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  • 20+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2015-December 31, 2015
    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 Ecuador in 2018. Data source: Ministerio de Agricultura, Ganaderia, Acuacultura y Pesca (MAGAP), 2015. The methodology used to estimate the flood hazard included parameters such as the soil texture, the terrain slope and the return period of the flood events, whose classifications have been combined into a final qualitative score. The main indicators used for the purposes of the analysis were the percentage of surface area at flood hazard and the most recurrent flood hazard class - according to MAGAP - by second-level administrative area. Original dataset title: ICA Ecuador, 2018 - Flood Hazard, 2015
  • 10+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2015-December 31, 2015
    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 Ecuador in 2018. Data source: Ministerio de Agricultura, Ganaderia, Acuacultura y Pesca (MAGAP), 2015. The methodology used to estimate the drought hazard included parameters such as the soil texture and ratio between the total precipitation and the average precipitation expected for a given region, whose classifications were combined into a final qualitative score. The main indicators used for the analysis were the percentage of surface at drought hazard and the most recurrent drought hazard class - according to MAGAP - by second-level administrative area. Original dataset title: ICA Ecuador, 2018 - Drought Hazard, 2015
  • 20+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2013-December 31, 2013
    This dataset updates: As needed
    This layer contains information about the flood hazard estimated - by second-level administrative area - during the Integrated Context Analysis (ICA) run in Democratic Republic of Congo in 2017. The analysis was performed in three hot-spot provinces in the north-east part of the country (Ituri, Nord-Kivu and Sud-Kivu) because of the recent conflict outbreak and increasing food insecurity levels. Data source: UNEP/UNISDR GAR 2013, EMDAT 1990-2016. The main indicators used for the analysis were the percentage of district surface at flood hazard, the maximum expected frequency of flood events and the number of flood events recorded by EMDAT between 1990 and 2017. Cette couche contient informations regard le risque d'inondations - par unité administrative de deuxième niveau - estimé pendant l'Analyse Integrée de Contexte (AIC) exécuté en République Démocratique du Congo en 2017. L'analyse a été executée dans trois provinces critiques dans le secteur nord-est du pays (Ituri, Nord-Kivu et Sud-Kivu) en raison du récent déclenchement des conflits et des croissants niveaux d'insécurité alimentaire. Sources des données: UNEP/UNISDR GAR 2013. Les indicateurs principales utilisés pour l'analyse étaient la pourcentage de surface à risque d'inondation et la combination de l'attente maximale attendue des inondations selon UNEP/UNISDR et l'attente des inondations enregistrées par EMDAT entre 1990 et 2016. Original dataset title: ICA Democratic Republic of Congo, 2017 - Flood Hazard, 2013
  • 20+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 1981-December 31, 2015
    This dataset updates: As needed
    This layer contains information about the interannual rainfall variability estimated during the Integrated Context Analysis (ICA) run in Democratic Republic of Congo in 2017. The analysis was performed in three hot-spot provinces in the north-east part of the country (Ituri, Nord-Kivu and Sud-Kivu) because of the recent conflict outbreak and increasing food insecurity levels. Data source: HQ VAM Analysis of CHIRPS Rainfall Estimates (RFE), 1981-2015. The main indicator used for the analysis was the maximum percentage of rainfall variability compared to the long-term average. Cette couche contient informations regard la variabilité interannulle des précipitations estimée pendant l'Analyse Integrée du Contexte (AIC) executée en République Démocratique du Congo en 2017. L'analyse a été executée dans trois provinces critiques dans le secteur nord-est du pays (Ituri, Nord-Kivu et Sud-Kivu) en raison du récent déclenchement des conflits et des croissants niveaux d'insécurité alimentaire. Source des donnés: HQ VAM Analyse de CHIRPS estimations des précipitations, 1981-2015. L'indicateur principal utilisé pour l'analyse était la pourcentage maxime de variabilité par rapport à la moyenne à long terme. Original dataset title: ICA Democratic Republic of Congo, 2017 - Inter-annual Rainfall Variability, 1981-2015
