Interactive Exploration and Explanation of Spatio- temporal Anomalies with Graph-LLM Integration

Understanding spatiotemporal anomalies is critical in domains such as urban safety, mobility, and environmental monitoring. These scenarios involve complex dynamics that are effectively modeled using graph-based representations, where the spatial structure is encoded through data connectivity, and each node corresponds to a time series. Anomaly detection in such data is crucial for identifying unusual or significant events, but it requires complex methods involving pattern recognition, prediction, and classification. Interpreting these anomalies remains challenging. To address this, we introduce an interactive system that combines spatiotemporal visualizations with Large Language Models (LLMs) to generate context-aware explanations by unifying temporal, spatial, and textual insights. We guide the LLM using a structured prompting strategy grounded in the data to reduce hallucinations and improve plausibility. As a demonstration of functionality, we analyze crime anomalies in São Paulo, uncovering links to events such as Carnival and religious holidays.