Replicating Group-Based Trajectory Models of Crime at Micro-Places in Albany,NY

Objectives

Replicate two previous studies of temporal crime trends at the street block level. We replicate the general approach of group-based trajectory modelling of crimes at micro-places originally taken by Weisburd et al. (Criminology 42(2):283–322, 2004) and replicated by Curman et al. (J Quant Criminol 31(1):127–147, 2014). We examine patterns in a city of a different character (Albany, NY) than those previously examined (Seattle and Vancouver) and so contribute to the generalizability of previous findings.

Methods

Crimes between 2000 and 2013 were used to identify different trajectory groups at street segments and intersections. Zero-inflated Poisson regression models are used to identify the trajectories. Pin maps, Ripley’s K and neighbor transition matrices are used to show the spatial patterning of the trajectory groups.

Results

The trajectory solution with eight classes is selected based on several model selection criteria. The trajectory of each those groups follow the overall citywide decline, and are only separated by the mean level of crime. Spatial analysis shows that higher crime trajectory groups are more likely to be nearby one another, potentially suggesting a diffusion process.

Conclusions

Our work adds additional support to that of others who have found tight coupling of crime at micro-places. We find that the clustering of trajectories identified a set of street units that disproportionately contributed to the total level of crime citywide in Albany, consistent with previous research. However, the temporal trends over time in Albany differed from those exhibited in previous work in Seattle but were consistent with patterns in Vancouver.

     

Assessing the Validity of the Law of Crime Concentration Across Different Temporal Scales

Objectives

The present study examined if Weisburd’s (Criminology 53(2):133–157, 2015) law of crime concentration held across different theoretically relevant temporal scales.

Methods

The cumulative percentages of Philadelphia, PA USA street blocks and intersections experiencing 25 and 50 % of street robberies by hour of the day, days of the week, and seasons of the year were compared to the bandwidth percentages established by Weisburd (2015). Different analyses were used to determine the stability of the micro-places’ street robbery levels within the three temporal scales.

Results

We found that the cumulative percentages of street blocks and intersections experiencing 25 and 50 % of street robberies at each of the three temporal scales closely matched the bandwidth percentages expected from Weisburd (2015) and some micro-places experienced street robberies across all temporal periods while others had more isolated temporal concentrations.

Conclusion

Weisburd’s (2015) law of crime concentration holds across different theoretically relevant temporal scales, and future criminology of place studies should not ignore temporal crime patterns. Further, it may be possible to refine hot spots policing approaches by incorporating spatial–temporal crime concentrations.

     

The Law of Crime Concentration: An Application and Recommendations for Future Research

Objective

We address four outstanding empirical questions related to the “law of crime concentration” (Weisburd in Criminology 53:133–157, 2015): (1) Is the spatial concentration of crime stable over time? (2) Do the same places consistently rank among those with the highest crime counts? (3) How much crime concentration would be observed if crimes were distributed randomly over place? (4) To what degree does the spatial concentration of crime depend on places that are crime free?

Methods

The data are annual counts of violent and property crimes in St. Louis between 2000 and 2014. Temporal stability in the spatial inequality of crime is measured by computing the fraction of crimes that occur in the 5% of street segments with the highest crime frequencies each year. The spatial mobility of crime is measured by computing the number of years each street segment appears in the top 5% of street segments. Poisson simulations are used to estimate the fraction of crimes that could appear in the top 5% of street segments on the basis of chance alone. The impact of crime-free locales on the spatial concentration of crime is evaluated by comparing results from analyses that include and exclude crime-free street segments from the crime distributions.

Results

The concentration of crime is highly unequal and stable over time. The specific street segments with the highest crime frequencies, however, change over time. Nontrivial fractions of street segments may appear among the 5% with the highest crime frequencies on the basis of chance. Spatial concentration of crime is reduced when crime-free street segments are excluded from the crime distributions.

Conclusions

The law of crime concentration is not a measurement artifact. Its substantive significance, however, should be assessed in future longitudinal research that replicates the current study across diverse social settings.

