Title

Law enforcement manpower analysis: an enhanced calculation model

Document Type

Article

Publication Date

6-2-2020

Subject Area

ARRAY(0x55c827f8b478)

Abstract

Purpose: This study aims to advance the existing analytic model to include staff allocation information at the district level. Maintaining adequate size of staff is essential to law enforcement agencies' ability to ensure social order, fight crime and, increasingly, deliver a widening range of social services. Review of the scientific literature on police size of force and staffing calculation models indicates that this line of inquiry (i.e. manpower analysis) is attentive to population size and workload demands but generally inattentive to how service demands are affected by community-level variables. Current staffing calculation models specify number of staff needed for a jurisdiction but do not inform the allocation of personnel across districts within the jurisdiction. Design/methodology/approach: To address this problem, the current study illustrates an enhanced analytic model to provide law enforcement staffing recommendations for a southern coastal county. An integrated per capita-workload manpower analysis model first factors the minimum number of law enforcement deputies needed per population size served and recent history workload demands and then executes the six-step workload model process. This study enhances staffing analysis by adding an additional seventh arithmetical step indicating the staffing needs by districts across a jurisdiction. Findings: The results from the integrated per capita-workload analysis indicate the need to hire additional deputies to meet current and future demands. Originality/value: Discussion centers on the need to include drivers of police services identified but not measured in this study's application of the hybrid manpower analysis model and its replication potential.

Publication Title

Policing

Volume

43

Issue

3

First Page

511

Last Page

523

Digital Object Identifier (DOI)

10.1108/PIJPSM-02-2020-0026

ISSN

1363951X

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