The International Federation of Red Cross and Red Crescent Societies (IFRC) is the world’s largest humanitarian network. Our secretariat supports local Red Cross and Red Crescent action in more than 192 countries, bringing together almost 15 million volunteers for the good of humanity.
We launch Emergency Appeals for big and complex disasters affecting lots of people who will need long-term support to recover. We also support Red Cross and Red Crescent Societies to respond to lots of small and medium-sized disasters worldwide—through our Disaster Response Emergency Fund (DREF) and in other ways.
There is also a global dataset.
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This dataset updates: Every week
This dataset is part of the data series [?]: IFRC - Appeals
Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.
"Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year.
"Internal displacements (New Displacements)" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.
Contains data from IDMC's Global Internal Displacement Database.
This table contains subnational multidimensional poverty trends data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 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 severe 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. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023).
Gabon administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata.
Global version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population
The dataset consists of settlement extents across Gabon, as well as accompanying settlement type based on the degrees of urbanization, place codes, and confidence level for each settlement extent.
Updates in this version include:
(1) The degree of urbanisation has replaced the previous classifications of built-up areas, small settlement areas, and hamlets
(2) Boundary names have been removed, since the OCHA dataset is not an official set of boundaries and may not be accurate
(3) Building count ranges have been included
(4) Predicted false positives have been included
(5) Population data have been removed until new constrained population numbers are available
(6) Settlement status has been included, as it pertains to Version 01 of the settlement extents
This work has been undertaken as part of the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) programme. The programme is funded by the Bill & Melinda Gates Foundation and the United Kingdom's Foreign, Commonwealth & Development Office. It is implemented by the Flowminder Foundation, WorldPop Project at the University of Southampton, the United Nations Population Fund, and the Center for International Earth Science Information Network (CIESIN) at Columbia University.
The Relative Wealth Index predicts the relative standard of living within countries using de-identified connectivity data, satellite imagery and other nontraditional data sources. The data is provided for 93 low and middle-income countries at 2.4km resolution. Please cite / attribute any use of this dataset using the following:
Microestimates of wealth for all low- and middle-income countries
Guanghua Chi, Han Fang, Sourav Chatterjee, Joshua E. Blumenstock
Proceedings of the National Academy of Sciences Jan 2022, 119 (3) e2113658119; DOI: 10.1073/pnas.2113658119
More details are available here: https://dataforgood.fb.com/tools/relative-wealth-index/
Research publication for the Relative Wealth Index is available here: https://www.pnas.org/content/119/3/e2113658119
Press coverage of the release of the Relative Wealth Index here: https://www.fastcompany.com/90625436/these-new-poverty-maps-could-reshape-how-we-deliver-humanitarian-aid
An interactive map of the Relative Wealth Index is available here: http://beta.povertymaps.net/
Education indicators for Gabon.
Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: SDG 4 Global and Thematic (made 2022 September), Other Policy Relevant Indicators (made 2022 September), Demographic and Socio-economic (made 2022 September)
This master list of health facilities was developed from a variety of government and non-government sources from 50 countries in sub-Saharan Africa. It uses multiple geocoding methods to provide a comprehensive spatial inventory of 98 745 public health facilities. Each data record represents a health facility and has 8 descriptive variables – Location identifiers including: country, first level administrative division, latitude, longitude and LL source (source of the coordinates). Coordinates are rounded off to four decimal places for uniformity, allowing an accuracy of 5–10 metres in decimal degrees coordinate format.
This geocoded master facility list has been made publicly and freely available through both the figshare repository and through the World Health Organization’s Global Malaria Programme in Microsoft Excel format.
TThe world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Gabon: (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).
There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.
Gabon administrative level 0-2 sex and age disaggregated projected 2022 population statistics
REFERENCE YEAR: 2022
These tables are suitable for database or GIS linkage to the Gabon - Subnational Administrative Boundaries layers using the ADM0 and ADM1_PCODE fields.
In partnership with Yale, Meta launched a climate change opinion survey that explores public climate change knowledge, attitudes, policy preferences, and behaviors. The 2022 survey includes respondents from nearly 200 countries and territories. We are sharing country level data from this survey, providing policymakers, research institutions, and nonprofits with an international view of public climate change opinion.
