AndreaBozzo/ceres-open-data-index huggingface dataset:
El Salvador: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in El Salvador: (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).

Tags: baseline population, children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Turks and Caicos: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Turks and Caicos: (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).

Tags: baseline population, births, children, demographics, elderly, geodata, health, maternity, men, women, youth
Organization: AI for Good at Meta
Trinidad and Tobago: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Trinidad and Tobago: (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).

Tags: baseline population, births, children, demographics, elderly, geodata, health, maternity, men, women, youth
Organization: AI for Good at Meta
Saint Vincent and the Grenadines: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Saint Vincent and the Grenadines: (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).

Tags: baseline population, births, children, demographics, elderly, geodata, health, maternity, women, youth
Organization: AI for Good at Meta
US Virgin Islands: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in US Virgin Islands: (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).

Tags: baseline population, births, children, demographics, elderly, geodata, health, maternity, men, women, youth
Organization: AI for Good at Meta
Argentina: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Argentina: (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).

Tags: baseline population, children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Bolivia: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Bolivia: (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).

Tags: baseline population, children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Brazil: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Brazil: (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).

Tags: baseline population, births, children, demographics, elderly, geodata, health, maternity, women, youth
Organization: AI for Good at Meta
Cook Islands: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Cook Islands: (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).

Tags: children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Federated States of Micronesia: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Micronesia: (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).

Tags: children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Chile: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Chile: (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).

Tags: baseline population, children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Colombia: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Colombia: (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).

Tags: children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Fiji: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Fiji: (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).

Tags: children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Guam: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Guam: (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).

Tags: children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Hong Kong: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Hong Kong: (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).

Tags: children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Cameroun : Malnutrition Aiguë

Ce jeu de données présente un tableau de population détaillant la situation actuelle pour les zones analysées, incluant 32 départements, 4 régions et des sites de réfugiés centrafricains et nigérians pour la période 2023-2024.

Tags: global acute malnutrition-gam, malnutrition, severe acute malnutrition-sam
Organization: HDX
Syria - Damage density 2016 of Daraa, Daraa Governorate.

This map illustrates satellite-detected damage density in the city of Daraa, Syrian Arab Republic. Using satellite imagery acquired 16 April 2016, 04 June 2015, 01 May 2014, 07 September 2013, and 14 December 2010, UNITAR - UNOSAT identified a total of 1180 affected structures, of which 217 were destroyed, 400 severely damaged, and 563 moderately damaged. This analysis does not include pre-war military bases and facilities. Trend analysis shows an increase in the number of damaged structures since 04 June 2015 and 190 structures are newly damaged. This analysis was done as part of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

Tags: complex emergency-conflict-security, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected water extent in Region 9 - Upper Takutu-Upper Essequibo, Guyana as of 16 August 2021

**UNOSAT code: FL20210817GUY**  This map illustrates satellite-detected surface waters in the Upper Takutu-Upper Essequibo Regions of Guyana using NOAA-VIIRS between 12th and 16th of August 2021. Within the analysed area of about 24,462 km2, a total of about 1417 km2 of lands were observed as flooded.

Based on Worldpop spatial demographic data,approximately 2153 people are exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
L’analyse des contraintes d’accès à l’est de la RDC

L’analyse des contraintes d’accès à l’est de la RDC pour septembre 2024.

Cet ensemble de données a été archivé. Vous trouverez [ici](https://data.humdata.org/dataset/drc-acces-humanitaire) l'ensemble de données régulièrement mis à jour sur l'accès humanitaire

Tags: humanitarian access
Organization: OCHA Democratic Republic of the Congo (DRC)
République Démocratique du Congo : Accès Humanitaire

L’analyse des contraintes d’accès à l’est de la RDC a été conduite au travers de consultations séparées avec les acteurs humanitaires - répartis par type d’organisation (ONGI, ONGN, NU).
Elle donne une indication des difficultés rencontrées d’une part par les partenaires humanitaires pour atteindre les populations dans le besoin, et d’autre part pour ces populations d’accéder à l’assistance humanitaire et services fournis par ces organisations.

