13 Police Q. 387 2010

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Hyunseok jang, PhD', Larry T. Hoover, PhD2 , and Hee-jong joo, PhD2

Abstract Compstat as a policing strategy became popular following the significant crime reduction in New York City during the 1990s. As an innovative management strategy in policing, Compstat attracted considerable attention from scholars and police practitioners. Despite its popularity, little empirical research has scientifically evaluated the effectiveness of the Compstat strategy. In addition, few studies have concentrated on Compstat strategies implemented during the 2000s outside New York City. This study examines the effectiveness of Compstat as implemented by the Fort Worth (Texas) Police Department (FWPD). Using monthly time-series arrest and crime data over a multiyear period, the study examines whether Compstat engendered a significant increase in "broken windows" arrests (minor nuisance offenses) and, using multivariate time-series analysis, the role of the Compstat strategy in explaining changes in violent, property, and total index crimes. Findings indicate that the implementation of Compstat significantly increased some types of broken windows arrests in the FWPD whereas others decreased. Analysis indicates significant decreases in property and total index crime rates after controlling for rival factors, but fails to show a significant change in violent crime rates. If the Fort Worth strategic approach to Compstat had to be described with a single word, it would be focusing. The Queensland study of Compstat also reported using a problem-oriented intervention model-focusing-in lieu of a broken windows approach (Mazerolle, Rombouts, & McBroom, 2007). Property crime was significantly reduced in both settings. Parallel findings from two differently constituted Compstat programs on two different continents provides evidence that the primary component of the Compstat model is focusing, not broken windows enforcement, and the primary impact is on property crime.

'Missouri Western State University, Saint Joseph, MO 2 Sam Houston State University, Huntsville, TX

Corresponding Author: Larry T. Hoover, College of Criminal justice, Sam Houston State University, Huntsville, TX 77341 Email: [email protected] 388 Police Quarterly 13(4)

Keywords Compstat policing, broken windows enforcement, time-series analysis, Fort Worth Police Department

Introduction

Following community-oriented or problem-solving strategies, Compstat, an alternative policing management strategy, has drawn significant attention from scholars and police practitioners. The crime decline in New York City following the introduction of Compstat fueled heated debates regarding the influence of this crime-focused strategy on crime rates since the 1990s (Eck & Maguire, 2000; Harcourt, 1998, 2001; Harcourt & Ludwig, 2006; Kelling & Sousa, 2001; Rosenfeld, Fornango, & Baumer, 2005; Willis, Mastrofski, & Weisburd, 2004). Partially due to the well-known success stories in New York City and subsequently several other metropolitan jurisdictions, the Compstat strategy quickly spread throughout the United States and has been characterized by some as the most popular police management and crime control strategy in American history (Weisburd, Mastrofski, McNally, Greenspan, & Willis, 2003). In a nationwide survey conducted in 1999, for example, one third of municipal police departments with more than 100 swom officers reported that they had implemented a "Compstat-like program" (Weisburd et al., 2003, p. 430). As such, the Compstat strategy became one of "the most quickly diffused forms of innovation" (Weisburd et al., 2003, p. 433) and was described as an organizational paradigm shift in policing due to the swiftness of its effect on crime prevention or reduction (Henry, 2002; Walsh, 2001). Despite Compstat's popularity, little empirical research has scientifically evaluated the effectiveness of the Compstat strategy. A number of the reviews of Compstat in New York City have been in the form of "advocacy" books which assume effectiveness (see, for example, Bratton & Knobler, 1998; Henry, 2002; Maple & Mitchell, 1999). A majority of the existing research has been anecdotal and qualitative, thus lacking generalizability. In addition, a few studies that measured the effectiveness of Compstat in New York City found conflicting results concerning the causal relationship between the Compstat strategy and the decline in crime rates (Harcourt, 1998, 2001; Harcourt & Ludwig, 2006; Kelling & Sousa, 2001; Rosenfeld et al., 2005; Willis et al., 2004). It is also important to recognize the potential role of other social structural variables in the reduction of crime rates in New York. Fagan, Zimring, and Kim (1998) reported that nonfirearm homicides began to decline in New York City prior to the implementation of the Compstat strategy. In relation to firearm homicides, however, they found consistent timing between Compstat and the homicide decline, leading them to conclude that the Compstat strategy may have contributed to the lower firearm homicide rate. Karmen (2000) examined the crime drop in New York City and concluded that the Compstat policing strategy may have influenced the reduction of crime. Karmen, however, argued that other factors such as an economic boom, escalated incarceration, change in drug markets, and the change in demographic composition may have played greater Jong et al 389 roles than the police strategy. Rosenfeld et al. (2005) also evaluated the influences of Compstat on the reduction of homicide rates in New York City compared with other large U.S. cities and found no significant differences. They argued that the reduction of homicide rates could be better explained by other social structural variables as most U.S. cities experienced similar reductions in homicide. Due to the inconsistent results of previous research, it has been difficult to develop a conclusion about the effectiveness of the New York City Compstat strategy in reducing crime rates. Further research is necessary to scientifically assess Compstat's impact on crime rates. Considering the widespread adoption of Compstat strategies throughout the nation, it is important to examine the effectiveness of strategies implemented outside of the New York City Police Department (NYPD). The Fort Worth Police Department (FWPD) in Texas implemented a Compstat strategy to address crime and quality of life issues in the early 2000s. While nationwide crime rates began to decline during the 1990s, crime rates reached a plateau in the early 2000s. Therefore, the evaluation of a Compstat strategy implemented during the 2000s provides important information to validate the effectiveness of Compstat in a different time period as well as in a different place. Considering the differences in geographic location, cultural background, organizational subculture, and study period, this study attempted to determine if any significant differences existed in effects as well as strategic approaches between the NYPD's Compstat and the FWPD's "All Staff Meeting" (ASM) version of Compstat. Using monthly time-series data and employing a time-series intervention analysis and a multivariate time-series analysis (regression with autoregressive error modeling), this study examines the relationship between the implementation of Compstat in the FWPD and misdemeanor arrest rates as well as changes in index crime rates. There are two major research questions: (a) Was there a significant increase in the arrest rate for broken windows offenses (i.e., minor nuisance offenses) in the FWPD following the implementation ofthe Compstat strategy? and (b) Was there a significant change in crime rates (i.e., Part I violent, property, and total crime) after Fort Worth implemented the Compstat strategy?

