Crime Hotspot Prediction, Risk-Aware Route Planning and Patrol Planning Using GIS and Machine Learning

Author

Assistant Professor Smita Mangesh Junnarkar ,Vineeth Ravichandran

Abstract

 Crime is not spread evenly across a city. A small number of streets, junctions and neighbourhoods account for a disproportionate share of incidents, and this regularity gives police departments a practical reason to look for forecasting tools. This paper presents an integrated framework that turns historical crime records into three connected outputs: a map of predicted crime probability, a set of risk-aware travel routes, and a patrol plan for available units. The study area is divided into a regular grid, and each cell is described by temporal, spatial, and contextual features. Spatial clustering and local statistics identify existing hotspots, while supervised learning models (Logistic Regression, Random Forest and Gradient Boosting) estimate the probability of at least one incident in a cell during the next time window. The predicted probabilities are then attached to the edges of a road network so that a shortest-path search can trade a small increase in travel distance for a lower exposure to risk. A greedy coverage procedure uses the same probabilities to allocate patrol units. The complete pipeline is implemented in Python and delivered through an interactive Streamlit dashboard. We describe the design, the evaluation protocol, and the ethical safeguards required when predictive tools are used in policing.

Keywords

crime hotspot prediction, geographic information systems, machine learning, risk-aware routing, patrol planning, spatial analysis, predictive policing

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