2025-11-30 23:22:42
See visuals like this from many other data creators on our Voronoi app. Download it for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
Forests cover nearly one-third of the world’s land area, playing a vital role in storing carbon, supporting biodiversity, and regulating the planet’s climate.
In this graphic, we visualize data from the UN FAO’s 2025 Forest Resources Assessment to map out the four major types of forests: tropical, subtropical, temperate, and boreal.
Tropical forests represent the largest share of global forest cover, at about 45%. These ecosystems thrive near the equator, in regions like the Amazon Basin, Central Africa, and Southeast Asia.
These are biodiversity hotspots, supporting millions of plant and animal species while also storing massive amounts of carbon.
| Type | Share | Description |
|---|---|---|
| Tropics | 45% | Lush, biodiverse forests near the equator with warm temperatures and abundant year-round rainfall. |
| Boreal | 27% | Cold northern coniferous forests with long winters, short summers, and low biodiversity. |
| Temperate | 16% | Forests with four distinct seasons, moderate climates, and a mix of deciduous and evergreen trees. |
| Subtropical | 11% | Warm, humid forests between tropics and temperate zones, with mixed vegetation and seasonal rainfall. |
Despite their ecological importance, tropical forests are under heavy pressure from deforestation, agriculture, and mining. For example, Brazil has lost 2.9 million acres of its tropical forests since 2015, an area equal to the size of Rwanda.
UN FAO data shows that 29% of forests are primarily used for production, referring to logging and other commercial activities. However, around 36% of global forests are designated for environmental or multiple-use purposes, including biodiversity conservation and water protection.
| Objective | Share of total |
|---|---|
Production |
29 |
Protection of soil & water |
9 |
Conservation of biodiversity |
12 |
Social services |
5 |
Multiple use |
15 |
Other |
7 |
No designation |
4 |
Unknown |
18 |
Interestingly, nearly one in five forests fall into the “unknown” category, underscoring gaps in global forest monitoring and classification.
If you enjoyed today’s post, check out Top 35 Countries With the Largest Forests on Voronoi, the new app from Visual Capitalist.
2025-11-30 21:02:32
This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
The U.S. dollar has steadily lost value over the past century. According to Federal Reserve data, the purchasing power of one dollar today is equal to just a few cents in 1913 (the year the Fed was created).
In this graphic, we track the decline in the purchasing power of the U.S. dollar since the early 1900s, illustrating how inflation has eroded its value.
The data for this visualization comes from Federal Reserve Economic Data (FRED). It measures the “Purchasing Power of the Consumer Dollar” across all U.S. city averages, indexed to consumer prices.
The higher the index, the more purchasing power the dollar has. As the index declines, goods and services become relatively more expensive.
| Date | Purchasing Power of the Consumer Dollar in U.S. City Average |
|---|---|
| 1913-01-01 | 1017.8 |
| 1914-01-01 | 994.2 |
| 1915-01-01 | 987.6 |
| 1916-01-01 | 956.2 |
| 1917-01-01 | 855 |
| 1918-01-01 | 715.9 |
| 1919-01-01 | 604.5 |
| 1920-01-01 | 517.7 |
| 1921-01-01 | 524.9 |
| 1922-01-01 | 590.2 |
| 1923-01-01 | 595 |
| 1924-01-01 | 578.8 |
| 1925-01-01 | 577.9 |
| 1926-01-01 | 557.3 |
| 1927-01-01 | 570.1 |
| 1928-01-01 | 578.8 |
| 1929-01-01 | 584.5 |
| 1930-01-01 | 584.5 |