     

The Influence of Community Areas,Neighborhood Clusters,and Street Segments on the Spatial Variability of Violent Crime in Chicago

Objectives

The influence of three hierarchical units of analysis on the total spatial variability of violent crime incidents in Chicago is assessed. This analysis seeks to replicate a recent study that found street segments, rather than neighborhood units of analysis, accounted for the largest share of the total spatial variability of crime in The Hague, Netherlands (see Steenbeek and Weisburd J Quant Criminol. doi: 10.1007/s10940-015-9276-3, 2015).

Methods

We analyze violent crime incidents reported to the police between 2001 and 2014. 359,786 incidents were geocoded to 41,926 street segments nested within 342 neighborhood clusters, in turn nested within 76 community areas in Chicago. Linear mixed models with random slopes of time were estimated to observe the variance uniquely attributed to each unit of analysis.

Results

Similar to Steenbeek and Weisburd, we find 56–65 % of the total variability in violent crime incidents can be attributed to street segments in Chicago. City-wide reductions in violence over the observation period coincide with increases in the spatial variability attributed to street segments and decreases in the variability attributed to both neighborhood units.

Conclusions

Our results suggest that scholars interested in understanding the spatial variation of crime across urban landscapes should be focused on the small places that comprise larger geographic areas. The next wave of “neighborhood-effects” research should explore the role of hierarchical processes in understanding crime variation within larger areas.

     

More Places than Crimes: Implications for Evaluating the Law of Crime Concentration at Place

Objectives

The crime and place literature lacks a standard methodology for measuring and reporting crime concentration. We suggest that crime concentration be reported with the Lorenz curve and summarized with the Gini coefficient, and we propose generalized versions of the Lorenz curve and the Gini coefficient to correct for bias when crime data are sparse (i.e., fewer crimes than places).

Methods

The proposed generalizations are based on the principle that the observed crime concentration should not be compared with perfect equality, but with maximal equality given the data. The generalizations asymptotically approach the original Lorenz curve and the original Gini coefficient as the number of crimes approaches the number of spatial units.

Results

Using geocoded crime data on two types of crime in the city of The Hague, we show the differences between the original Lorenz curve and Gini coefficient and the generalized versions. We demonstrate that the generalizations provide a better representation of crime concentration in situations of sparse crime data, and that they improve comparisons of crime concentration if they are sparse.

Conclusions

Researchers are advised to use the generalized versions of the Lorenz curve and the Gini coefficient when reporting and summarizing crime concentration at places. When places outnumber crimes, the generalized versions better represent the underlying processes of crime concentration than the original versions. The generalized Lorenz curve, the Gini coefficient and its variance are easy to compute.

     

Measuring the Spatial and Temporal Patterns of Police Proactivity

Objectives

To measure where officers engage in proactive, self-initiated activities, how much time they spend being proactive, and whether their proactive activities coincide with crime patterns.

Methods

This study uses Andresen’s Spatial Point Pattern Test to compare the spatial similarity between police proactivity and crime, as well as regression modeling to explore the relationship between proactivity and crime and the time spent on proactivity and crime.

Results

In the jurisdiction examined, high levels of proactivity are noted. This proactive activity is more likely to occur in places where crime is most concentrated. Additionally, the number of proactive calls and the proactive time spent per crime-and-disorder call remain high and stable across spatial scales. For each crime call received at a street block, police initiated 0.7 proactive activities and spent approximately 28 min carrying out proactive works.

Conclusions

This study develops a way of measuring proactive activity by patrol officers using calls for service data. We find that not only do officers in this jurisdiction exhibit higher levels of proactivity to prevent crime (compared to reacting to crime), but they also do so in targeted, micro-place ways. Agencies may consider using similar techniques to gauge the levels of proactivity in their agencies if proactive activity is a goal.

     

Measuring Crime Concentration Across Cities of Varying Sizes: Complications Based on the Spatial and Temporal Scale Employed

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.

