Acceptance Curves for the Positional Control of Geographic Databases
Publication: Journal of Surveying Engineering
Volume 134, Issue 1
Abstract
A simulation process is implemented in order to generate synthetic populations of positional-planimetric errors and later, by a bootstrap process, a family of acceptance curves (nomogram) has been derived. These curves allow us to learn the acceptance level, or pass level, of a geographic database when a positional control test based on bias and on variability is applied to it against a given standard. Acceptance curves allow us to report user’s risk and to determine the adequate size of the sample in order to reduce this risk to a desired level.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work has been partially funded by the National Ministry of Sciences and Technology of the Kingdom of Spain under Grant No. BIA2003-02234.
References
American Society of Photogrammetry and Remote Sensing (ASPRS). (1989). “Accuracy standards for large scale maps.” Photogramm. Eng. Remote Sens., 56(7), 1068–1070.
Ariza, F. J. (2002). Control de calidad en la producción cartográfica, Ra-Ma, Madrid, Spain.
ASCE. (1983). Map uses, scales and accuracies for engineering and associated purposes, Committee on Cartographic Surveying, Surveying and Mapping Division, New York.
Asociación Española para la calidad (AEC). (1990). Técnicas de control de calidad, Madrid, Spain.
Atkinson, A. (2005). “Control de calidad posicional en cartografía: análisis de los principales estándares y propuesta de mejora.” Thesis doctoral, Univ. de Jaén, Jaén, Spain.
Besterfield, D. (1994). Control de calidad, Prentice-Hall, Mexico, Mexico.
Bonferroni, C. E. (1935). “Il calcolo delle assicurazioni su gruppi di teste.” Studi in Onore del Professore Salvatore Ortu Carboni. Rome, 13–60.
Box, G., and Muller, M. (1958). “A note on the generation of random normal derivates.” Ann. Math. Stat., 29(2), 610–611.
Caridad, J. M. (1985). Calculo de probabilidades y análisis de datos, Servicio de Publicaciones de la Universidad de Córdoba, Córdoba, Spain.
Carmel, Y., Dean, D., and Flather, C. (2001). “Combining location and classification error sources for estimating multi-temporal data base accuracy.” Photogramm. Eng. Remote Sens., 67(7), 865–872.
Carmel, Y., Flather, C., and Dean, D. (2006). “A methodology for translating positional error into measures of attribute error, and combining the two error sources.” Proc., Accuracy 2006, 7th Int. Symp. on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, M. Caetano and M. Painho, eds., Lisbon, 3–17.
Caspary, W., and Joos, G. (2002). “Statistical quality control of geodata.” Spatial data quality, W. Shi, P. Fisher, and M. F. Goodchid, eds., Taylor and Francis, London, 106–115.
Church, R., Curtin, K., Fohl, P., Funk, C., Goodchild, M., Kyriakidis, P., and Noronha, V. (1998). “Positional distortion in geographic data sets as a barrier to interoperation.” 1998 American Congress on Surveying and Mapping Annual Conf. Technical Papers, Baltimore, 377–387.
Deming, W. E. (1986). Out of the crisis, Massachusetts Institute of Technology, Cambridge, Mass.
Department of Defense (DOD). (1990). MIL STD 60001: Mapping, charting and geodesy accuracy, Washington, D.C.
Federal Geographic Data Committee (FGDC). (1998). FGDC-STD-007: Geospatial positioning accuracy standards, Part 3. National standard for spatial data accuracy, Reston, Va.
Goodchild, M. F., and Gopal, S. (1989). Accuracy of spatial data bases, Taylor & Francis, London.
Gustafson, G. C., and Loon, J. C. (1982). “Contour accuracy and the National Map Accuracy Standards.” J. Surv. and Mapping Div., 42(4), 385–402.
Hansen, B. L., and Ghare, P. M. (1990). Control de calidad. Teoría y aplicaciones, Díaz de Santos, Madrid, Spain.
Hines, W. W., and Montgomery, D. C. (1990). Probability and statistics in engineering and management science, Wiley, New York.
