Mining large-scale human mobility data for long-term crime prediction

Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

Exploring the Behaviour of Foraging Burglary Offenders and Predictive Police Interventions to Prevent and Reduce their Offending

Drawn from ecology, the optimal forager predictive policing methodology has been identified as the primary tasking tool used by police services to tackle domestic burglary. Built upon established findings that the target selection behaviour of foraging domestic burglary offenders can be predicted, this thesis examines the physical offending and geographical characteristics of foraging offenders in greater detail. This study evolves established research evidence by drawing upon criminological methods that have potential to increase the approaches effectiveness before testing their applicability in respect of foraging criminals. Ecological research evidence relating to assumptions of foraging behaviour are used to devise theoretical manifestations within criminal behaviour which are subsequently tested for and used to build a theoretical model to combat them. The study achieves all of this through a number of key research chapters, these include (1) identifying predictive thresholds for linking burglary offences committed by foraging criminals (2) drawing on existing assumptions within ecology the study then seeks to identify their presence within foraging criminals, including the presence of significant crime displacement, and (3) geographical profiling is identified and tested as a potential solution to combat the evasive behaviour of foraging offenders as a response to the increased police presence that the optimal forager model is designed to co-ordinate. Underpinning the study throughout is an examination of the enablers and blockers present that impact upon the effectiveness of such transitions of theory into practice. Overall, the thesis provides new theoretical material by creating a framework of foraging offender typologies. The key practical implications for policing include a model for tackling the identified theoretical foraging typologies to increase the crime prevention and reduction efforts in respect of domestic burglary

Geospatial-based data and knowledge driven approaches for burglary crime susceptibility mapping in urban areas

The Damansara-Penchala region in Malaysia, is well-known for its high frequency of burglary crime and monetary loss based on the 2011-2016 geospatial burglary data provided by the Polis Diraja Malaysia (PDRM). As such, in order to have a better understanding of the components which influenced the burglary crime incidences in this area, this research aims at developing a geospatial-based burglary crime susceptibility mapping in this urban area. The spatial indicator maps was developed from the burglary data, census data and building footprint data. The initial phase of research focused on the development of the spatial indicators that influence the susceptibility of building towards the burglary crime. The indicators that formed the variable of susceptibility were first enlisted from the literature review. They were later narrowed down to the 18 indicators that were marked as important via the interview sessions with police officers and burglars. The burglary susceptibility mapping was done based on data-driven and knowledge-driven approaches. The data-driven burglary susceptibility maps were developed using bivariate statistics approach of Information Value Modelling (IVM), machine learning approach of Support Vector Machine (SVM) and Artificial Neural Network (ANN). Meanwhile, the knowledge-driven burglary susceptibility maps were developed using Relative Vulnerability Index (RVI) based on the input from experts. In order to obtain the best results, different parameter settings and indicators manipulation were established in the susceptibility modelling process. Both susceptibility modelling approaches were compared and validated with the same independent validation dataset using several accuracy assessment approaches of Area Under Curve - Receiver Operator Characteristic (AUC-ROC curve) and correlation matrix of True Positive and True Negative. The matrix is used to calculate the sensitivity, specificity and accuracy of the models. The performance of ANN and SVM were found to be close to one another with a sensitivity of 91.74% and 88.46%, respectively. However, in terms of specificity, SVM had a higher percentage than ANN at 57.59% and 40.46% respectively. In addition, the error term in classifying high frequency burglary building was also included as part of the measurements in order to decide on the best method. By comparing both classification results with the validation data, it was found that the ANN method has successfully classified buildings with high frequency of burglary cases to the high susceptibility class with no error at all, thus, proving it to be the best method. Meanwhile, the output from IVM had a very moderate percentage of sensitivity and specificity at 54.56% and 46.42% respectively. On the contrary, the knowledge-driven susceptibility map had a high percentage of sensitivity (86.51%) but a very low percentage of specificity (16.4%) which making it the least accurate model as it was not able to classify the high susceptible area correctly as compared to other modelling approaches. In conclusion, the results have indicated that the 18 indicators used in this research could be employed to successfully map the burglary susceptibility in the study area. Furthermore, it was also found that residential areas within the vicinity of Brickfields, Bangsar Baru, Hartamas and Bukit Pantai are consistent to be classified as high susceptible areas, meanwhile areas of Jalan Duta and Taman Tunku are both identified as the least susceptible areas across the modelling methods

A CPTED bibliography: Publications related to urban space, planning, architecture, and crime prevention through environmental design, 1975-2010

This compendium is the result of two different bibliographies. The first was completed by Sean Michael, Professor and Department Head of the Department of Landscape Architecture and Environmental Planing at Utah State University. The second was completed by Gregory Saville, urban planner and Principal of AlterNation Consulting, started during graduate work at the Faculty of Environmental Studies at York University. Consolidation and expansion of the two works was overseen by Joel Warren, during his Masters of Landscape Architecture at Utah State University. Our thanks go to the many students, colleagues, and friends who contributed to this work over the years. They include: Anna Brassard, Paul Cozens, Misty Fitch, Chuck Genre, and Diane Zahm. Earlier versions have appeared in different venues through the years such as the 2003 ICA CPTED Bibliography available on CD through the International CPTED Association and the Latin America CPTED Region Corporation. In addition, Emerald Press has published a detailed literature review of basic 1st Generation CPTED studies (P. Cozens, G. Saville and D. Hillier, “Crime prevention through environmental design: A review and modern bibliography”, Property Management. 23(5), 2005). Finally, an early version was available via The CPTED Page (www.thecptedpage.wsu.edu). Today, the resource is jointly hosted through the web site of Safe Cascadia (www.safecascadia.org

