Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2010, Article ID 974917, 20 pages
Research Article

Exploring the Fractal Parameters of Urban Growth and Form with Wave-Spectrum Analysis

Department of Geography, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

Received 16 October 2009; Revised 14 May 2010; Accepted 10 October 2010

Academic Editor: Michael Batty

Copyright © 2010 Yanguang Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The Fourier transform and spectral analysis are employed to estimate the fractal dimension and explore the fractal parameter relations of urban growth and form using mathematical experiments and empirical analyses. Based on the models of urban density, two kinds of fractal dimensions of urban form can be evaluated with the scaling relations between the wave number and the spectral density. One is the radial dimension of self-similar distribution indicating the macro-urban patterns, and the other, the profile dimension of self-affine tracks indicating the micro-urban evolution. If a city's growth follows the power law, the summation of the two dimension values may be a constant under certain condition. The estimated results of the radial dimension suggest a new fractal dimension, which can be termed “image dimension”. A dual-structure model named particle-ripple model (PRM) is proposed to explain the connections and differences between the macro and micro levels of urban form.

1. Introduction

Measurement is the basic link between mathematics and empirical research in any factual science [1]. However, for urban studies, the conventional measures based on Euclidean geometry, such as length, area, and density, are sometimes of no effect due to the scale-free property of urban form and growth. Fortunately, fractal geometry provides us with effective measurements based on fractal dimensions for spatial analysis. Since the concepts of fractals were introduced into urban studies by pioneers, such as Arlinghaus [2], Batty and Longley [3], Benguigui and Daoud [4], Frankhauser and Sadler [5], Goodchild and Mark [6], and Fotheringham et al. [7], many of our theories of urban geography have been reinterpreted using ideas from scaling invariance. Batty and Longley [8] and Frankhauser [9] once summarized the models and theories of fractal cities systematically. From then on, research on fractal cities has progressed in various aspects, including urban forms, structures, transportation, and dynamics of urban evolution (e.g., [1020]). Because of the development of the cellular automata (CA) theory, fractal geometry and computer-simulated experiment of cities became two principal approaches to researching complex urban systems (e.g., [2125]).

Despite all the above-mentioned achievements, however, we often run into some difficult problems in urban analysis. The theory on the fractal dimensions of urban space is less developed. We have varied fractal parameters on cities, but we seldom relate them with each other to form a systematic framework. Moreover, the estimation methods of fractal dimensions remain in need of further development. The common approaches to the fractal analysis of cities are limited by self-affine structures. In this instance, three methods, including scaling analysis, spectral analysis, and spatial correlation analysis, are helpful for us to evaluate fractal parameters. The mathematical models of urban density are significant in our research of the fractal form of cities. A density distribution model is usually a spatial correlation function of the distance from city center [26]. In the theory of spectral analysis, the correlation function and energy spectrum can be converted into one another using Fourier transform [27]. Using spectral analysis based on correlation functions, we can find the relations among different fractal parameters, which in turn help us understand urban structure and evolution.

This paper is devoted to exploring the relation between the radial dimension and the self-affine record dimension. The rest of the paper is arranged as follows. In the second section, the wave-spectrum scaling equations for estimating fractal dimensions of urban form are presented. In the third section, two mathematical experiments are implemented to determine the error-correction formula of fractal dimension estimation, and an empirical analysis of Beijing, China, is performed to validate the models and method presented in the text. In the fourth section, a new model of dual structure is proposed to explain urban evolution. Finally, the paper is concluded with a brief summary of this study.

2. Mathematical Models and Fractal Dimension Relations

2.1. Urban Density Functions—Special Spatial Correlation Functions

A fractal is a scale-free phenomenon, but a fractal dimension seems to be a measurement with a characteristic scale. Urban growth and form take on several features of scaling invariance, which can be characterized with fractal dimensions. Three basic concepts about city fractals and fractal dimensions can be outlined here. First, the models of fractal cities are defined in the 2-dimensional Euclidean plane. That is, we investigate the fractal structure of cities through 2D remotely sensed images, digital maps, and so forth. In short, the Euclidean dimension of the embedding space is [8]. On the other hand, the smallest image-forming units of a city figure can be theoretically treated as points, so the topological dimension of a city form is generally considered to be . In terms of the original definition of simple fractals [28], the fractal dimension value of urban form ranges from to . Empirically, the dimension of fractal cities is between 1 and 2. Second, the center of the circles for measuring radial dimension should be the center of a city. The box dimension of fractal cities is affirmatively restricted to the interval . However, the radial dimension denominated by Frankhauser and Sadler [5] can go beyond the upper limit confined by a Euclidean space. If the measurement center is the centroid of a fractal body, the dimension will not exceed . Otherwise, the radial dimension value may be greater than 2 [23]. Third, for the isotropic growing fractals of cities, the radial dimension is close to the box dimension or the grid dimension [29]. The radial dimension of a regular self-similar growing fractal equals its box dimension (see [8]). As for cities, if the measurement center is properly located within an urban figure on the digital map, the box dimension will be close to the radial dimension.

