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European overseas colonies by colonizer

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State Capacity

About this data

European overseas colonies by colonizer
Total number of overseas colonies per year colonized by an European colonial power, between Belgium, United Kingdom, France, Germany, Netherlands, Portugal, Spain, and Italy. Colonies are defined in terms of contemporary nation states: along current independent countries and their borders.
Source
Bastian Becker (2023); Gapminder, Population v7 (2022) – with major processing by Our World in Data
Last updated
September 18, 2023
Next expected update
May 2026
Date range
1462–2022
Unit
countries

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Sources and processing

This data is based on the following sources

Bastian Becker – Colonial Dates Dataset (COLDAT)

The Colonial Dates Dataset (COLDAT) aggregates information on the reach and duration of European colonial empires from renowned secondary sources. By aggregating secondary sources, rather than collecting from primary sources, the new dataset reflects the accumulated knowledge in the discipline and relieves researchers from making hard to justify choices between different historical datasets. The dataset only considers the colonial status of countries that are independent today. It also includes overseas colonies only.

Retrieved on
September 22, 2023
Retrieved from
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T9SDEW
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.
Becker, Bastian (2019), Introducing COLDAT: The Colonial Dates Dataset, SOCIUM/SFB1342 Working Paper Series, 02/2019. url: https://www.socium.uni-bremen.de/f/51ebd445c4.pdf

The Colonial Dates Dataset (COLDAT) aggregates information on the reach and duration of European colonial empires from renowned secondary sources. By aggregating secondary sources, rather than collecting from primary sources, the new dataset reflects the accumulated knowledge in the discipline and relieves researchers from making hard to justify choices between different historical datasets. The dataset only considers the colonial status of countries that are independent today. It also includes overseas colonies only.

Retrieved on
September 22, 2023
Retrieved from
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T9SDEW
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.
Becker, Bastian (2019), Introducing COLDAT: The Colonial Dates Dataset, SOCIUM/SFB1342 Working Paper Series, 02/2019. url: https://www.socium.uni-bremen.de/f/51ebd445c4.pdf

Gapminder – Population

Gapminder's population data is divided into two chunks: One long historical trend for the global population that goes back to 10,000 BC. And the second chunk is country estimates that only reaches back to 1800.

For the first chunk, several sources were used. You can learn more at https://docs.google.com/spreadsheets/d/1hkLbEilJbl630IG68q-aQJlUjuTFm9b_12nQMVd1sZM/edit#gid=0. For the second chunk, Gapminder uses UN population data between 1950 to 2100 from the UN Population Division World Population Prospects 2019, and the forecast to the year 2100 uses their medium-fertility variant.

For years before 1950, this version uses the data documented in greater detail by Mattias Lindgren in version 3. The main source was Angus Maddison's data, which CLIO Infra Project maintained and improved. Note that when combining version 3 with the new UN data, the trends for a few countries didn't match up in the overlapping year 1950.

Minor adjustments were made to the years before and after to smooth out discrepancies between the two sources and avoid spurious jumps in Gapminder's visualisations.

Visit https://www.gapminder.org/data/documentation/gd003/ to learn more about the methodology used and the data from back to 10,000 BC.

Retrieved on
March 31, 2023
Retrieved from
http://gapm.io/dpop
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.
Gapminder Population v7 (2022)

Gapminder's population data is divided into two chunks: One long historical trend for the global population that goes back to 10,000 BC. And the second chunk is country estimates that only reaches back to 1800.

For the first chunk, several sources were used. You can learn more at https://docs.google.com/spreadsheets/d/1hkLbEilJbl630IG68q-aQJlUjuTFm9b_12nQMVd1sZM/edit#gid=0. For the second chunk, Gapminder uses UN population data between 1950 to 2100 from the UN Population Division World Population Prospects 2019, and the forecast to the year 2100 uses their medium-fertility variant.

For years before 1950, this version uses the data documented in greater detail by Mattias Lindgren in version 3. The main source was Angus Maddison's data, which CLIO Infra Project maintained and improved. Note that when combining version 3 with the new UN data, the trends for a few countries didn't match up in the overlapping year 1950.

Minor adjustments were made to the years before and after to smooth out discrepancies between the two sources and avoid spurious jumps in Gapminder's visualisations.

Visit https://www.gapminder.org/data/documentation/gd003/ to learn more about the methodology used and the data from back to 10,000 BC.

Retrieved on
March 31, 2023
Retrieved from
http://gapm.io/dpop
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.
Gapminder Population v7 (2022)

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

This is the count of countries each European colonial power colonized each year. Colonized countries or countries not colonized show a null value.

A total for each year is estimated with the country (entity) "World".

Years are defined from last date aggregations available in the Colonial Dates Dataset. This means that when sources differ, years of colonization are defined from the last date between them. This is different from mean date aggregations (also available), which averages the dates of colonization across the original sources.

Years have been expanded to show the full range of years in the dataset, from 1462 to 2022. The original dataset only includes the years where a country was colonized.

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: European overseas colonies by colonizer”, part of the following publication: Bastian Herre and Pablo Arriagada (2023) - “State Capacity”. Data adapted from Bastian Becker, Gapminder. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/european-overseas-colonies-by-colonizer.html [online resource] (archived on March 4, 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:

Bastian Becker (2023); Gapminder, Population v7 (2022) – with major processing by Our World in Data

Full citation

Bastian Becker (2023); Gapminder, Population v7 (2022) – with major processing by Our World in Data. “European overseas colonies by colonizer” [dataset]. Bastian Becker, “Colonial Dates Dataset (COLDAT) 3.0”; Gapminder, “Population v7” [original data]. Retrieved April 3, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/european-overseas-colonies-by-colonizer.html (archived on March 4, 2026).

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

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

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