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An Association Model Based on Modus Operandi Mining for Implicit Crime Link Construction
Abstract
Link analysis has been an important tool in crime investigation. Explicit or implicit social links, such as kinship, financial exchange, telephone connection, links derived from modus operandi, time of day, and geographic relationship, are often used to construct links between criminals. This paper proposes an association model based on modus operandi mining to establish links among crime cases and chronic criminals. Two data sets of robbery and residential burglary crime records were collected from a local police department and were used for experiment to evaluate the performance of the proposed approach. Explicit social links (12), such as friendship, kinship, financial exchange, sexual relationships, and telephone connections, are often used to construct social network among suspects and victims in crime investigations. Although implicit crime links, such as links derived from modus operandi, geographic relationship, and crime time of day, are valuable and oftenly used in crime profiling, the effectiveness of their usage is mainly depending on the interpretation and expertise of an investigator. While social link analysis of solved crime cases could be based solely on explicit links, associations among unsolved ones can only be constructed by implicit links derived from mining crime databases. According to Locard's Exchange Principle: every contact of the perpetrators of a crime scene leaves a trace;the perpetrators will both bring something into the scene and leave with something from the scene(13). Furthermore, the type、quantity、position、and status of evidences left at a crime scene often provides clues for crime reconstruction (5). By analyzing evidences left at a crime scene, a criminal profiling specialist can derive the information needed to reconstruct a crime and narrow down the suspects (10). Criminal profiling is mainly based on 'method of operations' or modus operandi, such as preparation actions, crime methods and weapon(s), etc. It has been shown that after a criminal gets used to a certain method of operation, he/she will use the same modus operandi again in committing other cases (6). Therefore, modus operandi information not only can be used to identify the relationship between suspects and crime cases, but also to discover the association among unsolved cases (11). In Taiwan, the final step of crime investigation procedure is to file a report for each case. Besides essential case data, such as case category, location, time of a day, suspect's and victim's names, a crime record also includes five modus operandi variables, namely crime cause(CC), crime habit(CH), preparation action(PA), crime method(CM), and crime tool(CT). Each modus operandi variable has a set of predefined values for selection in report filing. This paper proposes an association model based on modus operandi mining to construct links among crime cases and chronic criminals. Two local police data sets of 1504 robbery cases and 5443 residential burglary cases were collected and used for experiment to evaluate the proposed approach. The organization of this paper is as follows: section 2 gives related literatures, section 3 describes the proposed model, section 4 presents the experimental results, and section 5 gives conclusions of this paper.
... In addition to the above, researchers also have utilized behavioral features of the crime pattern with machine learning algorithms including logistic regression (Bennell & Canter, 2002;Tonkin, Woodhams, Bull, & Bond, 2012), probability inference (Wang & Lin, 2011), etc. for eliciting associations between crime and criminals. Bache, Crestani, Canter, and Youngs (2010) applied unigram language model i.e. multinomial and multiple Bernoulli models over solved crimes dataset to link behavioral features with characteristics of offenders and found that Bernoulli models outperformed multinomial models. ...
... It is accomplished through a multi-level association model (shown in Fig. 2). This model is based on spatial-temporal characteristics (Ozgul, Gok, Erdem, & Ozal, 2012) and modus operandi behavior N. Qazi and B.L.W. Wong Information Processing and Management 56 (2019) 102066 (Wang & Lin, 2011) of the given crime pattern. We compared the similarity of the given crime pattern with the other crime entities to establish these multi-level associations. ...
... We here cite some of the related projects that involve association mining tasks.Among such projects is the Prep-Search [5] that answers only WHO and WHERE questions identifying suspects and their locations.The CrimeLink Explorer [6] identifies associations between people only, without revealing any associations of other entity types such as location or vehicle. They employed shortest path algorithm, co-occurrence analysis, and a heuristic approach over the structured crime incident data extracted from the Tucson Police Department(TPD) Records Management System.Some researchers [7] however have employed modus operandi based similarity component of the crime to establish associations among the crime cases and chronic criminals on burglary and robbery dataset. Introducing notion of prominent criminal communities, [8] have proposed a social network mining method to extract groups from unstructured textual data achieved from a suspects hard drive. ...
