Salīdzināt metodes

	Spatial Poverty Mapping	Asset Index Construction
Nozare	Development Studies	Development Studies
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2007	2001
Autors	World Bank poverty-mapping programme; Bedi, Coudouel & Simler	Deon Filmer & Lant Pritchett
Tips	Spatial-statistical and GIS method for analysing poverty distribution	Composite socioeconomic-status proxy index
Pirmavots	Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring Economic Growth from Outer Space. American Economic Review, 102(2), 994-1028.	Filmer, D., & Pritchett, L. H. (2001). Estimating Wealth Effects without Expenditure Data—or Tears: An Application to Educational Enrollments in States of India. Demography, 38(1), 115-132.
Citi nosaukumi	Poverty mapping, Geographic targeting, Poverty maps, Spatial poverty analysis	Wealth Index, Asset Index, PCA Wealth Index, Socioeconomic Status Index
Saistītās	4	4
Kopsavilkums	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.	Asset index construction builds a proxy for household wealth or socioeconomic status from observable possessions — durable goods, housing quality, and access to utilities — when reliable income or consumption data are unavailable. The dominant approach, popularized by Deon Filmer and Lant Pritchett in 2001, applies principal component analysis (PCA) to a set of asset variables and uses the first principal component as a set of weights, producing a single wealth score for each household. The method underlies the wealth quintiles reported in Demographic and Health Surveys and many other household surveys across low- and middle-income countries.