Dataset on grassland non-point source pollution management and control zones for the Kherlen River basin in 2022

In contrast to traditional spatial clustering methods, the STICC method utilized here achieves a harmonious balance between multidimensional patterns of attribute repetition and spatial continuity, thereby elevating clustering precision. Furthermore, it incorporates raw continuous numerical variables directly into the clustering process, bypassing conventional hierarchical operations.

This approach enables the zoning outcomes to offer a more nuanced depiction of non-point source pollution characteristics. Consequently, this dataset serves as a valuable tool for delving deeper into the specifics of non-point source pollution traits within the Kherlen River Basin, while also furnishing crucial data support for decision-making in managing and controlling such pollution within the basin.