Crime Patterns Research Articles

A Crime Data Analysis of Prediction Based on Classification Approaches

Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or living in it to assist people in recognizing between a secured and an unsecured environment. Geo-location, combined with new approaches and techniques, can be extremely useful in crime investigation. The aim is focused on comparative study between three supervised learning algorithms. Where learning used data sets to train and test it to get desired results on them. Various machine learning algorithms on the dataset of Boston city crime are Decision Tree, Naïve Bayes and Logistic Regression classifiers have been used here to predict the type of crime that happens in the area. The outputs of these methods are compared to each other to find the one model best fits this type of data with the best performance. From the results obtained, the Decision Tree demonstrated the highest result compared to Naïve Bayes and Logistic Regression.

Assaying the Statistics of Crime Against Women in India using Provenance and Machine Learning Models

Now-a-days, the surging of crime against women is occurring at a startling rate in India. According to the National Commission for Women, there was a 46% increase in reports of crimes against women in the initial months of the year 2021 in comparison with the same period in 2020. However, to handle this problem, the need of the hour is to fetch relevant and timely information about the various types of crime taking place and make specific predictions based on the existing information to safeguard women from future predictable contingencies. AI and Machine learning mechanisms have become a powerful tool in predicting the crime rate in India under various crime categories by analyzing the crime patterns, crime–centric areas, and the comparative study of various crime categories. Hence, from 2001 to 2019, a women's crime-based dataset from NCRB has been used in this paper, which included various crime sub-categories, for instance; molestation, sexual harassment, rape, kidnapping, dowry deaths, cruelty to family, importation of girls, immortal traffic, sati prevention act, and others. To acquire a better understanding of the data, a framework has been created which makes use of provenance and machine learning algorithms on the dataset, which has been grouped based on several factors such as distribution of cases convicted or reported every year, safest and un-safest states for women in India, etc. Different machine learning algorithms, such as gradient boosting and its many versions, Random forest, and many more, have been used on the dataset. Their performances are evaluated using various metrics such as accuracy, recall, precision, F1 score, and root mean error square.

The influence of permeable built environment on theft crime pattern and the moderation effects of social disorganization:Applicability of street eyes and defensible place theories

The influence of permeable built environment on theft crime pattern and the moderation effects of social disorganization:Applicability of street eyes and defensible place theories

Evaluation of crime pattern in Khanna local government using geographic information system

Evaluation of crime pattern in Khanna local government using geographic information system

Evaluation of crime pattern in Khanna local government using geographic information system

Sustainable development can only take place in an atmosphere of peace and tranquility. The state of insecurity in Rivers State and Nigeria at large has been on the rise over the past decade. The Nigerian security agencies have not been able to effectively tackle and nib crime activities in the bud. This has led to the loss of lives, businesses, and other attendant socio-economic effects. The distribution of crime across the landscape is geographically random since crime is a human phenomenon, hence geography plays an important role in law enforcement and criminal justice. Geographic information system (GIS) can help the security agency in tactical planning and response; allocating resources quickly and more effectively, determine mitigation priorities, analyze historical events, and predict future trends and patterns. The main aim of this paper is crime modeling and analysis using the Geo-information system. This paper uses GIS to visualize, analyze, and explain criminal activities within Khanna local government in a spatial context. To achieve this, crime data and records covering over seven years were obtained from the Nigerian Police Force in Bori. Supplementary and attribute data were collected through fieldwork using the Global Positioning System (GPS) to provide the coordinates of crime points and scenes in the local government and other associated data. A spatial database was created using ArcGIS software. The crime hotspot map and crime density map of the study area was produced using the ArcGIS pro tool- Gi-Bin fixed 234-FDR at 99%,95%, and 90% confidence level respectively for the hot spots and cold spots respectively. The maps and results from the spatial analysis performed can help the police to carry out patterns and trends analysis across multiple jurisdictions; enlist community action, enhancing implementation of various policies to reduce crime in the study area. It can also provide tools and techniques to capture, forecast future crime occurrence in the area and reduce crime incidences.

