A canonical index of common brand names, operators, and features for consistent tagging in OpenStreetMap.
Name Suggestion Index (NSI) is a canonical list of commonly used features for OpenStreetMap, providing standardized names and tags for brands, operators, transit, and flags. It solves the problem of inconsistent tagging and spelling in OSM by offering a single source of truth that mapping tools can reference. This helps improve data quality and mapper efficiency across the global OpenStreetMap database.
OpenStreetMap contributors, editors, and tool developers who need consistent tagging for common features like fast-food chains, retail brands, and transit systems. It's also valuable for data validators and community maintainers focused on OSM data quality.
Developers choose NSI because it's the de facto standard for canonical feature tagging in the OSM ecosystem, widely integrated into major editing tools. Its community-driven curation and Wikidata linking ensure accuracy and ongoing relevance, reducing fragmentation and cleanup efforts.
Canonical common brand names, operators, transit, and flags for OpenStreetMap.
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Provides standardized names and tags for thousands of brands, reducing tagging errors in OSM, as evidenced by its use in editors like iD and Rapid for real-time suggestions.
Seamlessly integrates into popular OSM editors including iD, Vespucci, and JOSM, offering built-in suggestions that streamline mapper workflow.
Links entries to Wikidata for accuracy verification, enhancing data reliability and enabling cross-referencing with external knowledge bases, as highlighted in the README.
Maintained through manual contributions and automated scans via the NSI Collector, ensuring ongoing relevance and crowd-sourced accuracy with active maintainer involvement.
Focuses primarily on common brands and operators; lacks support for highly niche or local entities without community submission, which can hinder customization.
Relies on community processes and automated scans, leading to delays in including new brands or updates, potentially resulting in outdated suggestions.
Effectiveness is tied to specific OSM editors; tools not integrated with NSI, such as some proprietary systems, won't benefit from its suggestions, limiting universality.