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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