AndreaBozzo/ceres-open-data-index huggingface dataset:
Thailand - Age and gender structures

<b>WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. 
Please make sure you have read our <a href="/methods/populations" target="_blank">Mapping Populations</a> overview page before choosing and downloading a dataset.</b> 
<br/><br/>   

A description of the modelling methods used for age and gender structures can be found in 
<a href="https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank">
Tatem et al</a> and 
<a href="https://dx.doi.org/10.1038/sdata.2017.89" target="_blank">Pezzulo et al</a>. Details of the input population count datasets used can be found <a href="https://www.worldpop.org/project/categories?id=3" target="_blank">here</a>, and age/gender structure proportion datasets <a href="https://www.portal.worldpop.org/demographics/data/global_agegender_proportions_totals_2020.zip" target="_blank">here</a>. 
<br/>
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined 
<a href="/methods/top_down_constrained_vs_unconstrained" target="_blank">here</a>. The  datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.  
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.  
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/WP00646

Tags: baseline population, demographics, geodata
Organization: WorldPop
Food Insecurity Mapping - Southern Africa (January - March 2014)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**.

It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.

The two shapefiles represent the two analytic periods:

* southernafrica201401_ML1	Most likely food security outcome for January-March 2014
* southernafrica201401_ML2	Most likely food security outcome for April-June 2014

Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:

* 66 = water
* 88 = parks, forests, reserves
* 99 = missing data (usually urban centers)

Tags: agriculture-livestock, climate-weather, complex emergency-conflict-security, food security
Organization: HDX
Food Insecurity Mapping - Southern Africa (April - June 2013)

This Archive contains shapefiles for FEWS NET Food Security Outlook for  **Southern Africa**.

It was last updated on June 14, 2013. The classification used is [IPC V2.0 Compatible](http://www.fews.net/nosso-trabalho/nosso-trabalho/classifica%C3%A7%C3%A3o-integrada-de-fases), aimed to address acute food insecurity.

The two shapefiles represent the two analytic periods:

* southernafrica201304_ML1	Most likely food security outcome for April-June 2013
* southernafrica201304_ML2	Most likely food security outcome for July-September 2013

Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used:

* 66 = water
* 88 = parks, forests, reserves
* 99 = missing data (usually urban centers)

Tags: agriculture-livestock, climate-weather, complex emergency-conflict-security, food security
Organization: HDX
Cook_Islands_Population_Grid_2020

Population Raster Cook Islands 2020
Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps https://data.humdata.org/dataset/cook-islands-high-resolution-population-density-maps-demographic-estimates
Population allocated proportionally using 2011 census population counts at enumeration area level.
Year Population Growth Rate of 0.23% has been applied to update population up to 2020
The human settlement footprint with census population allocated has been converted into a 100 m resolution raster.


_Access this dataset from the [Pacific Data Hub](https://pacificdata.org/data/dataset/cok_population_grid_2020)_

Tags: population
Organization: Pacific Community
Türkiye - Subnational Administrative Boundaries GEOSERVICES

Türkiye - Administrative LEVEL 0-2 boundaries and lines geoservices

These geoservices correspond to the (Türkiye - Subnational Administrative Boundaries)[https://data.humdata.org/dataset/cod-ab-tur] COD-AB.

Tags: administrative boundaries-divisions
Organization: OCHA Field Information Services Section (FISS)
Flooding in the Philippines on July 18, 2025

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of July 18, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

Tags: affected area, climate hazards, cyclones-hurricanes-typhoons, damage assessment, disaster risk reduction-drr, flooding, geodata, natural disasters
Organization: The Philippine Space Agency
Flooding in the Philippines on July 19, 2025

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of July 19, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

Tags: affected area, climate hazards, cyclones-hurricanes-typhoons, damage assessment, disaster risk reduction-drr, flooding, geodata, natural disasters
Organization: The Philippine Space Agency
Flooding in the Philippines on July 20, 2025

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of July 20, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

Tags: affected area, climate hazards, cyclones-hurricanes-typhoons, damage assessment, disaster risk reduction-drr, flooding, geodata, natural disasters
Organization: The Philippine Space Agency
Flooding in the Philippines on July 23, 2025

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of July 23, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

Tags: affected area, climate hazards, cyclones-hurricanes-typhoons, damage assessment, disaster risk reduction-drr, flooding, geodata, natural disasters
Organization: The Philippine Space Agency
Senegal - Population Density

