Evaluating the quality of public geocoding services for crime analysis in China

Abstract

Abstract Background Crime researchers often use publicly available geocoding services to obtain crime locations and conduct subsequent analysis. The quality of these geocoding platforms has not been extensively investigated, especially in the crime research field. The match rate is often employed for this purpose, but this measures only absolute performance at a specified scale and cannot reflect the performance of a platform at different accuracy levels. By expanding match rate to multiple scales, we compared the quality of publicly available geocoding services in China. Methods In this study, we develop a set of evaluation metrics by clarifying the definition of the match rate and considering the multiscale characteristics of geocoding errors. To interpret the geocoding errors of addresses, the positional errors are classified into more detailed types in accordance with their topological relationships with mapping units. Using burglary addresses recorded in N city, the quality of the geocoding services provided by four mainstream online geocoding services in China are compared based on these metrics. Results The match rates of the four geocoding platforms are high enough to maintain the burglary distribution patterns at the subdistrict and district levels but cannot satisfy this need at the building level. Three of the four platforms can geocode enough addresses for spatial analysis at the community level, while Tencent cannot. For commercial addresses, the match rates of the four platforms for residential addresses cannot satisfy the needs of burglary mapping at the building level but can satisfy these needs at any other level. For residential addresses, Tencent is the only platform that cannot provide satisfactory results for community-level mapping. For gated community-level mapping, Baidu and Tianditu can provide marginally satisfactory results. Based on the accuracy levels provided by each platform, the applicability of the results at each level are further analyzed. Conclusions Although this study is limited by address type and data volume, the results suggest users to select appropriate geocoding service based on mapping unit sizes and address types. The results can also help users determine whether the geocoding result at certain accuracy level meet their needs. This research also provides guidance for address-based users beyond crime analysis.