Multivariate analysis and characterization of low impact crime in Mexico City

Background: Multivariate analysis is fast becoming a key instrument that can be used to address crimes or incidents. It may be helpful to assess government policies on crime prevention. Methods: To distinguish between the 25 official crime categories reported in Mexico City (Jan 2019 - Jun 2019), principal coordinates analysis was used to determine the quality of a characteristic in this context. This study used cluster analysis via K-means and Biplot based on time and location in terms of crime occurrence. Results: The results obtained from this preliminary analysis indicates that around 70% of crime occurrence is shown in the following boroughs: Cuauhtémoc, Iztapalapa, Gustavo A. Madero, Benito Juárez, Álvaro Obregón, Coyoacán, and Miguel Hidalgo. Conclusion: There are two factors that contribute to the difficulties in crime analysis in Mexico City, namely, the lack of people’s trust in authorities and the insufficiency of tools for data analysis. The latter is an integral part in achieving justice for the victims of crimes because it impedes the process of observing patterns and predicting the perpetrators’ next actions, which may help in solving a number of types of crimes. It is then imperative for law enforcement to utilize data analysis tools that aid in identifying crime patterns and trends, such that the occurrences of crime show a downward trend and consequentially increase the people’s trust in law enforcement agencies.

Investigating honour-based abuse and other crimes in South Asian communities: the benefits, limitations and impacts of ‘ethnic matching’ and ethnic ‘difference’ of police officers

ABSTRACT This article considers the benefits, limitations and potential impacts of ‘ethic matching’ South Asian (SA) police officers to victims, or conversely allocating ‘different’ non-SA officers to reported honour-based abuse (HBA) cases and other crimes. To explore these themes, semi-structured interviews in one English police force with 15 police specialist detectives in Public Protection took place. Lack of cultural competence by some non-SA officers was evident, exposing stereotyping and tokenism. Some professional race anxiety features, but to a lesser extent. Cultural ignorance impacts an officer’s ability to identify crime patterns; results in misconstruing victim perspectives and means that officers may not recognise cues that victims are at risk. The opposing perspective identifies that there are enormous benefits to ‘ethnic matching’ SA officers and victims when investigating crimes, notably enhanced insight due to cultural competence, excellent victim rapport and the use of foreign language skills. Conversely, findings indicate that in exceptional circumstances cultural bias by some same ethnicity officers can act as an impediment, influencing officer decision-making and detrimentally affecting victims’ best interests. Extant literature reinforces the contention that victims do not want to be ‘ethnic matched’ to the police or other professionals, for fear that officials may leak information to the wider community. Albeit there are notable benefits to ethnic matching, factors such as training and experience are equally important. Nevertheless, the article affords valuable insights into the strengths and limitations of ethnic matching and ethnic difference, which will aid operational supervisory police officers when resourcing such incidents.

Risk of Outdoor Rape and Proximity to Bus Stops, Bars, and Residences.

Several researchers have linked the configuration of public places to the risk of sexual attack against women, including rape. Based on the routine activity approach and crime pattern theory, we expect to find the greatest risk of outdoor urban rape near public places that are "target rich," "offender rich," and "guardian poor." We apply the theory to 193 outdoor rape locations in Campinas, Brazil, 2010-2013. We measure distances to the nearest bus stops, bars, and residences, then compare each of these to the distance from random points in the same city. Consistent with theory, outdoor rape victimizations occur disproportionately near bus stops and bars, but at least 250 meters away from the location of the nearest residence of any type. This baseline model suggests that urban planners could make women more secure from outdoor sexual attack by improving the design and location of public places, reducing the concealment of such places, and enhancing guardianship.

