GIS data collection¶
An electrification analysis with gep_onsset is based on information collected by a number of GIS layers. These are used to provide all necessary, initial attributes that the model needs to run.
A basic analysis relies on the following “foundamental” GIS layers:
- Distribution of HV lines (current & planned)
- Distribution of MV line
- Location of Substations & Transformers
- Road network
- Global Horizontal Irradiation
- Wind speed
- Location of Small Hydropower potential sites
- Land Cover
- Elevation & Slope
- Administrative boundaries
- Population distribution
- Travel time to nearest town
- Nighttime lights
- Custom Residential Electricity Demand Indicative Target (CREDIT) Layer
Other supplementary layers may be used depending on their availability and support the electrification analysis accordingly.
Below we provide key features of these layers in the form of metadata. The list is not exhaustive but rather focuses on the latest, open access datasets providing global or at least regional coverage. These have informed the scenario analysis available on the GEP Explorer.
Note
It is important to highlight that the selection of these datasets is not set in stone. They are interchangeable and may be replaced by alternative datasets as per case study mandates. Note however, that any update should comply with the suggested data guidelines developed as part of the GEP project.
Infrastructure¶
HV lines (current & planned)¶
| Dataset | High Voltage (HV) lines |
| Data Type | Vector |
| Units | kV |
| Spatial Resolution | Regional, national |
| Description | Spatial distribution of (Existing & Planned) the transmission network. HV capacity definition depends on the country but usually refers to lines above 69 kV. |
| Why we are using this dataset | Identify where HV lines are; identify electrification status in the base year |
| Author | Open Street Map/The World Bank |
| Year | 2017 |
| Availability | Publickly available |
| Cleaned/Processed? | not available |
| Responsible Party | The World Bank |
| Learn More Link | https://energydata.info/dataset/africa-electricity-transmission-and-distribution-2017 |
| Download from Source | http://africagrid.energydata.info/ |
| Category | Transmission and distribution |
| Cautions | none |
| Supplementary Info | This dataset serves as an updated and improved replacement for the Africa Infrastructure Country Diagnostic (AICD) data that was published in 2007. |
| Geographic Coverage | Africa |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | March 24, 2017, 7:24 PM (UTC+01:00) |
| Date of Content | January 10, 2019, 12:06 PM (UTC+01:00) |
| Frequency of Updates | yearly |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 |
| Link to License | https://creativecommons.org/licenses/by/4.0/ |
| Citation | |
| Tags | Transmission; energy access; grid |
MV lines¶
| Dataset | Medium Voltage (MV) lines |
| Data Type | Vector |
| Units | kV |
| Spatial Resolution | Regional, national |
| Description | Spatial distribution of the medium voltage transmission network. What is defined as medium voltage depends on the country but usually refers to lines between 11-69 kV. |
| Why we are using this dataset | Identify where MV lines are; identify electrification status in the base year |
| Author | Christopher Arderne, Conrad Zorn, Claire Nicolas and Elco Koks |
| Year | 2020 |
| Availability | Publicly available |
| Cleaned/Processed? | not applicable |
| Responsible Party | not available |
| Learn More Link | https://gridfinder.org/ |
| Download from Source | https://zenodo.org/record/3628142#.XxhXF55KhPY |
| Category | Transmission and distribution |
| Cautions | none |
| Supplementary Info | none |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | January 16, 2020 |
| Date of Content | January 16, 2020 |
| Frequency of Updates | non available |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 International |
| Link to License | Creative Commons Attribution 4.0 International |
| Citation | https://www.nature.com/articles/s41597-019-0347-4 |
| Tags | Distribution; energy access; grid |
Sub-stations & Transformers¶
| Dataset | Substations & Transformers |
| Data Type | Vector |
| Units | kVA |
| Spatial Resolution | National |
| Description | The location of currently available substations and transformers. |
| Why we are using this dataset | Identify where sub-stations are; identify electrification status in the base year |
| Author | OpenStreetMap |
| Year | Up-to-date |
| Availability | Partially available |
| Cleaned/Processed? | Need to be processed and cross-validates |
| Responsible Party | OpenStreetMap |
| Learn More Link | none available |
| Download from Source | http://download.geofabrik.de/ |
| Category | Grid infrastructure |
| Cautions | |
| Supplementary Info | |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | not available |
| Date of Content | not available |
