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Although a growing body of research has examined and found a positive relationship between neighborhood crime and home foreclosures, some research suggests this relationship may not hold in all cities. This study uses city-level data to assess the relationship between foreclosures and crime by estimating longitudinal models with lags for monthly foreclosure and crime data in 128 cities from 1996 to 2011 in Southern California. We test whether these effects are stronger in cities with a combination of high economic inequality and high economic segregation; and whether they are stronger in cities with high racial/ethnic heterogeneity and high racial segregation. One month, and cumulative three month, six month, and 12-month lags of foreclosures are found to increase city level crime for all crimes except motor vehicle theft. The effect of foreclosures on these crime types is stronger in cities with simultaneously high levels of inequality but low levels of economic segregation. The effect of foreclosures on aggravated assault, robbery, and burglary is stronger in cities with simultaneously high levels of racial heterogeneity and low levels of racial segregation. On the other hand, foreclosures had a stronger effect on larceny and motor vehicle theft when they occurred in a city with simultaneously high levels of racial heterogeneity and high levels of racial segregation. There is evidence that the foreclosure crisis had large scale impacts on cities, leading to higher crime rates in cities hit harder by foreclosures. Nonetheless, the economic and racial characteristics of the city altered this effect., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Abstract: Research finds that immigration and crime are not related across neighbourhoods, contrary to social disorganization theory and consistent with the immigration revitalization thesis. This research, however, is largely silent as to any possible nonlinear effects. Yet social theory offers sound reasons for why the immigration–crime association may be nonlinear; explanations, including immigrant/ethnic enclave theory and immigrant victimization theory, underscore potential concentration effects—albeit in different ways. Using a novel dataset with information on crime in over 15,000 neighbourhoods across a diverse range of US cities, we examine whether or not the immigration–crime association is nonlinear. We find that for both violent and property crime, a nonlinear relationship best captures the relationship. In additional analyses, we determine the theoretical perspective with which the findings are most consistent.

This study compares the relationship between official crime rates in census tracts and resident perceptions of crime. Using a unique data set that links household-level data from the American Housing Survey metro samples over 25 years (1976-1999) with official crime rate data for census tracts in selected cities during selected years, this study finds that tract violent crime is the strongest predictor of residents' perception of crime. This standardized coefficient was 0.71 on average over the seven waves. Models simultaneously taking into account both violent and property crime found a consistently strong positive effect for violent crime but a consistently negative effect for property crime. Among types of violent crime, robbery and aggravated assault have the strongest effect on the perception of crime in the tract. Burglary showed a stronger effect on perceptions of crime in the 1970s but a steadily weakening effect since then. There was little evidence that the racial/ethnic composition of the tract affected these perceptions. (Contains 4 tables and 13 notes.)

Objectives: Longitudinal studies of the relationship between neighborhood change and changes in crime typically focus exclusively on the net level of change in key socio-demographic characteristics. Methods: We instead propose a demographic accounting strategy that captures the composition of neighborhood change: our measures capture which types of people are more likely to leave, stay, or enter the neighborhood. We use data for 3,325 tracts in the Southern California region over nearly two decades of 2000–2010 and 2010–2017 and construct flow measures based on race/ethnicity; the length of residence of owners and renters; the age structure. Results: These flow measures improve the predictive power of the models—implying important theoretical insights. Neighborhoods with higher percentages of middle-aged residents who recently entered the neighborhood exhibit larger increases in violent and property crime. The relative stability of those in the highest crime-prone ages (aged 15–29) is associated with the largest increases in violent and property crime. The greater loss of Black and Asian residents decreased crime while moderate outflows of Latinos increased crime. The mobility of long- and short-term renters was related to crime changes. Conclusions: This new technique will likely encourage further theoretical innovation for the neighborhoods and crime literature.

This study examines the proximity of service providers to recently released parolees in California over a 2-year period (2005-2006). The addresses of parolee residences and service providers are geocoded, and the number of various types of service providers within 2 miles (3.2 km) of a parolee are measured. "Potential demand" is measured as the number of parolees within 2 miles of a provider. Although racial and ethnic minority parolees have more service providers nearby, these providers appear to be particularly impacted based on potential demand. It is also found that the parolees arguably most in need of social services--those who have spent more time in correctional institutions, have been convicted of more serious or violent crimes in their careers, or are sex offenders--live near fewer social services, or the providers near them appear impacted. (Contains 4 tables, 2 figures and 6 notes.)

