, Volume 16, Issue 2, pp 221–243 | Cite as

Comparison of four line-based positional assessment methods by means of synthetic data

  • Francisco Javier Ariza-LópezEmail author
  • Antonio Tomás Mozas-Calvache


Positional accuracy of spatial data can be assessed by means of line-based methods. In this work we develop an analysis of the following four methods: Hausdorff Distance, Mean Distance, Single Buffer Overlay and Double Buffer Overlay, using a set of 12 synthetic cases. The synthetic cases incorporate specific shape features for bias, random errors and outliers which correspond to simplified versions of real world possibilities. The use of synthetic cases helps us to understand the basic behavioral differences between the methods. Numerical results for the positional accuracy estimations are different between methods and cases due to the different concepts of distance involved and the specific configurations of each case. When the method results in a function, patterns related to different types of errors can be detected in this function. The length-inclusion level of each method is revealed as the base criterion for comparison. The Single Buffer Overlay Method offers the more general solution because it includes the others’ results.


Positional accuracy assessment Positional control by line elements Buffer overlay methods Spatial data quality 



This work has been partially funded by the National Ministry of Sciences and Technology of the Kingdom of Spain under grant nº BIA2003-02234. The authors also acknowledge the Regional Government of Andalusia (Spain) for the grant P08-TIC-4199 and for the financial support since 1997 to their research group with code PAI-TEP-164 partially by means of the European Regional Development Fund (ERDF).


