Street Robbery Research Articles

Cognitive Pervasive Service Composition Applied to Predatory Crime Deterrence

Pervasive service composition is useful in many scenarios, for instance, in urban planning or controlled harvest. Currently, there is no standard to develop solutions using pervasive service composition. However, big companies propose their frameworks to develop complex services, but their frameworks are appropriate in specific applications, such as home automation and agriculture. On the other hand, there are different very well-grounded academic proposals for pervasive service composition. However, these do not solve the problems of traditional approaches that are appropriate to specific areas of application, and adaptation is needed to deal with the dynamism of the environment. This article presents a cognitive approach for pervasive service composition where InfoCom devices and the implementation of cognitive functions interact to create pervasive composite services. Our central hypothesis is that cognitive theory can help solve actual problems requiring pervasive service composition, as it addresses the above-mentioned problems. To test our approach, in this article we present a case of urban insecurity. Specifically, in different countries, street robbery using firearms is one of the problems with a high impact because of its frequency. This article proposes to compose a pervasive service for deterring criminals from committing their crimes. The results obtained by simulating our proposal in our case study are promising. However, more research needs to be achieved before applying the proposed approach to actual problems. The research needed ought to address various problems, some of which are discussed in this article.

Uticaj kriminalne karijere na način izvršenja uličnog razbojništva

Street development is a form of development and content of all elements of a significant criminal offense and was committed against one or more persons, who walked, known or unknown to the executor, performed in public places that should become proposed, parks, public garages, entrances and stairs in residential buildings, etc.). They belong to the more frequent types of robbery, characterized by less sophistication in execution and direct confrontation that determines the quantity of fear suffered, the scope and intensity of the studied consequences. The aim of the research was to examine the influence of the previous experience of the perpetrators of the robbery on the way of choosing the victim, the place of victimization and the presence of other persons who would be in the immediate or close environment of the street robbery victim. The sample consisted of 42 respondents who committed street robbery out of 120 examined perpetrators of the crime of robbery and serious cases of robbery and robbery, who were serving a prison sentence in the penitentiary in Sremska Mitrovica. Data were collected through an anonymous survey. The results show that there will be differences in the approach to victim first choice, place of execution, internal and external presence of others, between executor who have already and those who have no previous experience in development research. The executors of robbery who have already committed this act are not more inclined to type a potential victim, but they would rather decide to immediately commit a street robbery by encountering a victim with whom the "prey" is visually noticeable. When choosing the place where the crime was committed, they are more inclined to emphasize the possibility of safe removal, but they are more inclined to say that when they commit the crime at night, it will still be in a place that is not lit. Experience predisposes them to pay less attention to the external presence of other persons, while perpetrators who commit robbery for the first time give priority to victimization when the victim is alone. Proper understanding of these indicators can contribute to the qualitative improvement of the analysis, which will facilitate our identification of key starting points in the selection of the best solutions for effective intervention, in order to reduce street robberies.

Roubos em Maceió (AL): modus operandi e fatores associados à vitimização

ABSTRACT: The article presents a characterization of the modus operandi of crimes of street robberies that occurred in the city of Maceio (Alagoas State, Brazil) between the years 2015 and 2017. The data gathered in the study were provided by the Civil and Military Police and by the Alagoas State Re-socialization and Social Inclusion Secretariat. A descriptive approach was used to explain the configurational aspects of this type of robbery notified in the aforementioned city and period. In the analysis of the results, it was possible to identify patterns in the way criminals act, in their profile and in the victim's profiles. KEYWORDS: Robberies, Modus Operandi, Maceio.

