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The Model of Mixed Geographically Weighted Regression (MGWR) for Poverty Rate in Central Java

M. Y. Darsyah, R. Wasono, M. F. Agustina


This research aims to model the level of poverty in Central Java with spatial effect. The method of analysis that used in this study is Mixed Geographically Weighted Regression (MGWR).MGWR model parameter estimation is obtained by using Weighted Least Square (WLS) with gaussian kernel weighting function. Based on the result of the research, therewere five significant variables predictor that estimated to affect the level of poverty in the district or city in Central Java, namely a minimum wage (X1), the percentage of the number of families who work in the agricultural sector (X2), the percentage of the number of families that use national health insurance program 'Jamkesmas' (X4), the percentage of the number of families that have the facility to defecate(X6), and inflation (X8).Classfication accuracy (R2) of MGWR model is 73,95%, it means that MGWR model is able to explain the variation of the poverty level in 73,95%, the remaining of 26,05% is affected by another factor in the outside of the models with the AIC is180,49.There is no spatial effect or geographical factors influence the rate of poverty in Central Java. These variables predictors had asimiliar effect in each district or city.


MGWR, AIC, Poverty rate

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