A Spatial Big Data and Unsupervised Learning Framework for Prioritizing Patrol Areas for Illegal Waste Dumping

Illegal waste dumping remains a persistent urban management problem, yet enforcement is often reactive because cities lack spatially precise evidence on where risk is concentrated. This study presents an unsupervised machine learning and urban big data framework that converts routine administrative records into actionable patrol priorities. Using 1137 geocoded illegal-dumping complaint locations from Chuncheon, South Korea (January 2023 to May 2025), we integrate heterogeneous datasets describing residential intensity, population activity, educational facilities, waste disposal points, parks and public facilities, and monitoring infrastructure. A two-stage unsupervised pipeline is applied. First, DBSCAN aggregates nearby complaints to reduce spatial noise and supports feature screening by identifying effective influence ranges. Second, a grid-based K-means clustering classifies complaint occurrence areas into four interpretable typologies, selected using elbow and silhouette criteria. Hotspots are strongly associated with dense one-room housing, proximity to university districts, and high daily activity in mixed-use residential environments, while rural and predominantly non-residential zones show consistently low complaints. Infrastructure variables alone (e.g., disposal points, CCTV) have limited explanatory power. We derive a four-level enforcement priority scheme enabling targeted patrol and monitoring. Because complaint records reflect reporting behavior as well as underlying dumping activity, we interpret the resulting typologies as relative patrol-priority indicators rather than as a complete census of dumping risk.