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Determinants of Poverty: A Spatial Analysis
1fakulti sains bumi, universiti malaysia kelantan, jeli, kelantan, Malaysia
2fakulti sains bum, universiti malaysia kelantan, kelantan, kelantan, Malaysia
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Abstract : Eradicating poverty has become the main concern for Malaysian government since independence. Recognising the incidence of poverty through standard statistical data tables alone is no longer adequate. This study examines socio-demographic eﬀects on poverty and measure spatial patterns in poverty risk looking for high risk of areas. The poverty data were counts of the numbers of poverty cases occurring in each ten districts of Kelantan. To model these data, a spatial autocorrelation was detected prior to a Poisson Log Linear Leroux Conditional Autoregressive was fitted to the data. The result shows the variables household members, number of non-education of household head and log number of female household head significantly associated with the number of poor households. Tumpat was found as the highest risk area of poverty.
Keywords : poverty, Conditional autoregressive models,spatial autocorrelation