Luigi Sedda1, Andrew J Tatem2, David W Morley3, Peter M Atkinson4, Nicola A Wardrop4, Carla Pezzulo4, Alessandro Sorichetta4, Joanna Kuleszo4, David J Rogers5. 1. Geography and Environment, University of Southampton, Highfield, SO17 1BJ, Southampton, UK L.Sedda@soton.ac.uk. 2. Geography and Environment, University of Southampton, Highfield, SO17 1BJ, Southampton, UK Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA Flowminder Foundation, 17177 Stockholm, Sweden. 3. MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St. Mary's Campus, W2 1PG, London, UK. 4. Geography and Environment, University of Southampton, Highfield, SO17 1BJ, Southampton, UK. 5. Department of Zoology, University of Oxford, South Parks Road, OX1 3PS, Oxford, UK.
Abstract
BACKGROUND: Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI. METHODS: In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa. RESULTS: This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found. CONCLUSIONS: These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.
BACKGROUND: Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI. METHODS: In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa. RESULTS: This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found. CONCLUSIONS: These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.
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