This page follows a two-year simulation of how a patrol-planning specialist might use the system, how application-guided patrol choices reshape later incident data, and how that feedback loop produces long-run winners and losers across Tartu suburbs.
In the simulation, areas with stronger minority and social-vulnerability markers receive more patrol pressure, and that elevated attention carries into the next round of model inputs.
In this setup, the application changes recorded incident data as well as policing behavior, so targeted areas can continue to look worse over time even when the underlying story is more complicated.
The table below expresses the simulated policing strategy as social effects: who benefits from a lighter touch, who absorbs more surveillance pressure, and where prestige and property pressure diverge.
| Suburb | 2026 incidents | 2028 incidents | Patrol shift | Minority / vulnerable impact | Property price pressure | Prestige shift | Ghettoization risk |
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