Integrating Remote Sensing and Machine Learning for Crime-Risk Assessment: A Case Study of Bangkok Metro and Tourism Areas
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Abstract
This study aims to classify crime-risk areas in Bangkok to enhance safety in the tourism sector, specifically along metro lines. By integrating urban environmental data with satellite imagery including Sentinel-2 for vegetation (NDVI) and SNPP/VIIRS for nighttime lights, the research evaluates three machine learning models: Extra Trees (ET), Random Forest (RF), and LightGBM, utilizing 14 spatial parameters. The results demonstrate that the Extra Trees model achieved the highest performance with a test accuracy of 97.53%. Feature importance analysis revealed that business and industrial density (P8) is the most significant predictor of crime risk. Spatial analysis of 150 metro stations identified that 34 stations (22.67%) are in high-risk zones, particularly within key tourist districts such as Pathum Wan and Watthana. These findings confirm the effectiveness of combining geospatial technology with machine learning for urban safety assessment, providing policymakers with a data-driven tool to prioritize crime prevention strategies in high-density tourism corridors.
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