Banjul

Data is available for the 17 countries covered by the SWAC/OECD (Benin, Burkina Faso, Cabo Verde, Chad, Cote d'Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo).   Agglomerated population: Population of urban agglomerations above 10 000 inhabitants.

Agriculture Statistics of Gambia

Data Dictionary: https://github.com/wri/Aqueduct40/blob/master/data_dictionary_country-rankings.md#country-future  

Demographic Statistics of Gambia

Data citation: Data retrieved from the Area Database of the Global Data Lab, https://globaldatalab.org/areadata/, Version 4.4.1Smits, J. GDL Area Database. Sub-national development indicators for research and policy making.Smits GDL Working Paper 16-101 v44.pdf Smits GDL Working Paper 16-101 v44.docx

Geolocation of urban agglomerations in West Africa.

Household Using Energy Statistics of Gambia

This Dataset shows the modelled parasite rate for Plasmodium falciparum for the years 2000-2015 for all African countries where it is endemic. The Dataset shows the percentage of 2-10 year olds infected by the parasite for each year.

Population and Housing Census of Gambia, by LGA/District, 2013

Regional Directory of Settlements of Gambia

Data cited at: https://gambia.opendataforafrica.org/ipqdqie

Postharvest loss profiles (PHL profiles) quantify the expected loss – as a percentage – at each point along the postharvest chain. This loss data is collected by reviewing scientific literature and is broken down by crop, type of farm and climate type (based on the Köppen-Geiger climate classification). These profiles provide percentage loss figures for the various crops throughout the value chain under varying conditions and are updated as new research becomes available."   For complete reference information and definitions, Please visit: https://www.aphlis.net/en/page/20/data-tables#/datatables?year=20&tab=references&metric=prc

This dataset provides data on literacy rates, primary and secondary school attendance rates access to improved water and sanitation, household access to electricity, and household ownership of radio and television. Unlike other datasets, notably the World Bank’s World Development Indicators (WDI), this dataset provides data at the subnational level, specifically the first administrative district level. Furthermore, the data is comparable both within and across countries. This subnational level of data allows for assessment of education and household characteristics at a more relevant level for allocation of resources and targeting development interventions.