AndreaBozzo/ceres-open-data-index huggingface dataset:
Central African Republic Walking Travel Time to nearest Level IV health centre

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Central African Republic. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Cameroon Motorised Travel Time (in seconds) to nearest health centre

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Cameroon. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Cameroon Walking Travel Time to nearest Level IV health centre

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Cameroon. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Cabo Verde Motorised Travel Time (in seconds) to nearest health centre

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Cabo Verde. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Cabo Verde Walking Travel Time to nearest Level IV health centre

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Cabo Verde. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Burundi Motorised Travel Time (in seconds) to nearest health centre

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Burundi. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Burundi Walking Travel Time to nearest Level IV health centre

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Burundi. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Burkina Faso Motorised Travel Time (in seconds) to nearest health centre

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Burkina Faso. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Burkina Faso Walking Travel Time to nearest Level IV health centre

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Burkina Faso. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Botswana Motorised Travel Time (in seconds) to nearest health centre

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Botswana. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Botswana Walking Travel Time to nearest Level IV health centre

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Botswana. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Benin Motorised Travel Time (in seconds) to nearest health centre

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Benin. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Benin Walking Travel Time to nearest Level IV health centre

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Benin. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Angola Motorised Travel Time (in seconds) to nearest health centre

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Angola. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Angola Walking Travel Time to nearest Level IV health centre

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Angola. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Algeria Motorised Travel Time (in seconds) to nearest health centre

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Algeria. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available [here](https://doi.org/10.1038/s41597-022-01274-w) a video description of the data is also available [here](https://www.dataforchildrencollaborative.com/outputs/presentation-a-100m-resolution-travel-time-map)

Projection system is: GCS_WGS_1984 EPSG 4326 for all


Tags: africa, geodata, health facilities, transportation
Organization: Data for Children Collaborative
Geodata of UPDATE: AL Zaatari Refugee camp, Mafraq Governorate, Jordan

This map illustrates satellite-detected shelters and other buildings at the Al Zaatari refugee camp in Mafraq Governorate, Jordan. As of 11 November 2014 a total of 29,243 shelters were detected as well as 1,915 infrastructure and support buildings within the 534.4 hectares of the camp. Between 06 July 2014 and 11 November 2014, a total of 2,779 shelters closed or were moved, and a total of 1,910 shelters were constructed, and the number of shelters has thus decreased by about 739 since the previous UNITAR/UNOSAT assessment. This indicates an approximate 2.5% decrease in the number of shelters between 06 July 2014 and 11 November 2014. This is a preliminary analysis and has not yet been validated in the field; structure locations subject to a spatial error margin of +/- three meters. Shelters grouped under plastic sheeting were estimated by average household size and may be a source of error. Please send ground feedback to UNITAR/UNOSAT.

Tags: complex emergency-conflict-security, geodata
Organization: United Nations Satellite Centre (UNOSAT)
14 August 2021 M7.2 Haiti Earthquake. Preliminary Satellite-Based Comprehensive Damage Assessment Report

**UNOSAT code: EQ20210814HTI**  This report describes preliminary building damage analysis covering the departments of South, Grande Anse, and Nippes of Haiti, for a total area of approximately 550 Km2. A building damage analysis and a rapid assessment of transportation network conditions was conducted by comparing a series of satellite imageries acquired from the International Charter Space and Major Disasters, before and after the earthquake.  

The preliminary analysis extent within South, Grande  Anse, and Nippes departments identify a total of 1,842 buildings/structures with visible damages and approximately 130 locations with visible road obstacles and/or access constraints. 

Tags: earthquake-tsunami, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Damage Assessment  in Baraderes commune,  Nippes department,  Haiti as of 18 August 2021

**UNOSAT code: EQ20210814HTI**  This map illustrates potentially damaged structures/buildings in Baraderes commune,  Nippes department of Haiti as detected by using a satellite image acquired after the earthquake.

UNOSAT analysis used a Worldview-3 image acquired on 16 August 2021, and a Resurs-P image acquired on 18 August 2021, as a post-event images. Within the analyzed area, UNOSAT identified 7 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)
Colombia - PiN General y Severidad Intersectorial 2026

Para el año 2026 la estimación de las Personas en Necesidad (PiN) General y las Severidades intersectoriales para Colombia, se desarrolló siguiendo la metodología global JIAF 2.0 (Joint Intersectoral Analysis Framework, por sus siglas en inglés).

Para consultar el conjunto de datos del año anterior (PiN 2025), le invitamos a explorar el siguiente enlace: https://data.humdata.org/dataset/colombia-pin-general-y-severidad-intersectorial-2025

Tags: people in need-pin, severity
Organization: 3iS
Colombia - PiN General y Severidad Intersectorial 2025

Para el año 2025 la estimación de las Personas en Necesidad (PiN) General y las Severidades intersectoriales para Colombia, se desarrolló siguiendo la metodología global JIAF 2.0 (Joint Intersectoral Analysis Framework, por sus siglas en inglés). Esto se relaciona con el análisis del documento del Plan de Respuesta a Prioridades Comunitarias – PRPC- 2025.

Para consultar el conjunto de datos del año anterior (PiN 2024), le invitamos a explorar el siguiente enlace: https://data.humdata.org/dataset/pin-general-y-severidad-intersectorial-colombia-2024

Tags: people in need-pin, severity
Organization: 3iS
Zimbabwe Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative
Zambia Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative
South Africa Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative