<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Background Determining the location, boundaries and areas of land properties accurately in the land cadastre is essential. The named data are provided using coordinates, acquired from field measurements. Since 2008, the Slovenian land cadastre claims positioning in the national realization of the ETRS89, so the GNSS use is practically indispensable. Objectives: Contrary to real-time, we can change parameters in GNSS post-processing. The aim of this paper is to simulate different measurement conditions for GNSS in order to determine how to acquire the best possible coordinates for further use in land area calculation. Methods/Approach: Simulations of obstacles near points followed the increasing of the cut-off angle. Furthermore, shortening the observation interval resulted in different occupation duration. The final condition evaluation for coordinate quality acquisition followed from fuzzy logic. Results: The results show that for short baselines, occupation duration is the most important factor in acquiring high quality coordinates and avoiding the multipath. Differences in coordinates from specific strategies can sometimes exceed the tolerance and evidently affect the area calculation. Conclusions: The findings confirm that only good measurement conditions lead to high quality coordinates and well-defined areas of land properties, which are the fundamental factor in relation to the issues of property valuation and assessing land taxes or rents.</p></abstract>

Background Determining the location, boundaries and areas of land properties accurately in the land cadastre is essential. The named data are provided using coordinates, acquired from field measurements. Since 2008, the Slovenian land cadastre claims positioning in the national realization of the ETRS89, so the GNSS use is practically indispensable. Objectives: Contrary to real-time, we can change parameters in GNSS post-processing. The aim of this paper is to simulate different measurement conditions for GNSS in order to determine how to acquire the best possible coordinates for further use in land area calculation. Methods/Approach: Simulations of obstacles near points followed the increasing of the cut-off angle. Furthermore, shortening the observation interval resulted in different occupation duration. The final condition evaluation for coordinate quality acquisition followed from fuzzy logic. Results: The results show that for short baselines, occupation duration is the most important factor in acquiring high quality coordinates and avoiding the multipath. Differences in coordinates from specific strategies can sometimes exceed the tolerance and evidently affect the area calculation. Conclusions: The findings confirm that only good measurement conditions lead to high quality coordinates and well-defined areas of land properties, which are the fundamental factor in relation to the issues of property valuation and assessing land taxes or rents.

Editorial for the special issue: “Novel Solutions or Novel Approaches in Operational Research”

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Background Determining the location, boundaries and areas of land properties accurately in the land cadastre is essential. The named data are provided using...

Editorial for the special issue: “Novel Solutions or Novel Approaches in Operational Research”

Publié en ligne: 28 juil. 2018

Pages: 1 - 7

DOI: https://doi.org/10.2478/bsrj-2018-0014

Mots clés
interdisciplinary research, operations research, risk and uncertainty, autoregressive models, machine learning, clustering comparison measure, multicriteria decision making, big data, location-allocation, fuzzy logic, hidden economy

© 2018 Samo Drobne, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Editorial for the special issue: “Novel Solutions or Novel Approaches in Operational Research”

Publié en ligne: 28 juil. 2018

Pages: 1 - 7

DOI: https://doi.org/10.2478/bsrj-2018-0014

Mots clésinterdisciplinary research, operations research, risk and uncertainty, autoregressive models, machine learning, clustering comparison measure, multicriteria decision making, big data, location-allocation, fuzzy logic, hidden economy

© 2018 Samo Drobne, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
interdisciplinary research, operations research, risk and uncertainty, autoregressive models, machine learning, clustering comparison measure, multicriteria decision making, big data, location-allocation, fuzzy logic, hidden economy