Tracking Risk for Crime Throughout the Day: An Examination of Jersey City Robberies

This study examines temporal variations in the spatial influence of environmental features, such as bars and vacant buildings, on criminal behavior across microlevel places. Specifically, 17 environmental risk factors and their spatial influences are identified for calendar year 2014 street robberies in Jersey City, NJ. To explore temporal variation, risk factors and their spatial influences on crime are identified across 12 discrete 2-hr time intervals. The results demonstrate that the risk factors for street robbery varied across the course of a day. In fact, mapping the most vulnerable places for street robbery revealed that while many of the same environmental features remain high risk throughout the day, their influence varied. These results suggested that there was a temporality to robbery and that it is likely due to the interaction between physical vulnerabilities from the built environment and social behaviors of people at these places. This demonstrates the importance of considering the temporal dimension of criminal behavior as results show that people use and interact with their environment differently throughout the course of the day.

Block-Level Analysis of the Attractors of Robbery in a Downtown Area

This article examines the predictions of crime pattern theory in a unique neighborhood type. It tested potential crime attracting facilities against street robbery data from 2009 to 2013 in the Police Districts I & II in Downtown Houston. The analysis modeled the four daily human routine periods described in the American Time Use Survey (ATUS). Generalized linear simultaneous negative binomial regression model was used to determine the size of the influence of the variables (beta coefficients) and their significance for each model outcome. The findings show some distinct patterns of street robbery due to the immediate and lagged effects of the variables relatable to the study environment’s unique setting. Two variables, geographic mobility, and barbershops were particularly significant across three of the outcome models. The results suggest that the physical and social structure of neighborhoods determined by land-use regulations would enhance understanding of the time-based influence on robbery patterns due to crime-attracting facilities.

Using Eye-Tracking Technology to Measure Environmental Factors Affecting Street Robbery Decision-Making in Virtual Environments

There is a lack of quantitative data regarding how offenders make decisions about committing a crime or how situational factors influence such decisions. Detailed crime data on decision-making among criminals are required to improve the accuracy of research. Demonstrating a new methodology for assessing the factors impacting criminal decision-making among street robbery offenders, this study identifies visual data that influence criminal decision-making, and verifies the significance of the measured data. To this end, this study first identified and organized the physical aspects affecting criminal decision-making based on the Crime Prevention Through Environmental Design (CPTED) literature. Next, participants were informed of a street crime scenario and asked to replicate the behaviors of criminals in the virtual environment of Grand Theft Auto 5. Factors affecting criminals’ decision-making were then quantitatively assessed using eye-tracking technology. Multivariate logistic regression analysis was used to verify the significance of the measured data. Results show that windows placed adjacent to the street, balconies and verandas, and signs indicating territoriality have a significant effect on criminals’ decision-making. Confirming the influence of CPTED factors on the occurrence of street robbery, this study advances a new way of acquiring quantitative data through eye-tracker technology, a method hitherto unexplored by existing research on street robbery.

Linking Luojia 1-01 nightlight imagery to urban crime

Various environmental criminology theories and empirical studies have linked the urban environment to crime. The crime pattern theory, in particular, argues that edges, either social or physical, affect crime. A recent study has combined both social and physical edges to derive composite edges. A composite edge index measured by NPP-VIIRS satellite nightlights at the census tract level is found to be related to street robbery and burglary. Nightlight images of Luojia 1-01, launched in June 2018, have a much higher spatial resolution than that of NPP-VIIRS. This study applies Luojia 1-01 nightlight data to measure composite edges by nightlight gradients at the smaller census block group level. The effects of the composite edges on street robbery and burglary are explored by negative binomial models. Results show that composite edges measured by Luojia 1-01 nightlight data improve the fitness of models noticeably on street robbery but not on burglary. Nightlight gradients make a statistically significant and positive impact on the street robbery rate, but an insignificant and negative impact on the burglary rate. Furthermore, the composite edge effect on street robbery is more substantial than that on burglary. In sum, this study provides evidence that Luojia 1-01 nightlight imagery can help explain crime at the aggregated block group level, but its impact on crime varies by crime type.

