THE EFFECTS OF DIRECTED PATROL AND SELF-INITIATED ENFORCEMENT ON FIREARM VIOLENCE: A RANDOMIZED CONTROLLED STUDY OF HOT SPOT POLICING

THE EFFECTS OF DIRECTED PATROL ANDSELF-INITIATED ENFORCEMENT ON FIREARMVIOLENCE: A RANDOMIZED CONTROLLED STUDYOF HOT SPOT POLICING∗

RICHARD ROSENFELD,1 MICHAEL J. DECKARD,1

and EMILY BLACKBURN2

1University of Missouri—St. Louis2St. Louis Metropolitan Police Department

KEYWORDS: hot spots, enforcement tactics, crime reduction

Targeted policing has proven effective in reducing serious crime in areas where itis highly concentrated, but the enforcement mechanisms responsible for the successof so-called hot spots strategies remain poorly understood. This study evaluates theeffects of a 9-month randomized controlled hot spots field experiment on firearm as-saults and robberies in St. Louis, Missouri. Thirty-two firearm violence hot spots wererandomly allocated to two treatment conditions and a control condition. Directed pa-trols were increased in both treatment conditions, whereas the experimental protocollimited other enforcement activity in one of the treatment conditions and increased itin the other. The results from difference-in-difference regression analyses indicate thatthe intervention substantially reduced the incidence of nondomestic firearm assaults,with no evident crime displacement to surrounding areas, to times when the interven-tion was not active, or to nonfirearm assaults. By contrast, we find no effects of theintervention on firearm robberies. Less definitive results suggest that the certainty ofarrests and occupied vehicle checks account for the treatment effects on nondomesticfirearm assaults.

Place-based or “hot spots” policing is a well-researched and effective law enforcementstrategy for achieving short-run reductions in crime (National Research Council, 2004).Most large police departments claim to use the strategy (Police Executive Research Fo-rum, 2008) and report that they adopted computerized crime mapping primarily to fa-cilitate hot spots enforcement (Weisburd and Lum, 2005). The St. Louis MetropolitanPolice Department (SLMPD), the focus of the current study, has linked computerizedcrime mapping to hot spots enforcement, of one kind or another, for several years. In the

∗ Additional supporting information can be found in the listing for this article in the Wiley OnlineLibrary at http://onlinelibrary.wiley.com/doi/10.1111/crim.2014.52.issue-3/issuetoc.This study was supported by a grant from the National Institute of Justice (2012-IJ-CX-0042).The views expressed are the authors’ and do not necessarily reflect those of the National In-stitute of Justice. Direct correspondence to Richard Rosenfeld, Department of Criminology &Criminal Justice, University of Missouri—St. Louis, 537 Lucas Hall, St. Louis, MO 63121 (e-mail:richard [email protected]).Correction added on 11 July 2014 after original publication: the acknowledgment has beenamended.

C© 2014 American Society of Criminology doi: 10.1111/1745-9125.12043

CRIMINOLOGY Volume 52 Number 3 428–449 2014 428

THE EFFECTS OF DIRECTED PATROL 429

spring of 2012, the SLMPD implemented a 9-month randomized controlled field exper-iment to evaluate the impact of hot spots enforcement on firearm violence. The currentstudy reports the results of the experiment.

This study has three major objectives:

1. To analyze the effects of the experimental intervention on firearm violence hotspots in the city

2. To identify specific enforcement tactics that may have been responsible for theintervention effects

3. To investigate three forms of possible crime displacement—spatial and temporaldisplacement and displacement to other crimes—resulting from the intervention

The study’s main contribution is to add to the growing body of experimental research onhot spots policing by addressing the question of what the police do, or should do, to reducecrime in hot spots. A second important contribution is to broaden the assessment of crimedisplacement beyond the issue of spatial displacement that has dominated prior research.Temporal and offense-type displacement have been proposed as theoretically plausibleconsequences of place-based policing strategies (see Barr and Pease, 1990) but have notbeen included in prior evaluations of hot spots policing experiments. Finally, we devotesignificant attention to implementation issues and fidelity to experimental procedures,both because such revelations are helpful in interpreting the study’s results and becausethey may assist other researchers in addressing similar challenges.

BACKGROUND

Place-based targeted policing is now a common enforcement strategy in urban policedepartments. How extensively the practice is used and the degree to which it has displacedthe “standard model” of random beat patrol and rapid response to calls for service remainuncertain (Telep and Weisburd, 2011). But evidence for the effectiveness of the strategy,much of it derived from randomized or quasi-randomized experimental designs, is ro-bust. Systematic research reviews have revealed that most place-based policing strategiesyield small but significant reductions in crime that often diffuse into areas surroundingtargeted hot spots (Braga, 2005, 2007; Braga, Papachristos, and Hureau, 2012). Upend-ing a longstanding belief among the police, the accumulated evidence indicates that hotspots enforcement rarely displaces crime to surrounding areas (Eck, 1993; Green, 1995;Guerette and Bowers, 2009; Ratcliffe and Breen, 2011; Weisburd et al., 2006).

One often overlooked consideration in prior research has been the amount of time po-lice spend patrolling hot spots. Most studies have not specified the optimal duration of hotspots patrols. Many hot spots interventions have erred on the side of caution by assigningofficers to hot spots for longer than may be necessary to maximize patrol effectiveness.Koper (1995) found, however, that the optimal duration of targeted patrol in hot spotsis between 10 and 15 minutes. After 15 minutes, the presence of police officers yields di-minishing returns in crime reduction, resulting in wasted police resources that could bebetter used in another area. Koper (1995) also suggested that limiting the time spent pa-trolling one area may increase the effectiveness of officers by avoiding boredom. Koper’s(1995) results were based on patrols targeting single intersections, and it is not yet clearwhether they hold for larger areas (see Telep, Mitchell, and Weisburd, 2012, for similarresults). Nonetheless, the intervention under consideration here restricted the length oftime officers were directed to patrol hot spots.

430 ROSENFELD, DECKARD, & BLACKBURN

Hot spots interventions generally target serious offenses, including aggravated assaultand robbery, firearm-related crime, and street-level drug trafficking (Braga, Papachristos,and Hureau, 2012). Directed patrol is a policing strategy commonly evaluated in hot spotsresearch (Koper and Mayo-Wilson, 2006). Experimental evaluation has repeatedly foundthat directed patrols in crime hot spots can produce significant crime reductions (Bragaand Bond, 2008; McGarrell et al., 2001; Sherman and Weisburd, 1995; Taylor, Koper,and Woods, 2011). A similar controlled study of targeted foot patrols found that sat-urating hot spots with police officers on foot can yield significant reductions in violentcrime (Ratcliffe et al., 2011). This result conflicts with previous findings that foot patrolsreduce citizens’ fear of crime but have little effect on crime itself (Kelling et al., 1981).Police crackdowns on illegal gun possession and carrying have been found to reduce vio-lent crime (Koper and Mayo-Wilson, 2006; McGarrell et al., 2001; Sherman and Rogan,1995). Focused enforcement in drug hot spots has produced modest but significant crimereductions, although the duration of effects is uncertain (Sherman et al., 1995; Weisburdand Green, 1995).

