A Mathematical Study on the Application of Box Dimension in Measuring Coastline Length

Authors

  • Rui Du

DOI:

https://doi.org/10.61173/jztn0p09

Keywords:

Medical research, coastline, box dimension

Abstract

Coastline evaluation has always been difficult because of the complexity and mathematical functions of shores. Traditional techniques, such as the direct or wall strategy, often ignore coastlines’ nonlinear nature and reject their correct size. This review examines how to identify shores using the Box Dimension from fractal geometry. The box-counting algorithm and higher-resolution world coastline data from the International Self-Steady, Hierarchical, High-Resolution Geography Database (GSHHG) are used to determine the box dimension. The results demonstrate that measuring hard shores, especially in densely populated areas, is more accurate and strongly utilizes the Box Dimension. Also, the assessment examines the computational efficiency of this technique in comparison to regular people. The results suggest that the Box Dimension gives scholars and algebra a more suitable tool for exploring shores, with useful functions in environmental planning and coastal management. These findings provide geographers and mathematicians with more precise tools for analyzing coastline length and complexity.

References

[1] Richardson L F. The Problem of Contour Length. In Weather Prediction by Numerical Process. Cambridge University Press, 1961.

[2] Mandelbrot B B. The Fractal Geometry of Nature. Freeman, 1982.

[3] Klinkenberg B. A review of methods used to determine the fractal dimension of linear features. Mathematical Geology, 1992, 24(4): 421-434.

[4] Li X, Du Q. A comparison of Box Counting and Divider Dimensions in characterizing the complexity of the urban landscape in the Greater Toronto Area. Professional Geographer, 2007, 59(1): 104-117.

[5] Burrough P A. Fractal dimensions of landscapes and other environmental data. Nature, 1981, 294(5838): 240-242.

[6] Eke A, et al. Fractal characterization of complexity in temporal physiological signals. Physiological Measurement, 2002, 23(1).

[7] Smith T G, Lange G D, Marks W B. Fractal methods and results in cellular morphology-dimensions, lacunarity and multifractals. Journal of Neuroscience Methods, 1996, 69(2): 123-136.

[8] Goodchild M F, Mark D M. The fractal nature of geographic phenomena. Annals of the Association of American Geographers, 1987, 77(2): 265-278.

[9] Turcotte D L. Fractals and Chaos in Geology and Geophysics. Cambridge University Press, 1997.

[10] Clifford N J, French J R. Monitoring and modelling turbulent flows: Implications for scaling geomorphological processes. Journal of Hydrology, 1993, 150(2): 611-632.

[11] Mandelbrot B B. How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension. Science, 1967, 156(3775): 636-638.

[12] Burrough P A. Principles of Geographical Information Systems for Land Resources Assessment. Oxford University Press, 1986.

[13] Lovejoy S, Schertzer D. The Weather and Climate: Emergent Laws and Multifractal Cascades. Cambridge University Press, 2013.

[14] Barthelemy M. The Structure and Dynamics of Cities: Urban Data Analysis and Theoretical Modeling. Cambridge University Press, 2016.

[15] Mandelbrot B B, Wheeler J A. Fractals and Geometry of Nature: Science and Society. Scientific American, 1983.

[16] Ball P. Critical Mass: How One Thing Leads to Another. Macmillan, 2004.

[17] Burrough P A, McDonnell R A. Principles of Geographical Information Systems. Oxford University Press, 1998.

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Published

2024-12-31