Using OpenStreetMap to inventory bicycle infrastructure: A comparison with open data from cities

ABSTARCT With rapid growth in bicycling, timely and spatially rich bicycling infrastructure data are essential for understanding determinants of ridership, equity of access, and potential for future developments. OpenStreetMap (OSM) is a collaborative global map that was built by volunteers and is promising for active transportation research. In this article, we use OSM to inventory bicycling infrastructure in six Canadian cities, compare it to municipal open data, and provide guidance for practitioners using OSM data. We conducted an evaluation of OSM and open data, overall and for four categories of bicycle infrastructure: cycle tracks; on-street bicycle lanes; paths (bicycle only or multiuse); and local street bikeways. We found that the concordance in terms of total length of OSM infrastructure to open data infrastructure very high in two of the six cities (< ±2%), and reasonably high in all cities (maximum difference ±30%). Concordance for infrastructure categories was highest for on-street bicycle lanes, which were the most common, and easily identifiable type of bicycle infrastructure in the OSM data, and lowest for cycle tracks and local street bikeways, both of which are new or relatively rare infrastructure types in some Canadian cities. In some cases, OSM was more detailed and timely than open data. A challenge in OSM is consistent tagging of bicycle infrastructure types. We encourage practitioners to consider OSM data for multicity studies, but to be mindful of potential inconsistencies in attribution and local definitions. We also recommend users of OSM to publish data queries for repeatability.

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

[2]  Jaiteg Singh,et al.  Assessing OpenStreetMap Data Using Intrinsic Quality Indicators: An Extension to the QGIS Processing Toolbox , 2017, Future Internet.

[3]  N. Coops,et al.  Assessing the quality of forest fuel loading data collected using public participation methods and smartphones , 2014 .

[4]  B. Giles-Corti,et al.  Accessibility and connectivity in physical activity studies: the impact of missing pedestrian data. , 2008, Preventive medicine.

[5]  Billie Giles-Corti,et al.  Developing a reliable audit instrument to measure the physical environment for physical activity. , 2002, American journal of preventive medicine.

[6]  I. Sener,et al.  Understanding the role of equity in active transportation planning in the United States , 2017 .

[7]  Cheng Liu,et al.  Global System for Transportation Simulation and Visualization in Emergency Evacuation Scenarios , 2015 .

[8]  Eric B. Wolf,et al.  Metadata Squared: Enhancing Its Usability for Volunteered Geographic Information and the GeoWeb , 2013 .

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

[10]  Jeffrey S. Wilson,et al.  Using Google Street View to Audit the Built Environment: Inter-rater Reliability Results , 2013, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[11]  L. Martínez,et al.  International Freight and Related Carbon Dioxide Emissions by 2050 , 2015 .

[12]  Meghan Winters,et al.  Bike Score®: Associations between urban bikeability and cycling behavior in 24 cities , 2016, International Journal of Behavioral Nutrition and Physical Activity.

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

[14]  H. Nijland,et al.  Do the Health Benefits of Cycling Outweigh the Risks? , 2010, Environmental health perspectives.

[15]  Kenneth A. Perrine,et al.  Map-Matching Algorithm for Applications in Multimodal Transportation Network Modeling , 2015 .

[16]  Julian Hagenauer,et al.  Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks , 2012, Int. J. Geogr. Inf. Sci..

[17]  A. Bauman,et al.  Health benefits of cycling: a systematic review , 2011, Scandinavian journal of medicine & science in sports.

[18]  Jessica E. Schoner,et al.  The missing link: bicycle infrastructure networks and ridership in 74 US cities , 2014 .

[19]  Francisco Escobar,et al.  Assessing Walking and Cycling Environments in the Streets of Madrid: Comparing On-Field and Virtual Audits , 2015, Journal of Urban Health.

[20]  Adam Millard-Ball,et al.  The world’s user-generated road map is more than 80% complete , 2017, PloS one.

[21]  Janette Sadik-Khan,et al.  Urban Bikeway Design Guide , 2014 .

[22]  Jacek Malczewski,et al.  Quality Evaluation of Volunteered Geographic Information: The Case of OpenStreetMap , 2017 .

[23]  Kevin Curran,et al.  OpenStreetMap , 2012, Int. J. Interact. Commun. Syst. Technol..

[24]  Peter A Cripton,et al.  Route infrastructure and the risk of injuries to bicyclists: a case-crossover study. , 2012, American journal of public health.

[25]  Helen E. Roy,et al.  Lessons from lady beetles: accuracy of monitoring data from US and UK citizen-science programs , 2012 .

[26]  Thomas W. Sanchez,et al.  Public Participation, Social Equity, and Technology in Urban Governance , 2013 .

[27]  Daren C. Brabham THE MYTH OF AMATEUR CROWDS , 2012 .

[28]  Vyron Antoniou,et al.  How Many Volunteers Does it Take to Map an Area Well? The Validity of Linus’ Law to Volunteered Geographic Information , 2010 .

[29]  Shomik Raj Mehndiratta,et al.  Accessibility Analysis of Growth Patterns in Buenos Aires, Argentina , 2015 .

[30]  Greet Cardon,et al.  Assessing the environmental characteristics of cycling routes to school: a study on the reliability and validity of a Google Street View-based audit , 2014, International Journal of Health Geographics.

[31]  K. Teschke,et al.  Route Preferences among Adults in the near Market for Bicycling: Findings of the Cycling in Cities Study , 2010, American journal of health promotion : AJHP.

[32]  Hartwig H. Hochmair,et al.  Intermodal Door-to-Door Routing for People with Physical Impairments in a Web-Based, Open-Source Platform , 2014 .

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

[34]  M. Goodchild Citizens as sensors: the world of volunteered geography , 2007 .

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

[36]  Eric S. Raymond,et al.  The cathedral and the bazaar , 1998, First Monday.

[37]  T. Quirós Accessibility analysis of growth patterns in Buenos Aires , density , employment and spatial 1 form 2 3 , 2015 .

[38]  J. Pucher,et al.  Cycling to work in 90 large American cities: new evidence on the role of bike paths and lanes , 2012 .

[39]  Dennis Zielstra,et al.  Using Free and Proprietary Data to Compare Shortest-Path Lengths for Effective Pedestrian Routing in Street Networks , 2012 .

[40]  Andres F. Clarens,et al.  Estimating Spatially and Temporally Continuous Bicycle Volumes by Using Sparse Data , 2014 .