Crime In Urban Areas Research Articles

Amenities and crime: What is the association of amenities with crime in urban areas of Cuenca, Ecuador?

The study of crime and its associated factors is fundamental to improving the well-being of a society. In this context, this study aimed to understand the influence of amenities on crime in the urban areas of Cuenca (Ecuador). To this purpose, data was obtained from public entities and Google Places API. The empirical strategy employed involved the estimation of a set of spatial econometric models. The results show that amenities have an attractor effect on crime, thus reflecting that criminals act in places where there is a large accumulation of people. It was also found that the presence of government entities has no effect on crime.

Nigerian Police Force and Fight Against Urban Crimes in Nigeria: Assessment of Socio-Economic Implications

This study assessed the socio-economic implications of the fight against urban crimes in Nigeria by the Nigerian Police Force. The study adopted the descriptive survey research design and made use of both primary and secondary sources of data. The population of the study was the police men and women of the Force Criminal Investigation Department of the Nigerian Police Force State Commands in Imo, Lagos and Abuja numbering 165 (which also serves as sample population of the study). The study adopted the non-probability (judgmental) sampling technique; and the data collected through structured questionnaire instrument were presented and analyzed using frequency percentage and chi-square (X2) statistical instrument of analysis. The study revealed a number of drivers of crimes in urban areas as well as the socio-economic implication of crimes in urban areas in Nigeria. Based on the revelations, the researchers recommended among others that there should be a very proactive and responsive security template for security outfits in Nigeria to ensure adequate safety of lives and properties within the country as it would go a long way in addressing the operational inefficiency and logistical challenges faced by Commands of the Nigeria Police Force in responding adequately to meet residents’ demand for security of their lives and properties.

Analysis of Narcotics Circulation in Riau Province; inherited and studied in the View of Criminology Integrative Theory

<p class="TableParagraph"><em>The rise of narcotics smuggling in Indonesia is a complex issue that requires a comprehensive explanation. The use of integration theory, specifically the integration of Cultural Transmission and Differential Association, can provide a better understanding of the phenomenon of narcotics crime in urban areas. Cultural Transmission theory suggests that individuals learn their values, beliefs, and behaviors through the process of socialization within their culture. This theory highlights the role of family, peer groups, and the media in shaping an individual's attitudes and behaviors towards drugs. The theory argues that individuals who are exposed to drug use at an early age are more likely to engage in drug use themselves. On the other hand, Differential Association theory suggests that individuals learn deviant behaviors from their associations with others. The theory posits that individuals who associate with those who engage in deviant behavior, such as drug use, are more likely to adopt those behaviors themselves. By integrating these two theories, we can better understand the complex phenomenon of narcotics crime in Indonesia. The use of rat ports as entry points for illegal drugs can be explained by the influence of cultural transmission, where individuals are exposed to drug use through their socialization process. Furthermore, the use of these rat ports can also be explained by differential association, where individuals who associate with drug smugglers are more likely to engage in the smuggling of narcotics themselves. Overall, the integration of Cultural Transmission and Differential Association theory provides a more nuanced understanding of the complex phenomenon of narcotics crime in Indonesia. By considering the various factors that contribute to the rise of narcotics smuggling, we can develop more effective strategies to combat this issue.</em><em></em></p>

The Reason for Non-Reporting Crime Victimization: A Study on Urban Area in Bangladesh

This study has mainly explored the current extend of dark figure of crime in urban areas in Bangladesh. It was exploratory in nature. The samples were selected through randomly using a clustered sampling technique from 14 particular areas and respondents were selected as victimized person or their household heads in this study. It has conducted with a structured and standardized survey questionnaire for data collection and using SPSS statistical tools for analyzing data. The socio demographic findings of the study is that the total number of the respondents was 3,957, among them most of them are female about 57% who are belonging in a Muslim family with average family size of 5 members with average age of 37 years, with average income 37,000 TK and living in their own house in urban area and most of them are housewife and about 52% have completed the secondary level of education. On the other hand the major findings are indicated as about 74% did not go to any institution for reporting, while only 26% are approached for legal help. As per the study findings, theft constitutes the highest number for victimization, where 79% percent of the theft victim did not report for it. Even though within the total type of crime, about 49 percent of the victim who did not report for the crime found out to be a theft victim. There are various reasons have found for dark figure of crime such as Police behavior is not friendly to reporting, victim considered the crime as insignificant and police service needed monetary involvement etc. but most of respondents have said they wanted to avoid the inconvenience of calling the police in such petty cases (About 25.7%). Finally, in this study, it has been explained the findings as the three main perspectives such as problem of existing criminal justice system, victim’s social and also their personal perspectives.

Real Time Snatch Theft Detection using Deep Learning Networks

Snatch theft is a common crime in urban areas that poses a serious threat to public safety. It involves forcefully grabbing a victim's personal belongings, such as purses or mobile phones, before quickly fleeing the scene. Detecting snatch theft incidents in real-time is a challenging task due to the speed at which they occur. The current methods used to detect snatch theft incidents rely heavily on human intervention, which can lead to significant delays and potential errors. Therefore, there is a need for an automated technique that can accurately and efficiently detect these incidents in real-time. Hence, the study aims to detect snatch theft using a transfer learning approach based on eight pre-trained convolutional neural networks (CNNs) as classifiers: AlexNet, VGG16, VGG19, GoogleNet, InceptionV3, ResNet-18, ResNet-50, and ResNet-101. The modified pre-trained CNN models are evaluated in both offline and real-time modes. Based on the offline mode, VGG19 achieved 100% training accuracy, and ResNet50 had the highest testing accuracy of 98.9%. In the offline mode, all models accurately classified normal scenes, with ResNet-10 having the lowest false negative rate and ResNet-50 achieving the lowest false positive rate with only 44 misclassified anomaly frames related to snatch theft. The study further evaluated and validated the eight models in real-time mode, and the results showed that AlexNet and ResNet-18 were the only models capable of categorizing snatch theft scenarios with promising findings.

Information Fusion for the Development of a Composite Indicator of Criminogenic Factors Using OWA Operators

In this study, the issue of criminogenic factors in the Lizarzaburu parish of Riobamba-Ecuador is addressed, an area marked by a notable increase in crime. Recognizing the complexity of these factors and the need for an integrated approach for their analysis, the use of Ordered Weighted Averaging (OWA) operators for information fusion is proposed, aiming to create a composite indicator that allows for a holistic and accurate measure of criminality in the area. The implementation of OWA operators facilitates effective weighting of these factors, resulting in the creation of a composite indicator that more faithfully reflects the criminogenic dynamics of Lizarzaburu. This study not only provides a valuable tool for diagnosing crime in urban areas but also establishes a methodological foundation for future research and intervention policies in the field of public security.

Spatio-temporal stratified associations between urban human activities and crime patterns: a case study in San Francisco around the COVID-19 stay-at-home mandate

Crime changes have been reported as a result of human routine activity shifting due to containment policies, such as stay-at-home (SAH) mandates during the COVID-19 pandemic. However, the way in which the manifestation of crime in both space and time is affected by dynamic human activities has not been explored in depth in empirical studies. Here, we aim to quantitatively measure the spatio-temporal stratified associations between crime patterns and human activities in the context of an unstable period of the ever-changing socio-demographic backcloth. We propose an analytical framework to detect the stratified associations between dynamic human activities and crimes in urban areas. In a case study of San Francisco, United States, we first identify human activity zones (HAZs) based on the similarity of daily footfall signatures on census block groups (CBGs). Then, we examine the spatial associations between crime spatial distributions at the CBG-level and the HAZs using spatial stratified heterogeneity statistical measurements. Thirdly, we use different temporal observation scales around the effective date of the SAH mandate during the COVID-19 pandemic to investigate the dynamic nature of the associations. The results reveal that the spatial patterns of most crime types are statistically significantly associated with that of human activities zones. Property crime exhibits a higher stratified association than violent crime across all temporal scales. Further, the strongest association is obtained with the eight-week time span centred around the SAH order. These findings not only enhance our understanding of the relationships between urban crime and human activities, but also offer insights into that tailored crime intervention strategies need to consider human activity variables.

