Shiselweni

Operating unit-level results for 2016 and prior years represent aggregated totals. For 2015 and 2016, results are available at the subnational level. For 2014 results and prior, the data can only be viewed and explored in aggregate country or regional form. General patterns can be explored for all results, allowing the investigation of trends within and among different operating units. Some variation exists between indicator versions from PEPFAR during 2004-2010, 2011-2014, and 2015-2016. More detail regarding these differences can be found in the indicator reference documents and in reference materials attached to this dashboard.

Data citation: Data retrieved from the Area Database of the Global Data Lab, https://globaldatalab.org/areadata/, version v4.2.Smits, J. GDL Area Database. Sub-national development indicators for research and policy making. GDL Working Paper 16-101 (2016).

Household Population and Housing Characteristics of Swaziland, 2007

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 Statistics of Swaziland

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

Data cited at: https://swaziland.opendataforafrica.org/SEDSW2016

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.

Data cited at: The Water Footprint Network https://waterfootprint.org/en/ Topic: Product water footprint statistics Publication: https://waterfootprint.org/en/resources/waterstat/product-water-footprint-statistics/ Reference: Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600. License: https://creativecommons.org/licenses/by-sa/3.0/    

Data cited at: The Water Footprint Network https://waterfootprint.org/en/ Topic: Product water footprint statistics Publication: https://waterfootprint.org/en/resources/waterstat/product-water-footprint-statistics/ Reference: Mekonnen, M.M. & Hoekstra, A.Y. (2011) The green, blue and grey water footprint of crops and derived crop products, Hydrology and Earth System Sciences, 15(5): 1577-1600. License: https://creativecommons.org/licenses/by-sa/3.0/