Crime Patterns Research Articles

Prejudice in Past Pandemics

Accompanying the spread of SARS-CoV-2 (COVID-19) across the globe was a proliferation in anti-Asian hate crimes and political rhetoric. These discriminatory actions mirrored the prejudice exhibited towards other groups in past pandemics, such as Jewish populations in Europe during the Black Death between 1346 and 1353. This study aimed to determine similar patterns relating to and underlying the prejudice seen in past pandemics, focusing on three categories: hate crimes, political rhetoric, and religiosity. Prejudice against Jewish people during the Bubonic plague, Immigrants in America during the 1918 Flu, and Asian Americans during COVID-19 were investigated in this study because of the historical record of existing prejudice against these groups before the start of the pandemics. Overall, this study found that there was no general pattern of hate crimes during past pandemics without accompanying political rhetoric. In addition, there was no general pattern in religiosity in the pandemics studied, suggesting that the prejudice was not fueled by religious motivations. Given the small selection of pandemics analyzed in this study, further analysis with more pandemics could present more robust conclusions.

Applying Offending Trajectory Analyses to Men Adjudicated for Child Sexual Exploitation Material Offenses

We applied offending trajectory analyses to 387 men adjudicated for child sexual exploitation material (CSEM) offenses. After an average of 20 years, we found two trajectories of sexual offending and violent offending: one that peaked in late adolescence and was associated with higher rates of crimes, and one that peaked in the 30s and was associated with a lower rate of crime. We found four trajectories when modeling any crime. The findings highlight the heterogeneity of men with CSEM offenses. Although lifelong patterns of numerous sexual crimes were uncommon, men with more sexual crimes had greater indicators of sexual interest in children and a younger age of first contact with police. CSEM offenses were rarely the first offense in their criminal trajectories. As such, early intervention targeting youth before they are further advanced in their criminal careers may also reduce future CSEM offending.

The Geography of Hate: Spatial Patterns of Bias-Motivated Crimes in Minnesota, 2015–18

The Geography of Hate: Spatial Patterns of Bias-Motivated Crimes in Minnesota, 2015–18

Religious Fanaticism and Changing Patterns of Violent Crime in Nigeria

Fanaticism has brewed into different forms in the Nigerian context – and the gravest is religious fanaticism. It has taken hold of most clans, religions, and ethnic groups across Nigeria. Religion has always existed in Nigerian societies and also has fanaticism but the level of violence precipitated by this fanaticism is apparently unprecedented. This paper journeys into the beginning of religious fanaticism in Nigeria and its changing patterns of violence. It highlights the root causes of religious violence in Nigeria and visible patterns in this violence. The paper adopted structural-functionalism theoretical formation and gathered data from textbooks, e-books, journals, online articles, newspapers, and magazines. At the end, the paper recommends that Nigerian governments initiate comprehensive programs to reconcile the religious differences that have evolved over time between Christianity, Islam, and African Traditional Religions.

Could social media reflect acquisitive crime patterns in London?

Embraced within the framework of crime opportunities integrated with Social Disorganization theory and Broken Windows theory, this paper intends to explore the patterns of four types of acquisitive crimes, using social media data, i.e., Twitter, Foursquare and cross-sectional data acquired through text analysis technique. With Greater London as the study area, models like negative binominal regression (NBR) and geographically weighted regression (GWR) are performed to illustrate the aggregated relationships between acquisitive crimes and crime opportunities at London-wide and sub-regional MSOAs levels respectively. The results work towards to hypotheses that: the tweets sentiment could reflect property-related crime rates positively in light of Broken Windows Theory; more tweets with negative sentiment may incur increases in acquisitive crimes. It contributed to existing studies in (1) providing empirical evidence for integrating these three theories; (2) complementing current research on local discrepancies of acquisitive crimes by utilising both GWR and NBR models; (3) challenging the traditional stereotypes about racial disparities with the finding that ethnic heterogeneity and instrumental crimes have counterintuitive association, especially taking education factor into consideration; (4) implicating some localised acquisitive crime prevention strategies to policy makers in light of the reality that the relationship between local variations and different crime types may vary by place.

Assessing Impacts of New Subway Stations on Urban Thefts in the Surrounding Areas

Whether newly implemented public transit stations influence the nearby crime pattern has been debated for years. In ZG City, China, 2 new subway lines and 20 new stations were implemented in 2017. This intervention allows us to test the plausible relationship between new public transit stations and thefts in the surrounding areas. We use the difference-in-differences (DID) model to assess the theft in the treatment and control areas before and after the implementation of the new stations, with necessary socioeconomic and land-use variables and time from the addition of the station being controlled. We also explicitly examine the impacts of the proximity of the stations and the Spring Festival on theft. The results suggest the following: (1) theft around the new subway stations significantly increases after the stations’ implementation, while the control area does not see much change in thefts; (2) proximity between the neighboring stations’ increases thefts; and (3) theft near the new stations significantly decreases during the month of the Spring Festival. This study contributes to the literature on the relationship between the subway system and crime, especially from a Chinese perspective. The finding of the research can bring insights to urban transit planning, allocation of the police force, and crime prevention.