Prior hot spots research, therefore, has yielded evidence of the effectiveness of severalpolicing strategies for reducing crime. Nevertheless, what the police actually do or shoulddo in crime hot spots to prevent crime remains an important research issue. As two re-searchers have noted: “While the evidence on the effectiveness of hot spots policing is per-suasive, there still remains the question of what specifically police officers should be do-ing at hot spots to most effectively reduce crime. The literature thus far has not providedthe same level of guidance” (Telep and Weisburd, 2011: 6). Previous investigations typi-cally examined the effects of a single strategy in isolation from others. Investigators haverarely compared the crime-reduction effects of multiple enforcement strategies. Bragaet al. (1999) disaggregated the separate mechanisms associated with problem-orientedpolicing but did not evaluate the effectiveness of each of the mechanisms, focusing in-stead on the aggregate effect of the broader problem-oriented strategy. By contrast, thecurrent study compares the effects of multiple enforcement mechanisms on crime in hotspots.

We assess the crime-reduction effects of multiple enforcement activities, including di-rected patrol; arrests; foot patrols; problem solving; and pedestrian, vehicle, and buildingchecks. This assessment of the mechanisms underlying the hot spots strategy is more com-prehensive than in past research, but we use the term “effects” advisedly. The results re-sist conclusive causal interpretation because the enforcement activities themselves werenot randomly allocated across crime hot spots. Rather, officers generally had discretionregarding the specific enforcement activities they would pursue in a particular situation.This is standard practice in the SLMPD where, as elsewhere, enforcement tactics are re-ferred to as “self-initiated” activities. We can determine whether an association existsbetween variation in enforcement activities and firearm violence, within and across crimehot spots, but additional research is necessary to determine the causal influence of specifictactics.

SETTING AND EXPERIMENTAL INTERVENTION

St. Louis is an older Midwestern industrial city that has experienced rapid populationloss and elevated crime rates in recent decades. The city had more than 850,000 residentsin 1950. By 2010, the population had declined to approximately 319,000. The city’s currentviolent crime rates are far higher than those of most cities of similar size. In 2011, the rate


of firearm assaults and robberies in St. Louis (1,028 per 100,000 residents) was four timeshigher than that for all U.S. cities with populations greater than 250,000.1 Like most policedepartments in large cities with high levels of crime, the SLMPD has tried numerousstrategies to reduce crime, often with federal assistance, such as Operation Weed andSeed, Project Safe Futures, and locally initiated programs—all without much evidenceof success. In recent years, the department has adopted hot spots approaches to crimeprevention. Electronic crime maps are displayed at weekly Compstat meetings at whichcommanders discuss crime problems in their districts and strategies for addressing them.Under the current and former chiefs, nearly all strategies invoke the principle of hot spotsenforcement: Officers are to be deployed to the crime hot spots shown on the maps ofeach district during the days and times when crime is at its peak and are to engage in self-initiated activities when in the hot spots. In subsequent meetings, commanders review theeffects of the patrol deployments and self-initiated activities on district crime levels andplan future strategies based on updated crime maps.

The crime maps, Compstat meetings, and strategic planning represent an importantstep forward in the SLMPD’s approach to crime reduction. Indeed, they reflect the el-evation of crime prevention to a top departmental priority and hot spots policing as theprincipal means by which crime prevention is to be accomplished. But until recently, thedepartment had not systematically evaluated the effectiveness of its hot spots initiatives.In March 2012, the SLMPD launched a randomized controlled field experiment to as-sess the impact of hot spots policing on firearm violence, which is generally viewed asthe city’s most serious crime problem. The experiment was initiated as part of a formal“Public Safety Partnership” including the SLMPD, the St. Louis Mayor’s office, and re-searchers at the University of Missouri—St. Louis. The experiment was designed by thedepartment’s Crime Analysis Unit (CAU) and the university researchers in concert withpolice command staff. Originally scheduled to last 3 months, at the researchers’ request,it was extended to 6 months and then again to 9 months, ending in November 2012.

The CAU produced a list and accompanying map of 47 small geographic areas in thecity with the highest frequency and spatial concentration of firearm violence, based oncounts of homicides, nondomestic aggravated assaults committed with a firearm, andfirearm robberies that had occurred over several months during the year prior to theintervention.2 The CAU and university researchers proposed a research design that ran-domly allocated approximately half of the hot spots to a “treatment” condition and halfto a “control” condition subject to normal policing. To investigate the relative effective-ness of directed patrol and self-initiated enforcement tactics, two treatment conditionswere established. In the first, treatment 1, enhanced patrols were deployed to the hotspot. Officers were instructed to patrol slowly through the area and to avoid engaging inself-initiated activity, unless a crime was in progress or citizen or officer safety was at risk.In the second condition, treatment 2, enhanced patrols also were deployed, as in the firstcondition, and officers were instructed to engage in one or more types of self-initiated ac-tivity. The remaining hot spots were allocated to a control condition; officers were givenno special instructions, and the experiment was not mentioned.

The command staff rejected the proposed research design. They disliked the fact that itresulted in an unequal distribution of hot spots across the nine police districts in the city.

1. Uniform Crime Reports (http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/crime-in-the-u.s.-2011); St. Louis Metropolitan Police Department (http://www.slmpd.org/crime stats.shtml).

2. Specifically, March to May 2011 and December 2011 to February 2012.


Some districts had many hot spots, and others had few or none. The commanders wantedeach district to have the same number of hot spots and, therefore, equal participation inthe experiment. The CAU then revised the designation of hot spots by selecting from theoriginal list of 47 areas the four areas within each district with the greatest frequency andconcentration of firearm violence. One district in a comparatively low-crime section ofthe city was dropped from the experiment, leaving eight districts with four hot spots eachfor a total of 32 hot spots. The four hot spots in each district were then randomly allocatedto the treatment and control experimental conditions, with one assigned to treatment 1,one to treatment 2, and the remaining two assigned to the control condition.

The commanders’ insistence that the hot spots be equally divided among the eight po-lice districts and that random assignment to treatment and control conditions occur withineach district altered but did not destroy the experimental design. Randomization off abase of four geographic units of course is less likely to achieve pre-experimental crimeequivalence between the treatment and control conditions than randomization of 47 or32 cases. Moreover, as we will discuss, the subsets of four designated hot spots in eachdistrict were not equally “hot”; some were hotter than others. Nonetheless, a “block ran-domized” design was retained and, with a single exception, pre-experimental equivalencebetween the treatment and control conditions was achieved, as shown in the upcomingdiscussion.3 In addition, the commanders agreed to a fairly demanding set of experimen-tal procedures to evaluate the relative effectiveness of multiple enforcement practices,and they granted two extensions to the experimental period that increased the reliabilityof the design and the substantive significance of the results. As in all applied research,unavoidable compromises were made that resulted in departures from textbook researchprotocol. In this case, the compromises reduced but did not destroy the integrity of theresearch design or the value of the results.