International Organization for Standardization (ISO). (1999). “Sampling procedures for inspection by attributes. Part 1: Sampling schemes indexed by acceptance quality limit (AQL) for lot-by-lot inspection.” ISO 2859-1, Geneva Switzerland.
International Organization for Standardization (ISO). (2000). “Quality management systems—Requirements.” ISO 9001, Geneva, Switzerland.
International Organization for Standardization (ISO). (2002). “Geographic information—Quality principles.” ISO 19113, Geneva, Switzerland.
International Organization for Standardization (ISO). (2005). “Sampling procedures for inspection by variables. Part 1: Specification for single sampling plans indexed by acceptance quality limit (AQL) for lot-by-lot inspection for a single quality characteristic and a single AQL.” ISO 3951-1, Geneva, Switzerland.
Jakobsson, A., and Vauglin, F. (2002). Report of a Questionnaire on Data Quality in National Mapping Agencies. CERCO Working Group on Quality, Comite Europeen de Responsibles da la Cartographie Officielle, Marne-la-Vallée, France.
Juran, J. M., and Gryna, R. M. (1970). Quality planning and analysis; from product development through usage, McGraw-Hill, New York.
Krek, A., and Frank, A. U. (1999). “Optimization of quality of geoinformation products.” Proc., 11th Annual Colloquium of the Spatial Information Research Centre, SIRC’99, P. A. Whigham, ed., Dunedin, New Zealand, 151–159.
Leung, Y., and Yan, J. (1998). “A locational error model for spatial features.” Int. J. Geograph. Inf. Sci., 12(6), 607–620.
Li, Z. (1991). “Effects of check points on the reliability of DTM accuracy estimates obtained from experimental test.” Photogramm. Eng. Remote Sens., 57(10), 1333–1340.
McGwire, K. C. (1996). “Cross-validated assessment of geometric accuracy.” Photogramm. Eng. Remote Sens., 62(10), 1179–1187.
Mikhail, E. M. (1976). Observations and least squares, IEP-DunDonnely, New York.
Miller, R. G. (1991). Simultaneous statistical inference, Springer, New York.
Montgomery, D. C. (2001). Introduction to statistical quality control, 4th Ed., Wiley, New York.
Montgomery, D. C., and Runger, G. C. (1999). Applied statistics and probability for engineers, Wiley, New York.
Morrison, J. (1995). “Spatial data quality.” Elements of spatial data quality, S. C. Guptill and J. L. Morrison, eds., International Cartographic Association, Pergamon, Oxford, U.K., 1–12.
Pyzdek, T. (1989). What every engineer should know about quality control, ASQC-Quality, New York.
Schmidley, R. W. (1997). “Quality control in mapping: Some fundamental concepts.” Surv. Land Inf. Sys., 57(1), 31–36.
Sebastián, M. A., Bargueño, V., and Novo, V. (1999). Gestión y control de calidad, Univ. Nacional de Educación a Distancia, Madrid, Spain.
Sevilla, M. J. (1991). “Criterios de precisión cartográfica.” Catastro, 3(8), 12–20.
Shewhart, W. A. (1931). Economic control of quality manufactured product, Von Nostrand, New York.
Shi, W. (1998). “A generic statistical approach for modeling error of geometric features in GIS.” Int. J. Geograph. Inf. Sci., 12(2), 131–143.
Shi, W., and Liu, W. (2000). “A stochastic process-based model for the positional error of a line segments in GIS.” Int. J. Geograph. Inf. Sci., 14(1), 51–66.
Simley, J. (2001). “Improving the quality of mass produced maps.” Cartogr. Geogr. Inf. Sci., 28(2), 97–110.
Thompson, M. M., and Rosenfield, G. H. (1971). “On map accuracy specifications.” J. Surv. and Mapping Div., 31(1), 57–64.
U.S. Bureau of the Budget (USBB). (1947). United States National Map Accuracy Standards, Washington, D.C.
Information & Authors
Information
Published In
Copyright
© 2008 ASCE.
History
Received: Oct 24, 2006
Accepted: Apr 30, 2007
Published online: Feb 1, 2008
Published in print: Feb 2008
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.