Unsupervised Spatial Clustering

Unsupervised Spatial Clustering is an unsupervised learning method aimed at automatically identifying and dividing spatial regions with similar characteristics by analyzing the intrinsic structure of spatial data. This technique does not rely on prior knowledge or labels, effectively revealing hidden patterns and structures within the data. It is widely applied in geographic information systems, environmental science, urban planning, and other fields to support spatial data analysis and decision-making. Time series data can be used as input to enhance the temporal dimension of the clustering results, providing a more comprehensive interpretation.