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Share of population living in rural areas

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Urbanization

What you should know about this indicator

  • The Degree of Urbanization classifies areas as , , or based on population density and settlement size rather than administrative boundaries. Developed by six international organizations and endorsed by the UN Statistical Commission in 2020, it provides consistent definitions for comparing urbanization across countries.
  • Rural areas are places with less than 300 people per km² or a total population of less than 5,000.
  • The classification uses 1 km² grid cells, combining satellite imagery with census data to map where people actually live.
  • For the years 1950–1975, there are no detailed maps showing where people lived within countries. So instead of using grid-level or satellite data, the estimates are reconstructed using national statistics from the UN. From 1975 onwards, population is mapped to 1 km² grid cells by combining census data with satellite imagery of built-up areas from the Global Human Settlement Layer.
  • Different countries use different definitions and criteria to define urban and rural areas, such as population size, population density, infrastructure, employment patterns, or official city status. The Degree of Urbanization applies a single global standard using population density grids, meaning its classifications won’t always match official city boundaries and therefore urbanization rates may differ from country-reported figures.
  • For small countries, values can change sharply when an entire area shifts from one classification to another.
Share of population living in rural areas
Estimated share of population living in . Identified using satellite imagery and population data, applying the same density and size thresholds across all countries.
Source
European Commission, Joint Research Centre (JRC) (2025) – with major processing by Our World in Data
Last updated
December 10, 2025
Next expected update
December 2026
Date range
1950–2020
Unit
%

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Sources and processing

This data is based on the following sources

European Commission, Joint Research Centre (JRC) – Global Human Settlement Layer Dataset

The dataset includes population projections by degree of urbanisation and at the city level.

For every country and territory in the world, the authors estimated their population from 1950 to 2100 in cities, towns and semi-dense areas, and rural areas. It relies on the UN-endorsed Degree of Urbanisation methodology. As a result, the definitions used in each country are fully harmonised; while national definitions vary considerably.

The long time series consists of three parts:

  • From 1950 to 1970, it is based on backcasting by blending data using national definitions of urban and rural areas with data using the Degree of Urbanisation.
  • From 1975 to 2020, it is based on the Global Human Settlement Layer (GHSL), because it has the longest time series and uses a transparent and reproducible method.
  • From 2020 to 2100, it relies on a new model, "Cities and Rural Integrated Spatial Projections" (CRISP).

The CRISP model estimates population and built-up area change for a global grid of 1 km2 cells in an evidence-based, three-step process. First, the authors estimate population and built-up area change for roughly 1000 functional areas based on past trends and national population projections. Second, they allocate new built-up area to grid cells considering distance to settlements, roads, water, current share of built-up area and other characteristics. Finally, they add population to newly built-up areas and more suitable locations and reduce it in less suitable locations to capture internal migration and natural population decline.

Beyond population, the dataset also delivers maps showing the evolving spatial extent of cities, towns and rural areas. For every city in the world, it also provides updated boundaries, land area and built-up area at five-year intervals from 1975 to 2100.

Retrieved on
December 10, 2025
Retrieved from
https://human-settlement.emergency.copernicus.eu/ghs_wup2025.php
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Schiavina, Marcello; Alessandrini, Alfredo; Melchiorri, Michele; Jacobs-Crisioni, Chris; Dijkstra Lewis, (2025): GHS-WUP-COUNTRY-STATS R2025A – GHS-WUP country statistics by Degree of Urbanisation classes, multitemporal (1950-2100). European Commission, Joint Research Centre (JRC). PID: http://data.europa.eu/89h/ee52f334-aaee-4b9e-aa00-d38ecf6c11a9 , doi: 10.2905/ee52f334-aaee-4b9e-aa00-d38ecf6c11a9
Jacobs-Crisioni, C., Schiavina, M., Alessandrini, A., Dijkstra, L. (2025) Population by degree of urbanization and by urban agglomeration from 1950 to 2100: Analyses supporting the United Nations World Urbanization Prospects report. Publications Office of the European Union, Luxembourg, 2025, JRC144219

The dataset includes population projections by degree of urbanisation and at the city level.

For every country and territory in the world, the authors estimated their population from 1950 to 2100 in cities, towns and semi-dense areas, and rural areas. It relies on the UN-endorsed Degree of Urbanisation methodology. As a result, the definitions used in each country are fully harmonised; while national definitions vary considerably.

The long time series consists of three parts:

  • From 1950 to 1970, it is based on backcasting by blending data using national definitions of urban and rural areas with data using the Degree of Urbanisation.
  • From 1975 to 2020, it is based on the Global Human Settlement Layer (GHSL), because it has the longest time series and uses a transparent and reproducible method.
  • From 2020 to 2100, it relies on a new model, "Cities and Rural Integrated Spatial Projections" (CRISP).

The CRISP model estimates population and built-up area change for a global grid of 1 km2 cells in an evidence-based, three-step process. First, the authors estimate population and built-up area change for roughly 1000 functional areas based on past trends and national population projections. Second, they allocate new built-up area to grid cells considering distance to settlements, roads, water, current share of built-up area and other characteristics. Finally, they add population to newly built-up areas and more suitable locations and reduce it in less suitable locations to capture internal migration and natural population decline.

Beyond population, the dataset also delivers maps showing the evolving spatial extent of cities, towns and rural areas. For every city in the world, it also provides updated boundaries, land area and built-up area at five-year intervals from 1975 to 2100.

Retrieved on
December 10, 2025
Retrieved from
https://human-settlement.emergency.copernicus.eu/ghs_wup2025.php
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Schiavina, Marcello; Alessandrini, Alfredo; Melchiorri, Michele; Jacobs-Crisioni, Chris; Dijkstra Lewis, (2025): GHS-WUP-COUNTRY-STATS R2025A – GHS-WUP country statistics by Degree of Urbanisation classes, multitemporal (1950-2100). European Commission, Joint Research Centre (JRC). PID: http://data.europa.eu/89h/ee52f334-aaee-4b9e-aa00-d38ecf6c11a9 , doi: 10.2905/ee52f334-aaee-4b9e-aa00-d38ecf6c11a9
Jacobs-Crisioni, C., Schiavina, M., Alessandrini, A., Dijkstra, L. (2025) Population by degree of urbanization and by urban agglomeration from 1950 to 2100: Analyses supporting the United Nations World Urbanization Prospects report. Publications Office of the European Union, Luxembourg, 2025, JRC144219

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

Shares were calculated by dividing the area or population of each settlement type by the total, then multiplying by 100.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Share of population living in rural areas”, part of the following publication: Hannah Ritchie, Veronika Samborska, and Max Roser (2024) - “Urbanization”. Data adapted from European Commission, Joint Research Centre (JRC). Retrieved from https://archive.ourworldindata.org/20260330-182426/grapher/share-of-population-living-in-rural-areas.html [online resource] (archived on March 30, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

European Commission, Joint Research Centre (JRC) (2025) – with major processing by Our World in Data

Full citation

European Commission, Joint Research Centre (JRC) (2025) – with major processing by Our World in Data. “Share of population living in rural areas” [dataset]. European Commission, Joint Research Centre (JRC), “Global Human Settlement Layer Dataset” [original data]. Retrieved March 30, 2026 from https://archive.ourworldindata.org/20260330-182426/grapher/share-of-population-living-in-rural-areas.html (archived on March 30, 2026).