SLOWDOWN Data

Download excel spreadsheets and pdfs of data used in Slowdown, with additional supporting statistics

Guide to using the downloadable data on this site

Data is provided for every figure that appears in Slowdown in EXCEL format that you can download using the links below. To see the labels on the timelines you may need to download the plug in X-Y Labeller. You can also download a PDF of that spreadsheet if you don’t have access to excel or an equivalent.

The first sheet within each spreadsheet describes the contents of all the sheets in that spreadsheet. The second sheet describes the data. The third sheet is the data for the figure that appears in the book. The source of the data is given in cell A5 in that sheet which usually includes a web address that allows you to access the underlying data used to make the sheet during 2019 and if you would like, update the data and hence update the timeline. Within the third sheet column C contains the data that is being plotted. This is the column of data you can alter or update if you want to alter the timeline, column B contains the very simple formulae to calculate change over time. Usually this is change from the point in time before the point being plotted to the point in time after, but for the first and last points in any time series the formulae is different. If a different formula is ever used the cell is marked with a small red triangle and a comment explaining why. You can click on any cell to see the change formulae used at that point in time and to alter it, should you wish. Column A list the date in time and column D includes that date if the date is included as a label on the timeline. The timeline itself appears to the right of the data.

Additional data can be found in the fourth, fifth and subsequent sheets data not used in the book is shown, giving examples for different countries of what a similar timeline to the one in the book, but for that place, looks like. You can use these sheets as a template to draw your own timelines of a series for which there is good quality data.

Figures as they appear the book are provided for reference along with the data. Follow these links for high quality graphic files of the figures and their animations.

Population of an imaginary country, 1950–2020

Figure 1. Population of an imaginary country, 1950–2020
(accelerating at 2% per year)

2% per year: Total population and its absolute change of an imaginary country at a constant 2% growth per year, 1950-2020, (million people).

Additional data

5% per year: Total population and its absolute change of an imaginary country, at a constant 5% growth per year, 1950-2020, (million people).
2% and sudden increase: Total population and its absolute change of an imaginary country, at a constant 2% growth per year, with a sudden increase of 100 million people in 2010, 1950-2020, (million people).

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Fig 2-Population of an imaginary country, 1950–2070

Figure 2. Population of an imaginary country, 1950–2070 (initially rising but thereafter decelerating)

A hypothetical example of population acceleration and deceleration both slowing down over time.
2%+0.1% Total population and its absolute change of an imaginary country, at an initial 2% growth rate that decreases 0.1 percentage point per year, 1950-2160, (million people).

Additional data

2%+0.01% Total population and its absolute change of an imaginary country, at an initial 2% growth rate that decreases increasingly quickly with an acceleration rate of 0.01 percentage points, 1950-2130, (million people).
2%+0.1%+sudden decrease Total population and its absolute change of an imaginary country, at an initial 2% growth rate that decreases 0.1 percentage point per year, with a sudden¬¬ decrease of 20 million people in 2010, 1950-2160, (million people).

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 Fig 3-Population of an imaginary country, 1950–2650

Figure 3. Population of an imaginary country, 1950–2650

Hypothetical examples of increasing stability that looks like great change. Note: see file 4 for conventional graphs.
31.42+400 Total population and its absolute change of an imaginary country, total population oscillates every 31.42 years, absolute change drops (1/400). , 1950-2650, (million people).

Additional data

31.42+1000 Total population and its absolute change of an imaginary country, total population oscillates every 31.42 years, absolute change drops (1/1000). , 1950-2650, (million people).
100+400 Total population and its absolute change of an imaginary country, total population oscillates every 100 years, absolute change drops (1/400). , 1950-2650, (million people).

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Fig 4-Population of an imaginary country, 1950–2020

Figure 4. Population of an imaginary country, 1950–2020 (conventional depiction)

Hypothetical examples of increasing stability that looks like great change on a conventional graph. Note: see file 3 for timeline graphs, showing the fuller picture.
31.42+400 Total population and its absolute change of an imaginary country, total population oscillates every 31.42 years, absolute change drops (1/400). , 1950-2020, (million people).

Additional data

31.42+1000 Total population and its absolute change of an imaginary country, total population oscillates every 31.42 years, absolute change drops (1/1000). , 1950-2200, (million people).
100+400 Total population and its absolute change of an imaginary country, total population oscillates every 100 years, absolute change drops (1/400). , 1950-2650, (million people).

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Fig 5-Three different ways of describing the movement of the perpetual pendulum

Figure 5. Three different ways of describing the movement of the perpetual pendulum

Different ways of describing the movement of the perpetual pendulum.
Time on The velocity and position of a pendulum, against time: conventional graph
Time off The velocity and position of a pendulum, take time off any axis: phase diagram timeline graph

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Fig 6-US student debt, 2006–18

Figure 6. US student debt, 2006–18 (billions of dollars)

US Total student debt and its absolute change, US, 2006-2018, (billions of US dollars).

Additional data

UK Total student debt and its absolute change, UK (higher education), 2000-2018, (sterling billions).
Japan Total student debt and its absolute change, Japan, 2004-2016, (yen billions).

Numbers of students in higher education:
GermanyT Total students in higher education and its absolute change, Germany, 1995-2017
GermanyG German students in higher education and its absolute change, Germany, 1995- 2017
GermanyF Foreign students in higher education and its absolute change, Germany, 1995- 2017
China Total students in regular higher education and its absolute change, China, 1985- 2017

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Fig 7-US car-loan debt, 2003–18

Figure 7. US car-loan debt, 2003–18 (billions of dollars)

Car Loans US Total car-loan debt and its absolute change, US, 2003-2018, (billions of US dollars).

Additional data

Credit card debt:
Uscard Total credit card debt and its absolute change, US, 2003-2018, (billions of US dollars).
Ukcard Total credit card debt and its absolute change, UK, 1993-2019, (sterling billions).
Jpcard Total consumer card loans to households and its absolute change, Japan, 1989-2019, (yen billions).

