Complete the term project which is an 10-12 page APA Style paper. Your paper should have at least 10 full (not partial) pages of substance not counting the cover and reference pages. Please be sure that your paper is a Word Document (.doc). Students will be required to use at least six (6) scholarly – peer reviewed - sources. Students have access to peer reviewed sources through the APUS library. (Added as attachments) (Use additional sources also)
This is the absolute minimum and additional resources are encouraged to improve your work and impact your grade. Of course additional references do not have to be peer reviewed articles from the library but they should be, or at least from credible sources.
Question (do paper on this)
Directed patrol, as opposed to random and self-initiated patrol, reduces much discretion, but also reduces down time periods that may not be utilized performing police related duties. It is primarily a management tool to reduce non-productive patrol time while increasing the deterrent effect of properly employing patrol assets.
Hot spot patrol uses a variety of methods, generally crime-mapping, to determine areas of likely criminal activity. It is a popular crime prevention tactic and considered good use of scarce resources. It is felt that the high-profile appearance of law enforcement in the area will deter crime. Often it does, but unintended consequences include crime displacement, it just moves somewhere else.
Zero-tolerance patrol is aligned with the crime prevention doctrine of the Broken Windows Theory. By acting on even small incidents crime will be reduced through deterrence by fear, and also by the implication that action will be taken against virtually all offenders.
When looking for a patrol deployment model that works best with community policing concepts it has been though in the past that the geographic/sector method would be the most effective. Since the same officers are assigned to the same area their frequent contacts with local citizens would reduce the fear of crime and increase citizen interaction. This is seldom the case.
Actually, the overlay model works best for community policing since there are officers dedicated to problem solving and engaging members of the community in proactive crime prevention efforts. The fact that officers are specially trained and focused on these efforts makes it the most often utilized in agencies that promote community-oriented policing.
Exploring how an area’s crime-to-cop ratios impact patrol officer productivity
Luke Bonkiewicz Lincoln Police Department, Lincoln, Nebraska, USA
Abstract Purpose – The purpose of this paper is to examine how the combined crime rate and staffing levels of a patrol area affect patrol officers’ productivity. Specifically, the author identified and analyzed two macro-level correlates of patrol officer productivity: reported violent crimes per officer and reported property crimes per officer (a beat’s “crime-to-cop” ratios). Design/methodology/approach – Using hierarchical linear modeling, the author estimated the effects of a patrol area’s violent crimes per officer ratio and property crimes per officer ratio on the annual number of traffic citations, warrants, misdemeanor arrests, and felony arrests generated by patrol officers (n¼ 302). The author also examined the effect of these crime-to-cop ratios on a more advanced productivity metric. Findings – The results suggest that a patrol area’s rate of property crimes per officer is associated with a moderate decrease in an officer’s annual number of traffic citations, warrant arrests, and misdemeanor arrests; a patrol area’s rate of violent crimes per officer is also associated with a moderate decrease in an officer’s annual number of traffic citations; and a patrol area’s rate of violent crimes per officer is associated with a moderate increase in an officer’s annual number of warrant and misdemeanor arrests. Notably, the crime-to-cop ratios are not correlated with a more sophisticated patrol productivity metric. Research limitations/implications – The author analyzed data from a mid-sized US police department that uses a generalists policing style. It is unknown if these results translate to smaller or larger police departments, as well as those agencies practicing a specialized policing style. Practical implications – The findings suggest that police scholars should not only recognize how the crime-to-cop ratios of a patrol area might impact patrol officer productivity, but also incorporate more sophisticated metrics of patrol officer activity in future studies. These findings likewise signal to police practitioners that an area’s crime-to-cop ratios should be considered when allocating officers and other resources across patrol areas. Originality/value – To the authors knowledge, this is the first study to identify and examine the link between a patrol area’s crime-to-cop ratios and patrol officer productivity. Keywords Patrol, Productivity, Crime-to-cop ratio, VORC Paper type Research paper
Introduction Perhaps no civil servant undertakes the variety of tasks as a municipal police officer. As not only law enforcement agents, but also community caretakers and first-responders to every calamity great and small, patrol officers handle a broad array of calls for service (CFS) during their daily tour of duty. The regular duties of a patrol officer include reactive tasks, such as handling CFS, as well as proactive tasks, such as conducting traffic and scofflaw enforcement, searching for suspected
Policing: An International Journal of Police Strategies & Management
Vol. 39 No. 1, 2016 pp. 19-35
©Emerald Group Publishing Limited 1363-951X
Received 21 May 2015 Revised 22 July 2015 13 September 2015
Accepted 16 September 2015
The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1363-951X.htm
The author would like to thank Denver Police Department Crime Analyst Melissa Ohlhorst and Pennsylvania State University Professor Barry Ruback, as well as the three anonymous reviewers, for their insightful comments. The author also salute Lincoln Police Sergeant Jake Dilsaver for his logistical support of this project.
Patrol officer productivity
perpetrators, and patrolling for suspicious individuals or situations (e.g. drug deals, graffiti, vandalisms, or individuals “casing” a potential target).
Historically, both police researchers and practitioners have grappled with the lack of a valid and reliable method for evaluating patrol officer activity, as well as limited knowledge about how external factors affect officers’ productivity (Crank, 1990; Johnson, 2011; Shane, 2010). Although studies have demonstrated that differences in productivity between patrol officers may result from biographical factors and daily work experiences (Shane, 2011), operational variables (Amaranto et al., 2003), and organizational stressors (Brown and Campbell, 1990), scholars have devoted considerably less attention to how the characteristics of an area might impact police productivity. In other words, although individual-level variables impact a patrol officer’s productivity, how might the characteristics of a patrol area affect a police officer’s productivity?
This paper identifies and analyzes two macro-level correlates of patrol officer productivity: violent crimes per officer and property crimes per officer. We first review the extant literature on patrol officer productivity, discussing how an area’s violent and property crime per cop ratios might affect a patrol officer’s productivity. Next, we analyze productivity data from a mid-sized US police department to examine how these two variables impact the number of traffic citations, warrants, and misdemeanor and felony arrests generated by officers annually. We also examine the relationship between these two variables and a more sophisticated productivity metric, Value Over Replacement Cop (VORC). Finally, we discuss why police scholars and practitioners should pay closer attention to the practical, macro-level correlates of productivity, as well as incorporate more advanced patrol productivity metrics.
Literature review Productivity scholars have generally divided the concept of worker productivity into two spheres (Hatry, 1976). Effectiveness measures a worker or organization’s quality of service, while efficiency gauges how well a worker or organization produces a particular output using the least amount of resources, such as manpower, time, and money. Most police research has examined productivity in terms of efficiency, due mainly to the data available for analysis, such as CFS and arrests.
Police scholars have struggled with how to properly measure patrol officer productivity. There appear to be two main approaches. The first approach attempts to identify and quantify specific types of patrol productivity outputs. Some examples include evaluating productivity using an officer’s total arrests (Crank, 1990), clearance rates (Garicano and Heaton, 2010), number of traffic citations ( Johnson, 2011), and citizen complaints (Lersch, 2002). However, researchers have noted that evaluating productivity using these kinds of raw outputs is “conceptually simple,” shaped primarily by data availability (Chappell et al., 2006).
The second approach to analyzing patrol officer productivity has incorporated multi-dimensional indicators of police performance. For example, Shane (2011) conceptualized productivity using a z-score summary of multiple outputs, including arrests, traffic citations, field contacts, official reports, sick hours, and administrative complaints. Similarly, Van Meter’s (2001) Performance-Based Management, or “zero- based approach” uses three planks (non-scheduled absenteeism, cost of preventable error, and productive use of time) to evaluate patrol officers.
Notably, most existing police productivity research has ignored the concept of productive time, or the amount of time officers have for self-initiated activities after
handling CFS, investigating crimes, completing reports and follow-ups, and assisting other officers. Using productive time to evaluate officers is critical because two officers could achieve the same output (in terms of raw arrests or reports), but have done so with considerably different amounts of productive time due to CFS, follow-ups, etc. Recent research has proposed the use of VORC (Bonkiewicz, 2015), a metric that incorporates more than a dozen patrol activities, weights those activities, evaluates performance in terms of productive time, and compares an officer’s performance against a fixed, hypothetical estimate (i.e. a replacement officer).
In addition to conceptualizing patrol officer productivity, researchers have also studied potential antecedents of productivity, including not only biographical variables but also factors involving workplace environment. First, certain biographical characteristics might impact productivity (Hawkins, 2001). For instance, officers who have children might experience sleep deprivation and greater fatigue as a result of parenting responsibilities, thereby reducing their workplace energy and motivation, and in turn, their productivity (Shane, 2010). Marital status may also impact productivity. Although the relationship between marriage and worker productivity is well debated in the literature (Nakosteen and Zimmer, 2001; Becker, 1985), it seems reasonable to believe that married officers might be more productive than unmarried officers. Married officers have someone in whom to confide, a partner who hears about the challenges and rigors of the job, which may lead to stress reduction and improve workplace motivation. An officer’s age is another consideration. Older officers might be less productive than younger officers, perhaps due to burnout as well as the physical demands of law enforcement (Goodman, 1990). Officers’ perceptions of their job may also affect their proactivity, and in turn, productivity. Brehm and Gates (1993) found that officers who disliked certain aspects of law enforcement (e.g. taking people to jail) impacted whether officers engaged in proactive activities or shirked their duties.
Additionally, there are also operational features (the “day to day” rigors) of law enforcement which can decrease productivity and increase misconduct, such as the perceived danger of the job (Amaranto et al., 2003), work schedules and shift work (Vila et al., 2002), and fatigue (Vila and Kenney, 2002). Notably, in studying the relationship between daily work experience and performance, Shane (2011) found that workload was the “most important predictor of performance” (p. 13).
We believe this last finding merits further study. First, supervisors evaluate patrol officers, at least in part, using productivity outputs. If there are factors (i.e. heavy workload) beyond an officer’s control that are affecting his or her productivity, both line-level supervisors and command staff members should acknowledge and account for these variables during performance reviews. Second, if macro-level variables create different workloads, thereby affecting productivity outputs, then police researchers should include these measures in future studies on police activity. We suggest that there are at least two macro-level variables important to future police productivity research.
Crime-to-cop ratios and patrol officer productivity Criminological research has long recognized that crime rates are not uniform across neighborhoods and cities (Sherman et al., 1989; Weisburd et al., 1992). Instead of “policing people,” law enforcement agencies frequently identify “hot spots” and devote additional resources to reducing crime and disorder in specific problem areas. With this perspective in mind, it might seem natural to examine the relationship between the
Patrol officer productivity
crime rate of a patrol area and an officer’s productivity outputs (or vice versa). For example, a high crime area might net an officer more arrests (and therefore, more productivity outputs) than a low crime area. On the other hand, a high crime area might also occupy a great deal of an officer’s productive time, prohibiting the officer from engaging in proactive activities. However, crime rate does not fully capture how a particular area might affect an officer’s productivity.
Instead, we believe macro-level variables related to crime should be estimated in terms of the number of patrol officers in a patrol area, specifically, violent crimes per officer and property crimes per officer. For instance, some officers may patrol a lower crime area but end up responding to a greater number of violent crimes than officers in a high crime area simply because they have fewer officers working the street. This “crime per cop” ratio can dramatically affect officers’ productivity.
For example, when an officer investigates a crime, he or she might interview the victim, suspect, and witnesses, as well as take photographs, collect evidence, and make an arrest. There may be additional follow-up the next day or week, such as checking pawnshops for stolen property or tracking down and interviewing additional witnesses. All these activities could decrease an officer’s time for self-initiated activities, and in turn, an officer’s overall productivity. A high crime per cop ratio, therefore, could decrease productivity for officers, even if these officers work in an area with a low crime rate. Furthermore, an area’s crime per cop ratio could also affect officers’ productive time even if they are not the primary investigating officer because secondary officers are often needed during serious incidents, such as robberies or burglaries.
The “load shedding” hypothesis by Maxfield et al. (1980; see also Maxfield, 1982) also has important implications for any study examining patrol officer productivity. Maxfield et al. suggested that while variations in workload do not affect patrol officers’ decisions to record serious crimes, workload does affect officers’ decisions to record less serious crimes; it may also affect self-initiated activities as well (Herbert, 1998).
