Crime Patterns Research Articles

The influence of micro-places on the spatial patterns of property crime in Vancouver, Canada

ABSTRACT Research has shown that crime is concentrated at a small number of micro-places. This research has found that these spatial patterns are generalisable across different urban settings and are relatively stable over time. Despite this, little is known about the explanatory factors of crime at the micro-spatial scale. Using police incident data and land-use information obtained from the Vancouver Open-Data catalogue, zero-inflated negative binomial models are used to explain the spatial patterns of various types of property crimes at the street segment level. The results demonstrate that aspects of micro-places (multi-unit housing, restaurants (with and without liquor), and retail outlets) have a significant positive impact on these crime types at the micro-spatial level. Depending on the crime type, the strength of the relationship varies in magnitude and level of significance.

Состояние, структура и динамика преступности в сфере жилищно-коммунального хозяйства

Crimes in the housing and communal sphere cause considerable economic damage to the individual, society and the state. This problem is constantly addressed at all levels of government. The purpose of the study is to examine statistical indicators and to identify trends and patterns of crime in the field of housing and communal services.
 The article provides a criminological analysis of crime in the field of housing and communal services, its concept, structure, state and dynamics are considered, on the basis of general and special methods of scientific knowledge. The scientific study is based on statistical and analytical data of the Prosecutor’s Office Russia. It is based on the number and structure of registered crimes in the field of housing and communal services in the period from 2014 to 2020.
 The structure and dynamics of corruption and economic crime in the sphere of housing and communal services are presented. The author concludes that (44–52 %) of all crimes committed in the sphere of housing and communal services are corruptive. The author in a certain period observed an increase in crimes in the sphere of housing and communal services. In her opinion, this is due to changes in regulatory framework in the housing and utilities sector and an increased attention paid to the problems inherent to this sector by all the agents involved: state bodies, the country’s leadership and society. As a result of the analysis, the author concludes about the unstable dynamics, characterized by jumps of both positive and negative nature, increased latency and a high crime rates in the housing and communal services sector.

A data-driven agent-based simulation to predict crime patterns in an urban environment

Spatial crime simulations contribute to our understanding of the mechanisms that drive crime and can support decision-makers in developing effective crime reduction strategies. Agent-based models that integrate geographical environments to generate crime patterns have emerged in recent years, although data-driven crime simulations are scarce. This article (1) identifies numerous important drivers of crime patterns, (2) collects relevant, openly available data sources to build a GIS-layer with static and dynamic geographical, as well as temporal features relevant to crime, (3) builds a virtual urban environment with these layers, in which individual offender agents navigate, (4) proposes a data-driven decision-making process using machine-learning for the agents to decide whether to engage in criminal activity based on their perception of the environment and, finally, (5) generates fine-grained crime patterns in a simulated urban environment. The novelty of this work lies in the various large-scale data layers, the integration of machine learning at individual agent level to process the data layers, and the high resolution of the resulting predictions. The results show that the spatial, temporal, and interaction layers are all required to predict the top street segments with the highest number of crimes. In addition, the spatial layer is the most informative, which means that spatial data contributes most to predictive performance. Thus, these findings highlight the importance of the inclusion of various open data sources and the potential of theory-informed, data-driven simulations for the purpose of crime prediction. The resulting model is applicable as a predictive tool and as a test platform to support crime reduction.

Стан та тенденції застосування основних засобів виховного впливу на засуджених військовослужбовців

The analysis of military crimes committed from 2014 till 2020, the process of the development of the Armed Forces of Ukraine and a range of issues related to the process of correction and re-socialization of convicted members of the armed forces are represented in the article. Studying and determining the patterns of crimes committed while performing active duty in Ukraine, as well as the educational impact of remedies and re-socialization applied to convicted members of the armed forces is studied insufficiently and needs more attention from scholars. One of the main problems that remains relevant among scholars to this day is the development and implementation of optimal ways to correct convicted members of the armed forces. The state of affairs studied during the Anti-Terrorist Operation on Protection the Independence, Sovereignty and Territorial Integrity of Ukraine in Donetsk and Luhansk Regions showed that the criminal-executive system was not ready for the growth of the number military criminals. Taking into account the specifics of these subjects of criminal offenses, and the rapid development of the Armed Forces of Ukraine, which are in a state of undeclared war with the Russian Federation, we have proposed the necessary measures to correct and re-socialize convicted members of the armed forces in order to meet their needs and protect their rights and interests and, what is most importantly, to develop law-abiding behavior of the personality of a convicted member of the armed forces. The criminological portrait of a military criminal was also considered in the article. The changes through the prism of reforms in the defense sector were analyzed. Moreover, the international experience of studying the problems related to military crimes was analyzed. On the basis of the conducted analysis, corrective measures were proposed. Today, under the influence of legislative changes caused by Ukraine’s movement towards NATO membership, the transformation of the personnel of the Armed Forces of Ukraine was studied, and the ways to address the emerging needs of the members of the armed forces were proposed. Key words: correction, Military law enforcement service, members of the armed forces, offense, punishment, crime.

