Un método estadístico general para el control posicional de datos espaciales

  1. Ariza López, Francisco Javier
  2. Rodríguez Avi, José
Revista:
Geofocus: Revista Internacional de Ciencia y Tecnología de la Información Geográfica

ISSN: 1578-5157

Año de publicación: 2014

Número: 14

Tipo: Artículo

Otras publicaciones en: Geofocus: Revista Internacional de Ciencia y Tecnología de la Información Geográfica

Resumen

Se presenta un método de control de la calidad posicional adecuado a cualquier geometría y dimensión. Se aplica un contraste estadístico de hipótesis y, para ello, se propone el uso de dos modelos estadísticos: un modelo binomial (BiM) y un modelo de base (BaM). El BaM representa la hipótesis del comportamiento del error en la población, que puede ser una función de distribución paramétrica o con un modelo de distribución libre. El BiM se aplica sobre el BaM y consiste en contar el número F de eventos que superan una tolerancia. El parámetro  del BiM se deriva del BaM por medio de la tolerancia deseada. Mediante la comparación de las probabilidades asociadas a F y  se decide la aceptación/rechazo. Este método permite conocer los riesgos de usuario y productor. Se presentan varios ejemplos y, con el fin de facilitar su aplicación, se incluyen tablas que relacionan  con F y diversos los tamaños de las muestras de control.

