Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from la...Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from large amounts of data has become an important issue in the civil aviation passenger data analysis process.The uncertainty analysis and visualization methods of data record and property measurement are offered in this paper,based on the visual analysis and uncertainty measure theory combined with parallel coordinates,radar chart,histogram,pixel chart and good interaction.At the same time,the data source expression clearly shows the uncertainty and hidden information as an information base for passengers’service展开更多
Bus travel time is uncertain due to the dynamic change in the environment.Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks.To ...Bus travel time is uncertain due to the dynamic change in the environment.Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks.To quantify the uncertainty of the bus travel time prediction model,a visual analysis method about the bus travel time uncertainty is proposed in this paper,which can intuitively obtain uncertain information of bus travel time through visual graphs.Firstly,a Bayesian encoder–decoder deep neural network(BEDDNN)model is proposed to predict the bus travel time.The BEDDNN model outputs results with distributional properties to calculate the prediction model uncertainty degree and provide the estimation of the bus travel time uncertainty.Second,an interactive uncertainty visualization system is developed to analyze the time uncertainty associated with bus stations and lines.The prediction model and the visualization model are organically combined to better demonstrate the prediction results and uncertainties.Finally,the model evaluation results based on actual bus data illustrate the effectiveness of the model.The results of the case study and user evaluation show that the visualization system in this paper has a positive impact on the effectiveness of conveying uncertain information and on user perception and decision making.展开更多
Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always ...Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.展开更多
We present PuzzleSorter,a certainty-aware visual analytics system for cultural relic fragment restoration.Restoring cultural objects from broken fragments is a fundamental task in geometry and archaeology.Prior resear...We present PuzzleSorter,a certainty-aware visual analytics system for cultural relic fragment restoration.Restoring cultural objects from broken fragments is a fundamental task in geometry and archaeology.Prior research proposes automatic models to classify fragments by types and assemble matched pairs successively.However,eroded fragments lead to erroneous results,posing two challenges for restorers to correct:(1)numerous fragments conceal errors within an overwhelming number of object appearances,and(2)the unknown difficulty of restoration hinders correction strategy development.To address these challenges,PuzzleSorter provides multi-criteria analysis that helps users identify certainties of current solutions and alternatives at the type,object,and fragment levels.Moreover,our system visualizes these certainties through a relation graph,which implies alternative assembly solutions with geometric context and indicates correction difficulties through neighbor proximity,number of neighbors,and path length.We demonstrate the feasibility and utility of our system through two case studies and expert interviews.展开更多
文摘Aviation data analysis can help airlines to understand passenger needs,so as to provide passengers with more sophisticated and better services.How to explore the implicit message and analyze contained features from large amounts of data has become an important issue in the civil aviation passenger data analysis process.The uncertainty analysis and visualization methods of data record and property measurement are offered in this paper,based on the visual analysis and uncertainty measure theory combined with parallel coordinates,radar chart,histogram,pixel chart and good interaction.At the same time,the data source expression clearly shows the uncertainty and hidden information as an information base for passengers’service
基金supported by National Natural Science Foundation of China(Grant No.61872304,No.61802320)Excellent Youth Foundation of Si’chuan(Grant No.19JCQN0108).
文摘Bus travel time is uncertain due to the dynamic change in the environment.Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks.To quantify the uncertainty of the bus travel time prediction model,a visual analysis method about the bus travel time uncertainty is proposed in this paper,which can intuitively obtain uncertain information of bus travel time through visual graphs.Firstly,a Bayesian encoder–decoder deep neural network(BEDDNN)model is proposed to predict the bus travel time.The BEDDNN model outputs results with distributional properties to calculate the prediction model uncertainty degree and provide the estimation of the bus travel time uncertainty.Second,an interactive uncertainty visualization system is developed to analyze the time uncertainty associated with bus stations and lines.The prediction model and the visualization model are organically combined to better demonstrate the prediction results and uncertainties.Finally,the model evaluation results based on actual bus data illustrate the effectiveness of the model.The results of the case study and user evaluation show that the visualization system in this paper has a positive impact on the effectiveness of conveying uncertain information and on user perception and decision making.
基金the Ministry of National Education,Turkey for financially supporting the first author’s PhD study at Newcastle University,UK.
文摘Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.
基金supported by the National Natural Science Foundation of China(U22A2032,62302440)China Postdoctoral Science Foundation(2023TQ0288)the Laboratory of Art and Archaeology Image,Zhejiang University。
文摘We present PuzzleSorter,a certainty-aware visual analytics system for cultural relic fragment restoration.Restoring cultural objects from broken fragments is a fundamental task in geometry and archaeology.Prior research proposes automatic models to classify fragments by types and assemble matched pairs successively.However,eroded fragments lead to erroneous results,posing two challenges for restorers to correct:(1)numerous fragments conceal errors within an overwhelming number of object appearances,and(2)the unknown difficulty of restoration hinders correction strategy development.To address these challenges,PuzzleSorter provides multi-criteria analysis that helps users identify certainties of current solutions and alternatives at the type,object,and fragment levels.Moreover,our system visualizes these certainties through a relation graph,which implies alternative assembly solutions with geometric context and indicates correction difficulties through neighbor proximity,number of neighbors,and path length.We demonstrate the feasibility and utility of our system through two case studies and expert interviews.
