Geographically and Temporally Weighted Autoregressive to Modeling the Levels of Poverty Population in Java in 2012-2018
AbstractGeographically and temporally weighted regression (GTWR) is a method applied when there is spatial and temporal diversity in the observation. GTWR model just considers local influences of spatial-temporal response variable on the explanatory variables. The GTWR model can add an autoregressive component of response variable, the resulting model is known as a geographically and temporally weighted autoregressive model (GTWAR). This study aims to perform GTWAR modeling which is applied to the data on the proportion of poor people by districts/cities in Java in 2012-2018. The results showed that GTWAR produced Akaike Information Criterion (AIC) smaller than GTWR, and the coefficient of determination (R2) is higher than GTWR.
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