Characteristics of Cause of Death, Victim, Crime, Offender, and Familial Relationship

Broad personality or global traits are unlikely to assist in solving capital crimes, so forensic psychologists have begun to focus on characteristics of the crime to create differentiating profiles. The purpose of this study was to determine if offender and victim characteristics and method of murder could provide cluster profiles differentiating familial relationship between offender and victim. Guided by classical conditioning theory and social learning theory, an archival database of 147 capital offenders responsible for 506 victims was analyzed. Cluster analysis yielded 3 distinct profiles. Compared to other clusters, Cluster 1 offenders tended to be Black and unfamiliar with their victims, who tended to be male between 20 and 50 years old that were typically shot. Cluster 2 offenders tended to be White and familiar with their typically female victims under the age of 20 who they typically murdered by use of blunt force or strangulation. Cluster 3 offenders were distinguished from the other 2 clusters only by having accounted for 90.6% of all victims who were stabbed, but no other associations with variables in the data set were discovered to explain this finding. Though limited in sample size, range of variables, and supplemental insights that could have been gained from case files or interviews, the results contribute to positive social change with offender-victim characteristics and method of murder profiles that begin to differentiate the familial offender-victim relationship and that future research can prospectively build on to create retrospective profiling models, which could potentially lead to resolving unsolved serial murder cases

The Cybercrime Triangle

Information technology can increase the convergence of three dimensions of the crime triangle due to the spatial and temporal confluence in the virtual world. In other words, its advancement can lead to facilitating criminals with more chances to commit a crime against suitable targets living in different real-world time zones without temporal and spatial orders. However, within this mechanism, cybercrime can be discouraged “…if the cyber-adversary is handled, the target/victim is guarded, or the place is effectively managed” (Wilcox & Cullen, 2018, p. 134). In fact, Madensen and Eck (2013) assert that only one effective controller is enough to prevent a crime. Given this condition of the crime triangle, it must be noted that each of these components (the offender, the target, and the place) or controllers (i.e., handler, guardian, and manager) can play a pivotal role in reducing cybercrime. To date, scholars and professionals have analyzed the phenomenon of cybercrime and developed cybercrime prevention strategies relying predominantly on cybercrime victimization (suitable targets) but have yet to utilize the broader framework of the crime triangle commonly used in the analysis and prevention of crime. More specifically, the dimensions of cybercrime offenders, places, or controllers have been absent in prior scientific research and in guiding the establishment and examination of cybercrime prevention strategies. Given this gap, much remains to be known as to how these conceptual entities operate in the virtual realm and whether they share similarities with what we know about other crimes in the physical world. Thus, the purpose of this study is to extend the application of the “Crime Triangle,” a derivative of Routine Activity Theory, to crime events in the digital realm to provide scholars, practitioners, and policy makers a more complete lens to improve understanding and prevention of cybercrime incidents. In other words, this dissertation will endeavor to devise a comprehensive framework for our society to use to form cybersecurity policies to implement a secure and stable digital environment that supports continued economic growth as well as national security. The findings of this study suggest that both criminological and technical perspectives are crucial in comprehending cybercrime incidents. This dissertation attempts to independently explore these three components in order to portray the characteristics of cybercriminals, cybercrime victims, and place management. Specifically, this study first explores the characteristics of cybercriminals via a criminal profiling method primarily using court criminal record documents (indictments/complaints) provided by the FIU law library website. Second, the associations between cybercrime victims, digital capable guardianship, perceived risks of cybercrime, and online activity are examined using Eurobarometer survey data. Third, the associations between place management activities and cybercrime prevention are examined using “Phishing Campaign” and “Cybersecurity Awareness Training Program” data derived from FIU’s Division of Information Technology

The Use of Offender Profiling Evidence in Criminal Cases

This dissertation examined the use of offender profiling evidence in criminal cases. The meaning, history, approaches and legal admissibility of offender profiling have been discussed. The introduction of offender profiling into the courtroom has been controversial, problematic and full of inconsistencies. This dissertation therefore, examined the central problems with offender profiling evidence, and answered such questions as - Is offender profiling impermissible character evidence? Who is qualified to give expert profiling evidence? Is offender profiling too prejudicial than probative? Is offender profiling an opinion on the ultimate issue? Is offender profiling sufficiently reliable as to be admissible? This dissertation has noted that in United States, there are inconsistencies in the court decisions on offender profiling evidence as a result of the three conflicting rules governing the admissibility of expert evidence. After a critical examination of the three rules, the adoption of one rule has been suggested. The Frye test standard combined with the Federal Rules of Evidence 702 provides the best admissibility standard. Many people are confused as to the appropriate discipline of offender profiling. This dissertation has therefore, presented a step by step analysis of the history and development of offender profiling. Offender profiling is a multi-disciplinary practice that cuts across many disciplines. At the moment, it is best described as an art with the potential of becoming a science. This dissertation concludes that offender profiling is not sufficiently reliable as to be admissible. It is too prejudicial than probative. This dissertation also concludes that there is an uneasy relationship, lack of unity and absence of sharing information amongst the different segments involved with offender profiling, and that this problem has limited the potential of offender profiling. Hence, some courts are not convinced as to the reliability and validity of this technique. Several recommendations have been made

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia