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Accuracy assessment of planimetric large-scale map data for decision-making

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Języki publikacji
EN
Abstrakty
EN
This paper presents decision-making risk estimation based on planimetric large-scale map data, which are data sets or databases which are useful for creating planimetric maps on scales of 1:5,000 or larger. The studies were conducted on four data sets of large-scale map data. Errors of map data were used for a risk assessment of decision-making about the localization of objects, e.g. for land-use planning in realization of investments. An analysis was performed for a large statistical sample set of shift vectors of control points, which were identified with the position errors of these points (errors of map data). In this paper, empirical cumulative distribution function models for decision-making risk assessment were established. The established models of the empirical cumulative distribution functions of shift vectors of control points involve polynomial equations. An evaluation of the compatibility degree of the polynomial with empirical data was stated by the convergence coefficient and by the indicator of the mean relative compatibility of model. The application of an empirical cumulative distribution function allows an estimation of the probability of the occurrence of position errors of points in a database. The estimated decision-making risk assessment is represented by the probability of the errors of points stored in the database.
Rocznik
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3--12
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
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autor
  • University of Warmia and Mazury in Olsztyn, The Faculty of Geodesy, Geospatial and Civil Engineering, Department of Land Surveying and Geomatics, 12 Heweliusza St. Olsztyn, Poland
Bibliografia
  • [1] Act, (2010). Polish Parliament, the Act of March 4, 2010: on Spatial Information Infrastructure. Warsaw, Journal of Laws on 2010 no. 76, item 489.
  • [2] Andrew, M. E., Wulder M. A., Nelson, T. A. and Coops, N. C. (2015). Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review. GIScience and Remote Sensing, 52, 344–373. DOI: http://dx.doi.org/10.1080/15481603.2015.1033809.
  • [3] Bank, E. (2004). Importance of open spatial data infrastructure for data sharing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXV-B4, 271–276.
  • [4] Bielecka, E. and Całka, B. (2012). The analysis of the land exclusions from agricultural and forest production in the rural areas. Proceedings of the Polish Academy of Science Infrastructure and Ecology of Rural Areas, No. 2/III/2012, 163–173.
  • [5] Burkholder, E. F. (2005). Geomatics curriculum design issues. Surveying and Land Information Science, Vol. 65, No. 3, 151–157.
  • [6] Dąbrowski, W. and Doskocz, A. (2008). Estimation of accuracy of the large-scale digital topographic map data. Proceedings Paper of 7th International Conference on Environmental Engineering, Vol. 1-3, 1293–1299.
  • [7] Directive. (2007). European Parliament and of the Council, Directive 2007/2/EC of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). Official Journal of the European Union L108 (50) from 25 April 2007.
  • [8] Doskocz, A. (2005). The use of statistical analysis for estimation of positional accuracy of large-scale digital maps. Geodesy and Cartography, Vol. 54, No 3, 131–150.
  • [9] Doskocz, A. (2013). Methodology for assessing the accuracy of digital large-scale maps. Dissertations and Monographs 193, University of Warmia and Mazury in Olsztyn, Poland.
  • [10] Doskocz, A. (2014a). About accuracy of analytical determination of areas for cadastre and other purposes. Proceedings Paper of 9th International Conference on Environmental Engineering, Vol. II, 673–680. DOI: http://dx.doi.org/10.3846/enviro.2014.203.
  • [11] Doskocz, A. (2014b). Robust assessment of planimetric accuracy of large-scale map data. Unpublished manuscript.
  • [12] Doskocz, A. (2015). The current state of the creation and modernization of national geodetic and cartographic resources in Poland. Unpublished manuscript.
  • [13] Gładysz, B. and Mercik, J. (2007). Econometric modeling. Case study. Publishing House of Wrocław University of Technology.
  • [14] Guryev, E. S., Poluyan, L. V. and Timashev, S. A. (2014). Construction of dynamic risk maps for large metropolitan areas. Journal of Risk Analysis and Crisis Response, Vol. 4, No. 2, 72–76.
  • [15] Harding, J.L. (2013). Data quality in the integration and analysis of data from multiple sources: some research challenges. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XL-2/W1, 59–63.
  • [16] Hejmanowska, B. (2006). Influence of data quality on modeling of flood zones. Annals of Geomatics, Vol. 4, No 1, 145–150.
  • [17] ISO. (2004). Guide to the expression of uncertainty in measurement – Supplement 1: Numerical methods for the propagation of distributions. International Organization for Standardization.
  • [18] Lewandowicz, E. (2002). The role of cadastre in government information structure. Proceedings of the Wrocław University of Environmental and Life Sciences, No. 452, 223–228.
  • [19] LGA. (2014). Available online at: http://www.ensg.eu/Laboratory-for-Research-in-Applied-Geomatics.
  • [20] NSSDA. (1999). Positional accuracy handbook. Using the National Standard for Spatial Data Accuracy to measure and report geographic data quality. Available online at: http://www.mnplan.state.mn.us/pdf/1999/lmic/nssda_o.pdf.
  • [21] Pita, G.L., Francis, R., Liu, Y., Mitrani-Reiser, J., Guikema S. and Pinelli, J. P. (2011). Statistical tools for populating/predicting input data of risk analysis models. In: B. Ayyub (ed), Vulnerability, Uncertainty, and Risk: Analysis, Modeling, and Management, ASCE, 468–476.
  • [22] Pradhan, B., Mansor S., Pirasteh S. & Buchroithner M. F. (2011). Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. International Journal of Remote Sensing, Vol. 32, No. 14, 4075–4087.
  • [23] Shokin, Y. I., Moskvichev, V. V. and Nicheporchuk, V. V. (2011). Method of assessment of human-induced area risk and creation of risk map using geoinformation systems. In: B. Ayyub (ed), Vulnerability, Uncertainty, and Risk: Analysis, Modeling, and Management, ASCE, 442–449.
  • [24] Siegrist, J. (2011). Mixing good data with bad: how to do it and when you should not. In: B. Ayyub (ed), Vulnerability, Uncertainty, and Risk: Analysis, Modeling, and Management, ASCE, 368–373.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-a1735f04-b488-4dc5-8e3f-bdcee81abb7b
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