Tags: humanitarian access, severity
Organization: OCHA Democratic Republic of the Congo (DRC)
Zimbabwe: Integrated Context Analysis (ICA), 2015


    The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk.

The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.
    

Tags: geodata, hazards and risk
Organization: WFP - World Food Programme
Burundi Displacement - [IDPs] - Baseline Assessment [IOM DTM]

The dataset contains IDPs individuals and households at admin2 level. IOM has been developing a Displacement Tracking Matrix (DTM) since May 2015 aimed at effectively monitoring and evaluating the flows of Burundian IDPs and providing accurate information on the current IDP situation.

The DTM in Burundi has been successfully used in 2014, upon the request of the humanitarian community and the GoB when some areas of Bujumbura were flooded, which caused displacement. This tool allowed registering IDPs in four IDP sites and in host families in four locations and identifying their humanitarian needs.

The dataset includes returnees from abroad who are still displaced in the country. They are categorized as IDPs due to their continued displacement within the country.

Tags: affected population, displacement, gender and age disaggregated data-gadd, hxl, internally displaced persons-idp, migration
Organization: International Organization for Migration (IOM)
Satellite detected waters in Cortes, Atlantida, and Yoro departments of Honduras as of 23 November 2020

**UNOSAT code: TC20201116HND**  This map illustrates satellite-detected surface waters in Cortes, Atlantida, and Yoro departments of Honduras as observed from a Sentinel-1 image acquired on 23 November 2020 at 05:45 Local time. Within the analyzed area of about 3,500 km2, a total of about 170 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 35,000 people are potentially exposed or living close to flooded areas. 
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected waters in Quang Tri Province of Viet Nam as of 22 October 2020

**UNOSAT code: TC20201013VNM**  This map illustrates satellite-detected surface waters in Quang Tri Province of Viet Nam as observed from a Sentinel-1 image acquired on 22 October 2020. Within the analyzed area of about 2,500 km2, a total of about 180 km2 of lands appear to be flooded. The water extent appears to have receded of about 120 km2 since 18 October 2020. Based on Worldpop population data and the detected surface waters, about 45,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
GRID3 NGA - Roads v1.0

The GRID3 NGA - Roads v1.0 spatial dataset consists of road and path locations in Nigeria.

Dataset citation:
Center for Integrated Earth System Information (CIESIN), Columbia University. 2025. GRID3 NGA - Roads v1.0. New York: Columbia University. URL. Accessed DAY MONTH YEAR.

Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to:info@ciesin.columbia.edu


Tags: geodata, roads
Organization: GRID3
Harmonized Food Insecurity Dataset (HFID)

The Harmonized Food Insecurity Dataset (HFID) is an open-source resource consolidating four key data sources: the Integrated Food Security Phase Classification (IPC)/Cadre Harmonisé (CH) phases, the Famine Early Warning Systems Network (FEWS NET) IPC-compatible phases, and the World Food Program’s (WFP) Food Consumption Score (FCS) and reduced Coping Strategy Index (rCSI). 

Updated monthly and using a common reference system for administrative units, the HFID provides comprehensive spatial and temporal coverage to the extent permitted by the available data.

It serves as a vital tool for food insecurity experts and humanitarian agencies, providing a unified resource for analyzing food insecurity conditions and highlighting global data disparities and gaps. The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises.

This dataset is the first static version created and openly shared to accompany the publication of the paper ["A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling"](https://doi.org/10.1038/s41597-025-05034-4).
It includes data from 2007 until 2024.

A dynamic dataset, updated monthly with all of the above indicators will be shared on HDX as medium-term objective. 

