Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00033
Stefan Zellmann, M. Hellmann, U. Lang
While fast spatial index construction for triangle meshes has gained a lot of attention from the research community in recent years, fast tree construction algorithms for volume data are still rare and usually do not focus on real-time processing. We propose a linear time bounding volume hierarchy construction algorithm based on a popular method for surface ray tracing of triangle meshes that we adapt for direct volume rendering with sparse volumes. We aim at interactive to real-time construction rates and evaluate our algorithm using a GPU implementation.
{"title":"A Linear Time BVH Construction Algorithm for Sparse Volumes","authors":"Stefan Zellmann, M. Hellmann, U. Lang","doi":"10.1109/PacificVis.2019.00033","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00033","url":null,"abstract":"While fast spatial index construction for triangle meshes has gained a lot of attention from the research community in recent years, fast tree construction algorithms for volume data are still rare and usually do not focus on real-time processing. We propose a linear time bounding volume hierarchy construction algorithm based on a popular method for surface ray tracing of triangle meshes that we adapt for direct volume rendering with sparse volumes. We aim at interactive to real-time construction rates and evaluate our algorithm using a GPU implementation.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00043
Ko-Chih Wang, Jiayi Xu, J. Woodring, Han-Wei Shen
Cosmologists build simulations for the evolution of the universe using different initial parameters. By exploring the datasets from different simulation runs, cosmologists can understand the evolution of our universe and approach its initial conditions. A cosmological simulation nowadays can generate datasets on the order of petabytes. Moving datasets from the supercomputers to post data analysis machines is infeasible. We propose a novel approach called statistical super-resolution to tackle the big data problem for cosmological data analysis and visualization. It uses datasets from a few simulation runs to create a prior knowledge, which captures the relation between low-and high-resolution data. We apply in situ statistical down-sampling to datasets generated from simulation runs to minimize the requirements of I/O bandwidth and storage. High-resolution datasets are reconstructed from the statistical down-sampled data by using the prior knowledge for scientists to perform advanced data analysis and render high-quality visualizations.
{"title":"Statistical Super Resolution for Data Analysis and Visualization of Large Scale Cosmological Simulations","authors":"Ko-Chih Wang, Jiayi Xu, J. Woodring, Han-Wei Shen","doi":"10.1109/PacificVis.2019.00043","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00043","url":null,"abstract":"Cosmologists build simulations for the evolution of the universe using different initial parameters. By exploring the datasets from different simulation runs, cosmologists can understand the evolution of our universe and approach its initial conditions. A cosmological simulation nowadays can generate datasets on the order of petabytes. Moving datasets from the supercomputers to post data analysis machines is infeasible. We propose a novel approach called statistical super-resolution to tackle the big data problem for cosmological data analysis and visualization. It uses datasets from a few simulation runs to create a prior knowledge, which captures the relation between low-and high-resolution data. We apply in situ statistical down-sampling to datasets generated from simulation runs to minimize the requirements of I/O bandwidth and storage. High-resolution datasets are reconstructed from the statistical down-sampled data by using the prior knowledge for scientists to perform advanced data analysis and render high-quality visualizations.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130638354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00037
M. John, M. Baumann
The analysis of a novel's plot and characters are challenging and time-consuming tasks in literary criticism. Typically, humanities scholars want to describe and compare characters' personality traits, their roles, their relationships, and the evolution of these aspects over the course of a novel. Nowadays, due to the digitization of literature, humanities scholars can be supported in these endeavors with computational methods. In this paper, we present an approach that offers several means to analyze the plot and characters of a novel visually. Analysts can easily switch between an adjacency matrix and a node-link representation, which provide an overview of the characters and the relationships between them. Both views enable analysts to select different text ranges of the novel for studying the commonalities and differences of the character constellations within these ranges. We offer interactive visual representations to help investigate the relationships between the characters in more detail. Additionally, we link the visual representations with the novels' texts to support the inspection and verification of previously generated ideas and hypotheses. To demonstrate the benefits and limitations of our approach, we present two usage scenarios. The first one is based on a fictitious analysis and the second one discusses applications that were carried out during joint workshops with humanities scholars. Finally, we present and discuss the insights gained by an expert study and the design decisions of our approach.
{"title":"A Visual Approach for the Comparative Analysis of Character Networks in Narrative Texts","authors":"M. John, M. Baumann","doi":"10.1109/PacificVis.2019.00037","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00037","url":null,"abstract":"The analysis of a novel's plot and characters are challenging and time-consuming tasks in literary criticism. Typically, humanities scholars want to describe and compare characters' personality traits, their roles, their relationships, and the evolution of these aspects over the course of a novel. Nowadays, due to the digitization of literature, humanities scholars can be supported in these endeavors with computational methods. In this paper, we present an approach that offers several means to analyze the plot and characters of a novel visually. Analysts can easily switch between an adjacency matrix and a node-link representation, which provide an overview of the characters and the relationships between them. Both views enable analysts to select different text ranges of the novel for studying the commonalities and differences of the character constellations within these ranges. We offer interactive visual representations to help investigate the relationships between the characters in more detail. Additionally, we link the visual representations with the novels' texts to support the inspection and verification of previously generated ideas and hypotheses. To demonstrate the benefits and limitations of our approach, we present two usage scenarios. The first one is based on a fictitious analysis and the second one discusses applications that were carried out during joint workshops with humanities scholars. Finally, we present and discuss the insights gained by an expert study and the design decisions of our approach.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114241600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00026
Qi Han, Dennis Thom, M. John, Steffen Koch, T. Ertl, Florian Heimerl
Magic lens based focus+context techniques are powerful means for exploring document spatializations. Typically, they only offer additional summarized or abstracted views on focused documents. As a consequence, users might miss important information that is either not shown in aggregated form or that never happens to get focused. In this work, we present the design process and user study results for improving a magic lens based document exploration approach with exemplary visual quality cues to guide users in steering the exploration and support them in interpreting the summarization results. We contribute a thorough analysis of potential sources of information loss involved in these techniques, which include the visual spatialization of text documents, user-steered exploration, and the visual summarization. With lessons learned from previous research, we highlight the various ways those information losses could hamper the exploration. Furthermore, we formally define measures for the aforementioned different types of information losses and bias. Finally, we present the visual cues to depict these quality measures that are seamlessly integrated into the exploration approach. These visual cues guide users during the exploration and reduce the risk of misinterpretation and accelerate insight generation. We conclude with the results of a controlled user study and discuss the benefits and challenges of integrating quality guidance in exploration techniques.
{"title":"Visual Quality Guidance for Document Exploration with Focus+Context Techniques","authors":"Qi Han, Dennis Thom, M. John, Steffen Koch, T. Ertl, Florian Heimerl","doi":"10.1109/PacificVis.2019.00026","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00026","url":null,"abstract":"Magic lens based focus+context techniques are powerful means for exploring document spatializations. Typically, they only offer additional summarized or abstracted views on focused documents. As a consequence, users might miss important information that is either not shown in aggregated form or that never happens to get focused. In this work, we present the design process and user study results for improving a magic lens based document exploration approach with exemplary visual quality cues to guide users in steering the exploration and support them in interpreting the summarization results. We contribute a thorough analysis of potential sources of information loss involved in these techniques, which include the visual spatialization of text documents, user-steered exploration, and the visual summarization. With lessons learned from previous research, we highlight the various ways those information losses could hamper the exploration. Furthermore, we formally define measures for the aforementioned different types of information losses and bias. Finally, we present the visual cues to depict these quality measures that are seamlessly integrated into the exploration approach. These visual cues guide users during the exploration and reduce the risk of misinterpretation and accelerate insight generation. We conclude with the results of a controlled user study and discuss the benefits and challenges of integrating quality guidance in exploration techniques.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121851988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00010
Tiancheng Sun, Yucong Ye, I. Fujishiro, K. Ma
Solving complex data analysis problems can often benefit a collaborative effort. For synchronous co-located collaboration, a well-recognized challenge is to deliver different contents to people with different privileges and different responsibilities. This challenge is becoming more obvious with the use of a shared display space such as a wall-size display. In particular, scenarios often arise that a privileged participant needs to access sensitive information that other participants are not permitted to view. This is nearly impossible to achieve with only a single display. As a result, it becomes clear that additional devices are needed to provide some of the participants the capability to access and manage certain information in a private space. In this work, we investigate incorporating optical see-through head-mounted displays (OST-HMDs) with a wall-size display to deliver sensitive information in a synchronous co-located, collaborative setting. With our prototype system, we conduct a user study to observe the collaboration styles under this unique setup. We also present the lessons learned by reflecting on the iterative design process of our prototype system.