The Spatiotemporal Pattern and Driving Factors of Cyber Fraud Crime in China

As a typical cybercrime, cyber fraud poses severe threats to civilians’ property safety and social stability. Traditional criminological theories such as routine activity theory focus mainly on the effects of individual characteristics on cybercrime victimization and ignore the impacts of macro-level environmental factors. This study aims at exploring the spatiotemporal pattern of cyber fraud crime in China and investigating the relationships between cyber fraud and environmental factors. The results showed that cyber fraud crimes were initially distributed in southeastern China and gradually spread towards the middle and northern regions; spatial autocorrelation analysis revealed that the spatial concentration trend of cyber fraud became more and more strong, and a strong distinction in cyber fraud clustering between the north and the south was identified. To further explain the formative causes of these spatial patterns, a generalized additive model (GAM) was constructed by incorporating natural and social environmental factors. The results suggested that the distribution of cyber fraud was notably affected by the regional economy and population structure. Also, the high incidence of cyber fraud crime was closely associated with a large nonagricultural population, a high proportion of tertiary industry in GDP, a large number of general college students, a longer cable length, and a large numbers of internet users.

Environmental crimes and green criminology in Bangladesh

Bangladesh is an environmentally vulnerable country, where environmental crimes are massive and common. However, the environmental crime prevention mechanism in the country is very weak, and traditional policing is utilized to stop these crimes. Therefore, the green criminological approach to prevent environmental crimes in Bangladesh is underdeveloped in many ways, with a total absence of the green policing model. Hence, this study focuses on attaining a critical understanding of environmental crimes in Bangladesh by exploring the key underlying factors of environmental crimes. It also attempts to contribute to the environmental crime prevention mechanism by recommending a green policing model, while identifying the key weaknesses of the existing environmental crime prevention approach. This article implements the qualitative technique of data collection, and the analysis is based on an in-depth interview of 25 respondents, belonging to different categories of stakeholders, and participant observation. It also analyses the content of newspapers to understand the patterns of environmental crimes in Bangladesh. This article finds that environmental crimes are propagated by several political, economic, institutional, and social elements, such as the political affiliation of criminals, economic profit from natural resources, absence of institutional collaboration, and lack of social consciousness. The issue has become further aggravated due to the weakness of the crime prevention mechanism. Thus, the findings of this study suggest that environmental crimes in Bangladesh should be considered and understood from green criminological perspectives and the development of a green policing model would be effective in reducing environmental crimes in the country.

Crime hotspot prediction based on dynamic spatial analysis

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

Do Migrant and Native Robbers Target Different Places?

The spatial pattern of crime has been a central theme of criminological research. Recently, the spatial variation in the crime location choice of offenders by different population groups has been gaining more attention. This study addresses the issue of whether the spatial distribution of migrant robbers’ crime location choices is different from those of native robbers. Further, what factors contribute to such differences? Using a kernel density estimation and the discrete spatial choice modeling, we combine the offender data, POI data, and mobile phone data to explain the crime location choice of the street robbers who committed offenses and were arrested from 2012 to 2016 in ZG City, China. The results demonstrate that the crime location choices between migrant robbers and native robbers have obvious spatial differences. Migrant robbers tend to choose the labor-intensive industrial cluster, while native robbers prefer the old urban areas and urban villages. Wholesale markets, sports stadiums, transportation hubs, and subway stations only affect migrant robbers’ crime location choices, but not native robbers’. These results may be attributable to the different spatial awareness between migrant robbers and native robbers. The implications of the findings for criminological theory and crime prevention are discussed.

The Aftermath of IrMaria in the U.S. Virgin Islands: Temporal Patterns of Looting, Burglaries, and Community Solidarity

