STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity
This is an incremental improvement for researchers and practitioners in geography, remote sensing, transportation, and urban planning, addressing the challenge of clustering spatial data with both attributes and spatial relationships.
The paper tackled the problem of multivariate spatial clustering for discovering repeated geographic patterns while maintaining spatial contiguity, and the result showed that the proposed STICC method outperformed baseline methods significantly in terms of adjusted rand index and macro-F1 score, with spatial contiguity well preserved as indicated by join count statistics.
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.