Forecasting Spatio-Temporal Variation in Residential Burglary with the Integrated Laplace Approximation Framework: Effects of Crime Generators, Street Networks, and Prior Crimes

ObjectivesWe investigate the spatio-temporal variation of monthly residential burglary frequencies across neighborhoods as a function of crime generators, street network features and temporally and spatially lagged burglary frequencies. In addition, we evaluate the performance of the model as a forecasting tool.MethodsWe analyze 48 months of police-recorded residential burglaries across 20 neighborhoods in Amsterdam, the Netherlands, in combination with data on the locations of urban facilities (crime generators), frequencies of other crime types, and street network data. We apply the Integrated Laplace Approximation method, a Bayesian forecasting framework that is less computationally demanding than prior frameworks.ResultsThe local number of retail stores, the number of street robberies perpetrated and the closeness of the local street network are positively related to residential burglary. Inclusion of a general spatio-temporal interaction component significantly improves forecasting performance, but inclusion of spatial proximity or temporal recency components does not.DiscussionOur findings on crime generators and street network characteristics support evidence in the literature on environmental correlates of burglary. The significance of spatio-temporal interaction indicates that residential burglary is spatio-temporally concentrated. Our finding that recency and proximity of prior burglaries do not contribute to the performance of the forecast, probably indicates that relevant spatio-temporal interaction is limited to fine-grained spatial and temporal units of analysis, such as days and street blocks.

Understanding the Predictors of Street Robbery Hot Spots: A Matched Pairs Analysis and Systematic Social Observation

This study examines the environmental predictors that classify street robbery hot spots and control street segments in Indianapolis. Empirical controls were generated by matching each hot spot to a corresponding set of zero-crime control and low-crime control units. Then, units were evaluated based on the presence of crime generators and attractors, which were downloaded from open data sources and spatially joined to the street segments, and disorder indicators obtained via systematic social observation using Google Street View. The findings provide information about the influence environmental predictors have on the presence of street robbery hot spots, and whether the composition of hot spots significantly differs from that of similar places that experienced no crime or low counts of crime.

Firearms and Injuries during Home Robberies in Mexico, 2010–2017

According to data from Mexico’s National Crime Victimization and Public Security Surveys (ENVIPE), from 2010 through 2017, home robberies involving an interaction between a victim and an aggressor occurred every two minutes and resulted in more than 257,000 injuries. The objective of this study is to explore whether victims of home robberies in Mexico are more likely to report an injury if perpetrators are armed with a gun. This analysis compiles data from ENVIPE and runs a binary logistic regression. In contrast to literature on robberies in the United States and studies on street robberies in Mexico, results show that when perpetrators are armed with guns, victims of home robberies in Mexico are more likely to report an injury, even though these injuries are rarely caused by a gunshot. These discrepancies may be explained by different factors. Unlike in the United States, most gun-related crimes in Mexico are perpetrated by organized criminal groups that obtain firearms through illegal channels. Members of these groups are prepared to engage on physical violence against their victims. Additionally, unlike street robberies, the enclosed setting of home robberies might facilitate the use of physical violence as a strategy to coerce victims and successfully obtain profits without generating public attention. Overall, perpetrators use guns as a tool to exert control and avoid resistance. These findings suggest that policies to address organized crime and halt easy access to guns should be prioritized on Mexico’s national security agenda.

The Relationship between Near-Repeat Street Robbery and the Environment: Evidence from Malmö, Sweden

Near-repeat crime refers to a pattern whereby one crime event is soon followed by a similar crime event at a nearby location. Existing research on near-repeat crime patterns is inconclusive about where near-repeat patterns emerge and which physical and social factors influence them. The present research addressed this gap by examining the relationship between initiator events (i.e., the first event in a near-repeat pattern) and environmental characteristics to estimate where near-repeat patterns are most likely to emerge. A two-step analysis was undertaken using data on street robberies reported in Malmö, Sweden, for the years 2006–15. After determining near-repeat patterns, we assessed the correlations between initiator events and criminogenic places and socioeconomic indicators using a negative binomial regression at a street segment level. Our results show that both criminogenic places and socioeconomic indicators have a significant influence on the spatial variation of initiator events, suggesting that environmental characteristics can be used to explain the emergence of near-repeat patterns. Law enforcement agencies can utilize the findings in efforts to prevent further street robberies from occurring.

