updata

Sampling time as known information can certainly enhance the reliability of density estimation for space-time trajectories. Established kernel density estimation (KDE) methods utilize the sampling time to reshape the kernel or weights, but it is difficult to control two types of errors at the same time: overestimation or underestimation of the density due to temporal autocorrelation, and assignment of non-zero densities to non-reachable points. For this reason, this paper introduces a divide-and-conquer strategy for movement data with irregular sampling intervals, and proposes a time-KDE method under the dual drive of time geography and temporal autocorrelation. It establishes two mappings between time to kernels and to weights respectively in order to reconcile the above two types of errors by recalibrating the KDE, with the aim of maximizing the temporal information to abate the uncertainty of the density estimation, and thus providing a theoretical basis for unbiased density estimation.