     

The Impact of Field Court Attendance Notices on Property Crime in New South Wales,Australia

Objectives

This paper investigates the impact of Field Court Attendance Notices (FCANs) on rates of property crime in New South Wales (NSW), Australia. FCANs are used for relatively minor offenses, are issued ‘on the spot’, and provide an alternative to the time consuming process of arresting an alleged offender and taking them to the police station for processing. Despite their use in NSW for over 20 years, this study is the first to evaluate their impact on crime.

Methods

We use data provided by the NSW Bureau of Crime Statistics and Research, and the Australian Bureau of Statistics. We specify a general dynamic panel data model estimated via the Arellano and Bond (Rev Econ Stud 58:277–297, 1991) estimator, specifically the first-differenced twostep generalised method of moments (GMM) estimator.

Results

For property crime as a whole, in both the short- and long-run, we find no significant relationship between the use of FCANS and levels of offending. However, when offending rates are disaggregated into 11 sub-categories, we find that in the short-run an increase in the use of FCANs leads to statistically significant decreases in the rate of crime for five of the sub-categories offenses considered (break and enter dwelling; motor vehicle theft; steal from motor vehicle; steal from retail store and; steal from dwelling). The long-run results are largely consistent with the short-run results in terms of their signs and statistical significance, suggesting that the effects persist.

Conclusions

The empirical analysis presented in this paper suggests that the use of FCANs is an effective and potentially efficient policing strategy for a subset of property offenses, in that offenders can be processed at lower cost and long-run rates of certain crimes reduced.

     

A quasi-experimental evaluation of the impact of bike-sharing stations on micro-level robbery occurrence

Objectives

To examine if the implementation of bike-sharing stations is linked to robbery occurrence in micro-level street corner units in Cincinnati, OH, USA.

Methods

Propensity score matching was used to select comparison street corner units. The effect of bike-sharing station implementation on robbery occurrence across weekly, biweekly, and monthly observations was estimated using repeated measures multi-level logistic regression models.

Results

Bike-sharing stations did not statistically significantly link to robbery occurrence in immediate or nearby street corner units after implementation.

Conclusions

Numerous explanations consistent with Crime Pattern Theory may explain the null effect of bike-sharing stations on robbery occurrence. Future research should continue to examine how changes in the urban backcloth, such as bike-sharing stations, impact geographic crime patterns.

     

The Structure of Citizen Perceptions of Crime and Disorder: New Insights from a Caribbean Community

Objectives

This study builds on existing research from US cities on the construct and discriminant validity of perceptual measures of crime and disorder. It seeks to determine whether citizens distinguish between crime and disorder.

Methods

This study draws on quantitative and qualitative data from a high-crime community in Trinidad and Tobago, a small-island developing nation in the eastern Caribbean. Analysis of the quantitative data relies on exploratory and confirmatory factor analysis methods designed for use with categorical observed variables and continuous latent variables.

Results

In contrast to previous research, we find that citizens do distinguish between physical disorder and general crime, but there is a perceptual overlap for some drug-related offenses and types of social disorder.

Conclusions

This study raises questions about the external validity of research on the relationship between perceptions of crime and disorder conducted in the US, and contributes to ongoing discussions and debates about the meaning of disorder. The findings suggest the need for theory and research to explain how context shapes not only the magnitudes of these perceptions, but also their structures. The results also demonstrate the benefits of mixed-methods research approaches in this area of study.

     

Examining Systematic Crime Reporting Bias Across Three Immigrant Generations: Prevalence,Trends, and Divergence in Self-Reported and Official Reported Arrests

Objective

Mounting evidence reveals that foreign-born, first generation immigrants have significantly lower levels of criminal involvement compared to their US-born, second and third-plus generation peers. This study investigates whether this finding is influenced by differential crime reporting practices by testing for systematic crime reporting bias across first, second, and third-plus generation immigrants.

Methods

This study draws on data from the Pathways to Desistance Study, a longitudinal investigation of the transition from adolescence to young adulthood among a sample of serious adolescent offenders. Self-reported and official reports of arrest are compared longitudinally across ten waves of data spanning 7 years from adolescence into young adulthood for nearly 1300 adjudicated males and females.

