Goal-driven Elaboration of Crime Scripts

This research investigates a crime modelling technique known as crime scripting. Crime scripts are generated by crime analysts to improve the understanding of security incidents, and in particularly, the criminal modus operandi (i.e., how crimes occur) to help identify cost-effective crime prevention measures. This thesis makes four contributions in this area. First, a systematic review of the crime scripting literature that provides a comprehensive and up-to-date understanding of crime scripting practice, and identifies potential issues with current crime scripting methods. Second, a comparative analysis of crime scripts which reveals differences and similarities between the scripts generated by different analysts, and confirms the limitations of intuitive approaches to crime scripting. Third, an experimental study, which shows that the content of crime scripts is influenced by what scripters know about the future use of their scripts. And fourth, a novel crime scripting framework inspired from business process modelling and goal-based modelling techniques. This framework aims to help researchers and practitioners better understand the activities involved in the development of crime scripts, and guide them in the creation of scripts and facilitate the identification of suitable crime prevention measures

Development of Hotzone Identification Models for Simultaneous Crime and Collision Reduction

This research contributes to developing macro-level crime and collision prediction models using a new method designed to handle the problem of spatial dependency and over-dispersion in zonal data. A geographically weighted Poisson regression (GWPR) model and geographically weighted negative binomial regression (GWNBR) model were used for crime and collision prediction. Five years (2009-2013) of crime, collision, traffic, socio-demographic, road inventory, and land use data for Regina, Saskatchewan, Canada were used. The need for geographically weighted models became clear when Moran's I local indicator test showed statistically significant levels of spatial dependency. A bandwidth is a required input for geographically weighted regression models. This research tested two bandwidths: 1) fixed Gaussian and 2) adaptive bi-square bandwidth and investigated which was better suited to the study's database. Three crime models were developed: violent, non-violent and total crimes. Three collision models were developed: fatal-injury, property damage only and total collisions. The models were evaluated using seven goodness of fit (GOF) tests: 1) Akaike Information Criterion, 2) Bayesian Information Criteria, 3) Mean Square Error, 4) Mean Square Prediction Error, 5) Mean Prediction Bias, and 6) Mean Absolute Deviation. As the seven GOF tests did not produce consistent results, the cumulative residual (CURE) plot was explored. The CURE plots showed that the GWPR and GWNBR model using fixed Gaussian bandwidth was the better approach for predicting zonal level crimes and collisions in Regina. The GWNBR model has the important advantage that can be used with the empirical Bayes technique to further enhance prediction accuracy. The GWNBR crime and collision prediction models were used to identify crime and collision hotzones for simultaneous crime and collision reduction in Regina. The research used total collision and total crimes to demonstrate the determination of priority zones for focused law enforcement in Regina. Four enforcement priority zones were identified. These zones cover only 1.4% of the Citys area but account for 10.9% of total crimes and 5.8% of total collisions. The research advances knowledge by examining hotzones at a macro-level and suggesting zones where enforcement and planning for enforcement are likely to be most effective and efficient

An Examination of Livestock and Wildlife crimes in Agricultural areas of the UK

Wildlife crime is receiving increasing international media coverage, with much of the focus on the international Wildlife Trade (IWT) and iconic species (e.g. elephants, tigers and pangolins). Limited research exists on the impact of wildlife crime on native species in the UK. The majority of the UK landscape is categorised as rural and classified as Farmland. To account for the spatial overlap between Livestock and Wildlife, the thesis aimed to assess the incidence of these crime types on farmland in the UK. The thesis presents a multimethod analysis of livestock and wildlife crimes, beginning with a review of the existing research on the most effective prevention methods for crimes against terrestrial (land based) species (TS), which identified an overall dearth of empirical evidence. A victimization survey was then conducted of farmers in the UK. The survey received over 800 responses. Amongst the many survey findings, was the low level of reporting, with over 70% of wildlife crime incidents going unreported to the Police. The survey responses also identified an inverse relationship in the seasonal variation of these crime types. Finally, the thesis assessed Police data for Livestock and Wildlife crimes, between 2010 and 2015 from Dorset constabulary. The Police data was used to assess the seasonal variation in these crime types and identified the need to disaggregate the Police data into crimes involving different species to identify annual trends. Data quality issues associated with the recording of crimes in rural areas were identified and potential solutions for better location recording described. The thesis provides a comprehensive overview of the current state of Livestock and Wildlife crime in the UK, as well as highlighting the numerous avenues for further research

Data and evidence challenges facing place-based policing

PURPOSE: The purpose of this paper is to use an evaluation of a micro-place-based hot-spot policing implementation to highlight the potential issues raised by data quality standards in the recording and measurement of crime data and police officer movements. DESIGN/METHODOLOGY/APPROACH: The study focusses on an area of London (UK) which used a predictive algorithm to designate micro-place patrol zones for each police shift over a two-month period. Police officer movements are measured using GPS data from officer-worn radios. Descriptive statistics regarding the crime data commonly used to evaluate this type of implementation are presented, and simple analyses are presented to examine the effects of officer patrol duration (dosage) on crime in micro-place hot-spots. FINDINGS: The results suggest that patrols of 10-20 minutes in a given police shift have a significant impact on reducing crime; however, patrols of less than about 10 minutes and more than about 20 minutes are ineffective at deterring crime. RESEARCH LIMITATIONS/IMPLICATIONS: Due to the sparseness of officer GPS data, their paths have to be interpolated which could introduce error to the estimated patrol dosages. Similarly, errors and uncertainty in recorded crime data could have substantial impact on the designation of micro-place interventions and evaluations of their effectiveness. ORIGINALITY/VALUE: This study is one of the first to use officer GPS data to estimate patrol dosage and places particular emphasis on the issue of data quality when evaluating micro-place interventions

