Factores sociodemográficos en la dinámica del comportamiento delictivo: análisis descriptivo de criminalidad en Colombia, año 2022 / Socio-demographic factors in the dynamics of criminal behaviour: descriptive analysis of criminality in Colombia, year 2022 / Fatores sociodemográficos na dinâmica do comportamento criminal: análise descritiva da criminalidade na Colômbia, ano de 2022

El presente estudio de carácter descriptivo y analítico tiene como objetivo principal presentar el comportamiento criminal en Colombia para el 2022, desde un enfoque cuantitativo empleado para la extracción, análisis e interpretación de los registros administrativos del Sistema de Información Estadístico, Delincuencial, Contravencional y Operativo (SIEDCO), constituyéndose como un insumo para aquellos interesados en el estudio de la dinámica criminal, así como para quienes se encargan de diseñar estrategias para la contención del delito y la generación de política pública en materia de seguridad. En este sentido y en el marco de las dinámicas sociodemográficas, en una primera parte se aborda de manera general el proceso de homogenización de los registros administrativos llevado a cabo por la Policía Nacional y la Fiscalía General de la Nación. Y en una segunda parte, con especial énfasis en el homicidio intencional, se presenta el análisis de la información que permitió identificar las principales variables que influyen en la comisión del delito, de acuerdo con las cifras contenidas en el SIEDCO, en el periodo comprendido entre el 1 de enero y el 31 de diciembre de 2022, comparado con la misma temporalidad del 2021, en el que se detallan los delitos que afectan la integridad personal y el patrimonio económico de quienes habitan el territorio colombiano; se hallaron incrementos considerables en estos y se resaltan los factores de oportunidad para su comisión, situación contraria a la que se evidenció sobre las afectaciones a la vida y la integridad, conjunto de conductas que, según lo registrado, decrecieron en el periodo analizado. Finalmente, se ofrece un aporte a la contención desde la actividad de policía y una serie de conclusiones que permitan ampliar la visión sobre los diversos fenómenos y enriquecer la generación de conocimiento en el campo de la criminología.

The main objective of this descriptive and analytical study is to present criminal behaviour in Colombia for 2022, from a quantitative approach used for the extraction, analysis and interpretation of the administrative records of the Statistical, Criminal, Contraventional and Operational Information System (SIEDCO), constituting an input for those interested in the study of criminal dynamics, as well as for those responsible for designing strategies for the containment of crime and the generation of public policy on security. In this sense, and within the framework of socio-demographic dynamics, the first part of the paper deals in a general way with the process of homogenisation of administrative records carried out by the National Police and the Attorney General's Office. The second part, with special emphasis on intentional homicide, presents the analysis of the information that made it possible to identify the main variables that influence the commission of the crime, according to the figures contained in SIEDCO, in the period between 1 January and 31 December 2022, compared with the same period in 2021, in which the crimes that affect the personal integrity and economic patrimony of those who live in Colombian territory are detailed; considerable increases were found in these and the factors of opportunity for their commission are highlighted, contrary to the situation that was evidenced in the affectations to life and integrity, a group of conducts that, according to what was recorded, decreased in the period analysed. Finally, we offer a contribution to containment from the police activity and a series of conclusions that allow us to broaden the vision of the diverse phenomena and enrich the generation of knowledge in the field of criminology.

O principal objetivo deste estudo descritivo e analítico é apresentar o comportamento criminal na Colômbia para 2022, a partir de uma abordagem quantitativa utilizada para a extração, análise e interpretação dos registros administrativos do Sistema de Informação Estatística, Criminal, Contravencional e Operacional (SIEDCO), constituindo um insumo para os interessados no estudo da dinâmica criminal, bem como para os responsáveis pela elaboração de estratégias para a contenção do crime e a geração de políticas públicas de segurança. Nesse sentido, e dentro da estrutura da dinâmica sociodemográfica, a primeira parte do artigo trata de forma geral do processo de homogeneização dos registros administrativos realizado pela Polícia Nacional e pela Procuradoria Geral da República. A segunda parte, com ênfase especial no homicídio doloso, apresenta a análise das informações que permitiram identificar as principais variáveis que influenciam o cometimento do crime, de acordo com os números contidos no SIEDCO, no período entre 1º de janeiro e 31 de dezembro de 2022, em comparação com o mesmo período de 2021, no qual são detalhados os crimes que afetam a integridade pessoal e o patrimônio econômico daqueles que vivem em território colombiano; Neles foram encontrados aumentos consideráveis e são destacados os fatores de oportunidade para seu cometimento, ao contrário da situação que se evidenciou nas afetações à vida e à integridade, grupo de condutas que, segundo o que foi registrado, diminuiu no período analisado. Finalmente, oferecemos uma contribuição para a contenção da atividade policial e uma série de conclusões que nos permitem ampliar a visão dos diversos fenômenos e enriquecer a geração de conhecimento no campo da criminologia.

Spatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine Learning Algorithms.

The concept of social media began to gain popularity in the late 1990s and has played a significant role in connecting people across the globe. The constant addition of features to old social media platforms and the creation of new ones have helped amass and retain an extensive user base. Users could now share their views and provide detailed accounts of events from worldwide to reach like-minded people. This led to the popularization of blogging and brought into focus the posts of the commoner. These posts began to be verified and included in mainstream news articles bringing about a revolution in journalism. This research aims to use a social media platform, Twitter, to classify, visualize, and forecast Indian crime tweet data and provide a spatio-temporal view of crime in the country using statistical and machine learning models. The Tweepy Python module's search function and '#crime' query have been used to scrape relevant tweets under geographical constraints, followed by substring-keyword classification using 318 unique crime keywords. The Bokeh and gmaps Python modules create analytical and geospatial visualizations, respectively. Time series forecasting of crime tweet count is performed by comparing the accuracy of Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressivee Integrated Moving Average (SARIMA) models to determine the best model.

Operational psychology and cold case investigations in New Zealand.

Purpose: The purpose of this paper is to examine the application of psychology to the investigation of cold cases. The paper reviews the development of the discipline of operational psychology and reviews the role of the Behavioural Science Unit in assisting with cold case investigations within New Zealand. Design/methodology/approach: The paper integrates theory, academic principles, and practical investigative experience. Findings: As a field, operational psychology has a wide application to cold case investigations. The main areas where expertise can be provided, includes, indirect assessment and personality profiles, offender profiling, crime analysis, victimology and equivocal death analysis, and in the interviewing and engagement of offenders. Operational psychology advice should be based upon sound reasoning, evidenced based conclusion, and within the bounds of practitioner competence. Psychologists should seek to educate investigators on probabilities, likelihoods and error rates, and endeavour to indicate the strength of conclusions and statements provided within a report. Originality/value: The review aims to provide the necessary and relevant impetus for integrating operational, or forensic psychology expertise into cold case investigations. The application of psychological science to police investigations has been subject to numerous academic commentaries, however, there is a dearth of involvement from practitioners practicing in the field.

Impurities, adulterants and cutting agents in cocaine as potential candidates for retrospective mining of GC-MS data.

Cocaine is one of the most widely used illicit drugs worldwide. Cocaine powders seized by the Police may contain numerous other substances besides the drug itself. These can be impurities originating from the coca plant or the production process, or be purposely added to the drug formulation as adulterants and cutting agents. In forensic laboratories, identification of cocaine is routinely done through GC-MS analysis, but other components are often ignored even if the method allows for their detection. Yet, they can provide valuable insight into the history of a seizure and its potential connection to other samples. To explore this idea, an extensive review of common impurities and adulterants encountered in cocaine is presented. Based on their incidence, concentration in the end product and compatibility with GC-MS methods, their overall usefulness as candidates for the statistical investigation of existing forensic data is evaluated. The impurities cis- and trans-cinnamoylcocaine, tropacocaine, norcocaine and N-benzoylnormethylecgonine as well as the adulterants lidocaine, procaine, tetracaine, benzocaine, caffeine, acetylsalicylic acid, phenacetin, ibuprofen, levamisole, hydroxyzine and diltiazem are promising candidates to provide additional forensic intelligence. Future research on optimized routine GC-MS methods, signal reproducibility, comparison, statistics and databases is suggested to facilitate this concept. Ultimately, such an approach may significantly advance the amount of information that is extracted from routine casework data, elucidate developments in the cocaine markets in the past and facilitate Police work in the future. Preliminary assessment of existing data from the forensic laboratory of the Amsterdam Police has been included to show that the detection of the identified target impurities is feasible, and that small adjustments to the analysis method could significantly increase the detectability of these analytes in prospective drug screenings. Forensic intelligence based on retrospective data mining of cocaine containing casework samples may thus be realized with minimal additional laboratory efforts by using already available instrumentation, samples and data.

