Data-Driven Policing: From Early Intervention to Predictive Analytics

Policing is changing—and data is leading the way.

Join Charles Katz, author of McGraw Hill's The Police in America: An Introduction, for a session designed specifically for criminal justice instructors. Charles explores how advanced analytics and machine-learning models are transforming law enforcement from reactive to predictive—and what that means for how you teach the next generation of officers, administrators, and policy makers.

Drawing on real-world research with a large southwestern police agency, Charles reveals why traditional early intervention systems often fall short—and how newer probability-based approaches can dramatically improve the ability to identify risk before problems arise.

This session goes beyond the technology to tackle the bigger question: How can police agencies move from simply collecting data to making smarter, more accountable, and more effective decisions?—and how can you bring these conversations into your classroom?

Directly connected to the core concepts in The Police in America, this session gives instructors the context, research, and real-world examples to enrich course discussions and keep curriculum current.

You'll Learn:

• Why conventional early intervention systems have critical limitations—and how to frame this for students
• How machine-learning models improve predictive accuracy in law enforcement
• What a real-world agency case study reveals about implementing data-driven policing
• How these innovations connect directly to The Police in America: An Introduction
• How to prepare students for a field increasingly defined by data and analytics