A collaborative visual analytics of trajectory and transaction data for digital forensics: VAST 2014 Mini-Challenge 2: Award for outstanding visualization and analysis

Zhao, Ying; Peng, Yanni; Huang, Wei; Li, Yong; Zhou, Fangfang; Liao, Zhifang; Zhang, Kang

doi:10.1109/vast.2014.7042571

Cited by 2 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the fine interactivity, scalability, and retentive ability of original features, RadViz adequately analyzes the attribute values of moving objects. In the comprehensive analytic system for trajectory and transaction in Section 2.2 [10], Zhao et al [104] also used RadViz to address the uncertainty of employee-location types, assisting in depicting individual living habits and detecting suspects' abnormal behaviors (see Figure 40).…”

Section: Radvizmentioning

confidence: 99%

See 1 more Smart Citation

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Chen

et al. 2019

IJGI

View full text Add to dashboard Cite

Trajectory big data have significant applications in many areas, such as traffic management, urban planning and military reconnaissance. Traditional visualization methods, which are represented by contour maps, shading maps and hypsometric maps, are mainly based on the spatiotemporal information of trajectories, which can macroscopically study the spatiotemporal conditions of the entire trajectory set and microscopically analyze the individual movement of each trajectory; such methods are widely used in screen display and flat mapping. With the improvement of trajectory data quality, these data can generally describe information in the spatial and temporal dimensions and involve many other attributes (e.g., speed, orientation, and elevation) with large data amounts and high dimensions. Additionally, these data have relatively complicated internal relationships and regularities, whose analysis could cause many troubles; the traditional approaches can no longer fully meet the requirements of visualizing trajectory data and mining hidden information. Therefore, diverse visualization methods that present the value of massive trajectory information are currently a hot research topic. This paper summarizes the research status of trajectory data-visualization techniques in recent years and extracts common contemporary trajectory data-visualization methods to comprehensively cognize and understand the fundamental characteristics and diverse achievements of trajectory-data visualization.First, we should fully understand the conceptual distinction between "multidimensional" and "multivariate". Generally, "multidimensional" is used to describe the dimensionality of independent variables in physical space and concentrates on expressing spatial and temporal concepts, while "multivariate" is used for attribute-count description that indicates the attribute amount involved in the data. In fact, trajectory data are typically enormous, high-dimensional, spatiotemporal vector attribute data. We are interested not only in comprehending dimensional information for spatiotemporal-element depictions, but also in the visualization research with attribute-feature expression. Starting in Section 2, we present relevant visualization techniques and methods that function with multisource trajectory data. The chosen examples are for illustration and the list is not exhaustive-we only focus on small but representative choices. Universal Multivariate VisualizationThe trajectory visualization reflects the changing patterns of time-varying geolocations and corresponding attribute information, including basic-dimensional information and the attached thematic variables of space-time objects. However, these variables usually do not exist independently but are combined with each other, and the trajectory data of different sources, scales, or types are plotted in a hybrid manner. We separately list visualization techniques that integrate multiple attributes. As a powerful and portable carrier that encodes abundant information in methods of visual cha...

show abstract

Section: Radvizmentioning

confidence: 99%

“…1. [10], Zhao et al [104] also used RadViz to address the uncertainty of employee-location types, assisting in depicting individual living habits and detecting suspects' abnormal behaviors (see Figure 40).…”

Section: Radvizmentioning

confidence: 99%

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Chen

et al. 2019

IJGI

View full text Add to dashboard Cite

show abstract

Visual Analytics: A Comprehensive Overview

Cui

2019

IEEE Access

View full text Add to dashboard Cite

With the ever-increasing amount of data, the world has stepped into the era of ''Big Data''. Presently, the analysis of massive and complex data and the extraction of relevant information, have been become essential tasks in many fields of studies, such as health, biology, chemistry, social science, astronomy, and physics. However, compared with the development of data storage and management technologies, our ability to gain useful information from the collected data does not match our ability to collect the data. This gap has led to a surge of research activity in the field of visual analytics. Visual analytics employs interactive visualization to integrate human judgment into algorithmic data-analysis processes. In this paper, the aim is to draw a complete picture of visual analytics to direct future research by examining the related research in various application domains. As such, a novel categorization of visual-analytics applications from a technical perspective is proposed, which is based on the dimensionality of visualization and the type of interaction. Based on this categorization, a comprehensive survey of visual analytics is performed, which examines its evolution from visualization and algorithmic data analysis, and investigates how it is applied in various application domains. In addition, based on the observations and findings gained in this survey, the trends, major challenges, and future directions of visual analytics are discussed.

show abstract

A collaborative visual analytics of trajectory and transaction data for digital forensics: VAST 2014 Mini-Challenge 2: Award for outstanding visualization and analysis

Cited by 2 publications

References 2 publications

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Visual Analytics: A Comprehensive Overview

Contact Info

Product

Resources

About