  • 20+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2013-December 31, 2013
    This dataset updates: As needed
    This layer contains information about the flood risk - by second-level administrative area - estimated during the Integrated Context Analysis (ICA) run in Chad in 2017. The analysis is the result of a joint effort between the Regional Bureau in Dakar (RBD) and the HQ GIS Unit and Programme division. Data sources: UNEP/UNISDR GAR 2013. The main indicators used for the analysis were the percentage of department surface at flood risk and the maximum expected frequency of flood events with a 100-year return period. Cette couche contient informations regard le risque d’inondations – par unité administrative de deuxième niveau – estimé pendant l’Analyse Integrée du Contexte (AIC) executée en Tchad en 2017. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données : UNEP/UNISDR GAR, 2013. Les indicateurs principaux utilisées pour l’analyse étaient la pourcentage de surface a risque d’inondation et l’attente maximale attendue des inondations. Original dataset title: ICA Chad, 2017 - Flood Risk, 2013
  • 30+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 1981-December 31, 2015
    This dataset updates: As needed
    This layer contains information about the drought risk - by second-level administrative area - estimated during the Integrated Context Analysis (ICA) run in Chad in 2017. The analysis is a joint effort between the Regional Bureau in Dakar (RBD), the HQ GIS Unit and Programme division. 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. Cette couche contient informations regard le risque de sècheresse – par unité administrative de deuxième niveau – estimée pendant l’Analyse Integrée du Contexte (AIC) executée en Tchad en 2017. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données: HQ VAM Analyse des données CHIRPS d’estimation des precipitations, 1981-2015. L'indicateur principale utilisé pour l'analyse était le nombre de saisons qui ont connu un déficit hydrique (mauvaises saisons de croissance). Original dataset title: ICA Chad, 2017 - Drought Risk, 1981-2015
  • Updated 27 January 2020 | Dataset date: January 01, 2017-December 31, 2017
    This dataset updates: As needed
    This layer contains information about the natural shock risk (floods and droughts) estimated during the Integrated Context Analysis (ICA) performed in Chad in 2017. The analysis was a joint effort between the Regional Bureau in Dakar (RBD), the HQ GIS Unit and Programme division. Data sources: UNEP/UNISDR GAR 2013, HQ VAM Analysis of CHIRPS Rainfall Estimates (RFE) 1981-2015. Cette couche contient informations regard le risque des chocs naturels (inondations et sècheresse) estimé pendant l’Analyse Integrée du Contexte (AIC) executée en Tchad en 2017. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données : UNEP/UNISDR GAR 2013, HQ VAM Analyse des données CHIRPS d’estimation des precipitations 1981-2015. Original dataset title: ICA Chad, 2017 - Natural Shock Risk
  • 20+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2012-December 31, 2015
    This dataset updates: As needed
    This layer contains information about the flood risk - by livelihood zone - estimated during the Integrated Context Analysis (ICA) run in Burundi between 2014 and 2015. Data source: Strategie Nationale de Prévention des Risques et de Gestion des Catastrophes - Plan d'Action National, 2012-2015. The key indicator used for the analysis was a descriptive flood index, provided by the source with no information about the data and the methodology behind it. Cette couche contient informations regard le risque d'inondations - par zones de moyens d'existence - estimé pendant l'Analyse Integrée de Contexte (AIC) exécuté au Burundi entre 2014 et 2015. Sources des données: Strategie Nationale de Prévention des Risques et de Gestion des Catastrophes - Plan d'Action National, 2012-2015. L'indicateur principale utilisé pour l'analyse était un index des inondations, fourni par la source avec pas des informations regard les données utilisées ainsi que la méthodologie employée. Original dataset title: ICA Burundi, 2014 - Flood Risk, 2012-2015
  • 10+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 1999-December 31, 2013
    This dataset updates: As needed