Literature Review Compstat Strategy

The term Compstat originated from "Compare Stats," a computer file name (Silverman, 1999, p. 98). Similarly, according to Safir (1997), Compstat was short for computer comparison statistics. The term was abbreviated to the eight-character file name limita- tion in DOS programming at the time, "Compstat." Although the term appeared to emphasize a technical aspect of operational strategies, Compstat became the shorthand descriptor of a broad management strategy involving the entire police agency's operation and consisting of several elements (Bratton & Knobler, 1998). NYPD's commissioner, Bratton, and his executive team established four basic principles consisting of (a) accurate and timely intelligence, (b) rapid deployment of personnel and resources, (c) effective 390 Police Quarterly 13(4) tactics, and (d) relentless follow-up and assessment (Bratton & Knobler, 1998; Silverman, 1999; Walsh & Vito, 2004). In conclusion, Hoover (2004a) defined Compstat as "a combination of a strategy and a management style" (p. 1) that emphasized the specifica- tion of problems in a certain area and time, requiring tailor-made solutions for specific problems. As the impact of Compstat was so broad and swift in law enforcement society (see Weisburd et al., 2003), a few scholars described its introduction as a "paradigm shift" (Henry, 2002, p. 15; Walsh & Vito, 2004, p. 52). Intensive weekly or monthly meetings held between executives and middle managers to discuss the response to specific crime patterns and trends are vital to the Compstat strategy. They serve as a management tool for achieving organizational goals, crime reduction, and improvement in quality of life (Hoover, 2004b). Precinct commanders share their strategic plans, progress, and operations results in dealing with community crime and quality-of-life issues, leading to the creation, evaluation, and dissemination of new strategies.

Theoretical Background of Compstat Strategy Compstat policing entails several crime control strategies such as broken windows enforcement, hot spot policing, and problem-solving approaches (Bratton & Knobler, 1998; Henry, 2002; Silverman, 1999). To understand Compstat as an innovative approach, it is important to examine its most controversial foundation, the broken windows model (Kelling & Coles, 1996). The broken windows model was introduced by James Q. Wilson and George Kelling (1982) in the article, "Broken Windows: The Police and Neighborhood Safety." Wilson and Kelling argued that "disorder and crime are usually inextricably linked, in a kind of developmental sequence" (p. 31). They explained their theory using a broken windows analogy: "if a window in a building is broken and is left unrepaired, all the rest of the windows will soon be broken" (p. 31). A single unrepaired broken window becomes an indication that nobody cares about the property, thus increasing the likelihood of further vandalism. The broken windows model posited that criminal behavior was a joint result of indi- vidual criminal disposition and neighborhood social and physical disorder. Wilson and Kelling (1982) recognized a potential theoretical linkage between disorder and serious crime, whereas Skogan (1990) suggested that unconstrained disorderly behavior set in motion a downward spiral of decay. Wilson and Kelling believed that disorder and crime were similar types of social problems with different magnitudes. For instance, murder and graffiti might be two different crimes, but they were part of the same continuum. Therefore, if society tolerated nuisance offenses, serious offenses would follow. Based on this model, Wilson and Kelling proposed that the police focus on the improvement of social and physical disorder to control serious crime. One of the earliest attempts to test the broken windows model was completed by Wesley Skogan (1990). He surveyed 13,000 residents of forty neighborhoods in six major cities to examine the relationship between neighborhood disorder and perceptions Jong et al 391

of neighborhood crime problems, fear of crime, and experience of robbery victimization. In addition, he organized a field observation ofresidential areas by trained researchers, who obtained information regarding physical deterioration by recording graffiti, gang congregations, prostitution, drug dealing, public drinking, and other disorderly behaviors. Skogan found that perception of neighborhood crime problems, fear of crime, and experience of robbery victimization were positively related to actual neighborhood social and physical disorder. They were better explained by neighborhood disorder than other predictor variables such as ethnicity, poverty, and residential instability. This empirical finding supported Wilson and Kelling's broken windows model and provided justification for the police to arrest offenders for petty offenses to prevent serious crime (Kelling & Coles, 1996). Some questions have been raised regarding the validity of a causal link between disorder and serious crimes. Harcourt (1998) reexamined Skogan's data and found a significant influence of outliers in the statistical analysis. After excluding the outliers, the analysis revealed a nonsignificant relationship between disorder and serious crime. Sampson and Raudenbush (1999) tested the broken windows model using the systematic social observation method in which trained observers drove research vehicles through Chicago census tracts and recorded neighborhood disorder. They found that disorder was only moderately correlated to predatory crime. After controlling for antecedent neighborhood characteristics (e.g., concentrated disadvantage, residential stability, immigrant concentration, population density, mixed land use, and collective efficacy), the relationship between disorder and crime disappeared. They concluded that the relationship between public disorder and crime was spurious except for robbery and that the broken windows enforcement strategy was an "analytically weak strategy to reduce crime" (p. 638). The approach used by Sampson and Raudenbush has, itself, however, been criticized, particularly their "drive- by" measure of neighborhood disorder (Wilson & Kelling, 2006). Taylor (2001) conducted a comprehensive study examining incivilities2 and crime in 66 neighborhoods in Baltimore, focusing on the relationship between social and physical incivilities and neighborhood crimes. His findings partially supported the broken windows model; while certain types of incivilities were related to certain serious crimes, others were not.3 Therefore, Taylor (2001) proposed that the police, researchers, and policy makers look at various kinds of incivilities differently. He recommended policy makers consider not only broken windows enforcement but also different strategies to deal with various kinds of disorder problems in communities. With these conflicting findings, it would be difficult to acknowledge without question the validity of the broken windows model. Nevertheless, this model has become the theoretical foundation of broken windows enforcement, which is a major crime intervention strategy of Compstat policing. Bratton, who strongly supported the broken windows model, applied it to crime control policy in the subway system when he assumed command of the New York Transit Police in 1990 (Kelling & Coles, 1996). With the success of reducing disorderly behaviors and crimes in the New York subway system, Bratton implemented broken windows policing when he became New York City's police commissioner. As such, broken windows enforcement became an integral part of the Compstat policing model (Hoover, 2004a; 392 Police Quarterly 13(4)