Most prior research testing the hypothesis of the social disorganization theory that residential instability increases crime has used cross-sectional data. Using a unique dataset linking home sales address matched to census tracts with crime data in Los Angeles, we test the direction of this relationship using a six-year panel data design. We also test whether crime acts as a generator of transition and decline in neighborhoods by testing its effect on property values the following year. Our findings suggest little evidence that home sales volatility in one year leads to more property or violent crime the following year. Instead, higher levels of tract property and violent crime in one year lead to more home sales the following year. This effect of high crime rates is exacerbated in tracts with high levels of racial/ethnic heterogeneity, suggesting that such tracts may engender a distinct combination of fear and uncertainty in their residents, leading to more turn over. We also find that tracts with more violent crime one year have lower property values the following year, suggesting a general process of decline. (Contains 18 notes, 4 tables, and 4 figures.)

Objectives: Current strategies for locating where offenders live either focus exclusively on individual suspects or generalize to entire neighborhoods. However, better estimates of where offenders are located may improve models of the ecological distribution of crime, and forecasts of the locations of future crime incidents. Methods: We propose a novel reverse spatial patterning (RSP) strategy that estimates where offenders may live based on the spatial locations of crime events. We rely on a distance decay function – based on the consistent finding that offenders do not travel far to commit crime – and Hipp's (2016) general theory of spatial crime patterns, to work backwards from the locations of actual crime events to make predictions about where offenders may live in subsequent years. We then use these estimates in models predicting crime locations. We create two versions of the RSP: one which assumes everyone is equally likely to offend, and another that creates an estimate assuming disproportionate offending across persons. Results: We test the effectiveness of our proposed strategy for these two measures using offense and arrest data from St. Petersburg, FL, and assess how well they predict the location of offenders (proxied by arrestees) and future crime events. We find consistent evidence that our RSP strategy provides better predictions of the locations of where offenders are located and also future crime incidents across a variety of crime types compared to existing strategies. Conclusion: The RSP approach is useful for creating estimates of where offenders live, which allow for better predictions of the locations of future crime incidents. These better forecasts will allow for more efficient allocation of police resources and targeted crime suppression efforts.

We examine the impact of immigrant-serving organizations on neighborhood crime in the Los Angeles Metropolitan area, while accounting for other community correlates of crime as well as potential endogeneity. We estimate longitudinal negative binomial regression models that test for the main, mediating, and moderating effects of immigrant-serving organizations. We found that immigrant-serving organizations generally have crime-reducing effects for all types of crime. We also find that high immigrant concentration is associated with lower levels of crime in general, and this effect is moderated by the number of organizations, which underlines the importance of accounting for these organizations when studying the nexus of immigrant concentration and neighborhood crime.

Shaw and McKay advanced social disorganization theory in the 1930s, kick-starting a large body of research on communities and crime. Studies emphasize individual impacts of poverty, residential instability, and racial/ethnic heterogeneity by examining their independent effects on crime, adopting a variable-centered approach. We use a “neighborhood-centered” approach that considers how structural forces combine into unique constellations that vary across communities, with consequences for crime. Examining neighborhoods in Southern California we: (1) identify neighborhood typologies based on levels of poverty, instability, and heterogeneity; (2) explore how these typologies fit within a disorganization framework and are spatially distributed across the region; and (3) examine how these typologies are differentially associated with crime. Results reveal nine neighborhood types with varying relationships to crime.

Objectives: Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro−geographic units, measuring this is difficult. Methods: This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street segment in the city of Santa Ana, CA, and then uses machine learning to detect features of the environment. We capture eleven different features across four main dimensions, and demonstrate that their relative presence across street segments considerably increases the explanatory power of models of five different Part 1 crimes. Results: The presence of more persons in the environment is associated with higher levels of crime. The auto−oriented measures—vehicles and pavement—were positively associated with crime rates. For the defensible space measures, the presence of walls has a slowing negative relationship with most crime types, whereas fences did not. And for our two greenspace measures, although terrain was positively associated with crime rates, vegetation exhibited an inverted−U relationship with two crime types. Conclusions: The results demonstrate the efficacy of this approach for measuring the built environment.

In the current study, we theorized that businesses in place are subject to two processes: a crime generator effect in which they heighten crime due to increased opportunities and a crime inhibition effect in which certain types of businesses can increase guardianship capability. We explicitly compare the different effects of local vs. non-local and small vs. large businesses on crime in street segments using the data in cities across the Los Angeles metropolitan region by estimating a set of negative binomial regression models for small local, large local, small non-local, and large non-local consumer facing businesses (Retail, Restaurants, Food/Drug Stores, and Services) for violent and property crime. Although we found that most of the business coefficients were positive, local businesses, and particularly small local businesses, have considerably smaller crime-enhancing effects for both violent and property crime.