Spatial distribution of crimes in urban areas: An insight from Sri Lanka

Similar to other Asian countries, it is forecasted that the urban population in Sri Lanka will continue to increase at an accelerating rate and the majority will be living in urban areas by 2022. Urban crime which is a major challenge that cities face is assumed to spread with the increasing population. They are clustered geographically generating a substantial economic struggle. The related theories argue that criminal opportunity is the root cause of crime and physical environmental design makes an influence on the criminal opportunity. Therefore, a need of identifying how the built environment and different land uses become an opportunity and make an impact on the spatial distribution of urban crimes is evident. This study aims to do a hotspot analysis within the Colombo Municipal Council Area, Sri Lanka to identify the spatial relationship of urban crimes with different land uses. The data on 627 crime incidents occurred in 2018 were obtained from crime records of police stations in Colombo North, South and Central police divisions and were analyzed using Kernel Density Estimation in Geographical Information Systems (GIS) software. As per study findings, seven wards were identified as crime hotspots. Further, findings reveal that robbery and drug arrest incidents are common around low-income residential housing areas while property theft records are common around hospitals, cinemas and concentrated commercial areas. Vehicle thefts are often recorded near lakes, offices and playgrounds whereas housebreakings are recorded near housing schemes. Finally, implications to mitigate these impacts were also presented. Keywords: crime types, GIS, land use, spatial distribution, Sri Lanka, urban crime

Applying Spatial Video Geonarratives and Physiological Measurements to Explore Perceived Safety in Baton Rouge, Louisiana.

Spatial crime analysis, together with perceived (crime) safety analysis have tremendously benefitted from Geographic Information Science (GISc) and the application of geospatial technology. This research study discusses a novel methodological approach to document the use of emerging geospatial technologies to explore perceived urban safety from the lenses of fear of crime or crime perception in the city of Baton Rouge, USA. The mixed techniques include a survey, spatial video geonarrative (SVG) in the field with study participants, and the extraction of moments of stress (MOS) from biosensing wristbands. This study enrolled 46 participants who completed geonarratives and MOS detection. A subset of 10 of these geonarratives are presented here. Each participant was driven in a car equipped with audio recording and spatial video along a predefined route while wearing the Empatica E4 wristbands to measure three physiological variables, all of them linked by timestamp. The results show differences in the participants’ sentiments (positive or negative) and MOS in the field based on gender. These mixed-methods are encouraging for finding relationships between actual crime occurrences and the community perceived fear of crime in urban areas.

CuRbanIsME: A Photographic Self-Analysis to Evaluate the Likelihood of the Occurrence of Predatory Crimes in Downtown Hamburg

In this study, a triangulation of (a) spatial data, (b) self-awareness, and (c) behavioral self-analysis seeks to provide an explanation from an innovative perspective for the likelihood of the occurrence of predatory crimes in the city center. This study does not examine the circumstances in which criminal acts occur. Instead, it focuses on a broader concept that combines both the configurational factors and the behavioral interconnections in which criminal acts occur. We orient the occurrence probability of crime towards appropriate objectives in the presence or absence of attractors/detractors, with interesting variation in the behavior of the acting subject—in our case, a random walker (also called the Random Movement–displacement Agent, or RDMA, in the text), which is the key variable that triggers the occurrence probability of predatory crimes. The relationship between spatial and/or behavioral observations and the probability of the crimes that may result from such observations is limited in this text to “predatory crimes,” which are the most common and light forms of crimes that endanger both human quality of life and the related safety in the city. Such crimes include theft, damage (specifically crime against public property and all similar offensive acts, such as littering and incivility), physical attacks (restrained to attempted violence against defenseless people), robberies, and car thefts (i.e., the most frequent crimes in urban areas). The theory of complexity, specifically as illustrated by the in-depth work of the 20th century German sociologist Niklas Luhmann, also suggests the importance of self-analysis in specific contexts to construct a mosaic of social phenomena. We conducted both a behavioral self-survey and a metric-based self-analysis by measuring random walks (RWs) to achieve some common behaviors—for example, buying food, shopping, or just looking at shop windows—on the streets of downtown Hamburg, Germany. RWs are used in our article to indicate random walks in the city center and any activities that may arise from them, such as protecting oneself from potentially hostile contexts, seeking information, or conforming oneself to official signals and customs. The hundreds of images taken by us in October 2019 during their RWs in Hamburg form a reservoir of our pictures, with the aim of showing the acceptable patterns of random movements–displacements that emerge. This method was primarily discursive but based on the ongoing search for a transformative conduit of behaviors that were intuitively established and observable for us but actually involved a complex process of imaginative ideation that was impossible to promote and pass on to the reader.

The Effect of the Spatial Organization Characteristics in Reducing the Crime in Al-Hila City

"The present work is about an organized crime which is considered a serious phenomenon that generally comes upon the world and especially in the Iraqi area. In recent decades, Al- Hilla City has suffered from spread variety of crimes, which lead the citizens to lack the sense of security. For this reason, it is seen it is significant to study this phenomenon with a new parallel phenomenon which causes their appearance. In addition, it is thought that this topic has not been tackled yet. The problem of the study lies in the lack of holistic scientific knowledge about the role of the synthetic properties of spatial organization for the city of Al- Hilla in crime growing. The study aims at establishing a holistic knowledge of that role. To deal with the problem of the research, an inductive approach (descriptive- analytical) has been adopted. The data is got from the responsible security institutions and is analyzed through invest the calculation method (space syntax). The results of the research showed that there are four areas ((4 zones) where crime is concentrated in the city and the growth of crime is related to the change of the synthetic characteristics of spatial organization (Connectivity, control, and Integration) of urban space. On the other hand, the diversity of crime in urban areas is related to other parallel phenomena and not to the structural characteristics of urban space."

Multi-UAV Allocation Framework for Predictive Crime Deterrence and Data Acquisition

The recent decline in the number of police and security force personnel has raised a serious security issue that could lead to reduced public safety and delayed response to crimes in urban areas. This may be alleviated in part by utilizing micro or small unmanned aerial vehicles (UAVs) and their high-mobility on-board sensors in conjunction with machine-learning techniques such as neural networks to offer better performance in predicting times and places that are high-risk and deterring crimes. The key to the success of such operation lies in the suitable placement of UAVs. This paper proposes a multi-UAV allocation framework for predictive crime deterrence and data acquisition that consists of the overarching methodology, a problem formulation, and an allocation method that work with a prediction model using a machine learning approach. In contrast to previous studies, our framework provides the most effective arrangement of UAVs for maximizing the chance to apprehend offenders whilst also acquiring data that will help improve the performance of subsequent crime prediction. This paper presents the system architecture assumed in this study, followed by a detailed description of the methodology, the formulation of the problem, and the UAV allocation method of the proposed framework. Our framework is tested using a real-world crime dataset to evaluate its performance with respect to the expected number of crimes deterred by the UAV patrol. Furthermore, to address the engineering practice of the proposed framework, we discuss the feasibility of the simulated deployment scenario in terms of energy consumption and the relationship between data analysis and crime prediction.

Optoelectronic and Environmental Factors Affecting the Accuracy of Crowd-Sourced Vehicle-Mounted License Plate Recognition

License plate recognition (LPR) technology has been used to combat vehicle-related crime in urban areas in many developed contexts. However, commercially available LPR systems are expensive and not feasible for large scale adoption in developing countries. The development of a low-cost crowd-sourced solution requires an informed approach to the selection of an appropriate camera, as well as a realistic understanding of the system’s performance under various environmental conditions. This work investigates the effect of optoelectronic and environmental factors on the ability of a vehicle-mounted LPR system to correctly identify license plates, specifically for a mass-deployment crowd-sourced scenario. A theoretical LPR camera model was developed to estimate the effect of different cameras, while the effects of motion, orientation and lighting were evaluated in a series of experimental tests. The most influential optoelectronic factors were shown to be focus, focal length and image sensor resolution. Furthermore, recognition was impaired during high-speed turn maneuvers, angling of license plates away from the camera and certain night-time conditions. The optoelectronic model proved useful for the selection of a cost-effective camera for use in an open-source LPR system. Moreover, the study of environmental factors provided valuable insight into the limitations of LPR systems in various environmental and traffic conditions.

Is Restricting Sales of Malt Liquor Beverages Effective in Reducing Crime in Urban Areas?

We evaluated the effects of outlet and small area level malt liquor policies on crime in 10 U.S. cities and hypothesized that more restrictive malt liquor policies would be associated with greater reductions in crime. We used a pooled time-series study design (i.e., panel regression) with comparison areas to test our hypothesis. Quarterly crime rates in targeted areas were compared 3 years before and after policy adoption. Four crime outcomes were analyzed: Selected Part II crimes, assaults, vandalism/property damage, and disorderly conduct. Both the presence and absence of a malt liquor policy (yes, no) and degree of restrictiveness of the malt liquor policy were assessed using a newly created measure of malt liquor policy restrictiveness developed by the investigators. Results were analyzed using a series of linear mixed models. Adoption of malt liquor policies was not associated with Part II selected crimes. For individual crimes (e.g., assaults, vandalism, disorderly conduct), we found mixed results as more restrictive malt liquor policies were significantly associated with decreases as well as increases in crime. In general, the evaluated policies were considered to be not highly restrictive. Our findings suggest that malt liquor policies at the outlet or small area level alone may not be sufficient to reduce crime associated with malt liquor use. Policies that are more restrictive or cover larger areas than assessed in the current study may be required to have an independent effect on crime outcomes.

Crime in Urban Areas:

Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. Traditional urban crime research focused on leveraging demographic data, which is insufficient to capture the complexity and dynamics of urban crimes. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources as well as rich environmental and social information. The availability of big urban data provides unprecedented opportunities, which enable us to conduct advanced urban crime research. Meanwhile, environmental and social crime theories from criminology provide better understandings about the behaviors of offenders and complex patterns of crime in urban. They can not only help bridge the gap from what we have (big urban data) to what we want to understand about urban crime (urban crime analysis); but also guide us to build computational models for crime. In this article, we give an overview to key theories from criminology, summarize crime analysis on urban data, review state-of-the-art algorithms for various types of computational crime tasks and discuss some appealing research directions that can bring the urban crime research into a new frontier.

Trees and crime in urban areas: recommendations

The benefits of trees especially in urban settings are often limited to discussions centered on energy savings the removal of atmospheric carbon dioxide muting noise pollution and storm water control However several studies have examined the relationship between violence and vegetation in urban areas In fact the relationship between trees and crime was considered as early as by King Edward when he ordered property owners to clear highway edges of trees in an attempt to reduce robberies In this paper I will review studies on the relationship neighborhood trees have to crime and provide recommendations regarding how urban trees can assist to deter criminal activity Recommendations resulting from reviewing these studies are Enhancing the presence of current trees and green spaces in those areas Educating citizens on the need for trees and green spaces Creating incentives for homeowners and tenants to support efforts that increase the number of and condition trees and green spaces Finding partnerships with local businesses and industry to aid in tree plantings and greening efforts

Human Rights in the Context of Criminal Justice: A Study of Urban Crime

Urban areas are known by their facilities and enhanced opportunities of living and employment. With the availing all the facilities, many serious issues have emerged due to urbanization in developing countries. The most highlighted problems include security and crime in urban areas in developing countries. This paper is aimed to focus on the urban crime, the reasons behind the crime happened, the major indicators of crimes and what is the city response and what policy should be developed by police and society. To achieve the objective of this research secondary data was used. With the help of literature crime indicators were explored and explained well in this research. Eight countries were analyzed with the help of literature to check the overall crime situation there. Crime in those all eight countries was low to high level. It is in alarming in some areas of Africa and other developing countries. Study showed crime is increasing from last three years which is high. In any society urban crime prevails it created unsafe environment, threaten to lives, threaten to belonging and has impact on life quality and economy of the country. Law force agencies must be empowered and well equipped to control the situation. It’s government responsibilities to create employment opportunities for youth so that they cannot engage into illegal activities.

The Impact of Single-Container Malt Liquor Sales Restrictions on Urban Crime.

