Abstract Despite the significance of tracking human mobility dynamics in a large‐scale earthquake evacuation for an effective first response and disaster relief, the general understanding of evacuation behaviors remains limited. Numerous individual movement trajectories, disaster damages of civil engineering, associated heterogeneous data attributes, as well as complex urban environment all obscure disaster evacuation analysis. Although visualization methods have demonstrated promising performance in emergency evacuation analysis, they cannot effectively identify and deliver the major features like speed or density, as well as the resulting evacuation events like congestion or turn‐back. In this study, we propose a shot design approach to generate customized and narrative animations to track different evacuation features with different exploration purposes of users. Particularly, an intuitive scene feature graph that identifies the most dominating evacuation events is first constructed based on user‐specific regions or their tracking purposes on a certain feature. An optimal camera route, i.e., a storyboard is then calculated based on the previous user‐specific regions or features. For different evacuation events along this route, we employ the corresponding shot design to reveal the underlying feature evolution and its correlation with the environment. Several case studies confirm the efficacy of our system. The feedback from experts and users with different backgrounds suggests that our approach indeed helps them better embrace a comprehensive understanding of the earthquake evacuation.

Graph cut has proven to be an effective scheme to solve a wide variety of segmentation problems in vision and graphics community. The main limitation of conventional graph-cut implementations is that they can hardly handle large images or videos because of high computational complexity. Even though there are some parallelization solutions, they commonly suffer from the problems of low parallelism (on CPU) or low convergence speed (on GPU). In this paper, we present a novel graph-cut algorithm that leverages a parallelized jump flooding technique and an heuristic push-relabel scheme to enhance the graph-cut process, namely, back-and-forth relabel, convergence detection, and block-wise push-relabel. The entire process is parallelizable on GPU, and outperforms the existing GPU-based implementations in terms of global convergence, information propagation, and performance. We design an intuitive user interface for specifying interested regions in cases of occlusions when handling video sequences. Experiments on a variety of data sets, including images (up to 15 K × 10 K), videos (up to 2.5 K × 1.5 K × 50), and volumetric data, achieve high-quality results and a maximum 40-fold (139-fold) speedup over conventional GPU (CPU-)-based approaches.

Color mapping plays a critical role in visualization of time-varying data and also sets a challenge for researchers due to the consistency of mapping and great changes in time-varying data. In order to solve this problem and generate feature-prominent animation, we present a two phase optimization technique using bilateral filtering and global energy functions. In the first phase, for each time step, we use a weighted mapping function which combines linear mapping and feature histogram. In the second phase, an optimization function taking global color mapping and minimum color difference into consideration is designed. So users can clearly distinguish between data in variable time steps and easily understand the corresponding relationships between different structures. The experiments' results show that our method can achieve high quality visualization for both static and time-varying data.