Poverty, health and satellite-derived vegetation indices: Their inter-spatial relationship in West Africa

International Health

Volumn 7, Issue 2, 2015, Pages 99-106

  Sedda, Luigi ^a   Tatema, Andrew J ^a,b,c   Morley, David W ^d   Atkinson, Peter M ^a   Wardrop, Nicola A ^a   Pezzulo, Carla ^a   Sorichetta, Alessandro ^a   Kuleszo, Joanna ^a   Rogers, David J ^e  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

^c FLOWMINDER FOUNDATION   (Sweden)

^d Imperial College London   (United Kingdom)

^e UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Child mortality; Geostatistics; Multidimensional poverty index; Normalized difference vegetation index; Nutrition; Poverty

Indexed keywords

ARTICLE; BENIN; BURKINA FASO; CAMEROON; CHILD ATTENDANCE; CHILDHOOD MORTALITY; COKRIGING; COTE D'IVOIRE; DESCRIPTIVE RESEARCH; EDUCATION; FACTORIAL KRIGING; GEOSTATISTICAL ANALYSIS; GHANA; HUMAN; KRIGING; MALI; MALNUTRITION; NIGER; NIGERIA; NORMALIZED DIFFERENCE VEGETATION INDEX; POVERTY; PRIORITY JOURNAL; SPATIAL ANALYSIS; TOGO; VARIOGRAPHY; VEGETATION; AFRICA; BIOLOGICAL MODEL; CHILD; CHILD NUTRITION DISORDERS; CLIMATE; CLIMATE CHANGE; ECOLOGY; ECOSYSTEM; ENVIRONMENT; ENVIRONMENTAL MONITORING; HEALTH; PLANT; REGRESSION ANALYSIS; RURAL POPULATION; SATELLITE IMAGERY; TEMPERATURE;

AFRICA, WESTERN; CHILD; CHILD MORTALITY; CHILD NUTRITION DISORDERS; CLIMATE; CLIMATE CHANGE; ECOLOGY; ECOSYSTEM; ENVIRONMENT; ENVIRONMENTAL MONITORING; HUMANS; MODELS, BIOLOGICAL; PLANTS; POVERTY; REGRESSION ANALYSIS; RURAL HEALTH; RURAL POPULATION; SATELLITE IMAGERY; TEMPERATURE;

EID: 84924513969     PISSN: 18763413     EISSN: 18763405     Source Type: Journal    
DOI: 10.1093/inthealth/ihv005     Document Type: Article

References (60)

