CrimeGPT: Numerically Faithful Crime Forecasting with Situated Large Language Models

Crime prediction is essential for proactive community safety and efficient police resource allocation. Beyond strong spatio-temporal periodicity, criminal activity is closely intertwined with regional functional characteristics and neighborhood environments, contextual factors that conventional statistical models largely overlook. Large language models (LLMs) offer a natural pathway to jointly reason over heterogeneous urban descriptors and historical signals within a unified semantic framework. However, their discrete, token-based generation paradigm renders them inherently insensitive to numerical values, a limitation further amplified in crime forecasting where competing multi-source urban signals prevent the model from forming confident numerical outputs. To this end, we propose CrimeGPT, which rethinks numerically faithful LLM-based crime prediction along two complementary axes: situating LLMs in urban dynamics and ensuring numerical faithfulness in their outputs. CrimeGPT introduces a Crime Dynamics Encoder that situates the LLM in heterogeneous urban signals. Bidirectional ordinal supervision then organizes numerical-token representations, which are mapped to continuous crime-count estimates by a distribution-guided regression head. Extensive experiments on real-world datasets from multiple cities show that CrimeGPT achieves superior performance over state-of-the-art methods. Our code and data will be made publicly available.