Research Progress on Spatial Clustering Pattern Discovery: Concepts, Methods and Applications

[Significance] The spatial patterns of geographic features have a profound impact on the natural environment and human activities. Mining and discovering typical feature patterns from spatial-temporal data is a prerequisite for morphological analysis and planning, which can provide basic support for urban planning and watershed planning. Spatial clustering pattern is a significant and repeated orderly arrangement or combination of relationships between geographic features, which shows a significant distribution pattern and spatial morphology. The discovery of spatial clustering pattern of features is facilitated by spatial analysis, data mining, pattern recognition, and other related technical methods. This process helps to build a perception of the laws of the arrangement and combination of features within a complex and irregular collection of feature sets. Through analytical reasoning, it uncovers the spatial clustering and morphological structure of features with specific semantics. This discovery is of great significance in revealing the spatial distribution law of features, explaining the formation mechanism of geographic phenomena, and understanding the interaction process between humans and space. [Progress] On the basis of elaborating the connotation of spatial clustering patterns of features, this paper summarizes two types of methods for spatial clustering pattern discovery, including rule-oriented pattern extraction and data-driven pattern recognition. The rule-oriented pattern extraction methods rely on expert knowledge to summarize pattern characteristics. They express, constrain and guide the pattern discovery process with formal explicit rules, and extract the features of the specified spatial clustering patterns from the spatial data set. The data-driven pattern recognition methods draw knowledge from both 'experts' and 'data'. They learn the pattern characteristics of features from multiple scales and perspectives through a large number of samples automatically under the guidance of expert knowledge, and perform category prediction on a set of features in order to identify the spatial clustering patterns of the features. Subsequently, the spatial clustering pattern discovery of three types of typical features, namely buildings, roads and water systems, is reviewed. The data-driven approach represented by graph deep learning is usually superior to the rule-oriented pattern extraction approach in terms of pattern discovery accuracy due to its powerful pattern learning capability. In terms of the overall trend, spatial clustering pattern discovery of features is shifting from traditional methods to close integration with deep learning methods. [Prospect] In the future, knowledge aggregation of the rule base and sample set for feature spatial clustering pattern discovery, active discovery techniques for clustering patterns, graph deep learning models for efficient clustering pattern discovery, and pattern discovery based on generative AI will become the main research directions. © 2025 Science Press. All rights reserved.