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.

     

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.

     

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 Trajectories of Crime at Places: Understanding the Patterns of Disaggregated Crime Types

Objectives

Investigate the spatial concentrations and the stability of trajectories for disaggregated crime types on street segments and intersections in Vancouver, Canada.

Methods

A longitudinal analysis of 16 years of crime data using street segments and intersections as the units of analysis. We use the k-means non-parametric cluster analysis technique considering eight crime types: assault, burglary, robbery, theft, theft of vehicle, theft from vehicle, other, and total crime.

Results

The overall results for the individual crime types versus overall crime are similar: crime is highly concentrated regardless of crime type, most street segment and intersection trajectories are stable over time with the others decreasing, and most decreasing trajectories are in the same general areas. However, there are notable differences across crime types that need to be considered when attempting to understand spatial pattern changes and implement crime prevention initiatives.

Conclusions

The law of crime concentration at places holds in Vancouver, Canada for disaggregated crime types in the context of spatial concentrations and their stability over time. However, notable differences exist across crime types that should be accounted for when developing theory or policy.

     

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.

     

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.

     

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.

     

The Gains of Greater Granularity: The Presence and Persistence of Problem Properties in Urban Neighborhoods

Objectives

This study applies the growing emphasis on micro-places to the analysis of addresses, assessing the presence and persistence of “problem properties” with elevated levels of crime and disorder. It evaluates what insights this additional detail offers beyond the analysis of neighborhoods and street segments.

Methods

We used over 2,000,000 geocoded emergency and non-emergency requests received by the City of Boston’s 911 and 311 systems from 2011–2013 to calculate six indices of violent crime, physical disorder, and social disorder for all addresses (n = 123,265). We linked addresses to their street segment (n = 13,767) and census tract (n = 178), creating a three-level hierarchy that enabled a series of multilevel Poisson hierarchical models.

Results

Less than 1% of addresses generated 25% of reports of crime and disorder. Across indices, 95–99% of variance was at the address level, though there was significant clustering at the street segment and neighborhood levels. Models with lag predictors found that levels of crime and disorder persisted across years for all outcomes at all three geographic levels, with stronger effects at higher geographic levels. Distinctively, ~15% of addresses generated crime or disorder in one year and not in the other.

Conclusions

The analysis suggests new opportunities for both the criminology of place and the management of public safety in considering addresses in conjunction with higher-order geographies. We explore directions for empirical work including the further experimentation with and evaluation of law enforcement policies targeting problem properties.

     

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.

     

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.

     

Measuring Disorder: Observer Bias in Systematic Social Observations at Streets and Neighborhoods

Objectives

The present study focuses on Systematic Social Observation (SSO) as a method to investigate physical and social disorder at different units of analysis. The study contributes to the aggregation bias debate and to the ‘social science of ecological assessment’ in two ways: first, by presenting a new model that directly controls for observer bias in ecological constructs and second, by attempting to identify systematic sources of bias in SSO that affect the valid and reliable measurement of physical and social disorder at both street segments and neighborhoods.

Methods

Data on physical disorder (e.g., litter, cigarette butts) and social disorder (e.g., loitering adults) from 1422 street segments in 253 different neighborhoods in a conurbation of the greater The Hague area (the Netherlands) are analyzed using cross-classified multilevel models.

Results

Neighborhood differences in disorder are overestimated when scholars fail to recognize the cross-classified data structure of an SSO study that is due to allocation of street segments to observers and neighborhoods. Not correcting for observer bias and observational conditions underestimates the disorder–crime association at street segment/grid cell level, but overestimates this association at the neighborhood level.

Conclusion

Findings indicate that SSO can be used for measuring disorder at both street segment level and neighborhood level. Future studies should pay attention to observer bias prior to their data collection by selecting a minimum number of observers, offering extensive training, and collecting information on the urban background of the observers.

     

The efficacy of foot patrol in violent places

Objectives

This study examines the effectiveness of foot patrol in violent micro-places. A large urban police department deployed foot patrol in micro-places (hot spots) for a period of 90 days for two shifts each day. Our objective is to determine whether this activity impacted violent crime in these hot spots and whether spatial displacement of crime occurred.

Methods

Eight eligible foot beat locations were set by examining crime rates for previous years in order to identify micro-places of high criminal activity. We employed a quasi-experimental design comparing the four treatment to the four control areas, estimating panel-specific autoregressive models for 30 weeks prior to and 40 weeks after the treatment.

Results

Time series models revealed statistically significant reductions in violent crime in the micro-places receiving foot patrol treatment, while no such reductions were observed in the control areas. The deterrent effect, however, was short and dissipated quickly. Control areas did not experience any crime prevention benefit during this time period. No evidence of crime displacement to spatially contiguous areas was detected.

Conclusions

This contributes to the growing body of knowledge that focused police strategies within hot spots impact violent crime. Specifically, the implementation of foot patrol in high crime hot spots led to measurable reductions in aggravated assaults and robberies, without displacing crime to contiguous areas.

     

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.