Kelling & Coles, 1996), which emphasized the reduction of serious crimes through the improvement of quality of life in neighborhoods (Bratton & Knobler, 1998; Kelling & Coles, 1996). Kelling and Sousa (2001) evaluated NYPD's Compstat strategy focusing on broken windows arrests as a measure of police intervention and found a significant increase in misdemeanor arrests following the initiation ofthe Compstat strategy. To examine changes of violent crime over time in one New York City police precinct, they employed hierar- chical linear modeling with four explanatory predictors for the change in crime rates: (a) broken windows policing, (b) demographics, (c) drug usage, and (d) economic status.4 Of these, broken windows enforcement was found to be the strongest predictor of violent crime rates in the model (Kelling & Sousa, 2001). Using New York City's monthly crime and arrest data and socioeconomic data between 1974 and 1999, Corman and Mocan (2005) also tested the effectiveness of broken windows policing. Seven index crimes and misdemeanor arrests were used as dependent variables of broken windows policing. The time-series analysis reported that as the growth rate of misdemeanor arrests increased, the growth rate for robbery, motor vehicle theft, and grand larceny declined after controlling for other independent variables (i.e., each crime cate- gory's arrest rate, size ofthe police force, and prison population). This finding validated the broken windows theory for three crime categories-robbery, motor vehicle theft, and grand larceny. They explained the causal mechanism between the increased misdemeanor arrests and decreased growth rates for the three crime categories in terms of deterrence rather than incapacitation. As most misdemeanor arrests resulted in a small number or very short incarceration periods, incapacitation effect was minimal. In sum, Kelling and Sousa (2001) and Corman and Mocan's (2005) studies provide important empirical evidence supporting broken windows enforcement as a major Compstat strategy. However, broken windows enforcement may not be the critical component of Compstat suggested. The Compstat program of Queensland Police Services (QPS) was evaluated recently (Mazerolle et al., 2007). Following NYPD's Compstat model, the QPS implemented "Operational Performance Reviews" (OPRs) in early 2001. However, the approach in Queensland did not include an increase in broken window enforcement. Instead, the Operational Performance Reviews (Compstat strategy meetings) focused on problem- oriented approaches to a wide range of offenses, not simply the classic suppressible street crimes. Using time-series analysis, Mazerolle et al. (2007) examined monthly counts of offenses for 295 police divisions in the QPS. The results showed that the introduction of OPRs was significantly related to a decrease in the total number of reported offenses and a decrease of unlawful entry offenses. In addition, the OPRs were cost effective by saving more than a million dollars (Mazerolle et al., 2007).

Current Study Although several studies have examined organizational innovation and managerial improvements generated by Compstat (Bratton & Knobler, 1998; Hoover, 2004b; Silverman, 1999), little empirical research has scientifically evaluated the effectiveness Jong et al 393 ofthe Compstat strategy. Furthermore, there has been a lack of consistent findings from the studies that have examined the effects of Compstat policing and broken windows enforcement in particular. In addition, previous research has largely concentrated on New York City, thus providing little information about Compstat policing in other cities. As a result, this study attempts to address these problems by scientifically evaluating the effectiveness ofthe Compstat strategy used by a more typical police agency, specifi- cally the Fort Worth Police Department. Prior to conducting quantitative analyses, the researchers interviewed key personnel at the FWPD. With in-depth interviews, the researchers were able to identify details concerning the implementation of Compstat, which are not obvious in the statistical data. The questionnaire for the interview was created based on Hoover's (2004a) Compstat elements.s During the interview, the researchers requested data concerning the actual initiation date of the program for the department and seven major Compstat components: (a) real time crime analysis, (b) targeted enforcement, (c) broken windows enforcement, (d) crime response teams, (e) unit commander accountability, (f) police application of organizational development, and (g) reorientation of community policing to crime-specific policing. Thus, the researchers employed both qualitative and quantitative methods to analyze the Compstat strategy of the FWPD.

Description of Fort Worth's ASM Fort Worth, Texas' fifth largest city, is located in north central Texas with a population of 703,073 as of 2008. FWPD, with 1,409 sworn officers and approximately 350 non- sworn personnel, has employed the ASM as a Compstat strategy since September 2002 (Hoover, 2004b). The implementation of the ASM followed a pattern similar to the NYPD, with FWPD holding a crime strategy meeting every 2 weeks for which all regional commanders must prepare crime statistics on their areas as well as actionable strategies for responding to any problems. The overall goal of Fort Worth's ASM (Compstat meeting) was to reduce crime and problems to improve quality of life in the community. The FWPD initiated Compstat in September 2002 under the leadership of then Chief of Police Ralph Mendoza. FWPD conducted regular Compstat meetings at both headquarters and division levels. Mendoza himself chaired the headquarters meeting. The headquarters meetings were initially scheduled every 2 weeks. Based on a 28-day period crime analysis, division commanders were asked to identify problems in their areas and present potential solutions. For each division, Compstat meetings were scheduled weekly and line officers were required to participate with the district lieutenant. Therefore, every officer in the FWPD was directly involved in Compstat meetings. Department and division level GIS analysts at the FWPD prepared crime analyses for Compstat meetings based on GIS mapping. The lag time between an incident and GIS analysis was less than 24 hr, allowing division commanders to examine the results of crime incidents quickly. Agency personnel reported that the GIS mapping system significantly improved the Compstat strategy at the FWPD. Crime analysis data were 394 Police Quarterly 13(4) used to create accountability among division commanders at the department level while lieutenants in each division were held accountable by division commanders. In addition, crime analysis data were used to identify crime hot spots and develop crime prevention strategies. Crime mapping technology was described as the key of Compstat by the FWPD. The FWPD thus implemented the classic model of Compstat vis-?1-vis crime analysis and accountability. The FWPD did not create department-level special units for crackdowns against crime hot spots as part of the Compstat policy, although existing special units such as the drug enforcement unit and gang unit were deployed to crime hot spots. However, the FWPD created targeted enforcement units called Zero Tolerance Units (ZTU) at each of the four patrol divisions to address crime hot spots. The ZTU were each composed of approximately 12 officers who focused on targeted enforcement to solve prevalent crime problems instead of answering calls for service. The FWPD also implemented broken windows enforcement against disorderly conduct and public nuisances by employing in particular the agency's Neighborhood Police Officers (NPOs). According to agency personnel, broken windows enforcement had begun before the implementation of Compstat. However, Compstat made broken windows enforcement more effective by providing timely and accurate crime information. The NPO program began in 1991 as part of community-oriented policing in the FWPD. Even though the role of NPO became more enforcement oriented after the implementation of Compstat, their major role as liaison between the police and the community remained the same. The FWPD's Compstat emphasized accountability among geographic commanders. Division or district commanders were responsible for preparing plausible solutions regarding problems in their areas. After a solution had been implemented, commanders were responsible for reporting results. Successful results were shared with other commanders while commanders prepared alternative methods to solve the problem if it was not solved. Although this strict accountability brought higher levels of stress among officers, agency command staff reported it catalyzed the entire department to perform more proactively. Compared with the NYPD's Compstat strategy, however, the FWPD employed less "punitive" accountability for division or district commanders. While commanders were transferred, demoted, or forced to retire based on their performance at the NYPD, this did not occur at the FWPD. According to the agency, the Compstat strategy was assessed to generally improve the operation and management of the department. Confirming the implementation of Compstat policy in the FWPD, this study examines the effectiveness of Compstat policy using quantitative analysis.

