Fractal dimensions

Bai-Lian Li Volume 2, pp 821–825

in

Encyclopedia of Environmetrics (ISBN 0471 899976)

Edited by

Abdel H. El-Shaarawi and Walter W. Piegorsch

 John Wiley & Sons, Ltd, Chichester, 2002 by a constant parameter D known as the fractal (or Fractal dimensions fractional) dimension. There are many fractal dimensions introduced in The term ‘fractal’ (from the Latin fractus, meaning mathematical and physical literature (e.g. [13], [16], ‘broken’), introduced by Benoit Mandelbrot about [19] and [22]). Fractal dimensions can be positive, 25 years ago, is used to characterize spatial and/or negative, complex, fuzzy, multifractal, etc. The two temporal phenomena that are continuous but not most commonly used are the Hausdorf dimension and differentiable. Geometrically, a fractal is a rough capacity. Hausdorf in 1919 introduced the definition or fragmented geometric shape that can be subdi- of the Hausdorf dimension based on a method of cov- vided into parts, each of which is (at least approx- ering. Let D>0andε>0 be real numbers. Cover imately) a reduced-size copy of the whole. Fractal asetE by countable spheres whose diameters are all geometry is the study of geometric shapes that seem smaller than ε. Denoting the radii of the spheres by chaotic or irregular when compared with those of r1,r2,...,rk,theD-dimensional Hausdorf measure standard geometry (line, square, sphere, etc.) but is defined by the following equation: exhibit extreme orderliness because they possess a
D property of invariance under suitable contractions or MD E Á lim inf rk 1 ε!0 rk <ε dilations [13]. Fractal objects are highly nontrivial k representations of the two fundamental symmetries of For any given set E, this quantity is proved to vary nature, dilation (r ! ar) and translation (r ! r C b). from infinity to zero at a special value of D, denoted There are many mathematical structures that are frac- by D , which is the Hausdorf dimension. tals (e.g. Sierpinski gasket, Koch snowflake, Peano H Since a rigorous calculation of the Hausdorf curve, Mandelbrot set, and Lorenz attractor) and dimension is generally very difficult another fractal many examples of fractal-like objects in nature, such dimension, the capacity dimension, is more practi- as clouds, mountains, turbulence, coastlines, shells, cally useful. Kolmogorov introduced this dimension cauliflowers, and leaves, that do not correspond to in 1959 and like the Hausdorf dimension it is based simple, Euclidean geometric shapes. on the idea of covering. Let the considered shape be a Fractal properties include self-similarity or affin- bounded set in d-dimensional Euclidean space. Cover ity, scale symmetry, scale independence or invari- the set by d-dimensional spheres of identical radius ance, heterogeneity, complexity, and infinite length 1/ε. The capacity dimension is given by or detail. ‘Self-similar’ here has two meanings. One can understand ‘similar’ as a loose everyday syn- log N ε DC Á lim 2 onym of ‘analogous’. But there is also the strict ε!0 log 1/ε textbook sense of ‘contracting similarity’. It expresses that each part is a linear geometric reduction of the where N ε denotes the minimal number of spheres. whole, with the same reduction ratios in all directions. For example, consider a Koch snowflake curve Random fractals are self-similar only in a statistical formed by repeatedly replacing with ,where sense; to describe them it is more appropriate to use each of the four new lines is one-third the length the term ‘scale invariance’ than self-similarity. There of the old line. Blowing up the snowflake curve by are many different self-similar processes. However, a factor of three results in a snowflake curve four most studies have considered those that have a sta- times as large (one of the old snowflake curves can be tionary increment. More recent developments have placed on each of the four segments ,4D 3log3 4). extended, in particular, to include self-affine, in that So, the curve length has a dimension of log3 4 D the reductions are still linear but the reduction ratios log 4/ log 3 D 1.2618 . Since the dimension 1.261 is in different directions are different. Fractal structures larger than the dimension 1 of the lines making up do not have a single length scale, while fractal pro- the curve, the Koch curve is more complicated than a cesses (e.g. time series) cannot be characterized by line, but seems less complicated than the plane-filling a single time scale. Fractal theory offers methods for Peano curve. Another example is the Sierpinski gas- describing the inherent irregularity of natural objects. ket. This is perhaps the most studied two-dimensional In fractal analysis, the Euclidean concept of ‘length’ fractal structure because it can be regarded as a pro- is viewed as a process. This process is characterized totype of fractal lattices with an infinite hierarchy of 2 Fractal dimensions loops (see Figure 1). When constructing this fractal, εD,whereN ε is a number measure corre- three of the four equilateral triangles generated within sponding to squares with the side length scale the triangles obtained in the previous step are kept. unit ε and D is the fractal dimension); Since the linear size of the triangles is halved in every 2. using the fractal measure relations (i.e. peri- iteration, the fractal dimension of the resulting object meter/area/volume methods: L / S1/2 / V1/3 / is log 3/ log 2 D 1.5849. X1/D); Capacity dimension is a special case of the Haus- 3. using the correlation functions (i.e. autocorrela- dorf dimension with the restriction that the radii of tions, semivariograms, etc.); spheres are the same. This dimension often coin- 4. using the distribution functions (i.e. hyperbolic cides with the Hausdorf dimension but sometimes distribution, stable Levy distribution, etc.); and differs. Mathematically, we can prove the following 5. using the power spectrum (i.e. Fourier transfor- inequality for any shape: mation, filters, wavelets, etc.). For more informa- tion about estimating fractal dimensions, see [4], DC ½ DH 3 [5], [13], [19], [21] and [22].

Therefore, a fractal can be defined as a set for which Since the above-defined fractal dimensions are the Hausdorf dimension strictly exceeds the topo- scale-independent, they are only valid when N ε logical dimension [13]. Topological dimension is the follows an exact power law; therefore, they may ‘normal’ idea of dimension; a point has topological not be as useful for describing more complicated dimension 0, a line has topological dimension 1, a phenomena in nature with only one number [10, surface has topological dimension 2, etc. 19]. We need to extend the fractal dimension. Most These definitions of dimension are rigorous. How- important, we should be able to generalize the fractal ever, a basic difficulty arises when applying them dimension so as to depend on the scale of observation. to real problems. In both definitions dimensions are According to (2), it seems to be most natural to define defined in the limit ε ! 0, but length 0 is not a physi- a scale-dependent fractal dimension by the slope of cal concept as a consequence of the uncertainty prin- the graph of N ε , that is, the derivative of log N ε ciple. In addition to this ‘inner-length scale’ below with respect to log ε: which scale invariance breaks down, there is in each dlogN ε natural problem also an ‘outer-length scale’ above D ε D 4 which scale invariance does not hold. Thus, experi- dlogε mentally, there must be limitations on the observed Solving (4) inversely, we can have the following scaling range on both sides (e.g. [12] and [19]). expression: Many physically feasible methods of calculating    fractal dimensions have been developed recently. We E D r can classify them into five categories: N E D N ε exp  dr 5 ε r 1. changing coarse graining level (also called box- Equation (5) shows how the scale-dependent fractal counting methods) (i.e. the relationship N ε / dimension links observations of different scales. Another important extension is to introduce new quantities to describe spatial fluctuations of fractal dimensions in order to cope with objects the local fractal dimensions of which change from part to part. A generalized information dimension and multifractal or f–˛ spectra are introduced for the purpose [13, 19, 20, 22]. We will not discuss them further here. The concept and methodology of fractals have been used to study many multiscale ecological phe- nomena and provide us with a new insight on analyz- Figure 1 The first four steps in the construction of the ing and quantifying ecological patterns and dynam- Sierpinski gasket that has loops on all length scales ics [6–8, 10, 11, 15, 17, 18]. The fractal approach Fractal dimensions 3 is becoming mainstream in ecology because under- heterogeneity, and m and e are constant coefficients. standing the origins of fractal scaling can provide And ˇ0 >ˇis for limited growth of suitable habitat. valuable information about the origins of systems’ In equilibrium situations, both the species number complexity, and also the driving mechanisms inherent and suitable habitat area (as well as the total sampled to these systems with their intricate dynamical struc- area A) become ture [1, 7]. As a property of fractals, self-similarity – ln d/c repeating in time and/or space – can also substan- ln S D and   0 tially simplify the mathematical modeling of the 6 phenomena themselves [2]. In ecology, there are sev- ln e/m ln aO D / ln A eral widely accepted fractal scaling relations, espe- ˇ  ˇ0 cially in size–abundance spectra or distributions, species–area curves, allometric scaling [23], life his- Combining equations (6), and ln S / z ln A,wehave   tory invariants [3], self-thinning processes [9], and so ln d/c on. These scaling relations have been helping ecolo-   0 gists to achieve a better understanding of ecological z D z c, d, e, m, ˇ, ˇ0,,0 D   patterns and processes. ln e/m Due to limited space, only one example, namely ˇ  ˇ0 z the well-known species–area curve S D CA (the ˇ  ˇ0 ln d/c D 7 number of species is a power law function of a   0 ln e/m sampled patch of area A with scaling exponent z), is used here to show how a fractal approach can Therefore, the z-value appears related to the two help us to solve this ecological puzzle. Milne [14] mechanisms of regulation at the species and their and Yamasaki et al. [24] used a fractal approach corresponding habitat levels, and is the function of to this old problem and concluded that the number parameters of species dynamics and self-regulation of species on the island is constrained by not only processes coupling parameters from dynamics of the size of the island but also the shape of the suitable habitat area occupied by those species. When island. But the questions about why is the z-value of we remove the sampling effect, the z-value could the relationship generally between 0.15 and 0.4 and be an excellent index of diversity (see Diversity why is there so much variation in z among surveys measures) because it measures how fast the number remain. Here we use theories of island biogeography of species increases in relation to the increase of and fractal dynamics to construct a model coupling total suitable habitat area. At different scales in dynamics of species (colonization–extinction) and space and/or over time, such as local, regional, and habitat heterogeneity. Consider that the total energy global scales, parameters in the dynamics of species supporting the number of species on earth is limited. and habitat diversity can be different. Equation (7) We can generalize MacArther–Wilson’s model to predicts that the wide range of the z-values of write a simple fractal–logistic-type species dynamics species–area curve is due to the differences in the 0 as dS/dt D cS  dS ,whereS is the number of rates of dynamics at species and habitat levels. species,  and 0 are scaling exponents regulating The fractal approach is a powerful tool in eco- dynamics of species (i.e. colonization–extinction, logical studies. By ‘scale invariance’ in ecology, we competition–colonization trade-off, etc.), and c and mean that scales are ecologically equivalent so that d are constant coefficients. Now we assume that the the same ecological conclusions may be drawn from total area of suitable habitat, a, is also limited and any scale statistically. However, ecological systems it should be proportional to the total sampled area, are complex. Since a log–log plot of many ecologi- A,i.e.a / A. (We could consider a general fractal- cal data or power-law distributions displays a straight like increase of habitat space on ‘landscape’, i.e. a / line only over a certain range of parameters, such A , but that will not change our basic conclusion.) fractal measures alone may not give the whole system The dynamics of suitable habitat area for those information. We have to go beyond scale invariance species dynamics have similar equation as da/dt D to scale covariance and scale dynamics [7]. Studies of 0 maˇ  eaˇ ,whereˇ and ˇ0 are similarly scaling fractals in ecology have now evolved from the purely exponents for regulating dynamics of suitable habitat descriptive to the beginning of understanding. 4 Fractal dimensions

References [13] Mandelbrot, B.B. (1983). The Fractal Geometry of Nature, W.H. Freeman, New York. [1] Bak, P., Tang, C. & Wiesenfeld, K. (1987). Self-organi- [14] Milne, B.T. (1997). Applications of fractal geometry zed criticality: an explanation of 1/f noise, Physical in wildlife biology, in Wildlife and Landscape Ecol- Review Letters 59, 381–384. ogy, J.A. Bissonette, ed., Springer-Verlag, New York, [2] Barenblatt, G.I. (1996). Scaling, Self-similarity, and pp. 32–69. Intermediate Asymptotics, Cambridge University Press, [15] Milne, B.T. (1998). Motivation and benefits of com- Cambridge. plex systems approaches in ecology, Ecosystems 1, [3] Charnov, E.L. (1993). Life History Invariants: Some 449–456. Explorations of Symmetry in Evolutionary Ecology, [16] Pietronero, L. & Tosatti, E., eds (1986). Fractals in Oxford University Press, Oxford. Physics, North-Holland, Amsterdam. [4] Frantziskonis, G. & Hansen, A. (2000). Wavelet-based [17] Ritchie, M.E. & Olff, H. (1999). Spatial scaling laws multiscaling in self-affine random media, Fractals 8, yield a synthetic theory of biodiversity, Nature 400, 403–411. 557–560. [5] Jaggi, S., Quattrochi, D.A. & Lam, N.S. (1993). Imple- [18] Sugihara, G. & May, R.M. (1990). Applications of mentation and operation of three fractal measurement fractals in ecology, Trends in Ecology and Evolution 5, algorithms for analysis of remote-sensing data, Com- 79–86. puters and Geosciences 19, 745–767. [19] Takayasu, H. (1990). Fractals in Physical Sciences, [6] Krummel, J.R., Gardner, R.H., Sugihara, G., O’Neill, Manchester University Press, Manchester. R.V. & Coleman, P.R. (1987). Landscape patterns in [20] Tchiguirinskaia, I., Lu, S., Molz, J., Williams, T.M. disturbed environment, Oikos 48, 321–324. & Lavallee, D. (2000). Multifractal versus mono- [7] Li, B.L. (2000). Fractal geometry applications in fractal analysis of wetland topography, Stochastic description and analysis of patch patterns and patch Environmental Research and Risk Assessment 14, dynamics, Ecological Modelling 132, 33–50. 8–32. [8] Li, B.L., Loehle, C. & Malon, D. (1996). Microbial [21] Theiler, J. (1990). Estimating fractal dimension, Journal transport through heterogeneous porous media: Random of the Optical Society of America A: Optics and Image walk, fractal, and percolation approaches, Ecological Science 7, 1055–1073. Modelling 85, 285–302. [22] Vicsek, T. (1989). Fractal Growth Phenomena,World [9] Li, B.L., Wu, H. & Zou, G. (2000). Self-thinning rule: Scientific, Singapore. a causal interpretation from ecological field theory, [23] West, G.B., Brown, J.H. & Enquist, B.J. (1997). A Ecological Modelling 132, 167–173. general model for the origin of allometric scaling laws [10] Lin, Z.S. & Li, B.L. (2001). Scaling laws for the in biology, Science 276, 122–126. prey–predator interaction rates, in Integrative Systems [24] Yamasaki, K., Chiba, S. & Nagahama, H. (1999). Approaches to Natural and Social Dynamics – Systems Geometrical effect of island shape on the species Science 2000, M. Matthies, H. Malchow & J. Kriz, eds, richness, Fractals 7, 353–357. Springer-Verlag, Berlin, pp. 111–118. [11] Loehle, C., Li, B.L. & Sundell, R.C. (1996). Forest spread and phase transitions at forest–prairie ecotones (See also Criticality, self-organized; Point pro- in Kansas, USA, Landscape Ecology 11, 225–235. cesses, spatial–temporal; Population dynamics) [12] Malcai, O., Lidar, D.A. & Biham, O. (1997). Scal- ing range and cutoffs in empirical fractals, Physical Review E 56, 2817–2828. BAI-LIAN LI