Household debt:
DehousholdS Total short-term household loans and its absolute change, Germany, 1999-2018, (euro billions).
DehouseholdL Total long-term household loans and its absolute change, Germany, 1999-2018, (euro billions).
DehousholdT Total household loans and its absolute change, Germany, 1999-2018, (euro billions).
Deconsumer Total consumer loans and its absolute change, Germany, 2005-2017, (euro billions).

Credit card debt
China Cncard Total credit card debt and its absolute change, China, 2008-2018, (RMB billions).

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Fig 8-US mortgage debt, 2003–18

Figure 8. US mortgage debt, 2003–18 (billions of dollars)

US Total household's mortgage debt and its absolute change, US, 2003-2018, (billions of US dollars).

Additional data

UK Total lending to individuals secured on dwellings and its absolute change, UK, 1987-2019, (sterling billions).
Japan Total housing loans to households and its absolute change, Japan, 1974-2019, (yen billions).
Germany Total households' mortgage and its absolute change, Germany, 2005-2017, (euro billions).
China Total housing mortgage and its absolute change, China, 2010-2016, (RMB billions).

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Fig 9-US public debt, 1966–2018

Figure 9. US public debt, 1966–2018 (billions of dollars)

US Federal government public debt and its absolute change, US, 1966-2018, (billions of US dollars).

Additional data

UK General government gross debt and its absolute change, UK, 1980-2024, (sterling billions).
Japan General government gross debt and its absolute change, Japan, 1980-2024, (yen billions).
Germany General government gross debt and its absolute change, Germany, 1991-2024, (euro billions).
China General government gross debt and its absolute change, China, 1995-2024, (RMB billions).

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ig 10-Articles in Wikipedia

Figure 10. Articles in Wikipedia, 15 January 2001–1 January 2019

(Internet activity: Wikipedia, Domain names, Facebook, Weibo)
Wikipedia Number of articles in Wikipedia and its absolute change, worldwide, 2001-2019, (millions).

Additional data

Domain Number of domain name registration of the Top Level Domain Names (TLDs). and its absolute change, worldwide, 2010-2019, (millions).
Facebook Number of monthly active Facebook users and its absolute change, worldwide, 2009-2019, (millions).
DomainCN Number of domain name registration of the Top Level Domain Names (TLDs). and its absolute change, China, 2009-2014, (millions).
Weibo Number of monthly active Weibo users and its absolute change, China, 2014-2018, (millions).

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Fig 11-New books Netherlands, 1500–1680

Figure 11. New book titles published in the Netherlands, 1500–1680

(New book titles, historic: Netherlands UK, Germany, France, US, Russia. New titles per million people).
Netherlands Number of new book titles published and its absolute change, the Netherlands, 1500-1680 {1500-2000}

Additional data

UK Number of new book titles published and its absolute change, UK, 1500-1700 {1500-2000}
Germany Number of new book titles published and its absolute change, Germany, 1500-1700 {1500-1800}
France Number of new book titles published and its absolute change, France, 1500-1700 {1500-1800}
US Number of new book titles published and its absolute change, US, 1700-1800
Russia: Number of new book titles published and its absolute change, Russia, 1700-1800

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Fig 12-New books Netherlands, 1580–2009

Figure 12. New book titles published in the Netherlands, 1580–2009

(New book titles: Netherlands UK, Germany, France, Russia, Germany. New titles per million people).
Netherlands Number published and its absolute change, the Netherlands, 1580-2009 {1500-2009}, log scale

Additional data

UK Number published and its absolute change, UK, 1510-2009, log scale
Russia Number published and its absolute change, Russia, 1920-2009
Germany: Number published and its absolute change, Germany, 1920-1996

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Fig 13-Global fuel-industry CO2 emissions, 1750–1910

Figure 13. Global fuel-industry CO2 emissions, 1750–1910

Global Carbon Project (Fossil fuels+cement, Gas, Liquid fuel, Solid fuel. World, W.Europe, N.America. Billions of tonnes.).
Note: cement production accounted for 8% of CO2 emissions in 2018.
TotalW Total CO2 emissions from fossil fuels and cement production, worldwide, 1750-1910 {1750-2018}

Additional data

GasW CO2 emissions from gas fuel consumption, worldwide, 1750-1910 {1750-2014}
LiquidW CO2 emissions from liquid fuel consumption, worldwide, 1750-1910 {1750-2014}
SolidW CO2 emissions from solid fuel consumption, worldwide, 1750-1910 {1750-2014}
TotalWE Total CO2 emissions from fossil fuels and cement production, West Europe, 1750-1910 {1750-2014}
TotalNA: Total CO2 emissions from fossil fuels and cement production, North America, 1790-1910 {1750-2014}

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ig 14-Global fuel-industry CO2 emissions_1910_1960

Figure 14. Global fuel-industry CO2 emissions, 1910–60

Global Carbon Project (Fossil fuels, Gas, Liquid, Solid, Fuel+cement. Western Europe & North America. Billions of tonnes of CO2).
TotalW Total CO2 emissions from fossil fuels and cement production, worldwide, 1910-1960 {1910-2018}

Additional data

GasW CO2 emissions from gas fuel consumption, worldwide, 1910-1960 {1909-2014}
LiquidW CO2 emissions from liquid fuel consumption, worldwide, 1910-1960 {1909-2014}
SolidW CO2 emissions from solid fuel consumption, worldwide, 1910-1960 {1909-2014}
TotalWE Total CO2 emissions from fossil fuels and cement production, West Europe, 1910-1960
TotalNA Total CO2 emissions from fossil fuels and cement production, North America, 1910-1960 {1909-2014}

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Fig 15-Global fuel-industry CO2 emissions_1960_2018_conventional

Figure 15. Global fuel-industry CO2 emissions, 1960–2018 (conventional graph)

(Fossil fuels, Gas, Liquid, Solid, Fuel+cement. World, UK, US, France, Japan, China. Billions of tonnes of CO2).
TotalW Total CO2 emissions from fossil fuel consumption and cement production, worldwide, 1960-2018

