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Poverty mapping in Uganda: Extrapolating household expenditure data using environmental data and regression technique














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    Book (stand-alone)
    Processing food consumption data from household consumption and expenditure surveys (HCES)
    Guidelines for countries collecting data in line with the United Nations Statistical Commission-endorsed guidelines on food data collection in HCES
    2025
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    The food data processing guidelines presented in this document provide some basic principles to adopt when transforming the food data collected in household consumption and expenditure surveys (HCES) to data ready for poverty or food security analysis (among other things). The goal is to enable more and more timely, consistent and reliable statistics derived from food consumption data, while also improving the quality and transparency of data processing.The first part presents food consumption modules and provides some useful principles and general methods to consider before starting work. The analyst needs to assess the data collection tools and other available information before embarking on processing the data. Furthermore, the analyst should decide on the overall approach to cleaning the data.The second part provides a step-by-step description of food data processing, following 11 steps that describe how to bring the food consumption data from its raw form, as collected in the survey, to transformed data ready to be used for statistical analysis. The document was produced under the aegis of the United Nations Committee of Experts on Food Security, Agricultural and Rural Statistics (UN-CEAG), which reports to the United Nations Statistical Commission. It was prepared by members of the UN-CEAG task team on food security and consumption statistics, and with several rounds of consultation with a large group of experts from national statistical offices, international organisations and academia.
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    A rapid geospatial analysis of the flood impacts on crops in Eastern Cape province of South Africa in 2022 2023
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    An analysis to assess the impacts of floods on cropland in KwaZulu-Natal province was performed using existing data, GIS and remote sensing. The crop mask was derived from the South African National Land Cover map (SANLC, 2018). The water mask was derived from the Joint Research Centre (JRC) water body data (2020). Sentinel 1 SAR was used to assess flood extent.
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    Brochure, flyer, fact-sheet
    A rapid geospatial analysis of the flood impacts on crops in KwaZulu-Natal province of South Africa in 2022 2023
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    No results found.

    An analysis to assess the impacts of floods on cropland in KwaZulu-Natal province was performed using existing data, GIS and remote sensing. The crop mask was derived from the South African National Land Cover map (SANLC, 2018). The water mask was derived from the Joint Research RC) water body data (2020). Sentinel 1 SAR was used to assess flood extent.

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