## 3 Results
Furthermore, the spatial heterogeneity test was carried out by using the Gleiser test and it was found that there is spatial heterogeneity when the test statistics value \(G\) = 210.5993 is larger than \(\chi^{2}_{0.05;15}=24.996\). The parameter of the GWMPIGR model were estimated locally using spatial weighting so that each observation location had a different parameter estimate value. Spatial weighting represents the location between observations. The closer an observation location is to another observation location, the greater the weighting value and the greater the influence on these observations. In this study, spatial weighting was determined using a fixed Gaussian and bisquare kernel function. The optimum bandwidth, minimum CV and AICc values for each model are presented in Table 4.  
Based on Table 4, model 2 has the lowest CV minimum value. However, this does not mean that model 2 is the best model. The CV value is used to determine the optimum bandwidth for the spatial weight matrix. Therefore, the CV values in Table 4 refer to the minimum CV value of the optimum bandwidth for each model. On the other hand, the AICc can be used to evaluate how well a model fits the data and to determine the best model from multiple models for the same dataset. AICc uses the MLE of the model (log-likelihood) as a measure of fit. The model that fits the data the best has the maximum likelihood. Therefore, the model with high log-likelihood has a low AICc value. Based on Table 4, model 1 has the smallest AICc value. Thus, in this study, the GWMPIGR model with the fixed Gaussian kernel spatial weighting function and the number of live births as the exposure is the best model to determine the number of infant, under-5 and maternal deaths in East Java in 2019. Therefore, only model 1 will be discussed further. The value of the parameter estimator of the GWMPIGR model is different for each observation location, as is the significance of the parameters. The GWMPIGR model generated 722 coefficient estimates for 38 districts/cities in the province of East Java. Simultaneous testing of the GWMPIGR model parameters shows that at least one parameter has a significant effect on the response variable (\(G^{2}\) = 384.17 \(>\chi^{2}_{0.05;12}=21.026\)). Meanwhile, partial hypothesis testing was carried out on the parameters of the GWMPIGR model locally for each observation's location. For example, the parameter estimates for the GWMPGR model with the fixed Gaussian kernel spatial weighted function for Banyuwangi District (\(i\) = 10) and Surabaya City (\(i\) = 37) can be seen in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Bandwidth Optimum** & **CV Minimum** & **AICc** \\
**1** & 0.975566 & 505,601.5 & 64,309.95 \\
2 & 2.043404 & 496,850.6 & 64,445.95 \\
3 & 0.880499 & 529,872.7 & 74,930.46 \\
4 & 1.834768 & 524,106.5 & 74,962.95 \\ \hline \hline \end{tabular}  
* The best model.  
\end{table} 
Table 4: Bandwidth optimum, CV minimum and AICc for each model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Deviance/df** & \multicolumn{2}{c}{**LM**} \\ \cline{3-4}  & & \(\chi^{2}\) & \(p\)**-Value** \\ \hline \(Y_{1}\) & 38.78 & 211,028.5 & 0.000 \({}^{*}\) \\ \(Y_{2}\) & 8.13 & 1336.02 & 0.000 \({}^{*}\) \\ \(Y_{3}\) & 5.44 & 3532.65 & 0.000 \({}^{*}\) \\ \hline \hline \end{tabular}  
* Significant at \(\alpha\) = 5%.  
\end{table} 
Table 3: Overdispersion test. Table 5 shows that there are differences in the parameter coefficient values and in the parameter significance for Banyuwangi District and Surabaya City. Some of the predictors that were significant in Surabaya City were not significant in Banyuwangi District. Variable \(X_{3}\) and \(X_{5}\) did not significantly affect \(Y_{1}\) in the Banyuwangi District, while all of the predictors significantly affected \(Y_{1}\) in Surabaya City. Variables \(X_{1}\), \(X_{3}\) and \(X_{5}\) did not significantly affect \(Y_{2}\) in the Banyuwangi District, while only variable \(X_{3}\) did not significantly affect \(Y_{2}\) in Surabaya City.