Mortgage fraud: A risk factor analysis of affected communities

Mortgage fraud is a fast-growing form of white-collar crime that has received much press coverage in the United States of America. Mortgage fraud has an adverse effect on individual homeowners, communities, and many indirect victims of the crime. While past research has focused on the personal motivating factors behind the commission of white-collar crime, this particular article reviews several facets of the crime itself and explores the potential neighbourhood risk factors that help attract the crime. From a national perspective, mortgage fraud seems to occur more frequently in neighbourhoods that have low socioeconomic indicators. These associations become even more pronounced when the degree of fraud occurrences within the community is factored in as a variable. Upon disaggregating the data according to region, the fraud indicator variables also display differing trend levels, perhaps indicating that as mortgage fraud practices begin to mature within an area, its community dynamics tend to change as well. The article concludes with recommendations for policymakers, community organizations, and law enforcement officials as to how to address mortgage fraud once it appears within a community, and also addresses future avenues of research for what is largely an untapped area of financial crime research.     

Exporting and importing criminality: Incarceration of the Foreign Born

Since previous studies have found that crime rates vary by immigrant group there is a need to dis‐aggregate immigrants by country of birth in order to obtain a more accurate representation of the relationship between migrants and crime. This study examines data from six countries (Australia, Canada, France, Italy, the Netherlands, and the U.S.A.) on the country of birth of their inmate populations. The following observations are reasonable conclusions from the data available. First, the percentages of each home country's inmate population that is foreign‐born varies remarkably. Second, in general foreign‐born inmates tend to come from regions outside the region within which the host country was located, though in most cases from regions that were proximate. Third, given the small number of countries reporting, it is intriguing that just a small number of countries and regions can account for such a high proportion of a home country's inmate population if one includes the numbers of a country's citizens who are housed in foreign prisons as part of that original country's inmate population. The paper concludes with a discussion of a number of policy implications that flow from these findings.     

Prior Victimization, Region, and Neighborhood Effects on Fear of Crime in Mumbai, India

Research in Western countries has found that prior victimization, region, and neighborhood effects, such as high population density, residential instability and low social cohesion as well as related characteristics such as litter, public drunkenness, and abandoned storefronts, are all significant predictors of fear of crime. The present study examined the extent to which these factors were associated with predicting fear of crime in one of the fastest growing economies in the world – India. Data from the International Crime Victimization Survey (ICVS), conducted under the auspices of the United Nations Interregional Crime and Justice Institute, suggest that, similar to findings from Western literature (with the exception of car theft and burglary), prior victimization is strongly related to fear of crime. However, contrary to findings from the Western literature, fear of crime appeared to be stronger among the middle classes than among the lower and higher classes. Moreover, limitations of the study and suggestions for future research are also discussed.     

Using Social Media to Measure Temporal Ambient Population: Does it Help Explain Local Crime Rates?

A challenge for studies assessing routine activities theory is accounting for the spatial and temporal confluence of offenders and targets given that people move about during the daytime and nighttime. We propose exploiting social media (Twitter) data to construct estimates of the population at various locations at different times of day, and assess whether these estimates help predict the amount of crime during two-hour time periods over the course of the day. We address these questions using crime data for 97,428 blocks in the Southern California region, along with geocoded information on tweets in the region over an eight month period. The results show that this measure of the temporal ambient population helps explain the level of crime in blocks during particular time periods. The use of social media data appear promising for testing various implications of routine activities and crime pattern theories, given their explicit spatial and temporal nature.     

Bus Stops and Violence,Are Risky Places Really Risky?

Geographic forecasting of crime can be done by considering prior crime or by considering spatial risk factors, e.g., using risk terrain modeling (RTM). The present paper tests both methods, but primarily focuses on RTM and on increasing our understanding of forecasting by attempting to compare the spatial risk factors for where the number of crimes is high with the spatial risk factors for where the risk of victimization is high. This is performed by fitting negative binomial models on crime around bus stops and comparing them to the same models with the number of bus passengers as exposure variable. The models also take the surrounding environment into account by fitting multi-level models with neighborhood level predictors of concentrated disadvantage and collective efficacy. The results show that some types of facilities are risk factors for crime, but not for victimization. This results in new insights into how flows of people impact on forecasting, as for instance a school is a spatial risk factor for crime, while not being associated with an elevated risk per person. The results also show that the neighborhood level of collective efficacy is a stable and significant risk factor both for crime and for risk of victimization, highlighting a potential for better crime forecasting by combining different spatial and theoretical perspectives.     