Fractal research on urban growth and form is related to the concepts of size, scale, shape, and dimension [30, 31]. Two functions are basic and all-important for these kinds of studies. One is the negative exponential function, and the other is the inverse power function, both of which are associated with fractal cities. They are often employed as density models to describe urban landscapes. The former is mainly used to reflect a city’s population density [3234] while the latter is usually employed to characterize the urban land use density [8, 9]. In fact, the inverse power law can be sometimes applied to describing a city population’s spatial distribution [35]. If the fractal structure of a city degenerates to some extent, the land use density also follows exponential distribution. The negative exponential model can be written in the form where denotes the population density at the distance from the center of the city , refers to a constant coefficient, which theoretically equals the central density , and is the characteristic radius of the population distribution. The reciprocal of reflects the rate at which the effect of distance decays.

The inverse power law is significant in the spatial analysis of urban form and structure. Formally, given , the power function of urban density can be expressed as in which and fulfill the same roles as in (2.1), denotes a proportionality constant, is the dimension of the embedding space, and is the radial dimension of city form. When , there is a discontinuity and the urban density can be specially defined as . Equation (2.1) is the well-known Clark’s [34] model and (2.2) Smeed’s [36] model.

Urban density functions are in fact special correlation functions that reflect the spatial correlation between a city center and the areas around the center. In theory, almost all fractal dimensions can be regarded as a correlation dimension in a broad sense. For urban growth and form, the can be demonstrated as a one-point correlation dimension (the zero-order correlation dimension) while the spectral exponent, , of the power-law density function can be shown to be a point-point correlation dimension (the second-order correlation dimension). These two dimensions can be found within the continuous spectrum of generalized dimensions. By comparing the values of the two correlation dimensions, we can obtain useful information on urban evolution. A fractal dimension is a measurement of space-use extent. Both the box dimension and the can act as two indices for a city. One is the index of uniformity for spatial distribution and the other is the index of space filling, indicative of land use intensity and built-up extent. In addition, the box dimension is associated with information entropy while the is associated with the coefficient of spatial autocorrelation [12].

2.2. The Wave-Spectrum Relation of Urban Density

To simplify the analytical process of spatial scaling, a correlation function can be converted into an energy spectrum using Fourier transform [27]. One of the special properties of the Fourier transform is similarity. By this property, a scaling analysis can be made to derive useful relations of fractal parameters. Any function indicative of self-similarity retains scaling symmetry after being transformed. Consider a density function, , that follows the scaling law where is the scale factor, denotes the scaling exponent , and represents distance variable. Applying the Fourier transform to (2.3) will satisfy the following scaling relation: in which refers to the Fourier operator, to the wave number, and to the image function of the original function . From (2.4), the wave-spectrum relation can be derived as where denotes the spectral density of “energy”, which bears an analogy to the energy concept in engineering mathematics [37].

The numerical relation between the spectral exponent and fractal dimension can be revealed by comparison. Equation (2.1) fails to follow the scaling law under dilation, while (2.2) is a function of scaling symmetry. Thus, (2.2) can be related to the wave-spectrum scaling. Taking in (2.5) yields Thus, we have The precondition of (2.7) is . As stated above, the spectral exponent can be demonstrated to be the point-point correlation dimension. This implies that (2.7) is a dimension equation that shows the relation between the one-point correlation dimension and the point-point correlation dimension ().

The parameter is the fractal dimension of the self-similar form of cities. We can derive another fractal dimension, the self-affine record dimension, , from the wave-spectrum relation by means of dimensional analysis [3841]. The well-known result is as follows: where and are the fractal dimensions of the self-affine curve and the Hurst exponent, respectively [42]. The concept of the Hurst exponent comes from the method of the rescaled range analysis, namely, the analysis [43], which is now widely applied to nonlinear random processes. For the increment series of a space/time series , is the scaling exponent of the ratio of the range () to the standard deviation () versus space/time lag . In other words, is defined by the power function [42].

The parameter is mainly used to analyze the characters of spatial distribution at the macrolevel whereas is used to study the spatial autocorrelation at the microlevel. The latter is termed profile dimension because it can be estimated by the profile curve of urban form [37]. The is the local dimension of self-affine fractal records instead of self-similar fractal trails [26, 42]. A useful relation between the and can be derived under certain conditions. Combining (2.7) and (2.8) yields

The question is how to comprehend the relationships and differences between and . Let us look at the diffusion-limited aggregation (DLA) model (Figure 1), which was employed by Batty et al. [44] and Fotheringham et al. [7] to simulate urban growth. In a DLA, each track/trail of a particle has a self-affine record and [42]. However, the final aggregate comprised of countless fine particles takes on the form of statistical self-similarity. In fact, the random walk of the particles in the growing process of DLA is associated with Brownian motion. However, the spatial activity of the “particles” in real urban growth is assumed to be representative of fractional Brownian motion (fBm) rather than standard random walk, thus the   of real cities falls between 1 and 2 (see [42, 45] for a discussion on fBm).

