Updated
January 19, 2021
| Dataset date: March 01, 2020-August 31, 2020
This dataset updates: Every day
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/
Updated
January 13, 2021
| Dataset date: January 01, 2018-December 31, 2020
This dataset updates: Every week
The ACLED project codes reported information on the type, agents, exact location, date, and other characteristics of political violence events, demonstrations and select politically relevant non-violent events. ACLED focuses on tracking a range of violent and non-violent actions by political agents, including governments, rebels, militias, communal groups, political parties, external actors, rioters, protesters and civilians. Data contain specific information on the date, location, group names, interaction type, event type, reported fatalities and contextual notes.
Updated
January 12, 2021
| Dataset date: January 01, 2018-June 30, 2020
This dataset updates: Every six months
Data collated by UNHCR, containing information about forcibly displaced populations and stateless persons, spanning across almost 70 years of statistical activities. The data includes the countries / territories of asylum and origin. Specific resources are available for end-year population totals, demographics, asylum applications, decisions, and solutions availed by refugees and IDPs (resettlement, naturalisation or returns).
Updated
January 4, 2021
| Dataset date: August 27, 2020-August 27, 2020
This dataset updates: As needed
We use an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations. The Social Connectedness Index (SCI) is a measure of the social connectedness between different geographies. Specifically, it measures the relative probability that two individuals across two locations are friends with each other on Facebook.
Details on the underlying data and the construction of the index are provided in the โFacebook Social Connectedness Index - Data Notes.pdfโ file. Please also see https://dataforgood.fb.com/ as well as the associated research paper โSocial Connectedness: Measurement, Determinants and Effects,โ published in the Journal of Economic Perspectives (https://www.aeaweb.org/articles?id=10.1257/jep.32.3.259).
Updated
January 4, 2021
| Dataset date: September 21, 2020-September 21, 2020
This dataset updates: Every year
This data contains aggregated weighted statistics at the regional level by gender for the Survey on Gender Equality At Home fielded in July 2020. Facebookโs Survey on Gender Equality at Home generates a global snapshot of women and menโs access to resources, their time spent on unpaid care work, and their attitudes about equality. If you're interested in becoming a Survey on Gender Equality research partner, please email gendersurvey@fb.com.
Updated
December 16, 2020
| Dataset date: January 01, 1970-December 31, 2019
This dataset updates: Every three months
Education indicators for Martinique.
Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: National Monitoring (made 2020 September), SDG 4 Global and Thematic (made 2020 September), Demographic and Socio-economic (made 2020 September)
Updated
November 24, 2020
| Dataset date: January 01, 2000-December 31, 2020
This dataset updates: Every year
WorldPop produces different types of gridded population count datasets, depending on the methods used and end application.
Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.
Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 1km at the equator)
-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 โ derived from the corresponding
Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area.
These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 โ derived from the corresponding
Unconstrained individual countries 2000-2020 population UN adjusted count datasets 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 unconstrained top-down modelling method.
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
Updated
November 24, 2020
| Dataset date: January 01, 2000-December 31, 2020
This dataset updates: Every year
WorldPop produces different types of gridded population count datasets, depending on the methods used and end application.
Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.
Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below.
These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country.
They can also be visualised and explored through the woprVision App.
The remaining datasets in the links below are produced using the "top-down" method,
with either the unconstrained or constrained top-down disaggregation method used.
Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs.
Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively):
- Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019)
-Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019).
-Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets.
-Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using
constrained top-down methods for all countries of the World for 2020.
-Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using
constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national
population estimates (UN 2019).
Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent.
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/WP00645
Updated
October 13, 2020
| Dataset date: October 13, 2020-October 13, 2020
This dataset updates: Every day
FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.
Updated
Live
| Dataset date: January 01, 2019-January 01, 2020
This dataset updates: Live
Live list of active aid activities for Martinique 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
Updated
Live
| Dataset date: January 22, 2020-January 17, 2021
This dataset updates: Live
Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the Peopleโs Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github.
Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths.
On 23/03/2020, a new data structure was released. The current resources for the latest time series data are:
time_series_covid19_confirmed_global.csv
time_series_covid19_deaths_global.csv
time_series_covid19_recovered_global.csv
---DEPRECATION WARNING---
The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020:
time_series_19-covid-Confirmed.csv
time_series_19-covid-Deaths.csv
time_series_19-covid-Recovered.csv
Updated
July 14, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay')
Features may have these attributes:
natural
tunnel
depth
width
name
water
layer
covered
waterway
blockage
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
July 13, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
highway IS NOT NULL
Features may have these attributes:
smoothness
surface
highway
width
name
bridge
oneway
layer
lanes
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
July 13, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
building IS NOT NULL
Features may have these attributes:
addr:housenumber
addr:city
building
name
addr:full
building:materials
addr:street
building:levels
source
office
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
July 13, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL
Features may have these attributes:
man_made
rooms
amenity
addr:housenumber
shop
addr:city
opening_hours
name
tourism
addr:full
addr:street
beds
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
July 4, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity IN ('kindergarten','school','college','university') OR building IN ('kindergarten','school','college','university')
Features may have these attributes:
operator:type
amenity
addr:city
building
name
addr:full
capacity:persons
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
June 29, 2020
| Dataset date: January 01, 2000-December 31, 2020
This dataset updates: Every year
WorldPop produces different types of gridded population count datasets, depending on the methods used and end application.
Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.
A description of the modelling methods used for age and sex structures can be found in
Tatem et al and
Pezzulo et al.
The 'Unconstrained global per country 2000-2020' datasets represent the outputs from a project focused on construction of
consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020 structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency.
These are produced using the unconstrained top-down modelling method.
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
Updated
June 29, 2020
| Dataset date: January 01, 2017-December 31, 2017
This dataset updates: Every year
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 pregnancies to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age, age-specific fertility rates, still births and abortions to map the estimated distributions of pregnancies for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al and James et al..
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2017. Martinique 1km pregnancies. Version 2.0 2015 estimates of numbers of pregnancies per grid square, with national totals adjusted to match national estimates on numbers of pregnancies made by the Guttmacher Institute (http://www.guttmacher.org) DOI: 10.5258/SOTON/WP00502
Updated
June 29, 2020
| Dataset date: January 01, 2017-December 31, 2017
This dataset updates: Every year
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 Tatem et al. and James et al..
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2017. Martinique 1km births. Version 2.0 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/WP00394
Updated
June 5, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
aeroway IS NOT NULL OR building = 'aerodrome' OR emergency:helipad IS NOT NULL OR emergency = 'landing_site'
Features may have these attributes:
operator:type
aeroway
emergency:helipad
addr:city
building
name
addr:full
emergency
capacity:persons
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
June 5, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity = 'ferry_terminal' OR building = 'ferry_terminal' OR port IS NOT NULL
Features may have these attributes:
operator:type
amenity
addr:city
building
name
port
addr:full
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
June 5, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
healthcare IS NOT NULL OR amenity IN ('doctors','dentist','clinic','hospital','pharmacy')
Features may have these attributes:
operator:type
amenity
healthcare
addr:city
building
healthcare:speciality
name
addr:full
capacity:persons
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
June 5, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
place IN ('isolated_dwelling','town','village','hamlet','city')
Features may have these attributes:
place
is_in
name
population
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
June 5, 2020
| Dataset date: January 04, 2021-January 04, 2021
This dataset updates: Every month
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
railway IN ('rail','subway','station')
Features may have these attributes:
operator:type
railway
addr:city
name
addr:full
ele
layer
source
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