Uncovering dynamic textual topics that explain crime.

Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain. Some of these techniques may provide insights for understanding crime and disorder, especially, via interpreting the weights for the spatial covariates based on regression modelling. However, to date, the use of temporal covariates for the time domain has not played a significant role in the analysis. In this work, we collect time-stamped crime-related news articles, infer crime topics or themes based on the collection and associate the topics with the historical numeric crime counts. We provide a proof-of-concept study, where instead of adopting spatial covariates, we focus on temporal (or dynamic) covariates and assess their utility. We present a novel joint model tailored for the crime articles and counts such that the temporal covariates (latent variables, more generally) are inferred based on the data sources. We apply the model for violent crime in London.

Defining and Explaining Serial Murders in the United States / 대한법의학회지

Serial murder cases in the United States, due to the fact they are rare in numbers and each case has complex nature of the crime, have presented challenges to law enforcement officers and investigators. Academic scholars also have faced obstacles explaining causes of murder within a specific theoretical framework. A steadily rising number of serial murders in recent years prompted this paper to examine the nature of serial murders and their causes in search of answers to questions of ‘who they are’ and ‘why they commit such crimes’ Reviewing research studies on serial murder and/or empirical tests of typology of serial murder provided a mixed results and presented a difficulty of classifying serial murders into mutually exclusive categories.

"Hello are you available?" Dealing with online frauds and the role of forensic science.

On August 6, 2019, the 119 members of the School of criminal justice, forensic science and criminology at the University of Lausanne were the target of an online scammer. His/her modus operandi consisted of email masquerading as the Director of the School in an attempt to induce the victims to buy digital gift cards and to transmit the card usage code to the perpetrator. The first author of this paper is the Director of the School, and the second is an expert in digital forensic science and a professor of the School. They worked together in real time to deal with the fraud. Because the fraud occurred in a School of forensic science and criminology, it raised many questions on a variety of overlapping dimensions. The objective of this study was, therefore, to draw lessons from this case from several perspectives ranging from forensic science to cybersecurity, and from practical to academic. The response to the incident has been treated in four typical distinguishable phases: (1) fraud detection; (2) crisis management; (3) post-incident analysis; and (4) reporting to different communities. We conclude this paper by taking lessons from the case to express the essential role of forensic knowledge and crime analysis in interpreting the information conveyed by digital traces to develop innovative cross-disciplinary models for preventing, detecting, analysing, investigating and responding to online fraud.

Deliberate fires: From data to intelligence.

Deliberate fires are a very common problem affecting all countries around the world. They create a high sense of insecurity within communities, consuming and straining many resources (human and financial). Yet, despite various attempts, significantly tackling and reducing deliberate fires has remained largely ineffective, mainly due to the case-by-case approach implemented in responding to these incidents. Drawing on the repetitive nature of some types of deliberate fires, it was shown that adopting an intelligence-based approach is promising in tackling and reducing repetitive deliberate fires. This paper presents a two-fold procedure developed to produce intelligence on a dataset of fire events that were either deliberate or unknown in origin. Firstly, through the creation of a relevant dataset (which is a peculiar problem due to the specificities of the event of fire) and secondly through the application of specific analyses. This procedure was implemented on a dataset of fire events collated from a nine-year period in the State of Geneva, Switzerland. Results show that rudimentary data and simple processing can already generate valuable intelligence, often unsuspected until then. These results provide responding agencies with a clearer understanding of the problem, which can also support their decision-making process. This study proposes the basis for the development of an integrated real-time intelligence process. Such a process would allow the systematic and real-time monitoring of fire events in general and deliberate fires in particular by providing an immediate view of the problem, detecting recurrent events and revealing linkages between cases indicating repetitions. In terms of policies and governance, such a study should encourage institutions that deal with fires to collectively reshape their objectives, share data and analyses, and coordinate their actions to reduce harm.

