The spatial distribution of urban population can reflect significantly urban functions and development status. Shenyang, as a typical old industrial city in China, has experienced considerable changes in spatial distribution of population in the process of urban transformation, resulting in the change of urban spatial structure. Based on the subdistrict data of Chinese national population censuses in 1982, 1990 and 2000, this study simulates the evolution pattern of spatial distribution of urban population in Shenyang City. Using statistical method and exploratory spatial data analysis (ESDA), we found that the population distribution, on the whole, has presented a balanced and decentralized trend since the 1980s, which characterizes with Chinese suburbanization. Furthermore, based on the investigation of the pattern of population distribution, it is concluded that the negative exponential model fitted the distribution best, and population concentration in the inner suburb kept increasing gradually, meanwhile, the spatial structure of population distribution has presented a polycentric feature since the 1980s. The parameters of the model show that population in the urban core concentrate significantly all the time. The increase of population in the inner suburb influences the population distribution pattern more and more importantly, but the concentration intensity of population cores in inner suburb is still low.
Studying the characteristics of wind speed is essential in wind speed prediction. Based on long-term observed wind speed data, fractal dimension analysis of wind speed was first conducted at different scales, and persistence in wind speed was evaluated based on fractal dimensions in this paper. To propose a more accurate model for wind speed prediction, the wavelet decomposition method was applied to separate the high-frequency dynamics of wind speed data from the low-frequency dynamics. Chaotic behaviors were studied for each decomposed component using the largest Lyapunov exponents method. A proposed hybrid prediction method combining wavelet decomposition, a chaotic prediction method and a Kalman filter method was investigated for short-term wind speed prediction. Simulation results showed that the proposed method can significantly improve prediction accuracy.
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