Comparar métodos

	Poverty Mapping (Small-Area Estimation)	Spatial Poverty Mapping
Campo	Development Studies	Development Studies
Familia	Process / pipeline	Process / pipeline
Año de origen	2003	2007
Autor original	Chris Elbers, Jean O. Lanjouw & Peter Lanjouw	World Bank poverty-mapping programme; Bedi, Coudouel & Simler
Tipo	Census-survey small-area poverty estimation method	Spatial-statistical and GIS method for analysing poverty distribution
Fuente seminal	Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-Level Estimation of Poverty and Inequality. Econometrica, 71(1), 355-364.	Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring Economic Growth from Outer Space. American Economic Review, 102(2), 994-1028.
Alias	ELL Method, Poverty Mapping, Census-Survey Poverty Estimation, Small-Area Poverty Estimation	Poverty mapping, Geographic targeting, Poverty maps, Spatial poverty analysis
Relacionados	4	4
Resumen	ELL poverty mapping, named after Chris Elbers, Jean Lanjouw, and Peter Lanjouw, is a small-area estimation method that produces poverty and inequality estimates for geographic units far smaller than a household survey can support on its own. It combines two data sources: a detailed household survey that measures consumption but covers too few households per locality, and a population census that covers everyone but does not measure consumption. The method estimates a model of consumption on variables common to both, imputes consumption into the census, and simulates to generate poverty estimates — with statistically valid standard errors — for districts, communes, or even villages, which are then drawn as poverty maps.	Spatial poverty mapping visualises and analyses the geographic distribution of poverty using geographic information systems and spatial statistics, turning poverty estimates into maps that reveal where the poor live at fine spatial scales. It combines small-area poverty estimates with spatial covariates — remote-sensing data, night-time lights, accessibility, and terrain — examines spatial patterns and autocorrelation, and supports the geographic targeting of resources. Consolidated through the World Bank programme documented by Bedi, Coudouel, and Simler and energised by data such as the satellite night-lights series analysed by Henderson, Storeygard, and Weil, it has become a standard tool for evidence-based geographic targeting.