AndreaBozzo/ceres-open-data-index huggingface dataset:
Satellite detected water over Beledweyne City, Hiraan Region of Somalia as of 25 May 2023

**UNOSAT code FL20230327SOM, GDACS Id: 1101908**  This map illustrates the cumulative satellite-detected water affected sectors of Beledweyne City in Hiiran Region, Somalia as detected from the analysis of a satellite Pleiades Jilin-1 and Pleiades images acquired on 25 May 2023.  
Beletweyne is heavily affected by floods and about 42% of the town and its vicinity is inundated; the sectors called Kutimbo, Lamagalay and Radar Neighbourhood appear to be the most affected by floodwaters.  
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Puerto Rico - Subnational Population Statistics

Puerto Rico administrative level 0 (territory) and 1 (municipal) sex and age disaggregated population statistics

REFERENCE YEAR:  2010

These tables are suitable for database or GIS linkage to the [Puerto Rico administrative level 0 (territory) and 1 (municipio) boundaries](https://data.humdata.org/dataset/cod-ab-pri)

Tags: baseline population, gazetteer
Organization: OCHA Field Information Services Section (FISS)
UNOSAT Typhoon NORU  Population Exposure Analysis  in Lao People's Democratic Republic 28 September 2022

**UNOSAT code TC20220928LAO, GDACS Id: 1000922**  Typhoon Noru originated from a disturbance over the Philippine Sea. The storm made its first landfall over the Polillo Islands, Philippines on September 26 at 09:30 UTC and its second over Central Luzon five hours later as a high-end typhoon. The storm also made the third landfall in the early morning of 28 Sep 22 with winds of 117 km/h in the province of Quang Nam, home to the historic city of Hoi An and resort of Danang of Viet Nam. Typhoon Noru is expected to hit the central and southern provinces of Lao PDR on Wednesday, bringing heavy rain and thunderstorm to most of the country. According to GDACS, Typhoon Noru can have a high humanitarian impact based on the maximum sustained wind speed, exposed population, and vulnerability.  
  
Based on data of the forecasted the cyclone path of wind speeds zones from Joint Research Centre (Issued on 28 September 2022 at 07:00 local time), and population data from WorldPop 2020, UNITAR-UNOSAT has prepared a population exposure analysis for Lao PDR. About 2% of population of Lao PDR living inside wind speed zone exceed 120 km/h, 16% living inside wind speed zone of 60-120 km/h and 20% living inside wind speed zone below 60 km/h

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Malaysia - Births

The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of live births to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age and age-specific fertility rates to map the estimated distributions of births for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in <a href="https://ij-healthgeographics.biomedcentral.com/articles/10.1186/1476-072X-13-2"  target="_blank">Tatem et al.</a> and <a href="https://www.nature.com/articles/sdata201890"  target="_blank">James et al.</a>.  
Data for earlier dates is available directly from WorldPop.  
  
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2018. Malaysia 1km Births. Version 1.0 2015 estimates of numbers of live births per grid square, with national totals adjusted to match UN national estimates on numbers of live births (http://esa.un.org/wpp/).   DOI: 10.5258/SOTON/WP00566

Tags: births, geodata
Organization: WorldPop
Food Insecurity Mapping - East Africa (January - March 2013)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**.

It was last updated on January 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:

* eastafrica201304_ML1	Most likely food security outcome for January-March 2013
* eastafrica201304_ML2	Most likely food security outcome for April-June 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
Satellite detected surface water extents in Bulacan, Nueva Ecija, Pampanga, and Tarlac Provinces, Region III, Philippines  as of 3 November 2022

**UNOSAT code TC20221028PHL, GDACS Id: 1000942**  This map illustrates the extent of surface waters in Bulacan, Nueva Ecija, Pampanga, and Tarlac Provinces, Region III, Philippines  as observed from a TerraSAR-X image acquired on 3 November 2022 at 05:59 local time. Within the analysis extent of about 1,800 km2, approximately 160 km2 of land appears to be inundated and 140 km2 of croplands appear to be likely affected by the flood waters. Based on Worldpop population data and detected surface waters, approximately 84,000 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 United Nations Satellite Centre (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)
Damage Assessment in Killi (Idlib District), Syria as of 16 February 2023

**UNOSAT code EQ20230206SYR, GDACS Id: 1357372**  This map illustrates the potentially damaged structures/buildings affected by the 6th of February 2023 earthquake in Killi in Idlib district (Aleppo governorate), Syria as detected from a Pleiades very highresolution satellite image acquired on 16 February 2023. UNOSAT 11 potentially damaged structures.  
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

Tags: earthquake-tsunami, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Serbia - 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 - West Africa (July - September 2014)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**.