Referencias bibliográficas

  • Abbas, L.; Grussenmeyer, P. y Hottier, P. (1995): "Contrôle de la planimétrie d´une base de données vectorielles: une nouvelle méthode basée sur la distance de Hausdorff: la méthode du contrôle linéaire", Bul. Societé Française de Photogrammétrie et Télédétection 137, pp. 6-11.
  • AEC (1990): Técnicas de control de calidad. Asociación Española para la Calidad, Madrid.
  • Ariza López, F.J.; Atkinson-Gordo, A.D.; Rodríguez Avi, J. y García-Balboa, J.L. (2010): "Analysis of user and producer risk when applying the ASPRS Standards for Large Scale Maps", Photogrammetric Engineering and Remote Sensing, 76(5), pp. 625-632.
  • Ariza López, F.J. y Atkinson-Gordo, A.D.J. (2008): "Analysis of some positional accuracy assessment methodologies", Journal of Surveying Engineering 134 (2), pp. 404-407.
  • Ariza López, F.J.; Atkinson-Gordo, A.D.J. y Rodríguez Avi, J. (2008): "Acceptance curves for the positional control of geographic data bases", Journal of Surveying Engineering 134 (1), pp. 26-32.
  • Ariza López, F.J. y Mozas Calvache, A.T. (2012): "Comparison of four line-based positional assessment methods by means of synthetic data", J GeoInformatica, 16(2), pp. 221-243.
  • Ariza López, F.J.; Mozas Calvache, A.T.; Ureña Cámara, M.A.; Alba Fernández, V.; García Balboa, J.L.; Rodríguez Avi, J. y Ruiz-Lendínez, J.J. (2011): "Sample size influence on line-based positional assessment methods for road data", ISPRS Journal of Photogrammetry and Remote Sensing, 66(5), pp. 708–719.
  • Ariza López, F.J. y Rodríguez Avi, J. (2014): "A statistical model inspired by the National Map Accuracy Standard", Photogrammetric Engineering & Remote Sensing, doi: 10.14358/PERS.80.3.000
  • 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 York, USA.
  • ASPRS (1990): “Accuracy standards for large scale maps”, Photogrammetric Engineering and Remote Sensing 56(7), pp. 1068-1070.
  • Cai, H. B. y Rasdorf, W. (2009): "Accuracy evaluation and sensitivity analysis of estimating 3D road centerline length using LIDAR and NED", Photogrammetric Engineering and Remote Sensing, 75(6): 657-665.
  • Carmel, Y.; Flather C. y Dean, D. (2006): "A methodology for translating positional error into measures of attribute error, and combining the two error sources", Proceedings of Accuracy 2006. 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Lisbon, pp. 3-17.
  • Cuenin, R. (1972): Cartographie générale, Eyrrolles, Paris, 1972.
  • Chrisman, N.R. (1982): "A theory of cartographic error and its measurement in digital data bases", Proc. AutoCarto 5, Crystal City, Virginia, USA, pp. 159-168.
  • Church, R.; Curtin, K.; Fohl, P.; Funk, C.; Goodchild, M.; Kyriakidis, P. y Noronha, V. (1998): Positional distortion in geographic data sets as a barrier to interoperation. Technical Papers ACSM. American Congress on Surveying and Mapping. Bethesda, Maryland.
  • Dewberry, L.LC. (2004): Worcester County LIDAR 2002 Quality Assurance Report. Maryland Department of natural Resources.
  • DOD (1990): MIL STD 60001: Mapping, charting and geodesy accuracy. U.S. Department of Defense. Washington, D.C.
  • Dutton, G.H. (1999): "Scale, sinuosity and point selection in digital line generalization", Cartography and Geographic Information Science 26 (1), pp. 33-53.
  • Elberink, S.O. y Vosselman, G. (2011): "Quality analysis on 3D building models reconstructed from airborne laser scanning data", ISPRS Journal of Photogrammetry and Remote Sensing, 66 (2), pp. 157-165,
  • FGDC (1998): FGDC-STD-007: Geospatial Positioning Accuracy Standards, Part 3. National Standard for Spatial Data Accuracy, Federal Geographic Data Committee, Reston, USA.
  • Goodchild, M.F. y Hunter, G. (1997): "A simple positional accuracy measure for linear features", International Journal of Geographical Information Science, 11 (3), pp. 299-206.
  • ISO (2013): ISO 19157: Geographic information - Data quality. International Organization for Standardization. Geneva.
  • Joao, E.M. (1998): Causes and consequences of map generalisation, Taylor & Francis, London, UK.
  • Johnsosn, N.L.; Kemp, A.W. y Kotz. S. (2005): Univariate discrete distributions. Wiley.
  • Keefer, B.J.; Smith, J.L. y Gregoire, T.G. (1988): "Simulating manual digitizing error with statistical models", Proc. of GIS/LIS 88, San Antonio, Texas, pp. 475-483.
  • Li, R.; Niu, X.; Liu, C. y Wu, B. (2009): "Impact of Imaging Geometry on 3D Geopositioning Accuracy of Stereo IKONOS Imagery", Photogrammetric Engineering and Remote Sensing, 75(9), pp. 1119-1125.
  • Montgomery, D.C. (2001): Introduction to statistical quality control. 4th Ed. John Wiley & Sons, New York.
  • Sargent, I.; Harding, J. y Freeman, M. (2007): "Data quality in 3D: Gauging quality measures from users’ requirements", Proceedings of 5th International Symposium on Spatial Data Quality 2007. ITC, Enschede, The Netherlands.
  • Skidmore, A. y Turner, B. (1992): "Map accuracy assessment using line intersect sampling", Photogrammetric Engineering and Remote Sensing 58 (10), pp. 1453-1457.
  • STANAG (2002): Standardization Agreement 2215: Evaluation of land maps, aeronautical charts and digital topographic data. North Atlantic Treaty Organization. Bruxelles, Belgium.
  • Tveite, H. y Langaas, S. (1999): "An accuracy assessment method for geographical line data sets based on buffering"; International Journal of Geographical Information Science 13 (1), pp. 27-47.
  • USBB (1947): United States National Map Accuracy Standards. U.S. Bureau of the Budget, Washington, D.C.
  • Zandbergen, P.A. (2008): "Positional accuracy of spatial data: non-normal distributions and a critique of the National Standard for Spatial Data Accuracy", Transactions in GIS 12(1), pp. 103-130.