{"title":"Collaborative Visual Analysis with Multi-level Information Sharing Using a Wall-Size Display and See-Through HMDs","authors":"Tiancheng Sun, Yucong Ye, I. Fujishiro, K. Ma","doi":"10.1109/PacificVis.2019.00010","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00010","url":null,"abstract":"Solving complex data analysis problems can often benefit a collaborative effort. For synchronous co-located collaboration, a well-recognized challenge is to deliver different contents to people with different privileges and different responsibilities. This challenge is becoming more obvious with the use of a shared display space such as a wall-size display. In particular, scenarios often arise that a privileged participant needs to access sensitive information that other participants are not permitted to view. This is nearly impossible to achieve with only a single display. As a result, it becomes clear that additional devices are needed to provide some of the participants the capability to access and manage certain information in a private space. In this work, we investigate incorporating optical see-through head-mounted displays (OST-HMDs) with a wall-size display to deliver sensitive information in a synchronous co-located, collaborative setting. With our prototype system, we conduct a user study to observe the collaboration styles under this unique setup. We also present the lessons learned by reflecting on the iterative design process of our prototype system.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116834556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00019
Luiz Morais, N. Andrade, D. Sousa, Lesandro Ponciano
This work explores the user experience with two situated visualizations that lie on different points of design space. The first visualization — the Activity Clock — displays the aggregate presence of laboratory members into a wall clock. The second — Personal Activities — represents the same persons individually, in a conventional poster media. We interviewed 17 participants and leverage a theoretical lens of Continuous Engagement and Sense-Making to study how design decisions impact the user experience with respect to (1) which design factors attract users, (2) how design features affect users' understanding of the visualization, and (3) what kind of reflections are evoked by design. We discuss how the defamiliarizing effect of the Activity Clock plays a dual role in attracting users while also hindering their understanding of the data. We also consider the evidence that fine representation granularity in the Personal Activities evokes deeper reflections.
{"title":"Defamiliarization, Representation Granularity, and User Experience: A Qualitative Study with Two Situated Visualizations","authors":"Luiz Morais, N. Andrade, D. Sousa, Lesandro Ponciano","doi":"10.1109/PacificVis.2019.00019","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00019","url":null,"abstract":"This work explores the user experience with two situated visualizations that lie on different points of design space. The first visualization — the Activity Clock — displays the aggregate presence of laboratory members into a wall clock. The second — Personal Activities — represents the same persons individually, in a conventional poster media. We interviewed 17 participants and leverage a theoretical lens of Continuous Engagement and Sense-Making to study how design decisions impact the user experience with respect to (1) which design factors attract users, (2) how design features affect users' understanding of the visualization, and (3) what kind of reflections are evoked by design. We discuss how the defamiliarizing effect of the Activity Clock plays a dual role in attracting users while also hindering their understanding of the data. We also consider the evidence that fine representation granularity in the Personal Activities evokes deeper reflections.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114610854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00041
Fan Hong, Can Liu, Xiaoru Yuan
In this work, we propose a novel approach of volume visualization without explicit traditional rendering pipeline. In our proposed method, volumetric images can be interactively ‘reversed’ given the volumetric data and a static volume rendered image under the desired rendering effect. Our pipeline enables 3D-navigation on it for exploring the given volumetric data without explicit transfer function. In our approach, deep neural networks, combined usage of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNN) are employed to synthesize high-resolution and perceptually authentic images directly, inheriting the desired transfer function and viewing parameter implicitly given by the input images respectively.
{"title":"DNN-VolVis: Interactive Volume Visualization Supported by Deep Neural Network","authors":"Fan Hong, Can Liu, Xiaoru Yuan","doi":"10.1109/PacificVis.2019.00041","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00041","url":null,"abstract":"In this work, we propose a novel approach of volume visualization without explicit traditional rendering pipeline. In our proposed method, volumetric images can be interactively ‘reversed’ given the volumetric data and a static volume rendered image under the desired rendering effect. Our pipeline enables 3D-navigation on it for exploring the given volumetric data without explicit transfer function. In our approach, deep neural networks, combined usage of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNN) are employed to synthesize high-resolution and perceptually authentic images directly, inheriting the desired transfer function and viewing parameter implicitly given by the input images respectively.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126002045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00013
Sabrina Jaeger, Karsten Klein, Lucas Joos, Johannes Zagermann, M. D. Ridder, Jinman Kim, J. Yang, Ulrike Pfeil, Harald Reiterer, F. Schreiber
Analysing and understanding brain function and disorder is the main focus of neuroscience. Due to the high complexity of the brain, directionality of the signal and changing activity over time, visual exploration and data analysis are difficult. For this reason, a vast amount of research challenges are still unsolved. We explored different challenges of the visual analysis of brain data and the design of corresponding immersive environments in collaboration with experts from the biomedical domain. We built a prototype of an immersive virtual reality environment to explore the design space and to investigate how brain data analysis can be supported by a variety of design choices. Our environment can be used to study the effect of different visualisations and combinations of brain data representation, as for example network layouts, anatomical mapping or time series. As a long-term goal, we aim to aid neuro-scientists in a better understanding of brain function and disorder.
