This research, designed by the World Bank, and supported by the Department for International Development (DFID), aims to highlight the unprecedented transformation of the urban systems in the ECA region in the last decades, and to look at this shifts from the demographic, economic, and spatial prospectives. Cities in ECA database comprises data from 5,549 cities in 15 countries of the Eastern Europe and Central Asia region, as defined by the World Bank Group, and from the United Kingdom and Germany. Database information for each city is in three dimensions: demographic, spatial, and economic. The starting point to construct the Cities in ECA database was to obtain from each of the countries the list of official cities and these cities' population data. Population data collected for cities falls on or around three years: 1989, 1999, and 2010 (or the latest year available). The official list of "cities" was geo-referenced and overlaid with globally-available spatial data to produce city-level indicators capturing spatial characteristics (e.g., urban footprint) and proxies for economic activity. City-level spatial characteristics, including urban footprints (or extents) for the years 1996, 2000, and 2010 and their temporal evolution, were obtained from the Global Nighttime Lights (NTL) dataset. City-level proxies for economic activity were also estimated based on the NTL dataset. Nighttime Lights (NLS) data is produced by the Defense Meteorological Satellite Program (DMSP) Optical Line Scanner (OLS) database and maintained by the National Oceanic and Atmospheric Administration (NOAA).
Cities in Europe and Central Asia : A Shifting Story of Urban Growth and Decline Database
Detailed Managing Decline Data Dictionary
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 repository of information associated with tropical cyclones that is the most up to date. Topography was taken from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world. To account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). This was considered a good proxy of the spatial variation of surface roughness. A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009). Bathymetry is the information about the underwater floor of the ocean having direct influence on the formation of the storm surge. More information about the cyclone wind and strom surge hazard can be found in CIMNE et al., 2015a. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014). The vulnerability models used in the risk calculation for GAR correlate loss to the wind speed for 3-seconds gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.
East Africa coastal flood hazard 10 Year Return Period (GFDRR Geonode page)
East Africa coastal flood hazard 50 Year Return Period (GFDRR Geonode page)
East Africa coastal flood hazard 250 Year Return Period (GFDRR Geonode page)
East Africa coastal flood hazard 10 Year Return Period
East Africa coastal flood hazard 25 Year Return Period (GFDRR Geonode page)
East Africa coastal flood hazard 100 Year Return Period
East Africa coastal flood hazard 25 Year Return Period
East Africa coastal flood hazard 250 Year Return Period
East Africa coastal flood hazard 50 Year Return Period
East Africa coastal flood hazard 100 Year Return Period (GFDRR Geonode page)
Land Use / Land Cover (LULC) information product over Karachi (Pakistan) contains spatial explicit information about the different land covers / uses for current (2017) and past (2005) dates. The level of detail for the classification scheme mainly relies on the input data sources. LULC dataset provides detailed information (level-3) over core urban areas covered by very high resolution satellite imagery, and level-1 information over peri-urban areas covered by lower resolution satellite imagery.
Karachi (Pakistan) - Land Use/Land Cover (ESA EO4SD-Urban)
Land Use/Land Cover 2005 Map for Karachi (core urban areas) (ESA EO4SD-Urban)
Karachi (Pakistan) - Land Use/Land Cover (ESA EO4SD-Urban)(Feature Service)
Land Use/Land Cover 2017 Map for Karachi (core urban areas) (ESA EO4SD-Urban)
Land Use/Land Cover 2005 Map for Karachi (peri-urban areas) (ESA EO4SD-Urban)
Land Use/Land Cover 2017 Map for Karachi (peri-urban areas) (ESA EO4SD-Urban)
Link to Karachi Land Use / Land Cover datasets in the Urban Thematic Exploitation Platform (UTEP).
Urban Thematic Exploitation Platform (UTEP) Link - Karachi
National Daily Peak Demand for Electricity in Ghana for years 2009 and 2010. Citation: Ghana Grid Company (GRIDCo) & Negawatt challenge. A curated list of datasets for the World Bank Negawatt Challenge competition in Accra and Nairobi cities: https://datahub.io/organization/negawatt-challenge
Ghana - National Daily Peak Demand for Electricity
This shapefile contains industry zones in Ivory Coast.
Industry Zones Ivory Coast(Feature Service)
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Quickbird-2 (2007 / 2018)) by means of manual classification processing techniques.
Tanga (Tanzania) - Transport Infrastructure (ESA EO4SD-Urban)
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Quickbird-2 (2007)) by means of manual classification processing techniques.
Transport Infrastructure in 2007 for Tanga (ESA EO4SD-Urban)
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Worldview-2: 2018) by means of manual classification processing techniques.
Transport Infrastructure in 2018 for Tanga (ESA EO4SD-Urban)
Link to Tanga transport infrastructure datasets in the Urban Thematic Exploitation Platform (UTEP).
Urban Thematic Exploitation Platform (UTEP) Link - Tanga
Tanga (Tanzania) - Transport Infrastructure (ESA EO4SD-Urban)(Feature Service)
Survey data from Mexico, 1995, which provides detailed information on environmental performance and determinants, including plant, firm and market characteristics, pollution control costs, relationships with regulators, and measures to improve environmental performance. All plant and respondent identities were kept confidential. The data and survey document are all contained in the zip file. As preparation for their research paper on "What Improves Environmental Performance? Evidence from Mexican Industry," Susmita Dasgupta, Mala Hettige and David Wheeler used survey evidence to analyze the effects of regulation, plant-level management policies, and plant and firm characteristics on environmental performance in Mexican factories. They focused especially on management policies: the degree of effort to improve environmental performance and the type of management strategy adopted. The survey, conducted in the fall of 1995, focused on the food, chemicals, non-metallic minerals and metals sectors, which account for 75 percent to 95 percent of Mexico’s total industrial pollution. 236 facilities in the survey were chosen as representative of Mexican factories as defined by sector, size and location. The survey was designed by a World Bank team, conducted by the Monterrey Institute of Technology, and supported by the Mexico National Environment Ministry (SEMARNAP) and the Mexican National Association of Industries. The survey provides detailed information on performance and determinants, including plant, firm and market characteristics, pollution control costs, relationships with regulators, and measures to improve environmental performance. All plant and respondent identities were kept confidential. It is important to note the information derived from the survey was self-reported. No independently audited data on pollution and regulatory compliance was associated with the survey. Finally, with confidentiality assured, 49 percent of survey respondents admitted non-compliance with regulations. With no benchmarks, only educated guesses can be made about the degree of upward bias. Evidence from Indonesia, a country with similar regulations but weaker monitoring and enforcement, revealed a non-compliance rate of 64 percent. This suggests that the degree of upward bias in the Mexican self-assessment may be small. In any case, our analysis for this research focuses on relative, not absolute, performance.
What Improves Environmental Performance? Evidence from Mexican Industry
Contains data files in Lotus worksheets, along with the questionnaire in Spanish
Report: What improves environmental performance? evidence from Mexican industry (English)
This report, updated monthly, provides data on natural gas production, consumption, total imports and total exports for all OECD Member countries, and natural gas pipeline and LNG trade by origin and destination for all OECD regions.
Monthly natural gas statistics for OECD countries
The following surveys were carried out: • GIS mapping of bus routes, carried out by survey staff travelling bus routes with GPS enabled phones, using GPS Essantials The objective of these surveys was to develop a comprehensive picture of the urban transport sector, including, on the supply side, the financing and provision of vehicles and the incomes in the sector, and on the demand side, the needs of passengers and identification of the level of the number of passengers on different routes. The first phase of data gathering was in May 2019. This was entirely within Ramadan, which naturally throws off ordinary travel patterns. During this phase, only data on route geography was gathered, not on frequencies or capacities. Passenger responses may also have been skewed compared to a typical month. A second set of data was gathered in October (a more typical period), and it is on this phase of data gathering that the frequencies and capacities of routes are based on. GIS data on routes was processed and analysed using ArcMap. Data was gathered as location ‘pings’, which were processed into lines. These were in turn compiled into routes, based on matching the lines to the street network on Open Street Map. Some judgment and debate among the survey staff was used to resolve what constitutes a ‘route’. Some routes are circular, while others make a loop at the end. Some return trips are also carried out in a single beat by the vehicle, with no halt at the end. Passengers may or may not be expected to disembark, and/or to pay for another trip, depending on where they boarded. Different routes also have some variability in the streets they take, and others fully overlap longer routes. There was an attempt to capture all routes in the city, but given the level of manpower and time frame, this may have only been partially achieved. The mutability of routes and their proliferation mean that, while we believe we captured all major destinations, there may well be precise routes that are not captured. This is particularly the case in Balbala, where routes are served by rickshaws and 8-seat Bajaj vehicles, which have a great deal of flexibility and can be difficult to separate from taxi routes.
This dataset contains the location (point) of education facilities in Somaliland. It was provided by GoSL and was combined with open-source data available in UNICEF’s database.
This dataset,used for the SESRP project, contains the information on health facilities lacking adequate access to electricity in Somaliland
Somaliland - Health Facilities Lacking Adequate Access To Electricity
This shapefile contains airports in Ivory Coast.
The dataset developed by WHO contains the location(point) of health facilities in Somalia.
