August 5, 2026
U.S. policing agencies face three converging pressures on staffing levels: fewer applicants, departures to other fields, and increasing numbers of officers reaching retirement age (Police Executive Research Forum, 2019). Efforts to address these issues typically focus on piecemeal approaches such as recruitment or retention, with little systematic analysis of the demand for police services which drives the need for officers in the first place (Wilson and Weiss, 2014). But demand analysis isn’t just a simple calculation based on population or call estimates.
To understand the staffing levels needed to meet community demands, police agencies must understand what those demands are. Those demands can vary among jurisdictions, neighborhoods, and community members, often varying with the service-level standards and expectations they hold.
An effective staffing strategy should rely on detailed analysis of actual, measurable service needs, rather than generic, population-based ratios. In particular, workload (demand) analysis requires precise measurement of different layers of demand, accurate forecasting, and the ability to adapt as conditions change.
What to Measure: The Dimensions of Demand
A modern workload analysis requires a multilayered approach to capture the total workload that police must handle. There are several layers to this approach:
- Reactive Demand (Service Baseline)
The baseline of demand is determined by the typical total number of calls for service that an agency receives from the community. Beyond call volume, though, at National Policing Institute (NPI), our staffing studies also measure “time on task.” This measurement considers questions such as how long a typical domestic disturbance call takes to complete. Different kinds of cases can vary widely in the amount of time they require, so agencies should know both the number of calls they receive and their usual composition in order to get an accurate service baseline. - Proactive Demand (Service Level)
Answering calls for service isn’t the entirety of the job. Community members want to see officers engage in proactive work such as traffic stops, foot patrols, and problem-oriented policing. Treating this “discretionary time” as a planned category, rather than a residual one, is what allows agencies to sustain proactive policing under real workload pressure. An NPI workload analysis helps agencies quantify the proactive time they should protect and build it into their staffing model so leadership can direct officers toward the community’s changing needs. - Internal and Administrative Demand
Officers also spend significant time on the internal work that keeps an agency running and compliant. A workload analysis must consider internal and administrative demands such as roll calls and briefings, report writing, evidence packaging, equipment maintenance, court appearances, special assignments, temporary reassignments, training time, and breaks and wellness time.
What to Forecast: Moving from Retrospective to Predictive
Measuring reactive, proactive, and internal/administrative demand tells agencies where they have been. Forecasting can tell them where they need to go. To move beyond recent averages, agencies should consider several additional factors:
Temporal and Seasonal Trends
Demand for police services can vary by day of the week or month of the year (Wilson and Weiss, 2014). An NPI workload analysis can help identify such fluctuations for an agency and construct staffing models to address increased demands.
“Transient” Populations
Seemingly similar communities can differ in their economic activities and hence their “daytime” populations. For instance, the towns of Warren and Sterling Heights are adjacent communities north of Detroit, each with a population of about 135,000; but, in recent years, Sterling Heights has had about two-thirds the number of police officers that Warren has (Wilson and Grammich, 2024a). One reason for this may be the presence of several large employers in Warren and associated levels of economic activity, which can lead to demands for different types and levels of police services. An NPI workload analysis can help identify such populations and the police services they’re likely to need.
Growth and Development Modeling
As communities change and grow, so will their demand for police services. New housing developments or new business developments can lead to a demand for more police services, as can changes in community infrastructure, such as new roads or parks.
Predictive Workload Analysis
Historical data can help predict when peak demand is likely to occur. Demand for police services can vary, for example, by time of day (Wilson and Weiss, 2014). Special events may also lead to peaks in demand. An NPI workload analysis can help predict when such peaks are likely to occur. More importantly, it can suggest shift allocations that will meet demand at any hour of the day while maintaining the flexibility to meet peak demand hours.
Simulations and “What-if” Analyses
Even careful analyses may fail to predict future demands. Agencies may also see unexpected increases in demand for services or unexpected changes in staffing levels. In some cases, they may seek to shift some duties, including those in training and investigations, traditionally done by sworn officers to professional staff (Police Executive Research Forum, 2024).