It was last updated on September 29, 2014. 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:

* WA201304_ML1	Most likely food security outcome for July-September 2014
* WA201304_ML2	Most likely food security outcome for October-December 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 - Central America and The Caribbean (July - September 2014)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.

It was last updated on September 30, 2014. 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:

* LAC201304_ML1	Most likely food security outcome for July-September 2014
* LAC201304_ML2	Most likely food security outcome for October-December 2014

Where xx is one of the region codes listed above. 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 - Central America and The Caribbean (April - June 2015)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**.

It was last updated on July 01, 2015. 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:

* LAC201304_ML1	Most likely food security outcome for April-June 2015
* LAC201304_ML2	Most likely food security outcome for July-September 2015

 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, el nino-el nina, food security
Organization: HDX
Armenia - Births

The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of live births to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age and age-specific fertility rates to map the estimated distributions of births for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in <a href="https://ij-healthgeographics.biomedcentral.com/articles/10.1186/1476-072X-13-2"  target="_blank">Tatem et al.</a> and <a href="https://www.nature.com/articles/sdata201890"  target="_blank">James et al.</a>.  
Data for earlier dates is available directly from WorldPop.  
  
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2018. Armenia 1km Births. Version 1.0 2015 estimates of numbers of live births per grid square, with national totals adjusted to match UN national estimates on numbers of live births (http://esa.un.org/wpp/).   DOI: 10.5258/SOTON/WP00541

Tags: births, geodata
Organization: WorldPop
Iraq - 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
Uruguay - 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
Seychelles - 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
Satellite detected water extent between 04 October 2021 and 01 August 2019 in Muvaramu village, Ituri province, DR Congo

**UNOSAT code: FL20211008COD**  This map illustrates satellite-detected surface waters in Muvaramu village, Ituri province as observed from a Sentrinel-2 acquired on 04 October 2021. Within the analyzed area, waters appear to have slightly progressed inland since 27 January 2021.
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Geodata of Damage Assessment of Jilah Village, Shabwah Governorate, Yemen

This map illustrates satellite-detected damage and destruction in Jilah village, Shabwah Governorate, Yemen. Using satellite imagery acquired 5 November 2015 and comparing with imagery collected on 22 August 2015, UNITAR - UNOSAT identified an area severely affected by flash flood resulting from rains during Cyclone Chapala. Imagery shows that the town of Jilah is partially covered by mud and a total of 150 structures appear damaged. Approximately 58 of these were destroyed, 57 severely damaged, and 35 moderately damaged. Primary road N4 is also highly affected by mud as a consequence of the flash floods and it is impassable across several sections. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Geodata of Land Cover Classification of Galangan & Katingan, Central Kalimantan, Indonesia

This map illustrates a land cover classification of the Galangan site and Katingan catchment area in Central Kalimantan Province, Indonesia. It is derived from Landsat-8 multispectral imagery acquired on 24 September 2014 at 30 meter pixel resolution. The classification is divided into seven main classes: dense vegetation/forest, shrub/disturbed forest, agricultural areas, water, exposed sands/mining, affected waters/mining pits, and exposed soils. The inset table summarizes the total area for each class in the region analyzed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

Tags: geodata
Organization: United Nations Satellite Centre (UNOSAT)
Geodata of Damaged Structures in San Julian Area, Eastern Samar, Philippines

This map illustrates satellite-detected damaged structures in San Julian Area, Eastern Samar Province, Philippines. Using an image acquired by the WorldView-2 satellite on 12 December 2014 and compared with WorldView-1 image collected on 12 July 2014, UNOSAT identified a total of 279 affected structures in the area. Specifically, 66 structures were categorized as destroyed, 148 as severely damaged and 65 as moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)