| Frequency of Updates | Frequent |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | not available |
| Link to License | not available |
| Citation | not available |
| Tags | Grid infrastructure; Sub-stations |
Road network¶
| Dataset | Road Network |
| Data Type | Vector |
| Units | |
| Spatial Resolution | National |
| Description | Existing & planned road infrastructure. The road network that is to be used has to include major roads such as highways, primary and secondary roads. There is no need to include smaller desolate roads or trails. |
| Why we are using this dataset | Calibration of electrification heuristics; fuel cost for diesel; grid penalty costing |
| Author | OSM (through Mapzen) |
| Year | 2018 |
| Availability | Available |
| Cleaned/Processed? | Processed |
| Responsible Party | Mapzen |
| Learn More Link | https://www.mapzen.com/blog/osmlr-2nd-technical-preview/ |
| Download from Source | |
| Category | Transport |
| Cautions | OSMLR provides a stable linear-referencing system atop the ever-changing network of roadways in OpenStreetMap. It’s used by the Open Traffic platform to associate statistics like speeds and vehicle counts with roadway segments. |
| Supplementary Info | OSMLR segments are available as geographic tiles at three levels of roadway hierarchy. The highway level (0) includes drivable road segments with OSM highway tags: motorway, motorway_link, trunk, trunk_link, primary, and primary_link. The arterial level (1) includes drivable road segments with OSM highway tags: secondary, secondary_link, tertiary, and tertiary_link. The local level (2) includes drivable road segments with OSM highway tags: unclassified, unclassified_link, residential, and residential_link. |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | |
| Link to License | |
| Citation | |
| Tags | Roads; Transport |
Energy (and other) resources¶
Global Horizontal Irradiation (GHI)¶
| Dataset | Global Horizontal Irradiation (GHI) |
| Data Type | Raster |
| Units | kWh/m2/year |
| Spatial Resolution | 0.0083 deg |
| Description | Provide information about the Global Horizontal Irradiation (kWh/m2/year) over an area. |
| Why we are using this dataset | |
| Author | SOLARGIS |
| Year | 2017 |
| Availability | Available |
| Cleaned/Processed? | Processed |
| Responsible Party | Energy Sector Management Assistance Program (ESMAP) |
| Learn More Link | https://globalsolaratlas.info/downloads?c=22.755921,-17.753906,2 |
| Download from Source | |
| Category | Energy Resources |
| Cautions | |
| Supplementary Info | The Atlas covers areas between latitudes 60°N to 45°S. Areas north and south of these coordinates are not covered because the incline of the satellite imagery prohibits an accurate assessment of cloud cover. The primary grid resolution of solar resource data is approximately 3 to 7 km (depending on the latitude), which is enhanced by downscaling to a nominal resolution of approximately 1 km. The spatial resolution of other data parameters has been also harmonized to 1 km. |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution license (CC BY 3.0 IGO) |
| Link to License | https://creativecommons.org/licenses/by/3.0/igo/ |
| Citation | The following attribution is requested: “Solar resource data obtained from the Global Solar Atlas, owned by the World Bank Group and provided by Solargis.” |
| Tags | Solar; GHI; PV |
Wind¶
| Dataset | Wind speed or Power Density |
| Data Type | Raster |
| Units | m/s or W/m2 |
| Spatial Resolution | 0.01 deg |
| Description | Provide information about the wind velocity (m/sec) over an area. The wind power density map should provide information about the power density (W/m2) at a clearly stated altitude. |
| Why we are using this dataset | |
| Author | Technical University of Denmark (“DTU”) |
| Year | 2018 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | Energy Sector Management Assistance Program (ESMAP) |
| Learn More Link | https://globalwindatlas.info/downloads |
| Download from Source | |
| Category | Energy Resources |
| Cautions | |
| Supplementary Info | Vesrion 2.3. Wind resource mapping at 50, 100 and 200 m a.g.l. |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 International License. Full license text available at Creative Commons Attribution 4.0 |
| Link to License | https://creativecommons.org/licenses/by/4.0/ |
| Citation | By using the Works, you agree to provide attribution in accordance with the licensing conditions outlined above. To recognize the full partnership involved in developing the GWA App and Works, users are requested to use the following citation text: [Data/information/map obtained from the] “Global Wind Atlas 2.0, a free, web-based application developed, owned and operated by the Technical University of Denmark (DTU) in partnership with the World Bank Group, utilizing data provided by Vortex, with funding provided by the Energy Sector Management Assistance Program (ESMAP). For additional information: https://globalwindatlas.info” |