Figure 1: A DLA model showing the particle-ripple duality of city space. (Note that the cluster with a dimension is created in Matlab by using the DLA model. The center of the circles is the origin of growth as the location of the “seed” of DLA.)

Based on fBm, the relation between and the autocorrelation coefficient of a increment series can be given as [38, 42] where denotes the autocorrelation coefficient. For urban evolution, is a spatial autocorrelation coefficient that is different from Moran’s exponent (Moran’s ). Moran’s is based on the first-order lag 2-dimensional spatial autocorrelation [46] while is based on the multiple-lag 1-dimensional spatial autocorrelation. When , , indicating Brownian motion (random walk), an independent random process. When , , indicating positive spatial autocorrelation. Finally, when , , indicating negative spatial autocorrelation.

In light of (2.8), (2.9), and (2.10), we can reveal the numerical relationships between , , , , and . The examples are displayed in Table 1. Each parameter has its own valid scale. The , as shown above, ranges from 0 to 2 in theory and 1 to 2 in empirical results. The ranges from 1 to 2, the ranges from 0 to 1, and the ranges from −1 to 1. In sum, only when comes between 1.5 and 2, is the fractal dimension relation, (2.9), theoretically valid. There are two special points in the spectrum of the from 0 to 2. One is , corresponding to the distribution, and the other is , suggesting that a space is occupied and utilized completely. Only within this dimension range, from 1.5 to 2, can the city form be interpreted using the fBm process.

Table 1: The numerical relationships between different fractal dimensions, scaling exponents, and autocorrelation coefficients.

If an urban phenomenon, such as urban land use, follows the inverse power law, it can be characterized by a that varies from 0 to 2. However, what is the dimension of the urban phenomenon that follows the negative exponential law instead of the inverse power law? How can we understand the dimension of urban population if the population density conforms to the negative exponential distribution? These are difficult questions that have puzzled theoretical geographers for a long time. Batty and Kim [35] conducted an interesting discussion about the difference between the exponential function and the power function, and Thomas et al. [20] discussed the fractal question related to the exponential model.

Actually, the spectral density based on the Fourier transform of the negative exponential function approximately follows the inverse power law [37]. The spectral density of the negative exponential distribution meets the scaling relation as follows [38, 47]: in which is a theoretical value, indicating . In empirical studies, the calculations may deviate from this standard value and vary from 0 to 3.

The dimension relation, (2.9), can be employed to tackle some difficult problems on cities, including the dimension of urban population departing from self-similar fractal distributions and the scaling exponent of the allometric relation between urban area and population. If urban population density can be described by (2.1), according to (2.11), and thus we have according to (2.8). Substituting this result into (2.9) yields . This suggests that the dimensions of urban phenomena that satisfy the negative exponential distribution can be treated as .

To sum up, if we calculate the properly and the value falls between 1.5 and 2, we have a one-point correlation dimension and can estimate the , , and so forth. Using these fractal parameters, we can conduct spatial correlation analyses of urban evolution. There are often differences between the theoretical results and real calculations because of algorithms among others. However, we can find a formula to correct the errors in computation. For this purpose, a mathematical experiment based on noise-free spatial series is necessary. Moreover, an empirical analysis is essential to support the theoretical relations. The subsequent mathematical experiments consist of two principal parts: one is based on the inverse power law and the other on the negative exponential function. The empirical analysis will involve both the negative exponential distribution and the inverse power-law distribution.

3. Mathematical Experiments and Empirical Analysis

3.1. Mathematical Experiment Based on Inverse Power Law

All the theoretical derivations in Section 2.2 are based on the continuous Fourier transform (CFT), which requires the continuous variable to vary from negative infinity to infinity . However, in mathematical experiments or empirical analyses, we can only deal with the discrete sample paths with limited length . Because of this, the energy spectrum in (2.5), (2.6), and (2.11) should be replaced by the wave spectrum, thus we have where refers to the wave-spectral density and to the length of the sample path. In practice, CFT should be substituted with the discrete Fourier transform (DFT). The calculation error is inevitable owing to the conversion from continuity and infinity to discreteness and finitude.

For the power-law distribution, both and of the urban form can be estimated with the wave-spectrum relation. The procedures in the mathematical experiment are as follows: Create noise-free series of density data for an imaginary land use pattern using (2.2). A real space or time series often consists of trend component, period component, and random component (noise). However, the series produced by theoretical model contain no random component. The value is given in advance . The length of the sample path is taken as , where is a positive integer. Implement fast Fourier transform (FFT) on the data. Evaluate using (3.1). Estimate the fractal dimension value through the spectral exponent and (2.7); the result is notated as in contrast to the given value Compare the difference between the expected value, , and the estimated result, . The index of difference can be measured by the squared value of error, .