DATA AND METHODS

This study evaluates the results of a 9-month experimental intervention that soughtto reduce firearm violence in hot spots throughout the city of St. Louis. The two crimesof primary interest in the study are nondomestic aggravated assaults committed with afirearm and robberies committed with a firearm. Homicides and rapes were too infrequentto permit reliable analysis, and we retain data on nonfirearm assaults and robberies toinvestigate whether the intervention produced an increase in nonfirearm violence, as wewill discuss. The crime data consist of counts of each offense aggregated to 32 small hotspots of firearm violence distributed across the eight participating police districts. Thedata extend over the 9-month period of the hot spots intervention and 9 months prior tothe intervention.

The CAU and university researchers used the ESRI ArcMap geospatial mapping pro-gram (ESRI, Redlands, CA) to identify hot spots of firearm violence based on the dis-tribution of homicides, firearm assaults, and firearm robberies across street segments ineach of the eight participating police districts during the year prior to the intervention.The program’s kernel density function was used to fit a smoothly tapered surface repre-senting the areal concentration of crime incidents. This is accomplished by generating a

3. See Weisburd and Gill (2014) on the use of block randomized experimental designs in place-basedpolicing studies with small Ns.


grid, consisting of many small (10 ft2) cells, on which address-level (point) crime data areprojected. The crime point data were fitted to the grid by the ArcMap utility. A 400-ftsearch radius was used to exclude all but the most densely concentrated areas (see Gorrand Kurland, 2012). The kernel density function estimates the concentration of kernels,in this case crime incidents, by calculating the distance between the centroids of the gridcells in which each incident is located. The resulting raster image resembles a topograph-ical map, showing the magnitude of crime concentration per unit area in different col-ors, divided along standard deviation thresholds. Areas with crime concentrations greaterthan 2 standard deviations above the mean crime concentration were marked for furtheranalysis.

These hot spot areas were then analyzed by summing the number of violent crime inci-dents per street segment. Contiguous street segments with numerous crime incidents theprevious year were included in the determination of the experimental hot spots. The final32 hot spots contained 258 street segments, with an average of eight segments per area(standard deviation [SD] = 2.84, minimum = 3, maximum = 14). Block-level data fromthe 2007–2011 American Community Survey were used to generate approximate popula-tion totals for each hot spot. The average population in the hot spots was 128 (SD = 74.6,minimum = 3, maximum = 350).4

The four hot spots within each police district were randomly assigned to treatment andcontrol conditions, as described. We also examined the effects of directed patrols andmultiple self-initiated activities on crime in the hot spots. Directed patrols were measuredby instructing officers to “call out” (radio dispatch) each time they traversed a designatedintersection in a hot spot. Officers assigned to the experimental conditions were directedto patrol other areas as well, often in response to a crime. These cases are included in ourmeasure of directed patrol.

Following prior research on the optimal frequency and duration of hot spot patrols(Koper, 1995), officers were told by their supervisor to return to the designated intersec-tion at least three times during an 8-hour duty shift and to remain in that area for approxi-mately 15 minutes. Officers assigned to treatment 1 were told to patrol slowly through thearea and to refrain from engaging in self-initiated activities unless a crime was in progressor unless officer or citizen safety was at risk. Officers assigned to treatment 2 were giventhe same instructions pertaining to directed patrol and were told to engage in one ormore self-initiated activities while in a hot spot. These activities include arrest, pedestrianchecks, building checks, occupied vehicle checks, unoccupied vehicle checks, foot patrol,and problem solving. Officers were instructed to call out each time they engaged in aself-initiated activity. Those in the control condition were given no special instructions.

The hot spots intervention was restricted to the evening (3 p.m. to 11 p.m.) and overnightshifts (11 p.m. to 7 a.m.) when firearm violence was most frequent. Our primary analysesare limited to crimes committed during those periods.

METHOD

We present descriptive results on the degree of crime concentration in the hot spots,fidelity to the experimental procedures, and the pre-experimental equivalence in crimes

4. The population totals are approximations because the hot spots consisted of multiple census blocksin many cases.


across the treatment and control conditions achieved by the randomization of hot spotswithin police districts. Under ideal conditions of randomization and pre-experimentalequivalence in outcomes, experimental treatment effects would be evaluated simply bycomparing crime levels between the treatment and control conditions during the inter-vention period. Given the more limited randomization afforded by the research design,however, we employ a difference-in-difference (DID) design that contrasts the differencein outcomes between the treatment and control conditions before and during the inter-vention period. This procedure allows us to peer into the black box of hot spots policingand analyze the effects of specific enforcement activities on changes in crime outcomes.

We fit multilevel linear models to the rate of nondomestic firearm assault and firearmrobbery per 100 hot spot population. The outcomes are transformed to natural logs toreduce skewness.5 The fixed-effects portion of the models includes variables for the twotreatment conditions, with the control condition as the omitted contrast. An indicatorvariable (“exp”) is included as a fixed effect for the 9 months of the experimental pe-riod (exp = 1), with the 9 months before the intervention as the omitted contrast (exp =0). The variable of primary interest in the models is the product term, treatment × exp,which represents the difference between the experimental and control conditions duringthe intervention compared with the difference before the intervention. A negative coeffi-cient on the interaction term indicates a larger reduction (or smaller increase) in firearmviolence in the treatment areas than in the control areas between the pre-experimentaland experimental period. This DID specification is implemented in a mixed-effects frame-work that adjusts the crime estimates for the clustering of observations within both hotspots, which are the units of analysis, and police districts, which constitute the random-ization block. The multilevel effects are specified as random intercepts, but the districteffects are treated as random slopes in other analyses. Given the skewed and sparse dis-tribution of the firearm assaults and robberies, and the approximate hot spot populationtotals, analyses reported in the online supporting information estimate the effects of theexperimental treatments on firearm assaults and robberies in negative binomial countmodels.6,7

To identify the effects of specific enforcement mechanisms on crime, additional mod-els include a measure of the frequency of directed patrols carried out in the hot spotsand measures of each of the self-initiated activities officers were instructed to implementin treatment 2. These measures are constructed as logged ratios of directed patrols andself-initiated activities to the combined incidents of nondomestic firearm assaults and rob-beries in the 32 hot spots. We expect that the enforcement tactics should mediate theeffect of the experimental treatments on firearm violence in the hot spots.

Finally, we conducted analyses to determine whether the intervention resulted in crimedisplacement. We examined three types of possible displacement effects resulting fromthe intervention. Crime-type displacement is investigated in DID analyses of nonfirearmviolent crimes, on the assumption that some offenders might switch to these offenses if

5. To retain cases with zero offense counts, a constant of 1 was added to the offense rates beforelogging.

6. All multivariate analyses were conducted with the multilevel mixed-effects models implementedin Stata 13.1 (StataCorp, College Station, TX).

7. Additional supporting information can be found in the listing for this article in the Wiley OnlineLibrary at http://onlinelibrary.wiley.com/doi/10.1111/crim.2014.52.issue-3/issuetoc.


they believe the police are bearing down on crimes committed with a firearm. Spatialdisplacement, a common concern in hot spots research (Weisburd et al., 2006), is ad-dressed in DID analyses of firearm violence rates occurring within a 500-ft radius of thehot spot boundaries, not including the hot spots themselves. We investigate possible tem-poral displacement in crime, which has not been examined extensively in prior research,by analyzing firearm violence rates during the daytime duty shift when the interventionwas not active. We believe this threefold assessment is the most comprehensive analysisof possible crime displacement associated with hot spots policing to date.

