Early Identification of Violent Criminal Gang Members

Gang violence is a major problem in the United States accounting for a large fraction of homicides and other violent crime. In this paper, we study the problem of early identification of violent gang members. Our approach relies on modified centrality measures that take into account additional data of the individuals in the social network of co-arrestees which together with other arrest metadata provide a rich set of features for a classification algorithm. We show our approach obtains high precision and recall (0.89 and 0.78 respectively) in the case where the entire network is known and out-performs current approaches used by law-enforcement to the problem in the case where the network is discovered overtime by virtue of new arrests - mimicking real-world law-enforcement operations. Operational issues are also discussed as we are preparing to leverage this method in an operational environment.Comment: SIGKDD 201

Machine Learning para deteção de padrões e previsão de ocorrências criminais

The increase of the world population, especially in large urban centers, has resulted in new challenges such as the management of natural resources and infrastructures as well as the optimization of services to promote the quality of citizens’ life. One of the biggest and most important challenges is the management of public safety, since, in addition to being a factor of interest to both the general population and the authorities, it is also an area that influences other essential indicators in a city such as tourism and employment. Public Safety has impact on the economic growth and social development of a community. This dissertation proposes a solution for the prediction of criminal occurrences in a city based on historical data of incidents and demographic data. The entire life cycle of the model’s learning process will be presented to provide an organization with predictive capability: start with the data collection from its original source, the treatment and transformations applied to them, the choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location, as well as regression models to predict the number of crimes. Machine Learning algorithms, such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results of the chosen model show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models will be implemented on a platform that provides an API to enable other entities to request for predictions in real-time. An application will also be presented where it is possible to show criminal occurrences predictions visually.O aumento da população mundial, especialmente nos grandes centros urbanos, tem resultado em novos desafios tais como a gestão de recursos naturais, gestão de infraestruturas, bem como a otimização dos serviços para promover a qualidade de vida dos cidadãos. Um dos maiores e mais importantes desafios é a gestão da segurança pública. Para além de ser um fator de interesse quer da população em geral quer das autoridades, também é um domínio que influencia outros indicadores essenciais numa cidade como o turismo e o emprego. A segurança pública reflete-se no crescimento económico e no desenvolvimento social de uma comunidade. Nesta dissertação é proposta uma solução para previsão de ocorrências criminais numa cidade baseada em dados de histórico de incidentes e dados demográficos. Será apresentado todo o ciclo de vida do processo de aprendizagem do modelo para dotar uma organização da capacidade preditiva: desde a recolha dos dados da sua fonte de origem, o tratamento e transformações aplicadas aos mesmos, escolha, avaliação e implementação do modelo de Machine Learning até à camada de aplicação. Serão implementados modelos de classificação para previsão do risco criminal para um dado intervalo temporal e localização, e modelos de regressão para previsão do número de crimes. Irão ser utilizados algoritmos de Machine Learning como Random Forest, Redes Neuronais, K-Nearest Neighbors e Regressão Logística para a aprendizagem do modelo de previsão de ocorrências onde serão comparados os seus desempenhos de acordo com o tratamento e transformação dos dados utilizados. Os resultados do modelo escolhido evidenciam que a utilização de técnicas de Machine Learning auxiliam a antecipação de ocorrências criminais, o que contribuiu para o reforço da segurança pública. Por fim, irá ser procedida a implementação dos modelos numa plataforma que fornece uma API para que entidades externas possam solicitar previsões em tempo real. Será também apresentada a aplicação onde é possível mostrar visualmente as previsões de ocorrências criminais.Mestrado em Engenharia Informátic

Data Driven Inference in Populations of Agents

abstract: In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive.  More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.   This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Governing through ‘neutrality’: A Poststructural Analysis of Risk Assessment in the NSW Juvenile Justice System

Internationally, the assessment of risk via the application of standardised assessment tools has become routine practice across criminal justice and penal systems. Ostensibly, risk assessment tools enable the prediction, and thereby prevention, of reoffending and recidivism. The use of risk assessment tools in the juvenile justice system in NSW, Australia is less than 20 years old, yet since 2001 one specific tool, the Youth Level of Service Case Management Inventory Australian Adaptation (YLS/CMI-AA), has become a key technology in interventions with young people. Young offenders who come into contact with the justice system are guaranteed two things: to be assessed for their risk of reoffending, and then to be treated for their offending behaviour, based on their predicted risk. Poststructural analyses of risk assessments have highlighted the way that the concept of ‘risk’ has become central in modern day systems of discipline and punishment and is implicated in both the governing of juvenile offenders and the population more broadly. This thesis builds upon existing work on risk to closely interrogate how juvenile justice risk assessment tools constitute, or make, the ‘problem’ of juvenile offending. The study applies Carol Bacchi’s ‘What’s the problem represented to be?’ (WPR) policy analysis approach to the YLS/CMI-AA 2.0 risk assessment tool and to a corpus of related texts to illuminate how risk assessments regulate, and actively shape who is defined, marked and classified as ‘risky’. In this sense, they are understood to do more than simply ‘predict’ and ‘prevent’. The WPR analysis enables the interrogation of the problem representations, or problematisations, that are lodged within texts such as the assessment tool, user guides and so on. This study demonstrates how the risk assessment tool administered to young people in NSW problematises crime as fixed, static, and something that has always existed, thereby making the imagined standards of behaviour appear to be real and wholly ahistorical. It also produces offenders as having a set of specific and common characteristics that include deviancy, immorality, and various forms of failure. The concept of ‘criminogenic pathways’ is integral to these representations and the risk assessment tool also firms up and naturalises taken-for-granted ideas about how somebody becomes an offender. This thesis contributes to international scholarship on the uptake up of risk discourses in juvenile penality by demonstrating how risk assessment tools have introduced a new form of governing, one that is backed by the ‘neutrality’ of science, and by extension the ‘neutrality’ of the state. The supposed assurance of ‘neutrality’ is used to defend, explain and justify the overrepresentation of certain people in penal systems, and, in Australia, Indigenous young people in particular. In addition, it appears that risk assessment tools function to regulate and discipline both juvenile offenders and ‘non-offending’ people more generally. The thesis also underlines the importance and usefulness of poststructuralist analytic strategies such as the WPR approach to defamiliarise fields concerned with the juvenile offender problem