Method Research Questions

For the quantitative analyses, this research investigated two major research questions. The first research question included in the study was as follows: Jong et al 395

Research Question 1: Are there any significant increases in the arrest rate for broken windows offenses (minor nuisance offenses) in the FWPD following the implementation of the Compstat strategy?

The question is whether increased arrests for disorderly behaviors are an integral component of Compstat outside New York City. The second question examined the role of the Compstat strategy as a whole in explaining changes in crime rates. Previous studies suggested that structural variables be controlled to better isolate the net effect of Compstat strategy on crime trends. In this regard, this study included racial heterogeneity, residential mobility, unemployment rate, percentage of young males, and disrupted families as control variables. Using monthly time-series data, the study addressed the following research questions regarding the relationship between the Compstat strategy and changes in crime rates in Fort Worth:

Research Question 2a: Are there any significant changes in Part I violent offense rates following the implementation of the Compstat strategy after controlling for other explanatory variables? Research Question 2b: Are there any significant changes in Part I property offense rates following the implementation of the Compstat strategy after controlling for other explanatory variables? Research Question 2c: Are there any significant changes in Part I total index offense rates following the implementation of the Compstat strategy after controlling for other explanatory variables?

Data and Variables To examine the Research Question 1, a time-series intervention analysis was applied. The researchers obtained monthly arrest data for the six nuisance offenses identified earlier directly from the FWPD. To investigate three series of the second set of research questions (Research Questions 2a, 2b, and 2c), a multivariate time-series analysis was used, which required a number of variables to be controlled and a large sample size to maintain appropriate ratio of cases to independent variables (Hair, Black, Babin, Anderson, & Tatham, 2006; Mertler & Vannatta, 2005; Yaffee & McGee, 2000). As UCR crime data obtained from the FWPD did not span the entire 1990s, the researchers added UCR crime data from the Interuniversity Consortium for Political and Social Research (ICPSR). To obtain an adequate number of observations for time-series analyses, this research used monthly data from January 1995 to December 2006. For demographic and social structural variables, this study used the American Community Survey (ACS) data from 2000 through 2006, whereas city-level monthly unemployment rates were used from the U.S. Department of Labor's (USDL) Bureau of Labor Statistics for the economic variable (U.S. Department of Labor, n.d.). As this study incorporated data with a monthly time-series structure, each variable was measured in monthly intervals. 396 Police Quarterly 13(4)

Dependent Variables

To explore the first research question (i.e., any significant increases in the arrest rates for broken windows offenses?), several broken windows arrests were included as out- come measures ofthe Compstat policing. After considering previous indicators (Hoover, 2007; Kelling & Sousa, 2001; Worrall, 2006),6 data availability, and relevance to the broken windows theory, the following Part II arrests were chosen to measure broken windows enforcement for the Compstat strategy: (a) vandalism, (b) prostitution and commercialized vice, (c) drug abuse violations, (d) drunkenness, (e) disorderly conduct, and (f) vagrancy. The variable was measured by the sum of these six categories of Part II arrests per 100,000 persons. To examine Research Questions 2a, 2b, and 2c (i.e., any significant changes in major crime rates?), Part I violent offense, property offense, and total index crime rates were used as dependent variables. Violent offense rates were measured by the total number of murder and nonnegligent manslaughters, forcible rapes, robberies, and aggravated assaults per 100,000 persons. Part I property offense rates were measured by the total number ofburglaries, larcenies, and motor vehicle thefts per 100,000 persons (arson was excluded). Finally, the total index offense rates were measured by the sum of Part I violent and property offense rates.

Independent Variable The independent variable indicated the presence or nonpresence of the Compstat strategy. A simple dichotomy was used, with 0 assigned for the months prior to the implementation of Compstat and 1 assigned for months following the implementation, thus indicating the presence of Compstat.

Control Variables

As a measurement of economic status, this study used the monthly unemployment rate, which was obtained from the BLS. To measure demographic changes in Fort Worth, this study used the percentage of the young male population between the ages of 15 and 24. Social structural indicators included in this study were (a) racial heterogeneity, (b) residential mobility, and (c) disrupted family. These variables were obtained from the U.S. Census 2000 as well as the ACS 2000 through 2006. As a measure of racial heterogeneity, this study used Sampson and Groves' (1989) racial heterogeneity index (1 - ZPi2 , where Pi represents the fraction of the population for each racial group). Residential mobility was operationalized by the proportion of households that moved into their current address less than a year ago (Bursik & Grasmick, 1993). Disrupted family was measured by the proportion of female-headed households without a husband and with children below the age of 18. As this study used a monthly time-series data structure, the researchers estimated monthly data for the percentage of young male and social structural indicators using the annual ACS 2000 through 2006. Jong et al 397

Statistical Analysis

In this study, two different time-series analysis techniques were employed. For Research Question 1, a time-series intervention analysis was used to observe the immediate impact of the implementation of Compstat on arrest rates for broken window offenses. For Research Questions 2a, 2b, and 2c, a multivariate time-series analysis was used to examine the relationship between the implementation of the Compstat strategy and Part I offenses while controlling for other explanatory variables.

Time-Series Intervention Analysis The Box-Jenkins-Tiao strategy requires preintervention observations to estimate a reli- able time-series model. Generally, the ARIMA model requires a minimum of 50 observations. However, if there are not enough preintervention observations, researchers then consider another approach or a full-series modeling strategy, which does not require the separation of observations into pre- and postinterventions (Yaffee & McGee, 2000). Full-series modeling strategy estimates the impact model first and the residual noise later. As this study included a relatively small number of observations, a full-series modeling strategy for the intervention analysis was used. The significance of the intervention variable indicates the presence of significant impact of intervention in the series. Considering the possible lagged effect of the Compstat strategy, the researchers tested models with several lagged variables. Simultaneously, the researchers made sure the residuals were white noise (Yaffee & McGee, 2000, p. 286).

