The INFORM subnational model for South East Europe for 3 pilot countries: Albania, Montenegro and North Macedonia, was initiated by UNDRR Regional Office for Europe and Central Asia (ROECA) and the Secretariat of Disaster Preparedness and Prevention Initiative for South East Europe (DPPI SEE) upon prior consent of all DPPI SEE Member States. The INFORM model has been developed under supervision of the EC's JRC INFORM team and financial support from USAID BHA. The INFORM risk index results were produced in close collaboration with national and regional organization/data providers followed by regular meetings of the Regional Working Group (RWG) on INFORM. The RWG members are 2-3 representatives from each Government in the pilot countries (represented by their national civil protection authorities), including the Sendai Monitor National Focal Point, DPPI SEE Secretariat and UNDRR ROECA. 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 INFORM subnational model for South East Europe is developed at the first administrative level (corresponding to the subnational regions, capitals and municipalities) of Albania, Montenegro and North Macedonia. The INFORM risk index was calculated for 44 administrative units. The INFORM index supports a proactive disaster risk management framework. It will be helpful for an objective allocation of resources for disaster risk reduction and management as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies. It also identifies areas for improvement in national disaster data availability and compliance with implementation of Sendai Framework for DRR, SDGs and other global initiatives.

The INFORM subnational model for Caucasus and Central Asia was updated by CESDRR in collaboration with UNDRR and with financial support from USAID BHA in September 2021. The model developed by OCHA and EC’s Joint Research Center for the Regional Inter-Agency Standing Committee (RIASC) Task Force for Caucasus and Central Asia in April 2017. 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 INFORM subnational 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.

Disaster loss and damage data for Colombia at several levels of disaggregation

Disaster loss and damage dataset for Sri Lanka

Disaster loss and damage dataset for Senegal

Disaster loss and damage dataset for Niger

Disaster loss and damage dataset for Madagascar

Disaster loss and damage dataset for Kenya

Disaster loss and damage data for Colombia at several levels of disaggregation

Disaster loss and damage data for Albania at several levels of disaggregation

Disaster loss and damage data for Cambodia at several levels of disaggregation

A comprehensive set of information on global volcanic hazard, historical events, population exposure, vulnerability, and impact has been provided to GAR15 by Global Volcano Model (GVM) and The International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI). This work is the first of its kind in global coverage and level of contribution from a wide network of experts and institutions around the world.

Catalog of Earthquakes 1970-2014, Source: ANSS - USGS The ANSS Comprehensive Catalog (ComCat) contains earthquake source parameters (e.g. hypocenters, magnitudes, phase picks and amplitudes) and other products (e.g. moment tensor solutions, macroseismic information, tectonic summaries, maps) produced by contributing seismic networks.

The Global Exposure Database for GAR: A Global exposure database was developed for GAR15 by UNEP-GRID with close collaboration and inputs from WAPMERR (World Agency of Planetary Monitoring and Earthquake Risk Reduction), EU Joint Research Center (JRC), and Kokusai Kogyo. This database includes estimation on the economic value of the exposed assets, as well as their physical characteristics in urban and rural agglomerations. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools (De Bono et al., 2015).

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.