Visualizing criminal relationships: comparison of a hyperbolic tree and a hierarchical list

Xiang, Yang; Chau, Michael; Atabakhsh, Homa; Chen, Hsinchun

doi:10.1016/j.dss.2004.02.006

Cited by 40 publications

(22 citation statements)

References 23 publications

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“…1 The data were generated based on real-world maps by the network-based spatio-temporal data generating approach [28]. 1 http://www.fh-oow.de/institute/iapg/personen/brinkhoff/generator/.…”

Section: Experimental Settingmentioning

confidence: 99%

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PutMode: prediction of uncertain trajectories in moving objects databases

et al. 2009

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Objective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life.Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Tra-S. Qiao ( ) School jectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs.Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy.

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“…1 The data were generated based on real-world maps by the network-based spatio-temporal data generating approach [28]. 1 http://www.fh-oow.de/institute/iapg/personen/brinkhoff/generator/.…”

Section: Experimental Settingmentioning

confidence: 99%

“…Information technologies have been increasingly applied to fight against criminals and terrorists in recent years [1][2][3]. With the rapid development in the wireless and mobile technology, law enforcement agencies are provided with a large volume of trajectory data of "moving objects" such as the movement of vehicles and criminals.…”

Section: Introductionmentioning

confidence: 99%

PutMode: prediction of uncertain trajectories in moving objects databases

et al. 2009

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“…Other evaluations of the hyperbolic tree browser helped reveal its strengths and weaknesses for specific tasks [31,32,48]. A survey article covering overview+detail, zooming, and focus+context interfaces discussed tree browsing plus other applications, reviewed the empirical studies, and provided a principled analysis based on perceptual psychological issues [13].…”

Section: Adoption and Durabilitymentioning

confidence: 99%

Innovation trajectories for information visualizations: Comparing treemaps, cone trees, and hyperbolic trees

Shneiderman

Dunne

Sharma

et al. 2011

Information Visualization

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Abstract-This paper reviews the trajectory of three information visualization innovations: treemaps, cone trees, and hyperbolic trees. These three ideas were first published around the same time in the early 1990s, so we are able to track academic publications, patents, and trade press articles over almost two decades. We describe the early history of each approach, problems with data collection from differing sources, appropriate metrics, and strategies for visualizing these longitudinal data sets. This paper makes two contributions: (1) it offers the information visualization community a history of how certain ideas evolved, influenced others, and were adopted for widespread use and (2) it provides an example of how such scientometric trajectories of innovations can be gathered and visualized. Guidance for designers is offered, but these conjectures may also be useful to researchers, research managers, science policy analysts, and venture capitalists.

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“…Both inside and outside of the academic world large scale projects are underway. One of the larger academic projects is the COPLINK project, a policeuniversity collaboration in Arizona, where work has been done in the exploitation of data mining for cooperation purposes [4], the field of entity extraction from narrative reports [5], and social network analysis [6], [13]. The FLINTS project and FinCEN [9] aim at revealing links between crimes and criminals and to reveal money laundering networks by comparing financial transactions.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Approaches to Criminal Career Analysis

Bruin

Cocx

Kosters

et al. 2006

Sixth International Conference on Data Mining (ICDM'06)

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Abstract-Narrative reports and criminal records are stored digitally across individual police departments, enabling the collection of this data to compile a nation-wide database of criminals and the crimes they committed. The compilation of this data through the last years presents new possibilities of analyzing criminal activity through time. Augmenting the traditional, more socially oriented, approach of behavioral study of these criminals and traditional statistics, data mining methods like clustering and prediction enable police forces to get a clearer picture of criminal careers. This allows officers to recognize crucial spots in changing criminal behaviour and deploy resources to prevent these careers from unfolding.Four important factors play a role in the analysis of criminal careers: crime nature, frequency, duration and severity. We describe a tool that extracts these from the database and creates digital profiles for all offenders. It compares all individuals on these profiles by a new distance measure and clusters them accordingly. This method yields a visual clustering of these criminal careers and enables the identification of classes of criminals. The proposed method allows for several user-defined parameters.

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Visualizing criminal relationships: comparison of a hyperbolic tree and a hierarchical list

Cited by 40 publications

References 23 publications

PutMode: prediction of uncertain trajectories in moving objects databases

PutMode: prediction of uncertain trajectories in moving objects databases

Innovation trajectories for information visualizations: Comparing treemaps, cone trees, and hyperbolic trees

Data Mining Approaches to Criminal Career Analysis

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