| Tags | Wind speed; Power density |
Small Scale Hydropower¶
| Dataset | Small scale Hydropower potential |
| Data Type | Vector |
| Units | |
| Spatial Resolution | National |
| Description | Points showing potential mini/small hydropower potential. Dataset developed by KTH dESA including environmental, social and topological restrictions and provides power availability in each identified point. Other sources can be used but should also provide such information to reassure the proper model function. This information is regarding the location of the plants, their power output, the head and the discharge connected to each point. |
| Why we are using this dataset | |
| Author | Alexandros Korkovelos |
| Year | 2017 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | KTH Royal Institute of Technology |
| Learn More Link | https://energydata.info/dataset/small-and-mini-hydropower-potential-in-sub-saharan-africa |
| Download from Source | |
| Category | Energy Resources |
| Cautions | |
| Supplementary Info | |
| Geographic Coverage | Sub-Saharan Africa |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | June 19, 2017, 8:35 PM (UTC+02:00) |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 International License. Full license text available at Creative Commons Attribution 4.0 |
| Link to License | https://creativecommons.org/licenses/by/4.0/ |
| Citation | Korkovelos, A.; Mentis, D.; Siyal, S.H.; Arderne, C.; Rogner, H.; Bazilian, M.; Howells, M.; Beck, H.; De Roo, A. A Geospatial Assessment of Small-Scale Hydropower Potential in Sub-Saharan Africa. Energies 2018, 11, 3100. |
| Tags | Small scale hydro; GIS; Sub-Saharan Africa |
Land Cover¶
| Dataset | Land cover |
| Data Type | Raster |
| Units | 0-16, 254, 255 |
| Spatial Resolution | 0.00467 deg |
| Description | Land cover maps are used in a number of processes in the analysis (Energy potentials, restriction zones, grid extension suitability map etc.). Currently the land cover map used is divided into 17 classes. The classes are described in http://glcf.umd.edu/data/lc/. If this land cover map is replaced the land cover classification in OnSSET has to be altered. It is therefore advantageous if any land cover map that is used is classified similarly to the one described above. |
| Why we are using this dataset | |
| Author | GLCF |
| Year | 2010 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | |
| Learn More Link | http://glcf.umd.edu/data/lc/ |
| Download from Source | |
| Category | Land cover |
| Cautions | |
| Supplementary Info | Global Mosaics of the standard MODIS land cover type data product (MCD12Q1) in the IGBP Land Cover Type Classification are reprojected into geographic coordinates of latitude and longitude on the WGS 1984 coordinate reference system (EPSG: 4326). The data set boundaries are -180.0° <= longitude <= 180.0°; -64.0° <= latitude <= 84.0°. The data are organized as an array of values uniformly spaced across latitude and longitude with the indexed as [0, 0] at 84.0° latitude, -180.0° longitude. Spatially aggregated data for each year in the period 2001–2012 are available at two spatial resolutions: 5’ x 5’ resolution comprising 1776 rows x 4320 columns at a geographic pixel size of approximately 0.083333°; and 0.5° x 0.5° resolution comprising 296 rows x 720 columns of 0.5° pixels. The global land cover data sets are available as GeoTIFF format files (.tif) with embedded metadata or as ESRI ASCII Grid format files (.asc) with limited metadata in header lines. Native resolution data in the GLCF tile framework are available as GeoTIFF format files (*.tif). |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | 2010 |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | |
| License Type (if available) | |
| Link to License | |
| Citation | Data set development attribution: Channan, S., K. Collins, and W. R. Emanuel. 2014. Global mosaics of the standard MODIS land cover type data. University of Maryland and the Pacific Northwest National Laboratory, College Park, Maryland, USA. and MODIS standard data product attribution Friedl, M.A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley and X. Huang (2010), MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, 2001-2012, Collection 5.1 IGBP Land Cover, Boston University, Boston, MA, USA. |
| Tags | Land cover; MODIS |
Elevation¶
| Dataset | Elevation |
| Data Type | Raster |
| Units | meters |
| Spatial Resolution | 0.00083 deg |
| Description | Filled Digital Elevation Model (DEM) maps are used in a number of processes in the analysis (Energy potentials, restriction zones, grid extension suitability map etc.). |
| Why we are using this dataset | |
| Author | CGIAR-CSI |
| Year | 2008 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | |
| Learn More Link | http://www.cgiar-csi.org/data |
| Download from Source | |
| Category | Land cover |
