You want a system that reads patterns, not intuition. Start by logging every action of the suspect—time stamps, locations, communications. Feed that into a supervised learning model, like a random forest or a hidden Markov model, to capture sequential dependencies. Add contextual layers—traffic data, weather, known associates—to give the algorithm more variables. The key is to keep the feature set clean and avoid overfitting. Once you have a baseline prediction, run simulations against historical cases to validate accuracy. That’s the framework; the rest is tweaking the parameters until the false‑positive rate drops to an acceptable level.

Sure, let’s keep it tight. Start with the basics: hour of the day, day of the week, and whether it’s a holiday. Add a recency flag for the last known activity—how many minutes or hours ago the suspect was observed. Use a binary for “home vs. away” based on GPS. Include a simple count of communications in the last 24 hours, maybe split by type (call, text, social media). For context, pull in the local weather code and whether there’s a major event in the area. Keep the feature list to under fifteen items, then run a quick cross‑validation to watch the bias‑variance curve. If you hit too many false positives, try a L1‑regularized logistic regression before jumping to a tree‑based model. That way you get a lean pipeline and still catch the main patterns.