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Purpose: To determine the state of evidence on relationships among urban green space, violence, and crime in the United States. Methods and Results: Major bibliographic databases were searched for studies meeting inclusion criteria. Additional studies were culled from study references and authors’ personal collections. Comparison among studies was limited by variations in study design and measurement and results were mixed. However, more evidence supports the positive impact of green space on violence and crime, indicating great potential for green space to shape health-promoting environments. Conclusion: Numerous factors influence the relationships among green space, crime, and violence. Additional research and standardization among research studies are needed to better understand these relationships.

Crime is a major threat to society's well-being but lacks a statistical characterization that could lead to uncovering some of its underlying mechanisms. Evidence of nonlinear scaling of urban indicators in cities, such as wages and serious crime, has motivated the understanding of cities as complex systems-a perspective that offers insights into resources limits and sustainability, but that usually neglects details of the indicators themselves. Notably, since the nineteenth century, criminal activities have been known to occur unevenly within a city; crime concentrates in such way that most of the offenses take place in few regions of the city. Though confirmed by different studies, this concentration lacks broad analyses on its characteristics, which hinders not only the comprehension of crime dynamics but also the proposal of sounding counter-measures. Here, we developed a framework to characterize crime concentration which divides cities into regions with the same population size. We used disaggregated criminal data from 25 locations in the U.S. and the U.K., spanning from 2 to 15 years of longitudinal data. Our results confirmed that crime concentrates regardless of city and revealed that the level of concentration does not scale with city size. We found that the distribution of crime in a city can be approximated by a power-law distribution with exponent α that depends on the type of crime. In particular, our results showed that thefts tend to concentrate more than robberies, and robberies more than burglaries. Though criminal activities present regularities of concentration, we found that criminal ranks have the tendency to change continuously over time-features that support the perspective of crime as a complex system and demand analyses and evolving urban policies covering the city as a whole.

The jury trial is a critical point where the state and its citizens come together to define the limits of acceptable behavior. Here we present a large-scale quantitative analysis of trial transcripts from the Old Bailey that reveal a major transition in the nature of this defining moment. By coarse-graining the spoken word testimony into synonym sets and dividing the trials based on indictment, we demonstrate the emergence of semantically distinct violent and nonviolent trial genres. We show that although in the late 18th century the semantic content of trials for violent offenses is functionally indistinguishable from that for nonviolent ones, a long-term, secular trend drives the system toward increasingly clear distinctions between violent and nonviolent acts. We separate this process into the shifting patterns that drive it, determine the relative effects of bureaucratic change and broader cultural shifts, and identify the synonym sets most responsible for the eventual genre distinguishability. This work provides a new window onto the cultural and institutional changes that accompany the monopolization of violence by the state, described in qualitative historical analysis as the civilizing process.

In recent years, the crime rate has increased considerably and there is a need to properly identify the different types of crimes so that it can be tackled. In this paper, a Bi-LSTM neural network for classification is proposed that classifies the different types of crime on data collected from Google News and Twitter. The data is pre-processed and an initial step of labeling is performed with the help of Fuzzy c-means algorithm and Term Frequency – Inverse Document Frequency vectors. GloVe word embeddings were performed for feature extraction. Dynamically generated ontologies with minimal human supervision using a weighted graph modeled from Google News and Social Web like Twitter has been encompassed in order to enhance the quality of crime classification. The proposed method has proven, after experiments, to achieve evaluation metrics better than the existing methods; evaluated on four different datasets and compared with four different methods with an increase in Accuracy and decrease in FNR for four distinguished datasets.

This book confronts the problem of the legal uncertainty surrounding the definition and classification of ethnic cleansing, exploring whether the use of the term ethnic cleansing constitutes a valuable contribution to legal understanding and praxis. The premise underlying this book is that acts of ethnic cleansing are, first and foremost, a criminal issue and must therefore be precisely placed within the context of the international law order. In particular, it addresses the question of the specificity of the act and its relation to existing categories of international crime, exploring the relationship between ethnic cleansing and genocide, but also extending to war crimes and crimes against humanity. The book goes on to show how the current understanding of ethnic cleansing singularly fails to provide an efficient instrument for identification, and argues that the act, in having its own distinctive characteristics, conditions and exigencies, ought to be granted its own classification as a specific independent crime. Ethnic Cleansing: A Legal Qualification, will be of particular interest to students and scholars of International Law and Political Science.

