Geographic support of decision-making processes is based on various geographic products, usually in digital form, which come from various foundations and sources. Each product can be characterized by its quality or by its utility value for the given type of task or group of tasks, for which the product is used. They also usually have different characteristics and thus can very significantly influence the resulting analytical material. The aim of the paper is to contribute to the solution of the question of how it is possible to work with diverse spatial geographic information so that the user has an idea about the resulting product. The concept of fuzzy sets is used for representation of classes, whose boundaries are not clearly (not sharply) set, namely, the fuzzy approach in overlaying operations realized in ESRI ArcGIS environment. The paper is based on a research project which is being solved at the Faculty of Military Technologies of the University of Defence. The research deals with the influence of geographic and climatic factors on the activity of armed forces and the Integrated Rescue System. 1. Introduction Geographic support for decision-making processes is based on various geographic products, usually in digital form, which come from various foundations and sources. Each product can be characterized by its quality [1, 2] or by its utility value for the given type of task or group of tasks, for which the product is used [3, 4]. In both cases, among others also positional and thematic accuracy are evaluated either as an exactly given value, for example, mean square position error, probable error, and so forth, or as a level of fulfilment of user’s requirements expressed in percentage [5]. Decision-making processes are based on multicriteria decision-making, in which many factors are involved [6]. By a suitable combination of various factors, analytical products are created. They are the base for answering questions such as “What happens if…?” That is the reason why various foundations are used for geographic support. They also usually have different characteristics and thus can very significantly influence the resulting analytical material. The aim of the paper is to contribute to the solution of the question of how it is possible to work with diverse spatial geographic information so that the user has an idea about the resulting product. The paper is based on a research project which is being solved at the Faculty of Military Technologies of the University of Defence. The research deals with the influence of geographic and climatic factors on
References
[1]
ISO, ISO 19113—Geographic Information—Quality Principles, International Organization for Standardization, 2002, http://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?csnumber=26018.
[2]
ISO, ISO 19138—Geographic Information—Data Quality Measures, Interational Organization for Standardization, 2006, http://www.iso.org/iso/catalogue_detail.htm?csnumber=32556.
[3]
V. Talhofer and A. Hofmann, “Possibilities of evaluation of digital geographic data quality and reliability,” in Proceedigs of the 24th International Cartographic Conference, the World's Geo-Spatial Solutions, pp. 1–11, ICA/ACI, Santiago de Chilie, Chile, 2009.
[4]
V. Talhofer, S. Hoskova-Mayerova, and A. Hofmann, “Improvement of digital geographic data quality,” International Journal of Production Research, vol. 50, no. 17, pp. 4846–4859, 2012.
[5]
V. Talhofer, S. Hoskova, V. Kratochvil, and A. Hofmann, “Geospatial data quality,” in International Conference on Military Technologies (ICMT '09), pp. 570–578, Univerzita obrany, Brno, Czech Republic, 2009.
[6]
V. Kova？ík, “Use of spatial modelling to select the helicopter landing sites,” Advances in Military Technology, vol. 8, no. 2, pp. 1–10, 2013.
[7]
J. D. Brown and G. B. M. Heuvelink, “The data uncertainty engine (DUE): a software tool for assessing and simulating uncertain environmental variables,” Computers & Geosciences, vol. 33, no. 2, pp. 172–190, 2007.
[8]
R. Abbaspour, R. Mahmoud, K. Delavar, and B. Reihaneh, “The issue of uncertainty propagation in spatial decision making,” in Proceedings of the 9th Scandinavian Research Conference on Geographical Information Science (ScanGIS '03), K. V. Tveite, Ed., pp. 57–65, Department of Surveying, Helsinki University of Technology, Espoo, Finland, 2003.
[9]
M. F. Goodchild, S. Guoqing, and Y. Shiren, “Development and test of an error model for categorical data,” International Journal of Geographical Information Systems, vol. 6, no. 2, pp. 87–104, 1992.