The operation is very simple and all the steps can be carried out in Matlab or MS Excel. Taking , and 11, for example, we have four sample paths of noise-free series of urban land use densities with lengths of , and 2048, respectively. The length of a sample path is to a space or time series as the size of a sample is to population [48]. It is measured by the number of elements. Given the and values, the data can be produced easily using (2.2). Through spectral analysis, the value can be estimated using (2.7), and the value can be estimated using (2.8). Three conclusions can be drawn from the mathematical experiment. First, the longer the sample path is, the more precise the estimation results will be. The change in accuracy of the fractal dimension estimation over the sample path length is not very remarkable. Second, the closer the fractal dimension value is to , the better the estimated result will be. For instance, given and , the corresponding results of fractal dimension estimation are , respectively. When , we have and minimal errors are found (Figure 2). This value is very close to (Table 2). Third, if we add white noise (a random component) to the data series, the scaling relation between the wave number and the spectral density will not change. The white noise is the simplest series with various frequencies, and the intensity at all frequencies is the same. A formula of error correction can be found by the data in Table 2, that is, which can be used to reduce the error of the estimated fractal dimension. It is easy to apply the dimension estimation process to the fractal landscape of the DLA model displayed in Figure 1, from which we can abstract a sample of spatial series with random noise.

Table 2: Comparison between the fractal dimension values of an imaginary city form and its estimated results from the spectral exponent.
Figure 2: A log-log plot of the wave spectrum relation based on the inverse power function. (Note that a sample, with , can be produced by taking = 1.6654 and in (2.2). The spectral exponent of this data set is computed as , thus (2.7) yields a dimension estimation .)

One of the discoveries is that the estimated result becomes more precise the closer the value approaches 1.7. The relation between the dimension and the squared error produces a hyperbolic catenary, which can be converted into a concave parabola through the Taylor series expansion. For example, when , the empirical relation is The goodness of fit for this relation is . This suggests that when , the square error approaches the minimum .

Another discovery is that the best fit of data to the wave-spectrum relation appears when the fractal dimension approaches rather than when . The relation between the logarithm of the fractal dimension (ln) and the squared correlation coefficient is a convex parabola. For instance, taking , we have another parabola equation The goodness of fit is . This implies that when , the value approaches the maximum . If , we have (Table 1). In fact, when , the spectrum of short waves becomes divergent; when , the spectrum of long waves becomes divergent. Only when , does the wave spectrum converge in the best way [38].

3.2. Mathematical Experiment Based on Negative Exponential Function

For the negative exponential distribution, the of self-similar urban form does not exist. However, we can estimate the of self-affine curves by means of the wave-spectrum relation. The procedure is comprised of five steps. The first step is to use (2.1) to produce a noise-free series of the urban density by taking certain and  values. The length of the sample path is also taken as   . The next four other steps are similar to those used for estimating the in Section 3.1. The notation of the computed fractal dimension is , differing from the given dimension . The expected dimension value is , and the estimation of the fractal parameter can be illustrated with a log-log plot (Figure 3). The corresponding landscape of exponential distribution can be found in a real urban shape (Figure 4). The longer the sample path is, the closer the spectral exponent value is to and the closer the estimated value of the profile dimension is to (Table 3). The length of the spatial series is long enough in theory, so the spectral exponent will be infinitely close to 2 and the value will be infinitely close to 1.5.

Table 3: Spectral exponent, fractal dimension, and related parameter values based on the standard exponential distributions (partial results).
Figure 3: A log-log plot of a wave-spectrum relation based on negative exponential function. (Note that taking and in (2.1) yields a sample path of . A wave-spectrum analysis of this sample gives , which suggests that the fractal dimension of the self-affine record is around .)
Figure 4: A sketch map of the zonal system of Beijing with a system of concentric circles.

Random fractal forms can be associated with fBm, with varying from 0 to 1, thus varying from 1 to 2. If , then and , indicating Brownian motion instead of fBm. This suggests that the city form that satisfies the negative exponential distribution is based on the Brownian motion process with a self-affine fractal property. The local dimension value of the self-affine fractal record can be estimated as by the wave-spectrum relation. In this case, according to (2.9), the dimension of the urban form can be treated as . This is a special dimension value indicative of a self-affine fractal form.

3.3. Empirical Evidence: The Case of Beijing

The spectral analysis can be easily applied to real cities by means of MS Excel, Matlab, or Mathcad. Now, we take the population and land use of Beijing city as an example to show how to make use of the wave spectrum relation in urban studies. The fifth census data of China in 2000 and the land use data of Beijing in 2005 are available. Qianmen, the growth core of Beijing, is taken as the center, and a series of concentric circles are drawn at regular intervals (Figure 4). The width of an interval represents 500 meters on the earth’s surface. The land use area between two circles can be measured with the number of pixels on the digital map, and it is not difficult to calculate the area with the aid of ArcGIS software. Thus, the land use density can be determined easily. The population within a ring is hard to estimate because the census is taken in units of jie-dao (subdistrict) and each ring runs through different jie-daos. This problem is solved by estimating the weighted average density of the population within a ring [37]. We have 72 circles and thus 72 rings from center to exurb (suburban counties), but only the first 64 data points are adopted because of the algorithmic need of FFT () [27]. The study area is then confined to the field with a radius of 32 kilometers. This is enough for us to study the urban form of Beijing.