| 1931-01-01 | 628.8 |
| 1932-01-01 | 699.1 |
| 1933-01-01 | 775.4 |
| 1934-01-01 | 755.7 |
| 1935-01-01 | 733.5 |
| 1936-01-01 | 722.8 |
| 1937-01-01 | 709.3 |
| 1938-01-01 | 702.4 |
| 1939-01-01 | 715.9 |
| 1940-01-01 | 717.7 |
| 1941-01-01 | 709.3 |
| 1942-01-01 | 638.1 |
| 1943-01-01 | 591.4 |
| 1944-01-01 | 574.3 |
| 1945-01-01 | 561.4 |
| 1946-01-01 | 549.2 |
| 1947-01-01 | 464.8 |
| 1948-01-01 | 421.4 |
| 1949-01-01 | 415.7 |
| 1950-01-01 | 424.4 |
| 1951-01-01 | 393.2 |
| 1952-01-01 | 377.4 |
| 1953-01-01 | 375 |
| 1954-01-01 | 370.8 |
| 1955-01-01 | 373.5 |
| 1956-01-01 | 372.6 |
| 1957-01-01 | 361.5 |
| 1958-01-01 | 349.3 |
| 1959-01-01 | 344.8 |
| 1960-01-01 | 340.6 |
| 1961-01-01 | 335.2 |
| 1962-01-01 | 332.8 |
| 1963-01-01 | 328.6 |
| 1964-01-01 | 323.2 |
| 1965-01-01 | 319.6 |
| 1966-01-01 | 313.6 |
| 1967-01-01 | 303.5 |
| 1968-01-01 | 293.3 |
| 1969-01-01 | 280.4 |
| 1970-01-01 | 264.3 |
| 1971-01-01 | 251.1 |
| 1972-01-01 | 243 |
| 1973-01-01 | 234.3 |
| 1974-01-01 | 214.3 |
| 1975-01-01 | 191.8 |
| 1976-01-01 | 179.6 |
| 1977-01-01 | 170.6 |
| 1978-01-01 | 159.8 |
| 1979-01-01 | 146.3 |
| 1980-01-01 | 128.4 |
| 1981-01-01 | 114.9 |
| 1982-01-01 | 105.9 |
| 1983-01-01 | 102.1 |
| 1984-01-01 | 98.2 |
| 1985-01-01 | 94.6 |
| 1986-01-01 | 91.3 |
| 1987-01-01 | 89.9 |
| 1988-01-01 | 86.4 |
| 1989-01-01 | 82.6 |
| 1990-01-01 | 78.5 |
| 1991-01-01 | 74.3 |
| 1992-01-01 | 72.4 |
| 1993-01-01 | 70.1 |
| 1994-01-01 | 68.4 |
| 1995-01-01 | 66.5 |
| 1996-01-01 | 64.8 |
| 1997-01-01 | 62.8 |
| 1998-01-01 | 61.9 |
| 1999-01-01 | 60.8 |
| 2000-01-01 | 59.2 |
| 2001-01-01 | 57.1 |
| 2002-01-01 | 56.5 |
| 2003-01-01 | 55 |
| 2004-01-01 | 54 |
| 2005-01-01 | 52.4 |
| 2006-01-01 | 50.4 |
| 2007-01-01 | 49.4 |
| 2008-01-01 | 47.4 |
| 2009-01-01 | 47.4 |
| 2010-01-01 | 46.1 |
| 2011-01-01 | 45.4 |
| 2012-01-01 | 44.1 |
| 2013-01-01 | 43.4 |
| 2014-01-01 | 42.8 |
| 2015-01-01 | 42.8 |
| 2016-01-01 | 42.2 |
| 2017-01-01 | 41.2 |
| 2018-01-01 | 40.3 |
| 2019-01-01 | 39.7 |
| 2020-01-01 | 38.8 |
| 2021-01-01 | 38.2 |
| 2022-01-01 | 35.6 |
| 2023-01-01 | 33.4 |
| 2024-01-01 | 32.4 |
| 2025-01-01 | 31.5 |
| 2025-09-01 | 30.8 |
Major inflationary periods can be identified by looking at the steepest drops in the chart. For example, World War I and World War II strained government finances, leading to massive increases in public spending and money creation, which pushed prices sharply higher.
Similarly, the oil shocks of the 1970s caused energy costs to spike throughout the world, feeding into broad-based inflation. In each case, rising prices significantly eroded the purchasing power of the U.S. dollar.
Until 1971, the U.S. dollar was backed by gold.
This system was ended by President Nixon because the U.S. was creating more dollars than it had gold to support. Furthermore, foreign countries were increasingly demanding gold in exchange for their dollar reserves.
While ending this system gave policymakers more flexibility to manage the economy, money creation became easier, as shown by this chart of the M2 money supply. M2 comprises the most liquid forms of U.S. money, including physical currency, checking deposits, plus near-liquid assets like small-value time (CD) deposits, retail money-market funds, and other readily convertible savings vehicles.
An expanding money supply can be healthy when it grows in line with factors like population, economic output, and demand for credit, but becomes inflationary when it outpaces real economic growth.
If you enjoyed today’s post, check out Gold Production by Region in 2024 on Voronoi, the new app from Visual Capitalist.