Objectives: Although previous studies have theorized the importance of physical and social boundaries (edges) in understanding crime in place, the relationship between edges and the level of crime has been less studied empirically. The current study examines the effects of physical and social boundaries on crime in street segments. Methods: To empirically measure boundaries, we introduce an approach of looking at the differences of land use (physical boundary), socioeconomic status, or racial composition (social boundaries) on both sides of a street segment. We estimated a series of negative binomial regression models in which measures of the physical and social boundaries are included while controlling for the effects of structural characteristic and the conventional physical boundary measures of highways, parks, and rivers. Results: We observed that there are positive relationships between all three of these boundary measures and violent and property crimes. The results indicated that physical and social boundaries are important to consider in understanding the spatial patterns of crime. Moreover, the current study confirmed the moderating effects between social and physical boundaries. Conclusions: Our results indicate that although much empirical research focuses solely on physical boundaries, our measures of social and physical boundaries have important consequences for the spatial location of crime, and therefore are worthy of further research.

Criminological theories have posited that the built environment impacts where crime occurs; however, measuring the built environment is difficult. Furthermore, it is uncertain whether the built environment differentially impacts crime in high-disadvantage neighborhoods. This study extracts features of the built environment from Google Street View images with a machine learning semantic segmentation strategy to create measures of fences, walls, buildings, and greenspace for over 66,000 street segments in Los Angeles. Results indicate that the presence of more buildings on a segment was associated with higher crime rates and had a particularly strong positive relationship with robbery and motor vehicle theft in low-disadvantage neighborhoods. Notably, fences and walls exhibited different relationships with crime. Walls, which do not allow visibility, were strongly negatively related to crime, particularly for robbery and burglary in high-disadvantage neighborhoods. Fences, which allow visibility, were associated with fewer robberies and larcenies, but more burglaries and aggravated assaults. Fences only exhibited a negative relationship with violent crime when they were located in low-disadvantage neighborhoods. The results highlight the importance of accounting for the built environment and the surrounding level of disadvantage when exploring the micro-location of crime.

We compare the relative importance of four dimensions for explaining the micro location of robberies: 1) the micro spatial scale of street segments; 2) the meso spatial scale surrounding the street segment; 3) the temporal pattern, and 4) the macro-scale of the surrounding 2.5 miles. This study uses crime, business, and land use data from New York City and aggregates it to street segments and hours of the day. Although the measures capturing the micro-scale of the street segment explained the largest amount of unique variance, the measures capturing temporal scale across hours of the day (and weekdays) explained the next largest amount of unique variance. The measures of the characteristics in the 2.5 miles macro scale explained the next largest amount of unique variance, and combined with the measures at the meso-scale explained nearly as much of the variance as the street segment measures.

Objectives: Although theories suggest that street network configurations (pathways) are important factors for understanding the spatial patterns of crime, relatively less attention has been paid to the association between the physical configuration of the street network and the level of crime in place. Consequently, we employed the concept of betweenness centrality in the context of the street network to empirically measure the potential foot traffic passing through a given street segment. Methods: We introduce a methodological refinement by accounting for the characteristics of origin and destination of each potential trip (where travelers are from and tend to go) using residential population in origins and destinations and the number of various types of business employees in destinations. Moreover, we posit that the effect of potential foot traffic into a given street segment will be moderated by certain social environmental characteristics such as socioeconomic status of place. By using data on a sample of 300,000 street segments in the Southern California region across 130 cities, we estimate a set of negative binomial regression models including the betweenness measures. Results: Our results show that betweenness centrality has a curvilinear relationship with violent and property crime: At lower levels, increases in betweenness results in increased crime, yet the pattern becomes crime-reducing at higher values of the betweenness measure. We also found that the pattern is moderated by the socioeconomic status of the street segment. Conclusions: The current study highlights that there is an important relationship of the physical environment in terms of the street network configuration and crime in street segments.

Objectives: The current study proposes an approach that accounts for the importance of streets while at the same time accounting for the overlapping spatial nature of social and physical environments captured by the egohood approach. Our approach utilizes overlapping clusters of streets based on the street network distance, which we term street egohoods. Methods: We used the street segment as a base unit and employed two strategies in clustering the street segments: (1) based on the First Order Queen Contiguity; and (2) based on the street network distance considering physical barriers. We utilized our approaches for measuring ecological factors and estimated crime rates in the Los Angeles metropolitan area. Results: We found that whereas certain socio-demographics, land use, and business employee measures show stronger relationships with crime when measured at the smaller street based unit, a number of them actually exhibited stronger relationships when measured using our larger street egohoods. We compared the results for our three-sized street egohoods to street segments and two sizes of block egohoods proposed by Hipp and Boessen (Criminology 51(2):287–327, 2013) and found that two egohood strategies essentially are not different at the quarter mile egohood level but this similarity appears lower when looking at the half mile egohood level. Also, the street egohood models are a better fit for predicting violent and property crime compared to the block egohood models. Conclusions: A primary contribution of the current study is to develop and propose a new perspective of measuring neighborhood based on urban streets. We empirically demonstrated that whereas certain socio-demographic measures show the strongest relationship with crime when measured at the micro geographic unit of street segments, a number of them actually exhibited the strongest relationship when measured using our larger street egohoods. We hope future research can use egohoods to expand understanding of neighborhoods and crime.

