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Technical bookProcessing 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
2025Also available in:
No results found.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. -
Technical reportPoverty Mapping in Uganda: An Analysis Using Remotely Sensed and Other Environmental Data 2006
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No results found.This is the 36th of a series of Working Papers prepared for the Pro-Poor Livestock Policy Initiative (PPLPI). The purpose of these papers is to explore issues related to livestock development in the context of poverty alleviation. In order to reduce poverty we must first describe, explain and predict its spatial distribution over large areas with as high a level of local accuracy as possible. Poverty maps are traditionally produced by exploiting links between census (wide area) and survey (small er area coverage) data. The detailed relationships found within the survey data are extended to the census data that must share some predictor variables in common with the survey data. Both census and survey data tend to be socioeconomic in nature; the mapping thus exploits the internal correlations within potentially strongly correlated data sets one measure of poverty is often correlated with another. -
BrochureA 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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