<b>WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. 
Please make sure you have read our <a href="https://www.worldpop.org/methods/populations" target="_blank">Mapping Populations</a> overview page before choosing and downloading a dataset.</b> 
<br/><br/>
Datasets are available to download in Geotiff and ASCII XYZ format at a <u>resolution of 30 arc-seconds (approximately 1km at the equator)</u>
<br/> 

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding 
<a href="https://www.worldpop.org/geodata/listing?id=29">Unconstrained individual countries 2000-2020 population count datasets</a> by dividing the number of people in each pixel by the pixel surface area. 
These are produced using the <a href="https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained">unconstrained top-down modelling method</a>.
<br/> 
-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 
<a href="https://www.worldpop.org/geodata/listing?id=69">Unconstrained individual countries 2000-2020 population UN adjusted count datasets</a> 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 <a href="https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained">unconstrained top-down modelling method</a>.
  
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

Tags: baseline population, geodata
Organization: WorldPop
Flooding in the Philippines on July 22, 2025

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of July 22, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

Tags: affected area, climate hazards, cyclones-hurricanes-typhoons, damage assessment, disaster risk reduction-drr, flooding, geodata, natural disasters
Organization: The Philippine Space Agency
Ethiopia: Rainfall Anomaly

Rainfall anomaly represents the difference between observed rainfall and the long-term average for a specific location and period, indicating whether rainfall is above or below normal. Positive anomalies show higher-than-average rainfall, while negative anomalies indicate lower amounts. These anomalies help monitor climate patterns, droughts, floods, and their effects on agriculture and other sectors. For this analysis, we used CHIRPS daily rainfall data for Ethiopia and calculated anomalies for May to July 2025 by subtracting the 2000–2020 long-term average from the current values.

Tags: climate-weather, disaster risk reduction-drr, drought, food security
Organization: 3iS
Ethiopia: NDVI Anomaly

NDVI Anomaly is a geospatial metric used to measure how current vegetation conditions deviate from what is considered "normal" or average for a specific location and time period. It helps identify abnormal vegetation growth, whether better or worse than expected, and is especially valuable in agriculture, drought monitoring, food security, and environmental management. The dataset used for this analysis is the MOD13Q1 V6.1 product, which provides a Vegetation Index (VI) value on a per-pixel basis. For this study, we used MODIS data from July for the past three years, along with historical data spanning from 2010 to 2020.

Tags: affected area, disaster risk reduction-drr, drought, environment
Organization: 3iS
Mali - Surveillance Agricole à Tombouctou et Youwarou

Ces données sont accessibles librement à des fins humanitaires ou de recherche. L'exploitation commerciale de tout ou partie de ces données est interdite. Aucune compensation ne peut être exigée en échange de la diffusion de ces données.
Action Contre la Faim (ACF), International Rescue Committee (IRC) et la Direction du Développement et de la Coopération Suisse (DDC) doivent être mentionnés pour l'utilisation de ces données.

Pour plus d'informations, veuillez visiter : www.sigsahel.info

These data are freely accessible for humanitarian or research purposes. Any commercial exploitation of this data is prohibited. No compensation can be demanded in exchange for the dissemination of this data.
Action Against Hunger (ACF), the International Rescue Committee (IRC), and the Swiss Agency for Development and Cooperation (SDC) must be credited for the use of this data.

For more information, please visit: www.sigsahel.info

Tags: agriculture-livestock, climate hazards
Organization: Action Against Hunger ACF - Regional Office for West and Central Africa ROWCA
Mauritanie - Suivi des micro-entreprises à Bassikounou

Ce système de suivi des micro-entreprises et des activités génératrices de revenus AGR dans la Moughataa de Bassikounou, Wilaya de Hod El Chargui en Mauritanie a été élaboré dans le cadre du projet de Renforcement de la protection et inclusion socio-économique ainsi que la cohésion sociale entre communautés hôtes et réfugiés Maliens en Mauritanie financé par l'Organisation des Nations Unies pour les réfugiés (UNHCR).

Ce système de surveillance est mis en œuvre par la mission ACF en Mauritanie avec le support technique du bureau régional pour l'Afrique de l'Ouest et du Centre (ACF-ROWCA).