[10]
G. Hunter, Handling Uncertainty in Spatial Database [Ph.D. thesis], Department of Surveying and Land Information, University of Melbourne, Melbourne, Australia, 1993.
[11]
O. K？emenová, Fuzzy Modeling of Soil Maps, University of Technology, Helsinky, Finland, 2004.
[12]
P. Kubí？ek and ？. ？a？inka, “Thematic uncertainty visualization usability—comparison of basic methods,” Annals of GIS, vol. 17, no. 4, pp. 253–263, 2011.
[13]
NIST, Spatial Data Transfer Standard (FIPS 173), National Institute of Standards and Technology, US Department of Commerce, Washington, DC, USA, 1992.
[14]
P. D'Amico, F. Di Martino, and S. Sessa, “A GIS as a decision support system for planning sustainabel mobility in a case-study,” in Multicriteria and Multiagend Decision Making with Applications to Economics and Social Sciences, A. Ventre, A. Maturo, S. Hoskova-Mayerova, and J. Kacprzyk, Eds., Studies in Fuzziness and Soft Computing, pp. 115–128, Springer, Berlin, Germany, 2013.
[15]
F. De Felice and A. Petrillo, “Decision making analysis to improve public participation in stategic energy production management,” in Multicriteria and Multiagend Decision Making with Applications to Economics and Social Sciences, A. Ventre, A. Maturo, S. Hoskova-Mayerova, and J. Kacprzyk, Eds., Studies in Fuzziness and Soft Computing, pp. 129–142, Springer, Berlin, Germany, 2013.
[16]
N. Gershon, “Visualization of an imperfect world,” IEEE Computer Graphics and Applications, vol. 18, no. 4, pp. 43–45, 1998.
[17]
A. MacEachren, “Visualizing uncertain inforamtion,” Cartographic Perspectives, vol. 13, pp. 10–19, 1992.
[18]
J. Smith, D. Retchless, C. Kinkeldey, and A. Klippel, “Beyond the surface: current issues and future irections in uncertainty visualization research,” in Proceedigs of the 26th International Cartographic Conference, pp. 1–10, ICA, Dresden, Germany, 2013.
[19]
B. Ahmad and A. Kharal, “Fuzzy sets fuzzy s-open and s-closed mappings,” Advances in Fuzzy Systems, vol. 2009, Article ID 303042, 5 pages, 2009.
[20]
F. Di Martino, V. Loia, and S. Sessa, “Fuzzy transforms method in prediction data analysis,” Fuzzy Sets and Systems, vol. 180, no. 1, pp. 146–163, 2011.
[21]
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
[22]
F. Di Martino and S. Sessa, “Spatial analysis and fuzzy relation equations,” Advances in Fuzzy Systems, vol. 2011, Article ID 429498, 14 pages, 2011.
[23]
I. Cristea and S. Hoskova, “Fuzzy pseudotopological hypergroupoids,” Iranian Journal of Fuzzy Systems, vol. 6, no. 4, pp. 11–19, 2009.
M. Rybansky, Cross-Country Movement, the Imapct and Evaluation of Geographic Factors, Akademické nakladatelství CERM, s.r.o. Brno, Brno, Czech Republic, 1st edition, 2009.
[26]
M. Rybansky and M. Vala, “Relief impact on transport,” in International Conference on Military Technologies (ICMT '09), pp. 551–559, University of Defence, Brno, Czech Republic, 2010.
[27]
MoD-GeoS, Catalogue of the Topographic Objects DMU25, Ministry of Defence of the Czech Republic, Geographic Service, Dobruska, Czech Republic, 7.3 edition, 2010.
[28]
Tatra, Tatra is the solution. (Tatra, a.s.), TATRA, 2010, http://partners.tatra.cz/exter_pr/vp/new/typovy_listprospekt.asp?kod=341&jazyk=CZ.