Tags: food security, integrated food security phase classification-ipc
Organization: Joint Research Centre of the European Commission
Preliminary satellite-derived flood assessment in Lower Northeastern Part of Thailand as of 11 November 2021

**UNOSAT code: FL20210928THA**  Summary of finding
- Floodwaters observed in 13 provinces  as of 11 November 2021;
- Receding floodwaters observed in Lower Northeastern Region as of 11 November 2021;
- Receding floodwaters observed in Amnat Chareon, Buri Ram, Chaiyaphum, Khon Kaen, and Nakhon Ratchasima provinces as of 11 November 2021;
- Floodwaters increase observed in Kalasin, Maha Sarakham, Nong Bua Lam Phu, Roi Et, Si Sa Ket, Surin, Ubon Ratchathani, and Yasothon provinces as of 11 November 2021. 

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Sierra Leone: IBTrACS Storm Tracks

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.   
   
 Fields available:   
 SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.  
 ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)   
 BASIN: Basin of the current storm position   
 SUBBASIN: Sub-basin of the current storm position   
 NATURE: Type of storm (a combination of the various types from the available sources)   
 NUMBER: Number of the storm for the year (restarts at 1 for each year   
 LAT: Mean position - latitude (a combination of the available positions)   
 LON: Mean position - longitude (a combination of the available positions)   
 WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency   
 WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Tags: cyclones-hurricanes-typhoons, hxl
Organization: HDX
Senegal: IBTrACS Storm Tracks

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.   
   
 Fields available:   
 SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.  
 ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)   
 BASIN: Basin of the current storm position   
 SUBBASIN: Sub-basin of the current storm position   
 NATURE: Type of storm (a combination of the various types from the available sources)   
 NUMBER: Number of the storm for the year (restarts at 1 for each year   
 LAT: Mean position - latitude (a combination of the available positions)   
 LON: Mean position - longitude (a combination of the available positions)   
 WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency   
 WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Tags: cyclones-hurricanes-typhoons, hxl
Organization: HDX
Mauritania: IBTrACS Storm Tracks

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.   
   
 Fields available:   
 SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.  
 ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)   
 BASIN: Basin of the current storm position   
 SUBBASIN: Sub-basin of the current storm position   
 NATURE: Type of storm (a combination of the various types from the available sources)   
 NUMBER: Number of the storm for the year (restarts at 1 for each year   
 LAT: Mean position - latitude (a combination of the available positions)   
 LON: Mean position - longitude (a combination of the available positions)   
 WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency   
 WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Tags: cyclones-hurricanes-typhoons, hxl
Organization: HDX
Mali: IBTrACS Storm Tracks

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.   
   
 Fields available:   
 SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.  
 ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)   
 BASIN: Basin of the current storm position   
 SUBBASIN: Sub-basin of the current storm position   
 NATURE: Type of storm (a combination of the various types from the available sources)   
 NUMBER: Number of the storm for the year (restarts at 1 for each year   
 LAT: Mean position - latitude (a combination of the available positions)   
 LON: Mean position - longitude (a combination of the available positions)   
 WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency   
 WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Tags: cyclones-hurricanes-typhoons, hxl
Organization: HDX
Liberia: IBTrACS Storm Tracks

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.   
   
 Fields available:   
 SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.  
 ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)   
 BASIN: Basin of the current storm position   
 SUBBASIN: Sub-basin of the current storm position   
 NATURE: Type of storm (a combination of the various types from the available sources)   
 NUMBER: Number of the storm for the year (restarts at 1 for each year   
 LAT: Mean position - latitude (a combination of the available positions)   
 LON: Mean position - longitude (a combination of the available positions)   
 WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency   
 WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Tags: cyclones-hurricanes-typhoons, hxl
Organization: HDX
Guinea-Bissau: IBTrACS Storm Tracks

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.   
   
 Fields available:   
 SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.  
 ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)   
 BASIN: Basin of the current storm position   
 SUBBASIN: Sub-basin of the current storm position   
 NATURE: Type of storm (a combination of the various types from the available sources)   
 NUMBER: Number of the storm for the year (restarts at 1 for each year   
 LAT: Mean position - latitude (a combination of the available positions)   
 LON: Mean position - longitude (a combination of the available positions)   
 WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency   
 WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Tags: cyclones-hurricanes-typhoons, hxl
Organization: HDX