| Cautions | |
| Supplementary Info | Database v4.1 |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | 2008 |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | |
| License Type (if available) | |
| Link to License | |
| Citation | Jarvis, A., H.I. Reuter, A. Nelson, E. Guevara, 2008, Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (http://srtm.csi.cgiar.org). |
| Tags | DEM; elevation map |
Slope¶
| Dataset | Slope |
| Data Type | Raster |
| Units | degrees |
| Spatial Resolution | 0.00083 deg |
| Description | A sub product of DEM. The slope map visualizes the terrain slope in degrees. Any slope map that is to be used has to provide the slope in degrees. |
| Why we are using this dataset | |
| Author | KTH desa |
| Year | 2017 |
| Availability | Available |
| Cleaned/Processed? | Processed |
| Responsible Party | KTH dESA |
| Learn More Link | |
| Download from Source | |
| Category | Land cover |
| Cautions | |
| Supplementary Info | |
| Geographic Coverage | Africa |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | 2017 |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 |
| Link to License | https://creativecommons.org/licenses/by/4.0/ |
| Citation | |
| Tags | slope; elevation; Africa |
Socio-economic¶
Administrative units¶
| Dataset | Administrative Boundaries |
| Data Type | Vector |
| Units | |
| Spatial Resolution | National, sub-national |
| Description | Includes information (e.g. name) of the country(s) to be modelled and delineates the boundaries of the analysis. |
| Why we are using this dataset | |
| Author | GADM |
| Year | 2018 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | GADM |
| Learn More Link | https://gadm.org/download_country_v3.html |
| Download from Source | |
| Category | Socio-economic |
| Cautions | |
| Supplementary Info | Version 3.6 |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | 6 May 2018 |
| Date of Content | |
| Frequency of Updates | 3-6 months |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | The data are freely available for academic use and other non-commercial use. Redistribution, or commercial use, is not allowed without prior permission. Using the data to create maps for academic publishing is allowed. |
| Link to License | |
| Citation | |
| Tags | administrative boundaries |
Population¶
| Dataset | Population clusters - distribution & density |
| Data Type | Vector |
| Units | |
| Spatial Resolution | National |
| Description | Spatial quantification of the population for a selected area of interest (usually country or continent). |
| Why we are using this dataset | |
| Author | Babak Khavari, Andreas Sahlberg, Alexandros Korkovelos, Mark Howells |
| Year | 2019 |
| Availability | Available |
| Cleaned/Processed? | Processed |
| Responsible Party | KTH dESA |
| Learn More Link | |
| Download from Source | https://data.mendeley.com/datasets/z9zfhzk8cr/4 |
| Category | Socio-economic |
| Cautions | |
| Supplementary Info | |
| Geographic Coverage | Malawi |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | February 1 2019 |
| Date of Content | |
| Frequency of Updates | yearly |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 |
| Link to License | https://creativecommons.org/licenses/by/4.0/ |
| Citation | http://dx.doi.org/10.17632/z9zfhzk8cr.4 |
| Tags | population; clusters; settlements |
Travel time¶
| Dataset | Travel time |
| Data Type | Raster |
| Units | minutes |
| Spatial Resolution | 0.0083 deg |
| Description | Visualizes spatially the travel time required to reach from any individual cell to the closest town with population more than 50,000 people. The unit of these maps should preferably be in minutes but hours is also acceptable. |
| Why we are using this dataset | |
| Author | map |
| Year | 2015 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | |
| Learn More Link | https://map.ox.ac.uk/research-project/accessibility_to_cities/ |
| Download from Source | |
| Category | Transport; socio-economic |
| Cautions | |
| Supplementary Info | In the present study, we quantify and validate global accessibility to high-density urban centres at a resolution of 1×1 kilometre for 2015, as measured by travel time. The last global mapping effort to measure accessibility was for the year 2000, a time that predates both substantial investment and expansion of transportation infrastructure and an extraordinary improvement in the data quantity and quality of accessibility measures. The game-changing improvement underpinning this work is the first-ever, global-scale synthesis of two leading roads datasets – Open Street Map (OSM) data and distance-to-roads data derived from the Google roads database – which resulted in a nearly five-fold increase in the mapped road area relative to that used to produce the circa 2000 map. A major strength of the new roads data is its inclusion of minor roads (e.g., unpaved rural roads), which comprise a large proportion of roads