Regionalization Method

Regionalization methods aim to partition data into spatially contiguous and internally homogeneous regions, facilitating more accurate and efficient analysis of complex systems. Current research focuses on integrating regionalization with advanced modeling techniques, such as incorporating neural networks (e.g., multilayer perceptrons, Universal Differential Equations) into hydrological and epidemiological models, or employing algorithms like agglomerative clustering and K-means for improved spatial regime delineation. This work is significant for improving the accuracy of predictions in diverse fields, including disease modeling, hydrological forecasting, and social determinants of health analysis, by accounting for spatial heterogeneity and improving parameter estimation in large-scale datasets.