This study introduces filtering theory from housing economics to criminology and measures the age of housing as a proxy for deterioration and physical disorder. Using data for Los Angeles County in 2009 to 2011, negative binomial regression models are estimated and find that street segments with older housing have higher levels of all six crime types tested. Street segments with more housing age diversity have higher levels of all crime types, whereas housing age diversity in the surrounding ½-mile area is associated with lower levels of crime. Street segments with detached single-family units generally had less crime compared with other types of housing. Street segments with large apartment complexes (five or more units) generally have more crime than those with small apartment complexes and duplexes.

The present research examines the long-term, bidirectional relationships between calls for service, crime, and two police patrol strategies in Santa Monica, California: foot patrol and police stops. Using nine years of monthly data (2006–2014), we estimate two sets of block-level, longitudinal models to tease apart these relationships. In our first set of models, we use police actions and calls for service in the preceding month(s) to predict crime in the subsequent month. In our second set of models, we use calls for service and crime in the preceding month(s) to predict police actions in the subsequent month. We find that while changes in calls for service and crime often precede changes in police action, changes in crime also tend to follow them. For example, police stops appear to be particularly receptive to burglary: blocks with more burglaries receive greater numbers of police stops, and blocks with more police stops have reduced odds of experiencing burglary. We also find that the length of effects of predictors varies as a function of predictor and outcome: whereas some predictors exhibit short temporal effects (e.g. one month), other predictors exhibit much longer temporal effects (e.g. twelve months). Our results thus provide important insight into the spatial and temporal relationships between police actions and police incidents. Police actions must be neatly tailored to police incidents at precise levels if long-term deterrent effects at these levels are to be achieved.

A growing body of research has documented the consequences of neighborhood crime for a myriad of individual, household, and community outcomes. Given that neighborhood businesses figure into the link between neighborhood structure and crime as sources of employment or sites for neighbor interaction, the present study examines the extent to which neighborhood crime is associated with the survival, mobility, and destination locations of businesses in the subsequent year. Using business data from Reference USA (Infogroup, 2015) and crime data from the Southern California Crime Study (SCCS) we assess this question for neighborhoods across cities in the Southern California region. We find that in general, higher violent and property crime are significantly associated with both business failure and mobility, and that higher crime in a destination neighborhood reduces the likelihood that a business locates there. We also present findings specific to industries, and discuss the implications of our findings for future research.

Objectives: Given the evidence that crime events exhibit both a spatial and a temporal pattern, we explore whether certain social and physical environment characteristics have varying relationships with crime at different times of day. Methods: We assess this temporal question using a flexible nonlinear parametric approach on a large sample of street segments (and surrounding spatial area) in Southern California. Results: There are different temporal and spatial patterns for key measures. The presence of total employees in the surrounding area is associated with a reduced robbery risk during the daytime, but not at night. The risk of a robbery is elevated on a high retail segment on weekends during the daytime, and on high restaurant segments into the early evening on weekends. Furthermore, the presence of retail and restaurants in the surrounding area (evidence of shopping districts) was associated with elevated robbery risk in the afternoon and well into the evening. Conclusion: These different temporal patterns indicate the possibility of different mechanisms in operation.

The current study examines the relationship between the level of social activity and crime in place. We theoretically conceptualized the social activity as a combination of two essential elements of vitality and diversity. Our results suggest that level of social activity has a crime enhancing effect on both violent and property crime. We also found that there are positive interaction effects between the measures of vitality and diversity. This study contributes to the field by introducing a theoretically driven concept of social activity and empirically showing how the two dimensions of social activity—vitality and diversity—have independent effects that multiplicatively impact the level of crime at a location. [ABSTRACT FROM AUTHOR]

This study explored the dynamic nature of neighborhoods using a relatively novel approach and data source. By using a nonparametric holistic approach of neighborhood change based on latent class analysis (LCA), we have explored how changes in the socio-demographic characteristics of residents, as well as home improvement and refinance activity by residents, are related to changes in neighborhood crime over a decade. Utilizing annual home mortgage loan data in the city of Los Angeles from the years 2000-2010, we 1) conducted principle components factor analyses using measures of residential in-migration and home investment activities; 2) estimated LCA models to identify classes of neighborhoods that shared common patterns of change over the decade; 3) described these 11 classes; 4) estimated change-score regression models to assess the relationship of these classes with changing crime rates. The analyses detected six broad types of neighborhood change: 1) stability; 2) urban investors; 3) higher-income home buyers; 4) in-mover oscillating; 5) oscillating refinance; 6) mixed-trait. The study describes the characteristics of each of these classes, and how they are related to changes in crime rates over the decade.

