Visual Analytics Methods for Food Safety Risks

This document is a review article that discusses visual analytics methods applied to food safety risk analysis and prewarning (RAPW). It outlines the integration of human and machine intelligence in data analysis, emphasizing the importance of visual analytics in understanding large-scale food safety data. The review summarizes developments in the field over the past decade, detailing data sources, characteristics, and analysis tasks relevant to food safety. It categorizes data analysis methods into four main tasks: association analysis, risk assessment, risk prediction, and fraud identification. Furthermore, it reviews visualization and interaction techniques for various data types, including multidimensional and spatial-temporal data. The article also addresses opportunities and challenges in visual analytics for food safety, such as the application of artificial intelligence techniques and the need for expert involvement in the analysis process. Overall, it serves as a comprehensive resource for researchers and practitioners in the field of food safety.