Assessing the effects of bus stop relocation on street robbery

A number of studies have revealed a correlation between bus stops and crimes, especially street robberies. However, few have looked into the impact of bus stop location changes on the distribution of street robberies. Will newly added bus stops attract more street robberies? Will the removal of existing bus stops reduce street robberies? By assessing the change of street robberies in relation to the spatial change of bus stops of Cincinnati, OH, with the consideration of the controls from socioeconomic characteristics, point of interests (POI) and spatial heterogeneity, this study uses before-and-after comparisons and the difference-in-differences (DID) analysis in the context of quasi-experiment to answer these questions. This study assesses not only the influences of the relocation of bus stops, but also the influence on street robberies of the time elapsed from the addition or removal of bus stops. Besides the three typical variables representing the presence or absence of the intervention, before or after the intervention and the interaction of the two, we add the time from addition/removal to the DID analysis. Results suggest that, on average, adding bus stops to a new location significantly increases street robberies in the areas surrounding the stops. The longer the time from the addition of a new bus stop, the more the street robberies in its surrounding areas. Removing all bus stops from a location decreases street robberies in the areas nearby; however, this influence is not statistically significant. This suggests that the relationship between street robbery and time from removal may not be linear. There are multiple studies exploring the static relationship between the bus stop and street robbery, but none looked into their dynamic relationship. This study represents the first attempt to do so. Its findings add new evidence to the theories of rational choice, routine activity, crime pattern, and crime displacement.

Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of crime events. Spatio-temporal prediction of crimes activity can help the cities to have a better allocation of police resources and surveillance. Deep learning techniques are considered efficient tools to predict future events analyzing the behavior of past ones; however, they are not usually applied to crime event prediction using a spatio-temporal approach. In this paper, a Convolutional Neural Network (CNN) together with a Long-Short Term Memory (LSTM) network (thus CLSTM-NN) are proposed to predict the presence of crime events over the city of Baltimore (USA). In particular, matrices of past crime events are used as input to a CLSTM-NN to predict the presence of at least one event in future days. The model is implemented on two types of events: “street robbery” and “larceny”. The proposed procedure is able to take into account spatial and temporal correlations present in the past data to improve future prediction. The prediction performance of the proposed neural network is assessed under a number of controlled plausible scenarios, using some standard metrics (Accuracy, AUC-ROC, and AUC-PR).

Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.

Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection

A large body of research has found that crime is much more likely to occur at certain places relative to others. Attempting to capitalize on this finding to maximize crime prevention, many police administrators have sought to narrow their department’s operational focus and allocate resources and attention to the most problematic locations. However, in the face of a growing number of technological advances in crime forecasting that have facilitated this trend, it is still unclear how to best identify the most appropriate set of places to which resources and attention should be directed. Our goal was to examine this issue by exploring the ways in which spatial vulnerabilities and exposures could be used to identify the best target areas for policing. Using the Theory of Risky Places as a guide, we employed kernel density estimation (KDE) to measure crime exposures and risk terrain modeling (RTM) to identify crime vulnerabilities with the expectation that crime would be predicted more accurately by integrating the outputs from these two methods. To test this hypothesis, our analysis utilized 1 year of data on street robbery in Brooklyn, New York. A common metric, the prediction accuracy index (PAI), was computed for KDE, RTM, and the integrated approach, over 1 month and 3 month intervals. We found that the integrated approach, on average and most frequently, produces the most accurate predictions. These results demonstrate that place-based policing and related policies can be improved via actionable intelligence produced from multiple crime analysis tools that are designed to measure unique aspects of the spatial dynamics of crime.

The Impact of Business Improvement District Proximity on Street Block Robbery Counts

This study examined the impact of business improvement districts (BIDs) on street block robbery counts in Cincinnati, OH. The relationships among BIDs (as well as other relevant control measures) and street block robbery counts were then examined using negative binomial regression models. Results showed that BIDs played an important role in the spatial patterning of robbery at the street block level. Street blocks within BIDs experienced higher street robbery counts even after controlling for street network betweenness and the presence of potentially criminogenic places within and around BIDs. Although previous research suggests BID implementation leads to crime reductions, the present study shows BIDs still provide ample opportunities for criminal activity as one component of the environmental backcloth.