This study examines the effects of neighborhood racial in-group size, economic deprivation and the prevalence of crime on neighborhood cohesion among U.S. whites. We explore to what extent residents' perceptions of their neighborhood mediate these macro-micro relationships. We use a recent individual-level data set, the American Social Fabric Study (2012/2013), enriched with contextual-level data from the U.S. Census Bureau (2010) and employ multi-level structural equation models. We show that the racial in-group size is positively related to neighborhood cohesion and that neighborhood cohesion is lower in communities with a high crime rate. Individuals' perceptions of the racial in-group size partly mediate the relationship between the objective racial in-group size and neighborhood cohesion. Residents' perceptions of unsafety from crime also appear to be a mediating factor, not only for the objective crime rate but also for the objective racial in-group size. This is in line with our idea that racial stereotypes link racial minorities to crime whereby neighborhoods with a large non-white population are perceived to be more unsafe. Residents of the same neighborhood differ in how they perceive the degree of economic decay of the neighborhood and this causes them to evaluate neighborhood cohesion differently, however perceptions of neighborhood economic decay do not explain the link between the objective neighborhood context and neighborhood cohesion.

Objectives: Examining the immigration-crime nexus across neighborhoods in the Southern California metropolitan region, this study builds on existing literature by unpacking immigration and accounting for the rich diversity that exists between immigrant groups. Methods: Using data from a variety of sources, we capture this diversity with three different approaches, operationalizing immigrant groups by similar racial/ethnic categories, areas or regions of the world that immigrants emigrate from, and where immigrants co-locate once they settle in the U.S. We also account for the heterogeneity of immigrant populations by constructing measures of immigrant heterogeneity based on each of these classifications. We compare these novel approaches with the standard approach, which combines immigrants together through a single measure of percent foreign born. Results: The results reveal that considerable insights are gained by distinguishing between diverse groups of immigrants. In particular, we find that all three strategies explained neighborhood crime levels better than the traditional approach. Conclusion: The findings underscore the necessity of disaggregating immigrant groups when exploring the immigration-crime relationship.

Scholars have theorized how spatial boundaries (edges) can be important for understanding the location of crime, yet the empirical relationship between spatial boundaries in the environment and levels of crime is relatively less explored compared with other features of the environment. The current study extends the literature by not only studying three types of physical boundaries—interstate highways, parks, and rivers—but also one nonphysical and relatively less visible boundary—city boundaries. We analyze the relationship between crime in street segments and nearness to these four types of edges in the Southern California area. We measure nearness to these boundaries in two manners: (a) whether or not the segment is adjacent to the feature and (b) how far in physical distance the segment is to the feature.

Whereas there is a burgeoning literature focusing on the spatial distribution of crime events across neighborhoods or micro-geographic units in a specific city, the present study expands this line of research by selecting four cities that vary across two macro-spatial dimensions: population in the micro-environment, and population in the broader macro-environment. We assess the relationship between measures constructed at different spatial scales and robbery rates in blocks in four cities: 1) San Francisco (high in micro- and macro-environment population); 2) Honolulu (high in micro- but low in macro-environment population); 3) Los Angeles (low in micro- but high in macro-environment population); 4) Sacramento (low in micro- and macro-environment population). Whereas the socio-demographic characteristics of residents further than ½ mile away do not impact robbery rates, the number of people up to 2.5 miles away are related to robbery rates, especially in the two cities with smaller micro-environment population, implying a larger spatial scale than is often considered. The results show that coefficient estimates differ somewhat more between cities differing in micro-environment population compared to those differing based on macro-environment population. It is therefore necessary to consider the broader macro-environment even when focusing on the level of crime across neighborhoods or micro-geographic units within an area.

Objectives We argue that assessing the level of crime concentration across cities has four challenges: (1) how much variability should we expect to observe; (2) whether concentration should be measured across different types of macro units of different sizes; (3) a statistical challenge for measuring crime concentration; (4) the temporal assumption employed when measuring high crime locations. Methods We use data for 42 cities in southern California with at least 40,000 population to assess the level of crime concentration in them for five different Part 1 crimes and total Part 1 crimes over 2005–2012. We demonstrate that the traditional measure of crime concentration is confounded by crimes that may simply spatially locate due to random chance. We also use two measures employing different temporal assumptions: a historically adjusted crime concentration measure, and a temporally adjusted crime concentration measure (a novel approximate solution that is simple for researchers to implement). Results There is much variability in crime concentration over cities in the top 5 % of street segments. The standard deviation across cities over years for the temporally adjusted crime concentration measure is between 10 and 20 % across crime types (with the average range typically being about 15–90 %). The historically adjusted concentration has similar variability and typically ranges from about 35 to 100 %. Conclusions The study provides evidence of variability in the level of crime concentration across cities, but also raises important questions about the temporal scale when measuring this concentration. The results open an exciting new area of research exploring why levels of crime concentration may vary over cities? Either micro-or macro-theories may help researchers in exploring this new direction.

