Public Map Service Platforms (PMSPs) aggregate and disseminate the earth observation data. Leveraging spatiotemporal preference patterns derived from browsing targets within complex virtual trajectories on PMSPs aids in constructing user-profiles and comprehending their intentions. However, complex virtual trajectories, characterized by numerous trajectory points and overlapping pyramidal spatial structures, introduce inefficiencies and inaccuracies during browsing target extraction. To mitigate this, we propose an Optimized Spatial Structure Segmentation (OSSS) method that divides complex virtual trajectories into sub-trajectories with simplified spatial structures, enhancing the efficiency of browsing target extraction. Spatiotemporal reconstruction of these sub-trajectories establishes sequences of browsing targets, revealing patterns of interest transitions. Moreover, recognizing the spatial uncertainty inherent in complex virtual trajectories, which results in imprecise matching between browsing targets and spatial features, we introduce a spatial co-occurrence semantic modeling approach. This involves constructing a POI semantic space and introducing a semantic similarity matching method to reduce spatial uncertainty and refine the accuracy of mining spatiotemporal preference patterns. We evaluate these methods using real-world data from Tianditu. Results demonstrate that the OSSS improves extraction efficiency by 3.45 times and accuracy by 18.84%. Additionally, the semantic similarity approach combined with spatial co-occurrence effectively mitigates spatial uncertainty. This research contributes to advancing the intelligence of PMSPs.

中文翻译：

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从复杂的虚拟轨迹中进行浏览目标提取和时空偏好挖掘

公共地图服务平台（PMSP）汇总和传播地球观测数据。利用 PMSP 上复杂虚拟轨迹内的浏览目标衍生的时空偏好模式有助于构建用户配置文件并理解他们的意图。然而，复杂的虚拟轨迹以众多轨迹点和重叠的金字塔空间结构为特征，导致浏览目标提取过程中效率低下且不准确。为了缓解这个问题，我们提出了一种优化空间结构分割（OSSS）方法，将复杂的虚拟轨迹划分为具有简化空间结构的子轨迹，从而提高浏览目标提取的效率。这些子轨迹的时空重建建立了浏览目标序列，揭示了兴趣转变的模式。此外，认识到复杂虚拟轨迹固有的空间不确定性，导致浏览目标和空间特征之间的不精确匹配，我们引入了空间共现语义建模方法。这涉及到构建 POI 语义空间并引入语义相似度匹配方法来减少空间不确定性并提高挖掘时空偏好模式的准确性。我们使用天地图的真实数据来评估这些方法。结果表明，OSSS将提取效率提高了3.45倍，准确率提高了18.84%。此外，语义相似度方法与空间共现相结合，有效减轻了空间不确定性。这项研究有助于提高 PMSP 的智能。