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

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dc.contributor.authorWang, Yunzheen_US
dc.contributor.authorBaciu, Georgeen_US
dc.contributor.authorLi, Chenhuien_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2020-05-22T12:24:44Z
dc.date.available2020-05-22T12:24:44Z
dc.date.issued2020
dc.description.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 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.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.13882
dc.identifier.issn1467-8659
dc.identifier.pages405-419
dc.identifier.urihttps://doi.org/10.1111/cgf.13882
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13882
dc.publisher© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjecthuman–computer interfaces
dc.subjectinteraction
dc.subjectinformation visualization
dc.subjectvisualization
dc.subjectvisual analytics
dc.subjectHuman‐centred computing → Visualization
dc.titleVisualizing Dynamics of Urban Regions Through a Geo‐Semantic Graph‐Based Methoden_US
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