  For a long time, many methods are developed to make temporal signal analyses
based on time series. However, for geographical systems, spatial signal
analyses are as important as temporal signal analyses. Nonstationary spatial
and temporal processes are associated with nonlinearity, and cannot be
effectively analyzed by conventional analytical approaches. Fractal theory
provides a powerful tool for exploring complexity and is helpful for
spatio-temporal signal analysis. This paper is devoted to researching spatial
signals of geographical systems by means of wave-spectrum scaling. The traffic
networks of 10 Chinese cities are taken as cases for positive studies. Fast
Fourier transform and least squares regression analysis are employed to
calculate spectral exponents. The results show that the wave-spectral density
distribution of all these urban traffic networks follows scaling law, and the
spectral scaling exponents can be converted to fractal dimension values. Using
the fractal parameters, we can make spatial analyses for the geographical
signals. The analytical process can be generalized to temporal signal analyses.
The wave-spectrum scaling methods can be applied to both self-similar fractal
signals and self-affine fractal signals in the geographical world.
