Analysis of crime patterns through the integration of an agent-based model and a population microsimulation

In recent years, criminologists have become interested in understanding crime variations at progressively finer spatial scales, right down to individual streets or even houses. To model at these fine spatial scales, and to better account for the dynamics of the crime system, agent-based models of crime are emerging. Generally, these have been more successful in representing the behaviour of criminals than their victims. In this paper it is suggested that individual representations of criminal behaviour can be enhanced by combining them with models of the criminal environment which are specified at a similar scale. In the case of burglary this means the identification of individual households as targets. We will show how this can be achieved using the complementary technique of microsimulation. The work is significant because it allows agent-based models of crime to be refined geographically (to allow, for example, individual households with varying wealth or occupancy measures) and leads to the identification of the characteristics of individual victims.     

Introducing the Food Fraud Initial Screening model (FFIS)

Food Fraud is illegal deception for economic gain using food. There are many types of fraud including adulterant-substances, tampering, theft, diversion and gray marketing, simulations, misbranded, and intellectual property rights product counterfeiting. The concept is beginning to be addressed by laws, regulations, standards, and certifications. Regardless of the presence of an actual health hazard, Food Fraud incidents can: negatively impact sales, brand equity, market capitalization; violate regulations such as Sarbanes-Oxley; and even lead to the criminal prosecution of corporate leaders. Emerging regulations and industry standards are requiring risk and vulnerability assessments of Food Fraud as a prerequisite to countermeasures and decision-making systems. These assessments and risk management systems are not familiar food safety tools. It is effective and efficient to utilize an enterprise risk management (ERM) framework, such as developed by the Committee of the Sponsoring Companies of the Treadway Commission (COSO). ERM risk assessment occurs into two stages: (1) a qualitative initial screening followed by (2) a more detailed quantitative assessment. All types of Food Fraud can result in enterprise-wide risks so an enterprise risk management system must cover all types of vulnerabilities. The model developed in this paper addresses the unmet need of the first stage referred to here as the Food Fraud Initial Screening (FFIS).     

《公司法》修订对公司犯罪的影响

2005年10月27日通过的《公司法(修订案)》,是对我国1993年《公司法》的全面修改和完善。其对公司刑事责任的整合规定,体现了立法技术的进一步成熟;对公司注册资本制度的修订,将会对虚报注册资本罪、虚假出资罪的认定产生不容忽视的影响;对一人公司的增加规定,将会产生如何正确界定一人公司犯罪是公司(单位)犯罪还是自然人犯罪问题。     

浅谈计算机网络犯罪与安全问题

自世界上第一台计算机诞生到今天互联网的日益普及,计算机的发展速度非常迅猛,从而也把人类文明带入数码时代。互联网的广泛应用丰富了人们的生活,成为可以创造财富的生产力。但在虚拟的网络世界中,犯罪分子也会借助高智能、无现场、低风险及难防控等特性,采取一切手段,实施各种网络犯罪。     

我国保护土地资源的刑事立法研究

随着我国经济的飞速发展,土地升值,由此引发了形形色色土地违法犯罪案件,严重影响了我国经济健康、有序的发展。笔者从现阶段我国土地违法犯罪案件的特征着手,通过对现行刑法保护土地资源现状的分析,指出其中存在的问题,进而提出防范土地犯罪的相应法律对策。     

数字证据相关问题分析

随着计算机／网络犯罪问题日渐严重,通过规范化的作业程序获取数字证据的意义越来越突出。本文系统的分析了数字证据的特点、取证、保存、调查、使用等问题。     

An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

The sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level.     

浅谈中外有组织犯罪的几个问题

探讨了有组织犯罪的定义、类型,对比分析了中外有组织犯罪的特征,提出了打击有组织犯罪的对策.     

Regardless if it is comedy,action, thriller,or biography: A review of urban crime in the 1980s cinema of Chicago

This study investigates the cinematic representation of city crime transactions in Chicago in the 1980s. The narrative nature of cinema provides an imaginative context for interpreting the physical and nonphysical dimensions of urban crimes. From a critical interpretive position, based on Peirce's semiotics, this study uses “urban cinesemiotics” as the method to select image signs, identify their associated Chicagoan objects, and interpret their design-oriented meaning. The theoretical roots of crime prevention through environmental design constitute the basis for the interpretation of movies. A total of 27 crime-related scenes from 9 Chicagoan movies made in the 1980s illustrate that most urban settings suffer from the lack of crime-preventive environmental design. In particular, natural surveillance (eyes on the street), encounter and enclosure, and border vacuums are major environmental factors that affect urban crimes in Chicago. Some crime scenes also depict why environmental design cannot influence individuals' criminal intentions necessarily nor can they solve urban safety single-handedly.     

Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments

Steadily increasing urbanization is causing significant economic and social transformations in urban areas, posing several challenges related to city management and services. In particular, in cities with higher crime rates, effectively providing for public safety is an increasingly complex undertaking. To handle this complexity, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends. These technologies have potentially to increase the efficient deployment of police resources within a given territory and ultimately support more effective crime prevention. This paper presents a predictive approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and to reliably forecast crime trends in each region. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of crime-dense regions with associated crime predictors, each one representing a predictive model for estimating the number of crimes likely to occur in its associated region. The experimental evaluation was performed on two real-world datasets collected in the cities of Chicago and New York City. This evaluation shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.