Mathematics & Statistics

Mathematics & Statistics

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Robust Geographically Weighted Regression Model on Poverty Data in South Sulawesi in 2019

Volume: 131  ,  Issue: 1 , August    Published Date: 28 August 2023
Publisher Name: IJRP
Views: 88  ,  Download: 36 , Pages: 334 - 341    
DOI: 10.47119/IJRP1001311820235415


# Author Name
1 Aqilah Salsabila Rahman
2 Georgina Maria Tinungki
3 Erna Tri Herdiani


Geographically Weighted Regression (GWR) is a method of spatial analysis that can be used to perform analysis by assigning weights based on the geographical distance of each observation location and the assumption of having spatial heterogeneity. The result of this analysis is an equation model whose parameter values apply only to each observation location and are different from other observation locations. However, when there are outliers at the observation location, a more robust estimation method is needed. One of the robust methods that can be applied to the GWR model is the Least Absolute Deviation method. In this study, model estimation was carried out on the factors that affect poverty in South Sulawesi in 2019 using Robust Geographically Weighted Regression (RGWR) with the Least Absolute Deviation (LAD) method. Determination of weighting is done by using the adaptive kernel bisquare weighting function. The results obtained are Robust Geographically Weighted Regression (RGWR) models which are different and apply only to each district/city in South Sulawesi. In addition, it was also found that the Robust Geographically Weighted Regression (RGWR) model with the Least Absolute Deviation (LAD) method was the best model for data that experienced spatial heterogeneity and contained outliers.


  • Adaptive Kernel Bisquare
  • geographically weighted regression
  • least absolute deviation
  • poverty
  • robust