     

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.

     

Crime Clearance and Temporal Variation in Police Investigative Workload: Evidence from National Incident-Based Reporting System (NIBRS) Data

Objectives

Police workload’s relationship with crime clearance has been studied widely. In the challenging environment now facing police, even small and possibly temporary changes in investigative workload could harm clearance. However existing workload-clearance research either used only a yearly average that obscures temporal variability in caseload, or explored proxy rather than direct measures of workload’s short-term variation. Our improved workload measures capture caseload’s daily changes as crimes are reported, cleared, or remain uncleared but reach the end of active investigation. We examine relationships between clearance and both long- and short-term variability in workload.

Methods

Using NIBRS and LEMAS data, we calculated between-agency (typical or long-term) and time-varying, within-agency (daily fluctuating or short-term) workload measures. We used these and other agency/jurisdiction- and incident-level variables in multi-level survival analysis of clearance by arrest for serious violent incidents from 2007 NIBRS.

Results

Both workload measures were significantly and negatively related to the clearance hazard rate; higher long- and short-term workloads are associated with reduced chance of a case being cleared. The estimated relationship between longterm workload and clearance became progressively stronger (more negative) as the crime incident’s legal seriousness decreased. However, estimates indicated greater sensitivity of the clearance hazard to short-term workload fluctuations for more serious crimes, though the workload-clearance relationship remained negative for all crime types.

Conclusion

Crime clearance should be considered by police agency planners when addressing workload through staffing decisions. Refinement of our workload measures will require additional information, and should be considered in future agency- and incident-level data collection.

     

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.

     

Do Crime Hot Spots Exist in Developing Countries? Evidence from India

Objectives

To determine whether concentrations of crime documented in American cities such as Boston, Jacksonville, Minneapolis, Sacramento, and Seattle generalize to unique environments such as India.

Methods

Two years of motor vehicle theft (MVT) and burglary incidents from two police stations in Jaipur, India are analyzed. The degree to which crime clusters is documented using nearest neighborhood hierarchical clustering (NNHC). These results are compared to several widely cited studies documenting concentrations in the United States.

Results

The NNHC procedure identified five MVT hot spots, which accounted for just .09 % of the two station’s land, but over 13 % of these incidents, and four burglary hot spots, which accounted for less than 1 % of its land, but nearly 23 % of the incidents.

Conclusions

Given the stark differences in the built environment and sociological makeup of Jaipur, a better understanding of the forces that cause crime to concentrate to a high degree needs to be discerned before implementing law-enforcement driven policies derived from the scholarship of American cities. Additional research should also seek to replicate not just the degree to which crime clusters in these unique environments, but also its stability over time and micro place variation.

     

Policing a Negotiated World: A Partial Test of Klinger’s Ecological Theory of Policing

Objectives

Klinger’s (Criminology 35(2): 277–306, 1997) ecological theory of policing addresses the intersection of environment and police organizational structure on police patrol practices. The current study addresses the following question: ‘Is police response to calls for service influenced by the level of serious violent crime or the level of officer staffing?’

Methods

This question was addressed using crime, incident, and staffing data supplied by the Philadelphia Police Department. The dependent variable was the number of unfounded events per month, per police district, from 2004 to 2008. Analysis controlled for linear and non-linear trends, average monthly temperature, month length, and spatial effects. Data were analyzed using repeated measures multilevel modeling.

Results

Findings suggested that the quantity of unfounded events was associated with both workload and officer staffing levels. Consistent with theoretical predictions, higher workload was associated with more unfounded incidents while higher levels of officer staffing was associated with fewer unfounded incidents, net of ongoing trends and spatial effects.

Conclusions

These findings are consistent with the Ecological Theory of Policing and suggest that officers may shed workload in response to higher demands for service or lower levels of officer availability.

     

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.

     

Does Eligibility for Tertiary Education Affect Crime Rates? Quasi-Experimental Evidence

Objectives

This paper estimates the effect of tertiary education eligibility on crime in Sweden. The hypothesis tested is that continuing to higher education decreases crime rates since it allows young people to escape inactivity and idleness, which are known to trigger crime. However, to qualify for tertiary education, individuals have to meet the eligibility requirements in upper-secondary school. Tertiary education eligibility may therefore affect crime rates.

Methods

This paper uses a panel data set of 287 Swedish municipalities over the period 1998–2010 to estimate the tertiary education eligibility effect on crime. However, estimating educational effects on crime is challenging, because investment in education is an endogenous decision. In Sweden, substantial grade inflation, increased tertiary education eligibility by more than 6% points between 1998 and 2003. Thus, since the eligibility increase is exogenous to the educational achievements of a student cohort, i.e. not accompanied by a corresponding knowledge increase, we can use the increase to identify the effect of tertiary education eligibility on crime.

Results

It is found that increasing the tertiary education eligibility rate decreases both property and violent crime substantially.

Conclusions

The results show that when young people have the opportunity to attend tertiary education, and thus escape unemployment or inactivity, their propensity to commit crime decreases.