Urbanization Level Prediction Model And Its Applic Ation

Urbanization level is an important indicator to measure a country and region’s urbanization and its determination method is imperfect. The study of the scientific evaluation of the level of urbanization becomes an important subject in China. The level of urbanization in our country generally refers to the proportion of population residing in the cities, which reflects the level of a city region development and the overall development level of national economy. The region of Lanzhou city, Gansu Province, as an important province in western region has the following characteristics:a small number of cities, small size, and uneven distribution. How to grasp the enormous opportunities that urbanization brings for development, promote the further urbanization of Lanzhou city, Gansu Province, accelerate the regional economic and social development level and enhance the capacity of sustainable development, becomes an important research issue.This paper based on the analysis of current urbanization home and abroad, chooses the proportion of non-agricultural population as one-dimensional index which is the source of data for studying urbanization of Lanzhou city. By using simple linear regression model, Logistic growth model and time series model, they were used to analyze the level of urbanization in1986-2012in Lanzhou, respectively. Then complete model is set up; at last the thesis uses these models to predict the urbanization level of Lanzhou city in1986-2012. In the third part of this article, through the method of dividing the interval between fuzzy entropy and fuzzy clustering method and construct the fuzzy time series model based on deterministic state transition. At the same time it can be used to analyze the data of the level of urbanization from1986to2012. Then the author uses the models to predict the level of urbanization from1986to2012. In the last part of third chapter, through analyzing the error rate for analysis about the four model’s urbanization level in1986-2012, it concluded that forecasting precision of fuzzy time series model was obviously higher than that of existing models. So a new prediction method was born, which is about the forecast analysis of urbanization level. In the fourth part of the article, using time series prediction model forecasts urbanization level from2015to2020to Lanzhou city. The last part of the article summaries and guides the followings:prediction deficiencies of the urbanization level in view of the fuzzy time series to predict and further research contents.This paper is divided into five chapters.The first chapter is introduction. Mainly it illustrates the significance of topic selection, introduces present situation home and abroad, and puts forward the purpose of this study and research methods.The second chapter is a common urbanization level to forecast model analysis. Using simple linear regression model, logistic growth model and time series model respectively the thesis analyzes the urbanization level in Lanzhou city from1986to2012, and using those three models to predict urbanization level from1986-2012to this city.The third chapter is the fuzzy time series model and its urbanization level prediction study. By the method of dividing the interval between fuzzy entropy and fuzzy clustering method and construct the fuzzy time series model based on deterministic state transition. At the same time it can be used to analyze the data of the level of urbanization from1986to2012.Then the author uses the models to predict the level of urbanization from1986to2012. In the last part of chapters, comparing the urbanization level prediction error rate, about the above four models of Lanzhou city in1986-2012, it is concluded that the accuracy of fuzzy time series forecasting model is higher. At last the author, based on fuzzy time series model in2015-2020, predicts urbanization level of Lanzhou.The fourth chapter is summary. This part summaries analysis contrast research and points out the need for further research discussed in this paper.