Prediction of Series Criminals: An Approach Based on Modeling

Chen, Naichang; Shi, Wei; Song, Dawei; Wang, Yongsen

doi:10.1109/iccis.2010.25

Cited by 3 publications

(3 citation statements)

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“…The rapid miner tool plots the trend as a graph and the graph can be put to analytics to identify the criminal. The result is justified based on the crime that is already known and was committed by Peter Sutcliffe [4] and probable result that the graph shows.…”

Section: Experimental Results (Proving Yorkshire Ripper Case Convict)mentioning

confidence: 94%

Weight based Approach using Rossmo’s Equation for Criminal Policing

Syal¹,

Singh²

2015

CAE

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This paper discusses the approach of criminal policing by using Rossmo's equation and proposing weight based approach wherein nine types of crimes are put into four clusters and using raw data collected from online sources, weight is calculated based on the number of previous crimes committed. The result is put to use for analytics using rapid miner tool and graph is generated as the final result. Yorkshire ripper case is used as the experimental result to prove the convict taking some of his previous committed crime as data input along with other criminals in the area committing different crimes. The end result generated proves the algorithm discussed in the paper.

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Section: Experimental Results (Proving Yorkshire Ripper Case Convict)mentioning

confidence: 94%

Weight based Approach using Rossmo’s Equation for Criminal Policing

Syal¹,

Singh²

2015

CAE

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“…Then, stronger distance geometry techniques emerged and have leveraged the central tendency (e.g., geographic mean, median, and center of minimum distance) and diffusion (e.g., areal circle, standard deviational ellipse, and convex hull) to types of specific crime series [5]. These approaches have been demonstrated to be fast, intuitive, and cost-effective investigative tools because they capitalize on spatial behaviors characterized by the principle of least effort [7].…”

Section: Morphologymentioning

confidence: 99%

Prediction of Suspect Location Based on Spatiotemporal Semantics

Duan

et al. 2017

IJGI

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Abstract:The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity.

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“…Given a set of available clues, from the forensic labs and the clues collected at the crime spot, a methodology is presented to map the evidence and identify a criminal. In [11] Chen N. et. al., their model, firstly, we predict the residence of the offender based on the locations of the last crime scenes with three methods (distance analysis: the location that has the shortest distance to each crime site, circle fitting, probability theory); secondly, they predict the time of the next crime based on previous data with the method of fitting a straight line; next, predict the location of the next crime based on the locations of the last crime scenes and the time predicted in the second step with the method of weighted average; finally, generate a predicted location based on the three predicted locations with the method of weighted average.…”

Section: Related Workmentioning

confidence: 99%