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OpenStreetMap – building data completeness visualization in terms of “Fitness for purpose”

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EN
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EN
The purpose of this article was to provide the user with information about the number of buildings in the analyzed OpenStreetMap (OSM) dataset in the form of data completeness indicators, namely the standard OSM building areal completeness index (C Index), the numerical completeness index (COUNT Index) and OSM building location accuracy index (TP Index). The official Polish vector database BDOT10k (Database of Topographic Objects) was designated as the reference dataset. Analyses were carried out for Piaseczno County in Poland, differentiated by land cover structure and urbanization level. The results were presented in the form of a bivariate choropleth map with an individually selected class interval suitable for the statistical distribution of the analyzed data. The results confirm that the completeness of OSM buildings close to 100% was obtained mainly in built-up areas. Areas with a commission of OSM buildings were distinguished in terms of area and number of buildings. Lower values of completeness rates were observed in less urbanized areas. The developed methodology for assessing the quality of OSM building data and visualizing the quality results to assist the user in selecting a dataset is universal and can be applied to any OSM polygon features, as well as for peer review of other spatial datasets of comparable thematic scope and detail.
Rocznik
Strony
art. no. e35, 2023
Opis fizyczny
Bibliogr. 43 poz., rys., tab., wykr.
Twórcy
  • Military University of Technology, Warsaw, Poland
  • Military University of Technology, Warsaw, Poland
  • Military University of Technology, Warsaw, Poland
Bibliografia
  • 1. Baranowski, M., Gotlib, D., and Olszewski, R. (2016). Properties of cartographic modelling under contemporary definitions of a map. Polish Cartographical Review, 48(3), 91–100. DOI: 10.1515/pcr-2016-0011.
  • 2. Barron, C., Neis, P. and Zipf, A. (2014). A comprehensive framework for intrinsic OpenStreetMap quality analysis. Transactions in GIS, 18(6), 877–895. DOI: 10.1111/tgis.12073.
  • 3. Borkowska, S. and Pokonieczny, K. (2022) Analysis of OpenStreetMap Data Quality for Selected Counties in Poland in Terms of Sustainable Development. Sustainability, 14, 3728. DOI: 10.3390/su14073728.
  • 4. Calka, B. (2021). Bivariate choropleth map documenting land cover intensity and population growth in Poland 2006–2018. J. Maps, 17, 162–168. DOI: 10.1080/17445647.2021.2009925.
  • 5. Chrisman, N.R. (1984). The role of quality information in the long-term functioning of a geographic information system. Cartographica, 21(2), 79–87.
  • 6. Cliburn, D.C., Feddema, J.J., Miller, J.R. et al. (2002). Design and evaluation of a decision support system in a water balance application. Comput. Graph., 26, 931–949. DOI: 10.1016/S0097-8493(02)00181-4.
  • 7. Deitrick, S.A. (2007). Uncertainty visualization and decision making: Does visualizing uncertain information change decisions? In Proceedings of the 23rd International Cartographic Conference, 4-10 August 2007, 4–10. Moscow, Russia.
  • 8. Demetriou, D. (2016). Uncertainty of OpenStreetMap data for the road network in Cyprus. Proc. SPIE, 9688. DOI: 10.1117/12.2239612.
  • 9. Fan, H., Zipf, A., Fu, Q. et al. (2014). Quality assessment for building footprints data on OpenStreetMap. Int. J. Geogr. Inf. Sci., 28(4), 700–719. DOI: 10.1080/13658816.2013.867495.
  • 10. Frank, U.A. (2009). Why is scale an effective descriptor for data quality? The physical and ontological rationale for imprecision and level of detail. In Gerhard Navratil (Ed.) Research trends in geographic information science, pp. 39–61. Springer: Heidelberg.
  • 11. French, K., and Li, X. (2010). Feature-based cartographic modelling. Int. J. Geogr. Inf. Sci., 24(1), 141–164. DOI: 10.1080/13658810802492462.
  • 12. GEOFABRIK (2021). Retrieved August 28, 2022 from http://download.geofabrik.de/europe/poland.html.
  • 13. Glazewski, A., Kowalski, P.J., Olszewski, R. et al. (2009). New approach to multi scale cartographic modelling of reference and thematic databases in Poland. Cartography in Central and Eastern Europe, 89–106. Springer: Berlin, Heidelberg.
  • 14. GUS (2021). Area, population and ranking positions by powiats and cities with powiat status. Retrieved from https://stat.gov.pl/obszary-tematyczne/ludnosc/ludnosc/powierzchnia-i-ludnosc-w-przekroju- terytorialnym-w-2021-roku,7,18.html.
  • 15. Hanusz, Z. Tarasinska, J. and Zielinski, W. (2016). Shapiro–Wilk Test with Known Mean. Revstat Stat. J., 14, 89–100. DOI: 10.57805/revstat.v14i1.180.
  • 16. Hayakawa, T., Imi, Y. and Ito, T. (2012). Analysis of Quality of Data in OpenStreetMap. 2012 IEEE 14th International Conference on Commerce and Enterprise Computing, 131–134.
  • 17. Hecht, R., Kunze, C. nd Hahmann, S. (2013). Measuring Completeness of Building Footprints in Open-StreetMap over Space and Time. ISPRS Int. J. Geo-Inf., 2, 1066–1091. DOI: 10.3390/ijgi2041066.
  • 18. ISO (2013). Geographic information-Data Quality. ISO/TC 211 Geographic Information/Geomatics, In-ternational Organization for Standardization, Geneva, Switzerland, 2013.