2025-11-30 02:39:43
See visuals like this from many other data creators on our Voronoi app. Download it for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
In the U.S., alcohol consumption remains widespread, with nearly half the population aged 12 or older reporting that they consumed alcohol within the past month.
This visualization explores the scale of drinking behavior across America, including how many people drink, binge drink, or engage in heavier levels of alcohol use, using data from the Substance Abuse and Mental Health Services Administration as of 2024.
Out of the 288.8 million Americans aged 12 or older, 134 million (46.5%) reported drinking alcohol at least once in the past 30 days.
The data table below shows the number of regular alcohol drinkers in the U.S., along with binge drinkers and heavy drinkers.
| Group | Number of people (millions) | Share of all people | Share of alcohol users | Share of binge drinkers |
|---|---|---|---|---|
| All people in the U.S. aged ≥12 | 288.8 | n/a | n/a | n/a |
| Alcohol users in the past month | 134.3 | 46.6% | n/a | n/a |
| Binge alcohol users (drinking five or more drinks on the same occasion in the past 30 days) | 57.9 | 20.0% | 43.1% | n/a |
| Heavy alcohol users (binge drinking five or more days in the past 30 days) | 14.5 | 5.0% | 10.8% | 25.1% |
Binge drinkers are defined as those who consumed five or more drinks (four for women) on one occasion, and heavy drinkers are those who engaged in binge drinking at least five times in the past 30 days.
Despite alcohol drinkers making up nearly half of the U.S. population of those aged 12 or older, the share in 2024 (46.5%) has declined slightly since 2022 when it was 48.7%.
Of the 134.3 million alcohol drinkers in the U.S., 57.9 million people engaged in binge drinking, which represents 20.1% of the total population and 43.1% of all alcohol users.
This reveals a significant overlap between casual use and occasional high-risk consumption, highlighting how binge drinking behavior is deeply embedded within the broader drinking population.
Heavy alcohol users—those who binge drink on at least five days in the past month—number 14.5 million in America. This represents 5% of the total population above 12 years old and 10.8% of alcohol users.
While this group is much smaller than the broader categories of alcohol and binge drinkers, heavy drinkers make up one quarter of all binge drinkers, and account for one in every 10 regular alcohol drinkers in the country.
To learn more about alcohol consumption in the U.S., check out this graphic which breaks down which U.S. states drink the most beer.
2025-11-29 23:48:04
See visuals like this from many other data creators on our Voronoi app. Download it for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
The world’s criminal justice and prison systems vary significantly from country to country.
Regionally, Latin America and the Caribbean has the highest concentration of incarcerated people, accounting for six of the top 10 highest prison rates in the world. On the other hand, a number of West African countries sit on the opposite end of the spectrum.
This graphic shows the countries with the highest and lowest incarceration rates worldwide, based on data from the Prison Policy Initiative.
Below, we show the countries that sit at the extremes of global incarceration rates:
| Top 10 Highest Countries | Incarceration Rate (per 100,000 people) |
Top 10 Lowest Countries | Incarceration Rate (per 100,000 people) |
|---|---|---|---|
El Salvador |
1,086 |
Gambia |
22 |
Cuba |
794 |
Guinea-Bissau |
31 |
Rwanda |
637 |
Republic of Congo |
33 |
U.S. |
614 |
Guinea |
34 |
Turkmenistan |
576 |
Nigeria |
35 |
Panama |
499 |
Yemen |
35 |
Uruguay |
424 |
Japan |
36 |
Brazil |
390 |
Pakistan |
38 |
Thailand |
377 |
Burkina Faso |
39 |
Cabo Verde |
366 |
Central African Republic |
40 |
Today, at least 52,000 people are in prison in El Salvador, driven by its “state of exception” policy, which drastically reduces the constitutional rights of suspected criminals.
While this has led the homicide rate to fall 80% since 2022, thousands have been arbitrarily detained without access to a timely trial and other legal defenses in efforts to combat gang violence.
Like El Salvador, Cuba has faced mass arrests, typically for political dissidents. The country ranks second globally, with an incarceration rate of 795 per 100,000 people.
On the other hand, Gambia has an incarceration rate of just 22 per 100,000 inhabitants. Overall, Africa is home to seven of the 10 lowest incarceration rates, although prisons remain deeply underfunded.
As we can see, Japan stands as the only developed economy in the bottom 10. In addition, it has one of the lowest homicide rates globally, at 0.23 per 100,000 people—roughly 25 times lower than America.