Crime and COVID-19: effect of changes in routine activities in Mexico City

BackgroundThis study aimed to determine whether crime patterns in Mexico City changed due to the COVID-19 pandemic, and to test whether any changes observed were associated with the disruption of routine activities, as measured by changes in public transport passenger numbers.MethodThe first objective was assessed by comparing the observed incidence of crime after the COVID-19 pandemic was detected in the country with that expected based on ARIMA forecasts based on the pre-pandemic trends. The second objective was assessed by examining the association between crime incidence and the number of passengers on public transport using regressions with ARIMA errors.ResultsResults indicated that most crime categories decreased significantly after the pandemic was detected in the country or after a national lockdown was instituted. Furthermore, the study found that some of the declines observed were associated with the reductions seen in public transport passenger numbers. However, the findings suggested that the changes in mobility explain part of the declines observed, with important variations per crime type.ConclusionThe findings contribute to the global evaluation of the effects of COVID-19 on crime and propose a robust method to explicitly test whether the changes observed are associated with changes in routine activities.

Crime and COVID-19: effect of changes in routine activities in Mexico City

This study aimed to determine whether crime patterns in Mexico City changed due to the COVID-19 pandemic, and to test whether any changes observed were associated with the disruption of routine activities, as measured by changes in public transport passenger numbers. The first objective was assessed by comparing the observed incidence of crime after the COVID-19 pandemic was detected in the country with that expected based on ARIMA forecasts based on the pre-pandemic trends. The second objective was assessed by examining the association between crime incidence and the number of passengers on public transport using regressions with ARIMA errors. Results indicated that most crime categories decreased significantly after the pandemic was detected in the country or after a national lockdown was instituted. Furthermore, the study found that some of the declines observed were associated with the reductions seen in public transport passenger numbers. However, the findings suggested that the changes in mobility explain part of the declines observed, with important variations per crime type. The findings contribute to the global evaluation of the effects of COVID-19 on crime and propose a robust method to explicitly test whether the changes observed are associated with changes in routine activities.

Detection of Crime Patterns in Digital Forensic Investigation to Trace the Adversaries

The use of the internet has increased significantly over the past couple of years. Access to the internet has become so common that a person without computer knowledge can also use this facility easily. This ease of availability has provided a lot of benefits to society but on the other hand misuse of the internet for personal or corporate benefits is also increasing. To prosecute cybercriminals and make some lawful checks on everyone's digital activities, digital forensic science comes into the light. In this context, we developed a new framework that improves the digital forensic investigation process. This research paper proposes a method in which we can identify the illegal activities and trace the adversaries. We capture the TCP (Transmission Control Protocol) packets from the servers and workstations. This data collected from the TCP log is stored in the database and preprocessed to eliminate redundant data. Furthermore, the database also contains past data. The proposed framework has three major processes collection of TCP packets, storing and preprocessing of collected data in a database, and mining of the pattern through a digital forensic anomaly collection algorithm. For the evaluation of our proposed framework, we have developed a java based application. The results are shown in the form of reports and tables.