RESULTS

We begin the presentation of results with descriptive analyses of experimental fidelityand pre-experimental crime equivalence. The results of the main regression and supple-mentary analyses follow.

FIDELITY

Poor fidelity to experimental procedures is the downfall of many otherwise promisingfield experiments. Fidelity refers to whether the experiment was carried out the way itwas supposed to be carried out. Recall that officers in the two treatment conditions wereto engage in directed patrol in the hot spots and call out their presence at predesignatedlocations at least three times during a duty shift. In addition, officers assigned to treatment1 were to limit self-initiated enforcement activity, whereas those assigned to treatment 2were encouraged to engage in self-initiated activity. We should therefore observe roughlyequal frequencies of directed patrol in the hot spots assigned to the two treatment con-ditions, more directed patrols in the treatment conditions than in the control condition,and more self-initiated activity in treatment 2 than in treatment 1. Also, we should ob-serve more self-initiated activity in the control condition than in the treatment 1 hot spotswhere officers were told to limit self-initiated activity. The results shown in table 1 indi-cate appreciable, but not complete, fidelity with experimental procedures.

The frequency of directed patrol during the experimental period, as expected, was farhigher in the hot spots allocated to the treatment conditions than in those allocated tothe control condition. The combined number of self-initiated activities in the treatmentconditions exceeded the number in the control condition, and the number in treatment 2exceeded that in treatment 1. The greater number of self-initiated activities in treatment2 than in treatment 1 during the experimental period is the expected result, but if officersassigned to the treatment 1 hot spots limited their self-initiated activity, as experimen-tal procedures required, then we should have observed more self-initiated activity in thecontrol condition than in treatment 1, which is the opposite of the result shown in table 1.That result gives rise to two possible interpretations. On the one hand, the officers as-signed to treatment 1 may have failed to limit their self-initiated activity as instructed,which is a lapse in fidelity. On the other hand, perhaps even under “normal” patrol con-ditions, SLMPD officers do not engage in much self-initiated activity. The available evi-dence favors the former interpretation.

During the 9 months prior to the intervention, officers who patrolled the hot spotsthat would be assigned to treatment 1 during the experiment averaged 339 self-initiatedactivities per hot spot. As shown in table 1, average self-initiated activities in treatment


Table 1. Means and Standard Deviations by Experimental Condition andPeriod (No. of Observations = 64)a

Treatment 1 Treatment 2 Control

Variable Pre-exp Exp Pre-exp Exp Pre-exp Exp

Directed patrols 274.000 2,520.000 214.875 1,874.375 25.438 17.750(127.756) (1,442.982) (92.288) (745.860) (28.589) (13.864)

Self-initiated activities 338.625 388.250 242.125 483.750 229.125 170.250(271.907) (306.922) (108.702) (260.413) (119.676) (112.966)

Arrests 3.625 3.875 3.250 3.750 4.000 1.188(3.462) (3.907) (4.713) (4.464) (3.899) (2.198)

Pedestrian checks 9.750 9.375 5.125 16.375 4.125 3.000(13.667) (12.047) (5.540) (19.471) (3.364) (2.608)

Occupied vehicle checks 120.000 83.375 91.375 119.750 84.750 73.875(110.872) (59.170) (49.722) (83.459) (42.301) (46.712)

Unoccupied vehicle checks 102.875 110.750 51.875 56.250 72.250 62.688(117.383) (104.642) (39.095) (45.597) (54.960) (66.257)

Building checks 40.625 50.625 57.000 136.375 26.500 11.500(46.565) (56.153) (44.808) (157.971) (29.221) (10.627)

Foot patrols 59.375 127.125 33.000 148.250 35.938 16.562(78.675) (154.808) (35.202) (122.720) (66.142) (18.601)

Problem solving 2.375 3.125 .500 3.000 1.562 1.438(4.274) (4.357) (0.756) (1.852) (1.548) (1.965)

Firearm ViolenceCount 8.875 4.875 10.000 3.625 7.000 3.938

(6.490) (5.643) (4.721) (2.615) (5.538) (3.043)Logged rate 1.905 1.330 2.730 1.722 1.784 1.312

(.992) (1.079) (1.449) (1.602) (.802) (.585)Firearm Assault

Count 4.875 2.125 7.000 1.500 3.438 2.625(6.221) (4.051) (5.127) (1.309) (5.501) (2.826)

Logged rate 1.235 .776 2.349 1.125 .875 .881(1.313) (.842) (1.253) (1.473) (1.064) (.780)

Firearm RobberyCount 4.000 2.750 3.000 2.125 3.562 1.312

(2.507) (2.493) (2.828) (2.100) (2.449) (1.352)Logged rate 1.352 1.001 1.602 1.334 1.284 .604

(.521) (.962) (1.754) (1.519) (.701) (.609)Population 146.125 97.000 134.438

(102.385) (63.412) (63.208)Number of hot spots 8 8 16

aStandard deviations in parentheses.ABBREVIATIONS: exp = 9 months during intervention; pre-exp = 9 months before intervention.

1 hot spots increased to 388 during the experiment, which is an increase of 14.4%. Bycomparison, self-initiated activity in the control hot spots declined by 25.8% over the pre-intervention average. It does seem, then, that officers assigned to treatment 1 during theexperiment increased their self-initiated activity beyond what would have been expectedhad they minimized these additional enforcement activities as instructed. Fidelity wasachieved, however, with respect to self-initiated activity in treatment 2 hot spots, whichdoubled to 484 activities during the experiment over a pre-intervention average of 242activities per hot spot.

Several results for specific self-initiated activities indicate that officers followed ex-perimental protocol. Officers assigned to the treatment 2 hot spots undertook more


pedestrian checks, occupied vehicle checks, building checks, and foot patrols during theexperiment than did those assigned to treatment 1. Officers assigned to treatment 1, bycontrast, performed more checks of unoccupied vehicles than did those assigned to treat-ment 2 and were no less likely to make arrests or engage in problem solving.8 For theself-initiated activities, then, we observe substantial, albeit not complete, fidelity to exper-imental procedures when comparing the two treatment conditions but little fidelity whencomparing treatment 1 with the control condition. These results must be kept in mindwhen interpreting the effects of the experimental conditions on the crime outcomes.

PRE-EXPERIMENTAL EQUIVALENCE

Establishing equivalence in outcomes across treatment and control conditions, priorto applying the treatment, is the hallmark of randomized controlled experiments. With asingle exception, the random allocation of hot spots to treatment and control conditionsproduced pre-experimental equivalence across the experimental conditions, as shown intable 1. The frequency of overall firearm violence (nondomestic firearm assaults plusfirearm robberies) during the 9 months before the intervention differed little across thethree experimental conditions. The same was true for firearm robberies. The average fre-quency of firearm assaults in the hot spots allocated to the treatment 2 condition duringthe intervention, however, was twice that of the comparable hot spots in the control con-dition. This difference is not statistically significant (F = 2.33, p = .141). But the treatment2 hot spots also had substantially fewer residents than the control hot spots (97 vs. 134,respectively). When converted to population-based rates and logged to reduce skewness,the difference between the rate of firearm violence in the treatment 2 and control hotspots during the pre-experimental period is statistically significant (F = 6.21, p = .016).