This dataset contains solar rooftop potential data (suitable rooftop area, installable capacity, estimated yearly electricity generation, and building type ) at individual building structure level for a sample area of interest in SintMaarten Island. The data was gathered by extracting building rooftop footprint polygons from very high-resolution satellite stereo imagery of 0.5m resolution. The rooftop angle, obstruction, and shading were taken into account during suitable area calculation. The results can be aggregated by different sectors or administrative units for further analysis, which is useful for planning and decision making. Detailed methodology is available upon specific request.
Honduras - Rooftop Solar Potential Mapping
Yi Xiong, Laura Wendell Berman, Daron Bedrosyan
This vector dataset contains building footprint polygons for the area of study in Tegucigalpa. The metrics such as suitable area, installable capacity, estimated yearly potential, building height and roof tilt etc. are derived at individual building unit level. Another useful information attached is the building type.
The dataset with attributes is provided also in csv format. The available attribute information are : total surface area(m2), suitable area(m2), peak_installable_capacity(kw), energy potential(Kwh), estimated building height (m), estimated roof angle (degree) and assumed building type. This dataset covers the San Pedro Sula.
The excel contains a brief description about each attribute of the geodatabase layer.
This vector dataset contains building footprint polygons for the area of study in San Pedro Sula. The metrics such as suitable area, installable capacity, estimated yearly potential, building height and roof tilt etc. are derived at individual building unit level. Another useful information attached is the building type.
The dataset with attributes is provided also in csv format. The available attribute information are : total surface area(m2), suitable area(m2), peak_installable_capacity kw), energy potential(Kwh), estimated building height (m), estimated roof angle (degree) and assumed building type. This dataset covers the dense city area of Tegucigalpa
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 is the most up to date. Topography was taken from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world. To account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). This was considered a good proxy of the spatial variation of surface roughness. A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009). Bathymetry is the information about the underwater floor of the ocean having direct influence on the formation of the storm surge. More information about the cyclone wind and strom surge hazard can be found in CIMNE et al., 2015a. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014). The vulnerability models used in the risk calculation for GAR correlate loss to the wind speed for 3-seconds gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.
Global cyclone hazard 100 Year Return Period
Global cyclone hazard 250 Year Return Period (GFDRR Geonode page)
Global cyclone hazard 500 Year Return Period (GFDRR Geonode page)
Global cyclone hazard 1000 Year Return Period
Global cyclone hazard 250 Year Return Period
Global cyclone hazard 100 Year Return Period (GFDRR Geonode page)
Global cyclone hazard 50 Year Return Period
Global cyclone hazard 1000 Year Return Period (GFDRR Geonode page)
Global cyclone hazard 500 Year Return Period
Global cyclone hazard 50 Year Return Period (GFDRR Geonode page)
This shapefile contains river basins in Ethiopia.
This product provides information on the actual evapotranspiration (ET) on a coarse scale for the entire African Horn on the daily scale. The estimates are based on data from the Meteosat Second Generation SEVIRI sensor as well as some ancillary meteorological data. The ET estimates are produced by the Land Surface Analysis – Satellite Application Facility (LSA SAF) hosted at the Portuguese Met-service.
Kenya - Evapotranspiration - African Horn
Access UN COMTRADE including exports, re-exports and re-imports in Harmonized System (HS) and Standard Industrial Trade Classification (SITC). Convert into derived nomenclatures like ISIC, SIC, MTN, NACE, GTAP, BEC and more. Aggregate Trade Indicator to analyze a country's export competitiveness. Perform GVC analysis for country, region, intra-region and product of interest.
World Integrated Trade Solution UN COMTRADE
Help documents on fundamentals of trade data and details on how to use the query tool.
The dataset provides existing and planned road infrastructure in Afghanistan. Data Source: The World Bank Group. Open Street Maps have been used as a complementary source. Latest update: 2012 Comment: Many of the roads that are characterized as under construction, or planned, may now be completed.
Primary and secondary roads in Afghanistan
This dataset collected by SARA survey and GoSL contains the location (point) of health facilities in Somaliland.
The database is subject to the IEA’s Terms and Conditions, available at https://www.iea.org/terms. Monthly gas flow data by entry and exit point for 31 participating countries, principally covering the European natural gas network. The Gas Trade Flows (GTF) data service is intended to improve transparency in natural gas markets. This free service is based on an IEA data collection system that principally covers the European natural gas network, including pipeline and LNG physical flows by entry point.
This shapefile contains the rivers in Colombia.
The dataset contains the location(point) of hydro potential in Somalia
This raster file represents land cover classification in San Miguel based off of satellite imagery from November 30, 2014. This land cover classification was used in the following report: Mapping Land Cover in Secondary Cities in Central America. This work was initiated as an analytical effort to fill a gap on spatial form of secondary cities. While this is an independent output, this work is tightly linked to the work done under the Central America Urbanization Review. The analysis here described, was used as an input in the definition of urban agglomerations used in the Urbanization Review. The detailed analysis on secondary cities is seen as a complement to the work carried out in the Urbanization Review, in that it zooms into what is happening within a set of cities. The Urbanization Review instead provides a broader look at the system of cities in Central America, highlighting the key bottlenecks the regions faces in moving toward more inclusive, productive, and resilient cities. The Urbanization Review can be found here: http://documents.worldbank.org/curated/en/134151467994680764/pdf/106268-...
Mapping Land Cover in Secondary Cities in Central America: 2014 San Miguel, El Salvador Land Cover Classification
This data was collected during the project: Preparation of a high-level least-cost geospatial analysis for grid and off-grid electrification options Synoptic analysis of low-cost electrification solutions in Congo
Congo Brazzaville - HV Electricity Network (2019)
Health facilities in Kenya were acquired from a local consultant through the KEMRI Wellcome Trust Research Programme. These facilities were used to assess hospital preparedness based on available infrastructure data (GSM coverage, electrification, access (drive time)) and demographics (population and vulnerability to CoVID). Additionally summary statistics were generated at the ward level.
Kenya - Infrastructure assessment for health facilities
Access to health care facilities was summarized at the ward level, mostly though the intersection with population.
Github repo for the analysis that provides detailed data descriptions
The jupyter notebook used to generate the analysis may be difficult to implement without data access, but is a reasonable starting point
Field definitions for ward and hospital shapefiles.
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 1 (low) 2-3 2 (medium) =4 3 (high) The estimation of the area threatened by tidal waves and coast erosion is based on available reports and press releases as well as on the extrapolation of the mapped difference (done by visual interpretation) of the coastline between 2003 and 2019
Dakar (Senegal) - Flood Hazard Map (ESA EO4SD-Urban)
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 1 (low) 2-3 2 (medium) 4 and above 3 (high) The estimation of the area threatened by tidal waves and coast erosion is based on available reports and press releases as well as on the extrapolation of the mapped difference (done by visual interpretation) of the coastline between 2003 and 2019
Flood Hazard Map for Dakar (ESA EO4SD-Urban)
Link to Dakar flood hazard datasets in the Urban Thematic Exploitation Platform (UTEP).
Urban Thematic Exploitation Platform (UTEP) Link - Dakar
Dakar (Senegal) - Flood Hazard Map (ESA EO4SD-Urban)(Feature Service)
This raster file represents a land cover classification in San Pedro Sula based off satellite imagery from March 10, 2013. This land cover classification was used in the following report: Mapping Land Cover in Secondary Cities in Central America. This work was initiated as an analytical effort to fill a gap on spatial form of secondary cities. While this is an independent output, this work is tightly linked to the work done under the Central America Urbanization Review. The analysis here described, was used as an input in the definition of urban agglomerations used in the Urbanization Review. The detailed analysis on secondary cities is seen as a complement to the work carried out in the Urbanization Review, in that it zooms into what is happening within a set of cities. The Urbanization Review instead provides a broader look at the system of cities in Central America, highlighting the key bottlenecks the regions faces in moving toward more inclusive, productive, and resilient cities. The Urbanization Review can be found here: http://documents.worldbank.org/curated/en/134151467994680764/pdf/106268-...
Mapping Land Cover in Secondary Cities in Central America: 2013 San Pedro Sula, Honduras Land Cover Classification
Land Use / Land Cover (LULC) information product over Phnom Penh (Cambodia) contains spatial explicit information about the different land covers / uses for current (2017) and past (2003) dates. The level of detail for the classification scheme mainly relies on the input data sources. LULC dataset provides detailed information (level-3) over core urban areas covered by very high resolution satellite imagery, and level-1 information over peri-urban areas covered by lower resolution satellite imagery.
Phnom Penh (Cambodia) - Land Use/Land Cover Maps (ESA EO4SD-Urban)
Phnom Penh (Cambodia) - Land Use/Land Cover Maps (ESA EO4SD-Urban)(Feature Service)
Phnom Penh (Cambodia)_Land Use_Land Cover Maps (ESA EO4SD Urban) (Feature Service)
Land Use/Land Cover 2017 Very High Resolution Map for Phnom Penh (ESA EO4SD-Urban)
Land Use/Land Cover 2003 Very High Resolution Map for Phnom Penh (ESA EO4SD-Urban)
Land Use/Land Cover 2017 Map for Phnom Penh (ESA EO4SD-Urban)
EO4SD-Urban Phnom Penh Operations Report
Land Use/Land Cover 2003 Map for Phnom Penh (ESA EO4SD-Urban)
Link to LULC datasets of Phnom Penh on Urban Thematic Exploitation Platform (UTEP)
Urban Thematic Exploitation Platform (UTEP) Link - Phnom Penh
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.