    This layer contains information about the drought risk - by livelihood zone - estimated during the Integrated Context Analysis (ICA) run in Burundi between 2014 and 2015. Data source: HQ VAM Analysis of NDVI data, 1999-2013. The key indicator used for the analysis was the number of poor growing seasons during the time frame of interest. Cette couche contient informations regard le risque de sècheresse - par zones de moyens d'existence - estimé pendant l'Analyse Integrée de Contexte (AIC) executé au Burundi entre 2014 et 2015. Source des données: HQ VAM Analyse de NDVI données, 1999-2013. L'indicateur principale utilisé pour l'analyse était le nombre de saison qui ont connu un déficit hydrique (mauvaises saisons de croissance). Original dataset title: ICA Burundi, 2014 - Drought Risk, 1999-2013
  • 60+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2013-December 31, 2013
    This dataset updates: As needed
    This layer contains information about the flood risk - by first-level administrative area - estimated during the Integrated Context Analysis (ICA) run in Burkina Faso in 2018. The analysis is the result of a joint effort between the Regional Bureau in Dakar (RBD) and the HQ GIS Unit and Programme division. 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. Cette couche contient informations regard le risque d’inondations – par unité administrative de première niveau – estimée pendant l’Analyse Integrée du Contexte (AIC) executée en Burkina Faso en 2018. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données : UNEP/UNISDR GAR, 2013. Les indicateurs principaux utilisées pour l’analyse étaient la pourcentage de surface a risuqe d’inondation et l’attente maximale attendue des inondations. Original dataset title: ICA Burkina Faso, 2018 - Flood Risk, 2013
  • 40+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 1981-December 31, 2015
    This dataset updates: As needed
    This layer contains information about the drought risk - by first-level administrative area - estimated during the Integrated Context Analysis (ICA) run in Burkina Faso in 2018. The analysis is a joint effort between the Regional Bureau in Dakar (RBD), the HQ GIS Unit and Programme division. 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. Cette couche contient informations regard le risque de sècheresse – par unité administrative de première niveau – estimée pendant l’Analyse Integrée du Contexte (AIC) executée en Burkina Faso en 2018. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données: HQ VAM Analyse des données CHIRPS d’estimation des precipitations, 1981-2015. L'indicateur principale utilisé pour l'analyse était le nombre de saisons qui ont connu un déficit hydrique (mauvaises saisons de croissance). Original dataset title: ICA Burkina Faso, 2018 - Drought Risk, 1981-2015
  • 20+ Downloads
    Updated 27 January 2020 | Dataset date: January 01, 2018-December 31, 2018
    This dataset updates: As needed
    This layer contains information about the natural shock risk (floods and droughts) estimated during the Integrated Context Analysis (ICA) performed in Burkina Faso in 2018. The analysis was a joint effort between the Regional Bureau in Dakar (RBD), the HQ GIS Unit and Programme division. Data sources: UNEP/UNISDR GAR 2013, HQ VAM Analysis of CHIRPS Rainfall Estimates (RFE) 1981-2015. Cette couche contient informations regard le risque des chocs naturels (inondations et sècheresse) estimé pendant l’Analyse Integrée du Contexte (AIC) executée en Burkina Faso en 2018. L’analyse a été executée grâce à la collaboration entre le Bureau Régional de Dakar (RBD), l’unité GIS et la division de Programme au quartier générale du PAM. Source des données : UNEP/UNISDR GAR 2013, HQ VAM Analyse des données CHIRPS d’estimation des precipitations 1981-2015. Original dataset title: ICA Burkina Faso, 2018 - Natural Shock Risk
  • 200+ Downloads
    Updated 10 November 2019 | Dataset date: January 01, 2008-December 31, 2013
    This dataset updates: Every six months
    El IRSH está en capacidad de proporcionar a los analistas en el ámbito humanitario, una herramienta de uso general que muestre un resultado a nivel municipal entendido como la probabilidad de que exista una situación de crisis humanitaria enmarcado por las variables asociadas a los factores sociales, económicos, oferta institucional y el conflicto.
  • 400+ Downloads
    Updated 10 November 2019 | Dataset date: March 01, 2019-March 01, 2019
    This dataset updates: As needed
    The data is coming from Philippines Institute of Volcanology and Seismology (DOST-PHIVOLCS). It was also available in here: https://gisweb.phivolcs.dost.gov.ph/phivolcs_hazardmaps/?fbclid=IwAR1QpGFhkVl07XTCIEMtpBOG3v4jWV05v_1QhYPNyF2_1n0NsjY3do8TiZk by region.
