The researchers developed a model called Geographically Weighted Bivariate Poisson Inverse Gaussian Regression to handle over-dispersion in count data. This model considers different local conditions at each location to improve accuracy. By using Maximum Likelihood Estimation and Maximum Likelihood Ratio Test methods, the researchers were able to estimate parameters and conduct hypothesis testing effectively.