Results

This study reveals a high degree of correspondence between self-reports of arrest and official reports of arrest when compared within groups distinguished by immigrant generation. Longitudinal patterns of divergence, disaggregated by under-reporting and over-reporting, in self- and official-reports of arrest indicated a very high degree of similarity regardless of immigrant generation. We found no evidence of systematic crime reporting bias among foreign-born, first generation immigrants compared to their US-born peers.

Conclusions

First generation immigrants are characterized by lower levels of offending that are not attributable to a differential tendency to under-report their involvement in crime.

     

Where the Action is in Crime? An Examination of Variability of Crime Across Different Spatial Units in The Hague, 2001–2009

Objectives

To identify how much of the variability of crime in a city can be attributed to micro (street segment), meso (neighborhood), and macro (district) levels of geography. We define the extent to which different levels of geography are important in understanding the crime problem within cities and how those relationships change over time.

Methods

Data are police recorded crime events for the period 2001–2009. More than 400,000 crime events are geocoded to about 15,000 street segments, nested within 114 neighborhoods, in turn nested within 44 districts. Lorenz curves and Gini coefficients are used to describe the crime concentration at the three spatial levels. Linear mixed models with random slopes of time are used to estimate the variance attributed to each level.

Results

About 58–69 % of the variability of crime can be attributed to street segments, with most of the remaining variability at the district level. Our findings suggest that micro geographic units are key to understanding the crime problem and that the neighborhood does not add significantly beyond what is learned at the micro and macro levels. While the total number of crime events declines over time, the importance of street segments increases over time.

Conclusions

Our findings suggest that micro geographic units are key to understanding the variability of crime within cities—despite the fact that they have received little criminological focus so far. Moreover, our results raise a strong challenge to recent focus on such meso geographic units as census block groups.

     

Testing the “Law of Crime Concentration at Place” in a Suburban Setting: Implications for Research and Practice

Objective

To assess whether the “law of crime concentration at place” applies in a non-urban context. We test whether longitudinal trends in crime concentration, stability, and variability apply in a suburban setting.

Methods

We use group-based trajectory analysis to examine trends in recorded crime incidents on street segments in Brooklyn Park, a suburban city outside Minneapolis, Minnesota, over a 15-year period from 2000 to 2014.

Results

Consistent with the law of crime concentration at place, crime in Brooklyn Park is highly concentrated at a small percentage of micro-places. Two percent of street segments produced 50 % of the crime over the study period and 0.4 % of segments produced 25 % of the crime. The patterns of concentration are highly stable over time. However, the concentration of crime is substantially higher and there is much less street-by-street variability in Brooklyn Park compared to urban areas.

Conclusions

We find strong support for the application of the law of crime concentration at place to a non-urban setting, suggesting that place-based policing approaches tested in cities can also be applied to suburbs. However, there are also important differences in the concentration and variability of crime hot spots in suburbs that require further examination. Our study is based on a single setting that may not be representative of other suburban and rural areas. Finally, the clustering of hot spots raises questions about the use of street segments to analyze crime at suburban micro-places.

     

Violence in Urban Neighborhoods: A Longitudinal Study of Collective Efficacy and Violent Crime

Objectives

Cross-sectional studies consistently find that neighborhoods with higher levels of collective efficacy experience fewer social problems. Particularly robust is the relationship between collective efficacy and violent crime, which holds regardless of the socio-structural conditions of neighborhoods. Yet due to the limited availability of neighborhood panel data, the temporal relationship between neighborhood structure, collective efficacy and crime is less well understood.

Methods

In this paper, we provide an empirical test of the collective efficacy-crime association over time by bringing together multiple waves of survey and census data and counts of violent crime incident data collected across 148 neighborhoods in Brisbane, Australia. Utilizing three different longitudinal models that make different assumptions about the temporal nature of these relationships, we examine the reciprocal relationships between neighborhood features and collective efficacy with violent crime. We also consider the spatial embeddedness of these neighborhood characteristics and their association with collective efficacy and the concentration of violence longitudinally.