Evaluating the Effect of CCTV on Crime Occurrence and Case Clearances in Fayetteville, North Carolina: A Microsynthetic Control Quasi-Experiment

Closed circuit television (CCTV) surveillance cameras have become widely accepted as a traditional crime prevention measure used by law enforcement agencies across the globe. The proliferation of CCTV technology as a crime reduction mechanism has led to a corresponding growth in the evidence base of its effect on crime. While CCTV is generally associated with a moderate reduction in crime, these evaluations have suffered from certain limitations. First, rigorous quasi-experimental designs using advanced matching techniques have not been used enough in CCTV research and second, these evaluations remain largely one-dimensional, as additional outcome measures and other camera-specific features have gone largely underexplored. In light of these limitations, this dissertation sought to address these gaps by conducting a highly sophisticated assessment of the CCTV scheme in Fayetteville, North Carolina. To accomplish this, this dissertation applied the newly developed microsynthetic control (MSC) approach to conduct a longitudinal, matched quasi-experiment. The main research questions examined were: (1) how does CCTV effect crime occurrence, (2) are some CCTV cameras more effective at deterring crime than others, (3) to what degree does CCTV effect case clearances/closures, and (4) are some CCTV cameras more effective in closing cases than others? To examine both outcome measures, this dissertation utilized two separate units of analysis. Thus, the crime prevention effects were explored by using individual camera viewsheds as the unit of analysis (see Caplan, Kennedy, & Petrossian, 2011), whereas the investigative function was examined by using individual criminal incidents as the unit of analysis (see Jung & Wheeler, 2021). In the end, this dissertation generated several notable findings. First, this research found that CCTV in Fayetteville was associated with a significant decrease in felony crimes, which varied by phase of camera deployment. Importantly, these effects faded in both strength and significance over time, with the most robust effects observed a year after installation. Additionally, this CCTV scheme was associated with a diffusion of benefits, which varied in magnitude by phase. Secondly, this system not only consisted of both highly effective and less successful cameras, but it also revealed that several variables were related to the changes in crime experienced in the camera viewsheds. For example, areas experiencing more police activity also encountered smaller decreases in crime. Third, case closures increased after CCTV was installed, but these results were largely influenced by disorder crimes. Fourth, like the earlier crime prevention question, several cameras were associated with more case clearances than others. Moreover, for the case clearance analyses, the longer the camera was in place, the more likely it was to be classified as an effective camera. Finally, this dissertation discussed several strategic aspects of camera deployment that may increase the operational capabilities of the Fayetteville system and highlighted a handful of findings that the FPD may want to further explore to increase the effectiveness of this initiative

Victims, Vignettes, and Videos: Meta-Analytic and Experimental Evidence that Emotional Impact Enhances the Derogation of Innocent Victims

Research during the 1960s found that observers could be moved enough by an innocent victim’s suffering to derogate their character. However, recent research has produced inconsistent evidence for this effect. We conducted the first meta-analysis (k = 55) of the experimental literature on the victim derogation effect to test the hypothesis that it varies as a function of the emotional impactfulness of the context for observers. We found that studies which employed more impactful contexts (e.g., that were real and vivid) reported larger derogation effects. Emotional impact was, however, confounded by year of appearance, such that older studies reported larger effects and were more impactful. To disentangle the role of emotional impact, in two primary experiments we found that more impactful contexts increased the derogation of an innocent victim. Overall, the findings advance our theoretical understanding of the contexts in which observers are more likely to derogate an innocent victim

Transferring prisoners within the EU framework: its cosmopolitan reflections and existing European detention norms

A perverse side-effect of our interconnected world is that also crime crosses more and more borders. As a result, judicial cooperation in criminal matters is crucial before and after a criminal sentence. The increased global connectivity also gave rise to new paradigms in social sciences. As such, the paradigm of cosmopolitanism has been researched extensively in social sciences but has been largely neglected in criminology. By analyzing case law, European detention norms and EU legal instruments the submission critically evaluates cosmopolitanism in the area of EU judicial cooperation in criminal matters and more specifically to the transfer of prisoners. Cosmopolitanism is perfectly reflected in the mutual recognition principle as the cornerstone to develop the EU area of freedom, security and justice, based on notions of equivalence and trust. This principle is justified because every member state signed the European Convention of Human Rights and is a party of the EU Charter on Human Rights. On the other hand, reality revealed that mutual recognition is not absolute and mutual trust cannot be blind. An IRCP study, published in 2011, highlighted the various and often detrimental material prison conditions in different member states. These variances undermine the assumed mutual trust between member states although European detention norms - such as the European Prison Rules and CPT reports’ already exist. These norms aren’t legally binding and are still considered as “soft law”, simultaneously they gain importance due to increased reference in the ECtHR judgments. The cosmopolitan outlook by the member states related to the transfer of prisoners is in this submission highlighted as being both problematic and promising. Hereby it appears as if the EU rhetoric being a “unity in diversity”, by applying mutual recognition, is dominantly used to accommodate member states purposes rather than giving a central role to the individual

Multi-level Safety Performance Functions For High Speed Facilities

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users