Many US cities have adopted legal restrictions on high-alcohol malt liquor sales in response to reports of crime and nuisance behaviors around retail alcohol outlets. We assessed whether these policies are effective in reducing crime in urban areas. We used a rigorous interrupted time-series design with comparison groups to examine monthly crime rates in areas surrounding alcohol outlets in the 3years before and after adoption of malt liquor sales restrictions in two US cities. Crime rates in matched comparison areas not subject to restrictions served as covariates. Novel methods for matching target and comparison areas using virtual neighborhood audits conducted in Google Street View are described. In Minneapolis, Minnesota, sales of single containers of 16oz or less were prohibited in individual liquor stores (n = 6). In Washington, D.C., the sale of single containers of any size were prohibited in all retail alcohol outlets within full or partial wards (n = 6). Policy adoption was associated with modest reductions in crime, particularly assaults and vandalism, in both cities. All significant outcomes were in the hypothesized direction. Our results provide evidence that retail malt liquor sales restrictions, even relatively weak ones, can have modest effects on a range of crimes. Policy success may depend on community support and concurrent restrictions on malt liquor substitutes.

CAN HOT SPOTS POLICING REDUCE CRIME IN URBAN AREAS? AN AGENT‐BASED SIMULATION*

Over the past two decades, there has been a growing consensus among researchers that hot spots policing is an effective strategy to prevent crime. Although strong evidence exists that hot spots policing will reduce crime at hot spots without immediate spatial displacement, we know little about its possible jurisdictional or large‐area impacts. We cannot isolate such effects in previous experiments because they (appropriately) compare treatment and control hot spots within large urban communities, thus, confounding estimates of area‐wide impacts. An agent‐based model is used to estimate area‐wide impacts of hot spots policing on street robbery. We test two implementations of hot spots policing (representing different levels of resource allocation) in a simulated borough of a city, and we compare them with two control conditions, one model with constant random patrol and another with no police officers. Our models estimate the short‐ and long‐term impacts on large‐area robbery levels of these different schemes of policing resources. These experiments reveal statistically significant effects for hot spots policing beyond both a random patrol model and a landscape without police. These simulations suggest that wider application of hot spots policing can have significant impacts on overall levels of crime in urban areas.

Keeping Pace with Criminals: An Extended Study of Designing Patrol Allocation against Adaptive Opportunistic Criminals

Game theoretic approaches have recently been used to model the deterrence effect of patrol officers’ assignments on opportunistic crimes in urban areas. One major challenge in this domain is modeling the behavior of opportunistic criminals. Compared to strategic attackers (such as terrorists) who execute a well-laid out plan, opportunistic criminals are less strategic in planning attacks and more flexible in executing well-laid plans based on their knowledge of patrol officers’ assignments. In this paper, we aim to design an optimal police patrolling strategy against opportunistic criminals in urban areas. Our approach is comprised by two major parts: learning a model of the opportunistic criminal (and how he or she responds to patrols) and then planning optimal patrols against this learned model. The planning part, by using information about how criminals responds to patrols, takes into account the strategic game interaction between the police and criminals. In more detail, first, we propose two categories of models for modeling opportunistic crimes. The first category of models learns the relationship between defender strategy and crime distribution as a Markov chain. The second category of models represents the interaction of criminals and patrol officers as a Dynamic Bayesian Network (DBN) with the number of criminals as the unobserved hidden states. To this end, we: (i) apply standard algorithms, such as Expectation Maximization (EM), to learn the parameters of the DBN; (ii) modify the DBN representation that allows for a compact representation of the model, resulting in better learning accuracy and the increased speed of learning of the EM algorithm when used for the modified DBN. These modifications exploit the structure of the problem and use independence assumptions to factorize the large joint probability distributions. Next, we propose an iterative learning and planning mechanism that periodically updates the adversary model. We demonstrate the efficiency of our learning algorithms by applying them to a real dataset of criminal activity obtained from the police department of the University of Southern California (USC) situated in Los Angeles, CA, USA. We project a significant reduction in crime rate using our planning strategy as compared to the actual strategy deployed by the police department. We also demonstrate the improvement in crime prevention in simulation when we use our iterative planning and learning mechanism when compared to just learning once and planning. Finally, we introduce a web-based software for recommending patrol strategies, which is currently deployed at USC. In the near future, our learning and planning algorithm is planned to be integrated with this software. This work was done in collaboration with the police department of USC.