1. Griggs D, Stafford-Smith M, Gaffney O et al. Policy: Sustainable development goals for people and planet. Nature 2013;495:305-7.
2. Ravallion M. How long will it take to lift one billion people out of poverty?. Washington DC: World Bank; 2012. https://openknowledge. worldbank.org/bitstream/handle/10986/12166/wps6325.pdf?sequence=1 [accessed 13 January 2015].
3. World Bank. Poverty Data, A Supplement to World Development Indicators 2008. Washington DC: World Bank; 2008. http://siteresources. worldbank.org/DATASTATISTICS/Resources/WDI08supplement1216.pdf [accessed 13 January 2015].
4. Xiaoyun L, Remenyi J. Making poverty mapping and monitoring participatory. Dev Pract 2008;18:599-610.
5. Erenstein O. Livelihood assets as a multidimensional inverse proxy for poverty: a district-level analysis of the Indian Indo-Gangetic Plains. J Hum Dev Capabil 2011;12:283-302.
6. Nguyen CV. Poverty projection using a small area estimation method:evidence from Vietnam. J Comp Econ 2011;39:368-82.
7. Agostini CA, Brown PH. Cash transfers and poverty reduction in Chile. J Regional Sci 2011;51:604-25.
8. Sahn DE, Stifel DC. Poverty comparisons over time and across countries in Africa. World Dev 2000;28:2123-55.
9. Grolle J. High-resolution mapping of rural poverty and famine vulnerability in the Sahel: a possible approach for the Republic of Niger. Popul Environ 2012;34:1-30.
10. Okwi PO, Ndeng'e G, Kristjanson P et al. Spatial determinants of poverty in rural Kenya. Proc Natl Acad Sci U S A 2007;104:16769-74.
11. Hanjra MA, Gichuki F. Investments in agricultural water management for poverty reduction in Africa: case studies of Limpopo, Nile, and Volta river basins. Nat Resour Forum 2008;32:185-202.
12. Thornton PK, Kruska RL, Henninger N et al. Locating poor livestock keepers at the global level for research and development targeting. Land Use Policy 2003;20:311-22.
13. Hotez PJ, Thompson TG. Waging peace through neglected tropical disease control: a US foreign policy for the bottom billion. PLoS Negl Trop D 2009;3:e346.
14. Adhikari SR, Maskay NM. The economic burden of Kala-azar in households of the Danusha and Mahottari districts of Nepal. Acta Trop 2003;88:1-2.
15. Branca F, D'Acapito P. Seasonality. In: Benjamin CS (editor). Encyclopaedia of Human Nutrition (3rd ed.). Waltham: Academic Press, 2013, p. 178-85.
16. Tickamyer AR. Poverty, Rural. In: Rob K, Nigel TS (Editors). International Encyclopaedia of Human Geography. Oxford: Elsevier, 2009, p. 416-20.
17. Bernstein AS, Myers SS. Climate change and children's health. Curr Opin Pediatr 2011;23:221-6.
18. Mink SD, Mundial B. Poverty, population, and the environment. Washington DC: World Bank; 1993.
19. Staatz JM, Dembélé NN. Agriculture for development in sub-Saharan Africa. Washington DC: World Bank; 2007. http://siteresources. worldbank.org/INTWDR2008/Resources/2795087-1191427986785/ StaatzJ&DembeleN_AgriForDevtInSSA_ve19.pdf [accessed 13 January 2015].
20. Teklehaimanot A, Mejia P. Malaria and poverty. Reducing the impact of poverty on health and human development. Sci Approach 2008;1136:32-7.
21. Kaler SG. Diseases of poverty with highmortality in infants and children - malaria, measles, lower respiratory infections, and diarrheal illnesses. Reducing the Impact of Poverty on Health and Human Development. Sci Approach 2008;1136:28-31.
22. Ward FA. Financing irrigation water management and infrastructure: a review. Int J Water Resour D 2010;26:321-49.
23. WHO, UNICEF. Progress on Sanitation and Drinking-Water: 2013Update. Geneva: World Health Organization; 2013. http://apps.who.int/iris/ bitstream/10665/81245/1/9789241505390_eng.pdf [accessed 13 January 2015].
24. Zheng BH. Statistical inference for poverty measures with relative poverty lines. J Econometrics 2001;101:337-56.
25. Akinyemi F. A conceptual poverty mapping data model. T Gis 2010;14:85-00.
26. Foster J, Seth S, Lokshin M et al. A Unified Approach to Measuring Poverty and Inequality--Theory and Practice: Streamlined Analysis with ADePT Software. Washington DC: The World Bank; 2013.
27. Hwang M, Smith M. Integrating publicly available web mapping tools for cartographic visualization of community food insecurity: A prototype. Geo J 2012;77:47-62.
28. Alkire S, Santos ME. Acute multidimensional poverty: A new index for developing countries. Oxford: Oxford Poverty & Human Development Initiative; 2010. http://www.ophi.org.uk/wp-content/uploads/ophiwp38. pdf [accessed 13 January 2015].
29. Adebanji A, Achia T, Ngetich R et al. Spatial Durbin model for poverty mapping and analysis. Eur J Soc Sci 2008;5:194-204.
30. Bedi T, Coudouel A, Simler K. More Than a Pretty Picture: Using Poverty Maps to Design Better Policies and Interventions. In: Bedi T, Coudouel A, Simler K (editors). Maps for policy making: beyond the obvious targeting applications. Washington DC: World Bank Publications, 2007;3-22.
31. CIESIN. Where the poor are: an atlas of poverty. New York: Center for International Earth Science Information Network; 2006. http://sedac. ciesin.columbia.edu/povmap/downloads/maps/atlas/atlas.pdf [accessed 13 January 2015].