The DID design adjusts the estimates of treatment effects on firearm assault for thelack of pre-experimental equivalence by contrasting the change in, and not the level of,firearm assault across the experimental conditions between the pre-experimental and ex-perimental period. It does not, however, adjust the regression estimates for correlatedobservations within the hot spots or within police districts, the blocking unit in which ran-domization of the hot spots to treatment and control conditions occurred. That is the jobof the random intercepts estimated in the mixed-effects regression models. Nonetheless,it is useful to examine differences across the eight participating police districts in firearmviolence and other hot spot attributes, prior to the intervention, to gain a sense of howrandomization of the hot spots within districts affected pre-experimental equivalence.The results are shown in table 2.

With the exception of racial composition, we observe no significant differences in thehot spot attributes shown in the table, including the logged rate of firearm violence, acrossthe eight districts.9 Some of the pairwise differences, however, are large and statisticallysignificant. For example, the average population size of the hot spots in district 9 wasmore than three times that of the district 5 hot spots. That difference is highly significant(F = 16.41, p = .001). In addition, the difference between the logged firearm violence

8. In many instances, during a single shift, the same officers were assigned to patrol both the treatment1 and treatment 2 hot spots.

9. The attributes shown in table 2 are the only block-level characteristics available from the ACS foraggregation to the hot spots.


Table 2. Hot Spot Characteristics by Police District (No. ofObservations = 32)

District

Variable (1) (3) (4) (5) (6) (7) (8) (9) pa

Mean population 123.250 138.500 137.000 63.250 108.500 118.750 125.000 209.750 .312Percent 15–24 years of age 26.251 24.642 35.848 20.696 25.427 26.782 23.402 37.083 .230Percent Black 46.731 46.671 42.531 90.637 97.466 95.212 96.619 52.182 .000Percent vacant 20.004 31.348 14.768 23.314 16.605 33.307 30.248 24.147 .118Logged firearm violence rateb 1.662 2.196 2.851 2.880 1.942 1.270 2.220 1.387 .262

aEvaluated by F test.bPer 100 population during 9 months before intervention.

rate of the hot spots in district 5, the district with the highest rate, and in district 7, thedistrict with the lowest rate, is significant at the 5 percent level (F = 4.73, p = .047).Moreover, unmeasured but potentially important differences across the police districtsin informal norms governing police behavior, community relations, and supervisors’ andpatrol officers’ commitment to the experiment could have influenced the effect of theexperimental treatments on firearm violence. To investigate this possibility, we includedistrict-level random slopes in supplementary analyses.

TREATMENT EFFECTS: DESCRIPTIVE RESULTS

If the SLMPD hot spots intervention was effective, then we should observe a steeperdrop (or smaller rise) in firearm violence between the experimental and the pre-experimental period in the treatment than in the control conditions. The descriptive datain table 1 support this expectation for nondomestic aggravated assaults committed with afirearm and for the combined measure of firearm violence, but not for firearm robberies.In addition, the descriptive data show that the effect of treatment 2 on firearm violencewas stronger than the effect of treatment 1. The discussion that follows is based on av-erage offense counts in the hot spots. Table 1 presents the corresponding logged offenserates per 100 hot spot population, which are the focus of the subsequent regression anal-ysis.

The frequency of firearm violence in the control hot spots decreased by 43.7% betweenthe pre-experimental and the experimental period, from an average of 7.00 incidents to3.94 incidents per hot spot. These descriptive results suggest that the designated firearmviolence hot spots would have cooled considerably regardless of the experimental inter-vention to increase directed patrols and other targeted enforcement activity. Firearm vi-olence in the treatment 1 hot spots decreased by a nearly identical 45.0%, indicating littleif any effect of the intervention on firearm violence under the condition of enhanceddirected patrol and limited self-initiated activity. Firearm violence decreased in the treat-ment 2 hot spots, however, by 63.8%, from 10.00 to 3.62 incidents per hot spot. If theintervention was effective in reducing firearm violence, then it seems to have done sothrough a combination of enhanced directed patrol and self-initiated activity.

We observe a different pattern of results for nondomestic firearm assaults than forfirearm robberies. Both dropped in the control hot spots from the pre-experimental to theexperimental period: The firearm assaults decreased by 23.8% and the firearm robberiesfell by 63.2%. The decrease in firearm robberies in the control hot spots exceeded that


in both treatment hot spots, where firearm robberies fell by roughly 30% between thepre-experimental and the experimental period. It does not seem that the experimentaltreatments had any effect on firearm robberies beyond that of the factors, whatever theywere, that were driving down overall levels of firearm robbery in St. Louis.10 By contrast,firearm assaults decreased by 56.6% in the treatment 1 hot spots and by 78.6% in thetreatment 2 hot spots between the pre-experimental and the experimental period. Bothdeclines exceeded those in the control hot spots, with some indication of a stronger impactof treatment 2 (directed patrol plus self-initiated activity) than of treatment 1 (directedpatrol) on firearm assaults.

TREATMENT EFFECTS: REGRESSION RESULTS

The descriptive analyses, although suggestive of intervention effects on nondomesticfirearm assaults, are based on the raw frequencies and not on the population adjustedrates of firearm violence in the hot spots. Nor do they account for chance differencesin crime between the treatment and control conditions, the clustering of crimes in hotspots, or the district-level block randomization procedures. To address these issues, weestimated multilevel linear models of the combined and separate rates of nondomesticfirearm assaults and firearm robberies in the 32 firearm violence hot spots. As explained,the models implement a DID specification that estimates the change in crime rates inthe treatment and control hot spots between the pre-experimental and the experimentalperiod. The model estimates, therefore, are based on 64 “before–after” observations rep-resenting the average rate of firearm violence in each of the 32 hot spots in the 9 monthsbefore the experimental intervention and the average during the 9-month intervention.

Table 3 displays the regression results for the logged rate of firearm violence and itstwo components, the logged rates of nondomestic aggravated assault committed with afirearm and firearm robbery. Beginning with the results for the combined measure offirearm violence, we observe no significant “main effect” of treatment 1 on the rate offirearm violence and a significant and positive main effect of treatment 2 on firearm vio-lence. The latter result reflects the higher frequency of firearm violence in the treatment2 hot spots than in the control hot spots prior to the intervention, as shown in table 1.The negative interaction term (treatment 2 × exp), however, indicates that the reduc-tion in firearm violence in the treatment 2 hot spots between the pre-experimental andexperimental period exceeded the reduction in the control hot spots. That difference issignificant at the 10% level. The decrease in firearm violence in the treatment 1 hot spots,by contrast, did not differ significantly from that in the control hot spots.