This study investigates the aftermath of Hurricanes Irma and Maria (IrMaria) in the U.S. Virgin Islands and the dichotomy between media reports depicting mass looting and chaos with statements by public officials disputing these claims. Perceptions on post-hurricane criminality can influence sheltering and evacuation behavior as well as frame public policy. The authors review the post-disaster collective behavior literature and implement a mixed-methods approach to examine the veracity of these looting claims. Post-disaster interviews with impacted households, business owners, and public servants reveal a pro-social environment with strong community solidarity in the immediate aftermath of IrMaria. Sheltering behavior was not influenced by fears of looting, but was influenced by previous hurricane false alarms. Regression of available FBI-UCR data shows the rate of burglary has declined across the islands since 2010. An aggregate-level report by the U.S. Virgin Islands Police Department shows a decline in all crimes throughout the US territory compared to the fiscal year before IrMaria, but a four percent increase in burglary incidents. The comparable increase in incidents occurred within four months of IrMaria's landfall, followed by a precipitous decrease in incidents. This finding aligns with theoretical calls to consider temporal phases and patterns of post-disaster crime. Considering the delayed federal response and the decrease in all other crimes for the fiscal year, the authors attribute the momentary increase in burglary incidents as a misnomer and are likely survival appropriating acts that decreased with burgeoning humanitarian assistance. This study recommends disaster public policy and recovery efforts prioritize the unmet needs of hurricane survivors and greater scrutiny of the constraints of US colonialism that impede disaster resilience. Additionally, the paper highlights other concerns raised by participants during fieldwork interviews and emphasizes the necessity and value of ethical post-disaster qualitative research.

Density, diversity, and design: Three measures of the built environment and the spatial patterns of crime in street segments

PurposeThe current study simultaneously examines the effects of three different characteristics of the built environment based on the theoretical conceptualizations of density, diversity, and design (3D). MethodsBy using data of 211,155 street segments in the Southern California metropolitan region across 130 cities, we estimated a set of negative binomial regression models including the 3D measures of the built environment, while accounting for the effects of social structural characteristics of place. Furthermore, the current study examines the potential moderating effects of each 3D feature on crime. ResultsWe found that higher levels of business density are consistently associated with higher levels of crime. The diversity measure is associated with moderately higher levels of crime, whereas the design measure consistently exhibited a negative relationship with crime. Furthermore, we found that the diversity and design measures moderated the business density relationship with crime. ConclusionThe results of the current study suggest that it is necessary to examine the different types of physical environment simultaneously to understand the effects of physical environment and the spatial patterns of crime.

INFLUENCE OF THE PERFORMANCE OF NATIONAL POLICE SERVICE IN PREVENTION OF ORGANIZED CRIMES IN MOMBASA COUNTY, KENYA

This paper uses data collected for an MA Thesis on influence of the Performance of National Police Service in Prevention of organized Crimes in Mombasa County, Kenya. The study was necessitated by continued rise of criminal gangs despite police service efforts to contain the problem. The study was guided by three specific objectives, but for this paper first and second objectives will be discussed. First, the examined the influence of resource capacity on the performance of national police service in prevention of organized crimes in Mombasa County. Secondly, the study examined the influence of motivation on the performance of national police service in prevention of organized crimes. Thirdly, the last objective assessed the influence of external environment on the performance of National Police Service in prevention of organized crimes in Mombasa County, Kenya. The survey utilized Expectancy theory and Crime Pattern Theory and adopted a descriptive research design employing a mixed method paradigm.
 The study sample size comprised 306 National Police Service officers (NPS); Directorate of Criminal Investigations (DCI) officers, Kenya Police service (KP), and Administration Police (AP) officers both senior and junior. Qualitative data was analyzed using thematic analysis derived from the study objectives. On the other hand, quantitative data was analyzed through descriptive statistics. According to the findings, 17% of the police officers still undertake operations without intelligence briefing, and that despite the availability of criminal intelligence. Besides, access to criminal intelligence was still a major challenge to 45% of the officers while inter-agency coordination was a rare part of the fight against organized crime to 56% of the officers who were not involved. The findings also revealed that despite the importance of training towards the prevention of organized crime, 33% of police officers did not have access to these trainings. Furthermore, 53% of the officers, cited lack of recognition and motivation that could go a long way in raising performance of the police officers. The study makes two major recommendations; special attention to training and curriculum review that addresses the demands of emerging global security challenges. Secondly, officer’s welfare remains a thorny issue which can potentially stifle any crime prevention initiative. Welfare issues of concern such as merit and fairness in promotions, rewards and other incentives, and better compensation were said to be an integral part of any serious police reform agenda.