The population density distribution of Beijing follows Clark’s law and can be fitted to (2.1). An ordinary least squares (OLSs) calculation yields The goodness of fit is about . The population within a certain radius, , does not satisfy the power law. In this instance, Beijing’s population distribution cannot be described using the , but it can be depicted by the . That is, the human activities of the city may be based on Brownian motion and contain a set of self-affine fractal records.

The spectral density can be obtained by applying FFT to the population density, involving 64 concentric circles. The relation between the wave number and the spectral density follows the power law. A least squares computation gives the following result: The goodness of fit is around (Figure 5). The estimated value of (2.0549) is very close to the theoretically expected value (). Using (2.8), we can estimate the and have The result approaches the expected value of (1.5). This suggests that the population distribution of Beijing possess some nature of random walk. Then, according to (2.9), the city form’s can be estimated to be This value is close to the theoretical value of the Euclidean dimension, .

Figure 5: A log-log plot of the wave spectrum relation of Beijing’s population density (2000).

Because of underdevelopment of fractal structure, the land use density of Beijing seems to meet the negative exponential distribution rather than the power-law distribution. In a sense, the land use density follows the inverse power law locally. However, as a whole, the total quantity of land use within a certain radius follows the power law (Figure 6). The integral of (2.2) in the 2-dimensional space is where denotes the pixel number indicating the land use area within a radius of r from the city center and is a constant. Fitting the data of urban land use to (3.9) yields The goodness of fitness is about , and . Accordingly, , and .

Figure 6: A log-log plot of the relation between radius and corresponding land use quantity of Beijing (2005).

For the standard power-law distribution, the of urban form can be estimated by either (2.2) or (3.9). However, as indicated above, the of Beijing cannot be evaluated through (2.2) because the city’s land use density fails to follow the inverse power law properly. We can approximately estimate the fractal dimension through spectral analysis based on (2.2). The spectral density is still generated with FFT. The linear relation between the wave number and the spectral density is obvious in the log-log plot (Figure 7). A least squares computation yields The goodness of fit is about , and . Correspondingly, the can be estimated as which can be corrected to . Accordingly, the is This implies that the fractal dimension can be evaluated either by the integral result of (2.2) or by the wave spectrum relation based on (2.2). The former method is more convenient, while the latter approach can be used to reveal the regularity on a large scale due to the filter function of Fourier transform.

Figure 7: A log-log plot of the wave-spectrum relation of Beijing’s land use patterns.

To sum up, the of Beijing’s city form can be either directly evaluated or indirectly estimated through spectral analysis . The difference between these two results is due to algorithmic rules and random disturbance among others. The cannot be directly evaluated in this case. The spectral analysis is the most convenient approach to estimating it . Of course, it can be indirectly estimated with the number-radius scaling . The of Beijing’s urban population can be treated as , and . The main results are displayed in Table 4, which shows a concise comparison between the parameter values from different approaches.

Table 4: Fractal dimensions, spectral exponents, and related statistics of land use and population distribution in Beijing.

From the fractal perspective, the main conclusions about Beijing’s population and land use forms can be drawn as follows. First, the population density of Beijing follows Clark’s law, so the spatial distribution of the urban population bears no self-similar fractal property. Second, the land uses of this city take on self-similar fractal features, but the fractal structure degenerates to some extent. The quantity of land use within a radius of from the city center can be approximately modeled with a power function, and the scaling exponent is the radial dimension. Third, the dynamic process of population and land use possesses self-affine fractal properties. Both the population and land use can be associated with self-affine fractal records. The population pattern is possibly based on Brownian motion while the land use patterns are mainly based on fBm. Fourth, the human activity of Beijing is of locality while the land use is associated with action at a distance. The of the population distribution is near , which suggests that the is close to 0.5. Therefore, the of the spatial increment series is near zero, and this value reminds us of spatial locality [37]. The of land use is around 1.65, and the corresponding is 0.35. Thus, the is estimated to be about , which suggests a long memory and antipermanence of spatial correlation between the urban core and periphery.

4. Questions and Discussions

The obvious shortcoming of this work is that the wave-spectrum scaling is only applicable to static pictures of urban structures in mathematical experiments and empirical analyses. By means of computer simulation techniques, such as CA and multiagent systems (MASs) [21], perhaps we can base our urban analysis on the continuous process of urban evolution. This is one of the intended directions of spectral analysis for urban growth and form. The focus of this paper is on the theoretical understanding of fractal cities, rather than a case study of real cities. After all, as Hamming [49] pointed out, the purpose of modeling and computing is insight, not numbers.

