DATA Beta

The dataset results from WRI's calculation based on 1. NATIONAL FOREST AUTHORITY (NFA). 1996. Land Cover GIS Database. Kampala, Uganda: Government of Uganda. 2. KARANJA, F., L. EMERTON, J. MAFUMBO, AND W. KAKURU. 2001. Assessment of the Economic Value of Pallisa District Wetlands. Kampala,...

Some data were used in maps 4, 5, 6, 7, 8, 9, 10 of ""Mapping a Better Future: How Spatial Analysis Can Benefit Wetlands and Reduce Poverty in Uganda."" from Wetlands Management Department, Ministry of Water and Environment, Uganda; Uganda Bureau of Statistics; International Livestock Research...

Data used in map 2 of "Mapping a Better Future: How Spatial Analysis Can Benefit Wetlands and Reduce Poverty in Uganda." from Wetlands Management Department, Ministry of Water and Environment, Uganda; Uganda Bureau of Statistics; International Livestock Research Institute; and World Resources...

The dataset results from WRI's calculation based on: 1. NATIONAL FOREST AUTHORITY (NFA). 1996. Land Cover GIS Database. Kampala, Uganda: Government of Uganda, NFA. 2. UGANDA BUREAU OF STATISTICS (UBOS). 2002b. 2002 Uganda Population and Housing Census GIS Database. Kampala, Uganda:...

The poverty indicators produced by the Uganda Bureau of Statistics (UBOS) are based on household consumption expenditures, including both food and a range of nonfood items such as education, transport, health, and rent. Households are defined as poor when their total expenditures fall below...

Awareness around the physical, regulatory, and reputational water risks to companies and their investors is on the rise and robust, comparable and comprehensive data is needed to help assess these water-related risks. In response to this demand, the World Resources Institute developed the...

Companies can use this information to prioritize actions, investors to leverage financial interest to improve water management, and governments to engage with the private sector to seek solutions for more equitable and sustainable water governance. Aqueduct Global Maps 2.0 includes indicators of...

For the current scenario, we used hydrological data from 1960 through 1999 for generating flood inundations for 9 return periods, from 2-year flood to 1000-year flood, and 2010 GDP, population, and land use data for assessing flood impacts. For future projections, we used 5 GCMs (Global Climate...