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Time-awareness in kernel density estimation for movement data
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PRE
AI
DOI：10.1080/13658816.2025.2524856.png)
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Abstract
En 中文
Sampling timestamps enhance the reliability of spatiotemporal trajectory density estimation. Existing kernel density estimation (KDE) methods leverage sampling time to modify kernel shapes or weight specifications. However, controlling two concurrent errors remains challenging: density over/underestimation due to temporal autocorrelation, and nonzero density assignments to unreachable locations. This study proposes a time-aware KDE (t-KDE) method for irregular sampling intervals, adopting a divide-and-conquer strategy that synergistically integrates time geography and temporal autocorrelation. By establishing two temporal mappings—from time to kernel functions and to weight coefficients—t-KDE retrospectively mitigates the above errors through KDE recalibration. The approach aims to maximize temporal information utilization to reduce estimation uncertainty, providing a theoretical basis for unbiased density modeling. Empirical analysis demonstrates that t-KDE outperforms state-of-the-art methods in accuracy and reliability.
Keywords:
Kernel density estimation
temporal autocorrelation
time geography
space-time trajectory
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