The regression results for the overall rate of firearm violence largely reflect those forthe rate of nondomestic firearm assault. The measures of primary interest are the productterms for the two treatment conditions. The change in the average rate of firearm assaultin the treatment 1 hot spots did not differ significantly from that in the control hot spotsbetween the pre-experimental and experimental period (bt1 = −.466, p > .10). The re-duction in firearm assault was significantly greater in the treatment 2 hot spots than in thecontrol hot spots (bt2 = −1.23, p < .01). Neither of the treatment conditions, by contrast,had a significant effect on the change in the average rate of firearm robbery. Firearm rob-bery rates decreased significantly across the board, in both the control and treatment

10. Firearm robberies decreased by 20.3% citywide during 2012.


Table 3. Multilevel Linear Models of Experimental Treatment Effects onFirearm Violence in Hot Spots (No. of Observations = 64)a

Logged Firearm Logged Firearm Logged FirearmVariable Violence Rate Assault Rate Robbery Rate

Treatment 1b .121 .360 .068(.394) (.437) (.396)

Treatment 2b .946∗ 1.474∗∗ .318(.394) (.437) (.396)

Exp periodc −.472∗ .006 −.680∗∗(.183) (.234) (.143)

Treatment 1 × exp −.104 −.466 .329(.317) (.406) (.248)

Treatment 2 × exp −.536† −1.229∗∗ .412(.317) (.406) (.248)

Random Intercept VarianceDistrict .150 .063 .086

(.169) (.144) (.148)Hot spot .557 .581 .671

(.202) (.237) (.218)Residual variance .269 .439 .165

(.067) (.110) (.041)Log likelihood −77.479 −86.259 −70.008Wald chi2 30.430∗∗ 20.680∗∗ 29.180∗∗

aStandard errors in parentheses.bContrast is control.c1 = 9 months during intervention; 0 = 9 months before intervention.†p < .10; ∗p < .05; ∗∗p < .01 (two-tailed).

hot spots, between the pre-experimental and experimental periods (bexp = −.680,p < .01).11

The regression results generally confirm the descriptive results reported in table 1. Wefind a significant effect of the hot spot intervention on nondomestic firearm assault butnot on firearm robbery, and the reduction in firearm assault evidently required increasesin both directed patrol and self-initiated activity, as specified in the treatment 2 condi-tion. The descriptive results imply that the reduction in firearm assaults in the treatment2 hot spots was sizable, but we can obtain a more reliable estimate of the impact of theintervention from the regression analysis. We can use the estimated change in firearmassault between the pre-experimental and the experimental period in the control grouphot spots as the expected change that would have occurred in the treatment 2 hot spots,absent the intervention. That result can then be compared with the estimated change inthe treatment 2 hot spots to evaluate the magnitude of the crime-reduction impact of theintervention. By doing so, we estimate that the treatment 2 condition of the hot spotsintervention reduced the rate of firearm violence by two thirds from the level expectedwithout the intervention. (See appendix A in the online supporting information for dis-cussion and results.12)

11. The regression results are nearly identical when each treatment variable (plus the accompanyingproduct term) is entered into a separate model. All results not shown are available from the authorson request.

12. Additional supporting information can be found in the listing for this article in the Wiley OnlineLibrary at http://onlinelibrary.wiley.com/doi/10.1111/crim.2014.52.issue-3/issuetoc.


INTERPRETING THE TREATMENT EFFECTS

Studies of policing effects on crime often include the ratio of arrests to offenses as ameasure of the effect of police enforcement activity on crime rates (e.g., Baumer, 2008).The rationale for this approach derives from deterrence theory: the more arrests per unitof criminal activity (i.e., the greater the certainty of apprehension), the less likely individ-uals will engage in criminal behavior. That logic can be extended to other police enforce-ment tactics in addition to arrest, including the directed patrols and self-initiated activitiesrecorded by the SLMPD as part of the hot spots intervention. The police engage in di-rected patrols, pedestrian checks, building checks, and vehicle checks on the assumptionthat these activities have some deterrent effect on firearm violence by increasing the per-ceived certainty of detection and apprehension on the part of would-be offenders. If thatassumption is correct, then we should expect fewer crimes to be committed as the cer-tainty of enforcement activities increases. Two questions guide the current analysis:

1. Does the elevated enforcement associated with the SLMPD hot spots interventionexplain the impact of the intervention on firearm violence in the hot spots?

2. Which enforcement activities have the greatest impact on crime?

To measure the certainty of police enforcement activity in relation to firearm violence,we computed the ratio of directed patrols and self-initiated activities to the volume offirearm violence (the number of firearm assaults and robberies) in the 32 hot spots, andwe took the natural log of this measure to reduce skewness. We then entered the en-forcement measures into a mixed-effects DID regression model of the hot spot firearmassault rate containing the treatment 2 variable and related product term. If increasedenforcement activity explains the reduction in the rate of firearm assault in the treatment2 hot spots, then the coefficient on the treatment 2 variable should become nonsignificantwith the enforcement measures in the model. The results presented in table 4 support thisexpectation.

Model 1 in table 4 shows the significantly greater reduction in the rate of firearm assaultin the treatment 2 hot spots than in the control hot spots between the pre-experimentaland experimental period (bt2 × exp = −1.07, p < .01). When the directed patrol and self-initiated activity enforcement ratios are entered into model 2, the coefficient on the prod-uct term is reduced to nonsignificance (bt2 × exp = −.234, p > .10), which suggests thatthe reduction in firearm assault did in fact result from the increased enforcement activ-ity in the treatment 2 hot spots during the experimental period. Interestingly, however,only the greater certainty of self-initiated activity, not directed patrol, seems to have hadany effect on firearm assault. The estimated effect of directed patrol is negligible (bdir =.014, p > .10), whereas the effect of self-initiated activity is sizable and significant (bsia =−.702, p < .01). These results help to explain why we found no significant effect of thetreatment 1 condition, which was limited to increased directed patrols, on firearm assault.They also imply that directed patrol alone may have little deterrent value, at least with re-spect to firearm assault, unless coupled with more vigorous, purposeful, or coercive formsof police behavior.

The remaining question concerns the relationship between the specific self-initiated ac-tivities and firearm assault. The results displayed in model 3 of table 4 indicate that onlytwo of the self-initiated activities recorded by the SLMPD were significantly related to therate of firearm assault in the hot spots: arrest (barr = −.295, p < .05) and occupied vehicle


Table 4. Multilevel Linear Models of Effects of Experimental Treatment,Directed Patrols, and Self-Initiated Activities on FirearmAssault Rates in Hot Spots (No. of Observations = 64)a

Logged Firearm Assault Rate

Variable (1) (2) (3)

Treatment 2b 1.354∗∗ 1.114∗∗ .978∗∗(.415) (.379) (.366)

Exp periodc −.149 .092 .003(.195) (.168) (.134)

Treatment 2 × exp −1.074∗∗ −.234 −.142(.390) (.337) (.292)

Directed patrold — .014 —— (.070) —

Self-initiated activityd — −.702∗∗ —— (.142) —

Arrest — — −.295∗— — (.115)

Pedestrian check — — .089— — .102

Occupied vehicle check — — −.379∗— — (.178)

Unoccupied vehicle check — — −.056— — (.193)

Building check — — −.131— — (.093)

