Crime Trends Research Articles

INFORMATION AND ANALYTICAL SUPPORT FOR THE PREVENTION, DISCLOSURE AND INVESTIGATION OF CORRUPTION OFFENSES

The article deals with the features of implementation of informational and analytical support for the activities of law enforcement agencies in the prevention, discovery and investigation of corruption offenders. According to the author, such provision includes two relatively independent components: informational and analytical. The information provides for the collection, processing, accumulation and systematization of information about corruption offences and the persons who committed them. Analytical one is the determination of the state, structure and dynamics of corruption crime, negative deviations in its structure, the establishment of cause-and-effect relationships between various factors that determine it, as well as forecasting trends in its changes. The most effective means of informational and analytical activity for the prevention, detection and investigation of corruption offenders is the method of analytical intelligence and analytical search, focused on constant and continuous search, accumulation and processing of information about corrupt activities in a certain territory, in the economy or in the state as a whole. The division of analytical intelligence into types based on the range of tasks to be solved has theoretical and practical significance. Analytical intelligence for the purpose of solving operational problems allows one to establish the trends of corruption crime, find out the places and areas of concentration of corruption crimes, and determine the profile of the corruptor. Analytical intelligence with the aim of solving tactical problems pursues the goal of developing tactical measures to apprehend criminals, identify risks and warn specific corruption offenders. Strategic analytical intelligence involves the preparation of strategic management decisions and the identification of corruption-related risks. Considering the high latency of corruption offenders and the lack of necessary access to closed, confidential information by individual law enforcement agencies, the use of OSINT to search for hidden assets and connections of subjects of corruption offenders on the basis of open sources of information and data is an effective method of analytical intelligence in order to solve the problems of combating corruption offences. including on the Internet. Among the other directions of the analytical search, it is necessary to note the report on the state of anti-corruption, information from the unified state register of persons who have committed corruption or corruption-related offences, and the results of published relevant journalistic and public investigations.

Forecasting crime trends to support police strategic decision making

Forecasting crime trends to support police strategic decision making

Measuring the impact of the state of emergency on crime trends in Japan: a panel data analysis

Purpose/BackgroundCity-specific temporal analysis has been commonly used to investigate the impact of COVID-19-related behavioural regulation policies on crime. However, these previous studies fail to consider differences in the intensity of intervention among cities and the impact of these behavioural regulation policies on crime trends nationwide. This study performs panel data analyses to examine how the declaration of a state of emergency (SoE) affected ambient population and crime in Japan, taking advantage of the fact that the SoE was implemented at different times in different prefectures.MethodsThe current study uses two sets of panel data of 47 prefectures for 22 weeks from February to July 2020: (1) the data on ambient population in five types of locations provided by the Google Mobility Reports, and (2) official crime data of six types of crime: residential burglary, commercial burglary, theft of/from vehicle, bicycle theft, sexual assault, and violence and injury. Firstly, an ordinary least squares regression analysis was performed to examine the impact of the SoE on the ambient population. Then a negative binomial model with fixed effects was adopted to examine the effect of the ambient population on the crime trends.FindingsThe SoE declaration was found to increase the ambient population in ‘residential’, and decrease that in other settings including ‘workplaces’, ‘transit stations’, and ‘retail and recreation’ in targeted prefectures. Spill-over effects of the SoE were observed on the ambient population of non-SoE prefectures. The ambient population have impacted five out of the six types of crime examined, except for sexual assault. After controlling for the ambient population, we observed an increase in commercial burglary and theft of/from the vehicle in all prefectures during the SoE weeks, compared to the weeks when the SoE was not declared.ConclusionsThe declaration of the SoE during the COVID-19 pandemic changed the ambient population in the SoE-prefectures, resulting the changes in crime levels as well. In addition, the implementation of the SoE in specific prefectures was found to have a contextual impact on national-level crime trends. Furthermore, the implementation of the SoE caused changes in some crime types that could not be explained by the changes in the ambient population, suggesting that the implementation of the SoE affected offenders’ decision-making. It is also worth noting that the changes in ambient population and crime trends during the pandemic were observed in Japan where the behavioural regulation policy without law enforcement was introduced.

Энтропия законодательства в сфере благотворительности

In the conditions of modernization of the socially significant foundations of the state, the creation of new institutions of economic support for certain categories of citizens in order to realize their constitutional rights, a special demand of society for various types and forms of charitable activities is being formed. The implementation of charity in the modern Russian state has a number of constitutive obstacles, which are often expressed in the instability of the socio-economic sphere, significant gaps in special legislation regulating the activities of charitable foundations and in general charity as a phenomenon, lack of transparency in the implementation of charitable activities by relevant entities. These problems significantly affect the level of effectiveness of the type of activity under consideration and, as a result, contribute to the emergence of criminal determinants of the implementation of unscrupulous goals of intruders. The article analyzes the current legislation in the field of regulation of charitable activities, assesses its formal certainty, consistency and completeness of regulation; examines the existing trends in crime in the field of charity.