A two southern state comparison of the relationship between UCR crime rates and the anticipation of victimization

The purpose of this study is to investigate the relationship between UCR crime rates and a surveyed population’s anticipation of victimization within the next year. Separate surveys were conducted within the states of Tennessee and Texas. In both surveys, self-reported questionnaires were mailed to a random sample of 2,000 individuals drawn from the population of persons holding valid driver’s licenses within that state. A comparison was made between six Part I (rape, robbery, assualt, burglary, theft, and vehicle theft) UCR crime rates of the two states and the expectation of imminent victimization, as shown by the statewide surveys. Several past studies have suggested that there is little relationship between the official incidence of crime and the perceived likelihood of victimization. However, this study presents evidence in support of a positive relationship between UCR crime rates and the anticipation of victimization. In addition, it was found that the greater the anticipation of victimization level the more likely the respondents were to utilize defensive security devices (target hardening).     

Predictive Crime Mapping: Arbitrary Grids or Street Networks?

Objectives

Decades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent; a finding that Weisburd (Criminology 53:133–157, 2015) has recently termed the ‘law of crime concentration at places’. Such findings suggest that the future locations of crime should—to some extent at least—be predictable. To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. No studies of which we are aware have developed and tested the accuracy of methods for predicting the future risk of crime at the street segment level. This is surprising given that it is at this level of place that many crimes are committed and policing resources are deployed.

Methods

Using data for property crimes for a large UK metropolitan police force area, we introduce and calibrate a network-based version of prospective crime mapping [e.g. Bowers et al. (Br J Criminol 44:641–658, 2004)], and compare its performance against grid-based alternatives. We also examine how measures of predictive accuracy can be translated to the network context, and show how differences in performance between the two cases can be quantified and tested.

Results

Findings demonstrate that the calibrated network-based model substantially outperforms a grid-based alternative in terms of predictive accuracy, with, for example, approximately 20 % more crime identified at a coverage level of 5 %. The improvement in accuracy is highly statistically significant at all coverage levels tested (from 1 to 10 %).

Conclusions

This study suggests that, for property crime at least, network-based methods of crime forecasting are likely to outperform grid-based alternatives, and hence should be used in operational policing. More sophisticated variations of the model tested are possible and should be developed and tested in future research.

     

The Asian factor: Methodological barriers to the study of Asian gangs and organized crime

This paper examines methodological barriers that confront the study of Asian criminal gangs and organized crime. Researchers need to address definitional issues, appreciate the pluralistic nature of Asian communities, incorporate region as a unit of analysis, enhance the reliability of interviewees’ responses, and recognize the impact of news media on the public perception of Asian crime. A research agenda is offered to deal with these issues in future research endeavors.     

Predictive Policing as a New Tool for Law Enforcement? Recent Developments and Challenges

Decision-making processes are increasingly based on intelligence gained from ‘big data’, i.e., extensive but complex datasets. This evolution of analyzing complex data using methods aimed at prediction is also emerging within the field of quantitative criminology. In the context of crime analysis, the large amount of crime data available can be considered an example of big data, which could inform us about current and upcoming crime trends and patterns. A recent development in the analysis of this kind of data is predictive policing, which uses advanced statistical methods to make the most of these data to gain useable new insights and information, allowing police services to predict and anticipate future crime events. This article presents the results of a literature review, supplemented with key informant interviews, to give insight into what predictive policing is, how it can be used and implemented to anticipate crime, and what is known about its effectiveness. It also gives an overview of the currently known applications of predictive policing and their main characteristics.     

When crime reporting goes high-tech: An experimental test of computerized citizen response to crime

This study investigates the importance of the reporting mode as a factor contributing to citizen crime reporting decisions in the United States. A randomized experimental design involving 140 subjects was used to compare the treatment effects of two crime reporting modes: one which was telephonic and the other computer interactive. The findings of this exploratory study of citizen crime reporting within a controlled laboratory setting revealed significant increases in reporting rates for subjects assigned to the computer reporting mode condition relative to those assigned to the telephonic reporting mode condition. This relationship persisted for reporting behavior examined at both low and moderate levels of crime seriousness. The implications of these findings for predicting future changes in the incidence and distribution of reported criminal behavior or illegal incidents are presented and discussed.     

