2015
DOI: 10.1093/inthealth/ihv005
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Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa

Abstract: Background: Previous analyses have shown the individual correlations between poverty, health and satellitederived 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 r… Show more

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Cited by 32 publications
(23 citation statements)
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“…In 2011, some parts of southern Somalia were affected by famine in which approximately 4 million people, nearly half the country’s population, faced a humanitarian crisis [ 41 ]. In West African countries, the intensity of poverty was shown to be inversely associated with the vegetation cover [ 42 ].…”
Section: Discussionmentioning
confidence: 99%
“…In 2011, some parts of southern Somalia were affected by famine in which approximately 4 million people, nearly half the country’s population, faced a humanitarian crisis [ 41 ]. In West African countries, the intensity of poverty was shown to be inversely associated with the vegetation cover [ 42 ].…”
Section: Discussionmentioning
confidence: 99%
“…(19)(20)(21) Indicators such as night lights, (22) deforestation, (23) and environment (24) recorded with satellite imagery help reveal the distribution of poverty and its relationship with other factors such as food and health. (25,26) Food is a major fuel that drives human resources. The second SDG is zero hunger for which enough crops need to be produced worldwide.…”
Section: Application Of Rs and Geospatial Technologies For Sdgsmentioning
confidence: 99%
“…Recent work has highlighted the importance of remotely sensed (RS) features such as land use/land cover (LULC) for mapping demographic and socio-economic conditions at various geographic scales [5,[10][11][12][13][14][15][16][17]. In [5], satellite image features such as the number and density of buildings, type of roads and number cars explained roughly 60% of the variation in poverty models derived from census household consumption per capita estimates in Sri Lanka.…”
Section: Introductionmentioning
confidence: 99%