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Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products
Institution:1. Department of Physics, University of Idaho, Moscow, ID 83844-0903, United States;2. SETI Institute, Mountain View, CA 94043, United States;1. Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh;2. North South University, Dhaka, Bangladesh;3. International Centre for Diarrhoeal Disease Research, Bangladesh;1. Department of Computing Sciences, Texas A&M University – Corpus Christi, Corpus Christi, TX, USA;2. Department of Psychology and Sociology, Texas A&M University – Corpus Christi, Corpus Christi, TX, USA;3. Department of Mathematics and Statistics, Texas A&M University – Corpus Christi, Corpus Christi, TX, USA
Abstract:Poverty at the national and sub-national level is commonly mapped on the basis of household surveys. Typical poverty metrics like the head count index are not able to identify its underlaying factors, particularly in rural economies based on subsistence agriculture. This paper relates agro-ecological marginality identified from regional and global datasets including remote sensing products like the normalized difference vegetation index (NDVI) and rainfall to rural agricultural production and food consumption in Burkina Faso. The objective is to analyze poverty patterns and to generate a fine resolution poverty map at the national scale. We compose a new indicator from a range of welfare indicators quantified from Georeferenced household surveys, indicating a spatially varying set of welfare and poverty states of rural communities. Next, a local spatial regression is used to relate each welfare and poverty state to the agro-ecological marginality. Our results show strong spatial dependency of welfare and poverty states over agro-ecological marginality in heterogeneous regions, indicating that environmental factors affect living conditions in rural communities. The agro-ecological stress and related marginality vary locally between rural communities within each region. About 58% variance in the welfare indicator is explained by the factors of rural agricultural production and 42% is explained by the factor of food consumption. We found that the spatially explicit approach based on multi-temporal remote sensing products effectively summarizes information on poverty and facilitates further interpretation of the newly developed welfare indicator. The proposed method was validated with poverty incidence obtained from national surveys.
Keywords:Food security  Composite asset index  SPOT NDVI  TAMSAT rainfall  Geographical weighted regression
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