What to Change: Data-Driven Operational Shifts
Once an agency has a thorough analysis, it can explore options for matching demand with staffing levels. Among the options agencies may explore are the following:
Spatial Optimization
Redrawing beat boundaries to reflect current or anticipated demand can help balance the workload across a jurisdiction. For example, a recent analysis of Chicago police responses to 911 calls used historical data to calculate how much time individual calls required in order to devise a way to reassign cars as needed to optimize workload, all while ensuring that no car spent more than a specified threshold of 60% of its time responding to calls (Ander, 2022).
Differential Response Protocols
Not every call needs a sworn officer. An NPI analysis can help identify demand that can be diverted to professional staff responders, mental health clinicians, or digital reporting. Such diversion can help improve overall service delivery. A recent analysis of virtual responses to nonemergency calls for service found that they helped reduce waiting times and increase public satisfaction, trust, and confidence in the police (Gates, Ariel, and Assaraf, 2025).
Temporal Realignment
Developing “power shifts” to match exact hours of peak demand, as discussed earlier, can help agencies meet current needs with their available staffing. Compressed workweek schedules, such as those afforded by ten-hour shifts, can enable agencies to schedule overlapping shifts at times of peak demand for service (Amendola et al., 2011).
Relief Factor Reset
Calculating the “relief factor” shows how much time officers are available for patrol duties considering their scheduled time off, vacation, and training time. Often these totals will change over time and need to be recalibrated (Wilson and Weiss, 2014).
The table below highlights general areas of a traditional approach to workload analysis and improvements that an NPI workload analysis can introduce.
The Path Forward
The success of any workload analysis depends on timely and accurate data. While some agencies may not have this ready at hand, this shouldn’t delay the work. Agencies can begin with estimates or approximations and refine them as better data becomes available. Community needs and agency resources shift over time, so workload analysis is most useful when treated as a continuous feedback loop rather than a one-time exercise (Wilson and Grammich, 2024b).
Done well, this process produces benefits beyond staffing decisions. Public-facing dashboards and transparent reporting give governing bodies the evidence they need to approve budget requests, and they build community trust in the decisions agencies make about how officers spend their time.
Work with NPI on Your Agency’s Workload Analysis
The National Policing Institute helps law enforcement agencies translate research and data into practical operational decisions. Our team works with agencies of every size to design and conduct workload analyses, build the internal capacity to maintain them, and turn the findings into a clear staffing and deployment roadmap. The result is an agency that delivers stronger service to its community with a healthier, less-stressed workforce, all within the resources it has.
Contact NPI To Find Out What a Workload Analysis Looks Like for Your Agency.
References
Amendola, Karen E., David Weisburd, Edwin E. Hamilton, Greg Jones, Meghan Slipka, Anneke Heigmann, Jon Shane, Christopher Ortiz, and Eliab Tarkghen (2011). The Shift Length Experiment: What We Know About 8- 10- and 12-Hour Shifts in Policing. National Policing Institute. https://www.policinginstitute.org/publication/shift-length-experiment/
Ander, Roseanna (2022). “Workforce Allocation Summary.” University of Chicago Crime Lab. https://crimelab.uchicago.edu/wp-content/uploads/sites/2/2023/10/UChicago-Crime-Lab-WFA-Summary-02.22.pdf
Gates, Stewart, Barak Ariel, and Noy Assaraf (2025). “Responding to Nonemergency Calls for Service Via Video: A Randomized Controlled Trial.” Criminology & Public Policy, 24(3), 309-331. https://doi.org/10.1111/1745-9133.12690
Police Executive Research Forum. (2019). The Workforce Crisis, and What Police Agencies Are Doing About It. https://www.policeforum.org/assets/WorkforceCrisis.pdf
Police Executive Research Forum (2024). Embracing Civilianization: Integrating Professional Staff to Advance Modern Policing. https://www.policeforum.org/assets/Civilianization.pdf
Wilson, Jeremy M., and Clifford A. Grammich (2024a). “Debunking Myths of Police Staffing Benchmarks.” Michigan Police Chiefs, Issue 2, 20-22.