This study explores whether the broader context in which a city is located impacts the change in crime levels over the subsequent decade. This study uses a wide range of cities (those with a population of at least 10,000), over a long period of time (from 1970 to 2010). We test and find that although cities with larger population and those surrounded by a county with a larger population typically experience larger increases in crime over the subsequent decade, cities experiencing an increase in population during the current decade experience crime decreases. The study finds that cities with higher average income experience greater subsequent crime decreases, and those surrounded by counties with larger unemployment increases experience crime increases. Higher levels of income inequality and racial/ethnic heterogeneity are associated with increasing crime rates, and increasing inequality and racial/ethnic heterogeneity in the surrounding county are associated with further increases. Furthermore, this relationship has strengthened since 1970, suggesting that both scales of inequality are even more important from a public safety perspective. Finally, we tested the time invariance of these relationships, and showed that the magnitude of the relationship between city-level inequality and increasing crime has increased over the study period.

In the aftermath of one of the worst recessions in US history, high unemployment has placed millions of Americans in precarious financial positions. More than ever, Americans are opting out of traditional financial services, relying instead on “fringe lenders” such as check cashers, payday lenders, and pawnshops to manage their finances. Given their tremendous growth and the concern that consumers who are least able to pay for high-cost, high-risk financial products are most likely to use them, fringe lenders have been the subject of controversy and the focus of much research. Largely unknown, however, are the effects of fringe lenders on the communities where they are located. Given their spatial concentration in low-income neighborhoods with greater concentrations of racial and ethnic minorities—areas with typically more crime—of concern is whether fringe lenders themselves are criminogenic. We consider this by examining the impact of several types of fringe lenders on neighborhood crime rates in Los Angeles. Our findings reveal that the presence of fringe banks on a block is related to higher crime levels, even after controlling for a range of factors known to be associated with crime rates. The presence of a fringe bank also impacts crime, particularly robbery, on adjacent blocks. Whereas we find that pawnshops have little impact on crime levels, payday lenders and check cashers have a much stronger impact. Finally, we discover there are moderating effects, as the fringe lender–crime relationship is considerably reduced if the lender is located in a higher population density area.

© 2015 American Society of Criminology. Whereas one line of recent neighborhood research has placed an emphasis on zooming into smaller units of analysis such as street blocks, another line of research has suggested that even the meso-area of neighborhoods is too narrow and that the area surrounding the neighborhood is also important. Thus, there is a need to examine the scale at which the social ecology impacts crime. We use data from seven cities from around the year 2000 to test our research questions using multilevel negative binomial regression models (N = 73,010 blocks and 8,231 block groups). Our results suggest that although many neighborhood factors seem to operate on the microscale of blocks, others seem to have a much broader impact. In addition, we find that racially and ethnically homogenous blocks within heterogeneous block groups have the most crime. Our findings also show the strongest results for a multitude of land-use measures and that these measures sharpen some of the associations from social characteristics. Thus, we find that accounting for multiple scales simultaneously is important in ecological studies of crime.

Although a growing body of research has examined and found a positive relationship between neighborhood crime and home foreclosures, some research suggests this relationship may not hold in all cities. This study uses city-level data to assess the relationship between foreclosures and crime by estimating longitudinal models with lags for monthly foreclosure and crime data in 128 cities from 1996 to 2011 in Southern California. We test whether these effects are stronger in cities with a combination of high economic inequality and high economic segregation; and whether they are stronger in cities with high racial/ethnic heterogeneity and high racial segregation. One month, and cumulative three month, six month, and 12-month lags of foreclosures are found to increase city level crime for all crimes except motor vehicle theft. The effect of foreclosures on these crime types is stronger in cities with simultaneously high levels of inequality but low levels of economic segregation. The effect of foreclosures on aggravated assault, robbery, and burglary is stronger in cities with simultaneously high levels of racial heterogeneity and low levels of racial segregation. On the other hand, foreclosures had a stronger effect on larceny and motor vehicle theft when they occurred in a city with simultaneously high levels of racial heterogeneity and high levels of racial segregation. There is evidence that the foreclosure crisis had large scale impacts on cities, leading to higher crime rates in cities hit harder by foreclosures. Nonetheless, the economic and racial characteristics of the city altered this effect.