Multivariate Time-Series Analysis To test the second, third, and fourth hypotheses, the researchers used dynamic regression models with a dependent series and multiple independent series. By permitting the simultaneous testing of significance and magnitude of multiple input series, the dynamic regression modeling allows the researchers to test alternative causes with more sophisticated approaches (Yaffee & McGee, 2000):

Yt + t + 2X2t + + , structural equation =t t 1+ et disturbance for AR(1)

where Y = output series and X. = input series. However, one problem with dynamic regression modeling is that there is autocorrelation of the error (et). Autocorrelation of the errors can be related to the violation of four assumptions of the OLS regression: homogeneity of variance of errors, independent observations, zero sum of the errors, and nonstochastics independent variables (Yaffee & McGee, 2000). The Durbin-Watson d test was employed to statistically examine the presence of first-order autocorrelation. In addition, the researchers examined ACF and PACF plots to determine the presence of higher order autocorrelations. With the presence 398 Police Quarterly 13(4)

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Figure I. Sequence chart of broken windows enforcement rates of Fort Worth Police Department. of an autocorrelation process on residuals, researchers used corrective algorithms to prevent biased estimations. The regression analysis with Prais-Winsten, Cochrane- Orcutt, or maximum likelihood methods was used as a corrective algorithm (Yaffee & McGee, 2000). Based on three dependent series (e.g., property, violent, and total index crimes), three different multivariable time-series models were examined. The independent series for each model was the use of the Compstat strategy. The control variables of unemployment rates, the percentage of young males, and social structural indicators were included in the multivariate time-series models. Through these models, this study was able to determine the significance and magnitude of each variable in explaining changes in crime rates.

Results Time-Series Intervention Analysis on Broken Windows Enforcement

To examine the use of broken windows enforcement after the implementation ofASM, a time-series intervention analysis was employed. First, the sequence chart was examined for broken windows enforcement at the FWPD, which showed a nonstationary charac- teristic (see Figure 1). A seasonal variation in the series seemed to exist because arrest rates for broken windows offenses were generally higher during the summer season Jong et al 399 than the winter. In the sequence chart, however, there was a slight upward trend after September 2002, when the FWPD initiated the Compstat program. Therefore, this chart showed the potential impact of the FWPD's Compstat program on broken windows enforcement rates. This upward trend continued until around July 2005, after which the broken windows enforcement rates sharply declined. To identify a potential ARIMA model for broken windows enforcement rates at the FWPD, the researchers examined ACF and PACF plots. The ACF plot did not show a seasonal difference. The examination ofACF and PACF plots led the authors to consider ARIMA (0, 1, 2) as the potential nonseasonal ARIMA model. Therefore, the temporary model with a seasonal ARIMA component would be ARIMA (0, 1, 2)(0, 0, 1)12. The best ARIMA model should have all significant AR and MA coefficients and lower values of AIC and SBC scores. The parsimony of the model should also be considered. That is, the model with a smaller number of coefficients would be a better model. Considering these 7 estimation standards, the authors chose ARIMA (0, 1, 2)(0, 0, 1)12 as the final model. As the final stage of the time-series intervention analysis, the authors entered the intervention input into the analysis model to see if the FWPD's ASM implementation significantly increased broken windows enforcement. Considering the characteristics of the Compstat intervention, this study created intervention variables using a step function and considered a potential lagged effect after the implementation of Compstat. Lagged effects up to 5 months were analyzed in the intervention analysis. Results of the intervention analysis are shown in Table 1. All MA(p) and SMA(sq) coefficients were significant in the ARIMA intervention model. When the intervention on September 2002 was entered into the intervention analysis, the intervention variable showed a significant positive relationship with broken windows enforcement (b = 47.200, p <.05). That is, the Compstat implementation significantly increased broken windows enforcement in Fort Worth. One lagged intervention period (October 2002) was also significant (b = 55. 3 2 1, p <.01). Therefore, the ASM strategy at the FWPD did result in significant increases in broken windows enforcement after September 2002. This result indicated that the FWPD's ASM strategy also employed broken windows enforcement, similar to the NYPD's Compstat strategy (Bratton & Knobler, 1998; Henry, 2002). How- ever, all remaining lagged effects (November 2002-January 2003) were not significant. Most significantly, implementation of Compstat in Fort Worth did not result in increases in all six types of nuisance offense arrests. Indeed, the monthly mean of arrests for three of the six offenses decreased (see Table 2). The aggregate increase was driven primarily by a 69% increase in arrests for drunkenness, from a monthly mean of 330 arrests to 558. Concurrently, arrests for prostitution decreased by 35%, vandalism by 28%, and vagrancy by 16%. The pattern represents directed, geographically focused enforcement of nuisance offenses, not an across-the-board increase in "incivility" arrests as occurred in New York. Labeling here may be important. The Fort Worth enforcement effort associated with Compstat is better described as "hot spot" zero tolerance enforcement rather than a blanket increase in broken windows arrests. Indeed, as noted, the divisional targeted enforcement units in Fort Worth are called ZTU. on - -0 - uLJ 00- ... 0

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Table 2. Comparison of Monthly Mean for Six Nuisance Offenses Before and After Compstat (September 2002)

Monthly mean: Pre = 32 mos. Post = 67 months.

Drunkenness Drugs Disorderly Vagrancy Vandalism Prostitution Net Before Compstat 330 279 277 Ill 64 191 1,252 After Compstat 558 31 1 303 93 46 125 1,436 % change +69 +11 +9 -16 -28 -35 +15

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Date

Figure 2. Sequence chart of violent, property, and total index crime rates of Fort Worth.

