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The world's user-generated road map is more than 80% complete.

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OpenStreetMap (OSM) road data is approximately 83% complete globally, with over 40% of countries fully mapped. This crowdsourced geographic data is increasingly reliable for researchers and policymakers worldwide.

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Area of Science:

  • Geographic Information Systems (GIS)
  • Crowdsourced Data Analysis
  • Geospatial Science

Background:

  • OpenStreetMap (OSM) is a vital global, open-license source for geospatial road data.
  • Limited information exists on the reliability and completeness of OSM data for specific regions.
  • Assessing OSM's completeness is crucial for its effective use by researchers, policymakers, and citizens.

Purpose of the Study:

  • To comprehensively assess the completeness of OpenStreetMap road data across all countries globally.
  • To provide reliable metrics for understanding the usability of OSM road networks.
  • To evaluate the factors influencing OSM data completeness and saturation.

Main Methods:

  • Employed two independent methods: visual assessment against satellite imagery and statistical modeling (multilevel regression and poststratification).
  • Utilized sigmoid curve fitting on cumulative contribution length to estimate saturation levels for each country.
  • Developed techniques applicable to assessing other crowdsourced datasets.

Main Results:

  • Globally, OpenStreetMap road data achieves approximately 83% completeness.
  • Over 40% of countries, including several developing nations, exhibit a fully mapped street network.
  • Data completeness correlates positively with good governance and internet access, and shows a U-shaped relationship with population density.
  • Existing global datasets, like those used by the World Bank, may undercount roads by over 30%.

Conclusions:

  • OpenStreetMap provides a highly reliable and increasingly complete global road network dataset.
  • Researchers and policymakers can increasingly depend on OSM data for various applications.
  • The study highlights the potential of crowdsourced data and introduces robust methods for assessing its quality.