{"title":"Challenges for Brain Data Analysis in VR Environments","authors":"Sabrina Jaeger, Karsten Klein, Lucas Joos, Johannes Zagermann, M. D. Ridder, Jinman Kim, J. Yang, Ulrike Pfeil, Harald Reiterer, F. Schreiber","doi":"10.1109/PacificVis.2019.00013","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00013","url":null,"abstract":"Analysing and understanding brain function and disorder is the main focus of neuroscience. Due to the high complexity of the brain, directionality of the signal and changing activity over time, visual exploration and data analysis are difficult. For this reason, a vast amount of research challenges are still unsolved. We explored different challenges of the visual analysis of brain data and the design of corresponding immersive environments in collaboration with experts from the biomedical domain. We built a prototype of an immersive virtual reality environment to explore the design space and to investigate how brain data analysis can be supported by a variety of design choices. Our environment can be used to study the effect of different visualisations and combinations of brain data representation, as for example network layouts, anatomical mapping or time series. As a long-term goal, we aim to aid neuro-scientists in a better understanding of brain function and disorder.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125126069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid development of massively multiplayer online role-playing games (MMORPGs) has led operators to record huge amounts of fine-grained data from the in-game activities of players. These data provide considerable opportunities with which to study the dynamic interplay among player behaviors and investigate the roles of various social structures that underlie such interplay. However, modeling and visualizing these behavioral data remain a challenge. In this study, we propose a novel influence-susceptible model to measure the dynamic interplay among multiple behaviors. Based on this model, we introduce a new visual analytics system called BeXplorer. BeXplorer enables analysts to interactively explore the dynamic interplay between player purchase and communication behaviors and to examine the manner in which this interplay is bound by social structures where players are embedded.
{"title":"Visual Analytics of Dynamic Interplay Between Behaviors in MMORPGs","authors":"Junhua Lu, Xiao Xie, Ji Lan, Tai-Quan Peng, Wei Chen, Yingcai Wu","doi":"10.1109/PacificVis.2019.00021","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00021","url":null,"abstract":"The rapid development of massively multiplayer online role-playing games (MMORPGs) has led operators to record huge amounts of fine-grained data from the in-game activities of players. These data provide considerable opportunities with which to study the dynamic interplay among player behaviors and investigate the roles of various social structures that underlie such interplay. However, modeling and visualizing these behavioral data remain a challenge. In this study, we propose a novel influence-susceptible model to measure the dynamic interplay among multiple behaviors. Based on this model, we introduce a new visual analytics system called BeXplorer. BeXplorer enables analysts to interactively explore the dynamic interplay between player purchase and communication behaviors and to examine the manner in which this interplay is bound by social structures where players are embedded.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127398264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00027
Huan Liu, Sichen Jin, Yuyu Yan, Y. Tao, Hai Lin
GPS-based taxi trajectories contain valuable knowledge about movement behaviors for transportation and urban planning. Topic modeling is an effective tool to extract semantic information from taxi trajectories. However, previous methods generally ignore the direction of trajectories. In this paper, we employ the bigram topic model instead of traditional topic models to analyze textualized trajectories to take into account the direction information of trajectories. We further propose a modified Apriori algorithm to extract frequent sub-trajectories and use them to represent each topic as topical sub-trajectories. Finally, we design a visual analytics system with several linked views to facilitate users to interactively explore topics, sub-trajectories, and trips. We demonstrate the effectiveness of our system via case studies with Chengdu taxi trajectory data.
{"title":"Visual Analytics of Taxi Trajectory Data via Topical Sub-trajectories","authors":"Huan Liu, Sichen Jin, Yuyu Yan, Y. Tao, Hai Lin","doi":"10.1109/PacificVis.2019.00027","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00027","url":null,"abstract":"GPS-based taxi trajectories contain valuable knowledge about movement behaviors for transportation and urban planning. Topic modeling is an effective tool to extract semantic information from taxi trajectories. However, previous methods generally ignore the direction of trajectories. In this paper, we employ the bigram topic model instead of traditional topic models to analyze textualized trajectories to take into account the direction information of trajectories. We further propose a modified Apriori algorithm to extract frequent sub-trajectories and use them to represent each topic as topical sub-trajectories. Finally, we design a visual analytics system with several linked views to facilitate users to interactively explore topics, sub-trajectories, and trips. We demonstrate the effectiveness of our system via case studies with Chengdu taxi trajectory data.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124574946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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