Multivariate Time-Series Analysis on Crime Rates

Prior to the main analyses, sequence charts were examined for three dependent variables (violent, property, and total index crime rates) of the multivariate time-series analyses. As Figure 2 indicates, there were slight downward trends for both property crime and total index crime rates during the study period. However, violent crime rates did not show any upward or downward trend except seasonal variations. To determine the statistical significance ofCompstat policing on the changes in major crime rates after controlling for competing explanatory variables, multivariate time-series regression models were employed.8 By examining the ACF and PACF charts of each regression model's residuals, the researchers found the lags which had significant cor- relations. By including the significant lags in the model, the researcher controlled higher 402 Police Quarterly 13(4)

Table 3. Time-Series Regression Models for Violent Crime Rates in Fort Worth

Variables B SE T ratio SAR(1) 0.991 0.021 47.202*** SMA(1) 0.806 0.217 3.707*** Compstat -3.815 8.153 -0.468 Unemployment -1.004 3.320 -0.303 % young male -8.121 4.419 -1.838 Racial hetero -206.957 204.139 -1.014 Disrupted family -4.160 3.486 -1.193 Residential mobility -6.248 2.792 -2.238*

Constant 559.894 159.409 3.5 I2*** *p <.05.**p <.0 I.***p <.001.

order of serial autocorrelations (SPSS, 2007). After identifying all significant lags of AR or MA components, multivariate time-series regression models were employed to examine the impact of Fort Worth's ASM on violent crime rates while controlling for other competitive explanatory variables (see Table 3). Compstat was not found to be significant in explaining violent crime rates (B = -3.815, p = .641). Among other control variables, residential mobility showed a significant negative relationship (B = - 6 .2 4 8 , p <.05). In the case of Fort Worth, violent crime rates stabilized during the study period regardless ofthe Compstat implementation. This finding, however, is consistent with those of the previous studies (Jang, Hoover, & Lawton, 2008; Worrall, 2006), which reported nonsignificant relationships between broken windows enforcement and violent crimes. Also, in the Queensland Police Service's evaluation of Compstat in Australia, the researchers reported a nonsignificant effect of Compstat on violent crimes, including serious assaults, common assaults, sexual offenses, armed robberies, and unarmed robberies. Table 4 shows the results of a time-series regression model for property crime rates in Fort Worth.9 Unlike violent crime rates, Compstat was found to have a significant negative impact on property crime rates (B = -61.326, p < .001), meaning that the implementation ofASM was effective in reducing property crime rates while controlling for other competitive explanatory factors. Considering the plateau in crime trends in the United State since the early 2000s (see Zimring, 2007), the continuous decline of property crime rates in Fort Worth arguably can be at least partly attributed to the efforts of the police. This finding is consistent with the Queensland study of Compstat, where the researchers reported a significant reduction of unlawful entry offenses which would be similar to burglary offenses in the United States (Mazerolle et al., 2007). Among other independent input series, unemployment rates showed a significant positive relationship (B = 19.0 8 6 ,p <.01). As the unemployment rates decreased, property crime rates also decreased. This study supported previous findings which reported a Jong et al. 403

Table 4. Time-Series Regression Models for Property Crime Rates in Fort Worth

Variables B SE T ratio AR(1) 0.358 0.097 3.7I3#* AR(4) -0.273 0.103 -2.649# SAR(1) 0.695 0.078 8.9I2#* Compstat -61.326 17.578 -3.489#* Unemployment 19.086 6.858 2.783# % young male 25.809 9.098 2.837# Racial hetero 672.015 398.863 1.685 Disrupted family 12.573 6.846 1.837 Residential mobility -5.457 6.108 -0.893

Constant - I 60.808 302.732 -0.53 I

*p < .05. #p < .0 1. *p < .00 1.

Table 5. Time-Series Regression Models for Total Index Crime Rates in Fort Worth

Variables B SE T ratio AR(1) 0.305 0.096 3.18 1 ** AR(4) -0.298 0.100 -2.978** SAR(1) 0.771 0.066 I 1.748*** Compstat -78.208 21.471 -3.643*** Unemployment 23.262 8.258 2.817** % young male 21.862 10.695 2.044* Racial hetero 553.379 469.063 1.180 Disrupted family I 1.949 8.140 1.468 Residential mobility -7.641 7.412 -1.03 I

Constant 179.860 356.854 0.504

*p < .05. #*p < .0 1. #**p < .00 1. significant relationship between unemployment rate and crime rates while controlling for other factors (Donohue & Levitt, 2001; Gould, Weinberg, & Mustard, 2002). The percentage of young males was also significant in explaining property crime rates (B = 2 5. 8 09, p <.01). Considering the fact that most crimes were committed by young adult males (Blumstein & Wallman, 2000; Fox, 2000; Zimring, 2007), this finding confirmed the significant relationship between the percentage ofyoung males and property crime rates. Finally, Table 5 indicates the regression coefficients of explanatory variables for the multivariate time-series regression model for total index crime rates. Since all AR and SAR components were significant, this model was able to control both seasonal and nonseasonal autoregressive effects in the residual of the model. The independence 404 Police Quarterly 13(4) assumption was satisfied with the inclusion of these components in the model. The ASM strategy was found to have a significant negative impact on total index crime rates (B = -7 8 .2 0 8,p <.001) while controlling for other explanatory factors. Findings from the time-series regression model of total index crime rates was very similar to those of property crime rates since most of the total index crimes were property crimes. Therefore, interpretation of property crime rates could be applied to the total index crime rates of Fort Worth. Overall, FWPD's ASM was effective in reducing the total index crime rate. Among other independent input series, unemployment rates showed a significant positive relationship with total index crime rates (B = 2 3 .2 6 2 ,p <.01), which supported previous findings (Donohue & Levitt, 2001; Gould et al., 2002). Percentage of young males was also significant in explaining total index crime rates (B = 2 1. 8 62 , p <.05), which was also consistent with previous findings (Blumstein & Wallman, 2000; Fox, 2000; Zimring, 2007). Other variables such as racial heterogeneity, disrupted family, and residential mobility were not significant.

Discussion The Compstat policing strategy has been very popular among law enforcement agencies since the NYPD success story (Weisburd et al., 2003). Furthermore, Chief William Bratton and numerous scholars attributed a precipitous crime decline in New York City during the 1990s to the NYPD Compstat strategy (Bratton & Knobler, 1998; Henry, 2002; Kelling & Sousa, 2001; Silverman, 1999). However, it was not known whether the Compstat strategy would be effective in other major cities during the 2000s when the nationwide decline in crime rates began to flatten (Zimring, 2007). This study employed both qualitative and quantitative methods. The in-depth interviews with selected command staff in Fort Worth conveyed a positive message regarding implementation of Compstat as successful. The FWPD implemented its Compstat strategy in September 2002, with several important elements: regular ASMs at both headquarter and division levels, an advanced geographic information system (GIS) crime analysis system, and engagement of the following organizational units in crime- reduction efforts:

* Patrol-enhanced information and directed effort; * Divisional detectives-coordinated investigations, proactive approaches, such as stings; * Community policing neighborhood patrol officer engagement in direct crime control efforts, problem solving; * Structured tactical units-zero tolerance teams deployed to hot spots; * Patrol special duty assignments-saturation patrol, surveillance; * Centralized and specialized investigations units-directed effort of gang units, auto theft, narcotics. Jong et al 405

