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A.D. Sutmuller
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1
Homicide investigation in the digital era
The development and evaluation of a case-specific elements library (C-SEL)
The homicide rate dropped globally in the last decades, while in the same period the percentage of unsolved homicide cases increased. At the same time technological developments have drastically changed our lives and little is known about how this influenced the process of decision-making within homicide investigations. Therefore, the aim of this research is to develop a methodology that supports homicide investigators in the collection, prioritizing and elimination of persons of interest using pieces of evidence, in the new digital era, in such a way that the methodology is effective, reduces tunnel vision and obeys the laws for privacy.Currently used methodologies that use pieces of evidence to manage persons of interest were applied to three recent real-world homicide cases to evaluate whether these methodologies effectively collect, prioritize and eliminate persons of interest. The potential of a general approach, relying on big data and data science, to limit the number of persons of interest to be incorporated in homicide investigation, was explored. Subsequently, a methodology to incorporate and prioritize persons of interest was developed based on literature and the knowledge and expertise of experts in criminal investigation. This case specific elements library (C-SEL) consists of twenty-four elements and twelve underlying factors that can be used for the incorporation and prioritization of persons of interest. This new developed methodology was evaluated and compared to the currently used methodologies, not only on the performance measures of effectiveness, but also on tunnel vision reducing properties and the obedience to the laws of privacy.C-SEL effectively collects and prioritizes the perpetrator in all three real-world homicide cases and showed better results compared to the currently used methodologies on the tunnel vision reducing character and on the obedience to the laws of privacy. These results provide sufficient leads to further validate the new developed methodology.
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The homicide rate dropped globally in the last decades, while in the same period the percentage of unsolved homicide cases increased. At the same time technological developments have drastically changed our lives and little is known about how this influenced the process of decision-making within homicide investigations. Therefore, the aim of this research is to develop a methodology that supports homicide investigators in the collection, prioritizing and elimination of persons of interest using pieces of evidence, in the new digital era, in such a way that the methodology is effective, reduces tunnel vision and obeys the laws for privacy.Currently used methodologies that use pieces of evidence to manage persons of interest were applied to three recent real-world homicide cases to evaluate whether these methodologies effectively collect, prioritize and eliminate persons of interest. The potential of a general approach, relying on big data and data science, to limit the number of persons of interest to be incorporated in homicide investigation, was explored. Subsequently, a methodology to incorporate and prioritize persons of interest was developed based on literature and the knowledge and expertise of experts in criminal investigation. This case specific elements library (C-SEL) consists of twenty-four elements and twelve underlying factors that can be used for the incorporation and prioritization of persons of interest. This new developed methodology was evaluated and compared to the currently used methodologies, not only on the performance measures of effectiveness, but also on tunnel vision reducing properties and the obedience to the laws of privacy.C-SEL effectively collects and prioritizes the perpetrator in all three real-world homicide cases and showed better results compared to the currently used methodologies on the tunnel vision reducing character and on the obedience to the laws of privacy. These results provide sufficient leads to further validate the new developed methodology.
Getting the perpetrator incorporated and prioritized in homicide investigations
The development and evaluation of a case-specific element library (c-sel)
Journal article
(2020)
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August Daniel Sutmuller, Marielle Den Hengst, Ana Isabel Barros, Pieter van Gelder
Homicide investigators in the digital era have access to an increasing amount of data and the processing of all persons of interest and pieces of evidence has become nearly impossible. This paper describes the development and evaluation of a case-specific element library (C-SEL) that can be used to incorporate and prioritize persons of interest in homicide investigations. In a survey, 107 experts in the field of criminal investigation assigned an initial score to the elements. Each element was extended with underlying factors that can be used to adjust the initial score based on the relevance and credibility of the source. A case study was conducted using three Dutch real-world cases to evaluate the methodology. The results look promising and are better than four methodologies currently used in practice.
...
Homicide investigators in the digital era have access to an increasing amount of data and the processing of all persons of interest and pieces of evidence has become nearly impossible. This paper describes the development and evaluation of a case-specific element library (C-SEL) that can be used to incorporate and prioritize persons of interest in homicide investigations. In a survey, 107 experts in the field of criminal investigation assigned an initial score to the elements. Each element was extended with underlying factors that can be used to adjust the initial score based on the relevance and credibility of the source. A case study was conducted using three Dutch real-world cases to evaluate the methodology. The results look promising and are better than four methodologies currently used in practice.