Finally, we believe that macro-level variables affecting productivity should be divided between violent and property crimes to account for the time and resources needed to investigate each incident type. When officers respond to assaults, they often contact suspects and victims on scene, meaning that officers are able to interview all involved individuals, collect evidence, and make an arrest, sometimes in less than an hour. However, when officers respond to burglaries or larcenies, they may be required to dust for fingerprints, swab for DNA, canvass a neighborhood for witnesses, check pawnshops, and engage in other investigative activities that require hours and even days if the case requires follow-up.
With these issues in mind, our study addresses the following research questions:
RQ1. What is the relationship between a patrol area’s rate of violent crimes per officer and patrol officer productivity outputs (i.e. traffic citations, as well as warrant, misdemeanor, and felony arrests)?
RQ2. What is the relationship between a patrol area’s rate of property crimes per officer and patrol officer productivity outputs (again, traffic citations, and warrant, misdemeanor, and felony arrests)?
RQ3. What is the relationship between the rate of property and violent crimes per officer and a patrol officer’s productivity when we introduce a more sophisticated method for analyzing productivity, a metric based on productive time and not total time on duty?
Before discussing our data and methods, there are two important issues meriting brief discussion. First, we argue that the crime-to-cop ratio may affect the number of traffic stops, warrants, misdemeanors, and felonies that a patrol officer makes. It can also be argued that the number of arrests might impact the crime-to-cop ratio. Our intent is not to comprehensively discuss the relationship between the number of officers/officer arrests and an area’s crime rate, but it bears noting that the research is inconclusive on this point.
Some evidence suggests that the relationship between arrests and crime rates is non-linear, that is, crime rates decrease as arrests increase but eventually hit a “floor” and reach equilibrium (Kane, 2006). Second, when studies examine the arrest-crime rate relationship, they often find that the relationship only exists for specific crimes in their data sets, such as robbery and motor vehicle thefts (Corman and Mocan, 2005), robbery but not assaults (Kane, 2006), and homicide and auto thefts only (Cloninger and Sartorius, 1979), and so forth. Overall, the literature on the arrests-crime rate relationship is mixed – some research finds an effect between arrests and crime rates, some does not (Huizinga and Henry, 2008).
The second issue concerns whether a law enforcement agency operates using a generalist or specialist policing style. We analyze data from a generalist municipal police department, meaning that patrol officers investigate low and mid-level crimes, but investigators and detectives are responsible for handling serious crimes, cases involving lengthy investigations (e.g. narcotics trafficking or white-collar crimes), and incidents in which patrol officers may need assistance (e.g. an incident involving numerous victims and witnesses, as well as cases involving extensive follow-up or knowledge on a specific subject, such as gangs or narcotics).
By contrast, when patrol officers in a specialist-style police department respond to any incident potentially requiring follow-up, they might only contact the victim and either hold the scene until an investigator arrives or forward the information to a detective for follow-up. It is possible, indeed likely, that macro-level variables might affect generalist patrol officers differently than specialized officers. Due to data limitations, however, our study focusses on generalist police officers.
Data and methods We gathered data on patrol officers and their patrol areas from several sources. First, we collected patrol officer productivity data from the Lincoln (Nebraska) Police Department’s 2013-2014 productivity reports. Using Computer-Aided Dispatch (CAD) and a centralized productivity database, LPD measures numerous patrol officer activities. Every month (and year), LPD generates productivity reports which display each patrol officer’s activities and outputs (see Table I for an example of a squad’s annual productivity report). Each patrol activity and output merits further explanation.
CFS and officer assist time When dispatchers send patrol officers on a call, they use CAD to track the minutes the officer spends on the call. If the call is a priority incident, dispatchers send both a primary and secondary officer. Other unassigned officers might also respond to the CFS, meaning that multiple officers could be secondary officers. The annual productivity record tracks the amount of time an officer spent as a primary responder and a secondary responder.