Pandemic Restrictions and Spatiotemporal Crime Patterns in New York, São Paulo, and Stockholm

Studies are showing evidence of the effect of changes in routine activities due to the 2020 COVID-19 pandemic on crime levels in many cities worldwide. This study evaluates the potential impact of the COVID-19 pandemic on temporal and spatial patterns of crime in three major cities under very different national contexts. Each of the three countries and cities experienced different levels of pandemic restrictions and societal closure. The cities of New York (the United States), São Paulo (Brazil), and Stockholm (Sweden) were selected as cases. Temporal quantitative methods, spatial statistics techniques, and Geographical Information System (GIS) underlie the methodology used in this study. Findings show that there is a statistically significant break in the trend in crime levels after the stay-at-home orders were implemented in New York City, São Paulo, and Stockholm in the first months of 2020; the only exception was for murder. Such an impact varies by crime type and city context, but increases again after a few months, indicating how fast crime and criminals adapt. Residential burglary decreased, whereas nonresidential burglary increased overall. Changes in the levels and geography of vehicle thefts were observed, with an overall increase of significant cold spots but, in several cases, also solidification of existing crime concentrations in known crime attractors and in some deprived areas.

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas.

Crime drop in Ghana? Some insights from crime patterns and trends

This study examines crime rate trends in Ghana in the context of research into the international crime drop. Ghanaian police-recorded crime data are imperfect, but crime rates appear to have increased to the early 2000s then to have declined significantly. However, the national trend for all crime types masks significant variation by crime type and region. The national trends appear, broadly speaking, consistent with long-term crime increases and decreases in high-income countries, but with a lag that may reflect Ghana’s economic development trajectory. This may be consistent with the routine activity perspective if both crime opportunities and economic development increased in parallel, followed by a crime decline that reflected increased security measures or other change. The study raises many questions that require further research, and some suggestions to that end are outlined.

Situating Crime Pattern Theory Into The Explanation Of Co-Offending: Considering Area-Level Convergence Spaces

AbstractFeatures of the environment including activity nodes and the level of connectivity in spaces help offenders develop awareness spaces for criminal opportunities. Based on arguments forwarded by Felson, the current study argues these environmental features also facilitate convergence spaces that promote interaction among offenders that increase the likelihood of group crime. Data include street connectivity measures from the Environmental Protection Agency and publicly available arrest information from Baltimore City (2013–16). Findings provide support for the influence of certain activity nodes and pedestrian-oriented street connectivity in explaining group crime. The discussion evaluates how crime pattern theory can be extended to understand the social nature of crime.

A Time Series Analysis of the Trend of Crime Against Property in Oke-Ogun Region of Oyo State, Nigeria

Temporal variation in spatial crime occurrence has been a topical issue in environmental criminology and criminal justice research, especially in the area of generating early warning systems for preparedness against crime. However, the concentration of research effort on urban crime pattern with unjustified neglect of regional crime impedes the search for analytical explanations and effective strategies to eradicate crime. Hence, this study examined fluctuant pattern of property crime in Oke-Ogun region of Oyo State, Nigeria. It also assessed direction and incidence speed of the crime in the area. This is with a view to understanding the influence of changes in time (year factor) and other factors (a faulty system of education, erosion of traditional values, porous border, and unemployment among others). over property crime occurrence in the region. Crime reports were collated from the Nigerian Police records on nine typologies of property crime from 2005 to 2012. The geo-analytical techniques employed to examine temporal variations in crime were exponential trend smoothing technique with a smoothing constant of 0.3 and the least-squares trend analytical technique. Regression and correlation coefficients represented by “b” and “r” respectively were used to determine the changing rate of the crimes over the period of year under investigation. Analysis showed that out of the nine crime types that were analysed, three crimes were found to exhibit a rising trend with the effluxion of year while six exhibited a declining trend. Aggregately, crime against property had downward sloping trend lines. The negative values associated with (b = – 93.04, r = – 0.62) property crime confirmed the negative lapse rates and inverse relationships. The coefficient of determination (r2) in this case is 0.3969. However, the study concluded that 39.69% of variations in the level of property crime were influenced by changes in year and 60.31% was explained by other factors which included a faulty system of education, erosion of traditional values, porous border, and unemployment among others.

