Geomatics and Environmental Engineering, vol. 18, no. 4

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Vol. 18 No. 4 (2024): Geomatics and Environmental Engineering

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Submitted
May 6, 2024
Accepted
Jul 22, 2024
Published
Sep 11, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Weight Impact on Comparative Evaluation of Topographic Data

https://doi.org/10.7494/geom.2024.18.4.97
Sylwia Borkowska
Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geospatial Engineering and Geodesy
https://orcid.org/0000-0003-3183-1512
Elzbieta Bielecka
Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geospatial Engineering and Geodesy
https://orcid.org/0000-0003-3255-1264
Krzysztof Pokonieczny
Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geospatial Engineering and Geodesy
https://orcid.org/0000-0001-9114-5317

Corresponding Author(s) : Sylwia Borkowska

sylwia.borkowska@wat.edu.pl

Geomatics and Environmental Engineering, Vol. 18 No. 4 (2024): Geomatics and Environmental Engineering

Abstract

The paper addresses the problem of weighting in an analysis that supports the selection of a categorical data set according to user needs. Using the Relative Change (RC) of the Compound Correspondence Index (CCI), it is shown that weights have a significant impact on user choice – reaching extreme values in both urbanized and forested areas. Decreasing the weights from 0.25 to 0.17 in forested and built-up areas resulted in the maximum variations that were seen in the hot spot maps, with cold areas generally corresponding to builtup regions and hot areas to forested areas. The analysis covers seven counties that are located in different regions of Poland: Pomerania, Podlasie, Mazovia, Greater Poland and the Beskidy Mountains.