Crime Mapping and the Training Needs of Law Enforcement

This paper explores some of the more recent developments within crime mapping and the broader application of geographical information technology within law enforcement. The information technology (IT) revolution and the reduction in computing costs since the 1980s has brought a range of analytical tools within the budgets of most police services, and one of the most significant changes has been in the way that spatial data are handled. Law enforcement has strong geographic currents at all levels of the organisation, and this paper examines three applications of geographical information systems (GIS) within policing: hotspot mapping; CompStat; and geographic profiling. The paper concludes by discussing the future training needs using a simple model of intelligence-led crime reduction. This model suggests that training for managers to enable a greater understanding of the analyses presented to them, and how to use mapping to further crime prevention and reduction, may be as important as increasing the technical ability of crime analysts. The challenge for the immediate future of crime reduction practice in law enforcement is less to worry about the training of analysts, and more to address the inability of law enforcement management to understand and act on the crime analysis they are given.     

Perceived crime seriousness: Consensus and disparity

In this article, the statistical technique for setting up the consensus of perceived crime seriousness in previous studies is critically reviewed. The conventional method, when applied to a data set of crime seriousness, is found to have exaggerated the consensus because, by using a more appropriate model, which assumes perfect agreement in crime severity between subgroups, the consensus is reduced unanimously. By breaking down the population by gender, age, and educational level, sociodemographic differentials in crime seriousness are set up. The disparity is then further examined in details by paired comparisons between a target crime with fourteen other crimes. The three factors are all found to be important in affecting perceived crime seriousness. This conclusion is different from that of previous studies. The scaling method is found to be responsible for the difference. As the Thurstone method used in this study is more responsive and can produce more discriminating results, it is recommended for future research in crime severity. Finally, the implications of the findings on some important issues, like the appropriateness of legal penalty and the construction of a crime index, are discussed.     

Predictive crime mapping

Geographic Information Systems (GIS) have emerged as a key tool in intelligence-led policing and spatial predictions of crime are being used by many police services to reduce crime. Break and entries (BNEs) are one of the most patterned and predictable crime types, and may be particularly amendable to predictive crime mapping. A pilot project was conducted to spatially predict BNEs and property crime in Vancouver, Canada. Using detailed data collected by the Vancouver Police Department on where and when observed crimes occur, the statistical model was able to predict future BNEs for residential and commercial locations. Ideally implemented within a mobile GIS, the automated model provides continually updated predictive maps and may assist patrol units in self-deployment decisions. Future research is required to overcome computational and statistical limitations, and to preform model validation.     

Of bogus hunters,queenpins and mules: the varied roles of women in transnational organized crime in Southern Africa

Organized crime scholars have paid scant attention to gender and stereotyped roles of women in the commission of organized crime activities. Traditionally, organized crime is seen as a form of criminality perpetrated by men only. Women are usually portrayed as victims of organized crime or as “mean girls”, girlfriends, wives, lovers of brides of notorious gangsters and mobsters. In the southern African context, little historical or comparative data is available on the role of women in organized crime. Existing data is basic and proceeds on the assumption of gender-neutrality or the implied male composition of organized crime groups. The link of women to organized crime is one of suffering and exploitation. However, in reality women fulfill varied roles and functions within transnational organized crime networks in the region. In some instances, they are the foot soldiers of drug and human trafficking syndicates. Sometimes they are the intermediaries or powerful matriarchs at the apex of transnational organized crime networks. Reliant on empirical findings undertaken for a regional 3-year project on organized crime trends in southern Africa, this paper will examine the dynamism of the role of women in organized crime in the region and argues that women play a multifaceted role with implications for themselves, their families, society and organized crime. Gender mainstreaming within scholarly literature and policy research is in nascent stages, this paper pleads for a more gender-sensitive approach to organized crime analysis.     

U.S. SOCIAL STRUCTURE, CRIME, AND IMPRISONMENT

This research develops a structural model of crime and imprisonment in the United States from data on 49 states which was evaluated through a series of path and regression analyses. The major findings revealed that crime rates were effectively predicted by social structural characteristics, primarily urban population characteristics, and in turn that prison admissions were predicted by crime rates. Prison releases were not as strongly influenced by structural characteristics as crime rates and prison admissions; however, prison admissions were found to significantly affect releases. Variations in social structural determinants of violent and property crimes were also observed. The implication of these results ore discussed and suggestions for future research are presented.     

