Natural language interface for urban network analytics

Abstract We introduce the first natural language interface for complex urban analytics, leveraging Large Language Models (LLMs) and Spatio-Temporal Transactional Networks (STTNs). By combining intuitive natural language querying with structured data analytics, our framework simplifies complex urban analyses, such as identifying commuter patterns, detecting anomalies, and exploring mobility networks. We propose a comprehensive evaluation dataset that demonstrates that minor architectural improvements can significantly improve analytical accuracy. Our approach bridges the gap between non-expert users and sophisticated urban insights, paving the way for accessible, reliable, and scalable urban data analytics.