【摘要】 【意义】地理要素的空间聚类模式反映了要素的分布特征与空间格局，而模式发现对于揭示要素的空间分布规律、阐释地理现象的形成机制、理解人与空间的交互过程等具有重要意义。【进展】本文在阐述要素空间聚类模式内涵的基础上，梳理了空间聚类模式发现的两类方法，即规则导向的模式提取和数据驱动的模式识别。规则导向的模式提取方法根据专家知识对模式特点进行归纳，用形式化的显式规则表达并约束、指导模式发现过程；数据驱动的模式识别方法从“专家”和“数据”两方面汲取知识，在专家知识的指导下，通过大量样本自动化地从多尺度、多视角学习要素的模式特点。随后，具体针对建筑、道路和水系三类典型要素，系统归纳了三类要素模式的分类体系和空间聚类模式发现方法，尤其以图深度学习为代表的数据驱动方法由于其强大的模式学习能力，在模式发现精度上优于规则导向的模式提取方法。【展望】未来，要素空间聚类模式发现规则库和样本集的知识汇聚、聚类模式的主动发现技术、高效聚类模式发现的图深度学习模型以及基于生成式AI的模式发现等将成为主要研究方向。更多还原

【Abstract】 [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 datadriven 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.更多还原