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

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.

Keywords:

• Spatial clustering
• spatial partition
• regionalization
• GeoAI
• spatial contiguity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available in figshare.com with the link https://doi.org/10.6084/m9.figshare.15170898.v1 and on the Github repository https://github.com/GeoDS/STICC.

Additional information

Funding

Yuhao Kang acknowledges the support by the Trewartha Research Award, Department of the Geography, University of Wisconsin-Madison. Song Gao and Jinmeng Rao acknowledge the support by the American Family Insurance Data Science Institute at the University of Wisconsin-Madison and the National Science Foundation funded AI institute [Grant No. 2112606] for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE). Fan Zhang would like to thank the support by the National Natural Science Foundation of China under Grant 41901321. Ignavier Ng would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funders.

Notes on contributors

Yuhao Kang

Yuhao Kang is a Ph.D. student in GIScience at the Department of Geography, University of Wisconsin-Madison. He holds a B.S. degree in Geographic Information Science at Wuhan University. His main research interests include Place-Based GIS, GeoAI, and Social Sensing.

Kunlin Wu

Kunlin Wu is a graduate student in the School of Resource and Environmental Sciences at Wuhan University. His research interests include remote sensing, spatial data mining and auralization of spatial data.

Song Gao

Song Gao is an Assistant Professor in GIScience at the Department of Geography, University of Wisconsin-Madison. He holds a Ph.D. in Geography at the University of California, Santa Barbara. His main research interests include Place-Based GIS, Geospatial Data Science, and GeoAI approaches to Human Mobility and Social Sensing.

Ignavier Ng

Ignavier Ng is a Ph.D. student at Carnegie Mellon University. His research interests include causal discovery and machine learning.

Jinmeng Rao

Jinmeng Rao is a Ph.D. student at the Department of Geography, University of Wisconsin-Madison. His research interests include GeoAI, Privacy-Preserving AI, and Location Privacy.

Shan Ye

Shan Ye is a Ph.D. candidate at the Department of Geoscience, University of Wisconsin-Madison. His research focuses on quantitative stratigraphy and paleoclimate data science.

Fan Zhang

Fan Zhang is a Senior Research Associate at Senseable City Lab, Massachusetts Institute of Technology. His research interests include Urban Data Science, Visual Intelligence, GeoAI, and Social Sensing.

Teng Fei

Teng Fei is currently an Associate Professor in the School of Resource and Environmental Sciences at Wuhan University. His research focuses on remote sensing, urban data analysis, social sensing and ecological modelling.