... He has given three principles of associations which are resemblance contiguity in time and place and causality. References [7] also has emphasized the use of modus operandi to determine the association with a chronic offender. It has also been shown that after a criminal gets used to a certain method of operation, he/she will use the same modus operandi again in committing other cases. ...
... As redes sociais são usadas para diversas finalidades desde a análise da influência social, um fenômeno em que as ações de um usuário podem induzir seus amigos a se comportar de maneira similar , associação entre criminosos e crimes [Wang and Lin 2011], chegando até mesmo a medição de semelhanças entre grupos de terroristas [Ozgul et al. 2011] e sua evolução [Nizamani and Memon 2011]. Outro importante foco das redes sociaisé a análise da colaboração entre pesquisadores, através de redes sociais de co-autoria [Reijers et al. 2009] [Newman 2004b] [Newman 2004a]. ...
Using data from the Annals of SBSI, were created collaborative networks of researchers and collaborative networks of institutions for each of the symposiums. In this paper we present some concepts related to social networks and also some results obtained with two measures based on vertex cut. Resumo. Utilizando os dados dos Anais do SBSI, foram criadas redes de colabora ao entre pesquisadores e redes de colabora ao entre institui oes para cada um dos simpósios. Neste artigo são apresentados alguns conceitos re-lativos a redes sociais e também alguns resultados obtidos com duas medidas baseadas em corte de vértices.
Abstract
This paper reviews the crime linkage literature to identify how data were pre-processed for analysis, methods used to predict linkage status/series membership, and methods used to assess the accuracy of linkage predictions. Thirteen databases were searched, with 77 papers meeting the inclusion/exclusion criteria. Methods used to pre-process data were human judgement, similarity metrics (including machine learning approaches), spatial and temporal measures, and Mokken Scaling. Jaccard's coefficient and other measures of similarity (e.g., temporal proximity, inter-crime distance, similarity vectors) are the most common ways of pre-processing data. Methods for predicting linkage status were varied and included human (expert) judgement, logistic regression, multi-dimensional scaling, discriminant function analysis, principal component analysis and multiple correspondence analysis, Bayesian methods, fuzzy logic, and iterative classification trees. A common method used to assess linkage-prediction accuracy was to calculate the hit rate, although position on a ranked list was also used, and receiver operating characteristic (ROC) analysis has emerged as a popular method of assessing accuracy.
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Crimes have been existing since the dawn of human evolution. Its existence has been a threat to the society. The ever-changing nature of crimes in the world is creating challenges to the police department as well as the investigating agencies of various wings. To keep up with the dynamic nature of crimes and the complexity of the crimes committed are being surfaced every day, and solving them became an entanglement to an investigative officer. Due to the lack of perception or human error in judging an object in the crime scene as a potential evidence, many crimes over the decades took so many years to solve and some remained a mystery. The paper presents state of the art for ontologies-based approaches that will try to help certain investigative officer in not only piling up the evidences collected but also draw out a pattern that might suggest him the approach to solve the case in no time. This system focusses mainly on organized crimes due to most of the unsolved crimes that were being a part of it.
The improper use of law enforcement and data hinders the crime-fighting capabilities of government agencies. Police personnel lack in sharing information with other agencies. To overcome the challenges of handling excessive information, COPLINK, an integrated information and knowledge management environment was introduced. COPLINK helps in capturing, analyzing, visualizing and sharing law enforcement-related information in social and organizational contexts. COPLINK consists of two main components, COPLINK Connect whic has been designed to allow diverse police departments to share data seamlessly through an easy to use interface and COPLINK Detect which uncovers various types of criminal associations that exist in police databases.