  • 19. Keil, J., Edler, D., Kuchinke, L. et al.. (2020). Effects of visual map complexity on the attentional processing of landmarks. PLoS ONE, 15, e0229575. DOI: 10.1371/journal.pone.0229575.
  • 20. Korycka-Skorupa, J. and Paslawski, J. (2017). The beginnings of the choropleth presentation. Polish Cartographical Review, 49(4), 187–198. DOI: 10.1515/pcr-2017-0012.
  • 21. Korycka-Skorupa, J. and Nowacki, T. (2019). Cartographic presentation – from simple to complex map. .Miscellanea Geographica. 23(1), 16–22. DOI: 10.2478/mgrsd-2018-0023.
  • 22. Kraak, M.J., Roth, R.E., Ricker, B. et al. (2020). Mapping for a Sustainable World. The United Nations: New York.
  • 23. Leitner, M. and Buttenfield, B.P. (2000). Guidelines for the display of attribute certainty. Cartogr. Geogr. Inf. Sci.. 27, 3–14.
  • 24. Leonowicz, A. (2002). Prezentacja zależności zjawisk metodą kartogramu złożonego. Polski Przegląd Kartograficzny, 34, 273–85.
  • 25. Leonowicz, A. (2002). Z problematyki porównywalności kartogramów. Polski Przegląd Kartograficzny, 34(1), 22–33.
  • 26. Leonowicz, A. (2006). Two-variable choropleth maps as a useful tool for visualization of geographical relationship. Geografija, 42(1), 33–37.
  • 27. MacEachren, A.M., Brewer, C. and Pickle, L.W. (1995). Mapping health statistics: Representing data reliability. In Proceedings of the 17th International Cartographic Conference, Barcelona, Spain, 3-9 September 1995, 311-319.
  • 28. Mobasheri, A., Zipf, A. and Francis, L. (2018). OpenStreetMap data quality enrichment through awareness raising and collective action tools - experiences from a European project. Geo. Spat. Inf. Sci., 21:3, 234–246. DOI: 10.1080/10095020.2018.1493817.
  • 29. Mocnik, F.B., Fan, H. and Zipf, A. (2017). Data Quality and Fitness for Purpose. Conference: 20th AGILE Conference on Geographic Information Science. Wageningen: Netherlands. DOI: 10.13140/RG.2.2.13387.18726.
  • 30. Mocnik, F.B., Mobasheri, A., Griesbaum, L. et al. (2018). A grounding-based ontology of data quality measures. J. Spat. Inf. Sci., 16(16), 1–25. DOI: 10.5311/JOSIS.2018.16.360.
  • 31. Nelson, J. (2020). Multivariate Mapping. The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2020 Edition). DOI: 10.22224/gistbok/2020.1.5.
  • 32. Nowak Da Costa, J. (2016). Novel tool for examination of data completeness based on a comparative study of VGI data and official building datasets. Geodetski Vestnik, 60, 495–508. DOI: 10.15292/geodetski-vestnik.2016.03.495-508.
  • 33. Openshaw, S. (1983). The Modifiable Areal Unit Problem. Geo Books: Norwick, UK.
  • 34. OSM (2022). Retrieved August 25, 2022 from: https://wiki.openstreetmap.org/wiki/Pl:Key:building.
  • 35. PAP (2022). Najbogatsze i najbiedniejsze powiaty w Polsce część pierwsza (1–99). Serwis Samorządowy PAP.
  • 36. Ribeiro, A. and Fonte, C.C. (2015). A Methodology for Assessing OpenStreetMap Degree of Coverage for Purposes of Land Cover Mapping. ISPRS Annals of Photogrammetry. Remote Sensing and Spatial Information Sciences, II3, 297–303. DOI: 10.5194/isprsannals-II-3-W5-297-2015.
  • 37. RMDLT. (2021). Regulation of the Minister of Development, Labour and Technology of July 27, 2021 on the database of topographic objects and the database of general geographic objects, as well as standard cartographic studies, Dz.U. 2021, nr 30, poz. 1412.
  • 38. Roick, O., Hagenauer, J. and Zipf, A. (2011). OSMatrix - Grid based analysis and visualization of Open-StreetMap. In Proceedings of the 1st European State of the Map Conference(SOTM-EU), Vienna, Austria.
  • 39. Slocum, T., McMaster, R.B., Kessler, F.C. et al. (2005). Thematic cartography and geovisulization, second edition. Upper Saddle River: Pearson Prentice Hall. ISBN: 9780132298346.
  • 40. Tian, Y., Zhou, Q. and Fu, X. (2019). An Analysis of the Evolution, Completeness and Spatial Patterns of OpenStreetMap Building Data in China. ISPRS Int. J. Geo-Inf., 8, 35. DOI: 10.3390/ijgi8010035.
  • 41. Wang, M., Li, Q., Hu, Q. et al. (2013). Quality Analysis of Open Street Map Data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. – ISPRS Arch., XL2, 155–158. DOI: 10.5194/isprsarchives-XL-2-W1-155-2013.
  • 42. Zacharopoulou, D., Skopeliti, A. and Nakos, B. (2021). Assessment and Visualization of OSM Consistency for European Cities. ISPRS Int. J. Geoinf., 10, 361. DOI: 10.3390/ijgi10060361.
  • 43. Zhang, Y., Zhou, Q., Brovelli, M.A. et al.. (2022). Assessing OSM building completeness using population data. Int. J. Geogr. Inf. Sci.. 36(7), 1443–1466. DOI: 10.1080/13658816.2021.2023158.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-af70e8ba-a9de-49d7-aef2-fbfb9f28a2e1
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