STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity

TL;DR

STICC is a novel multivariate spatial clustering method that effectively identifies repeated geographic patterns while maintaining spatial contiguity, outperforming existing methods in accuracy and spatial coherence.

Contribution

The paper introduces STICC, a new clustering approach that integrates attribute dependencies and spatial relationships using a Markov random field and spatial consistency strategies.

Findings

STICC outperforms baseline methods in adjusted rand index and macro-F1 score.

Spatial contiguity is well preserved by STICC.

Demonstrated effectiveness in two real-world use cases.

Abstract

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.