Workforce Allocation Analysis
The Workforce Allocation (WFA) Analysis examined where personnel resources at the Chicago Police Department are allocated to improve efficiency, equity, and transparency in patrol staffing.
Challenge
The inequitable distribution of police officers contributes to disparities in 911 call response times across communities, with some areas receiving rapid responses to emergency and non-emergency calls. In contrast, others experience significantly longer response times for even violent incidents like robberies or shootings.
Opportunity
In 2019, the Crime Lab, in response to a request from Chicago Police Department (CPD) leadership, analyzed data to determine the status quo of how officers were distributed and then began developing a planning model to help the department better allocate officer resources. By allocating additional officers to areas of the city struggling to respond to calls for service adequately, we aim to reduce the overall imbalance in 911 responses across Chicago neighborhoods.
Project overview
In 2019, the Crime Lab, in response to a request from Chicago Police Department (CPD) leadership, analyzed data to determine the status quo of how officers were distributed and then began developing a planning model to help the department better allocate officer resources. The Crime Lab model was designed to optimize deployment by focusing on a key metric: the amount of time officers spend responding to 911 calls compared to the amount of time officers spend doing other work. By spreading officer workload more evenly, the speed with which officers can respond to calls and how much time they can spend addressing each call may improve.
Years Active
2019 – present
Topics
Project Leads
David Leitson
Senior Policy Analyst
Martin Barron
Senior Director of Data and Analysis
Workforce Allocation Summary
A Workforce Allocation Project Summary that addresses that the of allocation of law enforcement resources in a majority of US cities is typically subject to the discretion and intuition of pivotal decision-makers, leading to a high degree of politicization and inequality in the system.
The Crime Lab model uses an iterative process to allocate patrol cars to support busy officers until there are no more overworked officers and every officer can spend no more than 60% of their time answering calls. The fastest way to equalize 911 response times across the city would be to reallocate existing staff as needed. In practice, collective bargaining agreements and other institutional issues may constrain the ability to reallocate existing officers across areas or shifts. In that case, the model could also allocate new CPD staff as they join the workforce.
Our model and policy simulation represent the first passes at optimal staffing allocation. An actual implementation would ideally involve the deployment of the base model’s recommendations, accompanied by measurement, evaluation, and implementation of any refinements necessary to accomplish the city’s stated goals.