Application Of Markov Chain In Population Flow

In recent years,China's cities have developed rapidly and the scale of population movement has been increasing,which has caused many problems such as medical care,housing,transportation and education.In view of the above problems,this paper chooses guilin city to study the population flow between different urban regions and between urban and rural areas,so as to analyze and predict the future population situation.Based on the basic concept of Markov chain,Chapman-Kolmogorov equation and n-step transfer probability matrix,the paper explores the model principle by multiple multiplication of n-step transfer matrix,and then carries out Markov chain analysis according to its principle and uses Wilson maximum entropy model to supplement it.At the same time,it further improves the prediction of future population flow by means of linear programming analysis.Based on the above research methods,this paper draws the following conclusions :(1)regardless of the current situation of urban and rural population mobility,it will tend to be stable after many years,and the proportion of the population remaining in the city and the proportion remaining in the countryside is roughly a fixed value.(2)there is a large gap in the number of population flows between different regions and within each region in guilin,showing the characteristics of imbalance and asymmetry.(3)among the three districts selected in guilin,the short-term population distribution is mainly in lingui district,accounting for about 40%;xiufeng district and qixing district are relatively small,accounting for about 30% each.(4)combining the results of Wilson's maximum entropy model analysis with the results of Markov chain analysis,it can be seen that after many years,the residence of floating population is mainly in lingui district,accounting for about50%,seven-star district accounts for about 30%,and xiufeng district accounts for the least,accounting for about 20%.In view of the above results,it is hoped to provide reference for relevant departments in policy formulation and implementation,and further improve the efficiency of resource allocation and utilization.