Foot patrol — — .022— — (.083)

Problem solving — — −.135— — (.110)

Random Intercept VarianceDistrict .062 .112 .131

(.144) (.143) (.161)Hot spot .575 .505 .553

(.239) (.186) (.206)Residual variance .457 .261 .166

(.114) (.065) (.046)Log likelihood −86.958 −75.261 −68.385Wald chi2 18.670∗∗ 60.570∗∗ 105.320∗∗VIFMean 1.920 2.360Maximum 2.660 3.940

aStandard errors are in parentheses.bIncreased directed patrol and self-initiated activity; contrast is control.c1 = 9 months during intervention; 0 = 9 months before intervention.dLogged ratio of enforcement activity to firearm violence.∗p < .05; ∗∗p < .01.

checks (bocc veh chk = −.379, p < .05). The greater the certainty of arrest and inspection ofoccupied vehicles, the lower the rate of firearm assault in the most violent areas of thecity. Searching vehicles and their occupants, and of course making arrests, involve directcontact with citizens and are two of the most coercive actions in which police officers en-gage. For these reasons, perhaps, they carry more deterrent force than merely patrolling acrime hot spot, in a vehicle or on foot, or inspecting unoccupied vehicles or buildings. Asnoted, however, caution must be exercised in interpreting the effects of the self-initiated


activities on firearm violence in the hot spots because the individual activities were notsubject to experimental manipulation.

Not surprisingly, the various enforcement activities tended to be correlated during theexperimental period: Increases in one type of activity often occurred in the same hot spotswhere others were increasing. The variance inflation (VIF) results shown in table 4, how-ever, indicate that collinearity among the enforcement activities does not seriously affectthe estimates. In support of this conclusion, when the directed patrol and self-initiatedactivity measures are estimated separately in model 2 of table 4, the results are similar tothose shown (bdir = −.088, p > .10; bsia = −.637, p < .01).

ROBUSTNESS

The regression results presented thus far are based on population standardized rates offirearm violence, logged to reduce skewness, and analyzed in linear mixed-effects regres-sion models. As noted, however, the firearm violence data are quite sparse and the hotspot population totals are approximations from block-level census data. In addition, theaverage population of the treatment 2 hot spots, one of which had only three residents,was considerably smaller than that of the treatment 1 and control hot spots. To investi-gate whether these data limitations influenced the regression results, we replicated ouranalyses in multilevel negative binomial count models of firearm violence in the 32 hotspots, using a model with no adjustment for population size. The results are presented inappendix B of the online supporting information.13

The results from the multilevel count models largely confirm those reported previously.As shown in table B.1, we find no significant effect of the hot spots intervention on firearmrobberies. We find a greater reduction in firearm assaults in the treatment than in thecontrol hot spots between the pre-experimental and the experimental period, but onlyin the treatment 2 hot spots subject to both increased directed patrols and self-initiatedactivities. In keeping with this result, the reduction in firearm assaults was associated withthe greater certainty of self-initiated activity but not directed patrol in the treatment 2hot spots, as shown in table B.2. We were unable to estimate simultaneously the effectsof the specific self-initiated activities on firearm assaults because the negative binomialcount model failed to converge under this specification. In results not shown, however,we found significant negative effects of both the certainty of arrests and the occupiedvehicle checks on firearm assaults.

In a second robustness test, we fit the multilevel linear regression models with district-level random slopes for the treatment × exp interaction terms, on the assumption thatthe intervention effects could be related to unmeasured differences across the districts incompliance with the experimental protocol, staffing levels, community relations, or infor-mal norms governing police behavior. The results are nearly identical to those shown intable 3. For example, with the district-level random slopes included in the firearm assaultmodel, bt2 × exp = −1.23 (standard error = .409). The main regression results, therefore,seem to be robust across differing measurements and estimation procedures.

13. We obtained results from multilevel Poisson estimations very similar to those in appendix B of theonline supporting information and discussed subsequently.


Table 5. Multilevel Linear Models of Displacement Effects ofExperimental Treatment on Nondomestic Firearm Assault Rate(No. of Observations = 64)a

Spatial Displacement Crime-Type DisplacementVariable 500-Foot Buffer Nonfirearm Assault

Treatment 2b .922∗ .509(.456) (.395)

Exp periodc .273∗ .147(.139) (.132)

Treatment 2 × exp .027 −.102(.274) (.264)

Random Intercept VarianceDistrict .000 .058

(.000) (.146)Hot spot 1.035 .729

(.285) (.242)Residual variance .224 .209

(.057) (.052)Log likelihood −80.700 −74.911Wald chi2 10.080∗ 2.790

aStandard errors in parentheses.bIncreased directed patrol and self-initiated activity; contrast is control.c1 = 9 months during intervention; 0 = 9 months before intervention.∗p < .05.

DISPLACEMENT

Prior hot spots research has found little evidence that targeted enforcement displacescrime to nearby areas. On the contrary, several studies have revealed a “diffusion ofbenefits” of hot spots interventions beyond the hot spots themselves (e.g., Braga, 2005,2007; Clarke and Weisburd, 1994). Prior research, however, has focused mainly on spatialdisplacement and has typically neglected displacement to other types of crime or to timeperiods during which the intervention was not in force.14 Our data and research designpermit assessments of all three types of crime displacement or diffusion. Because bothcrime displacement and diffusion of benefits presuppose the effectiveness of the hot spotsintervention, these analyses are restricted to nondomestic firearm assaults.

We investigated the possibility of spatial displacement attributable to the hot spotsintervention by estimating the model presented in table 3 on the rate of nondomesticfirearm assault within 500 ft of the hot spot boundaries. The results are shown in table 5.We observe that the rate of firearm assault did increase in the area surrounding the treat-ment 2 hot spots between the pre-experimental and the experimental period (bexp = .273,p < .05), but the increase was no greater in the treatment 2 hot spots than in the controlhot spots (bt2 × exp = .027, p > .10). The latter result suggests that the increase in firearmassault in the area surrounding treatment 2 hot spots did not result from spatial displace-ment related to the intervention.

As noted, it is reasonable to suppose that some offenders will switch to nonfirearmcrimes if they believe the police are focusing on crimes committed with firearms. We

14. See Barr and Pease (1990) for an excellent discussion of different forms of crime displacement.


investigated this possibility by estimating the effect of the intervention on nondomesticaggravated assaults committed without a firearm. The results, displayed in table 5, indi-cate that the rate of nonfirearm assault did not increase between the pre-experimentaland the experimental period, either in the treatment 2 or the control hot spots. The hotspots intervention evidently did not displace firearm violence to other forms of violentoffending.

Finally, we sought to investigate whether the hot spots intervention, which took placeduring the evening and nighttime hours, may have displaced firearm assaults to the day-time hours. Before the intervention began, 13 firearm assaults occurred in the daytimeand the number dropped to 7 during the intervention. But all of those incidents tookplace in the control hot spots, which suggests the intervention did not cause offenders toswitch to the daytime to commit firearm violence. In summary, we find no evidence thatthe St. Louis hot spots intervention resulted in spatial, temporal, or crime-type displace-ment. Nor is there evidence that the benefits of the intervention diffused to other areas,other times, or other crimes.

