November 11, 2020
| Dataset date: Nov 21, 2020-May 23, 2026
This data is by request only
Contains data crowdsourced from Venezuelans through the Premise Data mobile application. The booklet included HERE provides more details on how crowdsourcing works. The data tracks delivery indicators for electricity, cooking gas, and garbage disposal at a weekly granularity.
November 10, 2019
| Dataset date: Jun 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.
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.
March 19, 2019
| Dataset date: Jan 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
The data set consists of district wise per capita gross national income for 2014. The variable used for per capita gross national income are agriculture and forestry, fishing, mining and quarrying, manufacturing, electricity, gas and water, construction, wholesale and retail trade, hotels and restaurants, transport, storage and communications, financial intermediation, real estate, renting and business activities, public administration and defense, education, health and social work, other community, social and personal services activities, total economy including financial intermediation service indirectly measured (total value added), total economy at basic price (total value added), GDP at market price, factor income, GNI, per capita income ( Rs. at), per capita income ($) and per capita income, PPP ($). The data is extracted from Nepal Human Developement Report (2014) by UNDP (http://www.npc.gov.np/new/uploadedFiles/allFiles/NHDR_Report_2014.pdf).