FGVis: visual analytics of human mobility patterns and urban areas based on F-GloVe

Understanding the relationship between urban areas is essential to the extraction of human mobility patterns. Most popular algorithms use word embedding technology to vectorize urban areas to calculate the similarity between them. However, noise generated by the traffic hub area is often ignored, such as crossroads and road toll stations, which affect the training time of the model and the accuracy of vectorization. In this paper, we propose a vectorized representation method of urban geographic areas based on the distribution of trajectories, combining with the filtering of traffic hub areas based on the law of similarity to eliminate their noise. A novel visual analytics system is designed, which enables experts to analyse the relationship between urban areas and explore human mobility patterns based on this vectorized representation method. Finally, we analyse the relevance to long-distance areas and campus crowd mobility patterns on real datasets, which proves the effectiveness of our method. Graphic abstract