Keywords

quantitative data OSM sensitivity analysis MCDA TOPSIS
Borkowska, S., Bielecka, E., & Pokonieczny, K. (2024). Weight Impact on Comparative Evaluation of Topographic Data. Geomatics and Environmental Engineering, 18(4), 97–116. https://doi.org/10.7494/geom.2024.18.4.97
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References
  1. Borkowska S., Bielecka E., Pokonieczny K.: Comparison of land cover categorical data stored in OSM and authoritative topographic data. Applied Sciences, vol. 13(13), 2023, 7525. https://doi.org/10.3390/app13137525.
  2. Aksoy E., San B.T.: Geographical information systems (GIS) and multi-criteria decision analysis (MCDA) integration for sustainable landfill site selection considering dynamic data source. Bulletin of Engineering Geology and the Environment, vol. 78(4), 2019, pp. 779–791. https://doi.org/10.1007/s10064-017-1135-z.
  3. Anthoff D., Tol R.S.J.: On international equity weights and national decision making on climate change. Journal of Environmental Economics and Management, vol. 60(1), 2010, pp. 14–20. https://doi.org/10.1016/j.jeem.2010.04.002.
  4. Li P., Qian H., Wu J., Chen J.: Sensitivity analysis of TOPSIS method in water quality assessment. Sensitivity to the parameter weights. Environmental Monitoring and Assessment, vol. 185(3), 2013, pp. 2453–2461. https://doi.org/10.1007/s10661-012-2723-9.
  5. Odu G.O.: Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management, vol. 23(8), 2019, pp. 1449–1457. https://www.ajol.info/index.php/jasem.
  6. Roszkowska E.: Rank ordering criteria weighting methods – a comparative overview. Optimum. Studia Ekonomiczne, vol. 5(65), 2013, pp. 14–33. https://doi.org/10.15290/ose.2013.05.65.02.
  7. Borkowska S., Bielecka E., Pokonieczny K.: OpenStreetMap – building data completeness visualization in terms of ‘Fitness for purpose’. Advances in Geodesy and Geoinformation, vol. 72(1), 2023, pp. 2–20. https://doi.org/10.24425/agg.2022.141922.
  8. Borkowska S., Pokonieczny K.: Analysis of OpenStreetMap data quality for selected counties in Poland in terms of sustainable development. Sustainability, vol. 14(7), 2022, 3728. https://doi.org/10.3390/su14073728.
  9. Behzadian M., Khanmohammadi Otaghsara S., Yazdani M., Ignatius J.: A stateof the-art survey of TOPSIS applications. Expert Systems with Applications, vol. 39(17), 2012, pp. 13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056.
  10. Zyoud S.H., Fuchs-Hanusch D.: A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, vol. 78, 2017, pp. 158–181. https://doi.org/10.1016/j.eswa.2017.02.016.
  11. Çelikbilek Y., Tüysüz F.: An in-depth review of theory of the TOPSIS method: An experimental analysis. Journal of Management Analytics, vol. 7(2), 2020, pp. 281–300. https://doi.org/10.1080/23270012.2020.1748528.
  12. Wang Z.X., Wang Y.Y.: Evaluation of the provincial competitiveness of the Chinese high-tech industry using an improved TOPSIS method. Expert Systems with Applications, vol. 41(6), 2014, pp. 2824–2831. https://doi.org/10.1016/j.eswa.2013.10.015.
  13. Kusumadewi S., Hartati S.: Sensitivity analysis of multi-attribute decision making methods in Clinical Group Decision Support System. [in:] 2007 International Conference on Intelligent and Advanced Systems: Kuala Lumpur, Malysia: 25–28 November 2007, IEEE, pp. 301–304. https://doi.org/10.1109/ICIAS.2007.4658395.
  14. Dalalah D., Hayajneh M., Batieha F.: A fuzzy multi-criteria decision making model for supplier selection. Expert Systems with Applications. 2011, vol. 38(7), pp. 8384–8391. https://doi.org/10.1016/j.eswa.2011.01.031.
  15. Choo E.U., Schoner B., Wedley W.C.: Interpretation of criteria weights in multicriteria decision making. Computers & Industrial Engineering, vol. 37(3), 1999, pp. 527–541. https://doi.org/10.1016/S0360-8352(00)00019-X.
  16. Bączkiewicz A., Wątróbski J., Kizielewicz B., Sałabun W.: Towards objectification of multi-criteria assessments: A comparative study on MCDA methods. [in:] Ganzha M., Maciaszek L., Paprzycki M., Ślęzak D. (eds.), Proceedings of the 16th Conference on Computer Science and Intelligence Systems: September 2–5, 2021, Annals of Computer and Information Systems, vol. 25, IEEE, pp. 417–425. https://doi.org/10.15439/2021F61.
  17. Kobryń A., Prystrom J.: A data pre-processing model for the TOPSIS method. Folia Oeconomica Stetinensia, vol. 16(2), 2016, pp. 219–235. https://doi.org/10.1515/foli-2016-0036.
  18. Pavić Z., Novoselac V.: Notes on TOPSIS Method. International Journal of Engineering Research and General Science, vol. 1(2), 2013, pp. 5–12.
  19. Więckowski J., Zwiech P.: Can weighting methods provide similar results in MCDA problems? Selection of energetic materials study case. Procedia Computer Science, vol. 192, 2021, pp. 4592–4601. https://doi.org/10.1016/j.procs.2021.09.237.
  20. Chen Y., Yu J., Khan S.: Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling & Software, vol. 25, 2010, pp. 1582–1591. https://doi.org/10.1016/j.envsoft.2010.06.001.
  21. Al-Mashreki M. H., Akhir J.B.M., Abd Rahim S., Tukimat L., Haider A.R.: GIS-based sensitivity analysis of multi-criteria weights for land suitability evaluation of sorghum crop in the Ibb Governorate, Republic of Yemen. Journal of Basic and Applied Scientific Research, vol. 1(9), 2011, pp. 1102–1111.
  22. Liern V., Pérez-Gladish B.: Multiple criteria ranking method based on functional proximity index: Un-weighted TOPSIS. Annals of Operations Research, vol. 311, 2022, pp. 1099–1121. https://doi.org/10.1007/s10479-020-03718-1.
  23. Dawid W., Pokonieczny K., Wyszyński M.: The methodology of determining optimum access routes to remote areas for the purposes of crisis management. International Journal of Digital Earth, vol. 15(1), 2022, pp. 1905–1928. https://doi.org/10.1080/17538947.2022.2134936
  24. Getis A., Ord K.: The analysis of spatial association by use of distance statistics. Geographical Analysis, vol. 24, 1992, pp. 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x.
  25. Ord K., Getis A.: Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, vol. 27(4), 2010, pp. 286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x.
  26. Jimenez J.: Fitness for purpose in relation to specification limits. Accreditation and Quality Assurance, vol. 17(1), 2012, pp. 27–34. https://doi.org/10.1007/s00769-011-0825-7.
  27. Sheng J., Wilson J.P., Chen N., Devinny J.S., Sayre J.M.: Evaluating the Quality of the National Hydrography Dataset for Watershed Assessments in Metropolitan Regions. GIScience & Remote Sensing, vol. 44(3), 2007, pp. 283–304. https://doi.org/10.2747/1548-1603.44.3.283.
  28. Bielecka E., Jenerowicz A.: Intellectual structure of CORINE land cover research applications in web of science: A Europe-wide review. Remote Sensing, vol. 11(17), 2019, 2017. https://doi.org/10.3390/rs11172017.
  29. Bielecka E.: Geographical data sets fitness of use evaluation. Geodetski Vestnik, vol. 59(2), 2016, pp. 335–348. https://doi.org/10.15292/geodetski-vestnik.2015.02.335-348.
  30. Bac-Bronowicz J., Dygaszewicz J., Grzempowski P., Nowak R.: Bazy danych referencyjnych jako źródła zasilania i aktualizacji warstw dotyczących budynków w Wielorozdzielczej Topograficznej Bazie Danych. Roczniki Geomatyki, t. 8, z. 5(41), 2010, pp. 7–22.
  31. Biljecki F., Chow Y. S., Lee K.: Quality of crowdsourced geospatial building information: A global assessment of OpenStreetMap attributes. Building and Environment, vol. 237, 2023, 110295. https://doi.org/10.1016/j.buildenv.2023.110295.
  32. Marczak S.: Ocena zaangażowania społeczeństwa w tworzenie danych przestrzennych w Polsce na przykładzie projektu OpenStreetMap. Roczniki Geomatyki, t. 13, z. 3(69), 2015, pp. 239–253.
  33. Chakravorty S.: Identifying crime clusters: The spatial principles. Middle States Geographer, vol. 28, 1995, pp. 53–58.
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