This study investigated hillslopes in Taichung City, collected basic data pertaining to the study areas,and examined the violations of hillslope regulations in these areas in recent years. Kernel density estimation was first
performed to determine the spatial distribution of violation hotspots in Taichung City before and after the merging of
Taichung City and Taichung County to form the Taichung City special municipality in 2010. The violation analysis
included the distance to a road, the distance to a community activity center, and the distance to a tourist attraction and a slope. Subsequently, logistic regression was performed to establish a model for predicting hotspots for hillsloperelated violations. The effects of grid resolution analysis on the model’s results were investigated. The results indicated that before the city–county merger, the hotspots for hillslope-related violations were identified in three districts of Taichung City, namely Nantun, Tanzi, and Beitun districts. After the city–county merger, hotspots were also identified in the districts of Shalu, Taiping, Wufeng, and Dongshi. The violations of hillslope regulations were distributed across the city and its key transportation areas, with the trend for violations changing over time. For model evaluation, the logistic regression model produced accurate predictions, achieving an area under the curve of >80% with the application of multiple grid resolutions. Maps displaying predicted violations were subsequently obtained. The results indicate that introducing preventive measures (e.g., increasing the number of patrols, enhancing education on the regulations, and establishing a violation bulletin) that target the predicted violation areas can prevent future violations and reduce the risk of related disasters.
Key Words: Slopeland, Violation prediction model, Logistic regression