Foundations and trends in the darknet-related criminals in the last 10 years: a systematic literature review and bibliometric analysis

After the Silk Road closure, many studies started focusing on the trend and patterns of darknet-related crimes in the 2010s. This first study combined a systematic literature review and bibliometric analysis in the field. This study clarifies 49 articles in criminology and penology among 1150 publications relating to the darknet on the Web of Science database to review and analyze the research evolutions of this topic in the last decade. The main findings point out (1) almost all leading authors with their most influential papers came from the Global South with predominant contributions; (2) unbalancing publications between regional scholars and their institutions and countries although the darknet-related criminals occurred and operated without border; and (3) some specialized themes have identified to call further extensive research such as policing interventions in the darknet and flows of the cryptocurrency in cryptomarkets, among others.

The “new” crimes of pandemics: observations and insights from Latin America

We study the emergence of new forms of law violations in Latin America during the coronavirus pandemic. Based on data from online news articles, we construct a typology of “new” crimes: (1) hate or fear crimes against health workers and hospitals; (2) illegal denials of public mobility out of fear of infection; (3) looting and other traditional crimes justified by the pandemic; and (4) violations of pandemic regulations. Studies of crime during the pandemic have focused on traditional crime and have been based on rational choice theory and a routine activities approach. We argue that in order to understand the recent crime trends in Latin America, these must be accompanied by other theories of crime, together with a better understanding of the role of the State. Our typology can be a starting point for discussions and further research on the complex relationship between criminal behavior and policy responses in pandemics.

軍 범죄예방을 위한 CPTED 적용효과 및 개선방안에 관한 연구

This research analyze the effect of the Application of CPTED and study improvement measures for the Prevention of Military Crime through various studies and CPTED application cases, military crime trends and crime trends. In particular, it was confirmed that the CPTED has a positive effect on military crime reduction, security and safety awareness through the first CPTED application case in the military. Therefore, this study suggested improvement measures to apply CPTED to the prevention of military crimes. First, it is the development of a CPTED-applied introduction model suitable for the military. In this study, a crime prevention activity model was presented in the order of pre-measures based on the CPTED principle, CPTED implementation, and follow-up management. Second, it is necessary to train CPTED experts (MCPO). Currently, the military lacks CPTED professionals necessary for CPTED application. In the future, it is essential to train experts for the operation of the Military Crime Prevention Officer (MCPO) of the military police. Third, it is necessary to form a CPTED consensus and activate a crime prevention consultative group. In order to successfully apply CPTED to the military, above all, responsibilities between crime prevention agents must be clearly defined and the consultative group must be reorganized and activated while forming a consensus on the application of CPTED among each agents. In this case, collaboration with specialized institutions inside and outside the military should also be carried out. Fourth, institutionalization for the application of CPTED to the military is necessary. In order to apply CPTED more systematically, it is important to expand the agents of crime prevention activities and clarify its responsibilities. To this end, the military also needs to establish a legal and institutional foundation and CPTED should be institutionalized by reflecting the concept of CPTED in military regulations and guidelines.

The State and Dynamics of the Criminal Situation at the Transport Facilities of the Russian Federation in the Context of the Developing Geopolitical Crisis

Introduction. The article describes the dynamics of the criminal situation at the transport infrastructure facilities of the Russian Federation in 2022 Based on the analysis of changes in the operational situation in transport over the past five years, taking into account the influence of previously non-existent factors that arose as a result of the introduction of anti-covert measures in 2020-2021 and openly hostile actions of Western countries directed against Russia, which has been conducting since February 2022 a special military operation in Ukraine, the conditions for the development of the criminal situation in transport are considered. The analysis of the criminal situation on transport in Russia during the period under study allows us to consider the trends of crime not only from the angle of its criminal-legal nature, but also from the perspective of the influence of ongoing social processes on it. The paper identifies the factors affecting crime rates at transport facilities in the current conditions, estimates of options for possible development of the criminal situation are derived, priority areas of law enforcement agencies' activities to minimize crime at transport facilities are formulated.Materials and methods. The theoretical basis of the study was the conclusions and achievements of criminological science on crime, scientific provisions on crime as a social phenomenon, on factors affecting the dynamics of crime. General scientific and private scientific research methods (synthesis, deduction, analysis, generalization, observation, etc.) were used in the work. The methodological basis of the research was a systematic approach, comparative and problem approaches.The results of the study. As a result of the analysis of the criminal situation at the transport facilities of the Russian Federation, it was found that, starting from 2020, new factors influenced the quantitative indicators of crime in transport. The introduction of restrictive measures related to the spread of coronavirus infection, their subsequent cancellation, the beginning of the geopolitical crisis in the world, provoked by the introduction of sanctions by Western countries against Russia, restrictions on international communications. closure of airports, borders, etc. they affected the directions of preventive activities of the internal affairs bodies in transport. The conclusions and forecasts made in the article determine the trends in the development of the criminal situation in transport in 2023 and allow us to determine the most important vectors of the direction of preventive activities of internal affairs bodies. Discussion and conclusion. The article identifies and substantiates the priority areas of activity of the internal affairs bodies of Russia in transport for the most effective organization of preventive activities and combating crime.

AIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction

Accuracy and interpretability are two essential properties for a crime prediction model. Accurate prediction of future crime occurrences along with the reason behind a prediction would allow us to plan the crime prevention steps accordingly. The key challenge in developing the model is to capture the non-linear and dynamic spatial dependency and temporal patterns of a specific crime category, while keeping the underlying structure of the model interpretable. In this article, we develop AIST, an A ttention-based I nterpretable S patio T emporal Network for crime prediction. AIST models the dynamic spatio-temporal correlations for a crime category based on past crime occurrences, external features (e.g., traffic flow and point of interest information) and recurring trends of crime. Extensive experiments show that AIST outperforms the state-of-the-art techniques in terms of accuracy (e.g., AIST shows a decrease of 4.1% on mean average error and 7.45% on mean square error for the Chicago 2019 crime dataset) and interpretability. 1

Effects of the onset of the COVID-19 pandemic restrictions on park crime in London, England: An interrupted time series analysis

IntroductionPark crimes may have increased during the COVID-19 pandemic as a result of lockdowns that limited the number of capable guardians in public spaces. Despite this, the impacts of the lockdowns on park crimes remain unknown. To help us understand the societal impacts of policies implemented during this period, we assessed how the onset of the COVID-19 restrictions impacted urban park crime levels in London, England. MethodsWe identified crimes that occurred in publicly accessible parks and gardens in the Greater London Authority (England, UK) between March 1, 2019 and February 28, 2021 by overlaying open-access crime data with greenspace data supplied by the Greater Information for Greater London service. Using interrupted time series analyses, we estimated seasonality-adjusted associations between the onset of COVID-19 restrictions and park crimes. ResultsOverall (1565.7, 95% confidence intervals [CI] 1021.9 to 2109.5) and antisocial behaviour crimes (1772.7, 95% CI 823.6–2721.7) increased in London parks during the first full month of COVID-19 restrictions (April 2020). There were no notable trends in park crimes in London prior to the onset of restrictions, but overall and antisocial behaviour crimes decreased after the onset of restrictions at a rate of 156.4 (95% CI −220.25 to −92.51) and 164.7 (95% CI −280.68 to −48.74) crimes/months, respectively. ConclusionsOverall park crimes increased during the first full month of the COVID-19 restrictions, largely driven by an increase in antisocial behaviours. Additional research is needed to identify the specific misdemeanours that accounted for this rise in antisocial behaviours and to investigate their downstream impacts (e.g. increases in policing costs or decreases in perceived park safety).

TRENDS IN THE DEVELOPMENT OF THE URBAN REAL ESTATE MARKET IN UKRAINE

The volatility of external and internal factors affecting the real estate market makes it necessary to study trends and predict the consequences of management decisions to prevent financial losses. The real estate market is a sphere of capital investment in real estate and a system of economic relations arising from real estate transactions. These relations arise when buying and selling real estate, leasing real estate, etc. Depending on the legal framework, external and internal financing conditions, and forms of property ownership, the real estate market is constantly changing and unique to each country, region, and city. To accurately evaluate real estate, it is necessary to monitor in detail all the changes taking place in the market. This article analyzes the factors that influence the formation of supply and demand in the real estate market. Economic factors have the greatest impact, namely: the level and dynamics of population income; differentiation of the population by income level; level of business development and its dynamics; changes in the structure of the business and its profitability; availability of financial resources; the state of alternative and related markets; prices in the real estate market; rental rates; risks of an investment in real estate; construction costs; prices and tariffs for utilities and energy resources. Social factors, such as population dynamics and age structure, the social structure of the population, crime rate and trends in crime, also significantly impact real estate market trends. The interaction of these factors ultimately determines the state and dynamics of supply and demand in the real estate market. At the same time, the direction of their influence may either coincide or differ for supply and demand. This can be better understood by identifying trends in supply and demand and their dependence on these factors. The article examines the history of price dynamics and systematizes the stages of the formation and development of the real estate market in Ukraine. The state of the real estate market during the crisis periods is analyzed and a forecast for the next year is made. Keywords: real estate market, real estate market dynamics, demand, offer, investment potential.