Friendly or unfriendly confines: impact of Chicago baseball stadia on vicinity crime patterns

ABSTRACT Rationale/Purpose: The purpose of this paper is to explore the impact of the two Major League Baseball stadia in Chicago, Wrigley Field and Guaranteed Rate Field (GRF) on stadium vicinity crime to assess changes in crime pattern on game day. Design/methodology/approach: This study compares the impact of stadia location on crime while employing an analysis on radii and concentric bands around baseball stadia in Chicago. An analysis of the influences of factors on crime counts within a 3-km radius of baseball stadia in Chicago from 2015 to 2017. Findings: The analysis suggested significant differences in crime pattern due to the presence of baseball stadia; more crime closer to Wrigley, in general, but more crime in the further areas of interest near GRF. Practical Implications: : The findings demonstrate how venues may influence crime patterns in a city including evidence of a displacement effect of crime further away from GRF while crime clusters closer around Wrigley Field. Research Contribution: This shows the need for different policing, crowd, risk, and communication strategies for different venues within the same city. Furthermore, the study confirms the importance of considering the impact of sporting events beyond the immediate vicinity of the venue property.

Impacts of Canada's cannabis legalization on police-reported crime among youth: early evidence.

Canada's 2018 Cannabis Act allows youth (age 12-17years) to possess up to 5g of dried cannabis (or equivalent) for personal consumption/sharing. This study assessed whether the Cannabis Act was associated with changes in police-reported cannabis offences among youth in Canada. Time series model using national daily criminal incident data from January 1, 2015-December 31, 2018 from the Canadian Uniform Crime Reporting Survey (UCR-2). Seasonal autoregressive integrated moving average time series models, stratified by sex, assessed the relations between legalization and youth cannabis-related offences. Canada, 2015-2018. Police-reported cannabis-related offenses among youth age 12-17years (male, n=32 178; female, n=9001). Outcomes: police-reported cannabis-related crimes, property crimes, and violent crimes. Covariate: calendar-month. For females, legalization was associated with a step-effect decrease of 4.56 (95% confidence interval [CI] = 3.32, 5.81; P<0.001) police-reported cannabis-related criminal offences per day, an effect equivalent to a 64.6% (standard error [SE] = 33.5%) reduction. For males, legalization was associated with a drop of 12.73 (95% CI = 8.82, 16.64; P<0.001) cannabis-related offences per day, equaling a decrease of 57.7% (SE = 22.6%). Results were inconclusive as to whether there were associations between cannabis legalization and patterns of property crimes or violent crimes. Implementation of the Cannabis Act in Canada in 2018 appears to have been associated with decreases of 55%-65% in cannabis-related crimes among male and female youth.

Prediction of Crime Occurrence in case of Scarcity of Labeled Data

In line with technological developments, machine learning/data mining studies have significantly scaled up in crime analysis. The prediction of crime occurrences, the detection of the spatial/temporal distribution of the criminal cases, forecasting the type of crime are some of these study areas. By taking crime data resulting from a substantial increase in crime rates into consideration, unlabeled data can be utilized to enhance exploring the patterns of crime for future events or to make crime-related predictions easily. Therefore, in this study, active learning, self-learning, and random sampling techniques are applied to predict the outcome of criminal searches in England using the police data of 2019. According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.

New York City: COVID-19 quarantine and crime

PurposeIn early 2020, the world faced a rapid life-changing pandemic in the form of the Coronavirus Disease of 2019 (COVID-19) crisis. Citywide lockdowns with stay-at-home orders and mass closings quickly became the “new normal.” With these new mandates, routine activity, mental health and financial securities all began to experience major deviations, and it became clear that this could prove to be rather valuable in providing the opportunity for large-scale criminology experiments. This study aims to explore New York City's (NYC) crime patterns during this unique social situation. Specifically, has crime as a whole increased or decreased, and have particular crimes increased or decreased during these stressful fluid times?Design/methodology/approachThe authors briefly review previous crises and worldwide trends but focus on NYC crime as collected by the New York Police Department's statistics unit, “CompStat.” An analysis of 13 crime types from March 30 to July 5 was completed, including percent differences and individual weekly incidence rates in citywide crimes compared to the same time in 2019.FindingsThe analysis demonstrated that all crimes analyzed, except for murder and burglary, exhibited a statistically significant difference during COVID-19 conditions compared to the same time the previous year. Grand larceny auto and gun violence crimes significantly increased during COVID-19 weeks, whereas rape, other sex crimes, robbery, felony assault, grand larceny, transit, housing, misdemeanor assault and petit larceny all significantly decreased.Originality/valueDue to the ongoing nature of the pandemic, this is amongst the first studies to examine trends in NYC crime during pandemic mandates. Expanding our knowledge in these situations can inform natural disaster responses, as well as criminal justice policy and practice to better protect the public in future crises.

