| 研究実績の概要 |
Consistently switching several visual contexts between geospatial information is an essential factor for map interaction. However, such information is hard to visually understand due to the complex transportation networks embedded in the present large datasets. Techniques have been developed for visualizing such networks, while a scale-aware integration of map interactions is still missing and challenging. This study aims to generate scale-aware dynamic maps by focusing (1) on algorithmic map schematization, (2) on annotated map illustrations, and (3) on map generalization between (1) and (2) across multiple levels of detail, while formulating them to be computationally efficient. In 2017, we concentrated on the spatial transition path optimization among multiple map scales to achieve the third aforementioned objectives. A Dependency Graph across Multiple Scales is automatically updated through user intervention, while the inherited visual relationship will be optimized using Label Active Ranges Maximization. The research was conducted step by step, where the rest of the techniques will be developed in the future.
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