Updated
4 December 2020
| Dataset date: July 01, 2019-September 30, 2019
This data is by request only
The dataset contains the full data collected in the field related to energy access. The assessment used the "Beyond Connection" methodology, developed by ESMAP (World Bank).
The full sample is based on 210 interviews at the household level in 68 different communities within the Hebron Governorate. All the households are in Area C.
As soon the data are fully analyzed, an open version will be released.
Updated
17 February 2020
| Dataset date: February 17, 2020-February 17, 2020
This dataset updates: As needed
Eight Geojson spatial layer of Syria power line and generator and power plant and substation
Two Geojson spatial layer of Syria Natural Water and waterway dam
Four Geojson spatial layer of Syria highway /motorway / link /primary / trunk
Updated
10 November 2019
| Dataset date: June 11, 2018-June 11, 2018
This dataset updates: Every three months
Rapid needs assessment conducted across 255 communities in Idleb Governorate and surrounding opposition held areas in north western Hama, and western Aleppo. Dataset includes demographics, IDP movement intentions, and sectoral information for shelter, food security, livelihoods, electricity and NFIs, WASH, Health, Education, and Protection. Data was collected from May 24th to the 31st.
Updated
19 March 2019
| Dataset date: January 24, 2019-January 24, 2019
This dataset updates: Never
Facebook has produced a model to help map global medium voltage (MV) grid infrastructure, i.e. the distribution lines which connect high-voltage transmission infrastructure to consumer-serving low-voltage distribution. The data found here are model outputs for six select African countries: Malawi, Nigeria, Uganda, DRC, Cote D’Ivoire, and Zambia. The grid maps are produced using a new methodology that employs various publicly-available datasets (night time satellite imagery, roads, political boundaries, etc) to predict the location of existing MV grid infrastructure. The model documentation and code are also available , so data scientists and planners globally can replicate the model to expand model coverage to other countries where this data is not already available. You can find the model code and documentation here: https://github.com/facebookresearch/many-to-many-dijkstra
Note: current model accuracy is approximately 70% when compared to existing ground-truthed data. Accuracy can be further improved by integrating other locally-relevant information into the model and running it again.
Resolution: geotiff is provided at Bing Tile Level 20