How to Measure Efficiency, Effectiveness, and Equity Within the Complex Role of Police in a Democratic Society: An ICURS Economics of Policing Study

Policing is complex. No easy measures exist for determining efficiency, effectiveness or equity in the overall economics of police service. Perhaps this is related to the fact that the debate on issues like core policing and tiered policing is both contentious and not well understood. For example, dealing with mental health issues in vulnerable communities may not be considered core policing in some discussions but it certainly remains an important element of and a key activity in contemporary policing. We are, nevertheless, making major advances in the 21st Century. Simple crime rate or response time measures have some meaning, but the multi-agency, multi-role character of policing calls for better measures that take into account the underlying public meaning of crime, the varying demands for police service in different jurisdictions, and the rapid increase in cyber crime

Insurance regulation for development: parametrics and agriculture

Provision of agricultural insurance is highly variable, with a deficit for the global rural poor faced with acute climate risk. Regulatory support is an enabler of widened supply of insurance, which can be pro-poor and climate adaptive in manner. The aims of this research are to: (i) assess organic evolution of agricultural parametric insurance provision; (ii) evaluate regulation of agricultural parametric insurance in Tanzania and related contexts; and (iii) construct an idealised regulatory framework to facilitate insurance as a climate risk management tool. This research draws on the launch of the WINnERS pilot programme in Tanzania, supported by literature analysis and interviews with critical market participants. It found parametric insurance regulation to be preclusive in all jurisdictions. Lack of legal certainty for multiple, foundational elements of already implemented parametric schemes prevents growth and deters market appetite. Though there is no suitable framework, there are useful elements indicative of supportive regulation. These are spatially disparate, preventing an individual insurance jurisdiction from building the necessary capacity, supply and demand for viable markets. A framework is presented which is practical and implementable, addressing the issues of regulatory comprehensiveness and coherence. It is intended to facilitate the identification of gaps and barriers as insurance regulators seek to develop the protective and promotive elements of parametric insurance as a public policy imperative.Open Acces

The Camera in conservation: determining photography’s place in the preservation of wildlife

This MA by research study is a reflection of photography’s past, current and future role within wildlife conservation, or whether there is indeed a necessity for it moving forwards. The following investigation and analysis of photography seeks to materialise how in fact the photographic medium can be both beneficial and negatively impactful to the preservation of wildlife, and how best it can be used by photographers in future conservation projects to ensure the preservation of wildlife. Several significant aspects of photography and external influences are engaged with in this study, firstly investigating the importance of empathy within wildlife conservation and how it can be elicited through imagery and photographic methods. Furthermore, I investigate the other side of conservation photography’s success, analysing what negative or neutral impacts it can bring with it, before researching the role that social media does and has the potential to play in conservation, and how photography can adapt to it to maximise its success. Lastly, I explore alternative visual media such as moving image, and how photography can best applicate successful techniques learned from them to reinterpret how conservation photography is perceived. Finally, using information and research from across my thesis, I have produced a ‘guide’ as to how conservation photography can be shaped to achieve its full potential for success, drawing upon previous successes and failures of other conservation attempts and photographers

Promoting prevention: Evaluating a multi-agency initiative to prevent youth offending in Swansea.

This thesis presents the research and evaluation of ‘Promoting Prevention’, a multi­agency, multiple intervention initiative to prevent youth offending in Swansea that is predicated on the generation of systematic information through official and self- reported sources. The thesis discusses how structures and processes within Promoting Prevention have developed through a rolling dynamic between information generation and system reproduction, with particular emphasis upon consultation with young people and key stakeholders.An individual study computer questionnaire, underpinned by the risk factor prevention paradigm, assessed young people’s self-reported attitudes, perceptions and behaviour in order to associate them with a range of risk and protective factors for offending. Statistical analysis identified that exposure to multiple risk factors in the key domains of the young person’s life (i.e. family, school, neighbourhood, lifestyle, personal/individual) was significantly linked to ever and active offending, particularly for males. Several key factors within each domain were highlighted as predictive of ever and active offending. When placed in the context of official and self-reported statistics locally, nationally and internationally, there was a clear overlap in salient issues for young people and identified risk factors, although levels of self-reported drug use and offending were generally higher in Swansea.Systems analyses adapted the grounded theory methodology and utilised interviews with key stakeholders to produce narrative reports and maps of Promoting Prevention components (organisations, committees, documents, individuals) to elucidate the complex, cross-cutting and reflexive nature of the initiative.Overall levels of (self-reported and official) permanent school exclusion and (self- reported and official) ever and active offending in Swansea have fallen since the inception of Promoting Prevention. This indicates that Promoting Prevention can tentatively claim to be successfully addressing offending behaviour by targeting interventions based on risk factors identified by young people. There is a commitment amongst key stakeholders to Promoting Prevention principles and strategies such as consultation and developing a reflexive relationship between research, information and practice. This highlights Promoting Prevention as a modem example of an holistic, rights-based crime prevention initiative underpinned by an ethos of consultation and responding to information relevant to the local context