With the proposed “carbon peaking” and “carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents' commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.

As the primary source of carbon dioxide (CO2) emissions, cities are the key to solving climate change. Prior works focus on CO2 emission drivers and CO2 emission city clusters. However, the methods of mining CO2 emission drivers include narrow factors affecting CO2 emissions, and ignore the interactions among these factors. The intelligibility of the results of the Geographical Weighted Regression (GWR) that is used to analyze the spatial heterogeneity of drivers is poor. Moreover, the relations between CO2 emission city clusters and economic ties among cities are ignored. The lack of visual analysis tools is also a problem that needs to be fixed. Hence we develop a novel analysis framework. Thereinto, a novel method based on the stepwise regression (SR) is used to mine drivers; the biclustering algorithm is introduced into the traditional GWR to improve the intelligibility of the GWR; the gravity-entropy model is adopted to analyze the relations between CO2 emission city clusters and economic correlation strength among cities. Furthermore, a visual analytics system is implemented to explore the situation of CO2 emissions. We demonstrate the effectiveness of our approach through case studies conducted with socioeconomic data from 169 Chinese cities and positive feedback from experts and volunteers.

A novel approach to visually represent meteorological data has emerged with the maturation of Immersive Analytics (IA). We have proposed an immersive meteorological virtual sandbox as a solution to the limitations of 2D analysis in expressing and perceiving data. This innovative visual method enables users to interact directly with data through non-contact aerial gestures (NCAG). Referring to the “What you see is what you get” concept in scientific visualization, we have proposed a novel approach for visual exploration of meteorological data that aims to immerse users in the analysis process. We hope this approach can inspire immersive visualization techniques for other types of geographic data as well. Finally, we conducted a user questionnaire to evaluate our system and work. The evaluation results demonstrate that our system effectively reduces cognitive burden, alleviates mental workload, and enhances users’ retention of analysis findings.

Urban traffic congestion is a major nuisance for residents' daily commute, but few studies have focused on the effective combination between urban traffic condition visualization and hybrid traffic travel route recommendation. In this paper, the visualization exploration of urban transportation patterns is realized by multilayer complex transportation networks, which are constructed by taxi transportation network, bike-sharing transportation network, and urban transportation community network. Based on multilayer complex traffic networks, a genetic algorithm modified by A * search algorithm is used to generate multi-modal travel routes. The case studies prove that this method can effectively reduce the time cost of commuting between congested areas by generating hybrid traffic routes.

Exploring campus lifestyle is conducive to innovating education management, optimizing campus resources allocation, and providing personalized services, but little attention had been paid to the exploration campus lifestyle. A novel interactive system based on behavioral data of campus cards is presented in this paper to provide new ideas and technical support for campus management. Interactive visualization techniques are utilized to help users analyze campus lifestyle via intelligible diagrams. The system contains three functional modules: providing a decision-making reference to educators on students' poverty subsidies, predicting students' academic performance by quantitative analysis, and scheduling cafeteria repast based on the scheduling model during the outbreak of COVID-19. Finally, three exploratory case studies are presented to demonstrate the effectiveness of the system.

Extended to immersive environment, volume visualization has the analytical superiority in spatial immersion, user engagement, multidimensional awareness and other aspects. But in a highly immersive virtual environment, traditional single-channel precise interactive methods cannot be applied directly to the immersive environment. Inspired by how users typically interact with everyday objects, a novel non-contact gesture interaction method base on What You See is What You Get(WYSIWYG)for volume rendering results is proposed in this paper. Just likes grab interaction in real scene, a full set of tools have been developed to enable direct volume rendering manipulation of color, saturation, contrast, brightness, and other optical properties by gestural motions in our method. Simultaneously, in order to improve the interactive experience in immersive environment, the evaluation model of motion comfort is introduced to design the interactive hand gestures, the cursor model is defined to estimating the gesture state combined with context gestural motions. Finally, the test platform is established with Oculus Rift + Leap Motion to verify the functionality and effectiveness of our method in improving the visual cognitive ability for volume visualization.

In order to clearly visualize the video structure and provide a user-friendly video content review and retrieve system, we firstly apply total variation (TV) metric to video shot segmentation and propose a novel video visualization method in this paper. Firstly, video data visualization design principles are proposed based on the analysis of video data characteristics. Secondly, a TV metric is proposed to measure the distance between two video frames. Then, video shots are detected by TV metric, and video key frames are extracted by K-means algorithm. Finally, according to the proposed video visualization design principles, a spiral video visualization method is proposed. Experimental results show that the proposed video visualization method can express the video content and the whole structure directly and clearly.