Browsing by Author "Li, Quan"

Now showing 1 - 3 of 3
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    SEEVis: A Smart Emergency Evacuation Plan Visualization System with Data-Driven Shot Designs
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Li, Quan; Liu, Yingjie J.; Chen, Li; Yang, Xingchao C.; Peng, Yi; Yuan, Xiaoru R.; Wijerathne, Maddegedara Lalith Lakshman; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    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.
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    Visual Analysis of Car-hailing Reimbursement Data for Overtime
    (The Eurographics Association, 2020) Liu, Qiang Qiang; Li, Quan; Tang, Chun Feng; Lin, Huan Bin; Peng, Zhen Hui; Li, Zhi Wei; Chen, Tian Jian; Byška, Jan and Jänicke, Stefan
    Compensation management is one of the most important elements of personnel management. One type of compensation is traffic supplementary pay for the overtime employees. Conventional analysis of the traffic reimbursement focuses on the basic financial statistics such as the expenditure trends and rankings among different departments in the company. However, it largely ignores the wellbeing of the individuals and their residential distribution that can help improve the effectiveness of compensation strategies. In this work, we propose a visual analytics system based on a company's traffic reimbursement data for the overtime. It assists the compensation managers in understanding the overtime employees' commuting status and providing more indirect compensation benefits for the employees. A user case confirms the efficacy of our system and experts' feedback also suggests that our approach indeed helps them better tackle the problem of analyzing the car-hailing reimbursement data for the overtime.
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    WarehouseVis: A Visual Analytics Approach to Facilitating Warehouse Location Selection for Business Districts
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Li, Quan; Liu, Qiangqiang; Tang, Chunfeng; Li, Zhiwei; Wei, Shuaichao; Peng, Xianrui; Zheng, Minghua; Chen, Tianjian; Yang, Qiang; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Selecting a proper warehouse location serving to satisfy the demands of the goods from a certain business area is important to a successful retail business. However, the large solution space, uncertain traffic conditions, and varying business preferences impose great challenges on warehouse location selection. Conventional approaches mainly summarize relevant evaluation criteria and compile them into an analysis report to facilitate rapid data absorption but fail to support a comprehensive and joint decision-making process in warehouse location selection. In this paper, we propose a visual analytics approach to facilitating warehouse location selection. We first visually centralize relevant information of warehouses and adapts a widely-used methodology to efficiently rank warehouse candidates. We then design a delivering estimation model based on massive logistics trajectories to resolve the uncertainty issue of traffic conditions of warehouses. Based on these techniques, an interactive framework is proposed to generate and explore the candidate warehouses. We conduct a case study and a within-subject study with baseline systems to assess the efficacy of our system. Experts' feedback also suggests that our approach indeed helps them better tackle the problem of finding an ideal warehouse in the field of retail logistics management.