"Prior research frequently observes a positive cross-sectional relationship between various neighborhood structural characteristics and crime rates, and attributes the causal explanation entirely to these structural characteristics.  We question this assumption theoretically, proposing a household-level model showing that neighborhood crime might also change these structural characteristics.  We test these hypotheses using data on the census tracts in 13 cities over a ten-year period, and our cross-lagged models generally find that, if anything, crime is the stronger causal force in these possible relationships.  Neighborhoods with more crime tend to experience increasing levels of residential instability, more concentrated disadvantage, a diminishing retail environment, and more African Americans ten years later.  Although we find that neighborhoods with more concentrated disadvantage experience increases in violent and property crime, there is no evidence that residential instability increases crime rates ten years later."

"ObjectivesPrevious criminological scholarship has posited that network ties among neighborhood residents may impact crime rates, but has done little to consider the specific ways in which network structure may enhance or inhibit criminal activity. A lack of data on social ties has arguably led to this state of affairs. We propose to avoid this limitation by demonstrating a novel approach of extrapolatively simulating network ties and constructing structural network measures to assess their effect on neighborhood crime rates.MethodsWe first spatially locate the households of a city into their constituent blocks. Then, we employ spatial interaction functions based on prior empirical work and simulate a network of social ties among these residents. From this simulated network, we compute network statistics that more appropriately capture the notions of cohesion and information diffusion that underlie theories of networks and crime.ResultsWe show that these network statistics are robust predictors of the levels of crime in five separate cities (above standard controls) at the very micro geographic level of blocks and block groups.ConclusionsWe conclude by considering extensions of the approach that account for homophily in the formation of network ties."

Defining “neighborhoods” is a bedeviling challenge faced by all studies of neighborhood effects and ecological models of social processes.  Although scholars frequently lament the inadequacies of the various existing definitions of “neighborhood”, we argue that previous strategies relying on non-overlapping boundaries such as block groups and tracts are fundamentally flawed.  The approach taken here instead builds on insights of the mental mapping literature, the social networks literature, the daily activities pattern literature, and the travel to crime literature to propose a new definition of neighborhoods:  egohoods.  These egohoods are conceptualized as waves washing across the surface of cities, as opposed to independent units with non-overlapping boundaries.  This approach is illustrated using crime data from nine cities:  Buffalo, Chicago, Cincinnati, Cleveland, Dallas, Los Angeles, Sacramento, St. Louis, and Tucson.  The results show that measures aggregated to our egohoods explain more of the variation in crime across the social environment than do models with measures aggregated to block groups or tracts.  Results also suggest that measuring inequality in egohoods provides dramatically stronger positive effects on crime rates than when using the non-overlapping boundary approach, highlighting the important new insights that can be obtained by utilizing our egohood approach.

Objectives: This study investigated the extent to which immigrant concentration is associated with reductions in neighborhood crime rates in the City of Los Angeles. Methods: A potential outcomes model using two-stage least squares regression was estimated, where immigrant concentration levels in 1990 were used as an instrumental variable to predict immigrant concentration levels in 2000. The instrumental variables design was used to reduce selection bias in estimating the effect of immigrant concentration on changes in official crime rates between 2000 and 2005 for 835 census tracts in the City of Los Angeles, holding constant other demographic variables and area-level fixed effects. Non-parametric smoothers were also employed in a two-stage least squares regression model to control for the potential influence of heterogeneity in immigrant concentration on changes in crime rates. Results: The results indicate that greater predicted concentrations of immigrants in neighborhoods are linked to significant reductions in crime. The results are robust to a number of different model specifications. Conclusions: The findings challenge traditional ecological perspectives that link immigrant settlement to higher rates of crime. Immigration settlement patterns appear to be associated with reducing the social burden of crime. Study conclusions are limited by the potential for omitted variables that may bias the observed relationship between immigrant concentration and neighborhood crime rates, and the use of only official crime data which may under report crimes committed against immigrants. Understanding whether immigrant concentration is an important dynamic of changing neighborhood patterns of crime outside Los Angeles will require replication with data from other U.S. cities.

Theories make varying predictions regarding the functional form of the relationship between neighborhood poverty and crime rates, ranging from a diminishing positive effect, to a linear positive effect, to an exponentially increasing or even threshold effect.  Nonetheless, surprisingly little empirical evidence exists testing this functional form.  The present study estimates the functional form of the relationship between poverty and various types of serious crime in a sample of census tracts for 25 cities, and finds that a diminishing positive effect most appropriately characterizes this relationship whether estimating the models nonparametrically or parametrically.  Only for the crime of murder is there some evidence of an accelerating effect, although this occurs in the range of 20 to 40% in poverty, with a leveling effect on crime beyond this point of very high poverty.  Thus, there is no evidence here in support of the postulate of scholars extending William Julius Wilson’s insight that neighborhoods with very high levels of poverty will experience an exponentially higher rate of crime compared to other neighborhoods.

