Refine your search: Clear all
Featured:
Locations:
More
Formats:
More
Organisations:
More
Tags:
More
Licenses:
  • 50+ Downloads
    Updated 1 April 2021 | Dataset date: May 05, 2020-May 05, 2020
    This dataset updates: Never
    This data was developed as part of the Modelling Exposure Through Earth Observation Routines (METEOR) project and is a Level 1, or a global-quality exposure data set. Minimal country-specific data was collected. The data is intended for CAT modeling and loss estimation. Repurposing this data for any reason other than assessing risk is not recommended. The data presents the estimated number of buildings, building area, and rebuilding value at a 15-arcsecond grid resolution (approximately 500 meters at the equator). This data set is in point shapefile format where the points represent the centroids of the 15-arcsecond grid. The results were created through a process of spreading the number of buildings to the 15-arcsecond level by a statistical assessment of moderate resolution EO data, which is described in more detail in the dasymetric mapping lineage processing step. The estimated building count at any given area is a result of statistical processes and should not be mistaken as a building count. The structural classes of buildings used for risk assessment are estimated given the building wall, floor, and roof material classes surveyed through 2002 Population and Housing Census - Volume 1. Analytical report. Additionally, the data is provided in Open Exposure Data (OED) import format, as a pair of CSV files. One CSV file contains the location details, and the other is an "account" file that is filled with default information to satisfy OED format requirements. The OED input files are set to use "All perils" (i.e. "AA1"). All required OED account-related fields are populated with "1" by default (such as PortNumber, AccNumber, PolNumber). If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes: https://forms.gle/DQjhE89CRegNKB3X8 Please see the METEOR project page for information about the METEOR Project: http://meteor-project.org/ Please see the METEOR map portal for interactive maps: https://maps.meteor-project.org/ For more information about the Open Exposure Data (OED) standard, please see https://github.com/OasisLMF/OpenDataStandards
  • 20+ Downloads
    Updated 1 April 2021 | Dataset date: May 05, 2020-May 05, 2020
    This dataset updates: Never
    This data was developed as part of the Modelling Exposure Through Earth Observation Routines (METEOR) project and is a Level 1, or a global-quality exposure data set. Minimal country-specific data was collected. The data is intended for CAT modeling and loss estimation. Repurposing this data for any reason other than assessing risk is not recommended. The data presents the estimated number of buildings, building area, and rebuilding value at a 15-arcsecond grid resolution (approximately 500 meters at the equator). This data set is in point shapefile format where the points represent the centroids of the 15-arcsecond grid. The results were created through a process of spreading the number of buildings to the 15-arcsecond level by a statistical assessment of moderate resolution EO data, which is described in more detail in the dasymetric mapping lineage processing step. The estimated building count at any given area is a result of statistical processes and should not be mistaken as a building count. The structural classes of buildings used for risk assessment are estimated given the building wall, floor, and roof material classes surveyed through 2002 Population and Housing Census - Volume 1. Analytical report. Additionally, the data is provided in Open Exposure Data (OED) import format, as a pair of CSV files. One CSV file contains the location details, and the other is an "account" file that is filled with default information to satisfy OED format requirements. The OED input files are set to use "All perils" (i.e. "AA1"). All required OED account-related fields are populated with "1" by default (such as PortNumber, AccNumber, PolNumber). If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes: https://forms.gle/DQjhE89CRegNKB3X8 Please see the METEOR project page for information about the METEOR Project: http://meteor-project.org/ Please see the METEOR map portal for interactive maps: https://maps.meteor-project.org/ For more information about the Open Exposure Data (OED) standard, please see https://github.com/OasisLMF/OpenDataStandards
  • 30+ Downloads
    Updated 1 April 2021 | Dataset date: May 05, 2020-May 05, 2020
    This dataset updates: Never
    This data was developed as part of the Modelling Exposure Through Earth Observation Routines (METEOR) project and is a Level 1, or a global-quality exposure data set. Minimal country-specific data was collected. The data is intended for CAT modeling and loss estimation. Repurposing this data for any reason other than assessing risk is not recommended. The data presents the estimated number of buildings, building area, and rebuilding value at a 15-arcsecond grid resolution (approximately 500 meters at the equator). This data set is in point shapefile format where the points represent the centroids of the 15-arcsecond grid. The results were created through a process of spreading the number of buildings to the 15-arcsecond level by a statistical assessment of moderate resolution EO data, which is described in more detail in the dasymetric mapping lineage processing step. The estimated building count at any given area is a result of statistical processes and should not be mistaken as a building count. The structural classes of buildings used for risk assessment are estimated given the building wall, floor, and roof material classes surveyed through 2002 Population and Housing Census - Volume 1. Analytical report. Additionally, the data is provided in Open Exposure Data (OED) import format, as a pair of CSV files. One CSV file contains the location details, and the other is an "account" file that is filled with default information to satisfy OED format requirements. The OED input files are set to use "All perils" (i.e. "AA1"). All required OED account-related fields are populated with "1" by default (such as PortNumber, AccNumber, PolNumber). If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes: https://forms.gle/DQjhE89CRegNKB3X8 Please see the METEOR project page for information about the METEOR Project: http://meteor-project.org/ Please see the METEOR map portal for interactive maps: https://maps.meteor-project.org/ For more information about the Open Exposure Data (OED) standard, please see https://github.com/OasisLMF/OpenDataStandards
