Several studies have analyzed traffic patterns using Vehicle Detector (VD) and Global Positioning System (GPS) data. VD records the speed of vehicles passing through detectors, GPS data captures traffic speed on the roads. However, unobserved data gaps may arise due to physical malfunctions of sensors in VD data or interruptions in satellite signal reception for GPS data. Unobserved data adds complexity to the analysis and prediction of urban traffic networks. To tackle this challenge, researchers have attempted to estimate unobserved data using spatiotemporal patterns, but approaches that rely solely on past time points are inherently less reliable. In this study, we propose a Virtual network-based Diffusion Convolutional Recurrent Neural Network (V-DCRNN) for estimating unobserved speed data in urban traffic networks using a virtual network. The virtual network is created by adding nodes and edges in virtual directions based on observed nodes at intersections, thereby enhancing the traffic network. The V-DCRNN, which utilizes the diffusion convolution process in the virtual network, uses the augmented traffic network as input to predict traffic speed. Unobserved speed data is estimated based on the values of virtual nodes predicted by V-DCRNN. We evaluate the proposed V-DCRNN model through unobserved speed data estimation experiments in the urban traffic network, where we randomly mask individual nodes and entire intersections. The main contributions of this work are as follows: 1) the design of a virtual network to model additional traffic dynamics at intersections, facilitating the estimation of unobserved speed data; 2) the development of the V-DCRNN model, which leverages a self-attention mechanism to capture spatiotemporal dependencies in urban traffic networks by incorporating the virtual network as input; and 3) an evaluation of the V-DCRNN’s ability to estimate unobserved speed data in urban traffic networks with up to 20% unobserved nodes, demonstrating robust and reliable performance.

The transportation network is an important element in an urban system that supports daily activities, enabling people to travel from one place to another. One of the key challenges is the network complexity, which is composed of many node pairs distributed over the area. This spatial characteristic results in the high dimensional network problem in understanding the 'cause' of problems such as traffic congestion. Recent studies have proposed visual analytics systems aimed at understanding these underlying causes. Despite these efforts, the analysis of such causes is limited to identified patterns. However, given the intricate distribution of roads and their mutual influence, new patterns continuously emerge across all roads within urban transportation. At this stage, a well-defined visual analytics system can be a good solution for transportation practitioners. In this paper, we propose a system, CATOM (Causal Topology Map), for the cause-effect analysis of traffic patterns based on Granger causality for extracting causal topology maps. CATOM discovers causal relationships between roads through the Granger causality test and quantifies these relationships through the causal density. During the design process, the system was developed to fully utilize spatial information with visualization techniques to overcome the previous problems in the literature. We also evaluate the usability of our approach by conducting a SUS(System Usability Scale) test and traffic cause analysis with the real-world data from two study sites in collaboration with domain experts.

The transfer function (TF) design is crucial for enhancing the visualization quality and understanding of volume data in volume rendering. Recent research has proposed various multidimensional TFs to utilize diverse attributes extracted from volume data for controlling individual voxel rendering. Although multidimensional TFs enhance the ability to segregate data, manipulating various attributes for the rendering is cumbersome. In contrast, low-dimensional TFs are more beneficial as they are easier to manage, but separating volume data during rendering is problematic. This paper proposes a novel approach, a two-level transfer function, for rendering volume data by reducing TF dimensions. The proposed technique involves extracting multidimensional TF attributes from volume data and applying t-Stochastic Neighbor Embedding (t-SNE) to the TF attributes for dimensionality reduction. The two-level transfer function combines the classical 2D TF and t-SNE TF in the conventional direct volume rendering pipeline. The proposed approach is evaluated by comparing segments in t-SNE TF and rendering images using various volume datasets. The results of this study demonstrate that the proposed approach can effectively allow us to manipulate multidimensional attributes easily while maintaining high visualization quality in volume rendering.

