One commonly used method for spatial clustering is the K-means clustering algorithm, which partitions the spatial domain into distinct clusters based on the similarity of data points. Another approach is DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which identifies clusters based on density and is particularly effective in detecting clusters of arbitrary shapes.

Spatial clustering finds applications in various fields, including geography, ecology, epidemiology, and urban planning. It assists in revealing spatial trends, identifying hotspots, and understanding the distribution of phenomena across geographical space. By grouping spatial entities with similar characteristics, spatial clustering facilitates insights into regional patterns and supports decision-making processes in areas such as resource allocation, urban development, and environmental management.