Poverty Data Analysis In Riau Province Using Geographically Weighted Regression Model With Exponential And Tricube Adaptive Kernels
Volume 4 - Issue 1, January 2020 Edition
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Mia Amiati, Arisman Adnan, Rado Yendra
Adaptive, Eksponential, Geographically Weighted Regression, Riau Poverty, Tricube.
one of the serious problems in Riau Province is the poverty rate. To overcome the problem the government has made various efforts, in the hope that poverty alleviation will become more directed, but these efforts have do not get effective results so a new aproach is needed to look specifically at poverty cases in each location. The regression analysis approach has often been used in predicting poverty rates, but still global and enforced at all observed locations without involving geographical location based on earth's longitude and latitude. One model could accommodate geographical effects on data is the Geographically Weighted Regression (GWR) model. The data used in this study is the poverty in Riau Province (y) and three independent factors (x) which will be modeled using the GWR model. The parameters of the model are calculated at each location, so it observation location has a local regression parameter value. The method for estimating the parameters of the GWR model is the Weighted Least Square (WLS) method. The weighti functions used are exponential and tricube adaptive kernels. The selection of optimum bandwidth use the Cross Validation (CV) method. The best selection criteria used are R^2, AIC and RMSE. The study show the GWR model with tricube adaptive kernel weighting function is better than the GWR model with an exponential adaptive kernel.
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