Physical disorder and crime revisited: New evidence from intensive longitudinal data

ObjectivesThe purpose of this study is to investigate whether the often-debated association between physical disorder and crime exists longitudinally and operates through a specific spatial process. MethodsWe combine four administrative and official databases to analyze patterns of crime occurring each month in New Orleans, LA, between 2012 and 2018 at the block group level. We adopt a generalized additive model (GAM) framework to efficiently account for potential spatial, temporal, and spatiotemporal autocorrelation to investigate this association. ResultsThe resident-driven self-report measure of physical disorder (i.e., 311 calls) consistently predicts neighborhood criminal activities in New Orleans. The spatial effects of 311 calls in adjacent areas on drug violations, property crime, and violence in a focal neighborhood are also observed. In contrast, official reports of physical disorder, captured with code violation, is either weakly related to crime or lacking an empirical relationship. ConclusionOverall, our findings suggest that the association between physical disorder and neighborhood crimes is robust over time. In addition, this association is conditioned by how physical disorder is operationalized, with dimensions such as residential report and official report of physical disorder playing a unique, and occasionally important role in understanding overall patterns of neighborhood criminal activity.

Improving the Creation of Hot Spot Policing Patrol Routes: Comparing Cognitive Heuristic Performance to an Automated Spatial Computation Approach

Hot spot policing involves the deployment of police patrols to places where high levels of crime have previously concentrated. The creation of patrol routes in these hot spots is mainly a manual process that involves using the results from an analysis of spatial patterns of crime to identify the areas and draw the routes that police officers are required to patrol. In this article we introduce a computational approach for automating the creation of hot spot policing patrol routes. The computational techniques we introduce created patrol routes that covered areas of higher levels of crime than an equivalent manual approach for creating hot spot policing patrol routes, and were more efficient in how they covered crime hot spots. Although the evidence on hot spot policing interventions shows they are effective in decreasing crime, the findings from the current research suggest that the impact of these interventions can potentially be greater when using the computational approaches that we introduce for creating hot spot policing patrol routes.

Concentration of Urban Violence in Fortaleza and Strategies for Crime Prevention

Despite the continued prevalence of violence in Latin America, there is a relative dearth of research investigating both spatial patterns of violent crimes and the effectiveness of evidence-based crime prevention policies in Brazil. This study aims to address this gap in extant knowledge by creating a Spatial Violence Index and a Restrictive Ambient Index to investigate the spatial dynamics of violent crimes and urban vulnerabilities in Fortaleza. Both exploratory spatial data analysis and spatial regression models were employed to visualize the associative patterns and measure the correlation between the two indexes. The results demonstrate how locations characterized by high levels of violence are spatially correlated with more vulnerable locations in terms of both socio-economic-demographics and urban disorder. Overall, the study identified 124 vulnerable micro-territories that would benefit from the allocation of resources in an effort to reduce violence in the city by enhancing the efficiency of policing and prevention strategies.

Crime Pattern Analysis, Visualization and Pridiction Using Data Mining

Crime against women these days has become problem of every nation around the globe many countries are trying to curb this problem. Preventive are taken to reduce the increasing number of cases of crime against women. A huge amount of data set is generated every year on the basis of reporting of crime. This data can prove very useful in analyzing and predicting crime and help us prevent the crime to some extent. Crime analysis is an area of vital importance in police department. Study of crime data can help us analyze crime pattern, inter-related clues& important hidden relations between the crimes. That is why data mining can be great aid to analyze, visualize and predict crime using crime data set. Classification and correlation of data set makes it easy to understand similarities & dissimilarities amongst the data objects. We group data objects using clustering technique. Dataset is classified on the basis of some predefined condition. Here grouping is done according to various types of crimes against women taking place in different states and cities of India. Crime mapping will help the administration to plan strategies for prevention of crime, further using data mining technique data can be predicted and visualized in various form in order to provide better understanding of crime patterns.

Unveiling the relationship between land use types and the temporal signals of crime: An empirical decomposition approach

Whilst some land uses are highly criminogenic, others remain largely free of crime. This patterning is a reflection of the types and timing of daily activities that take place in a given land use and the opportunities that this presents for crime. While the criminology literature has developed a rigorous understanding of geographic component of crime, relatively less emphasis has been placed on the temporal dimension. Here, we address this through applying a technique to examine micro-temporal variations in crime at places. This technique adopts a factor approach to model hourly counts of crime across seven land use types (commercial, residential, parkland, agricultural, medical/hospital, industrial and education) to unveil the number and distribution of crime signals across a 24-hour period along with how these signals mix across each land use type. Results reveal clear and distinct differences between crime type and land use, highlighting the diurnal nature of crime patterns and speak to the literature on risky places and risky times. The utility of our approach lies in its capacity to delineate common temporal rhythms and how these rhythms are shared across different land use types.