Wilson, Jeremy M., and Clifford A. Grammich (2024b). “Reframing the Police Staffing Challenge: A Systems Approach to Workforce Planning and Managing Workload Demand.” Policing: A Journal of Policy and Practice, 18, paae005. https://doi.org/10.1093/police/paae005
Wilson, Jeremy M., and Alexander Weiss (2014). A Performance-Based Approach to Police Staffing and Allocation. U.S. Department of Justice, Office of Community Oriented Policing Services. https://portal.cops.usdoj.gov/resourcecenter/ric/Publications/cops-p247-pub.pdf
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Facts Only
* U.S. policing agencies face pressures from fewer applicants, departures to other fields, and officers reaching retirement age.
* Staffing efforts typically use piecemeal approaches like recruitment or retention without systematic demand analysis.
* Demand analysis must account for variations in service-level standards among jurisdictions and community expectations.
* Workload analysis requires measuring reactive demand (calls for service) and "time on task."
* Proactive demand includes discretionary work such as traffic stops and foot patrols.
* Internal and administrative demands include roll calls, report writing, equipment maintenance, and training time.
* Forecasting should consider temporal and seasonal trends, transient populations, growth/development, and predictive analysis of peak demand times.
* Operational changes suggested include spatial optimization of beat boundaries, differential response protocols, temporal realignment for peak hours, and recalculating the relief factor.
* Workload analysis is most useful when treated as a continuous feedback loop rather than a one-time exercise.
Executive Summary
Staffing levels in U.S. policing agencies are pressured by fewer applicants, departures to other fields, and officers reaching retirement age. Traditional approaches focus on piecemeal recruitment or retention without systematically analyzing the demand for police services that drives staffing needs. Effective staffing strategies require detailed analysis of actual service demands rather than generic population ratios. Workload analysis must incorporate reactive demand (calls for service, including time on task), proactive demand (discretionary work like patrols), and internal/administrative demands (reporting, training, maintenance). Forecasting requires moving beyond averages to account for temporal trends, variations in "transient" populations, growth and development modeling, and predictive workload analysis based on historical data and potential peaks. Operational changes can involve spatial optimization of beat boundaries, implementing differential response protocols that divert non-emergency calls, temporal realignment through scheduling adjustments, and recalibrating the relief factor to account for time off and training.
Full Take
The necessity of moving from simplistic staffing ratios to multilayered workload analysis reveals a tension between administrative efficiency and community service expectations. The framework introduced—separating reactive, proactive, and internal demands—is crucial because treating time spent on discretionary policing as residual rather than planned allows agencies to build proactive capacity into their models, directly addressing the public expectation for visible community engagement. The shift from retrospective measurement to predictive modeling addresses the systemic lag where staffing is set based on historical averages, failing to account for dynamic community shifts like transient populations or infrastructure growth. A critical implication is that operational shifts, such as spatial optimization and differential response protocols, are not merely logistical tweaks; they are mechanisms for aligning institutional capacity with evolving social geography and service definitions. The reliance on data-driven feedback loops suggests a necessary structural change where accountability is tied to demonstrable service outcomes rather than static staffing formulas. What is missing in the narrative is a deeper exploration of how political incentives and organizational inertia resist these data-driven operational shifts, and what ethical obligations arise when diverting resources from sworn officer duties to professional staff or non-law enforcement responses.
Sentinel — Human
Confidence
This text presents a deeply structured argument built on existing research to propose a complex methodology for police staffing analysis, exhibiting characteristics of expert synthesis rather than pure generation.
Signals Detected
low severity: Moderate sentence length variance and appropriate use of specialized academic citations.
low severity: Strong, logical flow progressing from problem statement to detailed framework to actionable solutions; exhibits a coherent argumentative structure.
low severity: Systematic integration of external research (NPI, Wilson & Weiss) into the core argument; careful citation placement suggests human structuring.
low severity: Claims about specific metrics and theoretical frameworks (e.g., Reactive vs. Proactive Demand) are standard academic concepts, integrated correctly rather than fabricated.
Human Indicators
The integration of highly specific, cross-disciplinary citations from sources like the NPI, UChicago Crime Lab, and detailed temporal/spatial examples suggests human compilation synthesizing established research.
The shift in tone—moving from identifying a problem to proposing multi-layered analytical solutions (demand layers, forecasting methods, operational shifts)—demonstrates an authorial progression beyond simple data recitation.