A Polygon Constraint‐Based Multi‐Source Geocoding Optimization Method

ABSTRACT Online geocoding platforms transform textual addresses into geographic coordinates, yet often exhibit substantial geocoding errors. Most multi‐source geocoding approaches lack explicit spatial constraints, limiting the stability and interpretability of fused results. To address this issue, this study proposes a Polygon Constraint‐based Multi‐source Geocoding optimization method (PCMG) that integrates geocoding outputs from multiple platforms under explicit polygon constraints corresponding to address semantics. The method aggregates candidate coordinates and applies spatial filtering and POI‐based refinement to enhance spatial plausibility and consistency. It is evaluated using 1884 addresses in Nanjing, China, with ground‐truth locations collected through field surveys. Results demonstrate that PCMG significantly reduces mean error and suppresses extreme positional deviations. Error distributions are more concentrated and exhibit a clearer relationship with polygon scale, indicating improved spatial consistency. The findings suggest that polygon units can serve as active constraints for multi‐source geocoding optimization and improve the reliability of online geocoding services in complex urban environments.