Institutionalized ‘bad girls’: adolescent female folk devils in Canadian newspapers between 1991 and 2012

ABSTRACT Prior literature takes for granted that media do not reflect real crime trends, but present sensationalized depictions of girls and crime. A positive correlation exists between violent guilty findings among Canadian girls and the frequency of reporting in national newspapers. Media present simplistic and problematic depictions of girls’ involvement in crime with a qualitative content analysis finding two female-specific folk devils: the modern girl devoid of innocence and the girls wanting to attain masculine power. The construction of female folk devils, corresponding with traditional gendered tropes, highlights how moral panics surrounding girls’ criminal and violent behaviour are tied to gender inequality.

How the COVID-19 pandemic hit crime in Barcelona: Analysis of variation in crime trends.

To compare the observed and forecasted crime trends in Barcelona, using crime statistics from January 2018 to March 2021. We trained (seasonal) auto-regressive integrated moving average modelling (95% confidence intervals) using daily recorded crimes from January 2018 to February 2020. These models were then used to forecast crime data from March 2020 to March 2021 across four periods (lockdown, summer, fall and winter). Crime data were organized into two categories: property (burglary, theft) and violent crimes (robbery, assault, domestic violence and sexual offenses [rape, assault or abuse]). Overall, crime levels for property and violent crimes during lockdown declined sharply from the forecasted levels. Theft, burglary, assault, robbery and sexual offenses exhibited general decreases throughout the study period, with the same sharp declines during the lockdown, progressive recovery in the summer, and steady or slight reductions from fall to March 2021. Only domestic violence differed, reaching the forecasted levels for all periods and surpassing the forecast for summer 2020. Our findings show how the pandemic has affected mid-term crime trends. They help to place the measures applied in the last year into context and to determine the most suitable policies to reduce crime during societal change.

Analysis of LED street light conversions on firearm crimes in Dallas, Texas

This report analyses the effects of LED streetlight conversions on nighttime firearm crimes in Dallas, Texas. Using data from 2020 through May of 2022 on reported firearm crimes and LED conversions, using a differences-in-differences strategy (comparing trends in daytime firearm crimes compared to those at night), I find no evidence that LED conversions result in decreased nighttime firearm crimes in this sample. Code and Data to replicate the report findings can be obtained here <https://www.dropbox.com/sh/l5khtwss97relp8/AACx2TYMYCt7ICTOnKUHHtuWa?dl=0> .

‘Top 10’ policing as an alternative place-based strategy: responding to the overcomplication and underestimation of the law of crime concentration

ABSTRACT Recent trends in crime and place research emphasize a micro-level focus on the concentration, stability, and patrollability of crime hot spots. Empirical findings consistently suggest that hot spots are disproportionate in crime concentration, are time stable, and have high crime reduction potential for place-based interventions. Due to a growing literature on hot spot identification techniques, research may be overcomplicating and underestimating the degree to which these concepts manifest, creating unnecessary challenges to crime prevention strategies. The current study analyzes robbery in three cities to determine whether crime hot spot concentration, stability, and patrollability observed at different hot spot aggregations (1%, 0.1%, and the “Top 10”) have characteristics that make them amenable to more efficient crime-reduction initiatives at smaller aggregations. Collectively, current findings suggest that “Top 10” Policing, which focuses on just the 10 most criminogenic hot spots within an agency’s jurisdiction, could have a meaningful impact on overall crime rates.

Violent assault geographies in northeastern Australia.

As climate change produces more extreme weather, it is increasingly important to understand the impacts of these changes on social behaviour. The relationship between weather and crime has been studied across numerous contexts. However, few studies examine the correlation between weather and violence in southern, non-temperate climates. In addition, the literature lacks longitudinal research that controls for international changes in crime trends. In this study, we examine over 12 years of assault-related incidents in the state of Queensland, Australia. Controlling for deviations in trend for temperature and rainfall, we explore the relationship between violent crime and weather across Köppen climate classifications. Findings provide important insight into the impact of weather on violence across temperate, tropical, and arid climate regions.

