ladle: 11:30 A.M., air temperature 48.3°C, heat index 69°C. Which values appear with which paragraph is written in the HTML, as the attributes data-time, data-temp, and data-felt on each paragraph.

The table below shows how the data is mapped to visual forms.

Component	Data	Visual form / material	Specifics
Lead sentence	Maximum temperature given by the piece	Text	36°C
Four cards	Each person’s maximum heat index and its time	Portrait + name + one sentence + the person’s words	69 / 69 / about 66 / 56.8°C
Pinned panel: time	The time of the paragraph	Numbers animated like a clock	Counts in steps of 1 to 10 minutes depending on the time difference
Pinned panel: temperature	The temperature of the paragraph	Number + text color (5 levels)	Color changes at 30 / 33 / 36 / 39°C
Pinned panel: heat index	The heat index of the paragraph	Number + text color (5 levels)	Thresholds at 35 / 45 / 55 / 65°C, from #ffe5e5 to #f24405
Pinned panel: image	The scene of the paragraph	Swapping black-and-white line illustrations	Sathya1 to 4, Fisherman1 to 5, and so on
Time-series line	Heat index calculated from the sensor	A single red-orange (#f24405) line on black	Horizontal axis = time, vertical axis = heat index
Line annotations	Peaks, outliers, missing data	White circle and curved arrow + one sentence	“peaked to 69°C at around 1 p.m.” and others

The only place where values are turned into color is the text color of the panel’s temperature and heat index. The color thresholds for the heat index are the IMD’s four levels (35 / 45 / 55°C) with one more boundary, 65°C, added, making five levels. As a result, the deepest red-orange appears only in the scenes of the two people at 69°C and Prakash at 66°C. Leon (56.8°C), who falls in the same “above 55°C” class under the IMD’s division, stays one shade lighter. The extra step can be read as added to show differences among the four.

At the end of each scene, a line chart of that person’s heat index is placed. The line itself is a PNG image, while the axis numbers and annotations are HTML text. HTML comments show that the charts were finished in Adobe Illustrator and ai2html (v0.121.1).

The four charts differ from one another in both time range and vertical-axis range.

Person	Horizontal axis	Vertical axis	Annotations
Sathya	9:00 a.m. to 9:00 a.m. the next day (about 24 hours)	40 to 80 (steps of 10)	Outlier near the stove / “peaked to nearly 69°C at around 11.30 a.m.”
Anand	9:00 a.m. to 6:00 p.m.	30 to 70 (steps of 5)	“peaked to 69°C at around 1 p.m.” (desktop version)
Prakash	10:00 a.m. to 12:00 a.m.	40 to 65 (steps of 5)	“peaked to 66°C in the afternoon”
Leon	9:00 a.m. to 11:00 p.m.	40 to 55 (steps of 2.5)	“peaked to 56.8°C during one of his afternoon rides” / “Missing data”

The peak that reaches near the top of the frame is in the 56°C range in Leon’s chart and above 80°C in Sathya’s. The four are not built to be compared side by side. What lets readers compare one person with another is the numbers in the annotations and the four cards at the start. The charts can be read as cut to show the shape of each person’s day.

A red-orange line on black. A peak above 80 around noon, with a note that the device was placed near the stove and a note for 69°C, each marked with a circle and an arrow

Source: The Hindu Data team / The Hindu, “Heat index: how heat-stressed are you?”

In Sathya’s chart, the line rises past the 80 mark around noon. That peak is circled in white and annotated as the device having been placed near the stove. The “69°C” note points to the lower peak before it. The 69°C on the card can be read as a value chosen with the outlier peak excluded.

A red-orange line on black. A peak around 1 p.m. is circled, with a note that it reached 69°C

Source: The Hindu Data team / The Hindu, “Heat index: how heat-stressed are you?” (desktop version)

Anand’s chart contains an inconsistency. The page switches between two versions depending on screen width. In the desktop version, at widths of 1144px or more, the annotation reads “Anand’s heat index peaked to 69°C at around 1 p.m.” In the version for narrower screens it reads “peaked to 48°C at around 1 p.m.” Both versions have a vertical axis of 30 to 70, and the shape of the line is the same. The top of the line sits just under the 70 mark, which fits the desktop version’s 69°C. The 48°C on the small screen can be read as a missed rewrite. A person reading on a smartphone sees 69°C on the card and 48°C in the chart’s annotation.

A red-orange line on black. The afternoon peak carries a note of 56.8°C, and for several evening hours the line breaks off with the words Missing data

Source: The Hindu Data team / The Hindu, “Heat index: how heat-stressed are you?”

Leon’s chart does not connect the missing hours with a line; it leaves a blank and writes “Missing data.” The line color is the same single red-orange in all four charts, and it does not change with the value. Bands showing the IMD levels are not drawn in the background either.

How do third parties see this method?

I could not find any critique that discusses this piece. Instead, I list sources that assess the method, by point at issue.

The first is the meaning of measuring personal exposure. Kuras et al. write in a 2017 paper:

“Personal exposure research can help reveal the extent of exposure misclassification that occurs when individual exposure to heat is estimated using ambient temperature measured at fixed sites”

In short, estimating an individual’s exposure from temperature measured at fixed sites causes misclassification, and personal exposure research can reveal how large it is. The piece’s framing, the citywide high of 36°C against what people actually experienced, can be read as the journalistic version of this point.

The second is the limits of the heat index formula. The NWS Weather Prediction Center writes on its page explaining the formula:

“The Rothfusz regression is not valid for extreme temperature and relative humidity conditions beyond the range of data considered by Steadman.”

That is, the regression formula is not valid for extreme conditions beyond the range of Steadman’s data. In a piece for Down To Earth (June 18, 2024), Ashok Gadgil and Elif Kilic of UC Berkeley caution that the heat index assumes a healthy young adult moving lightly in the shade. The piece’s 69°C comes from conditions of 47 to 48°C air temperature. A fisherman at sea and a cook at a pot can be read as standing outside the formula’s assumptions. The piece reveals its outliers in annotations, but it does not mention the formula’s range of applicability.

The third is research using the same approach. According to an article from Princeton University Engineering (December 20, 2024), a team including Anu Ramaswami gave residents of Delhi and Chennai pendants with heat sensors. They are devices built on the same idea as the piece, computing a heat index from temperature and humidity. The difference can be read this way: the research gathers many participants, while the piece narrows to four people and one day and shows it as a story.

The piece made the shortlist of the Sigma Awards 2026. There were 543 entries from 84 countries, and 31 finalists. The Hindu’s name does not appear in the announcement of the ten winners.

At the end of the piece’s page, The Hindu’s making-of video, “Behind the scenes: How the data and science teams measured heat stress | In Focus podcast,” is embedded (YouTube). I have not watched the contents of the video.

Takeaways

What this piece lets you take away is to fix one thing to compare against and put it first. Because the lead sentence has 36°C, the 69°C on the cards reads as “high.” Show the reference number first and put the number people actually experienced beside it. This order works for reporting that re-measures something.

Next is following a small number of people with numbers and words. Four people over one day is not statistics. Instead, three numbers are tied to each person’s actions, paragraph by paragraph.

The third is to state gaps and anomalies inside the chart. The blank of “Missing data” and the note about the stove tell readers how far the numbers can be trusted. On the other hand, annotations that differ between versions and yardsticks that do not match leave readers who want to compare confused. When you build charts separately for different screen widths, check even the numbers in the annotations against both versions. If you change the vertical axis for each person, provide a separate place for comparison. This piece is an example of both.

References

Primary sources

The Hindu Data team, “Heat index: how heat-stressed are you?” (headline “TAKING ON THE HEAT”), The Hindu, 2025-07-12 (updated 2025-07-30). Reporting and writing: Nitika Francis / Production and data: Srinivasan Ramani / Hardware Assembly: Sanjeevi Maran / Interactive and Code: Areena Arora / Illustrations: Soumyadip Sinha, and others. The piece
The piece’s scroll handling and color levels (gif.v2.js). JavaScript
The piece’s CSS (color variables, two-column pinned layout). gif.v2.css / project_styles.v8.css
Sigma Awards 2026 entry “Chennai’s heat index: How heat stressed are you?” (2026 · Shortlist). Sigma Awards entries
The Hindu, “Behind the scenes: How the data and science teams measured heat stress | In Focus podcast”. YouTube
The Sigma Awards, “Remarkable diversity, confronting some of the most urgent issues of our time: announcing the 2026 Sigma Awards shortlist”. Announcement
The Sigma Awards, “From toxic supply chains in Africa to Baltic sabotage to stolen children in Syria: 10 data projects win 2026 Sigma Awards”. Announcement

Secondary sources

E. R. Kuras et al., “Opportunities and Challenges for Personal Heat Exposure Research,” Environmental Health Perspectives, 125(8):085001, 2017. PubMed / doi:10.1289/EHP556
NOAA/NWS Weather Prediction Center, “The Heat Index Equation”. Explanation
Ashok Gadgil, Elif Kilic, “Get the Heat Index right: an important first step to protect people from heatwaves,” Down To Earth, 2024-06-18. Op-ed
Molly Sharlach, “Measuring the human impacts of extreme heat to guide cities’ climate action plans,” Princeton Engineering, 2024-12-20. Article

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

Scrollytelling on Visualizing.JP

On a Day with a High of 36°C, the Heat Index Reached 69°C: The Hindu's 'Chennai's heat index'

On June 9, the high was 36°C

Handmade sensors and the NWS formula

Three numbers that change as you scroll

The four line charts do not share a yardstick

How do third parties see this method?

Takeaways

References

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

It starts with one mosque in Beijing

From 4,450 locations to 1,714 mosques

Teal for before, yellow for after

The regional scatter plot, and totals that do not match

How third parties saw the piece

Takeaways

References