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Abstract
Spatiotemporal raster (STR) data employ an array of grids to represent temporally varying and spatially distributed information, commonly utilized for recording environmental variables and socioeconomic indices. To reveal the geographic patterns embedded in STR data, the clustering by fast search and finding of density peaks (CFSFDP) algorithm is considered effective and suitable. However, this algorithm encounters limitations in identifying cluster centers, handling large data volumes, and measuring the coupled spatial-temporal-attribute distance when applied to STR data. To overcome these challenges, we propose an improved method named spatial temporal-adaptive density peak tree clustering (ST-ADPTC). This method leverages adaptive density peak tree segmentation to identify cluster centers and optimizes memory usage through the k-nearest neighbors (kNN) technique. By constructing a neighborhood that incorporates both spatiotemporal and thematic attribute similarities, ST-ADPTC computes the local density of STR data, facilitating the discovery of time-varying clusters. Based on the proposed method, we develop an open-source Python package (Geo_ADPTC). Experiments conducted using benchmarking datasets illustrate improvements in cluster identification and memory reduction. Additionally, a case study of sea surface temperature data demonstrates the feasibility and effectiveness of exploring spatial and temporal distribution patterns using the proposed method.
Acknowledgments
We appreciate the detailed suggestions and comments from editors and anonymous reviewers.
Data and code availability statement
The datasets and code used in the case studies can be accessed at https://doi.org/10.6084/m9.figshare.25752387.v1.
Disclosure statement
No potential conflicts of interest were reported by the author(s).
Additional information
Funding
Notes on contributors
Jie Song
Jie Song received the Mater degree in geographical information science from Nanjing Normal University. He is currently an assistant engineer at Wuhan Geomatics Institute. His research interests include spatiotemporal pattern analysis and data mining.
Songshan Yue
Songshan Yue is currently an associate professor at Nanjing Normal University. His research interests include geographic information system, geo-pattern mining and open geographic modelling and simulation.
Min Chen
Min Chen is currently a professor at Nanjing Normal University. His research interests include geographic modelling, open geographic modelling and simulation and virtual geographic environment.
Zhuo Sun
Zhuo Sun is currently a PhD candidate at Nanjing Normal University and her research interests focus on deep learning and geographical pattern mining.
Yongning Wen
Yongning Wen is currently a professor at Nanjing Normal University. His research interests include geographic modelling and virtual geographic environment.
Lingzhi Sun
Lingzhi Sun received the Mater degree in geographical information science from Nanjing Normal University. He is currently an assistant engineer at the 3rd Geoinformation Mapping Institute of Ministry of Natural Resources. His research interests include digital cartography and 3D visualization.