基于梁氏–克里曼信息流的江苏省人口流动预测
Prediction of Population Flow in Jiangsu Province Based on Liang-Kleeman Information Flow
摘要: 粮食产量作为一个牵动着基本民生福祉的问题,在影响人口流动方面也有着重要的作用。文章以江苏省为例,聚焦江苏省粮食生产比重最大的水稻,研究自1999年至2018年20年间,江苏省水稻产量和人口变化之间的联系。文章在研究过程中确定了将江苏省年均化肥使用量作为水稻产量的工具变量,将信息流的思想运用到因果分析上,利用梁氏–克里曼信息流计算出化肥使用量和江苏省人口流动之间的信息传递。结果表明,江苏省水稻产量和人口流动之间的因果关系是显著的,且江苏省水稻产量是人口流动的“因”。文章利用两种方法分别预测出江苏省未来5年和未来2年的非自然增长人口,均成上升趋势,表明政府需要采取相应的措施,应对大批人口涌入的情况。
Abstract: As an issue that affects basic people’s livelihood and well-being, food production also plays an important role in affecting population mobility. Taking Jiangsu Province as an example, this article focuses on rice, which accounts for the largest proportion of grain production in Jiangsu Province, and studies the relationship between rice production and population changes in Jiangsu Province during the 20 years from 1999 to 2018. In the process of research, the article determined that the average annual fertilizer use in Jiangsu Province was used as an instrumental variable for rice production, applied the idea of information flow to causal analysis, and used Liang-Kleeman information flow to calculate the information transfer between fertilizer use and population movements in Jiangsu Province. The results show that the causal relationship between rice production and population mobility in Jiangsu Province is significant, and that rice production in Jiangsu Province is the “cause” of population mobility. The article uses two methods to predict the unnatural population growth in Jiangsu Province in the next 5 years and the next 2 years, both of which are on the rise, indicating that the government needs to take corresponding measures to deal with the influx of large numbers of people.
文章引用:张克凡, 吕广迎. 基于梁氏–克里曼信息流的江苏省人口流动预测[J]. 应用数学进展, 2022, 11(3): 1355-1366. https://doi.org/10.12677/AAM.2022.113148

参考文献

[1] 藏媛, 郝枫. 空气质量对流动人口城市留居意愿强度的影响[J/OL]. 软科学, 2020(10): 48-61.
[2] 宫湛秋, 孙诚, 李建平, 冯娟, 谢飞, 杨韵, 薛佳庆. 基于信息流理论的因果分析在辨析大西洋多年代际振荡物理机制中的应用[J]. 大气科学, 2019, 43(5): 1081-1094.
[3] Liang, X.S. (2016) Information Flow and Causality as Rigorous Notions ab initio. Physical Review E, 94, 13-40. [Google Scholar] [CrossRef
[4] Liang, X.S. (2014) Unraveling the Cause-Effect Relation between Time Series. Physical Review E, 90, 105-116. [Google Scholar] [CrossRef