Abstract
Geographic information systems have usually implemented a module that allows areas to be delimited by an isotropic buffer. In this kind of buffer, the generating polygon is a circle, which implies a constant distance from the border of the buffer to the object. The simulation of anisotropic processes, in contrast, requires the use of generating polygons which determine directionally non-uniform areas of influence. In this paper, a review of how some of the commercial GISs address the problem of anisotropy is presented, concluding that commercial GIS have modules that allow to perform spatial analysis. However, the anisotropic analysis tools are not sufficiently developed, being restricted to implementations based on the study of distance costs. In addition, we proposed a method to check the anisotropy and the creation of a generator oval that allows to delimit zones of influence that represent phenomena with anisotropic characteristics.
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References
Bailey, T.C., Gatrell, A.C.: A Review of: Interactive Spatial Data Analysis. Longman, Harlow (2007)
Black, M., Ebener, S., Najera, P., Viaurre, M., El Morjani, Z.: Using GIS to measure physical accessibility to health care. In: Proceedings of the International Health Users Conference (2004)
Boost, B.: Boost C++ Libraries (2015). http://www.boost.org. Accessed 17 Feb 2017
Casciola, G., Montefuscoa, L.B., Morigi, S.: The regularizing properties of anisotropic radial basis functions. Appl. Math. Comput. 190(2), 1050–1062 (2007)
Chou, Y.H.: Exploring Spatial Analysis in GIS. Onward Press, Santa Fe (1997)
Cressie N.: Statistics for Spatial Data. Wiley, New York (1993)
Esri. The Gis software leader (2015). http://www.esri.com. Accessed 17 Feb 2017
Getis A. Homogeneity and Proximal Databases in Spatial Analysis and GIS. Taylor & Francis, London (1994)
Grass GIS (2015). https://grass.osgeo.org/documentation/manuals/. Accessed 17 Feb 2017
Mardia, K., Jupp, P.: Directional Statistics. Wiley, New York (1999)
Martinez, J.: Introducción a la lógica virtual (2008) (Spanish). https://composicionarqdatos.files.wordpress.com/2008/09/introduccion-a-la-logica-visual_juan-martinez-val.pdf. Accessed 17 Feb 2017
Mata, A., Muñoz, M.D., Corchado, E., Corchado, J.M.: Isotropic Image Analysis for improving CBR forecasting. J. Math. Imaging Vis. 42, 212–224 (2012)
Molina, A., Feito, F.R.: A method for testing anisotropy and quantifying its direction in digital images. Comput. Graph. 26, 771–784 (2002)
Molina, A., Muñoz, M.D.: Simulation and visualization of anisotropic expansion phenomena. In: 5th Agile Conference on Geographical Information Science (2002)
Muñoz, M.D., Mata, A., Corchado, E., Corchado, J.M.: (OBIS) isotropic image analysis for improving a predicting agent based systems. Expert Syst. Appl. 40, 5011–5020 (2013)
Ronald, J.: Idrisi Kilimanjaro Guía para SIG y Procesamiento de Imágenes. Clack Labs, Córdoba (2003)
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Muñoz, M.D., García, M.N.M. (2017). Simulation of a Directional Process by Means of an Anisotropic Buffer Operator. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_28
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DOI: https://doi.org/10.1007/978-3-319-59650-1_28
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