Researchers have been actively engaged in recent studies utilizing the denoising diffusion probabilistic model (DDPM), a generative model that shows stable convergence during training, unlike other generative models, and represents data distributions that approximate real-world data. Due to these advantages, DDPM is widely employed for image, video, and audio generation. Recent DDPM research extends beyond image, video, and audio to various datasets and applications. The majority of real-world scenarios involve spatiotemporal data. Consequently, researchers propose prediction models using spatiotemporal data across domains such as traffic forecasting, weather prediction, and disease tracking. While most spatiotemporal data prediction models focus on traffic data, weather and epidemic data present higher uncertainty due to regional variations, seasonal changes, fluctuations in infection rates, and uncertainties in reported cases. To address these challenges of spatiotemporal data, we propose a DDPM-based model. In contrast to previous DDPM studies, we propose a denoising network combining Graph Structure Learning (GSL) to utilize the dynamic nature of spatiotemporal data. We assess performance using three real-world datasets encompassing traffic, weather, and epidemics and conduct an ablation study comparing GSL methods.

Researchers have been persistently working to address the issue of missing values in time series data. Numerous models have been proposed, striving to estimate the distribution of the data. The Radial Basis Functions Neural Network (RBFNN) has recently exhibited exceptional performance in estimating data distribution. In this paper, we propose a time series imputation model based on RBFNN. Our imputation model learns local information from timestamps to create a continuous function. Additionally, we incorporate time gaps to facilitate learning information considering the missing terms of missing values. We name this model the Missing Imputation Multivariate RBFNN (MIM-RBFNN). However, MIM-RBFNN relies on a local information-based learning approach, which presents difficulties in utilizing temporal information. Therefore, we propose an extension called the Missing Value Imputation Recurrent Neural Network with Continuous Function (MIRNN-CF) using the continuous function generated by MIM-RBFNN. We evaluate the performance using two real-world datasets with non-random missing and random missing patterns, and conduct an ablation study comparing MIM-RBFNN and MIRNN-CF.

ML (Machine learning) models are used to mine inconspicuous information in big data. The model and data quality influence the performance of a machine-learning model. However, it is inefficient to modify the model, which is a black box, and low-quality data tends to cause biased learning of the model. Therefore, it is crucial to improve the data quality. Different techniques have been used to improve data quality depending on the data conditions and the data quality issues. Therefore, improving data quality is time-consuming and challenging for users with insufficient knowledge of data. Visual analytics techniques have been proposed to focus on decision support to improve data quality. However, existing studies are complicated for users to consider a comprehensive DQI (Data Quality Improvement) method for generating data suitable for ML models. Also, it remains limited in that users must directly consider all combinations of DQI processes. This paper presents a novel visual analytics system that manages data quality for use in ML models. The proposed system suggests an optimal quality improvement process with visualization techniques such as heatmap, histogram, and scatter plot to support DQI.

Machine learning (ML) models are used to mine inconspicuous information in big data. The model and data quality influence the performance of a ML model. However, modifying the ML model while measuring performance is impractical, and low-quality data causes biased model training. Therefore, improving the data quality is essential. Visual analytics systems supporting DQI (Data Quality Improvement) have been proposed in the past. However, in the studies, it is difficult for users to assess comprehensive data quality improvement methods for machine learning and to determine an appropriate data quality improvement process. In this paper, we propose a novel visual analytics system for managing data quality used in machine learning models.

Urban traffic congestion has become an important issue not only affecting our daily lives, but also limiting economic development. The primary cause of urban traffic congestion is that the number of vehicles is higher than the permissible limit of the road. Previous studies have focused on dispersing traffic volume by detecting urban traffic congestion zones and predicting future trends. However, to solve the fundamental problem, it is necessary to discover the cause of traffic congestion. Nevertheless, it is difficult to find a research which presents an approach to identify the causes of traffic congestion. In this paper, we propose a technique to analyze the cause of traffic congestion based on the traffic flow theory. We extract vehicle flows from traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the traffic congestion area. The N-curves are classified into four different traffic congestion patterns by a convolutional neural network. Analyzing the causes and influence of traffic congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated that our system can classify the causes of traffic congestion and can be used efficiently in road planning.