Results

Notably, our findings reveal no direct relationship between collective efficacy and violent crime over time. However, we find a strong reciprocal relationship between collective efficacy and disadvantage and between disadvantage and violence, indicating an indirect relationship between collective efficacy and violence.

Conclusions

The null direct effects for collective efficacy on crime in a longitudinal design suggest that this relationship may not be as straightforward as presumed in the literature. More longitudinal research is needed to understand the dynamics of disadvantage, collective efficacy, and violence in neighborhoods.

     

Measuring the Distribution of Crime and Its Concentration

Objectives

Generally speaking, crime is, fortunately, a rare event. As far as modelling is concerned, this sparsity of data means that traditional measures to quantify concentration are not appropriate when applied to crime suffered by a population. Our objective is to develop a new technique to measure the concentration of crime which takes into account its low frequency of occurrence and its high degree of concentration in such a way that this measure is comparable over time and over different populations.

Methods

This article derives an estimate of the distribution of crime suffered by a population based on a mixture model and then evaluates a new and standardised measurement of the concentration of the rates of suffering a crime based on that distribution.

Results

The new measure is successfully applied to the incidence of robbery of a person in Mexico and is able to correctly quantify the concentration crime in such a way that is comparable between different regions and can be tracked over different time periods.

Conclusions

The risk of suffering a crime is not uniformly distributed across a population. There are certain groups which are statistically immune to suffering crime but there are also groups which suffer chronic victimisation. This measure improves our understanding of how patterns of crime can be quantified allowing us to determine if a prevention policy results in a crime reduction rather than target displacement. The method may have applications beyond crime science.

     

Examining the Relationship Between the Structural Characteristics of Place and Crime by Imputing Census Block Data in Street Segments: Is the Pain Worth the Gain?

Objective

The current study proposes unique methods for apportioning existing census data in blocks to street segments and examines the effects of structural characteristics of street segments on crime. Also, this study tests if the effects of structural characteristics of street segments are similar with or distinct from those of blocks.

Methods

This study compiled a unique dataset in which block-level structural characteristics are apportioned to street segments utilizing the 2010 U.S. Census data of the cities of Anaheim, Santa Ana, and Huntington Beach in Orange County, California. Negative binomial regression models predicting crime that include measures of social disorganization and criminal opportunities in street segments and blocks were estimated.

Results

The results show that whereas some of the coefficients tested at the street segment level are similar to those aggregated to blocks, a few were quite different (most notably, racial/ethnic heterogeneity). Additional analyses confirm that the imputation methods are generally valid compared to data actually collected at the street segment level.

Conclusions

The results from the street segment models suggest that the structural characteristics from social disorganization and criminal opportunities theories at street segments may operate as crucial settings for crime. Also the results indicate that structural characteristics have generally similar effects on crime in street segments and blocks, yet have some distinct effects at the street segment level that may not be observable when looking at the block level. Such differences underscore the necessity of serious consideration of the issues of level of aggregation and unit of analysis when examining the structural characteristics-crime nexus.

     

Declines in Crime and Teen Childbearing: Identifying Potential Explanations for Contemporaneous Trends

Objectives

The previous 25 years have witnessed remarkable upheavals in the social landscape of the United States. Two of the most notable trends have been dramatic declines in levels of crime as well as teen childbearing. Much remains unknown about the underlying conditions that might be driving these changes. More importantly, we do not know if the same distal factors that are responsible for the drop in crime rates are similarly implicated in falling rates of teen births. We examine four overarching potential explanations: fluctuations in economic opportunity, shifting population demographics, differences in state-level public policies, and changes in expectations regarding health and mortality.

Methods

We combine state and year-specific data from existing secondary sources to model trajectories of violent crime and teen fertility over a 20-year period from 1990 to 2010 using simultaneous fixed-effects regression models.

Results

We find that 4 of the 20 predictors examined—growth in the service sector of the labor market, increasing racial diversity especially among Hispanics, escalating levels of migration, and the expansion of family planning services to low-income women—offer the most convincing explanations for why rates of violent crime and teen births have been steadily decreasing over time. Moreover, we are able to account for almost a quarter of the joint declines in violent crime and teen births.