Additional data

GasW CO2 emissions from gas fuel consumption, worldwide, 1960-2017
LiquidW CO2 emissions from liquid fuel consumption, worldwide, 1960-2017
SolidW CO2 emissions from solid fuel consumption, worldwide, 1960-2017, (billions of tonnes).
TotalUK Total CO2 emissions from fossil fuel consumption and cement production, UK, 1960-2014
TotalUS Total CO2 emissions from fossil fuel consumption and cement production, US, 1960-2014
TotalFR Total CO2 emissions from fossil fuel consumption and cement production, France, 1960-2014
TotalJP Total CO2 emissions from fossil fuel consumption and cement production, Japan, 1960-2014
TotalCN: Total CO2 emissions from fossil fuel consumption and cement production, China, 1960-2014

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Fig 16-Global fuel-industry CO2 emissions_1960_2018

Figure 16. Global fuel-industry CO2 emissions, 1960–2018 (timeline)

(Fossil fuels, Gas, Liquid, Solid, Fuel+cement. World, UK, US, France, Japan, China. Billions of tonnes of CO2).
TotalW Total CO2 emissions from fossil fuels and cement production, worldwide, 1960-2018

Additional data

GasW CO2 emissions from gas fuel consumption, worldwide, 1960-2017
LiquidW CO2 emissions from liquid fuel consumption, worldwide, 1960-2017
SolidW CO2 emissions from solid fuel consumption, worldwide, 1960-2017
TotalUK Total CO2 emissions from fossil fuels and cement production, UK, 1960-2014
TotalUS Total CO2 emissions from fossil fuels and cement production, US, 1960-2014
TotalFR Total CO2 emissions from fossil fuels and cement production, France, 1960-2014
TotalJP Total CO2 emissions from fossil fuels and cement production, Japan, 1960-2014
TotalCN: Total CO2 emissions from fossil fuels and cement production, China, 1960-2014

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Fig 17-Average world temperature (NASA), 1881–2018

Figure 17. Average world annual land and ocean temperature, 1881–2018

(World, land, ocean, US). Original data uses a five-year lowest smooth, with a base period 1951-1980. Note: Timelines, see file 20 for a bar chart.
LandOceanW Annual average land and ocean temperatures, world, 1880-2018, (degrees Celsius).

Additional data

LandW Annual average land temperatures, world, 1880-2018, (degrees Celsius).
OceanW Annual average ocean temperature, world, 1880-2018, (degrees Celsius).
LandOceanUS; Annual average land temperatures, US, 1880-2018, (degrees Celsius).

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Fig 18-Average world temperature (MET offfice), 1850–2018

Figure 18. Average world annual land and ocean temperature, 1850–2018

(World, land, ocean, UK). Original data uses an eleven-year smooth, with a base period 1961-1990. Note: Timelines, see file 20 for a bar chart.
LandOceanW Annual average land and ocean temperatures, worldwide, 1850-2018, (degrees Celsius).

Additional data

LandW Annual average land temperatures, worldwide, 1850-2018, (degrees Celsius).
LandOceanWS Annual average land and ocean temperatures, worldwide, 1940-1980, (degrees Celsius).
LandWS Annual average land temperatures with worldwide, 1940-1980, (degrees Celsius).

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Fig 19-Tropospheric temperature anomalies, 1978–2019

Figure 19. Tropospheric temperature anomalies, 1978–2019

Tropospheric temperature anomalies, base period 1981-2010. Note: Timelines, see file 20 for bar chart.
NonSmooth Annual average land and ocean temperatures without smooth, worldwide, 1978-2019, (Celsius).

Additional data

3ySmooth Annual average land and ocean temperatures with the 3-year smooth, worldwide, 1978-2019, (Celsius).
7ySmooth Annual average land and ocean temperatures with the 7-year smooth, worldwide, 1978-2019, (Celsius).
11ySmooth Annual average land and ocean temperatures with the 11-year smooth, worldwide, 1978-2019,(Celsius).

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Fig 20-Tropospheric temperature anomalies, 1978–2019

Figure 20. Tropospheric temperature anomalies, 1978–2019

Annual average land and ocean temperatures worldwide, (degrees Celsius). . Note: Bar charts, see files 18-20 for timelines
Alabama From Alabama University, base period 1981-2010, without smooth, worldwide, 1978-2019

Additional data

NASA From NASA, base period 1951-1980, with the five year lowess smooth, worldwide, 1880-2018
Met From the UK Met Office, base period 1961-1990, with 11-year smooth, worldwide, 1850-2018

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Fig 21-World - total population, years 1–2100

Figure 21. World - total population, years 1–2100

(2017 and 2019 projections, total and age groups 0-20,-40,-60,-80,-100). Angus Maddison Project and UN World Population Prospects Note: for these graphs using timelines with log-log scales, see file 22
Total2017 Total human population, with projections from 2017 UN report, worldwide, 1-2100

Additional data

Total2019 Total human population, with projections from 2019 UN report, worldwide, 1-2100
0-20_2019 Human population, aged 0-20, with projections from 2019 UN report, worldwide, 1950-2100
21-40_2019 Human population, aged 21-40, with projections from 2019 UN report, worldwide, 1950-2100
41-60_2019 Human population, aged 41-60, with projections from 2019 UN report, worldwide, 1950-2100
61-80_2019 Human population, aged 61-80, with projections from 2019 UN report, worldwide, 1950-2100
81-100_2019 Human population, aged 81-100, with projections from 2019 UN report, worldwide, 1950-2100

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Fig 22-World - total population, years 1–2100 (log scale)

Figure 22. World - total population, years 1–2100 (log scale)

(2017 and 2019 projections, and age groups 0-20,-40,-60,-80,-100). Angus Maddison Project and UN World Population Prospects Note: these timeline graphs use log-log scales. See file 21 for standard timeline graphs
Total2017 Total human population, with projections from 2017 UN report, worldwide, 1-2100

Additional data

Total2019 Total human population, with projections from 2019 UN report, worldwide, 1-2100
0-20_2019 Human population, aged 0-20, with projections from 2019 UN report, worldwide, 1950-2100
21-40_2019 Human population, aged 21-40, with projections from 2019 UN report, worldwide, 1950-2100
41-60_2019 Human population, aged 41-60, with projections from 2019 UN report, worldwide, 1950-2100
61-80_2019 Human population, aged 61-80, with projections from 2019 UN report, worldwide, 1950-2100
81-100_2019 Human population, aged 81-100, with projections from 2019 UN report, worldwide, 1950-2100