Ces données sont accessibles librement à des fins humanitaires ou de recherche. L'exploitation commerciale de tout ou partie de ces données est interdite. Aucune compensation ne peut être exigée en échange de la diffusion de ces données. Action Contre la Faim (ACF), et l'Organisation des Nations Unies pour les réfugiés (UNHCR) doivent être mentionnés pour l'utilisation de ces données. 

Pour plus d'informations, veuillez visiter : www.sigsahel.info

This system for monitoring micro-enterprises and income-generating activities (AGR) in the Moughataa of Bassikounou, Wilaya of Hod El Chargui in Mauritania was developed as part of the project to strengthen protection and socio-economic inclusion as well as social cohesion between host communities and Malian refugees in Mauritania, funded by the United Nations Refugee Agency (UNHCR).

This monitoring system is implemented by the ACF mission in Mauritania with technical support from the regional office for West and Central Africa (ACF-ROWCA).

These data are freely accessible for humanitarian or research purposes. Commercial exploitation of all or part of these data is prohibited. No compensation can be demanded in exchange for the dissemination of these data. Action Against Hunger (ACF) and the United Nations Refugee Agency (UNHCR) must be mentioned when using these data.

For more information, please visit: www.sigsahel.info



Tags: refugees, socioeconomics
Organization: Action Against Hunger ACF - Regional Office for West and Central Africa ROWCA
Ethiopia: IRC Ethiopia Sub Grantee Projects by Sectors and Donors

IRC indirectly implemented Projects through sub grants awarded to 9 implementing partners, which are intervened in the different part of Ethiopia and in a refugee camp (Bokolmayo). The data shows the interventions for the period of June 2024 by detail activities, Sectors, Donors and implementing partners. The programs are emergency responses, IDPs, local community development programs and refugee portfolios. 
Out of the implementing partners OSHID has got the highest coverage of the project (54.9%) followed by CVDA and FSA; 14.1% & 12.7%, respectively. Looking to project activities there are 5 sector of interventions, whereby Health sector has got the highest (89.3.%), while the rest 4 sectors have each below 5%. From donors perspective, 4 donors granted the sub grantee projects, while GAVI BHA has got the highest (88.4%) and similarly the rest each has got below 5%.
IRC’s GIS team created this database to allow for a more sustained approach to producing relevant mapping products and geospatial analysis for undergoing humanitarian responses and will fill gaps in information sharing and management between Donors. IRC’s GIS services provides information and analysis required to monitor the evolution of project / program results and to track project and program impacts across geographic locations. This dataset contains boundaries of Ethiopian administrative Woredas which are roughly consistent with the actual & updated administrative boundaries which bases CSA 2007 Census survey out puts. The Woreda boundary contains attribute data (, activities, Sector Donors Fields) for IRC Ethiopia for IRC Direct Projects- June 2024

Tags: health, nutrition, water sanitation and hygiene-wash
Organization: International Rescue Committee Ethiopia
DRC Displacement Data - Ituri Province - Baseline Assessment [IOM DTM]

The data collection exercise conducted in the villages of Aru, Djugu, Irumu, Mahagi and Mambasa territories. It contains number of IDPs, returnees and theri needs.

Tags: affected population, baseline population, conflict-violence, displacement, hxl, internally displaced persons-idp, migration, needs assessment, population, returnees
Organization: International Organization for Migration (IOM)
Hurricane Matthew gust footprint - Tropical Storm Risk, University College London

Tropical Storm Risk - University College London - gust footprint for Matthew
in kml format.
 
If you zoom in (using Google Earth) the footprint shows the towns and 
villages in western Haiti which UCL model predicts will be worst affected 
by wind damage (with peak gusts of 160 mph (260 kmph)).



Tags: cyclones-hurricanes-typhoons, geodata
Organization: British Red Cross Maps Team
Répartition désagrégée de la population (RGPHAE 2013) jusqu'au niveau commune d'arrondissement/communauté rurale.

Répartition de la population selon le genre, le milieu de résidence et le lien de parenté avec le chef de ménage, des ménages ordinaire, collectif et des âges médian et moyen par différents niveau de désagrégation : région, département, ville et commune d'arrondissement/communauté rurale. Ces données sont issues du Recensement général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage (RGPHAE 2013).


Tags: baseline population, gender, returnees
Organization: Agence Nationale de la Statistique et de la Démographie du Sénégal
Djibouti Medium Term Projection  FEWS NET Acute Food Insecurity Classifications Geographic Data for 2012

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived.

