Girma, Atreshiwal (2012) Remote Sensing and GIS Based Poverty Mapping Small- Area Estimation Approach in Rural Oromiya Regional State. Masters thesis, Addis Ababa University.
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Abstract
GIS is increasingly used in poverty mapping. This research raises awareness about the need for a generic poverty data model for use in poverty mapping, and applies a recently developed small-area estimation technique. The Small Area Estimation (SAE) of Poverty in Rural Oromiya Region was prepared with an objective to provide a more disaggregated picture of poverty in Oromiya Region down to the EA(Enumeration Area) and woreda level, based on detailed information from the 2004/5 household survey with the 2007 population census. The focus of this research is on the spatial representation of poverty. It helps to improve the targeting of public expenditures by identifying where the poorest populations are located. By integrating spatial measures of poverty with other data, access to services, water facility, road and other possible contributing factors, leading to a more complete understanding of different dimensions of human well-being. The research measures the estimation process in detail and describes results of statistical tests for quality checks. According to these tests, the poverty estimates at the Ea (Enumeration Area) and Woreda level are reliable. The report also enhances the transparency of the process and intends to serve as a guide for future updates. The results from the SAE are compared with other geo-referenced database. It is observed that, generally poor woreda tend to have limited access to road networks and similarly, access to water facility is relatively low in poor areas while densely populated. As such, overlaying a poverty map with other geo-referenced indicators is highly informative, and some of these findings can be used for designing, planning and monitoring poverty alleviation strategies at the regional or zonal or woreda or Kebele level. Therefore the high overall level of poverty in Oromiya Region, there are considerable spatial diversified in poverty levels across small administrative units (EA) within the region.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Poverty mapping, GIS, Small-Area Estimation, Census and Welfare, Data Model |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) H Social Sciences > H Social Sciences (General) Q Science > QE Geology |
| Divisions: | Africana |
| Depositing User: | Selom Ghislain |
| Date Deposited: | 07 Sep 2018 06:47 |
| Last Modified: | 07 Sep 2018 06:47 |
| URI: | http://thesisbank.jhia.ac.ke/id/eprint/4993 |
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