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<p>Overview »</p>
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Mapping for Results visualizes the locations of World Bank Bank-financed projects to better monitor development impact, improve aid effectiveness and coordination, and enhance transparency and social accountability. In combination with the subnational socio-economic indicators such as poverty, health, malnutrition, and population density it helps to better target development assistance.<br /><a class="readmore" href="/about">Read More »</a>
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<h2>Data Sources</h2>
<li><a target="_new" href="#worldbank_project_data">World Bank Project Data</a></li>
<li><a target="_new" href="#development_indicators">Development Indicators</a></li>
<li><a target="_new" href="#population_data">Population Data</a></li>
<li><a target="_new" href="#geographic_boundaries">Geographic Boundaries</a></li>
<h4 id="worldbank_project_data">World Bank Project Data</h4>
<p>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 <a href="" target="_new" title="World Bank Search - Projects (Portfolio) API">Open Data API</a>. 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</p>
<h4 id="development_indicators">Development Indicators</h4>
<p>Below is a list of the development indicators included in the Mapping for Results platform.</p>
<p>Human Development Indicators: Malnutrition (MDG1), Infant Mortality (MDG4) and Births Attended (MDG5)</p>
<p>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.</p>
<p><a href="" target="_new" title="Demographic and Health Surveys: MEASURE DHS Home">MEASURE DHS</a> (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 ICF International and is funded by the United States Agency for International Development (USAID) with contributions from other donors such as UNICEF, UNFPA, WHO, UNAIDS.</p>
<p><strong>Malnutrition (MDG1)</strong></p>
<p>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: MEASURE DHS, World Health Organization, Global Database on Child Growth and Malnutrition. Catalog Sources: <a href="" target="_new" title="World Development Indicators | Data">World Development Indicators</a></p>
<p><strong>Infant Mortality (MDG4)</strong></p>
<p>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: MEASURE DHS, Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UNPD, universities and research institutions). Catalog Sources: <a href="" target="_new" title="World Development Indicators | Data">World Development Indicators</a></p>
<p><strong>Births attended (MDG5)</strong></p>
<p>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: MEASURE DHS, UNICEF, State of the World's Children, and Childinfo. Catalog Sources: <a href="" target="_new" title="World Development Indicators | Data">World Development Indicators</a></p>
<p><strong>Poverty - Extreme Poverty (MDG1)</strong> </p>
<p><b>Countries</b>: Ecuador, Guatemala, Madagascar, Philippines, West Bank and Gaza, and Bolivia. </p>
<p>The <a href="" target="_new" title="Small Area Estimates (SAE) Poverty and Inequality Data Set">Center for International Earth Science Information Network (CIESIN) is</a> 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.</p>
<p>The data for the maps comes from <a href="" target="_new" title="Indirect Estimation Techniques">Small Area Estimates of Poverty and Inequality</a>. "In the late 1990's, researchers in the World Bank's Development Economics Research Group (DECRG), building on the work of Ghosh and Rao (1994), applied indirect estimation techniques to produce a census enumeration level poverty map for Ecuador, demonstrating that unbiased estimates of poverty can be derived for small areas by combining the richness of household surveys with the depth in coverage of censuses (Hentschel et al., 1998). Whereas survey data alone produce estimates that are representative nationally (or sub-national to the first administrative level) for hundreds of thousands of households, small area estimation generates estimates of a sub-national nature at a much finer-resolution, with comparable levels of statistical precision (Elbers at al. 2003).The ability to produce reliable estimates of poverty for small geographic areas, without the added costs of fielding additional household surveys, has made this technique attractive to policymakers.</p>
<p>These data sets involve econometric or quantitative indirect estimation procedures that combine spatial precision (such as censuses) with substantive depth (such as surveys). They have been developed and implemented by the World Bank Development Economics Research Group and colleagues, in collaboration with country teams for the implementation of Poverty Reduction Strategy Programmes.</p>
<p>Though spatial information may be used in the process of generating these estimates, the spatial data is generally separated prior to the analysis, reporting and dissemination of the poverty estimates. Thus, CIESIN's database of sub-national small area estimates contains poverty and inequality data with reconstructed boundary information, using basic geographic information system (GIS) tools."</p>
<p><b>World Bank Poverty Assessments</b></p>
<p><b>Countries: </b>Albania, Bangladesh, Belarus, Benin, China, Croatia, Georgia, Guinea-Bissau, Jordan, Kazakhstan, Kosovo, Kyrgyz Republic, Nepal, Nicaragua, Russian Federation, Tajikistan, and Zambia.</p>
<p>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. <a href=",,contentMDK:20210352~menuPK:435735~pagePK:148956~piPK:216618~theSitePK:430367,00.html">Poverty Assessments</a></p>
<p><b>Government Sources</b></p>
<p><b>Countries: </b>Kenya, Malawi, and Mozambique<a name="_GoBack"></a></p>
<p>Basic Report on Well-Being in Kenya (2005-2006)</p>
<p>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).</p>
<p>"Malawi - An Atlas of Social Statistics" </p>
<p>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. </p>
<p>Poverty and Well-Being in Mozambique: The Second National Assessment (2004)</p>
<p>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. </p>
<p><b>Socio-Economic Database for Latin America and the Caribbean and the World Bank (SEDLAC)</b></p>
<p><strong>Countries: Argentina, Haiti</strong></p>
<p>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.</p>
<p><strong>Population Density</strong></p>
<p><b>Countries:</b>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</p>
<p>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.</p>
<br />
<h4 id="#geographic_boundaries">Geographic boundaries</h4>
<p>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.</p>
<p>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.</p>
<p>The <a href="" target="_new">Global Administrative Unit Layers (GAUL)</a> is an initiative implemented by FAO within the <a href="" target="_new">EC-FAO Food Security Programme</a> 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.</p>
<p>Country: Bolivia</p>
<p>The <a href="" target="_new" title="UN SALB Home">United Nations Second Administrative Level Boundaries data set project (SALB)</a> 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.</p>
<p><strong>National Statistics Bureaus</strong></p>
<p>Countries: Kenya and Solomon Islands</p>
<p>· <a href="" target="_new" title="Welcome to Kenya National Bureau of Statistics">Kenya National Bureau of Statistics (KNBS)</a></p>
<p><strong>· </strong><a href="" target="_new" title="Solomon Islands Statistics">Solomon Islands</a></p>
<p>Countries: Albania, Chad, China, Kosovo, Macedonia, FYR, Moldova, Morocco, Nepal, Nigeria, Panama, Russian Federation, Samoa, St. Lucia, Turkmenistan, Ukraine, Uruguay, and Yemen, Republic of, </p>
<p><a href="" target="_new" title="Global Administrative Areas | Boundaries without limits">Global Administrative Areas (GADM)</a> 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.</p>
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