Geographically Weighted Regression Modeling Using Fixed and Adaptive Gaussian Kernel Weighting Functions in The Analysis of Maternal Mortality (MMR)

Suryowati, Kris and Ode Ranggo, Monalisa and Dwi Bekti, Rokhana and Sutanta, Edhy and Riswanto, Eko (2021) Geographically Weighted Regression Modeling Using Fixed and Adaptive Gaussian Kernel Weighting Functions in The Analysis of Maternal Mortality (MMR). 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA), - (-). pp. 115-120. ISSN 978-1-6654-3400-3

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Abstract

The weights have a very large influence on the parameter estimation of the geographically weighted regression (GWR). The weights show the relationship between observations or locations in the model. Types of weights that are often used in GWR are Gaussian kernels. This weighting can also be arranged into two forms. There are the fixed Gaussian kernel and the adaptive Gaussian kernel. Fixed is used when each location has the same bandwidth value. Adaptive is used when each location has a different bandwidth value. This study compares the results of the estimated parameters of the GWR model using the two weights. The modelling is carried out using data on maternal mortality in Central Sulawesi Province, consisting of 13 regencies/cities. The data used is secondary data. The result is that the adaptive Gaussian kernel weighting gives better results on the GWR model. It can be seen from the smaller standard error value (0.000398), the larger coefficient of determination (0.5468), and the smaller AIC (-152.52) than the model with the fixed Gaussian kernel weighting.

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