How does man-power affect response time in policing; The Case of NYPD

Allocation of Law Enforcement Assets

 Law enforcement in most jurisdictions, is often understaffed. This leaves administrators and management with asset allocation problems. Allocation of law enforcement resources requires objectivity and use of methodologies that can be justified and replicated to ensure that they can be improved to ensure shorter response timing out of learning and deliberate improvement.  Curtin, Hayslett-McCall, and Qiu (2010) explored the efficiency of police patrols in terms of maximal coverage of geographical areas. The study aimed to identify the most spatially efficient allocation of law enforcement resources. The study found out that the use of technologies such as GIS and linear programming can be applied in enhancing the efficiency of law enforcement. The study further found out that the model (Use of GIS and linear programming) helped in covering a more acceptable service distance and reducing the response distance and consequently the response time.

Asmild, Paradi, and Pastor (2012) explored ways of reallocating personnel resources between tasks in a specific unit, as well as, between similar units using data. The study explores six different ways of reallocating personnel resources and ensuring that there is operational efficiency. The data-based method was found to save resources and improve the efficiency of operations while at the same time allowing the management, time to consider other objectives. The method of using data to allocate resources was also found to be a critical element in morale preservation among personnel, especially when impacts among the personnel are minimized.

Adler, Hakkert, Kornbluth, Raviv, and Sher (2014) investigated the Traffic Routine Patrol Vehicle Assignment problem. The study acknowledges that the traffic police patrol vehicles are allocated based on the shortest path algorithm method. However, the vehicles are, tasked with other roles. Due to the complexity of the roles of the vehicles, allocation of the vehicles is highly dependent on arrival time and time to arrival constraint. Adler et al. found that a location-allocation configuration for every shift allocated to the vehicle should account for road safety and policing objectives. Each car should have a call-for-service coverage to maximize police presence and conspicuousness.

Law Enforcement and IT

Law enforcement has been experiencing increased use of IT in different facets of operations. IT in all businesses is applied to inculcate efficiency and high productivity of employees. In this regard, IT in law enforcement is necessary to enhance the productivity of officers, as well as, increase their ability to conclude cases after responding to them. Garicano and Heaton (2010) examined the role of increased investment in Information Technology (IT) by police departments. Police departments are expected to develop more problem-solving capabilities. Garicano and Heaton (2010) explained that IT intensity in police departments has no bearing with improvements in case solution rates, but rather has a dimension with higher offending rates. As a blanket observation, increases in police investment in IT have led to an increase in productivity.

Mishra (2017) explored the aspect of the use of smart management in emergency response system and its use of data to ensure productivity. The study explores the use of forecasting models developed after an analysis of historical call volume data, for seasonality and trend components. The study confirms that the use of forecast models is critical in the allocation of law enforcement resources, especially when they are scarce.

Response Times

Police response time has always been a contentious issue, with many arguing that the faster the police respond, the higher the chance of reducing crime, and the higher the chances of catching the perpetrator of a crime. In this case, we consider Clawson and Chang (1977) study, which evaluated arrests rates and response times of police officers. The study found that police officers who exhibited shorter response times had more arrests on the scenes of crime. The study attributed this relationship to instinctive relationships between dispatchers and police officers. The study further explained that dispatch times could not be related to arrests. On the same note, Cihan, Zhang, and Hoover (2012) observed that there is a relationship between police response time and neighborhood characteristics. Cihan et al. explained that the disadvantaged neighborhoods have shorter police responses. Further, the study provided that rapid responses increase the probability of in-progress burglary arrests.

Cihan (2012) evaluated whether the distribution of police response time in in-progress burglaries has a difference in terms of response time distribution, with respect to the social disorganization in different neighborhoods. The study found out that factors such as concentrated disadvantage, immigrant concentration, and residential stability are strong predictors of police response. In the study above by Cihan, Zhang, and Hoover (2012), disadvantaged neighborhoods received faster police responses as compared to other neighborhoods.

Coupe and Blake (2005) explored the response-related capture at residential burglary emergencies. To catch burglars was dependent on response times and the number of units being dispatched to attend to the issue, as well as, the incidence characteristics. The study found out that response urgency was informed by the numbers of patrol units with the capability of responding and hence, it had a dimension on workload placed on patrol units. Those patrol units with lighter workloads were found to have better chances of executing a capture mission. The study by Coupe and Blake (2005) was able to show that incidences of emergencies were more likely to be effectively attended by patrols with lighter loads. 

Stevens, Webster, and Stipak (1980) acknowledge that there are costs associated with patrol teams. The study recognizes that such costs are left on the taxpayers and as such, efficiency is required from law enforcement agencies. Police productivity, which could be assessed on parameters including response time require addressing. Stevens et al. found that predictors of response time included the type of service, location of crime and time of the day. The study further found that the efficiency of police increased with increased information sharing. The propensity of having shorter response periods was not tied to the number of burglars. The importance of having robust and short response times could not be emphasized due to the advantage the same has on neighborhoods. Myhill and Bradford (2012) assessed whether public confidence in the police force could be positively impacted due to the improved quality of service. Conducted in England and Wales, the study found out that public confidence increased and it came with enhanced trust and public confidence. Public confidence was found to be impacted on the criminal justice outcomes.

In conclusion, law enforcement is a concept that requires the establishment of methodologies that affect response times. This literature analysis has demonstrated that there are many predictors and consequences of quick response times. The society, the type of call, and the number of police on patrol are important determinants of how fast the police will respond to calls. Other dimensions such as the use of technology and mathematical models have been shown to have an impact on the same.

References

Adler, N., Hakkert, A. S., Kornbluth, J., Raviv, T., & Sher, M. (2014). Location-allocation models for traffic police patrol vehicles on an interurban network. Annals of Operations Research, 221(1), 9-31.

Asmild, M., Paradi, J. C., & Pastor, J. T. (2012). DEA based models for reallocations of police personnel. OR Spectrum, 34(4), 921-941.

Clawson, C., & Chang, S. K. (1977). The relationship of response delays and arrest rates. Journal of Police Science and Administration, 5(1), 53-68.

Curtin, K. M., Hayslett-McCall, K., & Qiu, F. (2010). Determining optimal police patrol areas with maximal covering and backup covering location models. Networks and Spatial Economics, 10(1), 125-145.

Cihan, A., Zhang, Y. and Hoover, L., 2012. Police response time to in-progress burglary: A multilevel analysis. Police Quarterly, 15(3), 308-327.

Cihan, A. (2012). The effects of community characteristics on police response time to crime: A multilevel analysis. Sam Houston State University.

Coupe, R. T., & Blake, L. (2005). The effects of patrol workloads and response strength on arrests at burglary emergencies. Journal of Criminal Justice, 33(3), 239-255.

Garicano, L., & Heaton, P. (2010). Information technology, organization, and productivity in the public sector: Evidence from police departments. Journal of Labor Economics, 28(1), 167-201.

Stevens, J. M., Webster, T. C., & Stipak, B. (1980). Response time: Role in assessing police performance. Public Productivity Review, 210-230.

Mishra, R. (2017). Optimal utilization of a smart police emergency response system: using forecasting models. International Journal of Business Excellence, 12(3), 376-385.

Myhill, A., & Bradford, B. (2012). Can police enhance public confidence by improving quality of service? Results from two surveys in England and Wales. Policing and society, 22(4), 397-425.