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Fig 23-United States - total population, years 1–2100

Figure 23. United States - total population, years 1–2100

(USA with 2017 & 2019 projections, Canada, Mexico, Cuba). Angus Maddison Project and UN World Population Prospects
USA2017 Total human population, with projections from 2017 UN report, USA, 1-2100

Additional data

USA2019 Total human population, with projections from 2019 UN report, USA, 1-2100
Canada2019 Total human population, with projections from 2019 UN report, Canada, 1-2100
Mexico2019 Total human population, with projections from 2019 UN report, Mexico, 1-2100
Cuba2019 Total human population, with projections from 2019 UN report, Cuba, 1820-2100

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Fig 24-China - total population, years 1–2100

Figure 24. China - total population, years 1–2100

(China, Singapore, Vietnam, Myamar, Indonesia). Angus Maddison Project and UN World Population Prospects
China2017 Total human population, with projections from 2017 UN report, China, 1-2100

Additional data

China2019 Total human population, with projections from 2019 UN report, China, 1-2100
Singapore2019 Total human population, with projections from 2019 UN report, Singapore, 1820-2100
Vietnam2019 Total human population, with projections from 2019 UN report, Vietnam, 1820-2100
Myanmar2019 Total human population, with projections from 2019 UN report, Myanmar, 1820-2100
Indonesia2019 Total human population, with projections from 2019 UN report, Indonesia, 1-2100

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Fig 25-Africa - total population, years 1–to 2100

Figure 25. Africa - total population, years 1–to 2100

Africa (Africa plus Nigeria, Uganda, Ethiopia, Egypt). Angus Maddison Project and UN World Population Prospects
Africa2017 Total human population, with projections from 2017 UN report, Africa, 1-2100

Additional data

Africa2019 Total human population, with projections from 2019 UN report, Africa, 1-2100
Nigeria2019 Total human population, with projections from 2019 UN report, Nigeria, 1950-2100
Uganda2019 Total human population, with projections from 2019 UN report, Uganda, 1950-2100
Ethiopia2019 Total human population, with projections from 2019 UN report, Ethiopia, 1-2100
Egypt2019 Total human population, with projections from 2019 UN report, Egypt, 1-2100

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ig 26-British Isles - total population, years 1–2100

Figure 26. British Isles - total population, years 1–2100

Europe (British Isles, Germany, France, Italy, Spain). Angus Maddison Project and UN World Population Prospects
British2017 Total human population, with projections from 2017 UN report, British Isles, 1-2100

Additional data

British2019 Total human population, with projections from 2019 UN report, British Isles, 1-2100
Germany2019 Total human population, with projections from 2019 UN report, Germany, 1-2100
France2019 Total human population, with projections from 2019 UN report, France, 1-2100
Italy2019 Total human population, with projections from 2019 UN report, Italy, 1-2100
Spain2019 Total human population, with projections from 2019 UN report, Spain, 1-2100

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Fig 27-Indian subcontinent - total population, years 1–2100

Figure 27. Indian subcontinent - total population, years 1–2100

(All and India, Pakistan, Bangladesh). Angus Maddison Project and UN World Population Prospects
Total2017 Total human population, with projections from 2017 UN report, India sub-continent, 1-2100

Additional data

Total2019 Total human population, with projections from 2019 UN report, India sub-continent, 1-2100
India2019 Total human population, with projections from 2019 UN report, India, 1950-2100
Pakistan2019 Total human population, with projections from 2019 UN report, Pakistan, 1950-2100
Bangladesh2019: Total human population, with projections from 2019 UN report, Bangladesh, 1950-2100

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Fig 28-Japan - total population, years 1–2100

Figure 28. Japan - total population, years 1–2100

Japan, Iceland, Norway, Finland, Denmark). Angus Maddison Project and UN World Population Prospects
Japan2017 Total human population, with projections from 2017 UN report, Japan, 1-2100

Additional data

Japan2019 Total human population, with projections from 2019 UN report, Japan, 1-2100
Iceland2019 Total human population, with projections from 2019 UN report, Iceland, 1950-2100
Norway2019 Total human population, with projections from 2019 UN report, Norway, 1-2100
Finland2019 Total human population, with projections from 2019 UN report, Finland, 1-2100
Denmark2019 Total human population, with projections from 2019 UN report, Denmark, 1-2100

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Fig 29-Remainder of Eurasia - total population, years 1–2100

Figure 29. Remainder of Eurasia - total population, years 1–2100

2100 (Eurasia without China, Japan, the Indian subcontinent and the British Isles – all already shown). plus Russia, Turkey, Iran, Afghanistan. Year 1 to 2100 Angus Maddison Project and UN World Population Prospects.
Eurasia2017 Total human population, with projections from 2017 UN report, Remainder of Eurasia, 1-2100

Additional data

Eurasia2019 Total human population, with projections from 2019 UN report, Remainder of Eurasia, 1-2100
Russia2019 Total human population, with projections from 2019 UN report, Russia, 1950-2100
Turkey2019 Total human population, with projections from 2019 UN report, Turkey, 1-2100
Iran2019 Total human population, with projections from 2019 UN report, Iran, 1-2100
Afghanistan2019 Total human population, with projections from 2019 UN report, Afghanistan, 1820-2100

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Fig 30-Oceania - total population, years 1–2100

Figure 30. Oceania - total population, years 1–2100

Australia, New Zealand, Papua New Guinea, Fiji. Year 1 to 2100 Angus Maddison Project and UN World Population Prospects
Total2017 Total human population, with projections from 2017 UN report, Oceania, 1-2100

Additional data

Total2019 Total human population, with projections from 2019 UN report, Oceania, 1-2100
Australia2019 Total human population, with projections from 2019 UN report, Australia, 1-2100
NewZealand2019 Total human population, with projections from 2019 UN report, New Zealand, 1000-2100
PNG2019 Total human population, with projections from 2019 UN report, Papua New Guinea, 1950-2100, (million people).
Fiji2019 Total human population, with projections from 2019 UN report, Fiji, 1950-2100

