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Online geocoding platforms are essential tools for location-based services (LBS) and spatial analysis because they convert textual addresses into geographic coordinates. However, their results often exhibit inconsistencies due to variations in geospatial databases and geocoding algorithms across platforms. A key challenge is the lack of standardized criteria to evaluate and integrate multi-source geocoding outputs. Integrating multi-platform results with public road network data may offer a viable path toward higher accuracy. Building on this idea, this study proposes a road-constrained optimization method for web geocoding. It utilizes results from three popular platforms (Baidu, Amap, and Tencent) and employs open-source road network data to impose spatial constraints. A rigorous spatial matching and filtering process is designed to eliminate erroneous coordinates and optimize results. We tested 3,000 addresses in Nanjing, China, comparing errors before and after applying our method. Results show the algorithm reduces errors from 112.36–210.58m to 72.07m on average. Error distribution also becomes more tightly clustered around the ground-truth locations. Statistical analysis further reveals that errors exceeding 500m can be corrected to within 100m. This demonstrates a significant accuracy improvement and offers a practical solution for users of online geocoding services.
Wang et al. (Wed,) studied this question.