The quality of OpenStreetMap in a large metropolis in northeast Brazil: Preliminary assessment of geospatial data for road axes

Abstract

This paper evaluates the data quality of road axes using the OpenStreetMap (OSM) collaborative mapping platform. OSM was chosen owing to the abundance of data and registered contributors (~ 6 million). We assumed the OSM collaborative data could complement the reference mappings by its quality parameters. We used the cartographic quality indicators of positional accuracy, thematic accuracy, and completeness to validate vector files from OSM. We analyzed the positional accuracy of linear features and we developed the automation of the positional accuracy process. The tool verified the completeness of road axes and thematic accuracy. The positional accuracy of linear features was also used, performed to obtain a range of scales, which reflected the characteristics of mapped areas and varied from 1:22,500 to 1:25,000. The completeness of road axes was 82% of the checked areas. By evaluating the thematic accuracy, we found that the absence of road axes toponymy in editions caused errors in the OSM features (i.e., 58% of road axes without information). As such, we concluded that collaborative data complements the reference cartography by measuring the heterogeneity of information in various regions and filtering the OSM data, despite its being useful for certain analyses.

Exploring spatio-temporal patterns of OpenStreetMap (OSM) contributions in heterogeneous urban areas

“…In addition, Paiva and Camboim (2022) worked with official and collaborative data from the capital city of Minas Gerais to search for intrinsic geospatial data quality models. Finally, Elias et al (2020) explored the extrinsic quality of road axes from the OSM's VGI platform in Salvador, Bahia, compared with the authoritative municipal dataset. The parameters analyzed were positional accuracy, thematic accuracy, and completeness, exposing data heterogeneity in the region.…”