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Fig 31-The Americas (excl. US) - total population, years 1–2100

Figure 31. The Americas (excl. US) - total population, years 1–2100

Brazil, Argentina, Venezuela, Peru. Year 1 to 2100 Angus Maddison Project and UN World Population Prospects.
Total2017 Total human population, with projections from 2017 UN report, the Americas (without the USA). , 1-2100

Additional data

Total2019 Total human population, with projections from 2019 UN report, the Americas (without the USA), 1- 2100
Brazil2019 Total human population, with projections from 2019 UN report, Brazil, 1000-2100
Argentina2019 Total human population, with projections from 2019 UN report, Argentina, 1820-2100
Venezuela2019 Total human population, with projections from 2019 UN report, Venezuela, 1820-2100
Peru2019 Total human population, with projections from 2019 UN report, Peru, 1000-2100

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Fig 32-World - total fertility rate, 1960–2016

Figure 32. World - total fertility rate, 1960–2016

European Union, North America, South Asia, High and Low income Countries UN World Population Prospects and other sources Note: for 1998-2016 only, see file 33
World Total fertility rate, world, 1960-2016, (children per woman).

Additional data

EU Total fertility rate, European Union, 1960-2017, (children per woman).
NorthAmerica Total fertility rate, North America, 1960-2017, (children per woman).
SouthAsia Total fertility rate, South Asia, 1960-2017, (children per woman).
HighIncome Total fertility rate, high income countries, 1960-2017, (children per woman).
LowIncome Total fertility rate, low income countries, 1960-2017, (children per woman).

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Fig 33-World - total fertility rate, 1998–2016

Figure 33. World - total fertility rate, 1998–2016

European Union, North America, South Asia, High and Low income Countries UN World Population Prospects and other sources Note: for 1960-2016, see file 32.
World Total fertility rate, world, 1998-2016 {1960-2016}, (children per woman).

Additional data

EU Total fertility rate, European Union, 1998-2017 {1960-2017}, (children per woman).
NorthAmerica Total fertility rate, North America, 1998-2017 {1960-2017}, (children per woman).
SouthAsia Total fertility rate, South Asia, 1998-2017 {1960-2017}, (children per woman).
HighIncome Total fertility rate, high income countries, 1998-2017 {1960-2017}, (children per woman).
LowIncome Total fertility rate, low income countries, 1998-2017 {1960-2017}, (children per woman).

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Fig 34-United States - total fertility rate, 1960–2016

Figure 34. United States - total fertility rate, 1960–2016

USA, Canada, Mexico, Cuba UN World Population Prospects and other sources Note: for 1973-2016 only, see file 35.
USA Total fertility rate, USA, 1960-2016, (children per woman).

Additional data

Canada Total fertility rate, Canada, 1960-2017, (children per woman).
Mexico Total fertility rate, Mexico, 1960-2017, (children per woman).
Cuba Total fertility rate, Cuba, 1960-2017, (children per woman).

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Fig 35-United States - total fertility rate, 1973–2016

Figure 35. United States - total fertility rate, 1973–2016

USA, Canada, Mexico, Cuba UN World Population Prospects and other sources Note: for 1960-2016, see file 34.
USA Total fertility rate, USA, 1973-2016 {1960-2016}, (children per woman).

Additional data

Canada Total fertility rate, Canada, 1973-2017 {1960-2017}, (children per woman).
Mexico Total fertility rate, Mexico, 1990-2017 {1960-2017}, (children per woman).
Cuba Total fertility rate, Cuba, 1985-2017 {1960-2017}, (children per woman).

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Fig 36-China - total fertility rate, 1960–2016

Figure 36. China - total fertility rate, 1960–2016

Singapore, Vietnam, Myanmar, Indonesia UN World Population Prospects and other sources Note: for 1999-2016 only, see file 37.
China Total fertility rate, China, 1960-2016, (children per woman).

Additional data

Singapore Total fertility rate, Singapore, 1960-2017, (children per woman).
Vietnam Total fertility rate, Vietnam, 1960-2017, (children per woman).
Myanmar Total fertility rate, Myanmar, 1960-2017, (children per woman).
Indonesia Total fertility rate, Indonesia, 1960-2017, (children per woman).

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Fig 37-China - total fertility rate, 1999–2016

Figure 37. China - total fertility rate, 1999–2016

Singapore, Vietnam, Myanmar, Indonesia UN World Population Prospects and other sources Note: for 1960-2016, see file 36.
China Total fertility rate, China, 1999-2016 {1960-2016}, (children per woman).

Additional data

Singapore Total fertility rate, Singapore, 1999-2017 {1960-2017}, (children per woman).
Vietnam Total fertility rate, Vietnam, 1999-2017 {1960-2017}, (children per woman).
Myanmar Total fertility rate, Myanmar, 1999-2017 {1960-2017}, (children per woman).
Indonesia Total fertility rate, Indonesia, 1999-2017 {1960-2017}, (children per woman).

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Fig 38-Niger - total fertility rate, 1960–2016

Figure 38. Niger - total fertility rate, 1960–2016

Niger, Somalia, Congo, Mali UN World Population Prospects and other sources
Niger Total fertility rate, Niger, 1960-2016, (children per woman).

Additional data

Somalia Total fertility rate, Somalia, 1960-2017, (children per woman).
Congo Total fertility rate, Democratic Republic of the Congo, 1960-2017, (children per woman).
Mali Total fertility rate, Mali, 1960-2017, (children per woman).

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Fig 39-East Timor - total fertility rate, 1960–2016

Figure 39. East Timor - total fertility rate, 1960–2016

East Timor, Afghanistan, Iraq, Yemen UN World Population Prospects and other sources
EastTimor Total fertility rate, East Timor, 1960-2016, (children per woman).

Additional data

Afghanistan Total fertility rate, Afghanistan, 1960-2017, (children per woman).
Iraq Total fertility rate, Iraq, 1960-2017, (children per woman).
Yemen Total fertility rate, Mali, 1960-2017, (children per woman).