Data mining of network data often focuses on classification methods from machine learning, statistics, and pattern recognition perspectives. These techniques have been described by many, but many of these researchers are unaware of the rich history of classification and clustering techniques originating in social network analysis. The growth of rich social media, on-line communities, and collectively produced knowledge resources has greatly increased the need for good analytic techniques for social networks. We now have the opportunity to analyze social network data at unprecedented levels of scale and temporal resolution; this has led to a growing body of research at the intersection of the computing, statistics, and the social and behavioral sciences. This talk discusses some of the current challenges in the analysis of large-scale social network data, focusing on the inference of social processes from data. The invasion of network science by computer scientists has produced much interesting, both good and bad, research.
This chapter discusses the general method, principles, and practice standards of behavioral evidence analysis (BEA). BEA is an ideo-deductive method of crime-scene analysis (CSA) and criminal profiling. It involves the examination and interpretation of physical evidence, forensic victimology, and crime-scene characteristics. For the purposes of criminal profiling, the results of these individual examinations can be analyzed for behavioral patterns and clusters that evidence offender characteristics of investigative or forensic relevance. BEA is idiographic in that it is concerned with studying the aspects of individual cases and offenders through the lens of forensic analysis, not groups of similar cases and offenders. It is deductive in that inferences and conclusions are not inductive theories or nomothetic predictions in disguise; they are based on critical thinking, the scientific method, and deductive logic. BEA is consequently guided by strict adherence to set principles and practice standards that embrace these concepts.
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This is the third edition of the premier professional reference on the subject of data mining, expanding and updating the previous market leading edition. This was the first (and is still the best and most popular) of its kind. Combines sound theory with truly practical applications to prepare students for real-world challenges in data mining. Like the first and second editions, Data Mining: Concepts and Techniques, 3rd Edition equips professionals with a sound understanding of data mining principles and teaches proven methods for knowledge discovery in large corporate databases. The first and second editions also established itself as the market leader for courses in data mining, data analytics, and knowledge discovery. Revisions incorporate input from instructors, changes in the field, and new and important topics such as data warehouse and data cube technology, mining stream data, mining social networks, and mining spatial, multimedia and other complex data. This book begins with a conceptual introduction followed by a comprehensive and state-of-the-art coverage of concepts and techniques. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability. relational data. -- A comprehensive, practical look at the concepts and techniques you need to get the most out of real business data. -- Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning, -- Scores of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. -- Complete classroom support for instructors as well as bonus content available at the companion website. A comprehensive and practical look at the concepts and techniques you need in the area of data mining and knowledge discovery.
Part I. Introduction: Networks, Relations, and Structure: 1. Relations and networks in the social and behavioral sciences 2. Social network data: collection and application Part II. Mathematical Representations of Social Networks: 3. Notation 4. Graphs and matrixes Part III. Structural and Locational Properties: 5. Centrality, prestige, and related actor and group measures 6. Structural balance, clusterability, and transitivity 7. Cohesive subgroups 8. Affiliations, co-memberships, and overlapping subgroups Part IV. Roles and Positions: 9. Structural equivalence 10. Blockmodels 11. Relational algebras 12. Network positions and roles Part V. Dyadic and Triadic Methods: 13. Dyads 14. Triads Part VI. Statistical Dyadic Interaction Models: 15. Statistical analysis of single relational networks 16. Stochastic blockmodels and goodness-of-fit indices Part VII. Epilogue: 17. Future directions.
The rapid development and integration of computer and communication technology have made the Internet one of the major media for communication. Nowadays, the Internet and world-wide-web (WWW) are used in every facet of modern society. This research proposes to develop a link analysis method based on Web page co-occurrence mining. The Google Internet search engine is used to gather Web pages relevant to a certain search subject. The proper nouns on each relevant page are then extracted using the Chinese Word Segmentation System, developed by Academia Sinica, Taiwan. The co-occurrence data of proper nouns is then analyzed and used for constructing link charts for visualization and further analysis.
Crime Pattern Analysis: An Investigative Tool
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Application of the Vector Space Model on Criminal Record Retrieval
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