Compared with the NYPD's Compstat strategy, however, the FWPD was found to employ no "punitive" accountability for division or district commanders. The NYPD's Compstat used broken windows enforcement as its main crime control strategy based on the hypothesis that serious crimes could be reduced if the police were able to control less serious disorderly behavior (Kelling & Coles, 1996; Wilson & Kelling, 1982). In this regard, the first research question asked if the Compstat strategy of the FWPD also employed broken windows enforcement as a major strategy. To answer this question, this study analyzed arrest data for the FWPD. Intervention analysis using the ARIMA technique showed a significant increase in the enforcement ofbroken windows offenses in the FWPD. The overall increase, however, must be understood in the context of a 69% increase in drunkenness arrests with concurrent decreasesin arrests for prostitution, vandalism, and vagrancy. In addition, multivariate time-series regression models were employed to examine the role of the Compstat strategy, broken windows arrests in particular, in explaining changes in crime rates for violent, property, and total index offenses. Since the NYPD's implementation of Compstat in the early 1990s, crime rates declined in the 1990s not only in New York but also in most major cities across the United States (Blumstein & Wallman, 2000; Zimring, 2007). However, nationwide downward crime trends began to flatten during the early 2000s when the FWPD implemented its Compstat strategy. Given this leveling in national crime trends, this study examined the role of the Compstat strategy in changes in major crime rates in the 2000s after controlling for rival explanatory variables. The results indicated that the FWPD's Compstat strategy played a significant role in reducing property crime rates and total index crime rates. This finding is consistent with previous studies (Jang et al., 2008; Mazerolle et al., 2007; Worrall, 2006). In particular, the Queensland Compstat evaluation reported a significant reduction of unlawful entry offenses and total reported offenses (Mazerolle et al., 2007). This finding provides more evidence supporting the effectiveness of Compstat in reducing property and total Part I offenses. However, Fort Worth's implementation of Compstat did not significantly reduce violent crime rates. This finding was same with the Queensland study of Compstat (Mazerolle et al., 2007).

Policy Implications If we accept the fact Compstat reduced crime in New York City, the question remains as to precisely what components of Compstat played a role. Compstat in New York City consisted of several distinctive elements, including the introduction of unit commandeer accountability, sophisticated crime analysis, and aggressive "broken windows" enforcement. It could be that any one of these three major components by itself explains the reduction in crime, or a combination of two components may account for the crime reduction, or the reduction may have required all three. We simply do not know. The situation is made even more difficult to assess because when William Bratton implemented Compstat in NYPD, many would argue that the agency had become bureaucratically dysfunctional. Thus, it is possible that the specifics do not matter, Compstat in NYPD was simply a 406 Police Quarterly 13(4) mechanism that in its totality invigorated an otherwise moribund organization. Certainly those who worked closely with NYPD, including one of these authors (Hoover), would never have held it up as exemplary of quality policing. Indeed, it is fair to characterize NYPD in 1993 as the epitome of "blue-collar policing." The fact that NYPD officers referred to calls for service as "jobs" reflects a mindset about their role and the tasks they perform. What is clear from the detailed descriptions of the implementation of Compstat in New York City is that it was a multifaceted program. One perspective has it that it was a precinct commander accountability program; it was also real time crime analysis for the first time in the history of the agency. Another perspective is that it was a strategic quick response program. It was also broken windows enforcement where there had been none before. It was uttering the words "you are under arrest" with the same frequency as other professional police departments. Finally, it was a classic "reinventing an organization" program. Given the circumstances in New York City in 1993, given the fact the agency under- went a radical organizational transformation, and given the fact that New York City is not typical of American cities, it is impossible to assess by analyzing data from NYPD whether Compstat is an effective program. Likewise, one would experience similar dif- ficulty in assessing the impact of the various elements of Compstat in cities where it was transported from New York City as a "reform" effort, New Orleans for example. Indeed, Compstat runs the same risk as community-oriented policing of suffering from a lack of a clear definition. Compstat is not a rigorously defined program. Before declaring it either successful or unsuccessful, one must first define the independent variable, in particular whether Compstat is a change in management style or a change in street-level enforcement strategy. The Fort Worth results argue for the import of strategy rather than management style. Nothing changed in Fort Worth except strategy. The management team stayed in place. City management stayed in place. Community policing efforts remained largely in place. The accountability model did not include a high- pressure environment or punitive repercussions for unit commanders. Thus, we must then look to strategy. The primary strategy question which can be answered with quantitative analysis is whether broken windows enforcement increased substantially. Importantly, Compstat in Fort Worth did not include blanket increases in enforcement of nuisance offenses. We cannot dismiss the possibility that focused enforcement of drunkenness contributed to the reduction in property crime, but the qualitative information from agency commanders argues that it is doubtful. Location-focused drunkenness arrests and reduction in property crime is a conceptual leap. In totality, broken windows enforcement was a minor component of Fort Worth's effort. Indeed, after the implementation of Compstat there were decreases in arrests for prostitution, vandalism, and vagrancy. Recall that Fort Worth is a "horizontal" western city. It lacks the concentration of disorderly behaviors of New York. Broken windows offenses are so scattered in most of Fort Worth as to be unenforceable as a blanket strategy. When broken windows enforcement was employed in Fort Worth, drunkenness arrests, it was concentrated in hot spots, particularly problematic liquor establishments. It must also be noted that the broken windows approach as implemented in New York involved more than nuisance offense arrests. It also entailed regulatory interventions by Jong et al 407 foot patrol officers. Described best by anecdotal description in Jack Maple's The Crime Fighter (1999), patrol officers most frequently intervened in street incivility without arrest. We had no way to measure such intervention in Fort Worth. (It has likewise never been quantified in the New York assessments.) However, outside of densely populated "vertical" cities, broken windows approaches, whether regulatory warnings or arrests, are likely irrelevant. If the Fort Worth strategic approach to Compstat had to be described with a single word it would be "focusing." The Queensland study of Compstat also reported using a problem-oriented intervention model-focusing-in lieu of a broken windows approach (Mazerolle et al., 2007). Property crime was significantly reduced in both settings. Parallel findings from two differently constituted Compstat programs on two different continents provides evidence that the primary strategic component of the Compstat model is focusing, not broken windows enforcement, and the primary impact is on property crime. Focusing is not as simplistic an approach as one might initially assume. One division commander in Fort Worth described the role of Compstat as the "headlight in the middle of night." That kind of superlative is not generated by a mere "we are going to pay more attention to crime." Observations and interviews by the authors indicate that Fort Worth's intervention approaches were (a) contemplative, (b) proactive, (c) planned, (d) monitored, and (e) modified. Within this context, specific interventions entailed the deployment of various combinations of several organizational units-patrol, divisional detectives, tactical (FWPD's ZTU), patrol special duty assignments, the gang unit, centralized/specialized investigative units, and community policing officers. At least 90% of the interventions involved targeted enforcement- specific offenses, at specific times, at specific locations, committed by specific offenders. Finally, focusing was as much about values and motivation as it was tactics. This observation is derived primarily from the authors' visits to the divisional Compstat meetings. The interventions discussed at the four patrol divisions were very specific. More important, discussion included operational personnel-patrol officers and detectives- not just command staff. It is also important to understand what the concept of focusing is "not." It is not a variant of community policing. Although Fort Worth's Neighborhood Patrol Officers were involved in Compstat, community engagement was employed only infrequently. Focusing is not a variant of problem-oriented policing. We never observed a single instance of formal employment of the classic SARA model. Although multiple police agency organizational units were frequently employed, other city or community orga- nizations were not. Interventions were enforcement oriented, not alleviation of "root causes," and tended to be short term. At the same time, focusing is not merely a form of hot spot deployment. Hot spot concentrated patrol was certainly employed, but usually was confined to deployment of the ZTU. Multiple approaches were the norm-more like Problem Oriented Policing than Hot Spot, but exclusively neither. The authors do not suggest that "focusing" be added to the already burdensome list of forms of policing. It is better regarded as a strategic approach to crime reduction described best as proactive engagement marked by flexibility in intervention tactics. '0 m 1 '0 Lj~ ~- ~

