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Poverty Mapping in Uganda: An Analysis Using Remotely Sensed and Other Environmental Data










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    Poverty mapping in Uganda: Extrapolating household expenditure data using environmental data and regression technique 2011
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    This ‘Working Paper’ series pulls together different strands of work on the wide range of topics covered by the Animal Production and Health Division with the aim of providing ‘fresh’ information on developments in various regions of the globe, some of which is hoped may contribute to foster sustainable and equitable livestock sector development. The work described in this paper follows directly on from earlier attempts to develop a novel approach to mapping poverty using environmental data. The aim was to get closer to understanding some of the underlying causes of poverty – something that is unlikely to be feasible using approaches based only on socio-economic data such as the traditional small area estimate (SAE) techniques. The environmental poverty mapping technique involved modelling geo-registered household expenditure estimates in Uganda, available from household surveys, using discriminant analysis of a range of environmental data – mostly derived from satellite remote sensing. This analysis was successful, resulting in a series of poverty maps and lists of environmental variables that were strongly correlated with poverty at different spatial resolutions.
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    Groundwater search by remote sensing: A methodological approach 2003
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    In the framework of the technical assistance provided to the Groundwater Unit (GWU) of the FAO-implemented IRAQ/SCR/986 "Three-year Agricultural Programme" for the three Iraqi Northern Governorates, a comprehensive remote sensing/GIS methodology was developed to identify potential sites for groundwater exploitation. The approach used in the study was a development of the traditional standard sequence of drainage, landforms, cover and lineaments analyses, to which several improvements and addit ions were made, such as: (1) all data were in digital format and stored in a geo-database as GIS layers; (2) all analyses and interpretations were performed directly from the computer screen; (3) on the basis of a previous positive experience, thermal lineaments analysis was performed; (4) a comprehensive geo-database was created including all GIS layers which were considered of interest for the study; (5) by using the potentiality of GIS software, which allows stacking of georeferenced data f or comparison and integration and data query for subsetting the needed information, selected layers of the database were superimposed on the Landsat image kept as background and a logical series of observations was made, leading to a well-substantiated set of interpretation assumptions. The creation of a GIS database, including the data format and entry, is a time-consuming and laborious exercise, as high accuracy is definitely mandatory. However, once the database is complete, interpretation of features leading to selection of promising sites for groundwater search is carried out easily and quickly. This as a result of data availability of all needed information in a GIS environment. Thirty test areas, selected by the field team, were investigated and 198 promising sites were identified for further ground survey and subsequent drilling.
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    Accessibility Mapping and Rural Poverty in the Horn of Africa 2010
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    This working paper has been prepared jointly for the Intergovernmental Authority on Development Livestock Policy Initiative (IGAD LPI) and the Pro-Poor Livestock Policy Initiative (PPLPI). The IGAD LPI was established by IGAD in collaboration with FAO and with financial support from the European Commission. Its main objective is to enhance the contribution of the livestock sector to sustainable food security and poverty reduction in the IGAD region. The initiative works towards the core outputs of IGAD’s programmes on policy harmonization, agriculture and the environment and regionally integrated information systems. The work described in this paper contributes towards making available standardised spatial data to help analyse policy options, to target policy interventions and to evaluate their impact – contributing to the evidence base underpinning pro-poor livestock policies.

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