ECOLOGICAL NETWORKS AND URBAN CRIME: THE STRUCTURE OF SHARED ROUTINE ACTIVITY LOCATIONS AND NEIGHBORHOOD‐LEVEL INFORMAL CONTROL CAPACITY

By drawing on the work of Jacobs (1961), we hypothesize that public contact among neighborhood residents while engaged in day‐to‐day routines, captured by the aggregate network structure of shared local exposure, is consequential for crime. Neighborhoods in which residents come into contact more extensively in the course of conventional routines will exhibit higher levels of public familiarity, trust, and collective efficacy with implications for the informal social control of crime. We employ the concept of ecological (“eco‐”) networks—networks linking households within neighborhoods through shared activity locations—to formalize the notion of overlapping routines. By using microsimulations of household travel patterns to construct census tract‐level eco‐networks for Columbus, OH, we examine the hypothesis that eco‐network intensity (the probability that households tied through one location in a neighborhood eco‐network will also be tied through another visited location) is negatively associated with tract‐level crime rates (N = 192). Fitted spatial autoregressive models offer evidence that neighborhoods with higher intensity eco‐networks exhibit lower levels of violent and property crime. In contrast, a higher prevalence of nonresident visitors to a given tract is positively associated with property crime. The results of these analyses hold the potential to enrich insight into the ecological processes that shape variation in neighborhood crime.     

“A Plague on both Your Houses?”: Risks,Repeats and Reconsiderations of Urban Residential Burglary

Research has shown that mapping techniques are useful in forecasting future crime events. However, the majority of prospective mapping techniques has focused on the event-dependent influence of instigator incidents on subsequent incidents and does not explicitly incorporate the risk heterogeneity of the setting. The study here discussed is a modest attempt to address this issue by using a two-step process: first, using risk terrain modeling, we operationalized the “environmental backcloth,” (the risk heterogeneity of an area) to forecast locations of residential burglaries in the urban city of Newark, New Jersey. Second, using the near repeat calculator, we assessed the variability of underlying risk between different types of residential burglaries. A discussion of the findings and the joint utility of these approaches is provided.     

THE LAW OF CRIME CONCENTRATION AND THE CRIMINOLOGY OF PLACE*

According to Laub (2004), criminology has a developmental life course with specific turning points that allow for innovations in how we understand and respond to crime. I argue that criminology should take another turn in direction, focusing on microgeographic hot spots. By examining articles published in Criminology, I show that only marginal attention has been paid to this area of study to date—often termed the criminology of place. I illustrate the potential utility of a turning point by examining the law of crime concentration at place, which states that for a defined measure of crime at a specific microgeographic unit, the concentration of crime will fall within a narrow bandwidth of percentages for a defined cumulative proportion of crime. By providing the first cross‐city comparison of crime concentration using a common geographic unit, the same crime type, and examining a general crime measure, I find strong support for a law of crime concentration. I also show that crime concentration stays within a narrow bandwidth across time, despite strong volatility in crime incidents. By drawing from these findings, I identify several key research questions for future study. In conclusion, I argue that a focus on the criminology of place provides significant opportunity for young scholars and has great promise for advancing criminology as a science.     

Crime,politics, and minority populations: Use of official statistics in the united states and Japan

Although the governments of the United States and Japan differ markedly in racial ideology, official crime statistics in both nations reflect political arrangements which marginalize minority populations. In both nations, official crime statistics reveal more about the attempts of majority populations to label minority populations as a criminal class than about variations in criminal behavior across racial populations. While there is no racially pure Black population in the United States, there is a “black” category within official statistics, and the statistics are used to justify crime control policies which have a disparate impact on the diverse peoples who are socially‐perceived as Black. While there are undeniably non‐Japanese populations in Japan, there are no racial categories for them in official statistics which define them out of existence; except where crime statistics are concerned, so that the police can monitor the criminality of “foreigners.” In both societies, official categorization of race in crime statistics implies that crime is a minority problem; government statistics reinforce official ideology that crimes by “foreigners” and “black violence” are the real threats to civil society.