DISCUSSION

This study investigated the effects of a 9-month hot spots experimental interventionon violent crimes committed with a firearm in St. Louis. We find that the hot spots in-tervention reduced nondomestic firearm assaults by a sizable margin and had no evidentdisplacement effects on surrounding areas, or at times when the intervention was notactive, or on nonfirearm aggravated assaults. These results are consistent with those ofprior hot spots research, although we extended previous analyses by examining tempo-ral and crime-type displacement in addition to spatial displacement. We find no effect ofthe intervention on firearm robberies, however, which declined substantially in both thetreatment and control experimental conditions.

The absence of an intervention effect on firearm robberies is puzzling. We can thinkof no reason why hot spots policing would have weaker effects on firearm robberies thanon nondomestic firearm assaults. Indeed, we might expect robberies to be even moreresponsive to place-based enforcement strategies in light of evidence that armed robberstake account of their surroundings when contemplating the act (Decker and Wright, 1997)and prior research that has shown that proactive policing curtails street robberies (Kubrinet al., 2010). We do know that firearm robberies had been trending downward for severalyears prior to the hot spots intervention. Between 2009 and 2012, firearm robberies in St.Louis fell by 38.4%, compared with a decrease of 20.2% in firearm assaults.15 Moreover,firearm robberies plummeted by nearly two thirds between the pre-experimental and theexperimental period in the hot spots randomly allocated to the control condition. It is notclear what local or extralocal factors were reducing firearm robberies in the city, but theymay have overwhelmed the effects of the hot spots intervention.16

Our evaluation sheds light on the enforcement mechanisms responsible for the drop innondomestic firearm assaults during the experimental period. We find that the certainty

15. http://www.slmpd.org/crime stats.shtml.16. Prior research has linked robbery trends to changing economic conditions (see Arvanites and

Defina, 2006; Rosenfeld and Fornango, 2007; and Rosenfeld and Messner, 2013).


of arrests and occupied vehicle checks, but none of the other enforcement activities weexamined, were associated with reductions in nondomestic firearm assaults during theexperimental period. In an apparent contradiction to the results of prior research (e.g.,McGarrell et al., 2001; Sherman and Weisburd, 1995; Sherman et al., 1995), enhanceddirected patrols, when not accompanied by other enforcement activity in hot spots, didnot produce significant reductions in firearm assaults.17 If these results are replicated insubsequent studies, they may have significant strategic implications.

As evidence accumulates regarding the crime-reduction benefits of specific enforce-ment tactics, police officials may want to rethink how those tactics are deployed. TheSLMPD is not alone in permitting officers broad discretion in the choice of specific en-forcement tactics to use while on patrol. But just as directed patrol has superseded whatmight be termed “self-initiated” patrol for crime prevention purposes, mounting researchevidence should prompt police commanders to reduce the enforcement discretion theirline officers now have and direct them to use only those strategies that have proven ef-fective in reducing crime. We have found that arrests and occupied vehicle checks seemto reduce firearm assaults, but it is possible that other less coercive or intrusive tacticsare effective with other types of crime. Additional research is clearly needed to develop asufficiently reliable evidence base to guide policy on what the police should do when theypolice crime hot spots.

We believe these results are suggestive and should serve as guideposts for future re-search. But we caution against drawing definitive conclusions from our comparative as-sessment of differing enforcement mechanisms, for two reasons. Because this is one ofthe first studies to evaluate systematically the crime-reduction effects of multiple enforce-ment activities, stronger conclusions—and as just noted, any resulting policy changes—must await replication of the results reported in this study. Equally important, however,is the absence of experimental manipulation of the specific enforcement activities in thecurrent study. Officers were told to engage in more self-initiated activities under the sec-ond experimental condition, but they were not told which activities to engage in. Theexperimental intervention was able to randomly assign differing “doses” of self-initiatedactivities across the two treatment conditions, but we cannot rule out selection effects orendogeneity bias as reasons why some of these activities and not others were associatedwith reduced levels of firearm assault in the hot spots. Causal claims regarding the mosteffective enforcement tactics in crime hot spots must await future randomized controlledstudies.

We are more confident in drawing causal inferences from the results of the differingexperimental treatments applied in this study. The intervention reduced firearm assaultsbut not firearm robberies, and the reduction in assaults stemmed from increased self-initiated activity and not from increased directed patrol alone. The experimental designwas not without limitations, however. We have pointed to the higher incidence of self-initiated activity in the treatment 1 hot spots than in the control hot spots as a lapse infidelity to experimental procedures. A second drawback is the limited randomization af-forded by the experimental design approved by police command staff. We acknowledgethe advantages of blocked randomization procedures in small N experimental studies of

17. An anonymous reviewer observed, however, that in prior hot spots research, officers involved inenhanced directed patrol often engaged in other enforcement activities as well. In such cases, theresults do not conflict with those of the current study.


place-based policing (Weisburd and Gill, 2014). Given the practical difficulties of conduct-ing experimental research in real-world settings—not to mention the inherently limitednumber of crime hot spots to be found in even high-crime cities—studies of hot spotspolicing often have to make do with samples that are smaller or “less random” than opti-mal for statistical purposes. As the evidence-based movement in criminal justice expands,however, and as randomized controlled trials continue to accumulate and are recognizedby policy makers, practitioners, and researchers alike as the standard against which allcriminal justice research is measured, we should expect police commanders and othercriminal justice leaders to become more open to, and in fact eager to facilitate, experi-mental research with maximum feasible statistical power.

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Richard Rosenfeld is the Founders Professor of Criminology and Criminal Justice atthe University of Missouri—St. Louis. His current research interests include the impactof policing on crime and the economic correlates of crime trends.

Michael Deckard is a doctoral candidate in the Department of Criminology and Crim-inal Justice at the University of Missouri—St. Louis. His research interests includeevidence-based practices, criminal justice policy evaluation, and experimental and mixed-methods research designs.

Emily Blackburn manages the Crime Analysis Unit of the St. Louis Metropolitan Po-lice Department. She is responsible for compiling the Department’s Compstat data, usedfor hot spot policing, crime analysis, and crime series forecasting. Her research interestsinclude the effect of legal change on crime, the impact of hot spot policing on crime, andthe influence of climate change on violent crime.

SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article atthe publisher’s web site:

Appendix A. Impact AssessmentTable A.1. Predicted Firearm Assault Rates in Hot Spots By Experimental Condition andPeriod (No. of Observations = 64)Appendix B. Count ModelsTable B.1. Multilevel Negative Binomial Count Models of Effects of Experimental Treat-ment on Firearm Violence in Hot Spots (No. of Observations = 64)Table B.2. Multilevel Negative Binomial Count Models of Effects of Experimental Treat-ment, Directed Patrols, and Self-Initiated Activities on Firearm Assaults in Hot Spots(No. of Observations = 64)

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THE EFFECTS OF DIRECTED PATROL AND SELF-INITIATED ENFORCEMENT ON FIREARM VIOLENCE: A RANDOMIZED CONTROLLED STUDY OF HOT SPOT POLICING