GIS is increasingly used in poverty mapping. This research raises awarenessrnabout the need for a generic poverty data model for use in poverty mapping,rnand applies a recently developed small-area estimation technique. ThernSmall Area Estimation (SAE) of Poverty in Rural Oromiya Region wasrnprepared with an objective to provide a more disaggregated picture of povertyrnin Oromiya Region down to the EA(Enumeration Area) and woreda level,rnbased on detailed information from the 2004/5 household survey withrnthe 2007 population census. The focus of this research is on the spatialrnrepresentation of poverty. It helps to improve the targeting of publicrnexpenditures by identifying where the poorest populations are located. Byrnintegrating spatial measures of poverty with other data, access to services,rnwater facility, road and other possible contributing factors, leading to a morerncomplete understanding of different dimensions of human well-being. Thernresearch measures the estimation process in detail and describesrnresults of statistical tests for quality checks. According to these tests, thernpoverty estimates at the Ea (Enumeration Area) and Woreda level arernreliable. The report also enhances the transparency of the process andrnintends to serve as a guide for future updates.rnThe results from the SAE are compared with other geo-referenced database.rnIt is observed that, generally poor woreda tend to have limited access tornroad networks and similarly, access to water facility is relatively low inrnpoor areas while densely populated. As such, overlaying a poverty maprnwith other geo-referenced indicators is highly informative, and some of thesernfindings can be used for designing, planning and monitoring povertyrnalleviation strategies at the regional or zonal or woreda or Kebele level.rnTherefore the high overall level of poverty in Oromiya Region, there arernconsiderable spatial diversified in poverty levels across smallrnadministrative units (EA) within the region.rnKeywords: Poverty mapping, GIS, Small-Area Estimation, Census andrnWelfare, Data Model