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Fig 40-Guatemala - total fertility rate, 1960–2016

Figure 40. Guatemala - total fertility rate, 1960–2016

Guatemala, Panama, Honduras, Nicaragua UN World Population Prospects and other sources
Guatemala Total fertility rate, Guatemala, 1960-2016, (children per woman).

Additional data

Panama Total fertility rate, Panama, 1960-2017, (children per woman).
Honduras Total fertility rate, Honduras, 1960-2017, (children per woman).
Nicaragua Total fertility rate, Nicaragua, 1960-2017, (children per woman).

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Fig 41-Haiti - total fertility rate, 1960–2016

Figure 41. Haiti - total fertility rate, 1960–2016

Dominican Republic, Grenada, Jamaica UN World Population Prospects and other sources
Haiti Total fertility rate, Haiti, 1960-2016, (children per woman).

Additional data

DominicanRepublic Total fertility rate, Dominican Republic, 1960-2017, (children per woman).
Grenada Total fertility rate, Grenada, 1960-2017, (children per woman).
Jamaica Total fertility rate, Jamaica, 1960-2017, (children per woman).

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Fig 42-France - total fertility rate, 1960–2016

Figure 42. France - total fertility rate, 1960–2016

France, Germany, Netherlands, Switzerland UN World Population Prospects and other sources
France Total fertility rate, France, 1960-2016, (children per woman).

Additional data

Germany Total fertility rate, Germany, 1960-2017, (children per woman).
Netherlands Total fertility rate, Netherlands, 1960-2017, (children per woman).
Switzerland Total fertility rate, Switzerland, 1960-2017, (children per woman).

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Fig 43-United Kingdom - total fertility rate, 1960–2016

Figure 43. United Kingdom - total fertility rate, 1960–2016

Ireland, New Zealand, Australia UN World Population Prospects and other sources
UK Total fertility rate, United Kingdom, 1960-2016, (children per woman).

Additional data

Ireland Total fertility rate, Ireland, 1960-2017, (children per woman).
NewZealand Total fertility rate, New Zealand, 1960-2017, (children per woman).
Australia Total fertility rate, Australia, 1960-2017, (children per woman).

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Fig 44-Republic of Korea - total fertility rate, 1960–2016

Figure 44. Republic of Korea - total fertility rate, 1960–2016

Republic of South Korea, Japan, Malaysia, Philippines UN World Population Prospects and other sources
Korea Total fertility rate, Republic of Korea, 1960-2016, (children per woman).

Additional data

Japan Total fertility rate, Japan, 1960-2017, (children per woman).
Malaysia Total fertility rate, Malaysia, 1960-2017, (children per woman).
Philippines Total fertility rate, Philippines, 1960-2017, (children per woman).

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Fig 45-Portugal - total fertility rate, 1960–2016

Figure 45. Portugal - total fertility rate, 1960–2016

Portugal, Spain, Italy, Greece UN World Population Prospects and other sources
Portugal Total fertility rate, Portugal, 1960-2016, (children per woman).

Additional data

Spain Total fertility rate, Spain, 1960-2017, (children per woman).
Italy Total fertility rate, Italy, 1960-2017, (children per woman).
Greece Total fertility rate, Greece, 1960-2017, (children per woman).

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Fig 46-Brazil - total fertility rate, 1960–2016

Figure 46. Brazil - total fertility rate, 1960–2016

UN World Population Prospects and other sources
Brazil Total fertility rate, Brazil, 1960-2016, (children per woman).

Additional data

Argentina Total fertility rate, Argentina, 1960-2017, (children per woman).
Venezuela Total fertility rate, Venezuela, 1960-2017, (children per woman).
Peru Total fertility rate, Peru, 1960-2017, (children per woman).

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Fig 47-World GDP per capita, 1–2018

Figure 47. World GDP per capita, 1–2018

Western Asia, Africa, Latin America, Western Europe. Year 1 to 2018 Maddison Project Database 2018, updated using World Bank and IMF data.
World-estimated GDP per capita, real mean annual average, constant 2011 US$, worldwide, 1-2018

Additional data

World-UN revised 2019, GDP per capita, real mean annual average, constant 2011 US$, worldwide, 1-2018
WesternAsia GDP per capita, real mean annual average, constant 2011 US$, Western Asia, 1-2016
Africa GDP per capita, real mean annual average, constant 2011 US$, Africa, 1-2016
LatinAmerica GDP per capita, real mean annual average, constant 2011 US$,
Latin America, 1-2018 WesternEurope GDP per capita, real mean annual average, constant 2011 US$, Western Europe, 1-2018

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Fig 48-US GDP per capita, 1500–2019

Figure 48. US GDP per capita, 1500–2019

USA, Canada, Mexico, Cuba. 1550 to 2019 Maddison Project Database 2018, updated using World Bank and IMF data
USA GDP per capita, real mean annual average, 'chained' 2012 US$, USA, 1650-2019

Additional data

Canada GDP per capita, real mean annual average, constant 2010 US$, Canada, 1820-2018
Mexico GDP per capita, real mean annual average, constant 2010 US$, Mexico, 1550-2018
Cuba GDP per capita, real mean annual average, constant 2010 US$, Cuba, 1690-2017

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Fig 49-China GDP per capita, 1978–2019

Figure 49. China GDP per capita, 1978–2019

China, Singapore, Japan, Indonesia. 1800 to 2019 Maddison Project Database 2018, updated using World Bank and IMF data
China GDP per capita, real mean annual average, constant 1978 Chinese yuan, China, 1978-2019

Additional data

Singapore GDP per capita, real mean annual average, constant 2010 US$, Singapore, 1960-2018
Japan GDP per capita, real mean annual average, constant 2010 US$,
Japan, 1800-2018 Indonesia GDP per capita, real mean annual average, constant 2010 US$, Indonesia, 1960-2018

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ig 50-US median full-time employee weekly real earnings, 1979–2019

Figure 50. US median full-time employee weekly real earnings, 1979–2019

US, UK, China, Japan
USA Median weekly wages, full time employees, USA, 1979-2019, ( real 1982-1984 US$,). U.S. Bureau of Labor Statistics, “Employed Full Time: Median Usual Weekly Real Earnings; Wage and Salary Workers; 16 Years and Over”

