Charts on sector distribution, list of financed activities, and number/volume of financed activities include all active Bank-financed activities under all 12 “lending” product lines. The dataset of financed activities is provided and regularly updated through the Open Data API. Out of these, so far, 2,500 activities been mapped by the Annual Meetings 2011 under the following product lines: IBRD/IDA, Global Environment Project, Special Financing, Montreal Protocol, GEF Medium Sized Programs, Guarantees, Debt Reduction Facility, Carbon Offsets, and large Recipient-Executed Activities. Locations of mapped financed activities are as of June 30, 2011
Below is a list of the development indicators included in the Mapping for Results platform.
Human Development Indicators: Malnutrition (MDG1), Infant Mortality (MDG4) and Births Attended (MDG5)
Countries: Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Cambodia, Cameroon, Central African Republic, Colombia, Congo, Democratic Republic of, Dominican Republic, Egypt, Arab Rep., Ethiopia, Gabon, Ghana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Moldova, Mozambique, Namibia, Nepal, Niger, Nigeria, Philippines, Senegal, Swaziland, Tanzania, Togo, Turkmenistan, Uganda, Ukraine, Zambia, and Zimbabwe.
MEASURE DHS (Demographic and Health Surveys) Project is responsible for collecting and disseminating accurate, nationally representative data on health and population in developing countries. The project is implemented by Macro International, Inc. and is funded by the United States Agency for International Development (USAID) with contributions from other donors such as UNICEF, UNFPA, WHO, UNAIDS.
Malnutrition prevalence, weight for age (% of children under 5): Prevalence of child malnutrition is the percentage of children under age 5 whose height for age (stunting) is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006. Source: World Health Organization, Global Database on Child Growth and Malnutrition. Catalog Sources: World Development Indicators, MEASURE DHS
Infant Mortality (MDG4)
Mortality rate, infant (per 1,000 live births): Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year. Source: Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UNPD, universities and research institutions).Catalog Sources: World Development Indicators, MEASURE DHS
Births attended (MDG5)
Births attended by skilled health staff (% of total): Percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns. Source: UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys by Macro International. Catalog Sources: World Development Indicators, MEASURE DHS
Poverty - Extreme Poverty (MDG1)
Countries: Ecuador, Guatemala, Madagascar, Philippines, West Bank and Gaza, and Bolivia.
The Center for International Earth Science Information Network (CIESIN) is a center within the Earth Institute at Columbia University. CIESIN works at the intersection of the social, natural, and information sciences, and specializes in on-line data and information management, spatial data integration and training, and interdisciplinary research related to human interactions in the environment. The Poverty Mapping Project at CIESIN is funded by the World Bank's Japan Policy and Human Resource Development (PHRD) Fund. This Project was a partnership between CIESIN, the World Bank, and the Earth Institute at Columbia University and was undertaken in 2004-2005.
The data for the maps comes from Small Area Estimates of Poverty and Inequality. According to the project website, "the data sets involve econometric or quantitative indirect estimation procedures that combine spatial precision (such as censuses) with substantive depth (such as surveys)." The database contains poverty and inequality data based on reconstructed boundary information.
World Bank Poverty Assessments
Countries: Albania, Bangladesh, Belarus, Benin, China, Croatia, Georgia, Guinea-Bissau, Jordan, Kazakhstan, Kosovo, Kyrgyz Republic, Nepal, Nicaragua, Russian Federation, Tajikistan, and Zambia.
Poverty Assessments (PA) are key instruments of the World Bank's poverty reduction strategy. They are designed to assess the extent and causes of poverty in a given country and to propose a strategy to ameliorate its effects. PAs review levels and changes over time and across regions in poverty indicators, assess the impact of growth and public actions on poverty and inequality, and review the adequacy of a country's poverty monitoring and evaluation arrangements. PAs generally feed into country-owned processes to develop strategies to reduce poverty, help build in-country capacity, and support joint work.
Countries: Kenya, Malawi, and Mozambique
Basic Report on Well-Being in Kenya: based on Kenya integrated household budget survey, 2005/06, Kenya National Bureau of Statistics, 2007.
The poverty estimates are household survey-based estimates at the stratum level; which happens to be ADM2 in Kenya. These are not small area estimates of poverty (which typically uses both household survey and population census data).
Malawi - An Atlas of Social Statistics, Todd David Benson, International Food Policy Research Institute, 2002
This document was produced by the National Statistics Office and IFPRI in 2002. The poverty data is based on the 1997-1998 Integrated Household Survey. The poverty line is based on the cost of minimum recommended daily calorie requirements plus some additional basic nonfood items. The weighted mean poverty line for Malawi in 1998 was approximately 41 U.S. cents per person per day. Those under that line were considered poor.