Predicting the offender
Frequency versus bayes
Conference paper
(2019)
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August Daniel Sutmuller, Marielle Den Hengst, Ana Isabel Barros, Bob Van der Vecht, Wouter Noordkamp, P. H.A.J.M. Van Gelder
In this paper two Bayesian approaches and a frequency approach are compared on predicting offender output variables based on the input of crime scene and victim variables. The K2 algorithm, Naïve Bayes and frequency approach were trained to make the correct prediction using a database of 233 solved Dutch single offender/single victim homicide cases and validated using a database of 35 solved Dutch single offender/single victim homicide cases. The comparison between the approaches was made using the measures of overall prediction accuracy and confidence level analysis. Besides the comparison of the three approaches, the correct predicted nodes per output variable and the correct predicted nodes per validation case were analyzed to investigate whether the approaches could be used as a decision tool in practice to limit the incorporation of persons of interest into homicide investigations. The results of this study can be summarized as: the non-intelligent frequency approach shows similar or better results than the intelligent Bayesian approaches and the usability of the approaches as a decision tool to limit the incorporation of persons of interest into homicide investigations should be questioned.
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In this paper two Bayesian approaches and a frequency approach are compared on predicting offender output variables based on the input of crime scene and victim variables. The K2 algorithm, Naïve Bayes and frequency approach were trained to make the correct prediction using a database of 233 solved Dutch single offender/single victim homicide cases and validated using a database of 35 solved Dutch single offender/single victim homicide cases. The comparison between the approaches was made using the measures of overall prediction accuracy and confidence level analysis. Besides the comparison of the three approaches, the correct predicted nodes per output variable and the correct predicted nodes per validation case were analyzed to investigate whether the approaches could be used as a decision tool in practice to limit the incorporation of persons of interest into homicide investigations. The results of this study can be summarized as: the non-intelligent frequency approach shows similar or better results than the intelligent Bayesian approaches and the usability of the approaches as a decision tool to limit the incorporation of persons of interest into homicide investigations should be questioned.
Comparison of Methodologies Used in Homicide Investigations to Collect, Prioritize, and Eliminate Persons of Interest
A Case Study of Three Dutch Real-World Homicide Cases
Journal article
(2018)
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A.D. Sutmuller, Mariëlle den Hengst-Bruggeling, Ana Isabel Barros, P.H.A.J.M. van Gelder
This paper provides a comparison between four methodologies that assist criminal investigators in homicide investigations. The Person of Interest Priority Assessment Tool, Trace Investigate and Evaluate, Rasterfahndung, and Analysis of Competing Hypotheses are compared on their performance in the collection, prioritization, and elimination phase of homicide cases in today’s digital era. Three recent Dutch homicide cases are used. The use of categories during collection can assist criminal investigators in the early inclusion of the perpetrator into the investigation, however, in this digital era, the number of persons of interest becomes too large to humanly handle. All four methodologies use techniques to assign weight to pieces of evidence; further research is required to evaluate the effectiveness of these techniques when the amount of pieces of evidence explodes. The use of pre-set elimination categories shows the least promising result leaving most persons of interest not-eliminated by the currently used methodologies.
...
This paper provides a comparison between four methodologies that assist criminal investigators in homicide investigations. The Person of Interest Priority Assessment Tool, Trace Investigate and Evaluate, Rasterfahndung, and Analysis of Competing Hypotheses are compared on their performance in the collection, prioritization, and elimination phase of homicide cases in today’s digital era. Three recent Dutch homicide cases are used. The use of categories during collection can assist criminal investigators in the early inclusion of the perpetrator into the investigation, however, in this digital era, the number of persons of interest becomes too large to humanly handle. All four methodologies use techniques to assign weight to pieces of evidence; further research is required to evaluate the effectiveness of these techniques when the amount of pieces of evidence explodes. The use of pre-set elimination categories shows the least promising result leaving most persons of interest not-eliminated by the currently used methodologies.