OpenStreetMap Data Quality

how the world's biggest open map stays correct

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Anyone can create an account on OpenStreetMap and start editing immediately.

This means that some people with bad intentions make malicious edits, creating places that don't exist, deleting places that do, or adding incorrect information.

To combat vandalism, OpenStreetMap superusers track recent changes constantly and flag changes that look wrong.

They can then revert - or undo - incorrect changes and notify the user. Users that repeatedly vandalize the map can have their editing rights suspended or removed entirely.

Missing Details

Since there's no perfect map of the world to compare ours to, finding areas where data is missing is difficult.

Data is often partial - a road is present, but unnamed, or a building lacks basic details that let it be categorized.

OpenStreetMap has multiple tools to score itself and identify missing data. The No Name layer highlights all roads without names and buildings without addresses. A worldwide concordance of Foursquare checkins versus OpenStreetMap data shows areas where people are and data isn't.

Routing Problems

One of the most important uses of OpenStreetMap is for routing: tools like OSRM and MapQuest Open provide driving directions based on of its data.

Problems with this routing network are invisible to other users of the map, and can include disconnected roads, improper turn restrictions, and unmarked speed limits.

A number of tools automatically comb through OSM data to find potential routing issues. MapRoulette finds highways without lane counts or speed limits, while Keep Right identifies many more routing issues, like unreachable roads oneway roads that dead-end.

Completeness & Analysis

This leaves open the question: how good is OpenStreetMap data? There are a few solid investigations of this question to point to.

A Quality Analysis Of OpenStreetMap by Aamer Ather at University College London concluded

The results of this analysis found the positional accuracy of OpenStreetMap data to be very good in comparison to OS MasterMap, with over 80% overlap between most the road objects tested between the two datasets. The results also found there to be a positive correlation between road name attribute completeness and number of users per area.

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