Additional data

GB Average weekly regular pay, all employees, Great Britain, 2000-2019, (real 2015 £). UK Office for National Statistics, EARN01: Average Weekly Earnings
China Average annual wages, all urban employees in formal sectors China, 2000-2018, (real 2000 Chinese yuan). National Bureau of Statistics of China adjusted using the price index
Japan Average monthly earnings, all regular employees, Japan, 1998-2018, (real 2015 Japanese yenJapanese yuan). Monthly (July used). Labor Survey, The Ministry of Health, Labor and Welfare Japan

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Fig 51-The Herengracht Dutch house-price index, 1628–1973

Figure 51. The Herengracht Dutch house-price index, 1628–1973

Amsterdam 1628-1973, UK 1975-2019, China 2000-2017
Amsterdam The Herengracht Index, real index, 1628-1973, (1628=100).

Additional data

UKnominal Average house prices, all houses, nominal price, UK, 1975-2019, (UK £).
UKreal Average house prices, all houses, current prices, UK, 1975-2019, (constant 2019 UK £).
China Average commercial houses prices, all houses, real 2000 Chinese yuan/square meter, China, 2000-2017

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Fig 52-The price of gold in US$, 1950–2019

Figure 52. The price of gold in US$, 1950–2019

Over the short term (quarterly, monthly, daily):
Yearly Price of one ounce of fine gold in London, annual average, US$ actual, 1950-2019 (US$).

Additional data

Quarterly Price of one ounce of fine gold in London, quarterly average, US$ actual, 1968-1979 (US$).
Monthly Price of one ounce of fine gold in London, monthly average, US$ actual, 1979-1982 (US$).
Daily Price of one ounce of fine gold in London, daily, US$ actual, August-October, 2008 (US$).

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Fig 53-The NASDAQ Composite Index, February 1971–December 1996

Figure 53. The NASDAQ Composite Index, February 1971–December 1996

Over the short term (quarterly, monthly, daily):
Yearly The NASDAQ Composite Index, annual average (1971=100). Feb 1971-Dec 1996 {1971-2019},

Additional data

Quarterly The NASDAQ Composite Index, quarterly average (1971=100). 1987-1996
Monthly The NASDAQ Composite Index, monthly average (1971=100). 1994-1996
Daily The NASDAQ Composite Index, daily (1971=100). May-July, 1995

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Fig 54-The NASDAQ Composite Index, February 1971–May 2019

Figure 54. The NASDAQ Composite Index, February 1971–May 2019

Over the short term (quarterly, monthly, daily):
Yearly The NASDAQ Composite Index, annual average (1971=100). Feb 1971-May 2019

Additional data

Quarterly The NASDAQ Composite Index, quarterly average (1971=100). 1999-2008
Monthly The NASDAQ Composite Index, monthly average (1971=100). 2014-2016
Daily The NASDAQ Composite Index, daily (1971=100). Feb-May, 2019

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Fig 55-Average height of adults worldwide, born 1896–1996

Figure 55. Average height of adults worldwide, born 1896–1996

Average height of adults, born in 1896–1996, aged 18 in 1914-2014 Worldwide (plus male-female difference). , UK, USA, Netherlands, China, Cuba
World Average adult height (cm). of people born 1896-1996, men and women combined, worldwide

Additional data

Gap Average adult height gap (cm). between men and women born 1896-1996, worldwide
UK Average adult height (cm). of people born 1896-1996, men and women combined, UK
USA Average adult height (cm). of people born 1896-1996, men and women combined, USA
Netherlands Average adult height (cm). of people born 1896-1996, men and women combined, Netherlands
China Average adult height (cm). of people born 1896-1996, men and women combined, China
Cuba Average adult height (cm). of people born 1896-1996, men and women combined, Cuba

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Fig 56-Enrollment in tertiary education worldwide, 1970–2014

Figure 56. Enrollment in tertiary education worldwide, 1970–2014

World, UK, USA, China, India, Ethiopia, Afghanistan Percentage of the total population of the five-year age group following on from secondary school leaving age.
World Enrollment in tertiary education (%). 1970-2014, worldwide

Additional data

UK Enrollment in tertiary education (%). 1971-2014, UK
USA Enrollment in tertiary education (%). 1971-2014, USA
China Enrollment in tertiary education (%). 1970-2014, China
India Enrollment in tertiary education (%). 1971-2013, India
Ethiopia Enrollment in tertiary education (%). 1971-2014, Ethiopia
Afghanistan Enrollment in tertiary education (%). 1970-2014, Afghanistan

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Fig 57-Opposite-sex marriages in England and Wales, 1947–2016)

Figure 57. Opposite-sex marriages in England and Wales, 1947–2016)

England and Wales, USA, China, Japan, Australia
EN Number of marriages each year, 1947-2016, England and Wales

Additional data

USA Number of marriages each year, 2000-2017, USA
China Number of marriages each year, 1999-2017, China
Japan Number of marriages each year, 1925-2016, Japan
Australia Number of marriages each year, 1997-2017, Australia

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Fig 58-New York State Democrat advantage in presidential elections, 1932–2016

Figure 58. New York State Democrat advantage in presidential elections, 1932–2016

Local minus national percentage for the Democrat candidate, 1932-2016 New York State, Massachusetts, California, Florida, Wyoming.
NewYork Presidential elections, New York, Democrat % point advantage

Additional data

Massachusetts Presidential elections, Massachusetts, Democrat % point advantage
California Presidential elections, California, Democrat % point advantage
Florida Presidential elections, Florida, Democrat % point advantage
Wyoming Presidential elections, Wyoming, Democrat % point advantage

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Fig 59-London Conservative advantage in general elections, 1835–2017

Figure 59. London Conservative advantage in general elections, 1835–2017

Local minus national vote for Conservatives, 1835-2017 London General elections, London, Conservative advantage (% point difference).

Additional data

SouthEast General elections, South East, Conservative advantages (% point difference).
SouthWest General elections, South West, Conservative advantages (% point difference).
NorthEast General elections, North East, Conservative advantages (% point difference).
NorthWest General elections, North West, Conservative advantages (% point difference).

