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Interpolacja fraktalna jako stochastyczna odwrotność generalizacji kartograficznej

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Warianty tytułu
EN
Fractal interpolation as a stochastic reversal of cartographic generalization
Języki publikacji
PL
Abstrakty
PL
W artykule pokazano możliwość zastosowania interpolacji mono- i multifraktalnej jako stochastycznej odwrotności procesu generalizacji kartograficznej. W celu lepszego zrozumienia zagadnienia omówiono podstawowe pojęcia geometrii fraktalnej.
EN
The article attempts to apply fractal analysis in cartographic research of selected components of natural environment. Fractal description, which bases on fractal geometry and analysis, invented by B.B.Mandelbrot in the seventies, became an integrating interdisciplinary tool for many scientific domains, particularly in natural sciences. Application of procedures, which originate from this division of mathematics, can also , according to the author, add to the development of analytical cartography. A fractal is a shape, which consists of parts in a way similar to whole. Fractal geometry can therefore become a tool in describing complex shapes of natural phenomena, like clouds, shorelines, mountain ridges. The parameter, which describes geometric complexity of those shapes is called fractal dimension. Fractal dimension (Dr) of a given object characterizes its degree of complexity and the extent, to which it fills the available space. In the result of the precess of cartographic generalization some of the source information is lost. Therefore it is vital to preserve the key metric parameters of somplified objects. Fractal dimension of an object ahows, how its metric parameters change in the process of cartographic generalization. Dr parameter of generalized geometric object makes it possible to recreate their approximate shapes in the process of fractal interpolation. This method can be treated as the reversal of the process of cartographic generalization. Basing on data of limited geometric accuracy and the fractal dimension of a given phenomenon, one can model topographic details on a level of complexity corresponding to the source materials. In this case fractal interpolation can also be treated as a method of stochastic decompression of spatial data with a known Dr parameter. Shape modeling of ganeralized objects through the process of fractal interpolation introduces a certain error. Geometric object, which represent river systems or shorelines are stochastic fractals. Statistically, a self-similar object does not consist of reduced copies of its entire self, but rather of reduced copies of its parts. This means, that in the process of fractal encoding we have to allow for a certain error, caused by the lack of homogeneity of transformed objects. The encoded picture, being a set of transformation, will not be a faithful copy of the original, but rather its approximation.
Rocznik
Strony
339--350
Opis fizyczny
Bibliogr. 24 poz., rys., wykr.
Twórcy
autor
  • Zakład Kartografii Politechniki Warszawskiej
Bibliografia
  • Adamczewski Z., 1992, Biała magia Ziemi i geometria fraktalna. „Przegl. Geodez.” R. 64, nr 6, s. 6–10.
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  • Berlant A. M., Musin O. R., Sobczuk T. W., 1998, Kartograficzeskaja gienieralizacija i tieorija fraktałow. Moskwa: Izd. Moskowskogo Uniwiersitieta.
  • Burrough P., 1993, Fractals and geostatistical methods in landscape studies. W: Fractals in geography. Englewood Cliffs, NJ: Prentice Hall.
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  • Dutton G. H., 1981, Fractal enhancement of cartographic line detail. „The Amer. Cartographer” Vol. 8, no. 1, s. 23–40.
  • Engelking R., 1977, Teoria wymiaru. Warszawa: PWN.
  • Falconer K., 1990, Fractal geometry. Mathematical foundations and applications. Chichester: John Wiley & Sons.
  • Feder J., 1988, Fractals. New York: Plenum Press.
  • Green D., 1995. Fractals and scale. http://life.csu.edu.au/complex/tutorials/tutorial3.html
  • Grygorenko W., 1991, Kierunki rozwoju naukowego współczesnej kartografii. W: Metody badań kartograficznych. „Materiały Ogólnopolskich Konferencji Kartograficznych” T. 16, s. 16–32.
  • Klinkenberg B., 1992, Fractals and morphometric measu-res: is there a relationship? „Geomorphology” Vol.5, Amsterdam: Elsevier Science Publishers B. V. s. 5–20.
  • Krak M.-J., Ormeling F., 1998, Kartografia. Wizualizacja danych przestrzennych. Warszawa: Wydawn. Naukowe PWN.
  • Lam N., De Cola L., 1993, Fractals in geography. Englewood Cliffs, NJ: Prentice Hall.
  • Mandelbrot B. B., 1967, How long is the coastline of Britain? Statistical self-similarity and fractional dimension. „Science” Vol. 156, no. 3775, s. 636–638.
  • Mandelbrot B. B., 1977, Fractals: form, chance and dimension. San Francisco: W. H. Freeman and Co.
  • Mandelbrot B. B., 1982, The fractal geometry of nature. New York: W. H. Freeman and Co.
  • Peitgen H.-O., Jurgens H., Saupe D., 1997, Granice chaosu. Fraktale. Warszawa: Wydawn. Naukowe PWN.
  • Quattrochi D. A., Lam N., Qiu H., Zhao W., 1997, Image characterization and modeling system. W: Scale in remote sensing and GIS. Ed. D. A. Quattrochi, M. F. Goodchild, New York: Lewis Publishers.
  • Ratajski L., 1989, Metodyka kartografii społeczno-gospodarczej. Wyd. 2. Warszawa: PPWK.
  • Saliszczew K. A., 1998, Kartografia ogólna. Warszawa: Wydawn. Naukowe PWN.
  • Tempczyk M., 1998, Teoria chaosu a filozofia. Warszawa: Wydawn. CiS.
  • Voss R. F., 1990, Fractals in nature: from characterization to simulation. W: The science of fractal images. Ed. H.-O. Peitgen, D. Saupe. Berlin: Springer Verlag, s. 21–69.
  • Xia Z., Clarke K., 1997, Approaches to scaling and geo-spatial data. W: Scale in remote sensing and GIS. Ed. D. A. Quattrochi, M. F. Goodchild. New York: Lewis Publishers, s. 309–361.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR5-0001-0200
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