The Impact of the Coronavirus (Sars-Cov-2) Lockdown on Crime in New York and London, March-June 2020: A Comparative Study

The objective of this paper is to assess the relationship between The Spring 2020 COVID-19 Lockdown and the levels of crime in New York City (NYC) and London. Our proposition, derived from the Routine Activity Theory (RAT), the ‘breaches’ theory and input from the 2020 research on lockdown and crime, hypothesised that lockdown measures would lead to reductions in crime. The crime categories selected for this study were: homicide, rape, robbery, violence against a person, burglary, theft and vehicle theft. T-test, F-test and the Ordinary Least Squares (OLS) regression calculations were used to test the hypotheses. The four-month lockdown period in 2020 produced a 15% and 31% crime reduction in NYC and London, respectively. In the case of London, the overall results indicate that changes in routine human activities were indeed largely correlated with the reduction in crime. However, crime patterns in NYC in spring 2020 turned out to be inconsistent. A comparison of crime patterns under lockdown proved dissimilarity between NYC and London. The two-city comparison indicates that crime change related to lockdown may vary across crime types, places, and timespans or may have a detrimental effect on crime levels. The study may be considered suitable for replication and elaboration, particularly in view of the extended longevity of lockdown measures.

When Crime Moves Where Does It Go? Analyzing the Spatial Correlates of Robbery Incidents Displaced by a Place-based Policing Intervention

Objective: Examine the place-based correlates of robbery activity displaced by a foot-patrol intervention in Newark, NJ. We use constructs from Crime Pattern and Social Disorganization theories to test hypotheses concerned with associations between the structure of the environment and the displacement of crime. Method: Robbery incidents were spatially joined to street segments to study micro-level displacement processes. Predictor variables were operationalized using data from the Newark Police Department and Infogroup USA. Generalized Linear models tested associations between the characteristics of street segments and displaced robbery in the target area as compared to a control. Results: Environmental structure is important to understanding the settings of displacement, though this varied between spatial and temporal displacement. Relationships between displaced crime activity and model covariates did not always appear in expected directions. For example, bus stops predicted increased spatial displacement while corner stores predicted decreased levels of temporal displacement. Conclusions: While testing for displacement has become commonplace in place-based policing interventions, less attention has been paid to the micro-level factors that may facilitate the displacement of crime events. Both bus stops and corner stores show consistent associations with displaced crime activity, but the directions of these relationships suggest more complex processes requiring further examination.

Location Choice of Snatching Offenders in Chennai City

ObjectivesWe aim to test the applicability of crime pattern theory in an Indian urbancontext by assessing the effects of offender residence, prior offending locations andpresence of crime generators and crime attractors on where offenders commit offences.MethodsThe data comprise 1573 police-recorded snatching offenses committed by 1152 identified offenders across the 201 wards of Chennai City. We used discrete crime location choice models to establish the choice criteria that snatching offenders use when they decide where to offend. Data on the locations retail businesses, religious and transportation facilities were collected using Google location services.ResultsThe results confirm that snatching offenders prefer to target locations closerto their residence and that they prefer to re-offend at or near their prior offendinglocations. The findings also demonstrate that some but not all crime attractors andgenerators influence the location choice of snatching offenders.ConclusionsBy replicating in an Indian context previously published crime locationchoice findings, our findings support the generality of crime pattern theory. We discusslimitations and make suggestions for future investigations.

서울시 행정동 수준의 범죄분포에 대한 탐색적 연구

서울시 행정동 수준의 범죄분포에 대한 탐색적 연구

Crime Concentration and Temporal Stability in Spatial Patterns of Crime in Niš, Serbia

The research objectives were to examine the concentration of crime and its temporal patterns in Nis and compare the related research results in spatial criminology. Longitudinal property and violent crime data were geocoded and assigned to three types of grid cells. Descriptive statistics were used to examine crime concentration, and spatial stability of crime was tested using a spatial point pattern test. The findings suggest an intense concentration of all crime types within all areal units and years of study. The higher rates of concentration were found in the analysis of less common robbery and violent crime. Findings of spatial pattern analysis suggest moderate to a high degree of similarity in spatial patterns among all crime types. However, sensitivity analysis indicates significant changes in the spatial distribution of both property and violent crime. Therefore, an alternative method is applied. Theoretical implications are discussed, highlighting routine activities and crime pattern theory. The study aims to instigate more spatial analysis of crime and evidence-based criminal policies within the region.

