Shinyanga Region

These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

The latest figures for 2020 presented in this dataset for some indicators are provisional and some indicators were revised according to the National Bureau of Statistics (NBS) Tanzania revision policies. Also, information in some tables is sourced from census and surveys which are mostly conducted in a lag of ten or five years. The data gaps attributed to the census and surveys shall be filled immediately upon the availability of respective reports.

Housing Characteristics of Tanzania

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.

Please refer below links for other relevant topic wise data:Population and Average Household size in Tanzania - http://knoema.com/TANPOAHS2016Population by Age in Single Years and Five-Year Age Groups of Tanzania - http://knoema.com/TANPOAGS2016Causes of Death, Inpatient and Outpatient Department Diagnosis, Tanzania - http://knoema.com/OPOID2016Health Statistics of Tanzania - http://knoema.com/OPHS2016

Population Projection of Tanzania

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://tanzania.opendataforafrica.org/TZSOCECD2016

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/  

Data cited at: The African Development Bank: Water Utility Database: https://www.infrastructureafrica.org/dataquery/ The Africa Infrastructure Country Diagnostic (AICD) was an unprecedented knowledge program on Africa’s infrastructure that grew out of the pledge by the G8 Summit of 2005 at Gleneagles to substantially increase ODA assistance to Africa, particularly to the infrastructure sector, and the subsequent formation of the Infrastructure Consortium for Africa (ICA). The AICD study was founded on the recognition that sub-Saharan Africa (SSA) suffers from a very weak infrastructural base, and that this is a key factor in the SSA region failing to realize its full potential for economic growth, international trade, and poverty reduction. The study broke new ground, with primary data collection efforts covering network service infrastructures (ICT, power, water & sanitation, road transport, rail transport, sea transport, and air transport) from 2001 to 2006 in 24 selected African countries. Between them, these countries account for 85 percent of the sub-Saharan Africa population, GDP, and infrastructure inflows. The countries included in the initial study were: Benin, Burkina Faso, Cameroon, Cape Verde, Chad, Côte d’Ivoire, Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, South Africa, Senegal, Sudan, Tanzania, Uganda, and Zambia. The study also represents an unprecedented effort to collect detailed economic and technical data on African infrastructure in relation to the fiscal costs of each of the sectors, future sector investment needs, and sector performance indicators. As a result, it has been possible for the first time to portray the magnitude of the continent’s infrastructure challenges and to provide detailed and substantiated estimates on spending needs, funding gaps, and the potential efficiency dividends to be derived from policy reforms.