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

Abstract

OpenStreetMap, a crowdsourced geographic database, provides the only global-level, openly licensed source of geospatial road data, and the only national-level source in many countries. However, researchers, policy makers, and citizens who want to make use of OpenStreetMap (OSM) have little information about whether it can be relied upon in a particular geographic setting. In this paper, we use two complementary, independent methods to assess the completeness of OSM road data in each country in the world. First, we undertake a visual assessment of OSM data against satellite imagery, which provides the input for estimates based on a multilevel regression and poststratification model. Second, we fit sigmoid curves to the cumulative length of contributions, and use them to estimate the saturation level for each country. Both techniques may have more general use for assessing the development and saturation of crowd-sourced data. Our results show that in many places, researchers and policymakers can rely on the completeness of OSM, or will soon be able to do so. We find (i) that globally, OSM is ∼83% complete, and more than 40% of countries-including several in the developing world-have a fully mapped street network; (ii) that well-governed countries with good Internet access tend to be more complete, and that completeness has a U-shaped relationship with population density-both sparsely populated areas and dense cities are the best mapped; and (iii) that existing global datasets used by the World Bank undercount roads by more than 30%.

Fig 1. Example visual assessment.

Street data from OSM is overlaid on satellite imagery of Kuwait City, Kuwait. Here, the network is 99% complete, with 2 out of 300 edges missing. The red lines indicate street edges in the OSM database. The green lines (highlighted with a white oval) are missing edges. While Google imagery was used in our actual inspection procedure of streets, lower-resolution public-domain imagery is shown in the figure. A version of this figure with the original imagery is available from the authors. Satellite imagery source: Landsat. Road network data source: OpenStreetMap.

Fig 2. Predicted partial effects in multilevel model.

The red line (intercept-only model) shows the baseline predictions, across the density spectrum, when all country-level variables are at their means. Each of the other lines shows the predicted fraction complete after a one standard deviation increase in one country-level predictor. 95% credible intervals are shaded. The thin grey line shows the cumulative distribution of grid-cell level densities in the world.

Fig 3. Visual assessments of completeness: Observations vs multilevel model.

The two sets of estimates correlate well at the country level, with no evidence of bias, adding confidence to our model predictions. The multilevel estimates are obtained from poststratification using out-of-sample predictions for each grid cell in a country. The red line indicates equality (i.e., the 45° line).

Fig 4. Growth in OSM dataset: Parametric fits and visual assessment.

The largest ten countries by road length are shown, along with the global data. The S1 Appendix provides similar plots for all countries. The thick red line shows the actual data for roads, along with the predicted values, asymptote and visual assessment. The thin red line shows that other paths, which are mainly pedestrian routes, continue to grow in some countries even where the road network is complete. The decline in the US is mainly due to the bulk import of TIGER data, which has subsequently been cleaned (e.g. forest tracks are retagged as tracks rather than roads). Years shown indicate January 1.

Fig 5. Completeness of the OSM dataset, by country, January 2016.

The fraction complete is estimated by the parametric model, where that estimate falls within five percentage points or the 95% confidence interval of the multilevel model. Otherwise, the multilevel model is used.

Fig 6. Completeness of the OSM dataset, by grid cell, January 2016.

The fraction complete is estimated by the multilevel model. The color intensity represents the number of estimated street edges, thus highlighting parts of the world with a denser street network. The full-resolution image is available online.

Fig 7. Comparison of methods.

The largest ten countries by road length are shown, and a similar plot for all countries is provided in the S1 Appendix. The bars indicate the bootstrapped and multilevel model estimates from the visual assessment. The green makers indicate the estimates from the parametric fits at the country level, and by subnational density quintile and subnational administrative geography.

Fig 8. Road length per capita.

Fig 9. Road length data from the International Road Federation are substantially lower than OSM-based estimates.

The red line indicates equality (i.e., the 45° line).