To reveal the essential properties of fractal cities in a simple way, a new model of monocentric cities, which can be termed the particle-ripple model (PRM), is proposed here (Figure 1). A city system can be divided into two levels: the particle layer and the wave layer. At the micro level, the city can be regarded as an irregular aggregate of “particles” taking on random motion. In contrast, at the macro level, the city can be abstracted as some deterministic pattern based on a system of concentric circles and the concept of statistical averages. The former reminds us of the fractal city model, which can be simulated with the DLA model, dielectric breakdown model (DBM), and CA model, among others [7, 21, 44, 50]. The latter remind us of von Thunen’s rings and the Burgess’s concentric zones, which can be modeled with (2.1), (2.2), or (2.6). A simple comparison between the power-law and exponential distributions can be made by means of PRM. The main similarities and differences of the two distributions are outlined in Table 5.

Table 5: The similarities and differences between inverse power law and negative exponential distributions.

The spatial feature of the particle level can be characterized by the fractal models based on the wave layer. In theory, we can use (2.2), (2.6), or (3.9) to estimate the of the cluster in Figure 1. For convenience, we will notate them as ,, and , respectively. The results are expected to be the same for each equation (i.e., ). However, the estimated values in empirical analyses are usually different, that is, . In most cases, the value of cannot be properly estimated by using the inverse power function. Taking Beijing as an example, the results are as follows: , (Table 4). However, is an unacceptable result because the dimensions of Beijing cannot be less than 1.

The three power functions are related to but different from one another. As a special density-density correlation function, (2.2) can capture more details at the micro level (particle layer). Thus the results are usually disturbed to a great extent by random noises. In contrast, as a function of correlation sum, (3.9) omits detailed information and reflects the geographical feature as a whole (wave layer). Equation (2.6) is based on (2.2). The noise and particulars can be filtrated by FFT so that (2.6) catches the main change trend. Both (2.2) and (3.9) characterize the form of the particle layer through the wave layer. Equation (2.6) describes the city form by projecting the particle layer onto the wave layer. The result of projection is defined in the complex number domain rather than in the real number domain.

The can also be used to characterize urban growth and form. A mathematical model is often defined at the macro level, while the parameters of the model, including fractal dimension, always reflect information at the micro level. Both and are the scaling exponents of spatial correlation based on the particle layer, but they are different from each other. The relationships and distinctions between the and can be summarized in several aspects (Table 6). First, the is a measurement of self-similar form while the is one of the measurements of self-affine patterns. Second, the represents the dimension of spatial distribution while the indicates the dimension of a curve or a surface [26]. Third, the represents density-density correlation at the wave layer, while indicates increment-increment correlation at the particle layer. The former is an exponent of spatial correlation of density distribution while the latter is an exponent of spatial autocorrelation of density increments. Finally, if the value falls between 1.5 and 2, the two dimensions can come into contact with each other .

Table 6: Comparison between the radial and profile dimensions.

By analogy with the fractal growth of DLA, we can understand city forms through their dimensions. Let us examine the DLA model displayed in Figure 1. For the cluster, and the goodness of fit is about . In the aggregation process, each particle moves by following a random path until it touches the growing cluster and becomes part of the aggregate. The track of a particle is a self-affine curve, which cannot be recorded directly and does not concern us. What interests us is the final distribution of all the particles with remnant information on the self-affine movements. For a profile from the center to the edge, on the average, . Thus, , and further, we have ., so as estimated at the micro level. At the macro level, the one-point correlation function is . The may be treated as a new fractal dimension termed the image dimension of urban forms because it always differs from in practice. This dimension can act as a complementary measurement of spatial analysis, which remains to be discussed in future work.

5. Conclusions

Spectral analysis based on Fourier transform is one of powerful tools for the studies of fractal cities. First of all, it can help reveal some theoretical equations, such as the relation between and . Next, it can be used to evaluate fractal dimensions, which are hard to calculate directly, such as the indicative of self-affine record of urban evolution. Finally, it can provide us with a supplementary approach to computing the fractal dimension, which can be directly determined by the area-radius scaling. When the urban density fails to follow the inverse power law properly, spectral analysis is an indispensable way of estimating latent fractal dimensions.

Based on the area-radius relation of cities, the main conclusions of this paper are as follows. First, to describe the core-periphery relationships of urban form, we need at least two fractal dimensions, the and the . The can be either directly calculated with the aid of the area-radius scaling or indirectly evaluated by the wave-spectrum relation. The is mainly estimated with the wave spectrum relation. When the ranges from 1.5 to 2, the sum of the two dimension values is a constant. Second, the dimensions of city phenomena satisfying the negative exponential distribution can be treated as . In spatial analysis, it is important to determine the dimensions of a geographical phenomenon. The dimension based on the power-law distribution is easy to evaluate. However, little is known about the dimensions of geographical systems following the exponential distribution. One useful inference of this study is that the dimension of exponential distribution phenomena is 2. If so, a number of theoretical problems, such as the allometric scaling exponent of urban area and population, can be readily solved. Third, city form bears no characteristic scale, but the fractal dimension of city form possesses a characteristic scale. Various fractal parameters, such as , , , and , have mathematical relations with one another. However, the rational ranges of these parameter values are not completely consistent with each other. Only when the value of the varies from 1.5 to 2, will all these fractal parameters become valid in value. This seems to suggest that the range of from 1.5 to 2 is a common scale for all these parameters, thus it is a reasonable scale for the . This scale of fractal dimension is revealing for unborn city planning and the spatial optimization of urban structures.