Impact of COVID-19 on financial institutions: navigating the global emerging patterns of financial crime

PurposeThe current COVID-19 pandemic has already proven to be one of the world’s deadliest crises in modern history with far-reaching impacts on different sectors of the global economy. The financial sector is among the most widely affected by the economic crisis occasioned by the COVID-19 pandemic. One of the most notable effects is related to financial crime. It is against this backdrop that the present study aimed to examine the impact of COVID-19 on financial institutions with the main focus being on financial crimeDesign/methodology/approachIts twofold objectives were to critically examine the global emerging patterns of financial crime and their association with the COVID-19 pandemic; and to investigate how financial institutions across the world have been responding to, managing, and dealing with the emerging patterns of financial crime brought about by (or linked to) the COVID-19 pandemic.FindingsIt was found out that as the pandemic ravages the world and pushes people and businesses to the very limits of their endurance, many financial sector stakeholders and players are responding in ways that put the entire financial sector and all its stakeholders at great risk. Specifically, COVID-19 pandemic has led to the emergence of new patterns of financial crime that were either unheard of or were not as rampant in the past.Originality/valueBoth the descriptive and correlation analyses produced by this study provide new insights into the impact of COVID-19 on financial institutions with a main focus on financial crime.

Interactive Visual Exploration of Human Mobility Correlation Based on Smart Card Data

Public transportation agencies call for an intuitive, interactive, and reusable visualization tool to detect patterns of crime (i.e. pickpockets and gangs) or missing commuters on public transportation systems. Few existing visualization techniques have visually explored mobility correlations of targets and their companions, who are characterized in diverse mobility types, by using discrete travel hints extracted from a massive amount of data. To fill this gap, a visual analytical system is provided to conduct a group-based and individual-based exploration of mobility correlations of passengers of interest, based on an auto integration of multiple queries. How passengers differ from or correlate with each other are further examined based on their spatiotemporal distributions in trajectories and ODs. Real-world case studies, as well as user feedback made by 30 participants, demonstrate the effectiveness of the system in detecting specific targets and their companions featured in diverse mobility types, or in characterizing their spatiotemporal aggregation patterns for a further tracking on public transportation systems.

The Covid-19 impact on financial crime and regulatory compliance in Malaysia

PurposeThe purpose of this paper is to provide a conceptual discussion and analysis of the Covid-19 impact on financial crime and regulatory compliance. The analysis is conducted to make a comparison of the financial crime and regulatory compliance patterns before and after the Covid-19 pandemic occurred.Design/methodology/approachThis paper contextualises the impact of Covid-19 on financial crime and regulatory compliance. Moreover, this paper explores different ways of conceptualising the Covid-19 impacts in terms of financial crimes and regulatory compliance patterns based on the surveys by PricewaterhouseCoopers and Deloitte.FindingsThe Covid-19 pandemic has brought both challenges and opportunities to financial crime and regulatory compliance. In the aspects of financial crime patterns, this study found a reduction in physical crime whilst on the other hand increment in cybercrime. Nevertheless, this study discovered regulatory compliance not at a satisfactory stage even before the Covid-19 pandemic, let alone during the pandemic.Practical implicationsThis study implies that the financial institutions must work together to combat the risks of financial crimes, not only amongst the institutions but also with the regulators. Digitalisation and robust risk management need to be improved at a massive level to beat the criminals’ high fintech skills and systems. The initiatives of fund packages from the governments to assist the companies especially the small firms need to be fully used by the companies to improve regulatory compliance.Originality/valueWhilst some studies discussed the impact of Covid-19 on the economy, there are still scarce resources on the comparative analysis on the financial crime and regulatory compliance, not to mention the before and after effect of the Covid-19 pandemic. This is the first paper to integrate the issues surrounding the Covid-19 impact, financial crimes and regulatory compliance in Malaysia.

Space Syntax-able Attributions for Safety Consideration of Heritage Area

Understanding crime has been in focus for a few decades. Significant research has begun to understand the relationship between spatial configuration and crime occurrence, which related back to human behaviour. In recent year, space syntax has been used as a method to analyse space and its spatial properties. Urban design researchers have employed the space syntax technique to analyse the geographic distribution of crime due to spatial and socio-demographic factors that could influence crime patterns. This paper describes how space syntax could be used to assess crime possibility in heritage areas, and the result supports the hypothesis. Keywords: Urban planning, Crime prevention theory, Space syntax, Spatial configuration eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI4.2903