Spatial and temporal patterns of violent crime in a Brazilian state capital: A quantitative analysis focusing on micro places and small units of time

The present research analyses a local variation of the problem of violence in Brazil and focuses on Florianópolis, the capital of the state of Santa Catarina, long considered the flagship of security in the country, although currently seeing an increase in violent crimes. A quantitative analysis has been implemented to test whether the law of crime concentration and the assumption of spatial-temporal dissimilarity of crime events were consistent for the specific case under study. The results show that crime concentration in Florianópolis is more accentuated than even previous literature would suggest and that the degree of concentration of street robberies and homicides is temporally stable. However, the specific locations where crime took place change over time depending on temporal dimensions such as the season, day of the week, and the hour of the day. Yet, homicide spatial patterns have shown to be less sensitive to seasonal variations. Although this conclusion challenged the initial expectations derived from previous literature, it provides a meaningful explanation of current trends of violent crime in Florianópolis, which are increasingly linked to organized crime and illegal drug trade.

Association between obesity in 4- to 7-year-old children and eight types of crime: a hierarchical linear modelling approach.

SummaryObjectiveEvidence of the association between childhood obesity and neighbourhood crime is inconclusive. Most previous studies have included children of all ages, and few have examined different types of crime. The objective of this study was to investigate the association between obesity and eight different types of crime (i.e. commercial robbery, street robbery, assault, other violence, commercial break and enter, residential break and enter, theft of vehicle and theft from vehicle) among 4‐ to 7‐year‐old children in a large western Canadian city.MethodsCross‐sectional, epidemiological study (N = 10,069) using spatial analysis and hierarchical generalized linear modelling. The outcome variable was normal weight or obesity. The exposure variable was the distance between the child's residential postal code and the closest occurrence of each type of crime.ResultsControlling for distance to the closest park, frequency of each type of crime in the child's neighbourhood and neighbourhood factors (proportion of visible minorities, education and median family income), there was no association between any of the crime types and childhood obesity.ConclusionsCrime did not contribute to obesity in this sample of 4‐ to 7‐year‐old children. Replication of this study in other jurisdictions would increase confidence in these results.

Aggravating effects of alcohol outlet types on street robbery and aggravated assault in New York City

ABSTRACTThe present study uses Risk Terrain Modeling (RTM) to analyze how nine different alcohol outlet types differentially influence the likelihood of aggravated assault and street robbery at micro-level places in New York City. Separate models were conducted for each of New York’s five boroughs to account for the differing environmental characteristics across the city. Results suggest that spatial influence of crime risk differed across alcohol outlet types, with off-premise grocery stores having the largest influence on the likelihood of both aggravated assault and street robbery and on-premise bars/taverns having a strong influence only on the likelihood of street robbery. In addition, although certain alcohol outlets affected crime in each borough, the spatial influence of others was more volatile. It is clear not all alcohol outlet types are equally criminogenic across boroughs. Thus, crime reduction efforts should be unique and localized according to the risk associated with outlet types and place.

A Typology of Street Robbery and Gang Organization: Insights from Qualitative Research in Scotland

Based on in-depth interviews with 42 current and ex- street offenders, this study explores the relationship between street gang organisation and robbery. The type of robbery is affected by the level of organisation a gang is at. For recreational and territorial young street gangs, robbery is opportunistic, occurring in a diffuse manner, and conducted individually, even when the rest of the group are present as ‘backup’. For criminal gangs, robbery is different in that it is often planned in advance and the proceeds of crime are divided more evenly amongst group members. Serious Organised Crime gangs are typically more specialised, thus robbery may often be the gang in question’s main ‘occupation’. For organized crime groups, robbery most often occurs in the illegitimate market, but can be aimed at legitimate and highly profitable institutions. We make sense of these findings with reference to street capital theory, and present implications for future research and practice.