This research was sponsored by the Natural Science Foundation of Beijing (Grant no. 8093033) and the National Natural Science Foundation of China (Grant no. 40771061). The supports are gratefully acknowledged. The author would like to thank Jingyi Lin of Peking University for providing the essential data on the urban land use and population of Beijing. Many thanks are to five anonymous reviewers whose interesting comments were very helpful in preparing the revised version of this paper.


  1. P. J. Taylor, Quantitative Methods in Geography: An Introduction to Spatial Analysis, Waveland Press, Long Grove, Ill, USA, 3rd edition, 1983.
  2. S. L. Arlinghaus, “Fractals take a central place,” Geografiska Annaler, Series B, vol. 67, no. 2, pp. 83–88, 1985. View at Google Scholar · View at Scopus
  3. M. Batty and P. A. Longley, “Urban shapes as fractals,” Area, vol. 19, no. 3, pp. 215–221, 1987. View at Google Scholar · View at Scopus
  4. L. Benguigui and M. Daoud, “Is the suburban railway system a fractal?” Geographical Analysis, vol. 23, no. 4, pp. 362–368, 1991. View at Google Scholar
  5. P. Frankhauser and R. Sadler, “Fractal analysis of agglomerations,” in Natural Structures: Principles, Strategies, and Models in Architecture and Nature, M. Hilliges, Ed., pp. 57–65, University of Stuttgart, Stuttgart, Germany, 1991. View at Google Scholar
  6. M. F. Goodchild and D. M. Mark, “The fractal nature of geographic phenomena,” Annals of Association of American Geographers, vol. 77, no. 2, pp. 265–278, 1987. View at Publisher · View at Google Scholar · View at Scopus
  7. A. S. Fotheringham, M. Batty, and P. A. Longley, “Diffusion-limited aggregation and the fractal nature of urban growth,” Papers of the Regional Science Association, vol. 67, no. 1, pp. 55–69, 1989. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Batty and P. A. Longley, Fractal Cities: A Geometry of Form and Function, Academic Press, London, UK, 1994.
  9. P. Frankhauser, La Fractalité des Structures Urbaines, Economica, Paris, France, 1994.
  10. L. Benguigui, D. Czamanski, M. Marinov, and Y. Portugali, “When and where is a city fractal?” Environment and Planning B, vol. 27, no. 4, pp. 507–519, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Benguigui, E. Blumenfeld-Lieberthal, and D. Czamanski, “The dynamics of the Tel Aviv morphology,” Environment and Planning B, vol. 33, no. 2, pp. 269–284, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. G. Chen, Fractal Urban Systems: Scaling, Symmetry, and Spatial Complexity, Scientific Press, Beijing, China, 2008.
  13. J. Cooper and R. Oskrochi, “Fractal analysis of street vistas: a potential tool for assessing levels of visual variety in everyday street scenes,” Environment and Planning B, vol. 35, no. 2, pp. 349–363, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Crompton, “The fractal nature of the everyday environment,” Environment and Planning B, vol. 28, no. 2, pp. 242–254, 2001. View at Google Scholar · View at Scopus
  15. M.-L. De Keersmaecker, P. Frankhauser, and I. Thomas, “Using fractal dimensions for characterizing intra-urban diversity: the example of Brussels,” Geographical Analysis, vol. 35, no. 4, pp. 310–328, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. D. S. Dendrinos and M. S. El Naschie, “Nonlinear dynamics in urban and transportation analysis,” Chaos, Soliton & Fractals, vol. 4, no. 4, pp. 497–617, 1994. View at Publisher · View at Google Scholar
  17. K. S. Kim, L. Benguigui, and M. Marinov, “The fractal structure of Seoul's public transportation system,” Cities, vol. 20, no. 1, pp. 31–39, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Lu and J. Tang, “Fractal dimension of a transportation network and its relationship with urban growth: a study of the Dallas-Fort Worth area,” Environment and Planning B, vol. 31, no. 6, pp. 895–911, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. I. Thomas, P. Frankhauser, and M.-L. De Keersmaecker, “Fractal dimension versus density of built-up surfaces in the periphery of Brussels,” Papers in Regional Science, vol. 86, no. 2, pp. 287–308, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. I. Thomas, P. Frankhauser, and C. Biernacki, “The morphology of built-up landscapes in Wallonia (Belgium): a classification using fractal indices,” Landscape and Urban Planning, vol. 84, no. 2, pp. 99–115, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Batty, Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals, MIT Press, London, UK, 2005.
  22. M. Batty and Y. Xie, “Self-organized criticality and urban development,” Discrete Dynamics in Nature and Society, vol. 3, no. 2-3, pp. 109–124, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  23. R. White and G. Engelen, “Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns,” Environment & Planning A, vol. 25, no. 8, pp. 1175–1199, 1993. View at Publisher · View at Google Scholar · View at Scopus
  24. R. White and G. Engelen, “Urban systems dynamics and cellular automata: fractal structures between order and chaos,” Chaos, Solitons & Fractals, vol. 4, no. 4, pp. 563–583, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  25. R. White, G. Engelen, and I. Uljee, “The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics,” Environment and Planning B, vol. 24, no. 3, pp. 323–343, 1997. View at Publisher · View at Google Scholar · View at Scopus
  26. H. Takayasu, Fractals in the Physical Sciences, Nonlinear Science: Theory and Applications, Manchester University Press, Manchester, UK, 1990.
  27. Y. Chen, “Urban gravity model based on cross-correlation function and Fourier analyses of spatio-temporal process,” Chaos, Solitons & Fractals, vol. 41, no. 2, pp. 603–614, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. B. B. Mandelbrot, The Fractal Geometry of Nature, W. H. Freeman and Company, New York, NY, USA, 1983.
  29. P. Frankhauser, “The fractal approach: a new tool for the spatial analysis of urban agglomerations,” Population: An English Selection, vol. 10, no. 1, pp. 205–240, 1998. View at Google Scholar
  30. M. Batty, “The size, scale, and shape of cities,” Science, vol. 319, no. 5864, pp. 769–771, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. P. A. Longley, M. Batty, and J. Shepherd, “The size, shape and dimension of urban settlements,” Transactions of the Institute of British Geographers, vol. 16, no. 1, pp. 75–94, 1991. View at Publisher · View at Google Scholar · View at Scopus
  32. M. T. Cadwallader, Urban Geography: An Analytical Approach, Prentice Hall, Upper Saddle River, NJ, USA, 1996.
  33. Y. G. Chen, “A new model of urban population density indicating latent fractal structure,” International Journal of Urban Sustainable Development, vol. 1, no. 1, pp. 89–110, 2009. View at Publisher · View at Google Scholar
  34. C. Clark, “Urban population densities,” Journal of Royal Statistical Society, vol. 114, no. 4, pp. 490–496, 1951. View at Google Scholar
  35. M. Batty and K. S. Kim, “Form follows function: reformulating urban population density functions,” Urban Studies, vol. 29, no. 7, pp. 1043–1069, 1992. View at Publisher · View at Google Scholar · View at Scopus
  36. R. J. Smeed, “Road development in urban area,” Journal of the Institution of Highway Engineers, vol. 10, no. 1, pp. 5–30, 1963. View at Google Scholar
  37. Y. G. Chen, “A wave-spectrum analysis of urban population density: entropy, fractal, and spatial localization,” Discrete Dynamics in Nature and Society, vol. 2008, Article ID 728420, 22 pages, 2008. View at Google Scholar · View at Zentralblatt MATH
  38. S. D. Liu and S. K. Liu, An Introduction to Fractals and Fractal Dimension, China Meteorological Press, Beijing, China, 1992.
  39. M. F. Barnsley, R. L. Devaney, B. B. Mandelbrot, H.-O. Peitgen, D. Saupe, and R. F. Voss, The Science of Fractal Images, Springer, New York, NY, USA, 1988.
  40. H.-O. Peitgen, H. Jürgens, and D. Saupe, Chaos and Fractals, Springer, New York, NY, USA, 2nd edition, 2004.
  41. Benoit B. Mandelbrot, Multifractals and 1/f Noise: Wild Self-Affinity in Physics (1963–1976), Springer, New York, NY, USA, 1999.
  42. J. Feder, Fractals, Plenum Press, New York, NY, USA, 1988.
  43. H. E. Hurst, R. P. Black, and Y. M. Simaika, Long-term Storage: An Experimental Study, Constable, London, UK, 1965.
  44. M. Batty, P. Longley, and S. Fotheringham, “Urban growth and form: scaling, fractal geometry, and diffusion- limited aggregation,” Environment & Planning A, vol. 21, no. 11, pp. 1447–1472, 1989. View at Publisher · View at Google Scholar · View at Scopus
  45. B. B. Mandelbrot and J. W. Van Ness, “Fractional Brownian motions, fractional noises and applications,” SIAM Review, vol. 10, no. 4, pp. 422–437, 1968. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  46. D. A. Griffith, Spatial Autocorrelation and Spatial Filtering: Gaining Understanding through Theory and Scientific Visualization, Springer, New York, NY, USA, 2003.
  47. Y. Chen and Y. Zhou, “Scaling laws and indications of self-organized criticality in urban systems,” Chaos, Solitons & Fractals, vol. 35, no. 1, pp. 85–98, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. F. X. Diebold, Elements of Forecasting, Thomson/South-Western, Mason, Ohio, USA, 3rd edition, 2004.
  49. R. W. Hamming, Numerical Methods for Scientists and Engineers, International Series in Pure and Applied Mathematics, McGraw-Hill, New York, NY, USA, 1962.
  50. R. White, “Cities and cellular automata,” Discrete Dynamics in Nature and Society, vol. 2, no. 2, pp. 111–125, 1998. View at Publisher · View at Google Scholar