This study links social network methodology with the social disorganization literature to test the effect of block-level social distance on neighborhood perceived crime and disorder. Employing a unique study design that allows creating matrices of social distance (based on demographic characteristics) between 11 residents on each of over 650 blocks at three time points, we find that more socially distant residents perceive more disorder than their neighbors. Consistent with the bridging social capital literature, overall social distance in the block has a curvilinear relationship with perceived crime. And blocks with two cohesive subgroups, based on social distance, have lower levels of perceived disorder. © 2009 Elsevier B.V. All rights reserved.

"Most prior research testing the hypothesis of the social disorganization theory that residential instability increases crime has used cross-sectional data. Using a unique dataset linking home sales address matched to census tracts with crime data in Los Angeles, we test the direction of this relationship using a six-year panel data design. We also test whether crime acts as a generator of transition and decline in neighborhoods by testing its effect on property values the following year. Our findings suggest little evidence that home sales volatility in one year leads to more property or violent crime the following year.  Instead, higher levels of tract property and violent crime in one year lead to more home sales the following year. This effect of high crime rates is exacerbated in tracts with high levels of racial/ethnic heterogeneity, suggesting that such tracts may engender a distinct combination of fear and uncertainty in their residents, leading to more turn over. We also find that tracts with more violent crime one year have lower property values the following year, suggesting a general process of decline."

This paper highlights the importance of seriously considering the proper level of aggregation when estimating neighborhood effects. Using a unique non-rural sub-sample from a large national survey (the American Housing Survey) at three time points that allows placing respondents in blocks and census tracts, this study tests the appropriate level of aggregation of the structural characteristics hypothesized to affect block-level perceived crime and disorder. A key finding is that structural characteristics differ in their effects based on the level of aggregation employed. While the effects of racial/ethnic heterogeneity were fairly robust to geographical level of aggregation, the stronger effects when measured at the level of the surrounding census tract suggest more far-flung networks are important for perceived crime and disorder. In contrast, economic resources showed a particularly localized effect only evident when aggregating to the block-level and differed based on the outcome: higher average income reduced disorder, but increased crime, likely by increasing the number of attractive targets. And the presence of broken households had a localized effect for social disorder, but a more diffuse effect for perceived crime. These findings suggest the need to consider the mechanisms involved when aggregating various structural characteristics in neighborhood studies of crime rates, as well as the broader neighborhood effects literature.

Whereas existing ecology of crime research frequently uses a cross‐sectional design, an open question is whether theories underlying such studies will operate similarly in longitudinal research. Using latent trajectory models and longitudinal data in half‐mile egohoods from the Southern California region over a 10‐year period (2000–2010), we explore this question and assess whether the changes in key measures of social disorganization theory are related to changes in violent or property crime through three possible relationships: 1) a monotonic relationship, 2) an asymmetric relationship, and 3) a perturbation relationship in which any change increases crime. We find evidence that measures can exhibit any of these three possible relationships, highlighting the importance of not assuming monotonic relationships. Most frequently observed are asymmetric relationships, which we posit are simultaneously capturing more than one theoretical process of neighborhoods and crime. Specific findings include asymmetric relationships between change in concentrated disadvantage, racial/ethnic minority composition, or population and violent crime, as well as relationships between change in Asian composition or population and property crime. We consider how this strategy opens a needed area of future research assessing how measures for other theories operate as environments change. [ABSTRACT FROM AUTHOR]

Objectives: Criminological scholars have long been interested in how macro-level characteristics of cities, counties, or metropolitan areas are related to levels of crime. The standard analytic approach in this literature aggregates constructs of interest, including crime rates, to the macro geographic units and estimates regression models, but this strategy ignores possible sub-city-level processes that occur simultaneously. Methods: One solution uses multilevel data of crime in meso-level units within a large number of cities; however, such data is very difficult and time intensive to collect. We propose an alternative approach which utilizes insights from existing literature on meso-level processes along with meso-level socio-demographic measures in cities to impute crime data from the city to the smaller geographic units. This strategy allows researchers to estimate full multilevel models that estimate the effects of macro-level processes while controlling for sub-city-level factors. Results: We demonstrate that the strategy works as expected on a sample of 91 cities with meso-level data, and also works well when estimating the multilevel model on a sample of cities different from the imputation model, or even in a different time period. Conclusions: The results demonstrate that existing studies aggregated to macro units can yield considerably different (and therefore potentially problematic) results when failing to account for meso-level processes. [ABSTRACT FROM AUTHOR]