  1. 1.
    Abbas L, Grussenmeyer P, Hottier P (1995) Contrôle de la planimétrie d´une base de données vectorielles: une nouvelle méthode basée sur la distance de Hausdorff: el méthode du contrôle linéaire. Bul. Societé Française de Photogrammétrie et Télédétection 137:6–11Google Scholar
  2. 2.
    Alesheikh AA, Blais JA, Chapman MA, Karimi H (1998) Rigorous geospatial data uncertainty models for GISs. In: Lowell K, Jaton A (eds) Spatial accuracy assessment: land information uncertainty in natural resources. Ann Arbor Press, Chelsea, pp 195–202Google Scholar
  3. 3.
    Ariza-López FJ (2002) Control de calidad en la producción cartográfica. Ra-Ma, MadridGoogle Scholar
  4. 4.
    Ariza-López FJ (2007) Control posicional en Cartografía por elementos lineales (ConPoCar). Informe final. Universidad de JaénGoogle Scholar
  5. 5.
    Ariza-López FJ, Atkinson-Gordo AD (2008) Variability of NSSDA estimations. J Surv Eng 134(2):404–407CrossRefGoogle Scholar
  6. 6.
    Ariza-López FJ, Atkinson-Gordo AD (2008) Analysis of some positional accuracy assessment methodologies. J Surv Eng 134(2):404–407CrossRefGoogle Scholar
  7. 7.
    Ariza-López FJ, Atkinson-Gordo AD, Rodríguez-Avi J (2008) Acceptance curves for the positional control of geographic data bases. J Surv Eng 134(1):26–32CrossRefGoogle Scholar
  8. 8.
    ASCE (1983) Map uses, scales and accuracies for engineering and associated purposes. American society of civil engineers, Committee on Cartographic Surveying, Surveying and Mapping Division, New YorkGoogle Scholar
  9. 9.
    ASPRS (1989) Accuracy standards for large scale maps. Photogramm Eng Remote Sens 56(7):1068–1070Google Scholar
  10. 10.
    Atkinson-Gordo AD, Ariza-Lopez FJ (2002) Nuevo enfoque para el análisis de la calidad posicional en cartografía mediante estudios basados en la geometría lineal. Proceedings XIV International Congress of Engineering Graphics, Santander, Spain, pp 1–10.Google Scholar
  11. 11.
    Blakemore M (1984) Generalization and error in spatial data bases. Cartographica 21(2&3):131–139CrossRefGoogle Scholar
  12. 12.
    Carmel Y, Flather C, Dean D (2006) A methodology for translating positional error into measures of attribute error, and combining the two error sources. In Caetano, M. and Painho, M. (eds.) Proceedings of Accuracy 2006. 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Lisbon, pp 3–17.Google Scholar
  13. 13.
    Chen CC, Knoblock CA, Shahabi C (2008) Automatically and accurately conflating raster maps with orthoimagery. GeoInformatica 12:377–410CrossRefGoogle Scholar
  14. 14.
    Chrisman NR (1982) A theory of cartographic error and its measurement in digital data bases, Auto-Carto 5 - Proceedings, Crystal City, Virginia, pp 159–168Google Scholar
  15. 15.
    Chrisman NR (1991) The error component in spatial data. In: Maguire DJ, Goodchild MF, Rhind DW (eds) Geographical information systems: principles and applications. Longman, Essex, pp 165–174Google Scholar
  16. 16.
    Church R, Curtin K, Fohl P, Funk C, Goodchild M, Kyriakidis P, Noronha V (1998) Positional distortion in geographic data sets as a barrier to interoperation. Technical papers ACSM. American Congress on Surveying and Mapping, BethesdaGoogle Scholar
  17. 17.
    Deza E, Deza MM (2006) Dictionary of distances. Elsevier, AmsterdamGoogle Scholar
  18. 18.
    Dutton GH (1999) Scale, sinuosity and point selection in digital line generalization. Cartogr Geogr Inf Sci 26(1):33–53CrossRefGoogle Scholar
  19. 19.
    EuroSDR. (2004) Positional Accuracy Improvement: Impacts of improving the positional accuracy of GI databases. Publication No. 48. <> (January 13, 2010).
  20. 20.
    FGDC (1998) FGDC-STD-007: geospatial positioning accuracy standards, part 3. National standard for spatial data accuracy. Federal Geographic Data Committee, RestonGoogle Scholar
  21. 21.
    Giordano A, Veregin H (1994) Il controllo di qualitá nei sistemei informative territoriali. Il Cardo Editore, VenetiaGoogle Scholar
  22. 22.
    Goeman W, Martínez-Fonte L, Bellens R, Gautama S (2005) Image based accuracy assessment of geographical line data sets. In Proceedings ProRISC 2005, pp 619–624Google Scholar
  23. 23.
    Goodchild MF, Hunter G (1997) A simple positional accuracy measure for linear features. Int J Geogr Inf Sci 11(3):299–206CrossRefGoogle Scholar
  24. 24.
    Gruber G, Menard C, Schachinger B (2008) Evaluation of the geometric accuracy of automatically recorded 3D City Models compared to GIS data. In Lecture Notes in Geoinformation and Cartography (The European Information Society). Springer, Berlin, pp 67–78Google Scholar
  25. 25.
    Hangouët JF (1995) Computation of the Hausdorff Distance between plane vector polylines. AutoCarto 12:1–10Google Scholar
  26. 26.
    Heo J, Woo J, Sang JS (2008) New line accuracy assessment methodology using nonlinear least-squares estimation. J Surv Eng 134(1):13–20CrossRefGoogle Scholar
  27. 27.
    ISO (2002) ISO 19113: geographic information - quality principles. International Organization for Standardization, GenevaGoogle Scholar
  28. 28.
    Jakobsson A, Vauglin F (2002) Report of a questionnaire on data quality in National Mapping Agencies. CERCO Working Group on Quality. Comité Européen des Responsables de Cartographie Officielle, Marne-la-ValléeGoogle Scholar
  29. 29.
    Joao EM (1995) The importance of quantifying the effects of generalization. In: Müller JC, Weibel R, Legrange JP (eds) GIS and generalization, methodology and practice. Taylor & Francis, London, pp 183–193Google Scholar
  30. 30.
    Joao EM (1998) Causes and consequences of map generalisation. Taylor & Francis, LondonGoogle Scholar
  31. 31.
    Johnston D, Timlin D, Szafoni D, Casanova J, Dilks K (2000) Quality assurance/quality control procedures for ITAM GIS databases. US Army Corps of Engineers, Engineer Research and Development CenterGoogle Scholar
  32. 32.
    Kagawa Y, Sekimoto Y, Shibaski R (1999) Comparative study of positional accuracy evaluation of line data. In proceedings of the asian association on remote sensing (ProRISC 2005)Google Scholar
  33. 33.
    Keefer BJ, Smith JL, Gregoire TG (1988) Simulating manual digitizing error with statistical models. In Proceedings of GIS/LIS 88, San Antonio, Texas, pp 475–483Google Scholar
  34. 34.
    Leung Y, Ma J (2004) A general framework for error analysis in measurement based GIS. Part 1: the basis measurement-error model and related concepts. J Geogr Syst 6(4):325–354CrossRefGoogle Scholar
  35. 35.
    Mustière S, Devogele T (2008) Matching networks with different levels of detail. GeoInformatica 12:435–453CrossRefGoogle Scholar
  36. 36.
    OS (2000) Information paper 4/2000. Positional accuracy of mapping data at 1:2500 scale. Resolving a problem in consultation with customers. Ordnance Survey GB, LondonGoogle Scholar
  37. 37.
    Perkal J (1966) On the length of empirical curves. Discussion paper 10, Ann Arbor, Michigan Inter-University Community of Mathematical GeographersGoogle Scholar
  38. 38.
    Shi W (1998) A generic statistical approach for modeling error of geometric features in GIS. Int J Geogr Inf Sci 12(2):131–143CrossRefGoogle Scholar
  39. 39.
    Shi W, Liu W (2001) A stochastic process-based model for the positional error of a line segments in GIS. Int J Geogr Inf Sci 14(1):51–66CrossRefGoogle Scholar
  40. 40.
    Skidmore A, Turner B (1992) Map accuracy assessment using line intersect sampling. Photogramm Eng Remote Sens 58(10):1453–1457Google Scholar
  41. 41.
    Tveite H, Langaas S (1995) Accuracy assessment of geographical line data sets, the case of the digital chart of the world. In Proceedings of the 5th Scandinavian Research Conference on Geographical Information Systems (ScanGIS’95)Google Scholar
  42. 42.
    Tveite H, Langaas S (1999) An accuracy assessment method for geographical line data sets based on buffering. Int J Geogr Inf Sci 13(1):27–47CrossRefGoogle Scholar
  43. 43.
    Van Niel T, McVicar TR (2002) Experimental evaluation of positional accuracy estimates from linear network using point and line based testing methods. Int J Geogr Inf Sci 16(5):455–473CrossRefGoogle Scholar
  44. 44.
    Veregin H (2000) Quantifying positional error induced by line simplification. Int J Geogr Inf Sci 14(2):113–130CrossRefGoogle Scholar
  45. 45.
    Wiedemann C (2003) External evaluation of road networks. In ISPRS Archives, vol. XXXIV, Part 3/W8, Munich, 17–19 Sept, 2003, pp 93–98Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Francisco Javier Ariza-López
    • 1
    Email author
  • Antonio Tomás Mozas-Calvache
    • 2
  1. 1.Grupo de Investigación Ingeniería CartográficaJaénSpain
  2. 2.Grupo de Investigación Sistemas Fotogramétricos y TopométricosJaénSpain

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