OpenStreetMap (OSM), the most popular VGI crowdsourcing project, is an excellent example of an open-license spatial database. But what is the quality of OSM road network datasets compared to authoritative counterparts? Several comparisons of this kind are detailed in literature but these cannot be easily adapted to other scenarios. Developing a generic automated procedure is very challenging. This paper proposes a FOSS4G-based procedure for automated quality comparison of OSM and any authoritative road network datasets. We detail work-in-progress which has great potential. Our procedure is currently implemented into a GRASS command with future plans to extend this to a QGIS plugin and a FOSS4G-based WPS

Brovelli, Maria Antonia

Minghini, Marco

Molinari, Monia

Mooney, Peter

BROVELLI, MARIA ANTONIA

MINGHINI, MARCO

MOLINARI, MONIA ELISA

Archivio istituzionale della ricerca - Politecnico di Milano

Geomatics Workbooks n° 12 – "FOSS4G Europe Como 2015"A FOSS4G-based procedure to compareOpenStreetMap and authoritative roadnetwork datasetsMaria Antonia Brovelli1, Marco Minghini1, Monia Molinari1, Peter Mooney21 Politecnico di Milano, Como Campus, DICA, Via Valleggio 11, 22100 Como, Italy2 Maynooth University, Department of Computer Science, Maynooth, Co. Kildare, IrelandAbstractOpenStreetMap  (OSM),  the  most  popular  VGI  crowdsourcing  project,  is  anexcellent example of an open-license spatial database. But what is the qualityof OSM road network datasets compared to authoritative counterparts? Severalcomparisons of this kind are detailed in literature but these cannot be easilyadapted to other scenarios. Developing a generic automated procedure is verychallenging.  This  paper proposes a  FOSS4G-based procedure  for  automatedquality comparison of OSM and any authoritative road network datasets. Wedetail work-in-progress which has great potential.  Our procedure is currentlyimplemented into a GRASS command with future plans to extend this to a QGISplugin and a FOSS4G-based WPS.KeywordsRoad network, FOSS4G, GRASS, open geo-data, OpenStreetMap, quality.1 IntroductionComparing  open  datasets  is  an  interesting  computational  and  societalchallenge. Open geographic datasets from Volunteered Geographic Information(VGI)  projects  like  OpenStreetMap  (OSM)  are  excellent  examples  ofcrowdsourced geographic  data of  real  world features  such as buildings androads. Recently some National Mapping Agencies (NMAs) have been makingtheir geographic datasets available as open data. This greatly increases theirdistribution and opportunities for access for citizens. Comparing OSM data andopen data from NMAs has been studied in the literature. There has been somework  published  reporting  comparison  of  OSM  and  other  official  datasetsdisplaying different levels  of  automation.  Mooney, Corcoran, and Winstanley(2009) developed an automated quality assessment measure to compare OSMwith authoritative datasets containing natural water features. Fairbairn and Al-Bakri (2013) compared OSM and authoritative large-scale databases in the UKand Iraq to address possible integration of these datasets. Ludwig, Voss, andKrause-Traudes  (2011)  developed  a  fully  automated  approach  to  matchingstreet objects in  Germany contained in  OSM and in  the commercial  Navteqdatabase.  Haklay  (2010)  compared  the  OSM dataset  and  Ordnance  Surveydataset in the UK, while Girres and Touya (2010) compared the quality of the235Geomatics Workbooks n° 12 – "FOSS4G Europe Como 2015"OSM dataset in France with the reference database from the French NationalMapping  Agency.  Siebritz  (2014)  performed  a  quantitative  and  qualitativecomparison between OSM and national mapping agency data in South Africa. Inthe studies outlined above (and some others in the literature) the authors havedesigned  and  developed  software  implementations  of  comparisonmethodologies  which  are  specific  and  tightly  coupled  to  the  OSM  andauthoritative datasets involved. We believe that this makes the comparisonsmore complex and can prevent other authors from replicating them. 2 Our proposed comparison procedureIn  this  paper  we  propose  a  novel  and  generic  procedure  to  performcomparisons of OSM and authoritative road network datasets in terms of spatialaccuracy  and  completeness.  The  procedure  is  designed  to  a  comparisonframework  which  is  general  and  applicable  (in  principle)  to  any  pair  ofcomparable  open  datasets  while  carefully  considering  their  specificcharacteristics.  When  executing  this  procedure  users  can  supervise  thecomputation by manipulating parameter values to reflect the specific featuresof the authoritative dataset under investigation such as its scale and nominalaccuracy.  These characteristics  make the procedure effectively adaptable tomost comparisons of this kind. 2.1 Procedure implementation and workflowThe procedure is developed as a GRASS module written in Python. Its mainsteps are as follows: preliminary comparison of the datasets and computationof global statistics; geometric preprocessing of the OSM dataset to extract itsnetworks representing the same road features as the authoritative dataset; andevaluation  of  the  OSM  dataset's  spatial  accuracy  through  a  grid-basedapproach.  These  steps  are  separately  described  below  by  outlining  theirpurpose, input/output data and GRASS modules used. REF (meaning “referencedataset”)  is  used  to  identify  the  authoritative  road  network  dataset  beingcompared with OSM.2.1.1 Preliminary comparison of the datasetsThe  first  step  prepares  the  OSM  and  REF  road  datasets  in  addition  toperforming an initial comparison of their spatial coverage similarity. The keyoperations are as follows:• import the OSM and REF datasets; if spatial clipping is required the user canimport a vector layer to be used as the clipping mask (v.in.ogr, v.overlay);• compute the total  length  of  the OSM and REF datasets  and their  lengthdifference, both in meters and percentages (v.to.db);• apply a user-specified buffer around the REF and OSM datasets and computethe length and the length percentage of the OSM and REF datasets includedin the buffer (v.buffer, v.overlay, v.to.db).2.1.2 Geometric preprocessing of OSM datasetThis  step  prepares  the  OSM  dataset  so  that  only  features  which  have  acorrespondence  in  the  REF  dataset  are  extracted.  This  correspondence  iscomputed using angular coefficients as follows: 236Geomatics Workbooks n° 12 – "FOSS4G Europe Como 2015"• apply a user-specified buffer around the REF dataset to extract only the OSMfeatures included (v.db.select, v.extract, v.buffer, v.overlay);• compute  the  angular  coefficient  of  each  feature  in  the  REF  dataset(v.db.addcolumn,  v.to.db,  v.db.update).  This  is  compared  to  the  angularcoefficient  of  all  OSM features  falling  inside  the  buffer  around  that  REFfeature.  If  the difference between the two angular coefficients exceeds auser-specified threshold (e.g. 30°), then that OSM feature is deleted as itdoes  not  correspond  to  the  REF  feature  considered  (see  Figure  1).Corresponding  OSM  features  are  instead  added  into  a  new  vector  layer(v.edit, v.patch).Before  these  operations  the  REF  line  features  must  be  split  into  segments(v.split) allowing the angular coefficient be computed on each segment. Thisconsiderably  increases  the  number  of  REF  features  for  comparison  andconsequently  the  computational  time required.  For  this  reason  the  splittingoperation  is  preceded by a  generalization  of  the REF dataset  (v.generalize)using  the  Douglas-Peucker  algorithm  (Douglas  and  Peucker,  1973).  