  • 40+ Downloads
    Updated 1 April 2021 | Dataset date: May 05, 2020-May 05, 2020
    This dataset updates: Never
    This data was developed as part of the Modelling Exposure Through Earth Observation Routines (METEOR) project and is a Level 1, or a global-quality exposure data set. Minimal country-specific data was collected. The data is intended for CAT modeling and loss estimation. Repurposing this data for any reason other than assessing risk is not recommended. The data presents the estimated number of buildings, building area, and rebuilding value at a 15-arcsecond grid resolution (approximately 500 meters at the equator). This data set is in point shapefile format where the points represent the centroids of the 15-arcsecond grid. The results were created through a process of spreading the number of buildings to the 15-arcsecond level by a statistical assessment of moderate resolution EO data, which is described in more detail in the dasymetric mapping lineage processing step. The estimated building count at any given area is a result of statistical processes and should not be mistaken as a building count. The structural classes of buildings used for risk assessment are estimated given the building wall, floor, and roof material classes surveyed through 2002 Population and Housing Census - Volume 1. Analytical report. Additionally, the data is provided in Open Exposure Data (OED) import format, as a pair of CSV files. One CSV file contains the location details, and the other is an "account" file that is filled with default information to satisfy OED format requirements. The OED input files are set to use "All perils" (i.e. "AA1"). All required OED account-related fields are populated with "1" by default (such as PortNumber, AccNumber, PolNumber). If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes: https://forms.gle/DQjhE89CRegNKB3X8 Please see the METEOR project page for information about the METEOR Project: http://meteor-project.org/ Please see the METEOR map portal for interactive maps: https://maps.meteor-project.org/ For more information about the Open Exposure Data (OED) standard, please see https://github.com/OasisLMF/OpenDataStandards
  • 60+ Downloads
    Updated 9 March 2021 | Dataset date: May 05, 2020-May 05, 2020
    This dataset updates: Never
    This data was developed as part of the Modelling Exposure Through Earth Observation Routines (METEOR) project and is a Level 1, or a global-quality exposure data set. Minimal country-specific data was collected. The data is intended for CAT modeling and loss estimation. Repurposing this data for any reason other than assessing risk is not recommended. The data presents the estimated number of buildings, building area, and rebuilding value at a 15-arcsecond grid resolution (approximately 500 meters at the equator). This data set is in point shapefile format where the points represent the centroids of the 15-arcsecond grid. The results were created through a process of spreading the number of buildings to the 15-arcsecond level by a statistical assessment of moderate resolution EO data, which is described in more detail in the dasymetric mapping lineage processing step. The estimated building count at any given area is a result of statistical processes and should not be mistaken as a building count. The structural classes of buildings used for risk assessment are estimated given the building wall, floor, and roof material classes surveyed through 2002 Population and Housing Census - Volume 1. Analytical report. Additionally, the data is provided in Open Exposure Data (OED) import format, as a pair of CSV files. One CSV file contains the location details, and the other is an "account" file that is filled with default information to satisfy OED format requirements. The OED input files are set to use "All perils" (i.e. "AA1"). All required OED account-related fields are populated with "1" by default (such as PortNumber, AccNumber, PolNumber). If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes: https://forms.gle/DQjhE89CRegNKB3X8 Please see the METEOR project page for information about the METEOR Project: http://meteor-project.org/ Please see the METEOR map portal for interactive maps: https://maps.meteor-project.org/ For more information about the Open Exposure Data (OED) standard, please see https://github.com/OasisLMF/OpenDataStandards
  • 300+ Downloads
    Updated 30 October 2020 | Dataset date: October 31, 2020-October 31, 2020
    This dataset updates: Every six months
    Eastern and Southern Africa Risk Analysis based on Inform, FEWSNET, OCHA, UNICEF and others
  • 300+ Downloads
    Updated 20 October 2020 | Dataset date: October 06, 2020-October 06, 2020
    This dataset updates: As needed
    The data shows the number of people, Household, houses damaged and destroyed by floods and was shared by the government of Sudan (HAC)
  • 3400+ Downloads
    Updated 12 October 2020 | Dataset date: September 12, 2020-September 12, 2020
    This dataset updates: As needed
    The INFORM COVID-19 Risk Index is a composite index that identifies: “countries at risk from health and humanitarian impacts of COVID-19 that could overwhelm current national response capacity, and therefore lead to a need for additional international assistance”. The INFORM COVID-19 Risk Index is primarily concerned with structural risk factors, i.e. those that existed before the outbreak. It can be used to support prioritization of preparedness and early response actions for the primary impacts of the pandemic, and identify countries where secondary impacts are likely to have the most critical humanitarian consequences. The main scope of the INFORM COVID-19 Risk Index is global and regional risk-informed resource allocation, i.e. where comparable understanding of countries’ risk is important. It cannot predict the impacts of the pandemic in individual countries. It does not consider the mechanisms behind secondary impacts - for example how a COVID-19 outbreak could increase conflict risk.
