Voronoi diagram is commonly used to visualize geographical point dataset with a collection of plane-partitioned facets. As the size of the geographical point dataset increases, facets are densely distributed, and present different sizes and irregular shapes, leading to overdrawing and confusion problems, and hampering the visual perception of Voronoi diagram and insightful exploration of geographical point data. In this paper, we propose a novel Voronoi diagram generation framework to visualize and explore large-scale geographical point datasets. Firstly, an attribute-based blue noise sampling model is designed to select a subset of points to generate the simplified Voronoi diagram, retaining both the spatial distribution and attribute relationship of the original large-scale geographical points. Then a couple of optimization schemes are integrated into the sampling model to replace the representative points, aiming to enhance the visual perception of Voronoi diagram, such as shape balance and color characterization. Furthermore, we implement an interactive online Voronoi diagram generation tool, GeoVoronoi, enabling users to generate meaningful facets according to their requirements. Quantitative comparisons, case studies and user studies based on real-world datasets have demonstrated the effectiveness of our proposed method in the generation of credible Voronoi diagram and in-depth exploration of geographical point datasets.

In the context of the exponentially increasing volume of narrative texts such as novels and news, readers struggle to extract and consistently remember storylines from these intricate texts due to the constraints of human working memory and attention span. To tackle this issue, we propose a visualization approachStoryExplorer, which facilitates the process of knowledge externalization of narrative texts and further makes the form of mental models more coherent. Through the formative study and close collaboration with two domain experts, we identified key challenges for the extraction of the storyline. Guided by the distilled requirements, we then propose a set of workflow (i.e., insight finding-scripting-storytelling) to enable users to interactively generate fragments of narrative structures. We then propose a visualization systemStoryExplorerthat combines stroke annotation and GPT-based visual hints to quickly extract story fragments and interactively construct storylines. To evaluate the effectiveness and usefulness ofStoryExplorer, we conducted two case studies and in-depth user interviews with 16 target users. The result shows that users can conveniently and effectively extract the storyline by usingStoryExploreralong with the proposed workflow.

Abstract Experimental economics is an important branch of economics to study human behaviours in a controlled laboratory setting or out in the field. Scientific experiments are conducted in experimental economics to collect what decisions people make in specific circumstances and verify economic theories. As a significant couple of variables in the virtual experimental environment, decisions and outcomes change with the subjective factors of participants and objective circumstances, making it a difficult task to capture human behaviour patterns and establish correlations to verify economic theories. In this paper, we present a visual analytics system, EBPVis, which enables economists to visually explore human behaviour patterns and faithfully verify economic theories, e.g. the vicious cycle of poverty and poverty trap. We utilize a Doc2Vec model to transform the economic behaviours of participants into a vectorized space according to their sequential decisions, where frequent sequences can be easily perceived and extracted to represent human behaviour patterns. To explore the correlation between decisions and outcomes, an Outcome View is designed to display the outcome variables for behaviour patterns. We also provide a Comparison View to support an efficient comparison between multiple behaviour patterns by revealing their differences in terms of decision combinations and time‐varying profits. Moreover, an Individual View is designed to illustrate the outcome accumulation and behaviour patterns of subjects. Case studies, expert feedback and user studies based on a real‐world dataset have demonstrated the effectiveness and practicability of EBPVis in the representation of economic behaviour patterns and certification of economic theories.

Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization, random walk, and deep learning. However, choosing the right method for different tasks can be challenging. Communities within networks help reveal underlying structures and correlations. Investigating how different models preserve community properties is crucial for identifying the best graph representation for data analysis. This paper defines indicators to explore the perceptual quality of community properties in representation learning spaces, including the consistency of community structure, node distribution within and between communities, and central node distribution. A visualization system presents these indicators, allowing users to evaluate models based on community structures. Case studies demonstrate the effectiveness of the indicators for the visual evaluation of graph representation learning models.

Abstract This paper presents the fireworks controller, a novel real‐time shape‐constrained fireworks animation system. We depict the shape of a firework by a 3D mesh. In order to approximate the mesh using evenly distributed points, we propose a fast point sampling method by extending the dual depth peeling algorithm. The samples are then taken as input to shape‐constrained fireworks whose physically plausible animations are based on inverse dynamics. We present a highly parallel iterative clustering algorithm to support multi‐level fireworks explosion. In order to simulate natural fuzzy fireworks, we impose extra random particles with a parallel random number generator. Several novel intuitive user interfaces are introduced to improve the usability of the system. Experimental results demonstrate the prettiness and efficiency of the proposed approach. Copyright © 2009 John Wiley & Sons, Ltd.