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Efficient cross-regional spatial dataset search with kernel density estimation
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DOI：10.1016/j.future.2026.108512.png)
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Abstract
En 中文
• A distribution pattern based DPSS framework is proposed for spatial dataset search. • Kernel density estimation is used to model and compare spatial data distributions. • DPSS+ integrates efficient indexing and candidate filtering for faster search. • High accuracy is achieved, with each query completed in approximately 10 seconds. • Experiments on 100,000 real-world datasets verify scalability and effectiveness.
Keywords:
distribution pattern
kernel density estimation
spatial dataset search
efficient indexing
candidate filtering
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