空间位置的关联分析及其向量化表示方法

理解地理空间位置的空间相关性，对于地理信息检索、推荐系统，城市交通管理，居民出行模式探究等应用研究具有重要支撑作用.为更具体表义空间位置及其关联关系，本文基于多种居民出行轨迹数据，提出一种基于深度学习的空间位置向量化表示方法，而后通过空间位置向量的向量运算，可计算得到空间位置的关联程度.首先将长、短距离出行轨迹进行匹配连接，构建大规模交通网络，覆盖多种出行模式，得到对不同位置间空间关联信息的完整识别.然后基于图神经网络模型，本文提出融合位置特征与轨迹信息的空间向量化表示方法，并优化其训练学习中节点采样方法，提高空间向量的表达能力.最后以北京市共享单车轨迹数据与公共交通路网数据进行实证分析，实验结果表明基于本文提出方法生成的空间向量在空间位置的关联分析、聚类分析中相比DeepMove等已有方法拥有更好的效果.

Correlation Analysis and Vectorization Method for Spatial Position

Understanding the spatial correlation of places plays an important role in geographic information retrieval and recommendation systems, urban traffic management, and resident travel pattern exploration. In order to represent the places and their spatial relationships specifically, we propose a deep learning-based vectorization method for places. The correlation between places can be calculated by the place vectors. Firstly, the trajectories of long-distance and short-distance are matched and connected to build a large-scale traffic network, which could cover multiple travel modes and obtain a complete cognition of spatial relations. Then we propose a spatial vectorization method which is based on graph neural network and combines place features and trajectory information. Besides, we improve the representation ability of latent representations for places by optimizing a node sampling method. Finally, the empirical analysis is performed on the shared bicycle track data and public traffic data in Beijing. The result demonstrates that the proposed method outperforms the existing methods such as DeepMove on place correlation analysis and cluster analysis.