  • 500+ Downloads
    Updated 1 May 2020 | Dataset date: April 23, 2020-April 23, 2020
    This dataset updates: As needed
    INFORM is a multi-stakeholder forum for developing shared, quantitative analysis relevant to humanitarian crises and disasters. INFORM includes organisations from across the multilateral system, including the humanitarian and development sector, donors, and technical partners. The Joint Research Center of European Commission is the scientific and technical lead for INFORM. In response to the COVID-19 pandemic, INFORM has released a COVID Risk Index to support the specific decision-making needs of humanitarian and other organisations. The following is an analysis that addresses key questions for humanitarian organisations by combining information from 3 INFORM products: • The new INFORM COVID Risk Index, which identifies “countries at risk from health and humanitarian impacts of COVID-19 that could overwhelm current national response capacity, and therefore lead to a need for additional international assistance”. The INFORM COVID Risk Index is • The INFORM Risk Index (Mid-2020 version), which identifies “countries at risk from humanitarian emergencies that could overwhelm current national response capacity, and therefore lead to a need for international assistance”. The INFORM Risk Index takes into account natural and human hazards, as well as vulnerability and lack of coping capacity. • The INFORM Severity Index (March 2020 version) is a regularly updated model for measuring the severity of humanitarian crises globally, which brings together indicators of impact, conditions of affected people, and complexity.
  • 1100+ Downloads
    Updated 21 April 2020 | Dataset date: March 31, 2020-March 31, 2020
    This dataset updates: As needed
    The INFORM EPIDEMIC RISK INDEX assesses the risk of countries to epidemic outbreak, which would exceed the national capacity to respond to the crisis.
  • 40+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 2002-December 31, 2012
    This dataset updates: As needed
    This layer contains information about the occurrence of tropical storms above Category 3 - according to the Saffir-Simpson scale and by second-level administrative area - used for the purposes of the Integrated Context Analysis (ICA) run in the Philippines in 2014. Data source: National Oceanic and Atmospheric Administration (NOAA) IBTrACS, 2002-2012. The main indicator used for the analysis was the number of times that a province was hit, in the time frame of interest, by a tropical storm above Category 3. Original dataset title: ICA Philippines, 2014 - Tropical Storm (above Category 3) Occurrence, 2002-2012
  • 60+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 2017-December 31, 2017
    This dataset updates: As needed
    This layer contains information about the combined natural shock (floods, droughts and cyclones) hazard estimated during the Integrated Context Analysis (ICA) run in Mozambique in 2017. Data source: Fewsnet 1975-2012, DRFI 2012, INGC, HQ VAM Analysis of CHIRPS Rainfall Estimates (RFE) 1981-2015, Fewsnet 1930-2000. Original dataset title: ICA Mozambique, 2017 - Natural Shocks Hazard
  • 100+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 1975-December 31, 2012
    This dataset updates: As needed
    This layer contains information about the flood hazard estimated during the Integrated Context Analysis (ICA) run in Mozambique in 2017. Data source: Fewsnet 1975-2012, Disaster Risk Financing and Insurance (DRFI), 2012. The indicators used for the analysis were the percentage of flood extent and a qualitative classification of the district at high or very high flood risk. It should be noted that the analysis did not consider information about the flood frequency and that, in the last 5 years, flood patterns are changing and affecting the northern part of the country, which is not registering extreme flood events anymore. Original dataset title: ICA Mozambique, 2017 - Flood Hazard, 1975-2012
  • 50+ Downloads
    Updated 17 April 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 Niger between 2017 and 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 deuxième niveau – estimé pendant l’Analyse Integrée du Contexte (AIC) executée en Niger entre 2017 et 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 Niger, 2018 - Flood Risk, 2013
  • 40+ Downloads
    Updated 17 April 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 Niger between 2017 and 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 deuxième niveau – estimée pendant l’Analyse Integrée du Contexte (AIC) executée en Niger entre 2017 et 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 Niger, 2018 - Drought Risk, 1981-2015
  • 20+ Downloads
    Updated 17 April 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 Niger between 2017 and 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 Niger entre 2017 et 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 Niger, 2018 - Natural Shock Risk
