GeoVis: a data-driven geographic visualization recommendation system via latent space encoding

As one of the effective means of representing geographic information, geographic visualization can directly improve the cognitive efficiency of users who are perceiving geospatial data. The existing geographic information visualization relies heavily on the background knowledge and visualization skills the data workers own. Therefore, the geographic visualization task is usually very time-consuming and challenging. To lower the barrier of visualization of geographical data, we propose a novel recommendation system of geographic information visualization called GeoVis. This system extracts the distribution characteristics with adaptive kernel density estimation and recommends the map type (scatter, bubble, hexbin and heatmap) that can best reflect the regularity of data distribution based on latent code. The key idea of how the data-driven recommendation works is to use latent code to express and decouple data features and then learn the mapping between data features and visual styles. At the same time, this system recommends design choices (e.g., map styles and color schemes). Users only need to browse the recommendation results to realize explorations and analyses of the dataset, which will greatly improve their work efficiency. We conduct a series of evaluation experiments on the proposed system, including a case study. The experiment results show that the system is practical and effective and can perform the task of recommending informative and esthetic geographical visualization results well.

Graphical abstract