RESUMEN
Extracting meaningful patterns of human mobility from accumulating trajectories is essential for understanding human behavior. However, previous works identify human mobility patterns based on the spatial co-occurrence of trajectories, which ignores the effect of activity content, leaving challenges in effectively extracting and understanding patterns. To bridge this gap, this study incorporates the activity content of trajectories to extract human mobility patterns, and proposes acontent-aware mobility pattern model. The model first embeds the activity content in distributed continuous vector space by taking point-of-interest as an agent and then extracts representative and interpretable mobility patterns from human trajectory sets using a derived topic model. To investigate the performance of the proposed model, several evaluation metrics are developed, including pattern coherence, pattern similarity, and manual scoring. A real-world case study is conducted, and its experimental results show that the proposed model improves interpretability and helps to understand mobility patterns. This study provides not only a novel solution and several evaluation metrics for human mobility patterns but also a method reference for fusing content semantics of human activities for trajectory analysis and mining.
RESUMEN
Geographical entity representation learning (GERL) aims to embed geographical entities into a low-dimensional vector space, which provides a generalized approach for utilizing geographical entities to serve various geographical intelligence applications. In practice, the spatial distribution of geographical entities is highly unbalanced; thus, it is challenging to embed them accurately. Previous GERL models treated all geographical entities uniformly, resulting in insufficient entity representations. To address this issue, this article proposes an anchor-enhanced GERL (AE-GERL) model, which utilizes the key informative entities as anchors to improve the representations of geographical entities. Specifically, AE-GERL develops an anchor selection algorithm to identify anchors from large-scale geographical entities based on their spatial distribution and entity types. To utilize anchors to guide geographical entities, AE-GERL constructs an anchor-enhanced graph to establish explicit connections between anchors and nonanchor entities. Finally, a graph neural network (GNN) based anchor to nonanchor node learning model is designed to impute missing information of nonanchor entities. Extensive experiments are conducted on four datasets, and the experimental results demonstrate that AE-GERL outperforms the baseline models in both sparse and dense scenarios. This study provides a methodological reference for embedding geographical entities in various geographical applications and also provides an effective approach to improve the performance of message-passing-based GNN models.