Creative Commons Attribution 4.0

This dataset is licensed under CC-BY 4.0

CC-BY 4.0

Denpasar (Indonesia) - Flood Risk Map (ESA EO4SD-Urban)

Description: 

Risk is defined as a combination of probability and consequences. A detailed and uniform land-use map is an important prerequisite to perform flood risk calculations, since it determines what is damaged in case of flooding. The land-use map was provided by GAF AG and recoded to pre-defined categories to ensure consistent results.
The exposition is classified integrating economic costs, social damage, physical damage and flood duration. Four land use damage levels (A, B, C, D) are defined based on this estimation.
The Flood Risk matrix is generated based on these results code...

Type: 
Geospatial

Denpasar (Indonesia) - Flood Hazard Map (ESA EO4SD-Urban)

Description: 

For the Denpasar area short-term flooding close to rivers and waterways in the rainy season (November – March) is typical.The present Geodatabase includes two shp-files for rough estimation of potential flooding zones: a. Denpasar_Flood_Hazard_Rivers Waterways are taken from OSM Layer and complemented by hydrologic modelling of potential catchment areas and flow routes based on SRTM and visual interpretation of VHR data. After classification in two classes based on Stream Order the lines were buffered with 50 m and 100 m respectively to roughly estimate potential flooding zones. b....

Type: 
Geospatial

Dakar (Senegal) - Transport Network Maps (ESA EO4SD-Urban)

Description: 

The product over Dakar (Senegal) contains spatial explicit information about transport network and its typology as identified from Open Street Map and updated by interpretation of VHR satellite imagery. The level of detail for the classification scheme mainly relies on the input data sources.

Type: 
Geospatial

Dakar (Senegal) - Land Use / Land Cover (Core City Area and Larger Urban Area)

Description: 

Land Use/Land Cover (LU/LC) information product contains spatial explicit information on different land use and land cover of the City of Dakar (SENEGAL), with aggregated LU/LC nomenclature within Larger Urban Area and detailed LU/LC nomenclature within Core City Area for 2006 and 2018.
The input data for the Larger Urban Area was the High Resolution (HR) data of Landsat-7 (2006) and Sentinel 2B (2018). For the Core City Area, input data was the Very High Resolution (VHR) data of Quickbird (2006) and Pléiades-1A (2018). The LU/LC product is the Baseline Product from which various...

Type: 
Geospatial

Dakar (Senegal) - Green Urban Area Classification (ESA EO4SD-Urban)

Description: 

The Green Urban Area classification 2006 and 2018 for Dakar was produced using Very High Resolution satellite imagery (2006: Quickbird, 2018: Pleiades). All steps for production of the dataset had a related Quality Control measure which is documented in the Annexes of the “EO4SD-Urban Dakar City Report, 2019.” The final thematic accuracy for this product was befilled.

Type: 
Geospatial

Dakar (Senegal) - Flood Risk Map (ESA EO4SD-Urban)

Description: 

Risk is defined as a combination of probability and consequences. A detailed and uniform land-use map is an important prerequisite to perform flood risk calculations, since it determines what is damaged in case of flooding.
Two different datasets regarding the urban landuse were made available:
• LULC product based on VHR data (WorldView-4 acquired on 29/11/2018) covering the core urban area (approx. 89,0 km²)
• LULC product based on HR data (Sentinel 2 acquired on 09/12/2018) covering the urban area (approx. 298,8 km²)
Both land-use classification results were...

Type: 
Geospatial

Dakar (Senegal) - Flood Extent Maps 2009-2018 (ESA EO4SD-Urban)

Description: 

For the urban and peri-urban area of Dakar, two main flood scenarios have to be considered:
a. Fluvial floods
b. Floods triggered by rainfall stagnation after heavy local cloudbursts
Scenario a.) and b.) may occur at the same time.

The shapefile includes 6 extents of floods between 2009 and 2018 based on HR optical imagery and 7 extents of floods based on visual interpretation of VHR data as available in GoogleEarth.

Type: 
Geospatial

Dakar (Senegal) - Flood Hazard Map (ESA EO4SD-Urban)

Description: 

For the urban and peri-urban area of Dakar, two main flood scenarios have to be considered:
a. Fluvial floods
b. Floods triggered by rainfall stagnation after heavy local cloudbursts
Scenario a.) and b.) may occur at the same time.

The flood hazard map was generated based on the occurrence of flood events during the past 10 years.
The flood hazard classification in three qualitative hazard levels was done by summing up the flood occurences and reclassifying according to the following list:

Number of events Flood Hazard Level
1...

Type: 
Geospatial

Abidjan (Ivory Coast) - Transport Network 2005 & 2018 Maps (ESA EO4SD-Urban)

Description: 

The product over Abidjan (Ivory Coast) contains spatial explicit information about transport network and its typology as identified from Open Street Map and updated by interpretation of VHR satellite imagery. The level of detail for the classification scheme mainly relies on the input data sources.

Type: 
Geospatial

Abidjan (Ivory Coast) - Urban Land Use / Land Cover (LULC) 2005 & 2018 (Larger Urban Area and Core City Area)

Description: 

Land Use / Land Cover (LULC) information product over Abidjan (Ivory Coast) contains spatial explicit information on different land use and land cover occurring in the Larger Urban Area and Core City Area of the City of Abidjan for past (2005) and current (2018) dates. The Larger Urban Area LU/LC nomenclature is at an aggregated Level 1 or 2. The input data for the Larger Urban Area was Ikonos (2005) and Sentinel-2 (2019). The Core City Area has detailed LU/LC nomenclature that is either at Level 3 or 4. The input data for the Core City Area was the Very High Resolution (VHR) data of...

