  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.
