Spatial clustering has been widely used for spatial data mining and knowledge
discovery. An ideal multivariate spatial clustering should consider both
spatial contiguity and aspatial attributes. Existing spatial clustering
approaches may face challenges for discovering repeated geographic patterns
with spatial contiguity maintained. In this paper, we propose a Spatial
Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both
attributes and spatial relationships of geographic objects for multivariate
spatial clustering. A subregion is created for each geographic object serving
as the basic unit when performing clustering. A Markov random field is then
constructed to characterize the attribute dependencies of subregions. Using a
spatial consistency strategy, nearby objects are encouraged to belong to the
same cluster. To test the performance of the proposed STICC algorithm, we apply
it in two use cases. The comparison results with several baseline methods show
that the STICC outperforms others significantly in terms of adjusted rand index
and macro-F1 score. Join count statistics is also calculated and shows that the
spatial contiguity is well preserved by STICC. Such a spatial clustering method
may benefit various applications in the fields of geography, remote sensing,
transportation, and urban planning, etc.

本文提出了一种基于 STICC 方法的多元空间聚类方法，该方法通过构建马尔可夫随机场以表征子区域之间的属性依赖关系，利用空间一致性策略鼓励附近的对象归属于同一聚类，应用实验表明 STICC 算法对于保持空间连续性和发现重复地理模式等方面优于其他基线方法。