8 datasets found
  1. H

    French Guiana - Population Density

    • data.humdata.org
    geotiff
    Updated Sep 19, 2021
    + more versions
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    WorldPop (2021). French Guiana - Population Density [Dataset]. https://data.humdata.org/dataset/worldpop-population-density-for-french-guiana
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    geotiffAvailable download formats
    Dataset updated
    Sep 19, 2021
    Dataset provided by
    WorldPop
    Area covered
    French Guiana
    Description

    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

    Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

    -Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
    -Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

    Data for earlier dates is available directly from WorldPop.

    WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

  2. A

    French Guiana: High Resolution Population Density Maps + Demographic...

    • data.amerigeoss.org
    json, zip
    Updated Apr 15, 2025
    + more versions
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    UN Humanitarian Data Exchange (2025). French Guiana: High Resolution Population Density Maps + Demographic Estimates [Dataset]. https://data.amerigeoss.org/dataset/french-guiana-high-resolution-population-density-maps-demographic-estimates
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    zip(520189), zip(2695620), zip(2695500), zip(2694109), zip(2694534), zip(520668), zip(520184), zip(2695383), zip(520305), zip(520117), zip(2694589), zip(519641), json(1447663), zip(2694839), zip(519184)Available download formats
    Dataset updated
    Apr 15, 2025
    Dataset provided by
    UN Humanitarian Data Exchange
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    French Guiana
    Description

    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in French Guiana: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

  3. Population density of the French overseas regions 1967-2020, by region

    • statista.com
    • ai-chatbox.pro
    Updated Aug 20, 2024
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    Statista (2024). Population density of the French overseas regions 1967-2020, by region [Dataset]. https://www.statista.com/statistics/1350451/population-density-french-overseas-regions/
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    Dataset updated
    Aug 20, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    France
    Description

    In 2020, there was an average of 344.7 inhabitants per square kilometer on the island of La Réunion, a French overseas region. It was in fact the most densely populated of the overseas regions, followed by Martinique and Guadeloupe. In contrast, there were only 3.4 inhabitants per square kilometer in French Guiana. In the same year, La Réunion, Martinique and Guadeloupe were the three most densely populated French regions after Île-de-France (1,021.6 inhabitants per square kilometer), while Guyane was at the bottom of the ranking.

  4. w

    French Guiana - Complete Country Profile & Statistics 2025

    • worldviewdata.com
    html
    Updated Jul 24, 2025
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    World View Data (2025). French Guiana - Complete Country Profile & Statistics 2025 [Dataset]. https://www.worldviewdata.com/countries/french-guiana
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    htmlAvailable download formats
    Dataset updated
    Jul 24, 2025
    Dataset authored and provided by
    World View Data
    License

    https://worldviewdata.com/termshttps://worldviewdata.com/terms

    Time period covered
    2025
    Area covered
    Variables measured
    Area, Population, Literacy Rate, GDP per capita, Life Expectancy, Population Density, Human Development Index, GDP (Gross Domestic Product), Geographic Coordinates (Latitude, Longitude)
    Description

    Comprehensive socio-economic dataset for French Guiana including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

  5. Distribution of French population as of 2025, by region

    • statista.com
    Updated Jul 4, 2025
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    Statista (2025). Distribution of French population as of 2025, by region [Dataset]. https://www.statista.com/statistics/608761/population-of-france-by-region/
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    Dataset updated
    Jul 4, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    France
    Description

    In 2025, the Ile-de-France region, sometimes called the Paris region, was the most populous in France. It is located in the northern part of France, divided into eight departments and crossed by the Seine River. The region contains Paris, its large suburbs, and several rural areas. The total population in metropolitan France was estimated at around ** million inhabitants. In the DOM (Overseas Department), France had more than *** million citizens spread over the islands of Guadeloupe, Martinique, Reunion, Mayotte, and the South American territory of French Guiana. Ile-de-France: the most populous region in France According to the source, more than ** million French citizens lived in the Ile-de-France region. Ile-de-France was followed by Auvergne-Rhône-Alpes and Occitanie region which is in the Southern part of the country. Ile-de-France is not only the most populated region in France, it is also the French region with the highest population density. In 2020, there were ******* residents per square kilometer in Ile-de-France compared to ***** for Auvergne-Rhône-Alpes, the second most populated region in France. More than two million people were living in the city of Paris in 2025. Thus, the metropolitan area outside the city of Paris, called the suburbs or banlieue in French, had more than ten million inhabitants. Ile-de-France concentrates the majority of the country’s economic and political activities. An urban population In 2024, the total population of France amounted to over 68 million. The population in the country has increased since the mid-2000s. As well as the other European countries, France is experiencing urbanization. In 2023, more than ** percent of the French population lived in cities. This phenomenon shapes France’s geography.

  6. d

    French Guianan mammal and bird population densities with spatial-capture...