Usersspecify the threshold value for the line feature generalization (see Figure 1).Figure 1: Geometric preprocessing of OSM dataset showing GRASS interface foruser inputs (left) and REF, original OSM and preprocessed OSM roads (right).2.1.3 Evaluation of OSM spatial accuracy using a grid-based approachThis step performs the comparison between the REF and the preprocessed OSMdatasets and evaluates the spatial accuracy of the latter. This process is basedon a grid approach which takes into account the possible heterogeneous nature(translating into an heterogeneous accuracy) of the OSM dataset. The singleoperations are the following:• define a grid, asking the user to either build it in real time (by setting thebounding box and the grid step in the two directions (v.mkgrid)) or uploadinga predefined polygon vector layer (v.in.ogr). The use of a grid is optional.• apply  one  or  more  user-customized  buffers  around the  REF  dataset  andcompute the length and the length percentage of the OSM dataset includedin the buffer (v.buffer, v.overlay, v.to.db).The  final  operation  is  executed  to  obtain  a  number  of  different  outputsaccording to user requirements. For example users can retrieve the grid cellswhere the deviation of OSM dataset from the REF dataset is lower than a fixedthreshold or within a fixed interval of thresholds. Moreover they can evaluate237Geomatics Workbooks n° 12 – "FOSS4G Europe Como 2015"which is the maximum deviation from the REF dataset for each grid cell.3 ConclusionsThe proposed procedure is  under active development and research.  We areworking to achieve improved computational performances. In addition to thegeometrical operations described here an analysis of the correctness of OSMroad  attributes  (i.e.  the  values  of  the  highway key)  compared  to  the  REFdataset is planned for future development. The procedure has been tested tocompare the OSM road network dataset with those authoritative open datasetsof the municipalities in the Lombardy Region in Northern Italy (scale 1:2000).Our  immediate  future  work  should,  at  first,  confirm  the  suitability  of  theprocedure – which, as shown, is heavily supervised by the user input values –on the input datasets. From a FOSS4G viewpoint the authors will develop thisprocedure to become a QGIS plugin before being provided as a Web ProcessingService (WPS) available for the whole community.AcknowledgementsThe authors grateful acknowledge support from COST Action TD1202 “Mappingand  the  Citizen  Sensor”  and  specifically  the  Short  Term  Scientific  Mission(STSM) COST-STSM-TD1202-24885.References✔ Douglas, D. H., & Peucker, T. K. (1973). Algorithms for the reduction of thenumber of points required to represent a digitized line or its caricature.Cartographica:  The International  Journal  for  Geographic  Information andGeovisualization, 10(2), 112-122.✔ Fairbairn, D., & Al-Bakri, M. (2013). Using Geometric Properties to EvaluatePossible  Integration  of  Authoritative  and  Volunteered  GeographicInformation. ISPRS International Journal of Geo-Information, 2(2), 349–370.✔ Girres,  J.-F.,  &  Touya,  G.  (2010).  Quality  Assessment  of  the  FrenchOpenStreetMap Dataset. Transactions in GIS, 14(4), 435–459.✔ Haklay, M. (2010) How good is OpenStreetMap information? A comparativestudy of OpenStreetMap and Ordnance Survey datasets for London and therest of England. Environment and Planning B, 37, 682-703.✔ Ludwig,  I.,  Voss,  A.,  &  Krause-Traudes,  M.  (2011).  A  Comparison  of  theStreet  Networks  of  Navteq  and  OSM  in  Germany.  In  S.  Geertman,  W.Reinhardt  &  F.  Toppen  (Eds.),  Advancing  Geoinformation  Science  for  aChanging World (pp. 65-84). Springer Berlin Heidelberg.✔ Mooney,  P.,  Corcoran,  P.,  &  Winstanley,  A.  C.  (2010).  Towards  QualityMetrics  for  OpenStreetMap.  In  Proceedings  of  the  18th  SIGSPATIALInternational Conference on Advances in Geographic Information Systems(pp. 514–517). New York, NY, USA: ACM. ✔ Siebritz,  L.-A.  (2014).  Assessing the  accuracy of  openstreetmap data  insouth africa for the purpose of integrating it with authoritative data. PhDThesis, University of Cape Town, SA.238

A FOSS4G-based procedure to compare OpenStreetMap and authoritative road network datasets

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A FOSS4G-based procedure to compare OpenStreetMap and authoritative road network datasets

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