It is not easy to predict incomplete panel data whose overall trend is not complete. For example, physical growth data for age 7-18 has been collected every a few months for up to seven years; therefore, only short growth pattern pieces exist in the data. When using previous prediction techniques, it is challenging to create a growth prediction model that reflects individual growth patterns. Also, uncertainties in predicted results emerge since the overall trend of the data is unknown. In this work, we present a predictive analysis and modeling to forecast incomplete data over time. We extend the Bayesian network model, which is relative data-driven approaches that explore similar data and weave them to create approximate inference margins. Besides, we propose a visual analytics system that enables us to design various predictive models that reflect individual growth pattern. Our visual analytics system assists us to discover new growth patterns in the process of analyzing the accuracy of previously designed predictive models. Moreover, the system allows us to optimize predictive models to fit unusual growth patterns.

Cybersickness is a symptom of dizziness that occurs while experiencing Virtual Reality (VR) technology and it is presumed to occur mainly by crosstalk between the sensory and cognitive systems. However, since the sensory and cognitive systems cannot be measured objectively, it is difficult to measure cybersickness. Therefore, methodologies for measuring cybersickness have been studied in various ways. Traditional studies have collected answers to questionnaires or analyzed EEG data using machine learning algorithms. However, the system relying on the questionnaires lacks objectivity, and it is difficult to obtain highly accurate measurements with the machine learning algorithms in previous studies. In this work, we apply and compare Deep Neural Network (DNN) and Convolutional Neural Network (CNN) deep learning algorithms for objective cy-bersickness measurement from EEG data. We also propose a data preprocessing for learning and signal quality weights allowing us to achieve high performance while learning EEG data with the deep learning algorithms. Besides, we analyze video characteristics where cybersickness occurs by examining the 360 video stream segments causing cybersickness in the experiments. Finally, we draw common patterns that cause cybersickness.

Recently, VR technology is rapidly developing and attracting public attention. However, VR Sickness is a problem that is still not solved in the VR experience. The VR sickness is presumed to be caused by crosstalk between sensory and cognitive systems [1]. However, since there is no objective way to measure sensory and cognitive systems, it is difficult to measure VR sickness. In this paper, we collect EEG data while participants experience VR videos. We propose a Deep Neural Network (DNN) deep learning algorithm by measuring VR sickness through electroencephalogram (EEG) data. Experiments have been conducted to search for an appropriate EEG data preprocessing method and DNN structure suitable for the deep learning, and the accuracy of 99.12% is obtained in our study.

Geographic visualization research has focused on a variety of techniques to represent and explore spatiotemporal data. The goal of those techniques is to enable users to explore events and interactions over space and time in order to facilitate the discovery of patterns, anomalies and relationships within the data. However, it is difficult to extract and visualize data flow patterns over time for non-directional statistical data without trajectory information. In this work, we develop a novel flow analysis technique to extract, represent, and analyze flow maps of non-directional spatiotemporal data unaccompanied by trajectory information. We estimate a continuous distribution of these events over space and time, and extract flow fields for spatial and temporal changes utilizing a gravity model. Then, we visualize the spatiotemporal patterns in the data by employing flow visualization techniques. The user is presented with temporal trends of geo-referenced discrete events on a map. As such, overall spatiotemporal data flow patterns help users analyze geo-referenced temporal events, such as disease outbreaks, crime patterns, etc. To validate our model, we discard the trajectory information in an origin-destination dataset and apply our technique to the data and compare the derived trajectories and the original. Finally, we present spatiotemporal trend analysis for statistical datasets including twitter data, maritime search and rescue events, and syndromic surveillance.

Welcome to the proceedings of the IEEE Pacific Visualization Symposium 2017 (IEEE PacificVis 2017), tenth in a series of successful events that have been sponsored by the IEEE Computer Society Visualization and Graphics Technical Committee (VGTC). Past IEEE PacificVis symposia were held in Kyoto (2008), Beijing (2009), Taipei (2010), Hong Kong (2011), Songdo (2012), Sydney (2013), Yokohama (2014), Hangzhou (2015), and Taipei (2016). This year, PacificVis is held at Seoul, Korea from April 18 to 21, 2017, hosted by Seoul National University.

