Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities. 

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A survey of urban visual analytics: Advances and future directions

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. CCS Concepts • Human-centered computing → Visualization; Visualization design and evaluation methods;

Huan Bin Lin

Papers

Visual Analysis of Car-hailing Reimbursement Data for Overtime