Remote Sensing and GIS Based Poverty Mapping Small- Area Estimation Approach in Rural Oromiya Regional State
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Date
2012-06
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Addis Ababa Universty
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.
Keywords: Poverty mapping, GIS, Small-Area Estimation, Census and
Welfare, Data Model
Description
Keywords
Poverty mapping, GIS, Small-Area Estimation, Census and Welfare, Data Model