Dakar (Senegal) - Flood Extent Maps 2009-2018 (ESA EO4SD-Urban)
Dakar (Senegal) - Flood Extent Maps 2009-2018 (ESA EO4SD-Urban)(Feature Service)
Link to Dakar flood extent datasets in the Urban Thematic Exploitation Platform (UTEP).
Flood Extent Map for Dakar 2009 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2018 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2010 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2011 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2014 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2016 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2017 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2013 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2012 (ESA EO4SD-Urban)
Flood Extent Map for Dakar 2015 (ESA EO4SD-Urban)
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains global irradiation for optimally tilted surfaces (GTI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: GTI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 300.57 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - Global Irradiation for Optimally Tilted Surfaces (GTI) GIS Data, (Global Solar Atlas)
GTI LTAy_AvgDailyTotals (GeoTIFF) Data format: raster (gridded), GEOTIFF File size : 300.57 MB
WORLD - GTI- Global Irradiation for Optimally Tilted Surfaces (Global Solar Atlas)
This layer contain existing distribution transformers data collected from field survey for the purpose of using in Liberia NEA project analysis and mapping.
Liberia - Electric Distribution Transformers
The dataset contains location of small scale hydro-potential points in Somaliland.
Somaliland - Small Scale Hydrological Power
The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis. Regional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes. This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).
Madagascar cyclone hazard 100 Year Return Period (GFDRR Geonode page)
Madagascar cyclone hazard 50 Year Return Period (GFDRR Geonode page)
Madagascar cyclone hazard 500 Year Return Period
Madagascar cyclone hazard 1000 Year Return Period
Madagascar cyclone hazard 250 Year Return Period (GFDRR Geonode page)
Madagascar cyclone hazard 50 Year Return Period
Madagascar cyclone hazard 500 Year Return Period (GFDRR Geonode page)
Madagascar cyclone hazard 250 Year Return Period
Madagascar cyclone hazard 25 Year Return Period
Madagascar cyclone hazard 25 Year Return Period (GFDRR Geonode page)
Madagascar cyclone hazard 100 Year Return Period
Madagascar cyclone hazard 1000 Year Return Period (GFDRR Geonode page)
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains global horizontal irradiation (GHI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: GHI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 268.11 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - Global Horizontal Irradiation (GHI) GIS Data, (Global Solar Atlas)
GHI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 268.11 MB
WORLD - GHI - Diffuse horizontal irradiation (Global Solar Atlas)
Congo Brazzaville - Biomass Potential (2019)
Land Use / Land Cover (LULC) information product over Dhaka (Bangladesh) contains spatial explicit information about the different land covers / uses for current (2017) and past (2006) dates. The level of detail for the classification scheme mainly relies on the input data sources. LULC dataset provides detailed information (level-3) over core urban areas covered by very high resolution satellite imagery, and level-1 information over peri-urban areas covered by lower resolution satellite imagery.
Dhaka (Bangladesh) - Land Use / Land Cover Maps (ESA EO4SD-Urban)
Dhaka (Bangladesh) - Land Use / Land Cover Maps (ESA EO4SD-Urban)(Feature Service)
Link to Dhaka Land Use / Land Cover datasets in the Urban Thematic Exploitation Platform (UTEP).
Urban Thematic Exploitation Platform (UTEP) Link - Dhaka
Land Use/Land Cover 2017 Map for Dhaka (VHR) (ESA EO4SD-Urban)
Land Use/Land Cover 2006 Map for Dhaka (VHR) (ESA EO4SD-Urban)
Land Use/Land Cover 2017 Map for Dhaka (ESA EO4SD-Urban)
Land Use/Land Cover 2006 Map for Dhaka (ESA EO4SD-Urban)
The data include results of the electrification modeling for the five Nigerian Federal States Cross River, Niger, Ogun, Plateau and Sokoto. Non-electrified village clusters were identified and a load was estimated. Small clusters with a low demand (demand <50kW) were assigend stand-alone solutions, while for the cluster with a larger demand techno-economically optimized hybrid mini-grids were contrasted to grid extension. Grid extension considers topography and existing infrastructure such as roads to identify a realistic grid extension pathway. Results included in the data set show identified clusters and electrification option created for a three-phased rural electrification approach towards 100% electrification and the respective grid development. This work partly results from the authors’ collaboration with the Nigerian Energy Support Programme (NESP) implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH in collaboration with Federal Ministry of Power, Nigeria and funded by the German Government and the European Union. The authors gratefully acknowledge the support from GIZ-NESP and Nigerian FMP. Any conclusions or recommendations expressed in this article do not necessarily reflect the views of GIZ-NESP or FMPWH.
Nigeria - Rural Electrification Modelling for five Nigerian Federal States
Dataset includes information on grid-connected cluster, un-connected cluster with a demand larger than 50 kW and small clusters. Each datasat contains information on population size, electricity demand, adminstrative area, number of schools and health facilties.
Dataset includes information on grid-connected cluster, un-connected cluster with a demand larger than 50 kW and small clusters. Each datasat contains information on population size, electricity demand, adminstrative area, numberDataset includes information on grid-connected cluster, un-connected cluster with a demand larger than 50 kW and small clusters.
This dataset contains the spatial explicit grid extension pathways considering landcover (water bodies and forest), slope, protected areas and roads in addition to the existing grid. This work partly results from the authors’ collaboration with the Nigerian Energy Support Programme (NESP) implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH in collaboration with Federal Ministry of Power, Nigeria and funded by the German Government and the European Union. The authors gratefully acknowledge the support from GIZ-NESP and Nigerian FMP. Any conclusions or recommendations expressed in this article do not necessarily reflect the views of GIZ-NESP or FMPWH.
Comoros cyclone hazard 100 Year Return Period (GFDRR Geonode page)
Comoros cyclone hazard 50 Year Return Period (GFDRR Geonode page)
Comoros cyclone hazard 250 Year Return Period
Comoros cyclone hazard 250 Year Return Period (GFDRR Geonode page)
Comoros cyclone hazard 25 Year Return Period (GFDRR Geonode page)
Comoros cyclone hazard 500 Year Return Period (GFDRR Geonode page)
Comoros cyclone hazard 50 Year Return Period
Comoros cyclone hazard 25 Year Return Period
Comoros cyclone hazard 1000 Year Return Period (GFDRR Geonode page)
Comoros cyclone hazard 100 Year Return Period
Comoros cyclone hazard 1000 Year Return Period
Comoros cyclone hazard 500 Year Return Period
This database contains parameters gathered after an assessment of the technological potential for development of floating solar photovoltaic (PV) projects on existing hydropower dams and other reservoirs, starting with a pilot in FY18 focused on West Africa and India gathered from World Bank hydro projects. This database has been expanded to include the International Hydropower Association (IHA) dataset for India: https://energydata.info/dataset/india-hydropower-databaseIt also includes data gathered from the FAO's public Aquastat database for Africa: http://www.fao.org/aquastat/en/databases/damsThis dataset also contains data gathered from the public Global Reservoir and Dam Database (GRanD) for Africa.The database will be included in the Global Solar Atlas (http://globalsolaratlas.info/) where they will be directly linked to the solar resource through the Floating Solar module. The purpose of the resulting Floating Solar Atlas is to enable potential developers or stakeholders to quickly navigate to the site of interest and obtain information about the potential for development of a floating solar PV project. Following the FY19 updates, the database will continue to expand to other regions until a global dataset has been completed.The current coverage spans the continent of Africa and India only, with upcoming updates to include other continents.
Contains descriptions of the listed fields and corresponding units in csv format.
This csv database contains hydropower dam locations (lat/long) as well as physical, electrical, and administrative information to help stakeholders identify opportunities for future hydro-connected solar installations.
Probable informal settlements over Karachi (Pakistan) contains spatial explicit information about position of slums as identified in 2005 and 2010 from ancillary data sources and in 2017 by interpretation of VHR satellite imagery. The level of detail for the classification scheme mainly relies on the input data sources.
Karachi (Pakistan) - Informal Settlements (ESA EO4SD-Urban)
Informal Settlements 2005 Map for Karachi (ESA EO4SD-Urban)
Link to Karachi Informal Settlements datasets in the Urban Thematic Exploitation Platform (UTEP).
Informal Settlements 2017 Map for Karachi (ESA EO4SD-Urban)
Karachi (Pakistan) - Informal Settlements (ESA EO4SD-Urban)(Feature Service)
Shapefile of national roads (Class A and Class B), digitized from Sri Lanka's National Road Master Plan. The road shapefile is topologically correct, i.e. ready to be used for a road network. Class A Roads: Inter-provincial trunk roads connecting major cities and ports Class B Roads: Intra-provincial arterial roads connecting major urban areas Sri Lanka's National Road Master Plan can be found online here: http://www.rda.gov.lk/supported/noticeboard/publications/nrmp_executiveS...
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 Ikonos (2005) and WorldView-2 (2018/2019)..
Abidjan (Ivory Coast) - Urban Land Use / Land Cover (LULC) 2005 & 2018 (Larger Urban Area and Core City Area)
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 of the City of Abidjan for current (2018) date. The Larger Urban Area LU/LC nomenclature is at an aggregated Level 1 or 2. The input data for the Larger Urban Area was Sentinel-2 (2019).
Abidjan (Ivory Coast) - Urban Land Use / Land Cover (LULC) 2018 (Larger Urban Area)
Link to Abidjan Land Use / Land Cover dataset in the Urban Thematic Exploitation Platform (UTEP).
Urban Thematic Exploitation Platform (UTEP) Link - Abidjan
EO4SD-Urban Project: Abidjan City Report
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 Core City Area of the City of Abidjan for past (2005) date. 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 Ikonos (2005).
Abidjan (Ivory Coast) - Urban Land Use / Land Cover (LULC) 2005 (Core City Area)
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 Core City Areas of the City of Abidjan for current (2018) date. The Core City Area has detailed LU/LC nomenclature that is either at Level 3 or 4. The input data for the Core City Aarea was the Very High Resolution (VHR) data of WorldView-2 (2018/2019).
Abidjan (Ivory Coast) - Urban Land Use / Land Cover (LULC) 2018 (Core City Area)
Abidjan (Ivory Coast) - Urban Land Use / Land Cover (LULC) 2005 & 2018 (Larger Urban Area and Core City Area)(Feature Service)
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 of the City of Abidjan for past (2005) date. 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). The LU/LC 2005 map of Abidjan was produced using High Resolution satellite data (2005: Ikonos) for the Larger Urban Area as input data. All steps for production of the dataset had a related Quality Control measure which is documented in the Annexes of the “EO4SD-Urban Abidjan City Report, 2019.”
Abidjan (Ivory Coast) - Urban Land Use / Land Cover (LULC) 2005 (Larger Urban Area)
"The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis. Regional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes. For Coastal Flood, rasters for each return period were interpolated from a 1km grid of points. This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR)."
United Republic of Tanzania coastal flood hazard
United Republic of Tanzania coastal flood hazard 50 Year Return Period
United Republic of Tanzania coastal flood hazard 250 Year Return Period
United Republic of Tanzania coastal flood hazard 500 Year Return Period
United Republic of Tanzania coastal flood hazard 25 Year Return Period
United Republic of Tanzania coastal flood hazard 25 Year Return Period (GFDRR Geonode page)
United Republic of Tanzania coastal flood hazard 100 Year Return Period (GFDRR Geonode page)
United Republic of Tanzania coastal flood hazard 100 Year Return Period
United Republic of Tanzania coastal flood hazard 250 Year Return Period (GFDRR Geonode page)
United Republic of Tanzania coastal flood hazard 50 Year Return Period (GFDRR Geonode page)
United Republic of Tanzania coastal flood hazard 1000 Year Return Period (GFDRR Geonode page)
United Republic of Tanzania coastal flood hazard 1000 Year Return Period
United Republic of Tanzania coastal flood hazard 500 Year Return Period (GFDRR Geonode page)
This raster file represents a land cover classification in San Isidro based off satellite imagery from December 23, 2014. This land cover classification was used in the following report: Mapping Land Cover in Secondary Cities in Central America. This work was initiated as an analytical effort to fill a gap on spatial form of secondary cities. While this is an independent output, this work is tightly linked to the work done under the Central America Urbanization Review. The analysis here described, was used as an input in the definition of urban agglomerations used in the Urbanization Review. The detailed analysis on secondary cities is seen as a complement to the work carried out in the Urbanization Review, in that it zooms into what is happening within a set of cities. The Urbanization Review instead provides a broader look at the system of cities in Central America, highlighting the key bottlenecks the regions faces in moving toward more inclusive, productive, and resilient cities. The Urbanization Review can be found here: http://documents.worldbank.org/curated/en/134151467994680764/pdf/106268-...
Mapping Land Cover in Secondary Cities in Central America: 2014 Land Cover Classification of San Isidro, Costa Rica
This is one of the most comprehensive datasets on public spending in the agricultural sector in Bolivia. The data on agriculture and agriculture-related expenditures was derived from the national accounting data obtained from the Public Accounting Department of the Ministry of Economy and Public Finance of Bolivia. The data is disaggregated by function (such as research and extension, irrigation, rural roads, rural electrification, etc.), economic classification (capital and current) and level of government (central, departmental and municipal) for a period of 13 years “ from 1996 to 2008. Sub-national units of observation include the 9 Departments of Bolivia and the 327 municipalities, which in 2009 represented all the municipalities in the country.
Bolivia Agricultural Public Expenditure Review
The Trade Costs Dataset provides estimates of bilateral trade costs in agriculture and manufactured goods for the 1995-2018 period. It is built on trade and production data collected in 208 countries. Symmetric bilateral trade costs are computed using the Inverse Gravity Framework (Nov. 2009), which estimates trade costs for each country pair using bilateral trade and gross national output. Trade costs are available for two sectors: trade in manufactured goods, and agriculture. Energy is excluded.
ESCAP-World Bank: International Trade Costs
For the urban and peri-urban area of St. Louis, three main flood scenarios have to be considered: a. Fluvial floods (seasonal floods from the Senegal River) after heavy tropical rains in the upper part of the catchment area (mainly from August to November) b. Floods triggered by rainfall stagnation after heavy local cloudbursts c. Floods caused by sea-level rise, tidal waves and coastal erosion 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 1 (low) 2-3 2 (medium) =4 3 (high) The estimation of the area threatened by tidal waves and coast erosion is based on available reports and press releases as well as on the extrapolation of the mapped difference (done by visual interpretation) of the coastline between 2003 and 2019
Saint Louis (Senegal) - Flood Hazard Map (ESA EO4SD-Urban)
Link to Saint Louis flood hazard datasets in the Urban Thematic Exploitation Platform (UTEP).
Urban Thematic Exploitation Platform (UTEP) Link - Saint Louis
Saint Louis (Senegal) - Flood Hazard Map (ESA EO4SD-Urban)(Feature Service)
EO4SD-Urban Saint Louis Operations Report
For the urban and peri-urban area of St. Louis, three main flood scenarios have to be considered: a. Fluvial floods (seasonal floods from the Senegal River) after heavy tropical rains in the upper part of the catchment area (mainly from August to November) b. Floods triggered by rainfall stagnation after heavy local cloudbursts c. Floods caused by sea-level rise, tidal waves and coastal erosion 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 1 (low) 2-3 2 (medium) 4 and above 3 (high) The estimation of the area threatened by tidal waves and coast erosion is based on available reports and press releases as well as on the extrapolation of the mapped difference (done by visual interpretation) of the coastline between 2003 and 2019
Flood Hazard Map for Saint Louis (ESA EO4SD-Urban)
Yearly sum of biomass production Burkina Faso at 30m resolution in 2017
Burkina Faso - Biomass production (yearly)
Original PTHA datasets are from GeoScience Australia. Probabilistic tsunami hazard assessments (PTHA) are analogous to probabilistic seismic hazard assessment (PSHA) which is the global standard for defining ground shaking levels for building codes and carrying out earthquake risk assessments. The PTHA method can be summarised as: 1. Determine the earthquake source zones (fault lines) to be included in the study; 2. For each earthquake fault, determine the characteristics (e.g. maximum magnitude, geometry etc.) of the earthquakes that could occur on the fault and the probability of such earthquakes; 3. Simulate all possible earthquakes for each fault and the resulting tsunami. For each coastal location calculate the tsunami waveheight that would result from the tsunami generated by each earthquake; 4. Combine these results to relate the maximum tsunami amplitude at the coast with a probability that they might occur. The characteristics of each fault were determined using the highest quality data available to the assessment team. This included historical catalogues of earthquakes and tsunami, physical laws on earthquake size, assessments made during the 2010 revision of Indonesia’s seismic hazard map, and expert scientific judgement by the assessment team. Numerical computations were performed to simulate the propagation of tsunami waves from the earthquake fault to the coast. In total, 100,000 synthetic tsunami were simulated and included in the assessment from a total of thirty earthquake faults. Twenty one faults were from around Indonesia and nine were from regional and distant sources in the Indian and Pacific Oceans. Results of the simulations were used to estimate the maximum tsunami waveheight at each coastal location around Indonesia for each earthquake. Partial pre-processing of local tsunami data is required in order to take into account the tsunami run-up and inundation process onshore. A simple procedure involving wave height and ground elevation analysis has been undertaken by the ThinkHazard! team for use within the model. More information on the GeoScience Australia process can be found at http://aifdr.org/wp-content/uploads/pdf/PTHA_Report_Final_EN.pdf Details on the pre-processing method can be found in the ThinkHazard! Methodology document.
Indonesia tsunami hazard 2500 Year Return Period
Indonesia tsunami hazard 100 Year Return Period (GFDRR Geonode page)
Indonesia tsunami hazard 500 Year Return Period (GFDRR Geonode page)
Indonesia tsunami hazard 2500 Year Return Period (GFDRR Geonode page)
Indonesia tsunami hazard 500 Year Return Period
Indonesia tsunami hazard 100 Year Return Period
Data for power plants in Congo, Rep. with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Congo, Rep. Power Plants
Data for power plants in Cape Verde with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Cape Verde Power Plants
Data for power plants in Eritrea with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Eritrea Power Plants
Data for power plants in Ethiopia with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Ethiopia Power Plants
Data for power plants in Gabon with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Gabon Power Plants
Data for power plants in Ghana with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Ghana Power Plants
Data for power plants in Gambia, The with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Gambia, The Power Plants
Data for power plants in Equatorial Guinea with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
XML Metadata: Equatorial Guinea Power Plants
Download Zipped Shapefile Here: Equatorial Guinea Power Plants
Data for power plants in Niger with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Niger Power Plants
Data for power plants in Sudan with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Sudan Power Plants
Data for power plants in Sierra Leone with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Sierra Leone Power Plants
Data for power plants in Swaziland with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Swaziland Power Plants
Data for power plants in Burkina Faso with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: Burkina Faso Power Plants
Data for power plants in South Africa with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006).
Download Zipped Shapefile Here: South Africa Power Plants
Lake Titicaca sub-basin land use of the years 2003 and 2010; and the land use changes between these years with thirteen Land Use classes defined. This dataset is one of the products produced under the 2008-2012 World Bank (WBG) European Space Agency (ESA) partnership, and is published in the partnership report: Earth Observation for Sustainable Development, June 2013
Lake Titicaca Land Use and Land Use Changes
3A. Lake Titicaca Sub-basin - Land Use Change 2003-2010
3B. GeoTIFF: Lake Titicaca Sub-basin - Land Use Change 2003-2010
2A. Lake Titicaca Sub-basin - Land Use Map 2010
2B. GeoTIFF: Lake Titicaca Sub-basin - Land Use 2010
1A. Lake Titicaca Sub-basin - Land Use Map 2003
1E. GeoTIFF: Lake Titicaca Sub-basin - Mosaic Landsat 2003
1D. Lake Titicaca Sub-basin - Mosaic Landsat 5TM Map 2003
1B. GeoTIFF: Lake Titicaca Sub-basin - Land Use 2003
2D. Lake Titicaca Sub-basin - Mosaic RapidEye Map 2010
2E. GeoTIFF: Lake Titicaca Sub-basin - Mosaic RapidEye 2010
Mauritius earthquake hazard 50 Year Return Period (GFDRR Geonode page)
Mauritius earthquake hazard 25 Year Return Period
Mauritius earthquake hazard 500 Year Return Period
Mauritius earthquake hazard 250 Year Return Period
Mauritius earthquake hazard 1000 Year Return Period
Mauritius earthquake hazard 50 Year Return Period
Mauritius earthquake hazard 1000 Year Return Period (GFDRR Geonode page)
Mauritius earthquake hazard 100 Year Return Period
Mauritius earthquake hazard 25 Year Return Period (GFDRR Geonode page)
Mauritius earthquake hazard 500 Year Return Period (GFDRR Geonode page)
Mauritius earthquake hazard 100 Year Return Period (GFDRR Geonode page)
Mauritius earthquake hazard 250 Year Return Period (GFDRR Geonode page)
The GIS Biomass Atlas of Pakistan is the final output from the biomass resource mapping component of the activity “ Renewable Energy Resource Mapping and Geospatial Planning – Pakistan” [Project ID: P146140]. You can find more information about the project here: https://www.esmap.org/re_mapping_pakistan To visualize the geospatial data generated in this study, please access: https://irena.masdar.ac.ae/GIS/?map=2636 Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass GIS Atlas, 2016, https://energydata.info/dataset/pakistan-biomass-gis-atlas"
Crop residue information aggregated from the Biomass Atlas project field survey raw data. * Min minimum crop residue yield; based on minimum crop yield recorded in the field survey, and the minimum Residue To Crop Ratio in the background data (see: industrial\Power_plant_model.xlsx in this dataset, sheet RCR) Max maximum crop residue yield; based on maximum crop yield recorded in the field survey, and the maximum Residue To Crop Ratio in the background data (see: industrial\Power_plant_model.xlsx in this dataset, sheet RCR) Mean mean crop residue yield (theoretical potential); based on mean crop yield recorded in the field survey, and the mean Residue To Crop Ratio in the background data (see: industrial\Power_plant_model.xlsx in this dataset, sheet RCR) Std standard deviation of the mean crop residue yield in the survey data N number of observations for the crop in the survey data AR availability of the crop residue based on the current residue use recorded in the field survey (technical potential) ARS standard deviation of the availability of the crop residue based on the current residue use AW availability of the crop residue based on the current residue use recorded in the field survey and farmers' willingness to participate in a feedstock supply chain (technical potential) AWS standard deviation of the availability of the crop residue based on the current residue use recorded in the field survey and farmers' willingness to participate in a feedstock supply chain where denotes an abbreviation for a crop residue, as listed below. WheStrMin Wheat straw CotStaMin Cotton stalk RicStrMin Rice straw RicHusMin Rice husk MaiStaMin Maize stalk MaiCobMin Maize cob MaiHusMin Maize husk SugTraMin Sugarcane trash SugBagMin Bagasse Feedstock summary by country and by district, including sampled district confidence intervals for yearly feedstock amounts, t/yr You can find more information about the project here: https://www.esmap.org/re_mapping_pakistan Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass Feedstock Crop Yield, 2016, https://energydata.info/dataset/pakistan-biomass-feedstock-cropyield"
The file is in a ERMapper ERS raster GIS data format. It can be opened with for example QGIS software (http://qgis.org) Coordinate reference system: WGS 84 / UTM zone 42N (EPSG:32642) Bands: 1 land use class Unit: categorical value indicating a land use class, value interpretations: 1 cotton-maize 2 cotton-other_crop 3 cotton-rice 4 cotton-sugarcane 5 cotton-wheat 6 maize-maize 7 maize-rice 8 maize-sugarcane 9 maize-wheat 10 other_crop-other_crop 11 rice-rice 12 rice-sugarcane 13 rice-wheat 14 sugarcane-sugarcane 15 sugarcane-wheat 16 wheat-wheat 17 bare area 18 low biomass 19 medium biomass 20 high biomass 21 road 22 urban 23 water The land use class values 1-16 indicate an agricultural field. The crop pairs indicate crops for the Kharif-Rabi yearly cropping seasons. Values 18-20 indicate natural vegetation and orchards. Further information: info@simosol.fi
This is the Biomass Atlas for Pakistan dataset. Each of the sub-folders has a metadata folder containing the dataset component descriptions in text files.
Pakistan Biomass FieldSurvey sourced from ESMAP World Bank. You can find more information about the project here: https://www.esmap.org/re_mapping_pakistan Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass Field Survey, 2016, https://energydata.info/dataset/pakistan-biomass-fieldsurvey"
Sugar and rice mill scenario analysis results. Sugar mill scenario: There is a potential for implementing cogeneration plant using bagasse generated at the sugar mills. For each sugar mill, the power capacity of the cogeneration plant was defined based on the NEPRA requirement of annual capacity factor (ACF) of a newly-established cogeneration plant running on bagasse produced by the sugar mill. The ACF approved by NEPRA in 2013 was 45% based on 180 days of operation (120 days milling season and 60 days off-milling season) and a plant availability factor of 92%. The conversion technology was assumed to be a high pressure cogeneration system including a steam boiler and an extraction-condensing steam turbine. The analysis results contain the sourcing area (km²/GWh) for the additional biomass feedstock needed for operating the cogeneration plant in power-only mode for additional 5 months of the off-milling season when bagasse is not available. The sourcing area matches the best-case scenario of being able to source all of the available technical potential of the crop harvesting residues suitable for the cogeneration plant from the immediate neighborhood of the mill. Therefore, it helps ranking the sugar mills in terms of the ease of sourcing the additional feedstock. Rice mill scenario: In this analysis, the power plant potential was estimated based on an assumption that the power plant will be run with a fuel mixture of rice husk and locally sourced crop harvesting residues. The minimum fixed power plant capacity of 3 MW is assumed for all 54 surveyed rice mills. The additional biomass feedstock was calculated in order to assure an annual capacity factor of 85% for all power plants. As for the sugar mills, the analysis results for each rice mill contain the sourcing area (km²/GWh) for the additional biomass feedstock needed to operate the power plant and the sourcing area matches the best-case sourcing scenario. You can find more information about the project here: https://www.esmap.org/re_mapping_pakistan Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass Industrial, 2016, https://energydata.info/dataset/pakistan-biomass-industrial"
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains air temperature at 2m above ground level in °C covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: TEMP GISdata (GeoTIFF) Data format: GEOTIFF File size : 121.03 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - Air Temperature at 2m Above Ground Level (TEMP) GIS Data, (Global Solar Atlas)
WORLD - TEMP- Diffuse horizontal irradiation (Global Solar Atlas)
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Quickbird-2 (2008) / Worldview-2: 2017) ) by means of manual classification processing techniques.
Mtwara (Tanzania) - Transport Infrastructure (ESA EO4SD-Urban)
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Worldview-2: 2017) by means of manual classification processing techniques.
Transport Infrastructure in 2017 for Mtwara (ESA EO4SD-Urban)
Mtwara (Tanzania) - Transport Infrastructure (ESA EO4SD-Urban)(Feature Service)
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Quickbird-2 (2008)) by means of manual classification processing techniques
Transport Infrastructure in 2008 for Mtwara (ESA EO4SD-Urban)
Link to transportation data for Mtwara on Urban Thematic Exploitation Platform (UTEP)
Urban Thematic Exploitation Platform (UTEP) Link - Mtwara
This shapefile contains railways in Ghana.
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Quickbird-2 (2005)) / Pleiades (2016) by means of manual classification processing techniques.
Kigoma (Tanzania) - Transport Infrastructure (ESA EO4SD-Urban)
Link to transportation infrastructure data of Kigoma on Urban Thematic Exploitation Platform (UTEP)
Urban Thematic Exploitation Platform (UTEP) Link - Kigoma
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Quickbird-2 (2005)) by means of manual classification processing techniques
Transport Infrastructure in 2005 for Kigoma (ESA EO4SD-Urban)
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Pleiades (2016)) by means of manual classification processing techniques.
Transport Infrastructure in 2016 for Kigoma (ESA EO4SD-Urban)
Kigoma (Tanzania) - Transport Infrastructure (ESA EO4SD-Urban)(Feature Service)
Global Horizontal Irradiation (GHI) raster file over the territory of Afghanistan. Aggregated annual average values. Spatial resolution 5km.
Afghanistan - Global Horizontal Irradiation
Madagascar earthquake hazard 500 Year Return Period (GFDRR Geonode page)
Madagascar earthquake hazard 25 Year Return Period (GFDRR Geonode page)
Madagascar earthquake hazard 50 Year Return Period (GFDRR Geonode page)
Madagascar earthquake hazard 250 Year Return Period
Madagascar earthquake hazard 500 Year Return Period
Madagascar earthquake hazard 1000 Year Return Period (GFDRR Geonode page)
Madagascar earthquake hazard 25 Year Return Period
Madagascar earthquake hazard 100 Year Return Period (GFDRR Geonode page)
Madagascar earthquake hazard 1000 Year Return Period
Madagascar earthquake hazard 50 Year Return Period
Madagascar earthquake hazard 100 Year Return Period
Madagascar earthquake hazard 250 Year Return Period (GFDRR Geonode page)
The dataset contains point locations of education facilities in Zambia. This is extracted from Humanitarian Open Street Map, 2018
Pacific coastal flood hazard 250 Year Return Period (GFDRR Geonode page)
Pacific coastal flood hazard 100 Year Return Period
Pacific coastal flood hazard 10 Year Return Period
Pacific coastal flood hazard 25 Year Return Period (GFDRR Geonode page)
Pacific coastal flood hazard 50 Year Return Period
Pacific coastal flood hazard 10 Year Return Period (GFDRR Geonode page)
Pacific coastal flood hazard 100 Year Return Period (GFDRR Geonode page)
Pacific coastal flood hazard 250 Year Return Period
Pacific coastal flood hazard 25 Year Return Period
Pacific coastal flood hazard 50 Year Return Period (GFDRR Geonode page)
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 that accounts for all potential seismic sources and their rates of earthquake activity, and we incorporate modeling uncertainty by using logic trees for source and ground-motion parameters. See the Appendix for an explanation of probabilistic seismic hazard analysis and discussion of seismic risk. Production of the seismic hazard maps is challenging because the geological and seismological data required to produce a seismic hazard model are limited. The data that are available for this project include historical seismicity and poorly constrained slip rates on only a few of the many active faults in the country. Much of the hazard is derived from a new catalog of historical earthquakes: from 1964 to the present, with magnitude equal to or greater than about 4.5, and with depth between 0 and 250 kilometers. We also include four specific faults in the model: the Chaman fault with an assigned slip rate of 10 mm/yr, the Central Badakhshan fault with an assigned slip rate of 12 mm/yr, the Darvaz fault with an assigned slip rate of 7 mm/yr, and the Hari Rud fault with an assigned slip rate of 2 mm/yr. For these faults and for shallow seismicity less than 50 km deep, we incorporate published ground-motion estimates from tectonically active regions of western North America, Europe, and the Middle East. Ground-motion estimates for deeper seismicity are derived from data in subduction environments. We apply estimates derived for tectonic regions where subduction is the main tectonic process for intermediate-depth seismicity between 50- and 250-km depth. Within the framework of these limitations, we have developed a preliminary probabilistic seismic-hazard assessment of Afghanistan, the type of analysis that underpins the seismic components of modern building codes in the United States. The assessment includes maps of estimated peak ground-acceleration (PGA), 0.2-second spectral acceleration (SA), and 1.0-second SA, with return periods of about 500 years (equal to a 10-percent probability in 50 years), 1000 years (equal to a 5-percent probability in 50 years), and 2,500 years (equal to a 2-percent probability in 50 years)
Afghanistan earthquake hazard 1000 Year Return Period (GFDRR Geonode page)
Afghanistan earthquake hazard 1000 Year Return Period
Afghanistan earthquake hazard 2500 Year Return Period (GFDRR Geonode page)
Afghanistan earthquake hazard 500 Year Return Period
Afghanistan earthquake hazard 2500 Year Return Period
Afghanistan earthquake hazard 500 Year Return Period (GFDRR Geonode page)
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains direct normal irradiation (DNI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 343.99 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - Direct Normal Irradiation (DNI) GIS Data, (Global Solar Atlas)
DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 343.99 MB
WORLD - DNI - Direct Normal Irradiation (Global Solar Atlas)
This dataset contains a population cluster (shapefile) of Somaliland with the electrification scenarios. The scenarios assess the effect of different roll-out plans, demand levels, diesel costs, grid electricity costs, PV costs, and different types of restrictions or limitations on grid expansion. Results are presented for 2025 and 2030.
Somaliland - Population Cluster With Electrification Scenarios
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains photovoltaic power potential (PVOUT) in kWh/kWp covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: PVOUT LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 3.6 GB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - Photovoltaic Power Potential (PVOUT) GIS Data, (Global Solar Atlas)
PVOUT LTAm_AvgDailyTotals (GeoTIFF) the temporal scale for this data set is longterm monthly average of daily totals Data format: raster (gridded), GEOTIFF File size : 3.6 GB
PVOUT LTAy_AvgDailyTotals (GeoTIFF) the temporal scale of this dataset is longterm average of yearly totals Data format: GEOTIFF File size : 286.34 MB
The 2014 U.S. Geological Survey (USGS) National Seismic Hazard Maps display earthquake ground motions for various probability levels across the United States and are applied in seismic provisions of building codes, insurance rate structures, risk assessments, and other public policy. The updated maps represent an assessment of the best available science in earthquake hazards and incorporate new findings on earthquake ground shaking, faults, seismicity, and geodesy. The USGS National Seismic Hazard Mapping Project developed these maps by incorporating information on potential earthquakes and associated ground shaking obtained from interaction in science and engineering workshops involving hundreds of participants, review by several science organizations and State surveys, and advice from expert panels and a Steering Committee. The new probabilistic hazard maps represent an update of the seismic hazard maps; previous versions were developed by Petersen and others (2008) and Frankel and others (2002), using the methodology developed Frankel and others (1996). Algermissen and Perkins (1976) published the first probabilistic seismic hazard map of the United States which was updated in Algermissen and others (1990). The 2014 U.S. Geological Survey (USGS) National Seismic Hazard Maps display earthquake ground motions for various probability levels across the United States and are applied in seismic provisions of building codes, insurance rate structures, risk assessments, and other public policy. The updated maps represent an assessment of the best available science in earthquake hazards and incorporate new findings on earthquake ground shaking, faults, seismicity, and geodesy. The USGS National Seismic Hazard Mapping Project developed these maps by incorporating information on potential earthquakes and associated ground shaking obtained from interaction in science and engineering workshops involving hundreds of participants, review by several science organizations and State surveys, and advice from expert panels and a Steering Committee. The new probabilistic hazard maps represent an update of the seismic hazard maps; previous versions were developed by Petersen and others (2008) and Frankel and others (2002), using the methodology developed Frankel and others (1996). Algermissen and Perkins (1976) published the first probabilistic seismic hazard map of the United States which was updated in Algermissen and others (1990). The 2014 U.S. Geological Survey (USGS) National Seismic Hazard Maps display earthquake ground motions for various probability levels across the United States and are applied in seismic provisions of building codes, insurance rate structures, risk assessments, and other public policy. The updated maps represent an assessment of the best available science in earthquake hazards and incorporate new findings on earthquake ground shaking, faults, seismicity, and geodesy. The USGS National Seismic Hazard Mapping Project developed these maps by incorporating information on potential earthquakes and associated ground shaking obtained from interaction in science and engineering workshops involving hundreds of participants, review by several science organizations and State surveys, and advice from expert panels and a Steering Committee. The new probabilistic hazard maps represent an update of the seismic hazard maps; previous versions were developed by Petersen and others (2008) and Frankel and others (2002), using the methodology developed Frankel and others (1996). Algermissen and Perkins (1976) published the first probabilistic seismic hazard map of the United States which was updated in Algermissen and others (1990). The 2014 U.S. Geological Survey (USGS) National Seismic Hazard Maps display earthquake ground motions for various probability levels across the United States and are applied in seismic provisions of building codes, insurance rate structures, risk assessments, and other public policy. The updated maps represent an assessment of the best available science in earthquake hazards and incorporate new findings on earthquake ground shaking, faults, seismicity, and geodesy. The USGS National Seismic Hazard Mapping Project developed these maps by incorporating information on potential earthquakes and associated ground shaking obtained from interaction in science and engineering workshops involving hundreds of participants, review by several science organizations and State surveys, and advice from expert panels and a Steering Committee. The new probabilistic hazard maps represent an update of the seismic hazard maps; previous versions were developed by Petersen and others (2008) and Frankel and others (2002), using the methodology developed Frankel and others (1996). Algermissen and Perkins (1976) published the first probabilistic seismic hazard map of the United States which was updated in Algermissen and others (1990). The 2014 U.S. Geological Survey (USGS) National Seismic Hazard Maps display earthquake ground motions for various probability levels across the United States and are applied in seismic provisions of building codes, insurance rate structures, risk assessments, and other public policy. The updated maps represent an assessment of the best available science in earthquake hazards and incorporate new findings on earthquake ground shaking, faults, seismicity, and geodesy. The USGS National Seismic Hazard Mapping Project developed these maps by incorporating information on potential earthquakes and associated ground shaking obtained from interaction in science and engineering workshops involving hundreds of participants, review by several science organizations and State surveys, and advice from expert panels and a Steering Committee. The new probabilistic hazard maps represent an update of the seismic hazard maps; previous versions were developed by Petersen and others (2008) and Frankel and others (2002), using the methodology developed Frankel and others (1996). Algermissen and Perkins (1976) published the first probabilistic seismic hazard map of the United States which was updated in Algermissen and others (1990). The 2014 U.S. Geological Survey (USGS) National Seismic Hazard Maps display earthquake ground motions for various probability levels across the United States and are applied in seismic provisions of building codes, insurance rate structures, risk assessments, and other public policy. The updated maps represent an assessment of the best available science in earthquake hazards and incorporate new findings on earthquake ground shaking, faults, seismicity, and geodesy. The USGS National Seismic Hazard Mapping Project developed these maps by incorporating information on potential earthquakes and associated ground shaking obtained from interaction in science and engineering workshops involving hundreds of participants, review by several science organizations and State surveys, and advice from expert panels and a Steering Committee. The new probabilistic hazard maps represent an update of the seismic hazard maps; previous versions were developed by Petersen and others (2008) and Frankel and others (2002), using the methodology developed Frankel and others (1996). Algermissen and Perkins (1976) published the first probabilistic seismic hazard map of the United States which was updated in Algermissen and others (1990).
United States of America earthquake hazard
United States of America earthquake hazard 475 Year Return Period (GFDRR Geonode page)
United States of America earthquake hazard 975 Year Return Period
United States of America earthquake hazard 975 Year Return Period (GFDRR Geonode page)
United States of America earthquake hazard 475 Year Return Period
United States of America earthquake hazard 100 Year Return Period (GFDRR Geonode page)
United States of America earthquake hazard 100 Year Return Period
United States of America earthquake hazard 2475 Year Return Period (GFDRR Geonode page)
United States of America earthquake hazard 2475 Year Return Period
This raster file represents a land cover classification in Limon based off satellite imagery from January 31, 2015. This land cover classification was used in the following report: Mapping Land Cover in Secondary Cities in Central America. This work was initiated as an analytical effort to fill a gap on spatial form of secondary cities. While this is an independent output, this work is tightly linked to the work done under the Central America Urbanization Review. The analysis here described, was used as an input in the definition of urban agglomerations used in the Urbanization Review. The detailed analysis on secondary cities is seen as a complement to the work carried out in the Urbanization Review, in that it zooms into what is happening within a set of cities. The Urbanization Review instead provides a broader look at the system of cities in Central America, highlighting the key bottlenecks the regions faces in moving toward more inclusive, productive, and resilient cities. The Urbanization Review can be found here: http://documents.worldbank.org/curated/en/134151467994680764/pdf/106268-...
Mapping Land Cover in Secondary Cities in Central America: 2015 Land Cover Classification of Limon, Costa Rica
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads in 2006 and 2015. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (QuickBird-2 (2006, 2008) and Pleiades (2015)) by means of manual classification processing techniques.
Semarang (Indonesia) - Transport Infrastructure (ESA EO4SD-Urban)
Link to Semarang transport network datasets in the Urban Thematic Exploitation Platform (UTEP).
Urban Thematic Exploitation Platform (UTEP) Link - Semarang
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (QuickBird-2 (2006, 2008)) by means of manual classification processing techniques.
Transport Infrastructure in 2006 for Semarang (ESA EO4SD-Urban)
Semarang (Indonesia) - Transport Infrastructure (ESA EO4SD-Urban)(Feature Service)
EO4SD-Urban Semarang City Operations Report
The Transport Infrastructure information product shows the classification of three road types: Arterial Roads, Collector Roads and Local Roads. The Transport Infrastructure dataset is based on Very High Resolution (VHR) satellite imagery (Pleiades (2015)) by means of manual classification processing techniques.
Transport Infrastructure in 2015 for Semarang (ESA EO4SD-Urban)
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains optimum tilt to maximize yearly yield in (°) covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: OPTA LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 2.08 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - Optimum Tilt to Maximize Yearly Yield (OPTA) GIS Data, (Global Solar Atlas)
OPTA LTAy_AvgDailyTotals (GeoTIFF) Data format: raster (gridded), GEOTIFF File size : 2.08 MB
WORLD - OPTA - Optimum Tilt to Maximize Yearly Yield (Global Solar Atlas)
Yearly biomass production Ethiopia 2013 & 2017
Ethiopia Biomass production (yearly) raster time-series files.
Congo Brazzaville - MV Electricity Network (2019)
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains solar resource data for: direct normal irradiation (DNI), global horizontal irradiation (GHI), diffuse horizontal irradiation data (DIF), and global irradiation for optimally tilted surfaces (GTI), all in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m). Due to the large amount of data, the coverage has been divided into eight segments. Four segments for the North hemisphere: WWN (West-west-north), WN (West-north), EN (East-north), EEN (East-east-north). Analogically four segments for the South hemisphere: WWS, WS, ES, EES. The data is hyperlinked under 'resources' with the following characteristics: DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: raster (gridded), GEOTIFF File size : 343.99 MB *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - High Resolution Solar Resource (GHI, DIF, GTI, DNI) GIS Data, (Global Solar Atlas)
Data format: raster (gridded), GeoTIFF File size: 807.7 MB
World - Solar Resource (GHI, DIF, GTI, DNI) GIS Data, Segment EES
Data format: raster (gridded), GeoTIFF File size: 1.15 GB
World - Solar Resource (GHI, DIF, GTI, DNI) GIS Data, Segment WN
Data format: raster (gridded), GeoTIFF File size: 1.04 GB
World - Solar Resource (GHI, DIF, GTI, DNI) GIS Data, Segment WS
Data format: raster (gridded), GeoTIFF File size: 1.07 GB
World - Solar Resource (GHI, DIF, GTI, DNI) GIS Data, Segment WWN
Data format: raster (gridded), GeoTIFF File size: 21.82 GB
World - Solar Resource (GHI, DIF, GTI, DNI) GIS Data, Segment WWS
Data format: raster (gridded), GeoTIFF File size: 699.46 MB
World - Solar Resource (GHI, DIF, GTI, DNI) GIS Data, Segment ES
Data format: raster (gridded), GeoTIFF File size: 1.81 GB
World - Solar Resource (GHI, DIF, GTI, DNI) GIS Data, Segment EEN
Data format: raster (gridded), GeoTIFF File size: 2.73 GB
World - Solar Resource (GHI, DIF, GTI, DNI) GIS Data, Segment EN
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains diffuse horizontal irradiation (DIF) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characeristics: DIF LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 198.94 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - Diffuse Horizontal Irradiation (DIF) GIS Data, (Global Solar Atlas)
WORLD - DIF - Diffuse horizontal irradiation (Global Solar Atlas)
DIF Diffuse horizontal irradiation [kWh/m²] DIF LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 198.94 MB
Seychelles earthquake hazard 100 Year Return Period (GFDRR Geonode page)
Seychelles earthquake hazard 500 Year Return Period
Seychelles earthquake hazard 50 Year Return Period
Seychelles earthquake hazard 250 Year Return Period (GFDRR Geonode page)
Seychelles earthquake hazard 500 Year Return Period (GFDRR Geonode page)
Seychelles earthquake hazard 100 Year Return Period
Seychelles earthquake hazard 25 Year Return Period
Seychelles earthquake hazard 50 Year Return Period (GFDRR Geonode page)
Seychelles earthquake hazard 25 Year Return Period (GFDRR Geonode page)
Seychelles earthquake hazard 1000 Year Return Period (GFDRR Geonode page)
Seychelles earthquake hazard 1000 Year Return Period
Seychelles earthquake hazard 250 Year Return Period
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.
Abidjan (Ivory Coast) - Transport Network 2005 & 2018 Maps (ESA EO4SD-Urban)
Transport Network 2005 Map for Abidjan (ESA EO4SD-Urban)
Transport Network 2018 Map for Abidjan (ESA EO4SD-Urban)
Link to Abidjan transport datasets in the Urban Thematic Exploitation Platform (UTEP).
Abidjan (Ivory Coast) - Transport Network 2005 & 2018 Maps (ESA EO4SD-Urban)(Feature Service)
Yearly biomass production change Ethiopia 2017 minus 2013
Ethiopia - Biomass production change (yearly)
For the urban and peri-urban area of St. Louis, three main flood scenarios have to be considered: a. Fluvial floods (seasonal floods from the Senegal River) after heavy tropical rains in the upper part of the catchment area b. Floods triggered by rainfall stagnation after heavy local cloudbursts c. Floods caused by sea-level rise, tidal waves and coastal erosion 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.
Saint Louis (Senegal) - Flood Extent Maps (ESA EO4SD-Urban)
Flood Extent Map for Saint Louis 2015 (ESA EO4SD-Urban)
Saint Louis (Senegal) - Flood Extent Maps (ESA EO4SD-Urban)(Feature Service)
Flood Extent Map for Saint Louis 2016 (ESA EO4SD-Urban)
Flood Extent Map for Saint Louis 2018 (ESA EO4SD-Urban)
Flood Extent Map for Saint Louis 2009 (ESA EO4SD-Urban)
Flood Extent Map for Saint Louis 2013 (ESA EO4SD-Urban)
Flood Extent Map for Saint Louis 2012 (ESA EO4SD-Urban)
Link to Saint Louis flood extent datasets in the Urban Thematic Exploitation Platform (UTEP).
Flood Extent Map for Saint Louis 2011 (ESA EO4SD-Urban)
Flood Extent Map for Saint Louis 2017 (ESA EO4SD-Urban)
The dataset provides potential sites for the deployment of smaller scale hydropower (1-10 MW) in Afghanistan. The analysis is based on a methodology developed at KTH dESA in 2016.
Afghanistan - Small-scale Hydropower Potential
Small scale hydropower potential in Afghanistan
The rainfall data are obtained from Vietnam's HydroMeteorological Data Center (http://www.hymetdata.gov.vn/), and cover daily observations from 172 weather stations. Most of them were actively operated through out the period 1975-2006. The list of weather stations with GIS coordinates is also provided.
Rainfall data from 172 weather stations in Vietnam
As natural disasters hit with increasing frequency, especially in coastal areas, it is imperative to better understand how much natural disasters affect economies and their people. This requires disaggregated measures of natural disasters that can be reliably linked to households, the first challenge this paper tackles. In particular, a methodology is illustrated to create natural disaster and hazard maps from first hand, geo-referenced meteorological data. In a second step, the repeated cross-sectional national living standard measurement surveys (2002, 2004, and 2006) from Vietnam are augmented with the natural disaster measures derived in the first phase, to estimate the welfare effects associated with natural disasters. The results indicate that short-run losses from natural disasters can be substantial, with riverine floods causing welfare losses of up to 23 percent and hurricanes reducing welfare by up to 52 percent inside cities with a population over 500,000. Households are better able to cope with the short-run effects of droughts, largely due to irrigation. There are also important long-run negative effects, in Vietnam mostly so for droughts, flash floods, and hurricanes. Geographical differentiation in the welfare effects across space and disaster appears partly linked to the functioning of the disaster relief system, which has so far largely eluded households in areas regularly affected by hurricane force winds.
Identifiers for each Vietnam weather station (incl. GPS coordinates)
The rainfall data are obtained from Vietnam's HydroMeteorological Data Center (http://www.hymetdata.gov.vn/), and cover daily observations from 172 weather stations. Most of them were actively operated through out the period 1975-2006. The list of weather stations with GIS coordinates is also provided. The data are provided in MS Excel files. Each file contains data from one weather station. Please note: (I) Blank cells in years for which the weather stations were active mean absolute no rainfall. (II) Cells with 0.0 (mm of rainfall) mean that there was some rainfall but it was too little to measure. (III) In years the weather stations were not active or for some reasons the data were not collected, the cells are marked by an X or a note showing that data in those years were not collected. We are revising the data so that errors will be eliminated and the data are better organized. Version 2 is expected to come out by April 15th, 2011. For any inqueries in regards to the use of data, please contact us at: Quy-Toan Do (The World Bank, qdo@worldbank.org) or Le Dang Trung (Univesity of Copenhagen, trung@foi.dk)
This raster file represents a land cover classification in Santa Ana based off satellite imagery from February 22, 2015. This land cover classification was used in the following report: Mapping Land Cover in Secondary Cities in Central America. This work was initiated as an analytical effort to fill a gap on spatial form of secondary cities. While this is an independent output, this work is tightly linked to the work done under the Central America Urbanization Review. The analysis here described, was used as an input in the definition of urban agglomerations used in the Urbanization Review. The detailed analysis on secondary cities is seen as a complement to the work carried out in the Urbanization Review, in that it zooms into what is happening within a set of cities. The Urbanization Review instead provides a broader look at the system of cities in Central America, highlighting the key bottlenecks the regions faces in moving toward more inclusive, productive, and resilient cities. The Urbanization Review can be found here: http://documents.worldbank.org/curated/en/134151467994680764/pdf/106268-...
Mapping Land Cover in Secondary Cities in Central America: 2015 Santa Ana, El Salvador Land Cover Classification
This shapefile contains airports in Ghana.
Land cover/land use (LULC) maps for the catchments of Kelani Ganga and Attanagalu Oya, and LULC Change comparing 1991, 2001 with the recent LCLU (2012). Classification includes two thematic levels (national 7-class scheme and 15 land cover/land use classes according to user definitions). This dataset is one of the products produced under the 2014-2016 World Bank (WBG) European Space Agency (ESA) partnership, and is published in the partnership report: Earth Observation for Sustainable Development, June 2016.
Sri Lanka - Land Use Land Cover LULC (Change) Mapping
Includes output map, statistics, metadata and satellite specifications, validation procedures and accuracy measures.
Product Sheet: Land Use Land Cover Change 1991-2001-2012
Shapefile: Land Cover Classification 2012
Shapefile: Land Cover Classification 2001
Ready Map for Printing: Land Cover Classification 2012 (JPG)
Sri Lanka - Land Use Land Cover LULC (Change) Mapping(Feature Service)
Includes output map, metadata and satellite specifications, validation procedures and accuracy measures.
Shapefile: Land Cover Classification 1991
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 is the most up to date. Topography was taken from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world. To account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). This was considered a good proxy of the spatial variation of surface roughness. A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009). Bathymetry is the information about the underwater floor of the ocean having direct influence on the formation of the storm surge. More information about the cyclone wind and storm surge hazard can be found in CIMNE et al., 2015a. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014). The vulnerability models used in the risk calculation for GAR correlate loss to the wind speed for 3-seconds gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.
Global coastal flood hazard 2 Year Return Period
Global coastal flood hazard 500 Year Return Period
Global coastal flood hazard 50 Year Return Period
Global coastal flood hazard 2 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 10 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 250 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 25 Year Return Period
Global coastal flood hazard 10 Year Return Period
Global coastal flood hazard 5 Year Return Period
Global coastal flood hazard 500 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 1000 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 5 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 100 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 250 Year Return Period
Global coastal flood hazard 50 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 25 Year Return Period (GFDRR Geonode page)
Global coastal flood hazard 100 Year Return Period
Global coastal flood hazard 1000 Year Return Period
This dataset is the result of a geospatial model that simulates how individuals and traded goods are moved in COD, taking both roads and navigable rivers into account. The Highway Development and Management Model was used to estimate the cost of the road network. The points of origin for the analysis were created by dividing the territory into more than 27,000 cells of approximately 10 kilometers on a side and estimating their centroid. Then, transport cost to the local market was estimated by calculating every possible transport route from every cell centroid to every possible market, and selecting the cheapest route-market combination as the most likely route to a destination. Full report here: http://documents.worldbank.org/curated/en/952691468195575937/Economic-bo...
Democratic Republic Congo (COD) - Transportation Prices (2016)
Democratic Republic Congo (COD) - Transportation Prices (2016)(Feature Service)
Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains terrain elevation above sea level (ELE) in [m a.s.l.] covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characeristics: ELE GISdata (GeoTIFF) Data format: GEOTIFF File size : 826.8 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month *For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) *For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).
World - Terrain Elevation Above Sea Level (ELE) GIS Data, (Global Solar Atlas)
ELE GISdata (GeoTIFF) Data format: raster (gridded), GEOTIFF File size : 826.8 MB
WORLD - ELE - Terrain Elevation Above Sea Level (Global Solar Atlas)
This layer contains town location that is Under Implementation from AfDB funded CLSG SW and MV project. There are also some additional settlements cluster and demand characteristics that identified under NEA project and proposed to be considered under the scope of CLSG SW and MV project.
Liberia - CLSG SW and MV Project Location-LNEA