This work is devoted to geodata clustering using discretization and its practical relevance in today's geographic information environment. The importance of geodata clustering for identifying patterns and trends in geographic data is analyzed, and the problems associated with non-discrete geodata, such as large data volume and the presence of noise, are highlighted. It is experimentally shown on real GPS data using clustering methods K-Means, DBSCAN and OPTICS that the use of discretization techniques can effectively address these problems by reducing data volume, removing noise and improving the quality of analysis. The advantages of using geodata discretization for clustering are highlighted and the importance of further research and development of these methods to expand their applications in various fields is emphasized.