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Share of population residing in urban areas

See all data and research on:
Urbanization

What you should know about this indicator

The projection method for urban population in the World Urbanization Prospects involves a two-step process using an established extrapolation method based on urban-rural ratios. Initially, the average annual rate of change in the urban-rural ratio is calculated using data from the last two censuses, which informs the rate of change in urban and rural populations. This rate is then extrapolated, assuming a logistic path of urban proportion growth. Subsequently, a "world norm" is applied, estimated from empirical urban-rural growth differences in two groups of countries categorized by population size. This norm uses a regression equation to establish a hypothetical urban-rural growth difference for different levels of initial urban percentage.

The country-specific urban-rural growth difference is then converged with this hypothetical difference over 25 years, allowing the urbanization process of a country to align with a global urbanization pattern. This method ensures that urban-rural growth differences evolve towards a worldwide trend rather than remaining constant.

Share of population residing in urban areas
Share of population living in urban areas in a country over time.
Source
United Nations, Department of Economic and Social Affairs, Population Division (2018); HYDE (2023) – with minor processing by Our World in Data
Last updated
January 17, 2024
Date range
10000 BCE – 2050 CE
Unit
%

Related research and writing

A dark blue background with a lighter blue world map superimposed over it. Yellow text that says Urbanization by Our World in Data

Urbanization

Hannah Ritchie, Veronika Samborska, and Max Roser

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Sources and processing

This data is based on the following sources

United Nations, Department of Economic and Social Affairs, Population Division – World Urbanization Prospects Dataset

This dataset is derived from the World Urbanization Prospects, providing comprehensive data on global urbanization trends, including population figures, urban and rural population distributions, and projections for future urban growth.

Retrieved on
January 17, 2024
Retrieved from
https://population.un.org/wup/Download/
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.
United Nations, Department of Economic and Social Affairs, Population Division (2018). World Urbanization Prospects: The 2018 Revision, Online Edition.

This dataset is derived from the World Urbanization Prospects, providing comprehensive data on global urbanization trends, including population figures, urban and rural population distributions, and projections for future urban growth.

Retrieved on
January 17, 2024
Retrieved from
https://population.un.org/wup/Download/
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.
United Nations, Department of Economic and Social Affairs, Population Division (2018). World Urbanization Prospects: The 2018 Revision, Online Edition.

PBL Netherlands Environmental Assessment Agency – History Database of the Global Environment

This database presents an update and expansion of the History Database of the Global Environment (HYDE, v 3.3) and replaces former HYDE 3.2 version from 2017. HYDE is and internally consistent combination of updated historical population estimates and land use. Categories include cropland, with a new distinction into irrigated and rain fed crops (other than rice) and irrigated and rain fed rice. Also grazing lands are provided, divided into more intensively used pasture, converted rangeland and non-converted natural (less intensively used) rangeland. Population is represented by maps of total, urban, rural population and population density as well as built-up area. The period covered is 10 000 BCE to 2023 CE. Spatial resolution is 5 arc minutes (approx. 85 km2 at the equator), the files are in ESRI ASCII grid format.

Retrieved on
January 2, 2024
Retrieved from
https://doi.org/10.24416/UU01-AEZZIT
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.
Utrecht University/PBL Netherlands Environmental Assessment Agency - History Database of the Global Environment (HYDE v 3.3, 2023).
Klein Goldewijk, C.G.M., Beusen, A., Doelman, J., Stehfest, E., 2017, Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953

This database presents an update and expansion of the History Database of the Global Environment (HYDE, v 3.3) and replaces former HYDE 3.2 version from 2017. HYDE is and internally consistent combination of updated historical population estimates and land use. Categories include cropland, with a new distinction into irrigated and rain fed crops (other than rice) and irrigated and rain fed rice. Also grazing lands are provided, divided into more intensively used pasture, converted rangeland and non-converted natural (less intensively used) rangeland. Population is represented by maps of total, urban, rural population and population density as well as built-up area. The period covered is 10 000 BCE to 2023 CE. Spatial resolution is 5 arc minutes (approx. 85 km2 at the equator), the files are in ESRI ASCII grid format.

Retrieved on
January 2, 2024
Retrieved from
https://doi.org/10.24416/UU01-AEZZIT
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.
Utrecht University/PBL Netherlands Environmental Assessment Agency - History Database of the Global Environment (HYDE v 3.3, 2023).
Klein Goldewijk, C.G.M., Beusen, A., Doelman, J., Stehfest, E., 2017, Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953

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

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.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

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 residing in urban areas”, part of the following publication: Hannah Ritchie, Veronika Samborska, and Max Roser (2024) - “Urbanization”. Data adapted from United Nations, Department of Economic and Social Affairs, Population Division, PBL Netherlands Environmental Assessment Agency. Retrieved from https://archive.ourworldindata.org/20260325-171315/grapher/urban-population-share-2050.html [online resource] (archived on March 25, 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:

United Nations, Department of Economic and Social Affairs, Population Division (2018); HYDE (2023) – with minor processing by Our World in Data

Full citation

United Nations, Department of Economic and Social Affairs, Population Division (2018); HYDE (2023) – with minor processing by Our World in Data. “Share of population residing in urban areas” [dataset]. United Nations, Department of Economic and Social Affairs, Population Division, “World Urbanization Prospects Dataset”; PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260325-171315/grapher/urban-population-share-2050.html (archived on March 25, 2026).

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Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/urban-population-share-2050.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/urban-population-share-2050.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/urban-population-share-2050.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/urban-population-share-2050.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/urban-population-share-2050.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/urban-population-share-2050.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/urban-population-share-2050.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/urban-population-share-2050.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear