Urban Allometric Characteristics And Evolution Model Research

The city is the region where politics,economy and culture are concentrated.It has a great radiation effect on the social and economic development,and the rapid development of the economy will certainly mean the rapid expansion of the city and the rapid rise of the proportion of urbanization.With the continuous progress of urbanization,the number of urban population increases rapidly,causing a series of urban diseases,such as traffic congestion,air pollution,and urban poverty.In order to solve the urban disease,it is important to accurately grasp the dynamics of urban population growth and the state of urban economic growth.Population is the basic component of city system,economy is an important measure of the development of urban system,and the allometric growth relationship between population and social economic indicators has attracted many city system researchers and economics researchers.As a complex and huge system,the city is self-similar.The complex network theory,which has arisen in recent years,is helpful to represent the urban system structure.Cross combination of population growth theory,economic theory model and complex network theory,provide a theoretical basis for understanding the interaction between the city system and its internal population and economic elements,and provide guidance for the macro-control of city economy and future city development planning.To study the allometric growth relationship in city system,this paper has the aid of the computer simulation technology to verify the allometric growth relationship between city population and GDP(Gross Domestic Product,GDP),and verify the temporal and spatial universality of this relationship at the same time.Then in order to reproduce the allometric growth relationship in city system,an attraction model and an exponent tunable network model for reproducing density driven superlinear relation is built combined with the theory of population growth,economic model and complex network theory.And verify the feasibility and reliability of the model through contrasting and analyzing the computer simulation results and empirical results.The specific work is as follows:(1)According to the analysis of the relationship between population and GDP in Japan from 1992 to 2012 by using the Japanese statistical yearbook,we verified the allometric growth relationship between population and GDP in the urban system.By improving the Agent model,then combined with the population growth and the concept of production function to further extend the model,this paper established the attraction model,and the simulation results prove that the allometric model can explain the allometric growth relationship between the population and GDP.(2)Through contrasting and analyzing the gross based and density based allometric relationship between population and GDP,to justify the density value is a better metric than gross to measure the allometric growth phenomenon in city system.In order to analyze the spatial and temporal universality of allometric growth relationship in urban system,the Chinese statistical yearbook from 1994 to 2013 is used to prove the time universality at first;then six different regional scales is defined,and by using the statistics yearbook of various countries and regions around the world,analyze the relative growth speed between population and city GDP system from two angles of the same regional scale level and different regional scale level,which proves the spatial universality of the allometric growth relationship in city system.(3)This paper studies the current density based models,and find that the existing models can’t explain the spatial and temporal universality of allometric growth relationship in urban systems.Therefore,we establish an exponent tunable network model for reproducing density driven superlinear relation,which actually reproduces the spatiotemporal universality of allometric growth phenomena.By computer simulation,the simulation results and empirical results are compared,which proves the feasibility and controllability of the model at last.