Abstract

n this paper, we propose a solution development of Business Intelligence and Geographic Information Systems (SIG) to an integrated management of information generated from Census of population, households and dwellings, and the Survey of Economic and Social Classification (CASEN). This, as an alternative to current methods in small area estimation (SAE) that are used to obtain disaggregated welfare indicators and estimate, for example, the income from the household attributes, ignoring the geographical location of the observations in the Survey. In this paper, we propose a change in the traditional implicit logic in these methods, to estimate the average income in small areas, georeferencing the survey observations using the matching method known as Matching Estimator and then extrapolating data using the Kriging prediction technique. The proposal suggests the use of an integrated database of both sources allowing to obtain data from the survey of economic characterization at levels of disaggregation not originally provided and are transferred by pairing with census data. Using spatial location codes incorporated into a SIG, cartographic visualization tools are added, which facilitates the observation and analysis of spatial relationships among geographic units, as well as observation and analysis of particularities in small areas. As an application, we describe the per capita spatial disaggregation income of households in Regions XIII, VI and VII of Chile.