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What you should know about this indicator

  • Estimated population density of each of the world's 100 most populous cities (as ranked in 2020), based on satellite imagery and census data.
  • are defined as areas with at least 1,500 people per km² and a total population of at least 50,000, identified using the Degree of Urbanization framework based on satellite imagery and census data.
  • City boundaries are fixed at their 2025 extent across all years, so historical values reflect conditions within today's boundaries. This can make fast-growing cities appear less dense in earlier periods.
  • Cities are also split at country borders, so a city that straddles two countries will appear as two separate entries.
  • City boundaries are model-derived and may not match official administrative limits. Data quality varies by region and tends to be lower where census data is sparse or outdated.
  • The underlying population figures have been rescaled to match UN World Population Prospects 2022 national totals, so country-level numbers are consistent with UN estimates.
  • For 1950–1975, estimates use UN national statistics. From 1975 onwards, population is mapped to 1 km² grid cells using the Global Human Settlement Layer (GHSL).
  • The ranking of the top 100 cities is fixed based on their population in 2020. Historical values show the density of those same cities across time.
Population density of the world's largest cities
The number of people per km² of land area for cities ranked among the top 100 most populous in 2020. are defined using a consistent global approach based on satellite imagery and population data.
Source
European Commission, Joint Research Centre (JRC) (2025) – with minor processing by Our World in Data
Last updated
December 10, 2025
Next expected update
December 2026
Date range
1975–2020
Unit
people per km²

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; Dijkstra, Lewis (2025): GHS-WUP-MTUC R2025A – GHS-WUP multitemporal urban centres, obtained from the Degree of Urbanisation grids (GHS-WUP-DEGURBA R2025A) and linked across epochs, multitemporal (1950-2100). European Commission, Joint Research Centre (JRC). PID: http://data.europa.eu/89h/1ea967e5-bedc-4cf3-a0b0-3851742ee7e2 , doi: 10.2905/1ea967e5-bedc-4cf3-a0b0-3851742ee7e2
Pesaresi, Martino, Marcello Schiavina, Panagiotis Politis, Sergio Freire, Katarzyna Krasnodębska, Johannes H. Uhl, Alessandra Carioli, et al. (2024). Advances on the Global Human Settlement Layer by Joint Assessment of Earth Observation and Population Survey Data. International Journal of Digital Earth 17 (1). doi:10.1080/17538947.2024.2390454
Jacobs-Crisioni, Chris et al (2025). Population projections by degree of urbanisation for the UN World Urbanization Prospects: introducing the CRISP model, Publications Office of the European Union, Luxembourg, 2025, doi:10.2760/7163875

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; Dijkstra, Lewis (2025): GHS-WUP-MTUC R2025A – GHS-WUP multitemporal urban centres, obtained from the Degree of Urbanisation grids (GHS-WUP-DEGURBA R2025A) and linked across epochs, multitemporal (1950-2100). European Commission, Joint Research Centre (JRC). PID: http://data.europa.eu/89h/1ea967e5-bedc-4cf3-a0b0-3851742ee7e2 , doi: 10.2905/1ea967e5-bedc-4cf3-a0b0-3851742ee7e2
Pesaresi, Martino, Marcello Schiavina, Panagiotis Politis, Sergio Freire, Katarzyna Krasnodębska, Johannes H. Uhl, Alessandra Carioli, et al. (2024). Advances on the Global Human Settlement Layer by Joint Assessment of Earth Observation and Population Survey Data. International Journal of Digital Earth 17 (1). doi:10.1080/17538947.2024.2390454
Jacobs-Crisioni, Chris et al (2025). Population projections by degree of urbanisation for the UN World Urbanization Prospects: introducing the CRISP model, Publications Office of the European Union, Luxembourg, 2025, doi:10.2760/7163875

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

Population density was calculated by dividing the population of the urban centre by its total land area.

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: Population density of the world's largest cities”, 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/20260610-110447/grapher/population-density-by-city.html [online resource] (archived on June 10, 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 minor processing by Our World in Data

Full citation

European Commission, Joint Research Centre (JRC) (2025) – with minor processing by Our World in Data. “Population density of the world's largest cities” [dataset]. European Commission, Joint Research Centre (JRC), “Global Human Settlement Layer Dataset” [original data]. Retrieved June 10, 2026 from https://archive.ourworldindata.org/20260610-110447/grapher/population-density-by-city.html (archived on June 10, 2026).

Download

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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/population-density-by-city.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/population-density-by-city.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/population-density-by-city.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/population-density-by-city.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/population-density-by-city.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

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

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