To learn more about this topic, check out this graphic on the average cost per prisoner by U.S. state.
2025-11-29 21:06:37
This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
The world’s forests are unevenly distributed, with countries like Canada and Russia containing hundreds of millions of hectares of forest area. But how that forest is shared among people varies dramatically around the globe.
In this graphic, we visualize each country’s forest area per capita, offering a unique perspective on the world’s tree coverage. While the average country has just 0.5 hectares of forest area per person, a select few boast significantly more.
The data for this visualization comes from the Food & Agriculture Organization of the United Nations. It measures each country’s total forest area in hectares, which we combined with 2025 population estimates to determine forest area per capita.
A hectare is equal to 10,000 square meters, which is about the size of an international rugby field. In city terms, it’s roughly two and a half acres, or enough space for 16 single-family homes.
| Rank | Country | Forest Area Per Capita (hectares) |
|---|---|---|
| 1 |
Guyana |
23.0 |
| 2 |
Suriname |
22.3 |
| 3 |
Gabon |
10.2 |
| 4 |
Canada |
8.9 |
| 5 |
Central African Republic |
8.2 |
| 6 |
Russian Federation |
5.7 |
| 7 |
Botswana |
5.7 |
| 8 |
Australia |
4.8 |
| 9 |
Bolivia |
4.4 |
| 10 |
Finland |
4.0 |
| 11 |
Mongolia |
4.0 |
| 12 |
Bhutan |
3.4 |
| 13 |
Belize |
3.2 |
| 14 |
Solomon Islands |
3.2 |
| 15 |
Papua New Guinea |
2.7 |
| 16 |
Namibia |
2.6 |
| 17 |
Sweden |
2.6 |
| 18 |
Vanuatu |
2.5 |
| 19 |
Palau |
2.3 |
| 20 |
Brazil |
2.3 |
| 21 |
Norway |
2.2 |
| 22 |
Zambia |
2.1 |
| 23 |
Paraguay |
2.1 |
| 24 |
Peru |
2.0 |
| 25 |
New Zealand |
1.9 |
| 26 |
Latvia |
1.9 |
| 27 |
Estonia |
1.8 |
| 28 |
Venezuela |
1.8 |
| 29 |
Lao P.D.R. |
1.7 |
| 30 |
Angola |
1.6 |
| 31 |
Equatorial Guinea |
1.5 |
| 32 |
Montenegro |
1.3 |
| 33 |
DRC |
1.3 |
| 34 |
Bahamas |
1.2 |
| 35 |
Fiji |
1.2 |
| 36 |
Colombia |
1.1 |
| 37 |
Liberia |
1.1 |
| 38 |
Guinea-Bissau |
1.0 |
| 39 |
Panama |
1.0 |
| 40 |
Belarus |
1.0 |
| 41 |
Argentina |
1.0 |
| 42 |
U.S. |
0.9 |
| 43 |
Mozambique |
0.9 |
| 44 |
Chile |
0.9 |
| 45 |
Georgia |
0.8 |
| 46 |
Brunei Darussalam |
0.8 |
| 47 |
Zimbabwe |
0.8 |
| 48 |
Lithuania |
0.8 |
| 49 |
Dominica |
0.8 |
| 50 |
Samoa |
0.8 |
| 51 |
Timor-Leste |
0.7 |
| 52 |
Nicaragua |
0.7 |
| 53 |
Ecuador |
0.7 |
| 54 |
Micronesia |
0.7 |
| 55 |
Tanzania |
0.6 |
| 56 |
Cameroon |
0.6 |
| 57 |
Bulgaria |
0.6 |
| 58 |
Bosnia & Herzegovina |
0.6 |
| 59 |
Slovenia |
0.6 |
| 60 |
Uruguay |
0.6 |
| 61 |
North Macedonia |
0.6 |
| 62 |
Malaysia |
0.6 |
| 63 |
Costa Rica |
0.6 |
| 64 |
Honduras |
0.5 |
| 65 |
Serbia |
0.5 |
| 66 |
World Average |
0.5 |
| 67 |
Croatia |
0.5 |
| 68 |
Mexico |
0.5 |
| 69 |
Myanmar |
0.5 |
| 70 |
Greece |
0.5 |
| 71 |
Senegal |
0.5 |
| 72 |
South Sudan |
0.5 |
| 73 |
Sudan |
0.4 |
| 74 |
Austria |
0.4 |
| 75 |
Mali |
0.4 |
| 76 |
Spain |
0.4 |
| 77 |
Romania |
0.4 |
| 78 |
Eswatini |
0.4 |
| 79 |
Cambodia |
0.4 |
| 80 |
Slovak Republic |
0.4 |
| 81 |
South Africa |
0.4 |
| 82 |
Albania |
0.3 |
| 83 |
Turkmenistan |
0.3 |
| 84 |
Indonesia |
0.3 |
| 85 |
Madagascar |
0.3 |
| 86 |
Portugal |
0.3 |
| 87 |
Congo |
0.3 |
| 88 |
Guinea |
0.3 |
| 89 |
Ukraine |
0.3 |
| 90 |
Somalia |
0.3 |
| 91 |
Thailand |
0.3 |
| 92 |
Sierra Leone |
0.3 |
| 93 |
Czech Republic |
0.3 |
| 94 |
Marshall Islands |
0.3 |
| 95 |
Türkiye |
0.3 |
| 96 |
Poland |
0.3 |
| 97 |
Seychelles |
0.3 |
| 98 |
France |
0.3 |
| 99 |
Saint Vincent & the Grenadines |
0.3 |
| 100 |
Ethiopia |
0.2 |
| 101 |
Sao Tome and Principe |
0.2 |
| 102 |
Jamaica |
0.2 |
| 103 |
Mauritania |
0.2 |
| 104 |
Hungary |
0.2 |
| 105 |
Saint Kitts and Nevis |
0.2 |
| 106 |
Nepal |
0.2 |
| 107 |
Benin |
0.2 |
| 108 |
Dominican Republic |
0.2 |
| 109 |
Andorra |
0.2 |
| 110 |
Japan |
0.2 |
| 111 |
Ghana |
0.2 |
| 112 |
Chad |
0.2 |
| 113 |
Guatemala |
0.2 |
| 114 |
Cyprus |
0.2 |
| 115 |
Saint Lucia |
0.2 |
| 116 |
Kyrgyz Republic |
0.2 |
| 117 |
Kazakhstan |
0.2 |
| 118 |
China |
0.2 |
| 119 |
Italy |
0.2 |
| 120 |
Trinidad and Tobago |
0.2 |
| 121 |
Iceland |
0.2 |
| 122 |
Republic of Moldova |
0.2 |
| 123 |
Grenada |
0.2 |
| 124 |
Ireland |
0.2 |
| 125 |
Morocco |
0.2 |
| 126 |
Liechtenstein |
0.1 |
| 127 |
Vietnam |
0.1 |
| 128 |
Switzerland |
0.1 |
| 129 |
Puerto Rico |
0.1 |
| 130 |
Germany |
0.1 |
| 131 |
Burkina Faso |
0.1 |
| 132 |
Luxembourg |
0.1 |
| 133 |
Togo |
0.1 |
| 134 |
Iran |
0.1 |
| 135 |
South Korea |
0.1 |
| 136 |
Azerbaijan |
0.1 |
| 137 |
Côte d'Ivoire |
0.1 |
| 138 |
Armenia |
0.1 |
| 139 |
El Salvador |
0.1 |
| 140 |
Denmark |
0.1 |
| 141 |
Uzbekistan |
0.1 |
| 142 |
Tuvalu |
0.1 |
| 143 |
Cabo Verde |
0.1 |
| 144 |
Tonga |
0.1 |
| 145 |
Malawi |
0.1 |
| 146 |
Saudi Arabia |
0.1 |
| 147 |
Gambia |
0.1 |
| 148 |
Antigua and Barbuda |
0.1 |
| 149 |
Kenya |
0.1 |
| 150 |
Nigeria |
0.1 |
| 151 |
Philippines |
0.1 |
| 152 |
Belgium |
0.1 |
| 153 |
Tunisia |
0.1 |
| 154 |
India |
0.050 |
| 155 |
Uganda |
0.049 |
| 156 |
UK |
0.047 |
| 157 |
Rwanda |
0.045 |
| 158 |
Tajikistan |
0.041 |
| 159 |
Comoros |
0.037 |
| 160 |
Niger |
0.036 |
| 161 |
Algeria |
0.036 |
| 162 |
Mauritius |
0.030 |
| 163 |
Haiti |
0.030 |
| 164 |
San Marino |
0.029 |
| 165 |
Libya |
0.029 |
| 166 |
UAE |
0.029 |
| 167 |
Barbados |
0.022 |
| 168 |
Netherlands |
0.020 |
| 169 |
Burundi |
0.019 |
| 170 |
Iraq |
0.015 |
| 171 |
Israel |
0.015 |
| 172 |
Lesotho |
0.014 |
| 173 |
Pakistan |
0.013 |
| 174 |
Yemen |
0.013 |
| 175 |
Bangladesh |
0.011 |
| 176 |
Maldives |
0.009 |
| 177 |
Kiribati |
0.008 |
| 178 |
Jordan |
0.006 |
| 179 |
Djibouti |
0.006 |
| 180 |
Aruba |
0.004 |
| 181 |
Bahrain |
0.003 |
| 182 |
Singapore |
0.003 |
| 183 |
Kuwait |
0.001 |
| 184 |
Malta |
0.001 |
| 185 |
Qatar |
0.000 |
| 186 |
Oman |
0.000 |
| 187 |
Egypt |
0.000 |
| 188 |
Nauru |
0.000 |
Guyana and Suriname are the two biggest outliers, each offering more than 22 hectares of forest area per person.
