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Top1. Introduction
With the rapid development of urbanization and industrialization after the reform and opening up, especially since the new century, the development gap between the east and west has become obvious, and the level of socio-economic development and employment opportunities in the eastern coastal areas have caused a strong siphon effect, resulting in the rapid growth and expansion of the number and scale of the cross-regional migrant population between the east and west and urban and rural areas, which has rapidly set off a large-scale wave of outbound workers (Soneka & Phiri, 2019). The rapidly increasing mobile population, along with the demographic dividend, has become a powerful booster for the country's rapid economic growth, promoting industrial restructuring and rational allocation of labor resources. Some experts and scholars analyze from the perspective of economics that the return of the migrant population is an important manifestation of the emerging wave of innovation and entrepreneurship in the process of transferring labor-intensive and resource-intensive industries to the central and western regions, which will be accompanied by the transfer and better allocation of human resources (AlBar & Hoque, 2019). If analyzed from the perspective of social integration of the mobile population, what are the important correlations between the emergence of the mobile population return phenomenon and the inability of the mobile population to integrate into the inflow area, and what are the factors affecting the social integration of the mobile population, all these questions need further research and response (Nyangarika & Bundala, 2020).
Based on the definition of the mobile population in existing statistical surveys, this paper defines the concept of population mobility with data and the problem under study, and enriches the theoretical connotation of the mobile population; this paper designs a new method for mobile population size estimation based on existing statistical survey methods, i.e., constructing a mobile population management model based on machine learning classification algorithm through data mining, on which Applying the capture-recapture sampling estimation method to measure the size of the mobile population and extrapolating the information such as the market share of mobile communication operators, this paper provides methodological support to be able to count more accurate data of the mobile population (Saediman et al., 2019). Compared with the existing mobile population survey methods, the population mobility measurement method based on mobile communication big data designed in this paper has been substantially improved in terms of timeliness and saving survey cost while maintaining higher accuracy, so the method can be applied to the practice of statistical survey of the mobile population, which enriches the methodological theory of mobile population survey in the statistical survey, and on this basis, for It also has an important theoretical value for the improvement and perfection of the statistical system of the mobile population. By using data mining and statistical methods, we can analyze the characteristics of communication behaviors of different groups through the use of mobile communication big data resources accumulated by mobile operators in real-time, and find a more accurate, timely, and effective method to obtain information of mobile population, to find a new way for the statistics of the mobile population (Kar et al., 2019).