Conclusions

Our conclusions underscore the far reaching effects that aggregate level demographic conditions and policies are likely to have on important social trends that might, at first glance, seem unrelated. Furthermore, the effects of policy efforts designed to target outcomes in one area are likely to spill over into other domains.

     

The Concentration and Stability of Drug Activity in Seattle,Washington Using Police and Emergency Medical Services Data

Objectives

Prior research demonstrates that crime is highly concentrated at place and that these concentrations are stable from year to year, highlighting the importance of place to crime control and prevention. A potential limitation is that most studies only use one data source to diagnose these patterns. The present study uses data from both police and emergency medical services (EMS) to explore the spatial concentration and stability of drug activity in Seattle, Washington from 2009 to 2014.

Methods

We use concentration graphs and group-based trajectory analysis to examine concentration and stability of calls related to drug activity in both data sources separately and combined. Additionally, we employ Andresen’s S-Index to determine the similarity of concentration within the SPD data, the EMS data and the combined data year to year as well as the degree of co-location between the SPD and EMS data during the study period.

Results

We find a high degree of concentration and group-based stability for both SPD and EMS drug calls across all street segments in Seattle. Conversely, we find only moderate local geographic stability of drug use across street segments as indicated by each of the data sources over the study period. Last, we find the spatial patterns in drug use as indicated by each data source are significantly different each year.

Conclusions

At the same time these findings provide support for the law of crime concentration, they also raise questions about local stability patterns. Additionally, they highlight the importance of expanding inquiries of crime and place research into new data sources. Our results serve to reinforce the importance of multiple data sets in quantifying, understanding, and responding to the drug problem in Seattle.

     

Dissecting the Prevalence and Incidence of Offending During the Crime Drop of the 1990s

Objectives

To evaluate whether the 1990s crime drop reflects a decrease in offending prevalence (the fraction of the population engaged in crime), offending incidence (the frequency of offending among active criminals), or some combination of the two.

Methods

We use individual-level longitudinal data on adolescent offending patterns from the Pittsburgh Youth study (PYS), integrating information from the youngest and oldest cohorts to compare offending among 17–18 year old males at the beginning and end of the 1990s. Logistic and negative binomial regression models are estimated to assess whether there are significant differences in offending prevalence and incidence during the 1990s.

Results

The reduction in property crime rates in the PYS sample during the 1990s can be attributed to declines in both offending prevalence and incidence. The overall decline in serious violence during the 1990s for the full sample was primarily the result of a falloff in prevalence. However, for black youth our results indicate significant reductions in both the prevalence and incidence of serious violence. We did not detect a significant difference in illegal drug sales during the period.

Conclusions

Using longitudinal data on individuals to decompose aggregate crime trends into changes in the prevalence and incidence of offending offers insights into the nature of the 1990s crime drop that cannot be discerned from aggregate crime data. Future research should build on the current study by examining the specific mechanisms that influence change over time in crime prevalence, incidence, or both.

     

The Murder Mystery: Police Effectiveness and Homicide

Objective

Previous aggregate analyses of the effect of police on crime show that increases in police staffing are especially effective at preventing homicide. This conflicts with evidence that suggests standard police methods should be more effective at preventing robbery, auto theft, and other property crimes. My objective is to reconcile the two.

Methods

Regression of crime rates on uniformed police staffing and other economic and demographic covariates, for a panel of 59 US cities for the period 1970–2013.

Results

Lagged crime rates are strong and statistically significant predictors of both policing staffing and crime rates, particularly homicide. When lags are included in the specification, the apparent effect of police on homicide drops by more than 70 %; there is little change in the effect of police on other crimes. Findings are robust with respect to specification and method.

Conclusions

Previous studies omitted lags and overstated the effectiveness of police on homicide. Because murder accounts for almost 40 % of all costs of crime in US cities, it is no longer clear whether increasing police force size is a cost-effective way to cut crime. Improving police tactics is more likely to work and less expensive.