Poverty and Well-Being in Mozambique: The Second National Assessment (PDF, 444 Kb), National Directorate of Planning and Budget, Ministry of Planning and Finance, Economic Research Bureau, Ministry of Planning and Finance, International Food Policy Research Institute, Purdue University, 2004
Poverty estimates for Mozambique refer a headcount index of people living below the poverty line. The data mapped is headcount based on a flexible bundle poverty line which accounts for changes in relative prices in order to make poverty comparable across regions of Mozambique.
Socio-Economic Database for Latin America and the Caribbean and the World Bank (SEDLAC)
Countries: Argentina, Haiti
The poverty data is based on the headcount ratios published by LAC governments, and several individual poverty indicators computed following SEDLAC methodology, and using the international poverty line: 2.5 a day at 2005 PPP. The US$ 2.5 line coincides with the median of the extreme poverty lines chosen by the governments of the Latin American countries.
Countries: Afghanistan, Albania, Algeria, Antigua and Barbuda, Argentina, Armenia, Bangladesh, Belarus, Belize, Benin, Bhutan, Bolivia, Bosnia-Herzegovina, Brazil, Bulgaria, Burkina Faso, Cambodia, Cameroon, Central African Republic, Chile, China, Colombia, Congo, Republic of, Costa Rica, Cote D'Ivoire, Croatia, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Equatorial Guinea, Ethiopia, Fiji, Gambia, The, Ghana, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, India, Indonesia, Iran, Islamic Rep., Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Laos, PDR, Lebanon, Lesotho, Macedonia, FYR, Malawi, Malaysia, Maldives, Mali, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Romania, Rwanda, Russian Federation, Samoa, Senegal, Serbia, Seychelles, Sierra Leone, Solomon Islands, South Africa, Sri Lanka, St. Lucia, Sudan, Suriname, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Ukraine, Uruguay, Uzbekistan, Vanuatu, Venezuela, RB, Vietnam, Yemen, and Zambia
The most recent census data at the provincial or district level was compiled from statistics bureaus and used to calculate population density. The population data was divided by the total area for each province or district. Official population density statistics were used in place of these calculations where available. These data are estimates only and should not be used as official numbers.
The maps displayed on the World Bank web site are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of the World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.
Countries: Afghanistan, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Belize, Benin, Bhutan, Bosnia-Herzegovina, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Central African Republic, Chile, Colombia, Comoros, Congo, Democratic Republic of, Congo, Republic of, Costa Rica, Cote D'Ivoire, Croatia, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Equatorial Guinea, Eritrea, Ethiopia, Fiji, Gabon, Gambia, The, Georgia, Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, India, Indonesia, Iran, Islamic Rep., Iraq, Jamaica, Jordan, Kazakhstan, Kiribati, Korea, Rep., Kyrgyz Republic, Laos, PDR, Lebanon, Lesotho, Liberia, Libya, Madagascar, Malawi, Malaysia, Maldives, Mali, Mauritania, Mauritius, Mexico, Mongolia, Montenegro, Mozambique, Myanmar, Namibia, Nicaragua, Niger, Pakistan, Palau, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Romania, Rwanda, Sao Tome and Principe, Senegal, Serbia, Seychelles, Sierra Leone, Somalia, South Africa, Sri Lanka, St. Kitts and Nevis, St. Vincent and the Grenadines, Sudan, Suriname, Swaziland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Uganda, Uzbekistan, Vanuatu, Venezuela, RB, Vietnam, West Bank and Gaza, Zambia, and Zimbabwe.
The Global Administrative Unit Layers (GAUL) is an initiative implemented by FAO within the EC-FAO Food Security Programme funded by the European Commission. The GAUL aims at compiling and disseminating the most reliable spatial information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units.
The United Nations Second Administrative Level Boundaries data set project (SALB) was launched in 2001 in the context of the activities of the UN Geographic Information Working Group (UNGIWG) and has for objective to provide access to a working platform for the collection, management, visualization and sharing of sub national data and information in a seamless way from the national to the global level. This platform is developed in collaboration with and validated by the National Mapping Agencies (NMA) of each UN Member State.
National Statistics Bureaus
Countries: Kenya and Solomon Islands
Countries: Albania, Chad, China, Kosovo, Macedonia, FYR, Moldova, Morocco, Nepal, Nigeria, Panama, Russian Federation, Samoa, St. Lucia, Turkmenistan, Ukraine, Uruguay, and Yemen, Republic of,
Global Administrative Areas (GADM) is a spatial database of the location of the world's administrative areas (or administrative boundaries) for use in GIS and similar software. Administrative areas in this database are countries and lower level subdivisions such as provinces, departments, bibhag, bundeslander, daerah istimewa, fivondronana, krong, landsvæðun, opština, sous-préfectures, counties, and thana. GADM describes where these administrative areas are (the "spatial features"), and for each area it provides some attributes, foremost being the name and variant names.