This is due to these countries’ vast rainforests combined with their relatively small populations (both under 1 million people). The forests in this region make up the Guiana Shield, one of the largest remaining blocks of primary tropical forest on earth.
Other countries that rank highly include Canada and Russia, two of the world’s largest countries by total area. While these countries have much bigger populations than #1 ranked Guyana, their sheer amount of forest land results in an above-average per capita figure.
If you enjoyed today’s post, check out Countries With the Most Freshwater Resources on Voronoi, the new app from Visual Capitalist.
2025-11-29 09:04:12
About 18 months ago, we launched Voronoi, our free new data discovery app.
Believe it or not, there are already more data-driven visuals on Voronoi than on Visual Capitalist (which has been around for 13 years!).
Every day there’s something new on Voronoi to see. And in aggregate, there are roughly 6,500 data stories to explore on the platform from nearly 200 world-class creators.
Let’s see what captivated users in November.
We’ll take a look at some of the best Voronoi visuals over the last month, including one standout Editor’s Pick, as well as the most viewed, most discussed, and most liked posts.
This month’s most viewed visual came from Julie Peasley, exploring the top-paying U.S. jobs that don’t require a college degree.
Based on data from the U.S. Bureau of Labor Statistics (May 2024), this visualization highlights 20 careers where experience, certification, or specialized training outweigh formal higher education. The top spot goes to air traffic controllers, earning a median wage of $144,580—without requiring an associate’s or bachelor’s degree.
Users were fascinated by how many six-figure opportunities exist for those with hands-on skills, trade experience, or niche expertise in logistics and public safety.
Explore the full dataset on Voronoi today.
This snapshot from Visual Capitalist sparked wide discussion this month by visualizing how citizens perceive changes in their country’s quality of life.
Using Numbeo’s Quality of Life Index, the chart combines data on costs, safety, healthcare, pollution, and more. While countries like Switzerland continue to rank among the world’s highest, others such as the Netherlands and Norway have climbed steadily.
The conversation heated up around the biggest declines: Canada (from 9th to 27th), Saudi Arabia (12th to 25th), the U.S. (4th to 14th), and Sweden (3rd to 13th)—prompting debate on affordability, policy, and post-pandemic priorities.
Join the discussion on Voronoi today.
This data-rich visualization from Visual Capitalist captured user attention worldwide, showing the global divide between cash-based and digital economies.
Cash use remains near-universal in lower-income nations such as Myanmar (98%), Ethiopia (95%), and Gambia (95%), where digital infrastructure is limited. In contrast, wealthy nations like Sweden (14%), Norway (10%), and South Korea (10%) have nearly eliminated physical cash.
Cultural outliers drew the most interest: Japan (60%) and Germany (51%) retain high cash use despite advanced economies—while China (10%) exemplifies a rapid leap to mobile payments, skipping the credit card era entirely.
See how your country compares on Voronoi today.
Our Editor’s Pick for November comes from MadeVisual, who turned the spotlight to outer space—mapping where water exists beyond Earth.
The visual reveals that oceans and ice reserves on moons like Titan and Ganymede vastly exceed Earth’s total water volume, hidden beneath thick crusts of ice. Even airless worlds like the Moon and Mercury harbor small pockets of frozen material in permanent shadow.
Together, these discoveries challenge assumptions about habitability—and hint that water, the foundation of life, may be far more common across the Solar System than once believed.
Dive deeper into the data on Voronoi today.