Identifying seasonal spatial patterns of crime in a small northern city

ObjectivesTo explore spatial patterns of crime in a small northern city, and assess the degree of similarity in these patterns across seasons.MethodsCalls for police service frequently associated with crime (theft, break and enter, domestic dispute, assault, and neighbor disputes) were acquired for a five year time span (2015–2019) for the city of North Bay, Ontario, Canada (population 50,396). Exploratory data analysis was conducted using descriptive statistics and a kernel density mapping technique. Andresen’s spatial point pattern test (SPPT) was then used to assess the degree of similarity between the seasonal patterns (spring, summer, autumn, winter) for each call type at two different spatial scales (dissemination area and census tract).ResultsExploratory data analysis of crime concentration at street segments showed that calls are generally more dispersed through the city in the warmer seasons of spring and summer. Kernel density mapping also shows increases in the intensity of hotspots at these times, but little overall change in pattern. The SPPT does find some evidence for seasonal differences in crime pattern across the city as a whole, specifically for thefts and break and enters. These differences are focused on the downtown core, as well as the outlying rural areas of the city.ConclusionsFor the various crime types examined, preliminary analysis, kernel density mapping, and the SPPT found differences in crime pattern consistent with the routine activities theory.

Spatial Distribution of Crime in Bangladesh: An Analysis

Crime mapping has become popular as a way of analysing the spatial distribution of crime, and its use enables effective crime prevention strategies to be employed based on the analysis of hotspots. However, in Bangladesh crime mapping using Geographic Information System (GIS) is still in its infancy. This paper intends to focus on the spatial distribution of crime throughout the major administrative divisions of Bangladesh and to explore how it varies according to region. Its objective is to fill the existing knowledge gap in terms of the spatial distribution of crime in Bangladesh with the help of GIS software. All associated issues behind any crime patterns in different regions have been analysed here using references from established research. This study also illustrates the trend analysis of crime in the last three years, from 2016 to 2018, to understand the patterns of crime distribution as per present administrative divisions. It also focuses on the relative frequency of incidents of crime in all the regions of Bangladesh. Additionally, this study indicates that areas with big cities (Dhaka and Chattogram) are more prone to harbouring a greater number of offenders than other areas. As an initial study using GIS-based computerised crime mapping in Bangladesh, this research may have an important role in showing division wise crime distribution.

Role of Traffic Network in Monitoring Crime and Violence Patterns in Karachi

Crime and geographical accessibility has very special relationship, if monitored on appropriate time it could resolve many complex issues of the crime dynamics. Karachi being the largest city of Pakistan is also very high in the intensity of street crimes that often has very close relationship with the network of the roads. This paper will assess the potential of Geographical Information Systems (GIS) for the analysis of crime pattern and its relationship with the road network in Karachi that would be beneficial for various crime agencies. Present research aims to provide a collective set of methods and techniques for geospatial analysis and 3D mapping of crime scenes. After identification of Hotspots assessment of relationships between robbery or snatching clusters and their spatial neighborhood is initiated by including the urban milieu. For obtaining the desired target copious geospatial data as well as a three-dimensional model is included for analysis. The combined and mutual effort of crime mapping methods with modern 3D modelling helps to facilitate on the spot clutch of multipart spatial phenomena in mapping of crime, and fruitful for both, the communal and responsible decision makers.

Economic Inequality Decomposition and Spatial Pattern of Crime in Indonesia

Rising crime victimization rate in Indonesia shows that crime is still a serious problem in the country. This study aims to identify the relationship between economic inequality and crime rate in Indonesia. It used the decomposed Theil indices, as the measure of inequality based on national household survey panel data, to disentangle the effect of economic inequality into within-district/city and between-district/city economic inequality components. This choice of inequality measure is due to the fact that most of the previous studies regarding economic inequality and crime overlooked the disentanglement between economic inequality effects. Another problem addressed by this study is that the effect of crime is not bounded by administrative boundaries. A high prevalence of crime in a region may cause a criminal spillover to the surrounding regions. To control for this effect, this study also evaluates the spatial effect of interregional crime rates using the panel spatial autoregressive model as the estimation method. The estimation results revealed that both within-region and across-region economic inequalities have substantial increasing impacts on the crime rate.

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.