in many low-resource settings and were largely absent or geographically inaccurate in previous roads databases. As such, the improvements in our accessibility map are most prominent in the areas where quality data are most needed for informing sustainable development policies and actions. To illustrate the far-reaching utility of our 2015 global accessibility map, we conduct exploratory analyses that enumerate geographic and wealth-based inequities in accessibility. We also show that shorter travel times to population centres in low- to middle-income countries is strongly associated with socioeconomic and health indicators (i.e., household wealth, educational attainment, and healthcare utilization), highlighting the vital role of accessibility in the pursuit of sustainable development worldwide. Beyond the socioeconomic and health domains, this work could be used to inform environmental and conservation efforts to balance infrastructure demands with ecosystem preservation. |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 |
| Link to License | https://creativecommons.org/licenses/by/4.0/ |
| Citation | D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181. |
| Tags | travel time; GIS |
Nighttime Lights¶
| Dataset | Nighttime Lights (NTL) |
| Data Type | Raster |
| Units | nW cm^-2 sr^-1 |
| Spatial Resolution | 0.00417 deg |
| Description | Nighttime light maps showing light pollution. The map shows stable light source wiht the unit nW cm^−2 sr^−1. Available on a yearly basis and monhtly basis. The monthly data is not cleaned of noise and outliers while the yearly one is. Latest yearly dataset is from 2016 |
| Why we are using this dataset | Night-time light maps capture anthropogenic light sources on the surface of the earth using satellite imagery. It is a good proxy for assessing where electrified human settlements are, as these tend to give light pollution. In OnSSET nighttime light maps are used to estimate the location of currently electrified population. |
| Author | NOAA National Centers for Environmental Information (NCEI) |
| Year | 2016 |
| Availability | Available |
| Cleaned/Processed? | Cloud free composite |
| Responsible Party | NOAA National Centers for Environmental Information (NCEI) |
| Learn More Link | https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html |
| Download from Source | https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html |
| Category | Night time lights; Socio-economic |
| Cautions | Nighttime light maps mostly capture light from outdoor sources; in many cases outdoor light is not a very good indicator of household electricity. |
| Supplementary Info | |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | |
| Date of Content | |
| Frequency of Updates | yearly |
| Summary of License (Open, Closed, Limited) | |
| License Type (if available) | |
| Link to License | |
| Citation | |
| Tags | nighttime lights; NOAA |
Residential Electricity Demand target layer¶
| Dataset | Residential demand |
| Data Type | Raster |
| Units | kWh/capita/year |
| Spatial Resolution | 0.0083 deg |
| Description | Layer that indicates electricity demand for residential sector (e.g. WRI’s perspective map) |
| Why we are using this dataset | |
| Author | KTH dESA |
| Year | 2019 |
| Availability | Potentially available |
| Cleaned/Processed? | Processed |
| Responsible Party | KTH dESA |
| Learn More Link | |
| Download from Source | |
| Category | Socio-economic |
| Cautions | |
| Supplementary Info | |
| Geographic Coverage | Malawi |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | |
| Date of Content | |
| Frequency of Updates | yearly |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 |
| Link to License | https://creativecommons.org/licenses/by/4.0/ |
| Citation | |
| Tags | electricity demand; households: energy access |
Supplementary layers¶
Power Plants (existing & Planned)¶
| Dataset | Power Plants (Existing & Planned) |
| Data Type | Vector |
| Units | kW |
| Spatial Resolution | National |
| Description | The locations of existing and planned power plants. It is also important that the dataset includes attributes regarding each plant’s minimum capacity. |
| Why we are using this dataset | |
| Author | |
| Year | 2018 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | World Resources Institute |
| Learn More Link | http://datasets.wri.org/dataset/globalpowerplantdatabase |
| Download from Source | |
| Category | Climate; Energy |
| Cautions | |
| Supplementary Info | The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights for one’s own analysis. Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. As of June 2018, the database includes around 28,500 power plants from 164 countries. It will be continuously updated as data becomes available. The most recent release of the Global Power Plant Database 1.1 includes the addition of two countries (China and Fiji), over 3,000 power plants, and nearly 1300 gigawatts of power capacity. We highly recommend using version 1.1, available online as of June 2018. |
| Geographic Coverage | Global |
| CRS of Original File | |
| Date of Publication | June 11, 2018 |
| Date of Content | |
| Frequency of Updates | Every 4-6 months |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 International License. Full license text available at Creative Commons Attribution 4.0 |
| Link to License | https://www.wri.org/publications/permissions-licensing |
| Citation | Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. 2018. Global Power Plant Database. Published on Resource Watch and Google Earth Engine; http://resourcewatch.org/ https://earthengine.google.com/ |
| Tags | Climate; Energy; Power Plants; Power Sector |
Poverty maps¶
| Dataset | Poverty maps |
| Data Type | Raster or vector |
| Units | % |
| Spatial Resolution | 0.0083 deg |
| Description | Poverty maps stating the headcount for the population below the poverty line. These poverty maps should be on the basis of a known administrative areas. The poverty line used should be clearly stated. If the poverty maps are available as raster maps for the studied countries it would preferable. |
| Why we are using this dataset | |
| Author | Worldpop |
| Year | 2018 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | |
| Learn More Link | http://www.worldpop.org.uk/data/get_data/ |
| Download from Source | |
| Category | Socio-economic |
| Cautions | |
| Supplementary Info | DATASET: Alpha version 2008 estimates of proportion of people per grid square living in poverty, as defined by the Multidimensional Poverty Index (http://www.ophi.org.uk/policy/multidimensional-poverty-index/), and associated uncertainty metrics. UNITS: Proportion of residents living in MPI-defined poverty (poverty dataset); 95% credible interval (uncertainty dataset). MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the DHS and/or LSMS programs. |
| Geographic Coverage | Kenya, Malawi, Nigeria, Uganda, Tanzania, Bangladesh, Pakistan |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | January 2013 |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons Attribution 4.0 |
| Link to License | https://creativecommons.org/licenses/by/4.0/ |
| Citation | Tatem AJ, Gething PW, Bhatt S, Weiss D and Pezzulo C (2013) Pilot high resolution poverty maps, University of Southampton/Oxford. |
| Tags | poverty map; GIS |
GDP PPP¶
| Dataset | GDP PPP |
| Data Type | Raster |
| Units | $ |
| Spatial Resolution | 0.0083 deg |
| Description | GDP map used should be a global raster map and show the purchasing power parity. |
| Why we are using this dataset | |
| Author | Kummu Matti, Taka Maija, Guillaume Joseph H.A. |
| Year | 2018 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | |
| Learn More Link | https://datadryad.org/resource/doi:10.5061/dryad.dk1j0/13 |
| Download from Source | |
| Category | Socio-economic |
| Cautions | |
| Supplementary Info | This global dataset represents the gross domestic production (GDP) of each grid cell. GDP is given in 2011 international US dollars. The data is derived from GDP per capita (PPP) which is multiplied by gridded population data HYDE 3.2 (the years of population data not available (1991-1999) were linearly interpolated at grid scale based on data from years 1990 and 2000). Dataset has global extent at 5 arc-min resolution for the 26-year period of 1990-2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself. |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | 2018-02-01 |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication |
| Link to License | http://creativecommons.org/publicdomain/zero/1.0/ |
| Citation | Kummu M, Taka M, Guillaume JHA (2018) Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015. Scientific Data 5: 180004. https://doi.org/10.1038/sdata.2018.4 and Kummu M, Taka M, Guillaume JHA (2018) Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015. Dryad Digital Repository. https://doi.org/10.5061/dryad.dk1j0 |
| Tags | global spatial data, gridded data, Gross Domestic Product (GDP), development indicator |
HDI¶
| Dataset | HDI |
| Data Type | Raster |
| Units | 0-1 |
| Spatial Resolution | 0.083 deg |
| Description | HDI map can be used in combination with GDP maps in order to assess electricity demand goals. These maps should be in raster format as HDI varies considerably within countries. |
| Why we are using this dataset | |
| Author | Kummu Matti, Taka Maija, Guillaume Joseph H.A. |
| Year | 2018 |
| Availability | Available |
| Cleaned/Processed? | |
| Responsible Party | |
| Learn More Link | https://datadryad.org/resource/doi:10.5061/dryad.dk1j0/10 |
| Download from Source | |
| Category | Socio-economic |
| Cautions | |
| Supplementary Info | HDI is a composite index of average achievement in key dimensions of human development (dimensionless indicator between 0 and 1). This index is based on method introduced 2010 and updated 2011. The subnational data for HDI were collected from multiple national-level datasets, and national-level HDI was collected from UNDP. Years with missing data were interpolated over time thin plate spines, assuming smooth trend over time. The dataset has a global extent at 5 arc-min resolution, and the annual data is available for each year over 1990-2015. HDI sub-national data covers 39 countries and 66% of global population in 2015. |
| Geographic Coverage | Global |
| CRS of Original File | EPSG:4326 - WGS 84 - Geographic |
| Date of Publication | 2018-02-01 |
| Date of Content | |
| Frequency of Updates | |
| Summary of License (Open, Closed, Limited) | Open |
| License Type (if available) | Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication |
| Link to License | http://creativecommons.org/publicdomain/zero/1.0/ |
| Citation | Kummu M, Taka M, Guillaume JHA (2018) Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015. Scientific Data 5: 180004. https://doi.org/10.1038/sdata.2018.4 and Kummu M, Taka M, Guillaume JHA (2018) Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015. Dryad Digital Repository. https://doi.org/10.5061/dryad.dk1j0 |
| Tags | global spatial data, gridded data, Human Development Index (HDI), development indicator |
Income level or Energy expenditure¶
| Dataset | Income level or Energy expenditure |
| Data Type | Vector or Raster |
| Units | $/year |
| Spatial Resolution | best available |
| Description | The income level or energy expenditure in an area could potentially be used for heat-maps identifying higher demand. These maps are preferably available on the basis of known administrative areas |
| Why we are using this dataset | |
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| Availability | Not available |
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Biomass¶
| Dataset | Biomass |
| Data Type | Raster |
| Units | not available |
| Spatial Resolution | not available |
| Description | Current and potentially productive agricultural activity as an indicator of agricultural residues. |
| Why we are using this dataset | |
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| Availability | Potentially available |
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Electricity demand for education facilities¶
| Dataset | Productive uses - Electricity demand for education |
| Data Type | Raster |
| Units | kWh/year |
| Spatial Resolution | best available |
| Description | Locations of schools.If there are additional data on school districts (in order to know to which school the population in a certain cell is going to) or the energy demand in the schools it would be useful. |
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| Availability | Not available |
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Electricity demand for health facilities¶
| Dataset | Productive uses - Electricity demand for health |
| Data Type | Raster |
| Units | kWh/year |
| Spatial Resolution | best available |
| Description | Locations of health clinics in the study area. If there are estimates of the energy demand in the health clinics this could also potentially be useful for the analysis. |
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| Availability | Not available |
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Electricity demand in commercial facilities¶
| Dataset | Productive uses - Electricity demand for commercial uses |
| Data Type | Raster |
| Units | kWh/year |
| Spatial Resolution | best available |
| Description | Maps showing electricity demand for commercial activity (mines, stores etc.). This is an important dataset since mines tend to use large quantities of electricity. |
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| Availability | Not (publicly) available |
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Electricity demand for agriculture activities¶
| Dataset | Productive uses - Electricity demand for Agriculture |
| Data Type | Raster |
| Units | kWh/year |
| Spatial Resolution | best available |
| Description | Maps showing the productive uses of electricity within the agricultural sector or areas that can be expected to have a large amount of agricultural activity are useful when estimating the productive uses. |
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| Availability | Potentially available |
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Mobile coverage¶
| Dataset | Mobile phone coverage |
| Data Type | Raster |
| Units | 0-1 |
| Spatial Resolution | best available |
| Description | Indication of where the is mobile phone coverage (service); usually in binary format (1:coverage, 0: no-coverage). It can work as a proxy of locations that are electrified |
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| Availability | Not (publicly) available |
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