Crime Prediction Utilizing ARIMA Model

With the development of society and the progress of human beings, the issue of crime has attracted more attention. This research uses one of the most common time series models named the ARIMA model as the main algorithm to predict the crime data by month in San Francisco from 2003 to 2015, obtained from Kaggle. This research is about to study all categories of crime and specific crime cases as well. The focus is on the number of cases that happened every month. By applying the ARIMA model, it can be tested whether the trend of happened crime cases fits the model and predict the future crime tendency. The verification and prediction of crime data are studied with the help of the ARIMA model. And the goal of studying this dataset is to improve the safety and security of human beings. In this research, the ARIMA model simulates the previous crime trend effectively and forecasts the future crime trend with high accuracy. This is helpful to improve the safety level and reduce the crime rates in San Francisco.

Rising Trends of Street Crime in Punjab

Crime is not the result of any single factor and so is the crime prevention which cannot be left to only one department. History of crime is as old as mankind and all crimes have socio-economic and psychological consequences for citizens. Amongst them crime against property has serious consequences for citizen safety and economic activity in any society. Street crimes which constitute major portion of crime against property has consistently increased in the province of Punjab in last few years. Street crime prevention is of great concern for the policy makers in general and police department in particular. Crime data in Punjab signifies that there is a sharp increase in street crimes in the province, but police department lack both resources and capacity to detect and investigate street crimes. As street crime has evolved over a period of time where offenders use modern communication tools and resort to violence, the state response has been found lacking. Various public policy initiatives against street crime in Punjab are mostly designed for controlling street crime and not for its prevention. The focus of these initiatives has remained on prevention through policing measures and increasing deterrence against crime. Despite all the challenges posed by street crime and offenders, some of the countries have successfully implemented strategies in prevention of street crimes. They have adopted a whole-of-system approach and handled the issue of street crimes not only from criminological perspective, but also as a social phenomenon. Various strategies adopted by these countries signify the importance of addressing various social factors contributing to rising trends of street crimes in urban centers. These initiatives not only increased the cost of committing crime for the offenders, but also worked on creating conditions where criminal offenders find it less lucrative to commit crime. Punjab has invested hugely in technological support to the police in Lahore but there is a dire need not only to replicate this model in all city district governments but addressing other social determinants of street crime. Street crime prevention require a focused approach and citizen safety should find priority in all these approaches.

Spatial-temporal meta-path guided explainable crime prediction

Exposure to crime and violence can harm individuals’ quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to crime prevention. The increasing availability of both fine-grained urban and public service data has driven a recent surge in fusing such cross-domain information to facilitate crime prediction. By capturing the information about social structure, environment, and crime trends, existing machine learning predictive models have explored the dynamic crime patterns from different views. However, these approaches mostly convert such multi-source knowledge into implicit and latent representations (e.g., learned embeddings of districts), making it still a challenge to investigate the impacts of explicit factors for the occurrences of crimes behind the scenes. In this paper, we present a Spatial-Temporal Meta-path guided Explainable Crime prediction (STMEC) framework to capture dynamic patterns of crime behaviours and explicitly characterize how the environmental and social factors mutually interact to produce the forecasts. Extensive experiments show the superiority of STMEC compared with other advanced spatial-temporal models, especially in predicting felonies (e.g., robberies and assaults with dangerous weapons).

New trends in extremist and terrorist crime and measures to counter threats to national security

The author analyzes the current state of extremist and terrorist crime, as well as the main trends in the commission of individual crimes in 2022 and 2023. The choice of this time period is due to the increased threats to the national security of the Russian Federation. Attention is drawn to the indicators of crimes provided for in Articles 205, 205.2, 281, 357 of the Criminal Code of the Russian Federation. Based on the evaluation of statistical data, information about the current criminogenic situation was obtained. There was a slowdown in the growth rate of the number of public calls for extremist activity (Article 280 of the Criminal Code of the Russian Federation) and the facts of incitement to hatred or enmity (Article 282 of the Criminal Code of the Russian Federation). The intensification of terrorist and sabotage activities is stated. The materials of separate foreign studies concerning destructive activity in the information sphere are analyzed. Conclusions are drawn about the need to consider extremist and terrorist activities in the context of information warfare and the use of information weapons. With this in mind, it is proposed to take a number of measures in the field of information security. As a measure to counteract new threats to national security, the urgent need to systematize and publish information that became known during the investigation of criminal cases initiated on the facts of the genocide of the Russian-speaking population in the period from 2014 and the Soviet people during the Great Patriotic War is called. It is proposed to consider the issue of securing in Russian legislation the powers of the Security Council of the Russian Federation to declare a State carrying out terrorist activities as a sponsor of terrorism.