论文标题
朝着轨迹嵌入时在细小的空间粒度下测量位置功能相似性
Towards Measuring Place Function Similarity at Fine Spatial Granularity with Trajectory Embedding
论文作者
论文摘要
从计算角度进行建模位置功能是一个普遍的研究主题。轨迹嵌入作为一种神经网络支持的降低技术，如果这些地方作为轨迹的一部分共享相似的时间顺序，则有可能将具有相似社会功能的位置放在嵌入空间的近距离位置。先前提出了嵌入相似性作为测量位置函数相似性的新指标。这项研究探讨了与以前的研究相比，这种方法对于地理粒度较小得多的地理单位是否有意义。此外，这项研究还研究了地理距离是否会影响嵌入相似性。基于大型车辆轨迹数据集的经验评估证实，嵌入相似性可以成为位置函数的度量代理。但是，结果还表明，嵌入相似性仍然受局部规模的距离界定。
Modeling place functions from a computational perspective is a prevalent research topic. Trajectory embedding, as a neural-network-backed dimension reduction technology, allows the possibility to put places with similar social functions at close locations in the embedding space if the places share similar chronological context as part of a trajectory. The embedding similarity was previously proposed as a new metric for measuring the similarity of place functions. This study explores if this approach is meaningful for geographical units at a much smaller geographical granularity compared to previous studies. In addition, this study investigates if the geographical distance can influence the embedding similarity. The empirical evaluations based on a big vehicle trajectory data set confirm that the embedding similarity can be a metric proxy for place functions. However, the results also show that the embedding similarity is still bounded by the distance at the local scale.