  • 400+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 2014-December 31, 2014
    This dataset updates: As needed
    This layer contains information about the average flood risk - by second-level administrative area - used for the purposes of the Integrated Context Analysis (ICA) run in the Philippines in 2014. Data source: National Household Targeting System and Mines and Geosciences Bureau. The main indicator used for the analysis was the average flood risk by barangay reaggregated by province level. Original dataset title: ICA Philippines, 2014 - Flood Risk, 2014
  • 100+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 1950-December 31, 2015
    This dataset updates: As needed
    This layer contains information about the flood risk estimated during the Integrated Context Analysis (ICA) performed in Pakistan in 2017. The indicator used was the number of flood events recorded between 1950 and 2015 and the severity by which they were affected by the super-flood in 2010. Source: National Disaster Management Agency (NDMA) for Pakistan. Original dataset title: ICA Pakistan, 2017 - Flood Hazard, 1950-2015
  • 50+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 1951-December 31, 2010
    This dataset updates: As needed
    This layer contains information about the drought risk estimated during the Integrated Context Analysis (ICA) performed in Pakistan in 2017. Data source: National Drought Monitoring Centre of the Pakistan Meteorological Department (PMD), 1951-2010. The key indicator used for the analysis was a drought hazard index based on SPI data. Original dataset title: ICA Pakistan, 2017 - Drought Hazard, 1951-2010
  • 100+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 1930-December 31, 2000
    This dataset updates: As needed
    This layer contains information about the cyclone hazard estimated during the Integrated Context Analysis (ICA) run in Mozambique in 2017. Data source: Fewsnet, 1930-2000. The indicator used for the analysis was the average frequency of cyclone events. Original dataset title: ICA Mozambique, 2017 - Cyclone Hazard, 1930-2000
  • 60+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 1981-December 31, 2015
    This dataset updates: As needed
    This layer contains information about the drought hazard estimated during the Integrated Context Analysis (ICA) run in Mozambique in 2017. Data source: HQ VAM Analysis of CHIRPS Rainfall Estimates (RFE), 1981-2015, National Institute of Disaster Management (INGC). The indicators used for the analysis were the number of poor growing seasons and the drought hazard classification provided by INGC. Original dataset title: ICA Mozambique, 2017 - Drought Hazard, 1981-2015
  • 20+ Downloads
    Updated 17 April 2020 | Dataset date: January 01, 2017-December 31, 2017
    This dataset updates: As needed
    This layer contains information about the reclassification of the natural shocks risk (floods and drought) into a single combined score according to the Integrated Context Analysis (ICA) performed in Pakistan in 2017. Original dataset title: ICA Pakistan, 2017 - Combined Hazard (Floods and Droughts)
  • 200+ Downloads
    Updated 10 March 2020 | Dataset date: May 19, 2017-May 19, 2017
    This dataset updates: Never
    The sub national 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.
  • 200+ Downloads
    Updated 10 March 2020 | Dataset date: May 05, 2017-May 05, 2017
    This dataset updates: Never
    INFORM Guatemala is a municipal risk index that identifies risks, threats, vulnerabilities and response capacities in the 340 municipalities of Guatemala. The municipal risk index simplifies information about crisis risk and is comprised of 29 indicators representing the three dimensions of risk: hazard and exposure, vulnerability, and lack of coping capacity. The results of the Guatemala INFORM index will be used by the National Coordination System for Disaster Risk Reduction (CONRED) for its analysis of risk of humanitarian crisis and disasters, municipal response capacities and potential humanitarian needs. Government institutions, civil society and international cooperation organizations can also use the results to focus the design and implementation of development programs and projects. The Guatemala INFORM initiative is supported by UNICEF, OCHA and WFP.
  • 200+ Downloads
    Updated 10 March 2020 | Dataset date: March 28, 2018-March 28, 2018
    This dataset updates: Never
    The INFOM Honduras tool has been implemented and will be updated by the Permanent Contingency Commission (COPECO). COPECO and other government institutions will jointly implement the INFORM initiative in the context of the National Risk Management System (SINAGER). The national Humanitarian Network, Civil Society Organizations, Academic institutions, and the Association of Municipalities of Honduras (AMHON) will also collaborate with the initiative.