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Appendix B Durbin-Watson d Test for Regression Models of Fort Worth Independent variable Dependent variables d statistics Compstat and other variables Violent crime rates 1.099* Property crime rates 1.305* Total index crime rates 1.198* *p<.05.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

Funding The author(s) received no financial support for the research and/or authorship of this article.

Notes 1. This was measured by asking questions on the extent of local problems with robbery, assault, and burglary. 2. Incivilities include both social and physical incivilities. Social incivilities are "street activi- ties that is disorderly, troublesome, and threatening" (Taylor, 2001, p. 5). Social incivilities include "rowdy teens; hey honey hassles; panhandlers; street crazies; public drunkenness; fights on the street; disorderly or sick drug users; and large numbers of persons hanging out, especially at odd hours" (Taylor, 2001, p. 5). Physical incivilities include local physical conditions such as "deteriorated housing; abandoned housing; poorly maintained properties, lots, sidewalks, and playgrounds; trash; graffiti; abandoned or burned-out cars; and vacant lots" (Taylor, 2001, p. 5). 3. For example, homicide was significantly related to the assessed incivilities, perceived physical incivilities, and perceived social incivilities; rape was significantly related to the assessed incivilities and perceived social incivilities; and robbery was not significantly related to any kind of incivilities (Taylor, 2001, p. 188). 4. Broken windows policing was measured by the precinct's arrests for misdemeanor offenses. Demographics was measured by the number of males enrolled in public high schools in each precinct. Drug usage was measured by the borough's number of hospital discharges for cocaine-related incidents. Economic status was measured by the borough's number of unemployed individuals. 5. Because Hoover already examined several Compstat strategies in Texas, his elements might better explain the program in Fort Worth. 6. Kelling and Sousa (2001) used the number of misdemeanor arrests as a measurement of NYPD's broken windows enforcement. Worrall (2006), however, considered three different types of broken windows enforcement variables. He first included "the number of 410 Police Quarterly 13(4)

misdemeanor arrests divided by total arrests for all felonies and misdemeanor" (p. 56). The second variable was measured by "adding the number of arrests for disturbing the peace, disorderly conduct, and public drunkenness, then dividing by the total number of arrests" (p. 56). The final broken windows variable was measured by "dividing the sum of arrests of disturbing the peace, disorderly conduct, and public drunkenness by the total number of misdemeanor arrests" (p. 56). Hoover (2007) also specified broken windows arrests as (a) disorderly conduct, (b) illegal solicitation, (c) vandalism/criminal mischief/littering, (d) public intoxication, (e) loitering, (f) prostitution, and (g) drug distribution. 7. To ensure the best fit ARIMA model, the author analyzed several other competitive models such as ARIMA (0, 1, 0)(0, 0, 1) 12, ARIMA (1, 1, 0)(0, 0, 1)12, ARIMA (0, 1, 1)(0, 0, 1)12' ARIMA (1, 1, 1)(0, 0, 1) 12, ARIMA (1, 1, 2)(0, 0, 1)12, ARIMA (0, 1, 3)(0, 0, 1)12, and ARIMA (0, 1, 3)(1, 0, 1)12 (see Appendix A). Also, as the diagnosis phase, the authors examined the ACF plot on the residual of the ARIMA (0, 1, 2)(0, 0, 1)12. The Box-Ljung statistic was not statistically significant at any lags. Therefore, the residual of the final ARIMA model was acceptable. 8. The Durbin-Watson's d statistics were examined to check the presence of first-order autocorrelation in the residual of the regression models. All regression models for Fort Worth required at least first-order autocorrelation components in the model (see Appendix B). 9. Multivariate time-series regression model requires two AR and one SAR components to control serial autocorrelation in the residual. In the model, all AR and SAR components were significant. The examination of the ACF plot for the residual series did not show significance at any lags. All seasonal and nonseasonal autoregressive effects were appropriately controlled by these factors in the model.

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Bios Dr. Hyunseok Jang received his Ph.D. from Sam Houston State University. He is on the faculty of the Department of Criminal Justice and Legal Studies, Missouri Western State University. He has authored several articles addressing police strategy, police use of force, and citizen confidence in the police.

Dr. Larry T. Hoover received his Ph.D. from Michigan State University, has been on the faculty at Sam Houston State University since 1977, and directs its Police Research Center. He is a past president ofACJS, and recipient oftheAcademy's OW. Wilson award. He has authored or edited several books.

Dr. Hee-Jong Joo is an Associate Professor in the College of Criminal Justice at Sam Houston State University. His current research interests involve contextual analysis of crime and crime control, the analysis of hot spots with crime mapping, prediction of offender recidivism, and institutional and community corrections.