    • datadryad.org
    zip
    Updated Jan 8, 2025
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    (Yannick) J. N. Wiegers; Cécile Richard-Hansen; Julia Blok; Romée van der Kuil; Margot Gradoz; Marijke van Kuijk (2025). French Guianan mammal and bird population densities with spatial-capture recapture, line transect distance sampling, and 'unmarked' density models [Dataset]. http://doi.org/10.5061/dryad.fttdz091v
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    zipAvailable download formats
    Dataset updated
    Jan 8, 2025
    Dataset provided by
    Dryad
    Authors
    (Yannick) J. N. Wiegers; Cécile Richard-Hansen; Julia Blok; Romée van der Kuil; Margot Gradoz; Marijke van Kuijk
    Description

    Validating density estimation methods for unmarked wildlife with camera traps

    https://doi.org/10.5061/dryad.fttdz091v

    Description of the data and file structure

    The scripts for the unmarked density models are provided as follows:

    • o image-processing-unmarked-2024.R: this script takes as input the annotated image database (which we made in the TimeLapse2 sofware) as a.csv and processes the images. The camera model is calibrated following the CTtracking protocol and various unmarked parameters are estimated such as encounter rate, activity level, and animal movement speed.

      o unmarked-models-2024: this script takes the output from the former script and uses the parameters to estimate the effective detection zones and eventually the densities with CTDS, the TTE model, and the REM.

    The data that are required as input for the first script are provided; the input for the second script is the output from the first script. For ...

  7. f

    Risk factors linked to mobility outside the area around one’s home for P....

    • plos.figshare.com
    xls
    Updated Feb 13, 2024
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    Hélène Tréhard; Lise Musset; Yassamine Lazrek; Felix Djossou; Loïc Epelboin; Emmanuel Roux; Jordi Landier; Jean Gaudart; Emilie Mosnier (2024). Risk factors linked to mobility outside the area around one’s home for P. vivax carriage among the whole study population, among children (under 18 years old) and among adults (18 years old and above. [Dataset]. http://doi.org/10.1371/journal.pgph.0002706.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Feb 13, 2024
    Dataset provided by
    PLOS Global Public Health
    Authors
    Hélène Tréhard; Lise Musset; Yassamine Lazrek; Felix Djossou; Loïc Epelboin; Emmanuel Roux; Jordi Landier; Jean Gaudart; Emilie Mosnier
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Risk factors linked to mobility outside the area around one’s home for P. vivax carriage among the whole study population, among children (under 18 years old) and among adults (18 years old and above.

  8. f

    Functional Traits Help Predict Post-Disturbance Demography of Tropical Trees...

    • plos.figshare.com
    tiff
    Updated May 31, 2023
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    Olivier Flores; Bruno Hérault; Matthieu Delcamp; Éric Garnier; Sylvie Gourlet-Fleury (2023). Functional Traits Help Predict Post-Disturbance Demography of Tropical Trees [Dataset]. http://doi.org/10.1371/journal.pone.0105022
    Explore at:
    tiffAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Olivier Flores; Bruno Hérault; Matthieu Delcamp; Éric Garnier; Sylvie Gourlet-Fleury
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    How tropical tree species respond to disturbance is a central issue of forest ecology, conservation and resource management. We define a hierarchical model to investigate how functional traits measured in control plots relate to the population change rate and to demographic rates for recruitment and mortality after disturbance by logging operations. Population change and demographic rates were quantified on a 12-year period after disturbance and related to seven functional traits measured in control plots. The model was calibrated using a Bayesian Network approach on 53 species surveyed in permanent forest plots (37.5 ha) at Paracou in French Guiana. The network analysis allowed us to highlight both direct and indirect relationships among predictive variables. Overall, 89% of interspecific variability in the population change rate after disturbance were explained by the two demographic rates, the recruitment rate being the most explicative variable. Three direct drivers explained 45% of the variability in recruitment rates, including leaf phosphorus concentration, with a positive effect, and seed size and wood density with negative effects. Mortality rates were explained by interspecific variability in maximum diameter only (25%). Wood density, leaf nitrogen concentration, maximum diameter and seed size were not explained by variables in the analysis and thus appear as independent drivers of post-disturbance demography. Relationships between functional traits and demographic parameters were consistent with results found in undisturbed forests. Functional traits measured in control conditions can thus help predict the fate of tropical tree species after disturbance. Indirect relationships also suggest how different processes interact to mediate species demographic response.

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WorldPop (2021). French Guiana - Population Density [Dataset]. https://data.humdata.org/dataset/worldpop-population-density-for-french-guiana

French Guiana - Population Density

Explore at:
3 scholarly articles cite this dataset (View in Google Scholar)
geotiffAvailable download formats
Dataset updated
Sep 19, 2021
Dataset provided by
WorldPop
Area covered
French Guiana
Description

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

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