Type: 
Geospatial

Zambia - Multi-Tier Framework (MTF) Survey

Description: 

The MTF survey is a global baseline survey on household access to electricity and clean cooking, which goes beyond the binary approach to look at access as a spectrum of service levels experienced by households. Resources included are raw data, codebook, questionnaires, sampling strategy document, and country diagnostic report. Formats include zip file (which includes raw data sets of dta format), excel spreadsheet, pdf, and docx.

Type: 
Other

São Tomé and Príncipe - Multi-Tier Framework (MTF) Survey

Description: 

The MTF survey is a global baseline survey on household access to electricity and clean cooking, which goes beyond the binary approach to look at access as a spectrum of service levels experienced by households. Resources included are raw data, codebook, questionnaires, sampling strategy document, and country diagnostic report. Formats include zip file (which includes raw data sets of dta format), excel spreadsheet, pdf, and docx.

Type: 
Other

Ghana - Road and building predictions

Description: 

Machine learning was applied to a satellite basemap of Ghana (purchased from Maxar, née DigitalGlobe) to map building footprints and roads. Building prediction was performed by Keith Garrett and Benjamin Stewart of GOST, and the road prediction was done by a firm - Development Seed. Predictions have been made as raster datasets, and no attempt at vectorization has been attempted (yet).

Type: 
Geospatial

Enabling the Business of Agriculture

Description: 

The World Bank's Enabling the Business of Agriculture project assesses laws and regulations in agriculture in 101 countries. The data set identifies actionable reforms to remove obstacles for farmers seeking to grow their business. The eight core indicators are: supplying seed, registering fertilizer, securing water, registering machinery, sustaining livestock, protecting plant health, trading food and accessing finance.

Type: 
Time Series

Afghanistan earthquake hazard

Description: 

Earthquakes represent a serious threat to the people and institutions of Afghanistan. As part of a United States Agency for International Development (USAID) effort to assess the resource potential and seismic hazards of Afghanistan, the Seismic Hazard Mapping group of the United States Geological Survey (USGS) has prepared a series of probabilistic seismic hazard maps that help quantify the expected frequency and strength of ground shaking nationwide. To construct the maps, we do a complete hazard analysis for each of ~35,000 sites in the study area. We use a probabilistic methodology...

Type: 
Geospatial

Solomon Islands earthquake hazard

Description: 

An earthquake hazard map provides, at any location, the value of a ground motion intensity measure (for example, horizontal peak ground acceleration, PGA) that is expected to be exceeded at least once in 100 year mean return period. The earthquake hazard maps are developed by determining the simulated ground motion intensities at every gridded location for 10,000 realizations of next-year activity of earthquake events. At each grid location, the intensities are ranked and the ground motion intensity of the mean return period of interest is recorded. The size of the finest grid is 9 arc...

Type: 
Geospatial

Afghanistan river flood hazard

Description: 

The geographical location of Afghanistan and years of environmental degradation in the
country make Afghanistan highly prone to intense and recurring natural hazards such as
flooding, earthquakes, snow avalanches, landslides, and droughts. These occur in addition to
man-made disasters resulting in the frequent loss of live, livelihoods, and property. The
creation, understanding and accessibility of hazard, exposure, vulnerability and risk
information is key for effective management of disaster risk. Assuring the resilience of new
reconstruction efforts...

Type: 
Geospatial

South Asia coastal flood hazard

Description: 

This dataset has been clipped from the GAR15 storm surge model by GFDRR for inclusion into ThinkHazard! The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the...

Type: 
Geospatial

Pacific coastal flood hazard

Description: 

This dataset has been clipped from the GAR15 storm surge model by GFDRR for inclusion into ThinkHazard! The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the...

Type: 
Geospatial

Caribbean coastal flood hazard

Description: 

This dataset has been clipped from the GAR15 storm surge model by GFDRR for inclusion into ThinkHazard! The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the...

Type: 
Geospatial

East Asia coastal flood hazard

Description: 

This dataset has been clipped from the GAR15 storm surge model by GFDRR for inclusion into ThinkHazard! The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the...

Type: 
Geospatial

East Africa coastal flood hazard

Description: 

This dataset has been clipped from the GAR15 storm surge model by GFDRR for inclusion into ThinkHazard! The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the...

Type: 
Geospatial

Global earthquake hazard

Description: 

Earthquake hazard For the GAR15, a fully probabilistic seismic hazard assessment at global level was developed by International Centre for Numerical Methods in Engineering (CIMNE) and INGENIAR Ltda. The purpose was to conduct a probabilistic hazard assessment at global level intended to be used in the probabilistic risk assessment estimating the order of magnitude of potential losses. From GAR13 version, there has been some changes, updates and improvements in input data, but the same methodology has been used which detail information can be found in GAR13 technical background paper (CIMNE...

Type: 
Geospatial

Global cyclone hazard

Description: 

Tropical Cyclonic Wind and Storm Surge hazard The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the repository of information associated with tropical cyclones that...

Type: 
Geospatial

Global extreme heat hazard

Description: 

Extreme Heat hazard is classified based on an existing and widely accepted heat stress indicator, the Wet Bulb Globe Temperature (WBGT, in °C) – more specifically the daily maximum WGBT. The WBGT has an obvious relevance for human health, but it is relevant in all kinds of projects and sectors, including infrastructure related, as heat stress affects personnel and stakeholders, and therefore the design of buildings and infrastructure.
Heat stress studies in the scientific literature that make use of the WBGT apply thresholds of 28°C and 32°C to categorise heat stress risk. The...

Type: 
Geospatial