  • 200+ Downloads
    Updated 3 September 2019 | Dataset date: June 01, 2019-August 01, 2019
    This dataset updates: Every three months
    These data are collected by sentinel sites in Mali, Burkina-Faso, Senegal and Niger. They provide weekly data on water availability, market prices, animal diseases and pasture conditions. The data are agregated at the end of a 2-months period and published via (1) a bulletin available on the sigsahel.info platform and maps available on a mapping platform : geosahel.info. These dataset are the summary of the data collected by sentinel sites during June and July 2019.
  • 600+ Downloads
    Updated 18 July 2019 | Dataset date: September 12, 2018-September 12, 2018
    This dataset updates: Every year
    The Index for Risk Management INFORM is a composite indicator developed by JRC as a tool for understanding the risk of humanitarian crisis and disasters.
  • 100+ Downloads
    Updated Live | Dataset date: April 12, 2019-April 12, 2019
    This dataset updates: Live
    Layer provides distribution of landslide hazard, describing by an index of very low to very high hazard. Data produced under the ADRF Sub Saharan Africa Risk Profiles project. The project provides also risk data produced, exposure and hazard data used in the analyses.
  • 100+ Downloads
    Updated 21 July 2018 | Dataset date: July 22, 2018-July 22, 2018
    This dataset updates: Every month
    Flood-affected populations by settlement type as of June 2018. The dataset presents the data to LGA level (Admin 2) in all the three crisis-affected states of north eastern Nigeria, thus Borno, Yobe and Adamawa. The affected populations include households and individuals. The settlement types include Households in IDP Camps, Households in Host Communities and provides a computation of affected people by settlement type; Camp flood risk mapping as of June 2018. The dataset presents the data to LGA level (Admin 2) in IDP camps across all the three crisis-affected states of north eastern Nigeria, thus Borno, Yobe and Adamawa; Host Community flood risk analysis and mapping as of June 2018. Also includes a CSV file on Flood-affected populations by settlement type as of June 2018.
  • 400+ Downloads
    Updated 17 July 2018 | Dataset date: December 31, 2016-December 31, 2016
    This dataset updates: Every year
    The subnational INFORM model for Caucasus and Central Asia was initiated by the Regional Inter-Agency Standing Committee (IASC) Task Force for Caucasus and Central Asia and is managed by OCHA. The INFORM model is being used to support coordinated preparedness actions. Partners hope to use the model to improve cooperation between humanitarian and development actors in managing risk and building resilience across the region. INFORM identifies areas at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM model is based on risk concepts published in scientific literature and envisages three dimensions of risk: Hazards & Exposure, Vulnerability and Lack of Coping Capacity. The INFORM model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk. The regional subnational INFORM model for Caucasus and Central Asia is developed at the first administrative level (corresponding to the provinces/oblasts/regions and few independent cities) of the eight countries in South Caucasus and Central Asia. The INFORM index supports a proactive crisis management framework. It will be helpful for an objective allocation of resources for disaster management as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies.
  • 4000+ Downloads
    Updated 11 July 2018 | Dataset date: April 01, 2016-June 30, 2016
    This dataset updates: Never
    This database contains information about road accidents happening in Kenya
  • 200+ Downloads
    Updated 25 May 2018 | Dataset date: May 25, 2018-May 25, 2018
    This dataset updates: Every year
    El indicador estima el porcentaje de población adulta alfabetizada que es capaz de usar palabras escritas en la vida diaria y de continuar aprendiendo. Refleja el logro acumulado de la educación en la difusión de la alfabetización.
  • 100+ Downloads
    Updated 25 May 2018 | Dataset date: May 25, 2018-May 25, 2018
    This dataset updates: Every year
    La tasa de cobertura de la población en edad de estudiar en ese grado especifico.
  • 100+ Downloads
    Updated 25 May 2018 | Dataset date: May 25, 2018-May 25, 2018
    This dataset updates: Every year
    La tasa de cobertura de la población en edad de estudiar en ese grado especifico.
  • 100+ Downloads
    Updated 25 May 2018 | Dataset date: May 25, 2018-May 25, 2018
    This dataset updates: Every year
    La tasa de cobertura promedio de la población en edad de estudiar en ciclo I y II.
  • 100+ Downloads
    Updated 25 May 2018 | Dataset date: May 25, 2018-May 25, 2018
    This dataset updates: Every year
    Describe la proporción estudiante-maestro en el sistema de educativo pre básica, ciclo común y media.