For more information please see https://dataforgood.facebook.com/dfg/tools/climate-change-opinion-survey
If you're interested in becoming a research partner and accessing record level data, please email dataforgood@fb.com.
NOTE: We plan to no longer update this dataset after May 22 2022.
These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day.
Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/
This data contains aggregated weighted statistics at the regional level by gender for the 2020 Survey on Gender Equality At Home as well as the country and regional level for the 2021 wave. The Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality. Researchers and nonprofits interested in access to survey microdata can apply at:
https://dataforgood.facebook.com/dfg/tools/survey-on-gender-equality-at-home
This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2.
Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.
West and Central Africa Administrative boundaries, administrative level 0 to 2. Notice: The boundaries and names shown and the designations used on these shapefiles do not imply official endorsement or acceptance by the United Nations.
West and Central Africa settlements with administrative capitals
Provinces and Departments of Gabon
The dataset represents the provinces and Departments of Gabon with harmonized PCODE of ROWCA and Humanitarian Response pcodes.
Settlement extents are polygons representing areas where there is likely a human settlement based on the presence of buildings detected in satellite imagery. Settlement extents are not meant to represent the boundaries of an administrative unit or locality. A single settlement extent may be made up of multiple localities, especially in urban areas. Each settlement extent has an associated population estimate. Provided is information on the common operational boundary that the extent fully resides within along with their associated place codes (PCodes). The data are in geodatabase format and consist of a single-feature class.
This work has been undertaken as part of the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) programme. The programme is funded by the Bill & Melinda Gates Foundation and the United Kingdom's Foreign, Commonwealth & Development Office. It is implemented by the Flowminder Foundation, WorldPop at the University of Southampton, the United Nations Population Fund, and the Center for International Earth Science Information Network (CIESIN) at Columbia University.
Suggested Data Set Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University and Novel-T. 2021. GRID3 Gabon Settlement Extents, Version 01.01. Palisades, NY: Geo-Referenced Infrastructure and Demographic Data for Development (GRID3). https://doi.org/10.7916/d8-bdtg-c209. Accessed DAY MONTH YEAR.
Live list of active aid activities for Gabon shared via the International Aid Transparency Initiative (IATI). Includes both humanitarian and development activities. More information on each activity (including financial data) is available from http://www.d-portal.org
Overview
The dataset contains harmonized indicators created from high-frequency phone surveys collected by the World Bank and partners. The surveys capture the socioeconomic impacts of the COVID-19 pandemic on households and individuals from all developing regions. Data are available for over 90 indicators in 14 topic areas, including education, food security, income, safety nets, and others. For more information, please refer to our Technical Note and Data Dictionary.
Unit of Measure
Percentages.
Aggregation Method:
The data is aggregated by Urban/Rural/National and Industry Sector
Disclaimer:
This harmonized dataset is an ongoing collation and harmonization of COVID-19 high-frequency phone survey (HFPS) data. Harmonization involves redefining indicators and categories so that they are comparable across countries. As a result, even if the names and definitions of indicators appear similar, numbers in this global database might differ slightly from those of each country's publications or dashboard. If you see large discrepancies or other issues, please reach out.
Version Notes:
COVID-19 Harmonized Household Data Feb 18 • Temporarily suppressed select income, labor, and government assistance indicators collected after wave 2 surveys for harmonization review • Added need for, and access to medical care in multiple countries • Temporarily suppressed select income, labor and government assistance indicators collected after wave 2 surveys for harmonization review
Funding Name, Abbreviation, Role:
The project received support from the Trust Fund for Statistical Capacity Building III (TFSCB-III). TFSCB-III is funded by the United Kingdom’s Foreign, Commonwealth & Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea.
Other Acknowledgments:
This dashboard was created by the Data for Goals (D4G) team and the Regional High-Frequency Phone Survey (HFPS) Focal Points in the EFI Poverty and Equity Global Practice (POV GP), under the guidance of POV GP management, using data collected under the World Bank-wide COVID-19 HFPS initiative.
Time Periods:
March, 2021