A Geo-Classification Model for Mapping Mixed Discrete and Continuous Response Data: An Application to Poverty Mapping

International Journal of Mathematics Trends and Technology (IJMTT)
© 2022 by IJMTT Journal
Volume-68 Issue-1
Year of Publication : 2022
Authors : Mark Adjei, Elphas Okango, Richard Puurbalanta, Henry Mwambi, Naiga Babra Charlotte


MLA Style: Mark Adjei, Elphas Okango, Richard Puurbalanta, Henry Mwambi, Naiga Babra Charlotte "A Geo-Classification Model for Mapping Mixed Discrete and Continuous Response Data: An Application to Poverty Mapping" International Journal of Mathematics Trends and Technology 68.1 (2022):143-157. 

APA Style: Mark Adjei, Elphas Okango, Richard Puurbalanta, Henry Mwambi, Naiga Babra Charlotte.(2022). Weighted Sharing and Uniqueness of Entire Functions whose Difference Polynomials Sharing a Polynomial of Certain Degree  International Journal of Mathematics Trends and Technology, 68(1), 143-157.

Welfare data for monitoring poverty are usually gathered over a wide geographical area, and as such proximal observations are more likely to be affected by common environmental elements and therefore share similar characteristics than distant observations. This is known as spatial dependence. However, poverty analysts have largely ignored this spatial property in welfare data. This work seeks to quantify relationships between poverty-severity and potential covariates while accounting for spatial dependence using a geo-classification model. The source of data for this study, is the seventh round of the Ghana Living Standards Survey (GLSS). We asserted that social and economic characteristics which are bounded in socially constructed spaces affect poverty-generating process. To investigate the interactive association, we use a statistical regime which has the benefit of parsimoniously analyzing all location specific circumstances simultaneously, thus yielding a broad view of the processes generating poverty in Ghana. Bayesian estimation was adopted in our model computation. This was due to the hierarchical and highly parameterized nature of our model. Evident from our preliminary results, spatial effect and variation is empirical in the GLSS 7 data and cannot be ignored in the bid to understand poverty and its correlates in the study region. In general, the posterior means and 95% credible intervals show that fixed effect estimates (household size, income level of householder, ecological zone and location/area of residence) and spatial effects significantly influence poverty levels and distribution patterns in Ghana.


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Keywords : Household Expenditure, Posterior Densities, Poverty-Severity, Kriging, Spatial Variation.