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies.

Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.

Adopting data interpretation on mining fine-grained near-repeat patterns in crimes.

The near-repeat effect is a well-known phenomenon in crime analysis. The classic research methods focus on two aspects. One is the geographical factor, which indicates the influence of a certain crime risk on other similar crime incidents in nearby places. The other is the social network, which demonstrates the contacts of the offenders and explain "near" as degrees instead of geographic distances. In our work, these coarse-grained patterns discovering methods are summarized as bundled-clues techniques. In this paper, we propose a knotted-clues method. Adopting a data science perspective, we make use of a data interpretative technology and discover that the near-repeat effect is not always so near in geographic or network structure. With this approach, we analyze the near-repeat patterns in all districts of the dataset, as well as in different crime types. Using open source data from Crimes in Chicago provided by Chicago Police Department, we find interesting relationships and patterns with our mining method, which have a positive effect on police deployment and decision making.

Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver.

The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime-related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non-game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary-based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft-from-vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.

L'utilisation des données des médias sociaux pour l'analyse spatiale des tendances de la criminalité durant des activités sociales s'est avérée très instructive. Cette étude analyse la géographie de la criminalité compte tenu des journées où il y a une partie de hockey, le comportement criminel et l'activité sur Twitter. Plus précisément, nous examinons les relations entre la criminalité et l'activité sur Twitter reliée à la criminalité géolocalisée. Nous analysons six types de crimes contre les biens qui sont agrégés par aire de diffusion à Vancouver pour deux saisons de hockey au moyen d'une résolution temporelle avec et sans partie. Utilisant la même méthode, les messages géolocalisés sur Twitter et les variables environnementales sont agrégés aux aires de diffusion. Nous utilisons le regroupement spatial, l'extraction basée sur le dictionnaire pour les gazouillis, l'autocorrélation spatiale ainsi que les modèles locaux et globaux de régression (décalage spatial et régression pondérée géographiquement). Les conclusions indiquent une influence importante des données de Twitter pour les méfaits et les vols dans les véhicules, principalement lors des journées où il y a une partie de hockey. Les relations des modèles de régression pondérée géographiquement indiquent que les gazouillis sont une variable indépendante utile qui peut être utilisée pour expliquer et comprendre les tendances de la criminalité.

The polymorphism of crime scene investigation: An exploratory analysis of the influence of crime and forensic intelligence on decisions made by crime scene examiners.

A growing body of scientific literature recurrently indicates that crime and forensic intelligence influence how crime scene investigators make decisions in their practices. This study scrutinises further this intelligence-led crime scene examination view. It analyses results obtained from two questionnaires. Data have been collected from nine chiefs of Intelligence Units (IUs) and 73 Crime Scene Examiners (CSEs) working in forensic science units (FSUs) in the French speaking part of Switzerland (six cantonal police agencies). Four salient elements emerged: (1) the actual existence of communication channels between IUs and FSUs across the police agencies under consideration; (2) most CSEs take into account crime intelligence disseminated; (3) a differentiated, but significant use by CSEs in their daily practice of this kind of intelligence; (4) a probable deep influence of this kind of intelligence on the most concerned CSEs, specially in the selection of the type of material/trace to detect, collect, analyse and exploit. These results contribute to decipher the subtle dialectic articulating crime intelligence and crime scene investigation, and to express further the polymorph role of CSEs, beyond their most recognised input to the justice system. Indeed, they appear to be central, but implicit, stakeholders in intelligence-led style of policing.

Expanding forensic science through forensic intelligence.

Research and Development ('R&D') in forensic science currently focuses on innovative technologies improving the efficiency of existing forensic processes, from the detection of marks and traces at the scene, to their presentation in Court. R&D approached from this perspective provides no response to doubts raised by recent criminological studies, which question the effective contribution of forensic science to crime reduction, and to policing in general. Traces (i.e. forensic case data), as remnants of criminal activity are collected and used in various forms of crime monitoring and investigation. The aforementioned doubts therefore need to be addressed by expressing how information is conveyed by traces in these processes. Modelling from this standpoint expands the scope of forensic science and provides new R&D opportunities. Twelve propositions for R&D are stated in order to pave the way.