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

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%.

[1]  Steven P. Jackson,et al.  Assessing the impact of demographic characteristics on spatial error in volunteered geographic information features , 2015 .

[2]  P. Mooney,et al.  Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps , 2010 .

[3]  Peter Mooney,et al.  Analysis of Interaction and Co‐editing Patterns amongst OpenStreetMap Contributors , 2014, Trans. GIS.

[4]  Guillaume Touya,et al.  Quality Assessment of the French OpenStreetMap Dataset , 2010, Trans. GIS.

[5]  Alexander Strickland Pfaff Talikoff What Drives Deforestation in the Brazilian Amazon? Evidence from Satellite and Socioeconomic Data , 1996 .

[6]  L. Yapa,et al.  OpenStreetMap and Food Security: A Case Study in the City of Philadelphia , 2016 .

[7]  Monica Stephens Gender and the GeoWeb: divisions in the production of user-generated cartographic information , 2013, GeoJournal.

[8]  A. Pfaff What drives deforestation in the Brazilian Amazon? Evidence from satellite and socioeconomic data , 1997 .

[9]  Ahmed Loai Ali,et al.  Tackling the thematic accuracy of areal features in OpenStreetMap , 2016 .

[10]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[11]  M. Haklay How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[12]  Pascal Neis,et al.  Quality assessment for building footprints data on OpenStreetMap , 2014, Int. J. Geogr. Inf. Sci..

[13]  C. Haythornthwaite,et al.  Motivation for Open Collaboration , 2013 .

[14]  Pascal Neis,et al.  The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007-2011 , 2011, Future Internet.

[15]  A. Millard‐Ball,et al.  A century of sprawl in the United States , 2015, Proceedings of the National Academy of Sciences.

[16]  Jeffrey R. Lax,et al.  How Should We Estimate Public Opinion in the States , 2009 .

[17]  Zhi Liu,et al.  Motorization and the Provision of Roads in Countries and Cities , 1999 .

[18]  Alexander Zipf,et al.  An Introduction to OpenStreetMap in Geographic Information Science: Experiences, Research, and Applications , 2015, OpenStreetMap in GIScience.

[19]  Arnold J. Toynbee,et al.  Cities on the move , 1972 .

[20]  Jiqiang Guo,et al.  Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.

[21]  Krzysztof Janowicz,et al.  What you are is when you are: the temporal dimension of feature types in location-based social networks , 2011, GIS.

[22]  Guillaume Touya,et al.  Quality analysis of the Parisian OSM toponyms evolution , 2016 .

[23]  Krishna Prapoorna Biligiri,et al.  Modeling climate change impacts of pavement production and construction , 2010 .

[24]  Alexander Zipf,et al.  Fine-resolution population mapping using OpenStreetMap points-of-interest , 2014, Int. J. Geogr. Inf. Sci..

[25]  Kay W. Axhausen,et al.  A multiscale classification of the urban morphology , 2016 .

[26]  Jonathan Rodden,et al.  How Should We Measure District-Level Public Opinion on Individual Issues? , 2012, The Journal of Politics.

[27]  Marco Minghini,et al.  Towards an Automated Comparison of OpenStreetMap with Authoritative Road Datasets , 2017, Trans. GIS.

[28]  Karl Rehrl,et al.  Digging into the history of VGI data-sets: results from a worldwide study on OpenStreetMap mapping activity , 2014, J. Locat. Based Serv..

[29]  Anthony Stefanidis,et al.  Assessing Completeness and Spatial Error of Features in Volunteered Geographic Information , 2013, ISPRS Int. J. Geo Inf..

[30]  Elizabeth Kopits,et al.  Why Have Traffic Fatalities Declined in Industrialized Countries? Implications for Pedestrians and Vehicle Occupants , 2005 .

[31]  João Vitor Meza Bravo,et al.  An Investigation into the Completeness of, and the Updates to, OpenStreetMap Data in a Heterogeneous Area in Brazil , 2015, ISPRS Int. J. Geo Inf..

[32]  K. Gwilliam Cities on the Move : A World Bank Urban Transport Strategy Review , 2002 .

[33]  Jianghua Zheng,et al.  Assessing the Completeness and Positional Accuracy of OpenStreetMap in China , 2014 .

[34]  Mahmoud Reza Delavar,et al.  A Quality Study of the OpenStreetMap Dataset for Tehran , 2014, ISPRS Int. J. Geo Inf..

[35]  Lew Fulton,et al.  The prospect for modal shifts in passenger transport worldwide and impacts on energy use and CO2 , 2012 .

[36]  Pascal Neis,et al.  Assessing the Completeness of Bicycle Trail and Lane Features in OpenStreetMap for the United States , 2015, Trans. GIS.

[37]  Piet Rietveld,et al.  Why fuel prices differ , 2005 .

[38]  Pascal Neis,et al.  Assessing the Effect of Data Imports on the Completeness of OpenStreetMap – A United States Case Study , 2013, Trans. GIS.

[39]  Angi Voß,et al.  A Comparison of the Street Networks of Navteq and OSM in Germany , 2011, AGILE Conf..

[40]  Hansi Senaratne,et al.  A review of volunteered geographic information quality assessment methods , 2017, Int. J. Geogr. Inf. Sci..

[41]  C. Calderon,et al.  Volume and Quality of Infrastructure and the Distribution of Income: An Empirical Investigation , 2001 .

[42]  Jaap Schellekens,et al.  Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model , 2014, Environ. Model. Softw..

[43]  Monika Sester,et al.  Quality Analysis of OpenStreetMap Data Based on Application Needs , 2011, Cartogr. Int. J. Geogr. Inf. Geovisualization.

[44]  Pascal Neis,et al.  Analyzing the Contributor Activity of a Volunteered Geographic Information Project - The Case of OpenStreetMap , 2012, ISPRS Int. J. Geo Inf..

[45]  Pascal Neis,et al.  A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis , 2014, Trans. GIS.

[46]  David Canning,et al.  A database of world stocks of infrastructure for 1950-95 , 1998 .

[47]  Anita Graser,et al.  Towards an Open Source Analysis Toolbox for Street Network Comparison: Indicators, Tools and Results of a Comparison of OSM and the Official Austrian Reference Graph , 2014, Trans. GIS.