Updated
March 28, 2020
| Dataset date: May 20, 2019
This dataset updates: As needed
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in South Africa: (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).
Updated
March 28, 2020
| Dataset date: May 20, 2019
This dataset updates: As needed
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Togo: (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).
Updated
March 28, 2020
| Dataset date: Oct 1, 2018
This dataset updates: As needed
The population of the world, allocated to 1 arcsecond blocks. This refines CIESINโs Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.
There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.
Updated
March 28, 2020
| Dataset date: May 20, 2019
This dataset updates: As needed
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Sao Tome and Principe: (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).
Updated
March 28, 2020
| Dataset date: May 20, 2019
This dataset updates: As needed
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Sierra Leone: (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.
Updated
March 28, 2020
| Dataset date: May 20, 2019
This dataset updates: As needed
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Mayotte: (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).
Updated
March 28, 2020
| Dataset date: May 20, 2019
This dataset updates: As needed
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Namibia: (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).
Updated
March 28, 2020
| Dataset date: May 20, 2019
This dataset updates: As needed
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Mozambique: (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).
Updated
March 28, 2020
| Dataset date: May 20, 2019
This dataset updates: As needed
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Lesotho: (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).
Updated
March 24, 2020
| Dataset date: Jan 1, 2013-Dec 31, 2013
This dataset updates: As needed
This layer contains information about the flood hazard - by second-level administrative area - estimated during the Integrated Context Analysis (ICA) run in Eswatini in 2019.
Data sources: UNEP/UNISDR GAR 2013.
The main indicators used for the analysis were the percentage of district surface at flood risk and the maximum expected frequency of flood events with a 100-year return period.
Original dataset title: ICA Eswatini, 2019 - Flood hazard, 2013
Updated
March 24, 2020
| Dataset date: Jan 1, 1981-Dec 31, 2018
This dataset updates: As needed
This layer contains information about the drought hazard - by second-level administrative area - estimated during the Integrated Context Analysis (ICA) run in Eswatini in 2019.
Data source: HQ VAM Analysis of CHIRPS Rainfall Estimates (RFE), 1981-2015.
The main indicator used for the analysis was the number of poor growing seasons observed in the time window of interest.
Original dataset title: ICA Eswatini, 2019 - Drought hazard, 1981-2018
Updated
March 24, 2020
| Dataset date: Jan 1, 2019-Dec 31, 2019
This dataset updates: As needed
This layer contains information about the severity of natural hazards (floods and droughts) estimated during the Integrated Context Analysis (ICA) performed in Eswatini in 2019.
Data sources: UNEP/UNISDR GAR 2013, HQ VAM Analysis of CHIRPS Rainfall Estimates (RFE) 1981-2018.
Original dataset title: ICA Eswatini, 2019 - Natural shocks hazard
Updated
March 24, 2020
| Dataset date: Jan 1, 2011-Dec 31, 2019
This dataset updates: As needed
This layer contains information about the food security trend analysis - by second-level administrative unit - used for the purposes of the Integrated Context Analysis (ICA) run in Eswatini in 2019.
Data source: Eswatini Vulnerability Assessment and Analysis coordinated by the Eswatini Vulnerability Assessment Committee and the Central Statistics Office, 2011-2019.
The key indicators used for the purposes of the analysis were the Food Consumption Score (FSC) and the Coping Strategy Index (CSI), with a threshold set at 20% because national average of food insecurity, throughout the available rounds, was found to be 18.75%.
Original dataset title: ICA Eswatini, 2019 - Recurrence of food insecurity, 2011-2019
Updated
March 24, 2020
| Dataset date: Jan 1, 2011-Dec 31, 2019
This dataset updates: As needed
This layer contains information about the numbers of food insecure people - by second-level administrative unit - estimated for the purposes of the Integrated Context Analysis (ICA) run in Eswatini in 2019.
Data source: Eswatini Vulnerability Assessment and Analysis coordinated by the Eswatini Vulnerability Assessment Committee and the Central Statistics Office, 2011-2019.
The main indicators used for the analysis were the percentage of food insecure population for long-term planning and the additional population figures in case of a major shock.
Original dataset title: ICA Eswatini, 2019 - Estimates of food insecure people, 2011-2019
Updated
March 24, 2020
| Dataset date: Jan 1, 2001-Dec 31, 2016
This dataset updates: As needed
This layer contains information about the land degradation phenomenon - by second-level administrative area - observed for the purposes of the Integrated Context Analysis (ICA) run in Eswatini in 2019.
Data sources: HQ OSE GIS Analysis of NASA MODIS 2001-2016, WorldClim 1970-2000, FAO and NASA SRTM Digital Elevation Model data.
The main indicators used for the analysis were the average ecological changes observed between 2001-2006 and 2011-2016 and the percentage of erosion-prone surface.
Original dataset title: ICA Eswatini, 2019 - Land Degradation, 2001-2016
Updated
March 24, 2020
| Dataset date: Jan 1, 2019-Dec 31, 2019
This dataset updates: As needed
This layer contains information about the final classification deriving from the Integrated Context Analysis (ICA) run in Eswatini in 2019, showing the areas of convergence between recurrence of food insecurity and propensity to natural shocks (floods and droughts).
Original dataset title: ICA Eswatini, 2019 - ICA Categories & Areas
Updated
March 24, 2020
| Dataset date: Jan 1, 2009-Dec 31, 2018
This dataset updates: As needed
This layer contains information about the recurrence of prevalence of stunting - by first-level administrative unit - used for the purposes of the Integrated Context Analysis (ICA) run in Eswatini in 2019.
Data source: Eswatini Vulnerability Assessment and Analysis coordinated by the Eswatini Vulnerability Assessment Committee and the Central Statistics Office, 2009-2018.
The key indicator used for the purposes of the analysis was the prevalence of stunting in children under the age of 5, with a threshold set at 20% because, according to the guidelines suggested by the World Health Organization (WHO), this is the break between the classes 'Medium' and 'High'.
Original dataset title: ICA Eswatini, 2019 - Recurrence of prevalence of stunting, 2009-2018
Updated
March 24, 2020
| Dataset date: Jan 1, 2016-Dec 31, 2016
This dataset updates: As needed
This layer contains information about the most predominant livelihood zones - by second-level administrative area - identified during the Integrated Context Analysis (ICA) run in Eswatini in 2019.
Data source: Eswatini VAC Livelihood Baseline Update, 2016.
Original dataset title: ICA Eswatini, 2019 - Most predominant livelihood zones, 2016
Updated
March 22, 2020
| Dataset date: Mar 22, 2020
This dataset updates: Every week
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office')
Features may have these attributes:
network
name
operator
amenity
addr:full
source
addr:city
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
March 22, 2020
| Dataset date: Mar 22, 2020
This dataset updates: Every week
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
name
population
source
is_in
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated
March 22, 2020
| Dataset date: Mar 22, 2020
This dataset updates: Every week
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:
tourism
name
shop
man_made
opening_hours
amenity
beds
addr:full
addr:street
source
rooms
addr:housenumber
addr:city
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