Is It Dangerous to Live in Neighborhoods with More Immigrants? Assessing the Effects of Immigrant Concentration on Crime Patterns

The immigration-crime nexus has been the subject of much empirical attention and research findings consistently indicate that neighborhoods with large immigrant populations exhibit comparatively lower crime rates. However, it is still imperative to explain how these effects take place in different contexts of structural circumstances of communities. This study aims to examine the effects of immigrant concentration as well as its conditioning effects for racial/ethnic segregation and concentrated disadvantage in Dallas, Texas. Results show that immigrant concentration is negatively associated with crime counts and, most importantly, that immigrant concentration moderates the effect of structural conditions on crime. Generally, immigration has crime-reducing effects and helps ameliorate the negative effects of structural conditions on crime.

Topography and crime in place: The effects of elevation, slope, and betweenness in San Francisco street segments

ABSTRACT Few studies have investigated how features of the land surface and the street network affect spatial crime patterns. Accordingly, for the current study, we estimated negative binomial regression models to test for main and moderating effects of elevation, slope, and betweenness on crime across San Francisco street segments. While significant effects were observed for all topography measures assessed, we found that elevation differences in the surrounding ¼ mile (i.e., hilliness) reduces the risk for crime more so than the elevation and slope of the segment itself. In comparison, betweenness based on the street network produced a higher risk for crime. We also determined a conditional effect between elevation differences in the surrounding ¼ mile and betweenness. To supplement the regression analysis, we produce maps that show the predicted values of the different crime outcomes for each segment in our sample, thereby underscoring certain policy and practical implications of our findings.

A Criminological Analysis on Crime Scene Related Environmental Factors in the Western Province of Sri Lanka

Environmental criminology, as a subfield of criminology, focuses on crime patterns within specific natural and built environments and investigates the effects of these external variables on people's cognitive behaviour. The primary goal of criminology is to prevent and control crime, and when it comes to the environment, it can be divided into two broad categories: crimes against the environment and crime scenes involving environmental factors. Identifying crime scenes based on environmental factors is divided into three broad categories: natural, built, and social. Architectural and landscape factors are the two types of built factors. This research study is expected to identify how cities, neighbourhoods, public buildings, and private dwellings can control and prevent criminal incidents and the natural environment based on natural and built environmental factors. The purposive sampling method was used in the research study and the purpose of this study was to select a diverse range of reported crime cases from the Western Province of Sri Lanka for 18 months (2017-2019). Murder, rape, child sexual abuse, grave assault, theft, burglary, and robbery were chosen as grave crimes for the study, and 115 crime scenes were observed. The main objective of this criminological research study was to focus on and identify the related ecological factors. In urban and semi-urban areas, built and social eco factors have been major criminogenic factors. Natural eco-elements were a source of crime in semi-urban areas. Each type of serious crime observed had dominant built and social eco factors that varied depending on the crime's location and the offender and victimised parties' routine activities. Following more in-depth research, controlling and preventing crime can be achieved through the management of environmental elements.

Breaking the Chain: How Arrests Reduce the Probability of Near Repeat Crimes

Objectives: Near repeat patterns have been identified for a host of different crimes, but effective strategies to reduce near repeats have had more variable results. This study identifies near repeat crime patterns in Dallas, TX, and examines the effects of an arrest on reducing the probability of future crime. Method: Using open-source crime data from the Dallas Police Department from July 2014 through June 2018, we identified near repeat patterns for shootings, interpersonal robberies, residential burglaries, and thefts from motor vehicles. Logistic regression models were used to test the effect of an arrest on reducing near repeat crimes; controls for geographic, demographic, and temporal factors were included in each model. Results: Near repeat calculations suggest violent crime clustered closely in time and space, with property crime dispersed over larger spatial and temporal dimensions. Across all four crime types, findings suggest arrests resulted in 20%–40% reductions in a near repeat follow-up crime. Conclusions: In line with past research on shootings, arrests reduced the likelihood of subsequent crimes. This suggests policing strategies to increase arrests may be a fruitful way to reduce near repeat crime patterns.