A Knowledge-Guided Large Language Model Approach for Standardising Unstructured Addresses

Unstructured geographic descriptions containing spatial relations, such as landmark references, directions, and relative distances, remain a major challenge for accurate geocoding and spatial analysis. Existing approaches based on string matching or sequence modelling often fail to interpret complex spatial semantics embedded in human-generated addresses. This study proposes a knowledge-guided large language model (LLM) approach for geocoding-oriented address standardisation. The proposed approach represents addresses using a dual-layer structure consisting of a standard address that can be geocoded and ancillary spatial information. By incorporating explicit address structural knowledge into LLM prompting, five representative address patterns are constructed to guide semantic classification and structured extraction of address components, landmarks, directions, and distances. A deterministic spatial module is further developed to transform the ancillary information extracted by the LLM into coordinate constraints, thereby linking semantic interpretation with geometric computation. Experiments on 7931 real-world crime records from Nanjing, China, demonstrate that the proposed approach significantly improves geocoding accuracy across multiple mapping platforms. Compared with raw addresses, the method reduces both mean positional errors and error variability, with particularly notable improvements for orientation-based descriptions. The results indicate that combining LLM-based semantic parsing with spatial computation provides an effective solution for transforming unstructured geographic descriptions into machine-readable spatial information.