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Fig 60-World GDP per capita, log scale, 1–2018

Figure 60. World GDP per capita, log scale, 1–2018

Real (2011 US$). mean annual average. Year 1 to 2018 Note: unlike most of the timelines, these show the relative percentage change over time, not te absolute change Data adapted from Maddison Project Database 2018, updated using World Bank and IMF data and UN data
World-estimated Global GDP per capita, using UN 2017 data. Year 1-2017

Additional data

World-UN Global GDP per capita, updated using UN 2019 revision of old plus new data. Year 1-2018
WesternAsia Western Asia GDP per capita. Year 1-2016
Africa Africa GDP per capita. Year 1-2016
LatinAmerica Latin America GDP per capita. Year 1-2018
WesternEurope Western Europe GDP per capita. Year 1-2018

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ig 61-Living Planet Index - species lost 1970–2013

Figure 61. Living Planet Index - species lost 1970–2013

Species loss according to lower confidence interval (worst case, mean, best case scenarios). Living Planet Index. Baseline 1970. Percentage loss since 1970.
LCI Species loss, worldwide, lower confidence interval (worst case scenario), 1970-2014

Additional data

MCI Species loss, worldwide, middle confidence interval, 1970-2014
UCI Species loss, worldwide, upper confidence interval (best case scenario), 1970-2014

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Fig 62-Passengers on air flights worldwide, 1970–2017

Figure 62. Passengers on air flights worldwide, 1970–2017

Passenger trips on air flights. Number of occupied seats in all flights per year, 1970-2017 Worldwide, USA. UK, China, Japan, Malawi. World Bank Data adapted from International Civil Aviation Organization, Civil Aviation Statistics of the World, and ICAO staff estimates.
World Passenger trips on aircraft flights per year, worldwide, 1970-2017, (occupied seats).

Additional data

USA Passenger trips on aircraft flights per year, USA 1970-2017, (occupied seats).
UK Passenger trips on aircraft flights per year, UK 1970-2017, (occupied seats).
China Passenger trips on aircraft flights per year, China 1974-2017, (occupied seats).
Japan Passenger trips on aircraft flights per year, Japan 1970-2017, (occupied seats).
Malawi Passenger trips on aircraft flights per year, Malawi 1970-2017, (occupied seats).

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Fig 63-Babies worldwide, 1950–2100

Figure 63. Babies worldwide, 1950–2100

UN 2019 World Population Prospects with projections to 2100. Worldwide, World minus China, China, UK, USA, Japan, Nigeria
World Total number of babies worldwide (people aged 0). , world, 1950-2100

Additional data

ExcludeChina Number of babies (people aged 0), everywhere except
China, 1950-2100 China Number of babies (people aged 0), China 1950-2100
UK Number of babies (people aged 0), UK 1950-2100
USA Number of babies (people aged 0). USA, 1950-2100
Japan Number of babies (people aged 0), Japan, 1950-2100
Nigeria Number of babies (people aged 0), Nigeria 1950-2100

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Fig 64-Worldwide life expectancy, men and women combined, 1950–2099

Figure 64. Worldwide life expectancy, men and women combined, 1950–2099

UN 2019 World Population Prospects with projections to 2099 Life expectancy at birth based on current mortality rates (Period LEB). .
World Life expectancy, men and women combined, worldwide average, 1950-2099, (years).

Additional data

Male Life expectancy, men, worldwide average, 1950-2099, (years).
Female Life expectancy, women, worldwide average, 1950-2099, (years).
UK Life expectancy, men and women combined, UK average, 1950-2099, (years).
USA Life expectancy, men and women combined, USA average, 1950-2099, (years).
Japan Life expectancy, men and women combined, Japan average, 1950-2099, (years).
China Life expectancy, men and women combined, China average, 1950-2099, (years).

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Fig 65-The spatial agglomeration and deglomeration of Tokyo, 1920–2010

Figure 65. The spatial agglomeration and deglomeration of Tokyo, 1920–2010

The ROXY Index is an indicative instrument to measure the speed of spatial concentration and deconcentration of populations and other socio-economic changes. It is used here to measure the urban growth of both within and between metropolitan and inter-metropolitan areas. Adapted from Ushijima Chihiro, “The Urban Life Cycle in the Tokyo 60km Area and the Expansion and Contraction of City”.
TokyoDistance Rise in population in central Tokyo compared to suburban growth, 1920-2010 The spatial agglomeration and deglomeration, measured by ROXY Index, weighted by distance

Additional data

TokyoDichotomy Rise in population in central Tokyo compared to suburban growth, 1947-2005 The spatial agglomeration and deglomeration, measured by ROXY Index, weighted by type
TokyoChuo Rise in population in central Tokyo compared suburban growth 1947-2005 The spatial agglomeration and deglomeration, measured by ROXY Index, weighted by distance, Chuo railway-line region in Tokyo
JPlargeUrban Rise in population in larger metropolitan areas compared smaller ones. Japan, 1947-1995 The spatial concentration and deconcentration, measured by ROXY index weighted by population size, for the eight largest metropolitan areas in 1995
FinlandUrban Rise in population in larger urban units compared smaller ones, functional urban regions, Finland, 1875-1995 The spatial concentration and deconcentration, measured by ROXY index weighted by population size

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Fig 66-Personal_coffee_consumption

Figure 66. Personal coffee consumption, 1995–2020

Simple timeline examples with commentary.
Coffee * Personal coffee consumption, 1995-2020, (Cups/day).

Additional data

Beer Personal beer consumption, 1995-2020, (Pint/week).
Water Personal water consumption, 1995-2020, (Cups/day).
Wine Personal wine consumption, 1995-2020, (Glasses/week).

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Fig 67-Three different ways of describing the movement of the slowing pendulum

Figure 67. Three different ways of describing the movement of the slowing pendulum

Three different ways of describing the movement of a slowing down pendulum
Time on The velocity and position of a pendulum, both against time, on a conventional graph
Time off Simultaneously showing position and velocity over unlimited time period Time taken off both axes to produce a phase portrait timeline

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