Updated
28 July 2021
| Dataset date: January 01, 1979-December 31, 2019
This data is by request only
The UEMS Database gathers information on accidental explosions of abandoned, damaged, improperly stored, or properly stored stockpiles of ammunition and explosives at munitions sites. It documents more than 600 incidents occurred from 1979 onwards and it is periodically updated. The UEMS Database provides details on the location, the number of casualties (fatalities and injuries) as well as the alleged cause of the incident, depending on available information.
Updated
21 September 2020
| Dataset date: January 01, 2016-August 31, 2020
This dataset updates: Every month
This dashboard provides aggregated global data on the safety & security incidents affecting NGOs in those countries covered by INSO*. It is intended to improve the visibility of macro-trends in humanitarian safety in order to raise awareness, inform research and strengthen operational practise. All data is sourced from INSO and assumed correct at the time of publishing. Please read below for advanced definitions & meanings. The information contained in this dashboard may be cited or reproduced only with credit to INSO.
Updated
27 January 2020
| Dataset date: August 18, 2019-August 18, 2019
This dataset updates: As needed
Accessibility information by WFP Operation/Security
Units,18 August 2019.
Original dataset title: Afghanistan: District Accessibility for WFP and Partners Staff as of 18 August 2019
Updated
16 January 2020
| Dataset date: November 13, 2019-November 13, 2019
This dataset updates: Every year
With its Global Militarisation Index (GMI), BICC is able to objectively depict worldwide militarisation for the first time. The GMI compares, for example, a countryโs military expenditure with its Gross Domestic Product (GDP) and its health expenditure.
It contrasts the total number of military and paramilitary forces in a country with the number of physicians. Finally, it studies the number of heavy weapons available to a countryโs armed forces. These and other indicators are used to determine a countryโs ranking, which in turn makes it possible to measure the respective level of militarisation in comparison to other countries.
The latest GMI of 2018 covers 155 countries and is based on the latest available figures (in most cases data for 2017). Israel, Singapore, Armenia, Cyprus, South Korea, Russia, Greece, Jordan, Brunei and Belarus are the top 10 worldwide. These countries allocate particularly high levels of resources to the military in comparison to other areas of society. See project website for more information
Updated
10 November 2019
| Dataset date: November 01, 1990-December 31, 2012
This dataset updates: Every year
Drawing from UN archival records, the IPI Peacekeeping Database presents the first publicly available database of total uniformed personnel contributions of each contributing country by month, by type (troop, police, or expert/observer) and by mission, from November 1990 to 2012. See Trends in Uniformed Contributions to UN Peacekeeping Operations: A New Dataset, 1991-2012 (Chris Perry and Adam C. Smith) for a full description of the database and some initial findings from the data regarding overall trends in uniformed contributions to UN peacekeeping.
For more information, please visit: http://www.providingforpeacekeeping.org/contributions/
Updated
12 April 2016
| Dataset date: April 12, 2016-April 12, 2016
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for East Africa.
It was last updated on April 12, 2016. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
EA201304_ML1 Most likely food security outcome for January-March 2016
EA201304_ML2 Most likely food security outcome for April-June 2016
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)
Updated
8 December 2015
| Dataset date: October 31, 2014-July 31, 2015
This dataset updates: Never
Maisha is VoicesAfricaโs online pan Africa study on lifestyle and viewpoints on various aspects of life. The research report covers the following information areas:
Income and expenditure
โข If earn a personal income
โข Share of wallet
โข Whether overall expenditure has gone up, down, or remained the same compared to the previous year
Values
โข Determinants of well being
โข Aspects that have made life better/worse
โข Threats to self and country
Role models
โข Most admired personality
โข Part of the world/Africa that offers greatest inspiration and hope
Africa unity
โข If would support the integration of all African countries
Technology
โข Devices used
โข Device that has made life better/worse
โข Device that has had greatest influence in life
Attitudes
โข Response to statements on politics, insecurity, family, religion, sports, patriotism, economy, corruption, health, relationships, among others
Updated
15 October 2015
| Dataset date: July 01, 2015-July 01, 2015
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on August 19, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
SA201304_ML1 Most likely food security outcome for July-September 2015
SA201304_ML2 Most likely food security outcome for October-December 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)
Updated
15 October 2015
| Dataset date: June 01, 2015-June 01, 2015
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on June 01, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
southernafrica201304_ML1 Most likely food security outcome for April-June 2015
southernafrica201304_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)
Updated
15 October 2015
| Dataset date: February 10, 2015-February 10, 2015
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on February 10, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
southernafrica201304_ML1 Most likely food security outcome for January-March 2015
southernafrica201304_ML2 Most likely food security outcome for April-June 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).
Updated
15 October 2015
| Dataset date: October 01, 2014-October 01, 2014
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on November 13, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
southernafrica201304_ML1 Most likely food security outcome for October-December 2014
southernafrica201304_ML2 Most likely food security outcome for January-March 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).
Updated
15 October 2015
| Dataset date: July 01, 2014-July 01, 2014
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on September 26, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
southernafrica201307_ML1 Most likely food security outcome for July-September 2014
southernafrica201407_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).
Updated
15 October 2015
| Dataset date: April 01, 2014-April 01, 2014
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on July 17, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
southernafrica201304_ML1 Most likely food security outcome for April-June 2014
southernafrica201304_ML2 Most likely food security outcome for July-September 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).
Updated
15 October 2015
| Dataset date: January 01, 2014-January 01, 2014
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
southernafrica201401_ML1 Most likely food security outcome for January-March 2014
southernafrica201401_ML2 Most likely food security outcome for April-June 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)
Updated
15 October 2015
| Dataset date: October 01, 2015-October 01, 2015
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on February 05, 2016. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
SA201304_ML1 Most likely food security outcome for October-December 2015
SA201304_ML2 Most likely food security outcome for January-March 2016
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).
Updated
15 October 2015
| Dataset date: July 01, 2013-July 01, 2013
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on July 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
southernafrica201307_ML1 Most likely food security outcome for July-September 2013
southernafrica201307_ML2 Most likely food security outcome for October-December 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)
Updated
15 October 2015
| Dataset date: June 01, 2013-June 01, 2013
This dataset updates: Never
This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa.
It was last updated on June 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity.
The two shapefiles represent the two analytic periods:
southernafrica201304_ML1 Most likely food security outcome for April-June 2013
southernafrica201304_ML2 Most likely food security outcome for July-September 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)
