A semantic-enhanced trajectory visual analytics for digital forensic

Liao, Zhifang; Li, Yong; Peng, Yanni; Zhao, Ying; Zhou, Fangfang; Liao, Zhining; Dudley, Sandra; Ghavami, Mohammad

doi:10.1007/s12650-015-0276-z

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…Crime map [8,9] is a typical application of introducing semantic analytics into the field of digital forensics and criminal investigation. Liao et al [10] designed a comprehensive analytic system for trajectory data and transaction data, which enables multiple perspectives in a semantically enhanced manner, depicting individual life routines and…”

Section: Trajectory Semantics Studymentioning

confidence: 99%

“…Crime map [8,9] is a typical application of introducing semantic analytics into the field of digital forensics and criminal investigation. Liao et al [10] designed ISPRS Int. J. Geo-Inf.…”

Section: Trajectory Semantics Studymentioning

confidence: 99%

“…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%

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

Chen

et al. 2019

IJGI

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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...

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Section: Trajectory Semantics Studymentioning

confidence: 99%

“…Crime map [8,9] is a typical application of introducing semantic analytics into the field of digital forensics and criminal investigation. Liao et al [10] designed ISPRS Int. J. Geo-Inf.…”

Section: Trajectory Semantics Studymentioning

confidence: 99%

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

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“…On the perspective of human urban life, Liao et al [21] combined conditional random field model with visual analytics methods to analyze the anomalies in urban taxi Global Positioning System (GPS) traces. Peng et al [22] performed a visual analysis on heterogeneous trajectories and credit card records of suspects to help investigators obtain their abnormal behaviors and gain evidence. Rohlig et al [23] presented a novel visual analytics approach for parameter-dependent activity recognition.…”

Section: Related Workmentioning

confidence: 99%

A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

et al. 2017

Self Cite

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Abstract:With the development of science and technology, it is possible to analyze residents' daily behaviors for the purpose of smart healthcare in the smart home environment. Many researchers have begun to detect residents' anomalous behaviors and assess their physical condition, but these approaches used by the researchers are often caught in plight caused by a lack of ground truth, one-sided analysis of behavior, and difficulty of understanding behaviors. In this paper, we put forward a smart home visual analysis system (SHVis) to help analysts detect and comprehend unusual behaviors of residents, and predict the health information intelligently. Firstly, the system classifies daily activities recorded by sensor devices in smart home environment into different categories, and discovers unusual behavior patterns of residents living in this environment by using various characteristics extracted from those activities and appropriate unsupervised anomaly detection algorithm. Secondly, on the basis of figuring out the residents' anomaly degree of every date, we explore the daily behavior patterns and details with the help of several visualization views, and compare and analyze residents' activities of various dates to find the reasons why residents act unusually. In the case study of this paper, we analyze residents' behaviors that happened over two months and find unusual indoor behaviors and give health advice to the residents.

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“…As such, there are emerging research trends focusing on the development of data-driven pattern discovery for human mobility patterns Barabasi (2005). While there has been much recent work on the visual exploration of traffic patterns and semantic events based on trajectory data (e.g., Chen et al (2015), , Zeng et al (2014), Doraiswamy et al (2014), Liao et al (2015), Zheng et al (2015), Wu et al (2015), visualizing the mobility patterns from a city-wide population is still a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Spatial–temporal visualization of city-wide crowd movement

Zhu

Wang

et al. 2016

J Vis

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Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macro-patterns of crowd movement, or analyze the trajectory of an individual object; however, they focus on geographical characteristics. In this paper, we propose a novel data representation, the mobility transition graph, to characterize spatio-temporal mobility transition of crowd from city-wide human mobility data. We describe the design, creation, and manipulation of the mobility transition graph and demonstrate the efficiency of our approach by a case study.

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A semantic-enhanced trajectory visual analytics for digital forensic

Cited by 13 publications

References 26 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

A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

Spatial–temporal visualization of city-wide crowd movement

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