In recent years, people can do most of their personal tasks, such as banking on smart devices like personal computers (PCs). Especially, Malware targeted at personal information stored on mobile are hard to detect and risks from usage patterns are even more difficult. Therefore, a means for easy recognition of the problems and the smartphone usage is necessary. In this paper, we present a personal visual analytics (PVA) system for Android security risk lifelog using app permissions to recognize the risk. Our system stores the security-related personal information on the smartphone device and utilizes it to analyze the security risk lifelog. For the risk analysis, we define security risk scores based on the app and permission statistics. Then, several linked visualizations are designed to present the risk lifelog. We have collected the security lifelog data from eight Android smartphone users and analyzed their security matters. Our PVA system enables Android smartphone users to observe, mitigate the security risk, and eventually understand how Android security risk affects their lives. Moreover, we present a user study to evaluate the PVA system with user feedback.

Abstract Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.

Digital big data provide the vast potential of increasing effectiveness and efficiency for decision making. Since the volume of the data is enormous, the data analysis requires large amount of time and effort. It is more problematic when predictive analysis is necessary for futuristic decision making. In order for predictive analysis, there have been many studies to forecast future trends spatiotemporally. However, most of studies provide just future tendency per event using graphs or maps without contextual compositive analysis. In this paper, we present a predictive visual analytics system to provide predictive event patterns. We infer the future event evolution by combining contextually similar cases occurring in the past. We utilize social media data to detect interesting abnormal events and match the detected abnormal events within the past news media data in order to retrieve similar event patterns. Then, we extract future event patterns through compositing contextual relationship among topics included in the similar past patterns. In order to evaluate our VA system, we demonstrate two use cases in this paper and validate our system with possible predictive story lines.

The advent of modern smart phones and handheld devices has given analysts, decision-makers, and even the general public the ability to rapidly ingest data and translate it into actionable information on-the-go. In this paper, we explore the design and use of a mobile visual analytics toolkit for public safety data that equips law enforcement agencies with effective situation awareness and risk assessment tools. Our system provides users with a suite of interactive tools that allow them to perform analysis and detect trends, patterns and anomalies among criminal, traffic and civil (CTC) incidents. The system also provides interactive risk assessment tools that allow users to identify regions of potential high risk and determine the risk at any user-specified location and time. Our system has been designed for the iPhone/iPad environment and is currently being used and evaluated by a consortium of law enforcement agencies. We report their use of the system and some initial feedback.

The perception of transparency and the underlying neural mechanisms have been subject to extensive research in the cognitive sciences. However, we have yet to develop visualization techniques that optimally convey the inner structure of complex transparent shapes. In this paper, we apply the findings of perception research to develop a novel illustrative rendering method that enhances surface transparency nonlocally. Rendering of transparent geometry is computationally expensive since many optimizations, such as visibility culling, are not applicable and fragments have to be sorted by depth for correct blending. In order to overcome these difficulties efficiently, we propose the illustration buffer. This novel data structure combines the ideas of the A and G-buffers to store a list of all surface layers for each pixel. A set of local and nonlocal operators is then used to process these depth-lists to generate the final image. Our technique is interactive on current graphics hardware and is only limited by the available graphics memory. Based on this framework, we present an efficient algorithm for a nonlocal transparency enhancement that creates expressive renderings of transparent surfaces. A controlled quantitative double blind user study shows that the presented approach improves the understanding of complex transparent surfaces significantly.

We present Bristle Maps, a novel method for the aggregation, abstraction, and stylization of spatiotemporal data that enables multiattribute visualization, exploration, and analysis. This visualization technique supports the display of multidimensional data by providing users with a multiparameter encoding scheme within a single visual encoding paradigm. Given a set of geographically located spatiotemporal events, we approximate the data as a continuous function using kernel density estimation. The density estimation encodes the probability that an event will occur within the space over a given temporal aggregation. These probability values, for one or more set of events, are then encoded into a bristle map. A bristle map consists of a series of straight lines that extend from, and are connected to, linear map elements such as roads, train, subway lines, and so on. These lines vary in length, density, color, orientation, and transparency—creating the multivariate attribute encoding scheme where event magnitude, change, and uncertainty can be mapped as various bristle parameters. This approach increases the amount of information displayed in a single plot and allows for unique designs for various information schemes. We show the application of our bristle map encoding scheme using categorical spatiotemporal police reports. Our examples demonstrate the use of our technique for visualizing data magnitude, variable comparisons, and a variety of multivariate attribute combinations. To evaluate the effectiveness of our bristle map, we have conducted quantitative and qualitative evaluations in which we compare our bristle map to conventional geovisualization techniques. Our results show that bristle maps are competitive in completion time and accuracy of tasks with various levels of complexity.

Currently, user centered transfer function design begins with the user interacting with a one or two-dimensional histogram of the volumetric attribute space. The attribute space is visualized as a function of the number of voxels, allowing the user to explore the data in terms of the attribute size/magnitude. However, such visualizations provide the user with no information on the relationship between various attribute spaces (e.g., density, temperature, pressure, x, y, z) within the multivariate data. In this work, we propose a modification to the attribute space visualization in which the user is no longer presented with the magnitude of the attribute; instead, the user is presented with an information metric detailing the relationship between attributes of the multivariate volumetric data. In this way, the user can guide their exploration based on the relationship between the attribute magnitude and user selected attribute information as opposed to being constrained by only visualizing the magnitude of the attribute. We refer to this modification to the traditional histogram widget as an abstract attribute space representation. Our system utilizes common one and two-dimensional histogram widgets where the bins of the abstract attribute space now correspond to an attribute relationship in terms of the mean, standard deviation, entropy, or skewness. In this manner, we exploit the relationships and correlations present in the underlying data with respect to the dimension(s) under examination. These relationships are often times key to insight and allow us to guide attribute discovery as opposed to automatic extraction schemes which try to calculate and extract distinct attributes a priori. In this way, our system aids in the knowledge discovery of the interaction of properties within volumetric data.

Abstract Competitive intelligence is a systematic approach for gathering, analyzing, and managing information to make informed business decisions. Many companies use competitive intelligence to identify risks and opportunities within markets. Point of sale data that retailers share with vendors is of critical importance in developing competitive intelligence. However, existing tools do not easily enable the analysis of such large and complex data. therefore, new approaches are needed in order to facilitate better analysis and decision making. In this paper, we present MarketAnalyzer, an interactive visual analytics system designed to allow vendors to increase their competitive intelligence. MarketAnalyzer utilizes pixel‐based matrices to present sale data, trends, and market share growths of products of the entire market within a single display. These matrices are augmented by advanced underlying analytical methods to enable the quick evaluation of growth and risk within market sectors. Furthermore, our system enables the aggregation of point of sale data in geographical views that provide analysts with the ability to explore the impact of regional demographics and trends. Additionally, overview and detailed information is provided through a series of coordinated multiple views. In order to demonstrate the effectiveness of our system, we provide two use‐case scenarios as well as feedback from market analysts.

We present a method for automatically building typographic maps that merge text and spatial data into a visual representation where text alone forms the graphical features. We further show how to use this approach to visualize spatial data such as traffic density, crime rate, or demographic data. The technique accepts a vector representation of a geographic map and spatializes the textual labels in the space onto polylines and polygons based on user-defined visual attributes and constraints. Our sample implementation runs as a Web service, spatializing shape files from the OpenStreetMap project into typographic maps for any region.