Patrol officer productivity
O ff ic er
Pr os ec ut io n
m in ut es
F/ U P
m in ut es
O ff ic er
as si st
m in ut es
In ci de nt
re po rt s
re po rt s
Se le ct iv es
W ar ni ng
s O ff ic ia ls
W ar ra nt s
D U Is
M is de m ea no rs
Fe lo ni es
22 ,8 12
5, 20 8
6, 13 2
30 ,0 72
5, 42 4
4, 11 6
72 32 ,4 96
6, 31 2
6, 60 0
33 ,0 00
5, 23 2
4, 98 0
91 41 ,7 24
4, 82 4
6, 04 8
83 23 ,9 16
7, 05 6
6, 19 2
95 24 ,4 68
6, 84 0
5, 89 2
90 39 ,9 72
5, 19 6
4, 92 0
25 ,5 00
4, 06 8
5, 10 0
87 21 ,9 84
5, 41 2
4, 52 4
Table I. Example of the Lincoln Police Department’s annual productivity report
Report writing time Standard operating procedures dictate that LPD patrol officers complete specific types of reports for incidents. An incident report (IR) details biographical information of the individuals involved in the incident and summarizes the incident. An Additional Case Information (ACI) is a more detailed report that documents additional investigative steps or the actions of assisting officers. Although the time spent on an IR or ACI could vary depending on the call for service, the average IR and ACI take roughly twenty minutes to complete.
The Lincoln Police Department also tracks self-initiated activities. Most productivity results from the following proactive activities.
Selectives The Lincoln Police Department tracks specific officer patrol activity by coding its neighborhoods, schools, parks, and business districts. When an officer patrols an at-risk business district or a high-crime neighborhood, for example, the officer notifies dispatch using that area’s specific code. Girded by the principles of “hot spots” policing research, officers use selectives to identify potential candidates for POP projects and heighten the perception of risk among criminals (Braga and Bond, 2008).
Traffic warnings and official citations Every Lincoln patrol officer drives a police cruiser and conducts traffic enforcement, issuing traffic warnings and official citations. The LPD records department tracks patrol officers’ warnings and citations.
Misdemeanors, felonies, and warrants Patrol officers also make misdemeanor and felony arrests during their productive time. Officers might detain a motorist and arrest him or her for driving under a suspended license or driving while intoxicated, or contact a pedestrian and make an arrest for a sex offender registration violation. Officers can also search their department’s electronic database for individuals with outstanding warrants in their patrol areas and attempt to arrest these offenders. As with warnings and citations, the LPD records department documents and tracks misdemeanor, felony, and warrant arrests. Moreover, the court system also monitors these types of outputs to process cases, thereby adding an additional layer of reporting accuracy and reliability.
Field information reports We also measured the number of field information or “intelligence” reports. Officers complete these internal reports to document suspicious or dangerous individuals or circumstances, though these reports are not publicly available.
VORC In addition to the raw outputs of traffic citations, warrants, misdemeanors, and felonies, we also include an advanced metric for measuring a patrol officer’s productivity. VORC is a method for evaluating officers that assigns different weights to productivity outputs and then assesses officer productivity in terms of available time for self-initiated activities, or productive time (Bonkiewicz, 2015). We use a variation of this formula that multiplies an officer’s officials, misdemeanors, and felonies by his or her
Patrol officer productivity
prosecution rate. Our modified use of VORC can best be explained using the following equation and five-step process:
VORC ¼ 100� Η þ Θþ Kð Þ þ Ιð Þ þ Λð Þ þ Νð Þð Þ � Ρð Þð Þ Α− Βþ Γ þ Δþ 20 Ε þ Ζð Þð Þ þ 10 Mð Þð Þð Þ
−100� 0:9ð Þ � Ξð Þ Ο Π
� � Ξ
where Α is the total monthly on-duty minutes, Β the CFS minutes, Γ the follow-up time and meetings, Δ the officer assist time, Ε the no. of IRs, Ζ the no. of ACIs, M the no. of intelligence reports, Η the no. of selectives, Θ the no. of warnings times two, Κ the no. of warrants times three, Ι the no. of officials times three, Λ the no. of misdemeanor arrests times their respective weights, Ν the no. of felonies types their respective weights, Ρ an officer’s prosecution rate, Ξ an officer’s productive time (Α−(Β+Γ+Δ+20(Ε+Ζ))), Ο, the department average P-score, or average (Η+((Θ)+(Ι)+(Κ)+(Λ)+(Μ)+(Ν)×Ρ)), and Π the department average self-initiated time.
First, VORC calculates an officer’s total minutes spent on CFS, follow-ups, officer assists, and reports. This amount is an officer’s total work time, or service minutes. Second, the formula subtracts an officer’s service minutes from the total on-duty minutes. This is the officer’s amount of productive time. Third, VORC calculates an officer’s productivity score (or P-score) using a weighted index. Departments can choose different weights for productivity outputs, but for the purpose of this study, we used our weights based on the statutes governing the Lincoln Police Department. Citations and arrests are weighted based on whether the offense was a Traffic Offense, a Class III, II, or I Misdemeanor, or a Class V, IV, III, II, or I Felony. Accordingly, a patrol selective is worth one point, a traffic warning two points, a traffic citation three points, a warrant arrest three points, a Class III misdemeanor arrest four points, a Class II misdemeanor arrest five points, a Class I misdemeanor arrest six points, a Class V Felony seven points, a Class IV Felony eight points, a Class III Felony nine points, and Class II and I Felonies (e.g. homicides) ten points. The sum of the weighted productivity outputs is the officer’s P-score. The P-score is divided by amount of productive time and then multiplied by 100 (for ease of understanding). This value, the productivity quotient, represents the officer’s productivity given the available minutes for proactivity, such as searching for wanted individuals or traffic enforcement.
Fourth, VORC creates a hypothetical replacement officer against which to measure the patrol officer’s output. This replacement player might be imagined as a rookie patrol officer, or a recruit who has not completed the academy, performing at 90 percent the capacity of fully trained police officer. Using LPD’s data, VORC creates a hypothetical replacement officer by dividing the departmental average P-score by the departmental average for productive time, and then multiplying it by the officer’s productive time to determine what an average officer would have generated given the officer’s amount of productive time. This equation is then multiplied by 0.9 (the capacity of a replacement officer), divided by the officer’s productive time, and then multiplied by 100 to generate the replacement officer’s productivity quotient. Finally, VORC subtracts the replacement officer’s score from the patrol officer’s score to calculate the officer’s value over the replacement officer. This number, or VORC, indicates the officer’s productivity per time for self-initiated activities compared to the replacement officer’s productivity. For example, a VORC of 1.2 means than officer would produce 0.012 more productivity points (some combination of warnings, citations, and arrests) per minute than a replacement officer.
Biographical variables We gathered biographical information about the officers in the sample, including gender, years of LPD experience, marital/cohabiting status, and number of children. We also controlled for whether the officer worked first, second, or third shift because our data showed significant differences in the number of CFS each shift handled, as well as the number of reports each shift completed.
Geographic variables It is necessary to measure the characteristics of patrol areas to account for variation across patrol areas. For example, some areas may feature different crime rates or levels of social disorganization which command more department resources and police officers. We gathered data by first mapping out every squad’s patrol area and then obtaining tract-level data from the 2010 US Census. Our models included measures of established of correlates of crime, including population density (Hipp and Roussell, 2013), household income (Gould et al., 2002), and racial composition (Light and Harris, 2012). Specifically, we use each area’s population density, median household income, and percent white. We also included measures for each area’s violent and property crime rate. Finally, we gathered information about the number of violent crimes (murders, assaults, aggravated assaults, sexual assaults, and robberies), property crimes (larcenies, burglaries, vandalisms, frauds, forgeries, and motor vehicle thefts), and number of police officers in each patrol area, thereby creating variables for violent crime per officer and property crime per officer.
Since LPD’s patrol officers are grouped with squads, it is reasonable to believe that an officer’s productivity might be more similar to an officer within their squad compared …
Police officer scheduling using goal programming
Dragana Todovic, Dragana Makajic-Nikolic, Milica Kostic-Stankovic and Milan Martic
Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia
Abstract Purpose – The purpose of this paper is to develop a methodology for automatically determining the optimal allocation of police officers in accordance with the division and organization of labor. Design/methodology/approach – The problem is defined as the problem of the goal programming for which the mathematical model of mixed integer programming was developed. In modeling of the scheduling problem the approach police officer/scheme, based on predefined scheduling patterns, was used. The approach is applied to real data of a police station in Bosnia and Herzegovina. Findings – This study indicates that the determination of monthly scheduling policemen is complex and challenging problem, which is usually performed without the aid of software (self-rostering), and that it can be si