Praise for Crime Classification Manual \"The very first book by and for criminal justice professionals in the major case fields... The skills, techniques, and proactive approaches offered are creatively concrete and worthy of replication across the country... Heartily recommended for those working in the 'front line' of major case investigation.\" —John B. Rabun Jr., ACSW, Executive Vice President and Chief Operating Officer, National Center for Missing and Exploited Children \"[CCM] is an outstanding resource for students pursuing forensic science degrees. It provides critical information on major crimes, which improve the user's ability to assess and evaluate.\" —Paul Thomas Clements, PhD, APRN-BC, CGS, DF-IAFN Drexel University Forensic Healthcare Program The landmark book standardizing the language, terminology, and classifications used throughout the criminal justice system Arranged according to the primary intent of the criminal, the Crime Classification Manual, Third Edition features the language, terms, and classifications the criminal justice system and allied fields use as they work to protect society from criminal behavior. Coauthored by a pioneer of modern profiling and featuring new coverage of wrongful convictions and false confessions, the Third Edition: * Tackles new areas affected by globalization and new technologies, including human trafficking and internationally coordinated cybercrimes * Expands discussion of border control, The Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF), and Homeland Security * Addresses the effects of ever-evolving technology on the commission and detection of crime The definitive text in this field, Crime Classification Manual, Third Edition is written for law enforcement personnel, mental health professionals, forensic scientists, and those professionals whose work requires an understanding of criminal behavior and detection.

This paper proposes a novel approach to utilizing open-source legal databases in academic education, especially in the fields of law and police investigations. Our framework provides a way to organize and analyze this data and extract reports that are associated with crime scenes, addressing the challenge of classifying unstructured legal documents by using text mining, natural language processing, and machine learning techniques. We developed a supervised machine learning model capable of accurately classifying court documents based on two classifiers: one identifies the documents containing crime scenes, and the other classifies them into five types of crimes. The experimental results were promising, as the random forest algorithm achieved an accuracy of 91.07% for the first classifier and support vector machines achieved an accuracy of 82.46% for the second classifier. What distinguishes our work is the creation of a crime dictionary that includes 70 crime tools and 151 related terms extracted from various forensic sources. It is considered relatively small, but it contributed to giving good classification results. The proposed crime dictionary can be generalized, developed, used in advanced searches, and integrated with police databases to improve crime scene analysis. Overall, the research highlights the use of court databases in police academic education and attempts to utilize them in a more effective manner.

Increasing numbers of public and private locations now have surveillance cameras installed to make those areas more secure. Even though many organizations still hire someone to monitor the cameras, the person hired is more likely to miss some unexpected events in the video feeds because of human error. Several researchers have worked on surveillance data and have presented a number of approaches for automatically detecting aberrant events. To keep track of all the video data that accumulate, a supervisor is often required. To analyze the video data automatically, we recommend using neural networks to identify the crimes happening in the real world. Through our approach, it will be easier for police agencies to discover and assess criminal activity more quickly using our method, which will reduce the burden on their staff. In this paper, we aim to provide anomaly detection using surveillance videos as input specifically for the crimes of arson, burglary, stealing, and vandalism. It will provide an efficient and adaptable crime-detection system if integrated across the smart city infrastructure. In our project, we trained multiple accurate deep learning models for object detection and crime classification for arson, burglary and vandalism. For arson, the videos were trained using YOLOv5. Similarly for burglary and vandalism, we trained using YOLOv7 and YOLOv6, respectively. When the models were compared, YOLOv7 performed better with the highest mAP of 87. In this, we could not compare the model’s performance based on crime type because all the datasets for each crime type varied. So, for arson YOLOv5 performed well with 80% mAP and for vandalism, YOLOv6 performed well with 86% mAP. This paper designed an automatic identification of crime types based on camera or surveillance video in the absence of a monitoring person, and alerts registered users about crimes such as arson, burglary, and vandalism through an SMS service. To detect the object of the crime in the video, we trained five different machine learning models: Improved YOLOv5 for arson, Faster RCNN and YOLOv7 for burglary, and SSD MobileNet and YOLOv6 for vandalism. Other than improved models, we innovated by building ensemble models of all three crime types. The main aim of the project is to provide security to the society without human involvement and make affordable surveillance cameras to detect and classify crimes. In addition, we implemented the Web system design using the built package in Python, which is Gradio. This helps the registered user of the Twilio communication tool to receive alert messages when any suspicious activity happens around their communities.

Safety and security are essential to social stability since their absence disrupts economic, social, and political structures and weakens basic human needs. A secure environment promotes development, social cohesion, and well-being, making national resilience and advancement crucial. Law enforcement struggles with rising crime, population density, and technology. Time and effort are required to analyze and utilize data. This study employs AI to classify Arabic text to detect criminal activity. Recent transformer methods, such as Bidirectional Encoder Representation Form Transformer (BERT) models, have shown promise in NLP applications, including text classification. Applying these models to crime prevention motivates significant insights. They are effective because of their unique architecture, especially their capacity to handle text in both left and right contexts after pre-training on massive data. The limited number of crime field studies that employ the BERT transformer and the limited availability of Arabic crime datasets are the primary concerns with the previous studies. This study creates its own X (previously Twitter) dataset. Next, the tweets will be pre-processed, data imbalance addressed, and BERT-based models fine-tuned using six Arabic BERT models and three multilingual models to classify criminal tweets and assess optimal variation. Findings demonstrate that Arabic models are more effective than multilingual models. MARBERT, the best Arabic model, surpasses the outcomes of previous studies by achieving an accuracy and F1-score of 93%. However, mBERT is the best multilingual model with an F1-score and accuracy of 89%. This emphasizes the efficacy of MARBERT in the classification of Arabic criminal text and illustrates its potential to assist in the prevention of crime and the defense of national security.