In many scientific simulations, the temporal variation and analysis of features are important. Visualization and visual analysis of time series data is still a significant challenge because of the large volume of data. Irregular and scattered time series data sets are even more problematic to visualize interactively. Previous work proposed functional representation using basis functions as one solution for interactively visualizing scattered data by harnessing the power of modern PC graphics boards. In this paper, we use the functional representation approach for time-varying data sets and develop an efficient encoding technique utilizing temporal similarity between time steps. Our system utilizes a graduated approach of three methods with increasing time complexity based on the lack of similarity of the evolving data sets. Using this system, we are able to enhance the encoding performance for the time-varying data sets, reduce the data storage by saving only changed or additional basis functions over time, and interactively visualize the time-varying encoding results. Moreover, we present efficient rendering of the functional representations using binary space partitioning tree textures to increase the rendering performance.

Simulation and computation in chemistry studies have been improved as computational power has increased over decades. Many types of chemistry simulation results are available, from atomic level bonding to volumetric representations of electron density. However, tools for the visualization of the results from quantum chemistry computations are still limited to showing atomic bonds and isosurfaces or isocontours corresponding to certain isovalues. In this work, we study the volumetric representations of the results from quantum chemistry computations, and evaluate and visualize the representations directly on the GPU without resampling the result in grid structures. Our visualization tool handles the direct evaluation of the approximated wavefunctions described as a combination of Gaussian-like primitive basis functions. For visualizations, we use a slice based volume rendering technique with a 2D transfer function, volume clipping, and illustrative rendering in order to reveal and enhance the quantum chemistry structure. Since there is no need of resampling the volume from the functional representations, two issues, data transfer and resampling resolution, can be ignored, therefore, it is possible to interactively explore large amount of different information in the computation results.

Abstract Functional approximation of scattered data is a popular technique for compactly representing various types of datasets in computer graphics, including surface, volume, and vector datasets. Typically, sums of Gaussians or similar radial basis functions are used in the functional approximation and PC graphics hardware is used to quickly evaluate and render these datasets. Previously, researchers presented techniques for spatially‐limited spherical Gaussian radial basis function encoding and visualization of volumetric scalar, vector, and multifield datasets. While truncated radially symmetric basis functions are quick to evaluate and simple for encoding optimization, they are not the most appropriate choice for data that is not radially symmetric and are especially problematic for representing linear, planar, and many non‐spherical structures. Therefore, we have developed a volumetric approximation and visualization system using ellipsoidal Gaussian functions which provides greater compression, and visually more accurate encodings of volumetric scattered datasets. In this paper, we extend previous work to use ellipsoidal Gaussians as basis functions, create a rendering system to adapt these basis functions to graphics hardware rendering, and evaluate the encoding effectiveness and performance for both spherical Gaussians and ellipsoidal Gaussians. Categories and Subject Descriptors (according to ACMCCS): I.3.3 [Computer Graphics]: Scientific Visualization, Ellipsoidal Basis Functions, Functional Approximation, Texture Advection

Understanding and analyzing complex volumetrically varying data is a difficult problem. Many computational visualization techniques have had only limited success in succinctly portraying the structure of three-dimensional turbulent flow. Motivated by both the extensive history and success of illustration and photographic flow visualization techniques, we have developed a new interactive volume rendering and visualization system for flows and volumes that simulates and enhances traditional illustration, experimental advection, and photographic flow visualization techniques. Our system uses a combination of varying focal and contextual illustrative styles, new advanced two-dimensional transfer functions, enhanced Schlieren and shadowgraphy shaders, and novel oriented structure enhancement techniques to allow interactive visualization, exploration, and comparative analysis of scalar, vector, and time-varying volume datasets. Both traditional illustration techniques and photographic flow visualization techniques effectively reduce visual clutter by using compact oriented structure information to convey three-dimensional structures. Therefore, a key to the effectiveness of our system is using one-dimensional (Schlieren and shadowgraphy) and two-dimensional (silhouette) oriented structural information to reduce visual clutter, while still providing enough three-dimensional structural information for the user's visual system to understand complex three-dimensional flow data. By combining these oriented feature visualization techniques with flexible transfer function controls, we can visualize scalar and vector data, allow comparative visualization of flow properties in a succinct, informative manner, and provide continuity for visualizing time-varying datasets.