区域经济发展与人口流动关系的实证模型研究

发布时间：2018-04-21 12:09

本文选题：人口流动 + 经济发展　；参考：《天津财经大学》2013年硕士论文

【摘要】：建国以来,人口问题一直是困扰中国经济发展的重要因素,而随着改革开放的推进,城市工业化的发展,人口大规模的流动也越来越深刻地影响着中国经济社会的变革。天津市作为中国四大直辖市之一,被定义为环渤海经济圈的中心,在北方有及其重要的经济地位。在这种背景下,将管理学、数据挖掘技术引进到天津市人口流动的研究中,分析人口流动与经济发展关系,探讨流动人口规模发展趋势,对天津市流动人口的管理具有现实意义。本文主要做了两方面的工作：首先根据天津市经济数据(生产总值)、流动人口数据、总人口数据,利用灰色关联度模型,分析了天津市流动人口数量、总人口数量对天津市生产总值的关联系数。结果显示流动人口数量与生产总值的关联系数较高,并且要高于总人口数量与生产总值的关联系数。说明天津市经济的高速发展与人口流动有较大关系,人口流动促进了天津市的经济发展。基于上述结论,第二项工作即通过数据挖掘模型预测未来天津市人口流动的规模,并依据此预测结果,分析将会对天津的经济发展带来什么样的影响,并提出相应的政策建议。在对人口流动规模进行预测时,运用了两种不同的预测方法,第一种是Logistic曲线模型,分别采用高、中、低三种方案进行拟合,得到高方案的拟合效果相对较好,预测到2030年天津市流动人口数量将达到498万人,并达到饱和,不再增长。第二种方法是基于BP神经网络的预测模型。通过神经网络128次的训练和学习,期望误差达到合理范围,得到所要建立的预测神经网络模型。预测结果到2020年天津的流动人口达到531万人,并在2021年首次出现下降。比较两种模型的预测结果相对误差,BP神经网络模型误差相对较小,适合作为天津市人口流动规模的预测模型。最后针对人口流动规模的预测结果,在户籍制度改革、加强社会保障、打造滨海新区为吸引流动人口试验区等方面对天津市的人口流动管理提出相应的政策建议。
[Abstract]:Since the founding of the people's Republic of China, population problem has been an important factor puzzling China's economic development. With the development of reform and opening up, the development of urban industrialization and the large-scale population flow, the economic and social changes in China have been more and more deeply affected. As one of the four municipalities directly under the Central Government of China, Tianjin is defined as the center of the economic circle around the Bohai Sea, and has an important economic status in the north. In this context, the management and data mining techniques are introduced into the study of population mobility in Tianjin, and the relationship between population mobility and economic development is analyzed, and the development trend of floating population scale is discussed. It has practical significance to the management of floating population in Tianjin. This paper mainly does two aspects of work: firstly, according to Tianjin economic data (GDP, floating population data, total population data, the use of grey correlation model, the number of Tianjin floating population analysis, The correlation coefficient of the total population quantity to the gross product of Tianjin. The results show that the correlation coefficient between the number of floating population and GDP is higher than that of total population and GDP. It shows that the rapid development of Tianjin's economy is closely related to population flow, which promotes the economic development of Tianjin. Based on the above conclusion, the second work is to predict the population flow scale of Tianjin through data mining model, and based on the prediction results, the paper analyzes what kind of impact will be brought to Tianjin's economic development, and puts forward corresponding policy recommendations. In forecasting population flow scale, two different forecasting methods are used. The first is the Logistic curve model, which uses three schemes, high, middle and low, respectively. It is predicted that the floating population in Tianjin will reach 4.98 million by 2030, reaching saturation and no longer increasing. The second method is based on BP neural network prediction model. Through 128 times of training and learning of neural network, the expected error reaches a reasonable range, and the predictive neural network model is established. Tianjin's migrant population will reach 5.31 million by 2020 and will decline for the first time in 2021. Comparing the relative error between the two models the model error of BP neural network is relatively small which is suitable for the prediction of population flow scale in Tianjin. Finally, according to the forecast results of population mobility scale, the paper puts forward corresponding policy recommendations on Tianjin population mobility management in the aspects of household registration system reform, strengthening social security, building Binhai New area to attract floating population experimental area and so on.
【学位授予单位】：天津财经大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F127;C924.2

【参考文献】