32. Davis B. Choosing a method for poverty mapping. Rome: Food and Agriculture Organization of the United Nations; 2003. http://www. fao.org/3/a-y4597e.pdf [accessed 13 January 2015].
33. Elbers C, Fujii T, Lanjouw P et al. Poverty alleviation through geographic targeting: How much does disaggregation help? J Dev Econ 2007; 83:198-13.
34. Imran M, Stein A, Zurita-Milla R. Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products. Int J Appl Earth Obs Geoinf 2014;26:322-34.
35. Johnson K, Brown ME. Environmental risk factors and child nutritional status and survival in a context of climate variability and change. Appl Geogr 2014;54:209-21.
36. Haglund E, Ndjeunga J, Snook L et al. Dry land tree management for improved household livelihoods: farmer managed natural regeneration in Niger. J Environ Manage 2011;92:1696-705.
37. Paré S, Savadogo P, Tigabu M et al. Consumptive values and local perception of dry forest decline in Burkina Faso, West Africa. Environ Dev Sustain 2009;12:277-95.
38. Weeks JR, Getis A, Stow DA et al. Connecting the dots between health, poverty and place in Accra, Ghana. Ann Assoc Am Geogr 2012;102:932-41.
39. Reij C, Tappan G, Belemvire A. Changing land management practices and vegetation on the Central Plateau of Burkina Faso (1968-2002). J Arid Environ 2005;63:642-59.
40. OPHI. Multidimensional Poverty Index (MPI) Data Bank. Oxford: Oxford Poverty & Human Development Initiative; 2015. http://www.ophi.org. uk/multidimensional-poverty-index/mpi-data-bank/ [accessed 13 January 2015].
41. Alkire S, Conconi A, Roche JM. Multidimensional Poverty Index 2013:Brief Methodological Note and Results. Oxford: Oxford Poverty & Human Development Initiative; 2013. http://www.ophi.org.uk/wpcontent/ uploads/MPI-2013-Brief-Methodological-Note-and-Results1. pdf?0a8fd7 [accessed 13 January 2015].
42. UNDP. Human development report 2013. The Rise of the South: Human Progress in a Diverse World. New York: United Nations Development Programme; 2013 http://hdr.undp.org/sites/default/files/reports/14/ hdr2013_en_complete.pdf [accessed 13 January 2015].
43. Alkire S, Foster J. Understandings and misunderstandings of multidimensional poverty measurement. J Econ Inequal 2011;9:289-14.
44. UNEP. Global Environment Outlook: environment for development (GEO-4). Nairobi: United Nations Environment Programme; 2007. http:// www.unep.org/geo/geo4/report/geo-4_report_full_en.pdf [accessed 13 January 2015].
45. Hountondji YC, Sokpon N, Ozer P. Analysis of the vegetation trends using low resolution remote sensing data in Burkina Faso (1982-1999) for the monitoring of desertification. Int J Remote Sens 2006;27:871-84.
46. Scharlemann JPW, Benz D, Hay SI et al. global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS Data. Plos One 2008;3:e1408.
47. Hijmans RJ, Cameron SE, Parra JL et al. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 2005;25:1965-78.
48. Mitchell A. The ESRI Guide to GIS Analysis, Vol. 2: Spatial Measurements and Statistics. Redlands CA: Environmental Systems Research Institute (ESRI); 2005.
49. Webster R, Oliver MA. Geostatistics for Environmental Scientists. London: Wiley; 2007.
50. Petrucci A, Salvati N, Seghieri C. The application of a spatial regression model to the analysis and mapping of poverty. Rome: Food and Agriculture Organization of the United Nations; 2003. http://www.fao. org/docrep/006/y4841e/y4841e00.htm [accessed 13 January 2015].
51. Journel AG, Huijbregts CJ. Mining Geostatistics. Caldwell NJ: Blackburn Press; 2003.
52. Goulard M, Voltz M. Linear coregionalization model - tools for estimation and choice of cross-variogram matrix. Math Geol 1992;24:269-86.
53. Goovaerts P. Geostatistics for Natural Resources Evaluation. Oxford:Oxford University Press; 1997.
54. Jaquet O. Factorial kriging analysis applied to geological data from petroleum exploration. Math Geol 1989;21:683-91.
55. Delfiner P. Linear estimation of non stationary spatial phenomena. In:Guarascio M, David M, Huijbregts CS (Editors). Advanced Geostatistics in the Mining Industry. Amsterdam: Springer; 1976, p. 49-68.
56. Rogers D, Emwanu T, Robinson T. Poverty mapping in Uganda: An analysis using remotely sensed and other environmental data. Rome: Food and Agriculture Organization of the United Nations, Pro-Poor Livestock Policy Initiative; 2006. http://r4d.dfid.gov.uk/pdf/ outputs/livestock/pplpiwp36.pdf [accessed 13 January 2015].
57. Watmough GR, Atkinson PM, Hutton CW. Predicting socioeconomic conditions from satellite sensor data in rural developing countries: A case study using female literacy in Assam, India. App Geogr 2013;44:192-200.
58. Hall O, Duit A, Caballero LNC. World poverty, environmental vulnerability and population at risk for natural hazards. J Maps 2008:151-60.
59. Diffenbaugh NS, Giorgi F, Raymond L et al. Indicators of 21st century socioclimatic exposure. Proc Natl Acad Sci U S A 2007;104:20195-8.
60. UNCCD, UNDP. The forgotten billion: MDG achievement in the drylands. New York: United Nations Development Programme; Bonn: United Nations Convention to Combat Desertification; 2011. http://www. unccd.int/Lists/SiteDocumentLibrary/Publications/Forgotten%20Billion. pdf [accessed 13 January 2015].