Djibouti Medium Term Projection  FEWS NET Acute Food Insecurity Classifications Geographic data for 2012

Tags: food security, integrated food security phase classification-ipc
Organization: FEWS NET
Projection de la population par localité et par sexe de 2014 à 2025 au Sénégal

Ce jeu de données concerne les projections démographiques basées sur le dernier recensement de la population et de l'habitat, de l'agriculture et de l'élevage (RGPHAE) de 2013

Tags: baseline population, census
Organization: Agence Nationale de la Statistique et de la Démographie du Sénégal
Cameroon Displacement - [IDPs, Returnees, Refugees] - Baseline Assessment [IOM DTM]

The dataset contains IDPs, Returnees and Refugees at sub national level with information on IDPs in camps and host communities.
IOM set up and rolled out the first round of the DTM in November 2015 with the objective of providing regular, accurate and updated information on displaced populations within the Far North region of Cameroon to better support the response of the Government of Cameroon and the humanitarian community.

Tags: covid-19, displacement, geodata, hxl, internally displaced persons-idp, migration, populated places-settlements, refugees, returnees
Organization: International Organization for Migration (IOM)
ShakeMap Data for the Earthquakes in Southeastern Türkiye and Northwestern Syria

On 6 February 2023 at 4:17h, local time in Türkiye (01:17 GMT), an earthquake with a magnitude of 7.8 hit Türkiye and northwestern Syria. It
mainly affected the cities of Adana, Adiyaman, Diyarbakir, Gaziantep, Hatay, Kahramanmaras, Kilis, Malatya, Osmaniye, and Sanliurfa in southeastern Türkiye and Aleppo, Hama, Idlib, Lattakie, and Tartous governorates in northwestern Syria.
The earthquake’s epicentre was 23km east of the district of Nurdagi, in Türkiye’s Gaziantep city, at a depth of 24.1km. Türkiye is
vulnerable to earthquakes because it lies on several fault lines, and the 7.8 quake seems to be the strongest since 1939
On the same day, at 13:45h, local time in Türkiye, another earthquake with a magnitude of 7.6 struck Türkiye. It hit four kilometres
southeast of Ekinozu town in Kahraman Maras province, where the 7.8 earthquake hit hours earlier. As at 6 February, there had been at least 78 aftershocks.

Tags: earthquake-tsunami, geodata
Organization: HDX
Benin Displacement - [IDPs] - Mobility Tracking [IOM DTM]

Due to Sahel Crisis, IOM DTM conducted mobility tracking in Alibori and Atacora departments in Benin. The datasets show the total number of IDP individuals identified and the origin of the majority of IDPs.

Tags: affected population, displacement, gender and age disaggregated data-gadd, hxl, internally displaced persons-idp
Organization: International Organization for Migration (IOM)
Lao PDR - Equity Analysis

UNICEF Lao PDR Equity Analysis (Base Layer) is a child rights-focused statistical tool developed by UNICEF's Division of Data, Analysis, Planning and Monitoring (DAPM). Launched in 2025, it aims to identify and assess disparities affecting children by analyzing both deprivation and hazard dimensions using selected national and global datasets, with data collected from 2011 to 2024.

1) Deprivation Analysis examines critical areas impacting children's well-being, including: Health, Nutrition, Education, Protection, Water, Sanitation and Hygiene (WASH), and Social Policy.
2) Hazard Analysis evaluates the prevalence and impact of climatic hazards such as floods, droughts, landslides, storms, heat wave, and air pollution.

By highlighting inequities across population group and sub-national levels, Equity Analysis provides critical insights that support not only program planning, monitoring, and effectiveness for child-focused initiatives, but also inform decision-making for a wide range of stakeholders.


Tags: affected area, children, climate hazards, drought, education, flooding, hazards and risk, health, indicators, malnutrition, mortality, natural disasters, nutrition, poverty, protection, severe acute malnutrition-sam, severity, vaccination-immunization, water sanitation and hygiene-wash
Organization: UNICEF Lao PDR
Mali: Operational Presence

The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working and what they are doing in order to identify gaps and plan for future humanitarian response. This dataset includes a list of humanitarian organizations operating in Mali at Admin 3.


Tags: hxl, operational capacity, operational presence, who is doing what and where-3w-4w-5w
Organization: OCHA Mali