Visualizing Dynamics of Urban Regions Through a Geo‐Semantic Graph‐Based Method

Abstract

AbstractIn urban analysis, it is desirable to find regions where a primary socio‐economic activity dominates as a key endeavour. This can be accomplished by aggregating neighbouring locations where similar activities take place. However, people move and their activities change over time. Furthermore, the boundaries of regions are not stationary. Thus, it is challenging to update region divisions and track their evolution. Geo‐textual data embody geographical information and activity descriptions. We obtain changes in regional boundaries by iteratively applying a community detection process to a sequence of latent graphs that are constructed from geo‐textual data. Region characteristics are interpreted by topics learned by the latent Dirichlet allocation model. We also propose a matching algorithm to expose region transformations between different timestamps. Interesting patterns of evolution emerge after clustering the migration trajectories of region centroids. In our visual system, users can explore the evolution of regions through animations and linked snapshots. To facilitate visual comparisons, we represent regions by hexagonal tiling that better construct arbitrary regional shapes. The effectiveness of our method is evaluated on two case studies using real‐world datasets, and a user study shows that our visual analytics system is highly effective in performing studies on such regional maps.