The spatial display of clustered data using machine learning (ML) as regions (bordered areas) is currently unfeasible. This problem is commonly encountered in various research fields that utilize clustering algorithms in their workflow. We present in this study an approach utilizing ML algorithm models that can be trained to any specific dataset to produce decision boundaries. These boundaries are overlaid onto the geographic coordinate system (GCS) to generate geographic clustering regions. The proposed approach is implemented in the Python Package Index (PyPI) as a geovisualization library called geographic decision zones (GeoZ). The efficiency of GeoZ was tested using a dataset of groundwater wells in the State of California. We experimented with 13 different ML models to determine the best model that predicts the existing regional distribution (subbasins). The support vector machine (SVM) algorithm produced a relatively high accuracy score and fulfilled the required criteria better than the other models. Consequently, the tested SVM model with optimized parameters was implemented in the GeoZ open-source library. However, it is important to note that limitations in the application of GeoZ may arise from the nature of the SVM algorithm, as well as the volume, discontinuity, and distribution of the data. We have attempted to address these limitations through various suggestions and solutions.
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