How do I add a new brand to the Name Suggestion Index?

Follow the guidelines in CONTRIBUTING.md by submitting a pull request with the brand's details and Wikidata link; it undergoes community review before inclusion, ensuring accuracy and consistency.

What's the difference between NSI and OpenStreetMap's default editor presets?

NSI provides a curated, canonical list for consistency across multiple tools, while default presets vary by editor; NSI is more standardized and widely adopted for reducing tagging fragmentation.

Can I use NSI data in a non-OSM mapping project?

Yes, it's licensed under BSD 3-Clause, but the data is optimized for OSM tagging schemas; you may need to adapt it, and integration might require custom handling for non-OSM contexts.

How accurate are the brand tags in NSI?

Accuracy is high due to Wikidata linking and community validation, but occasional errors can occur; users are encouraged to report issues via GitHub to maintain quality.

Does NSI support local or regional brands outside major chains?

It includes some regional entries, but coverage is sparse for hyper-local brands; reliance on community contributions means gaps exist, and manual additions are often needed.

How to integrate NSI with a custom OpenStreetMap editor?

Use the npm package or raw JSON data from the repository; refer to the project wiki for developer documentation on implementing suggestion features and data parsing.

Open-Awesome

name-suggestion-index

BSD-3-ClauseJSON

A canonical index of common brand names, operators, and features for consistent tagging in OpenStreetMap.

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What is name-suggestion-index?

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.

Target Audience

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.

Value Proposition

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.

Overview

Canonical common brand names, operators, transit, and flags for OpenStreetMap.

Use Cases

Best For

Ensuring consistent brand tagging in OpenStreetMap editors
Building OSM data validation and cleanup tools
Developing mapping applications with automated feature suggestions
Linking OSM features to Wikidata for enriched data
Creating presets for JOSM or other OSM editing software
Scanning OSM planet data for tagging inconsistencies

Not Ideal For

Mapping projects that require proprietary or custom tagging schemas not aligned with OpenStreetMap standards
Real-time applications needing instant updates to brand information without community review cycles
Small-scale, one-off editing tasks where manual tagging suffices and NSI integration overhead isn't justified

Pros & Cons

Pros

Canonical Brand Consistency

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.

Wide Editor Integration

Seamlessly integrates into popular OSM editors including iD, Vespucci, and JOSM, offering built-in suggestions that streamline mapper workflow.

Wikidata Enrichment

Links entries to Wikidata for accuracy verification, enhancing data reliability and enabling cross-referencing with external knowledge bases, as highlighted in the README.

Community-Driven Curation

Maintained through manual contributions and automated scans via the NSI Collector, ensuring ongoing relevance and crowd-sourced accuracy with active maintainer involvement.

Cons

Limited to Pre-Defined Features

Focuses primarily on common brands and operators; lacks support for highly niche or local entities without community submission, which can hinder customization.

Curation Lag

Relies on community processes and automated scans, leading to delays in including new brands or updates, potentially resulting in outdated suggestions.

Editor Dependency

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.

Frequently Asked Questions

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