In spatial data, multicollinearity and spatial heterogeneity are often encountered simultaneously. To overcome the problem of heterogeneity in spatial data, GWR method can be used but this method can only overcome heterogeneity but not multicollinearity. Therefore, another method is needed to overcome multicollinearity in spatial data. The purpose of this study is to look at the ability of LCR-GWR and GWL methods to overcome multicollinearity problems simultaneously. The best method is determined by the results of the study which has smaller AIC and RMSE values. The results showed that the GWL method has lower AIC and RMSE values compared to the LCR-GWR model. Therefore, it can be said that GWL is better able to overcome multicollinearity and spatial heterogeneity in Income data compared to LCR-GWR.