Full-Stack Development of an Intelligent System for the Development of Population Migration
DOI:
https://doi.org/10.31861/sisiot2023.1.01004Keywords:
geodata, population migration, full-stack development, intelligent system, decision-makingAbstract
A generalised model of population migration is proposed. On the basis of the model of population migration, the article develops models of: a set of directions of population flows, which are formed on internal and external flows of the State; duration of migration, which is determined by its nature in time, including permanent or irreversible duration of migration, movement for a relatively short time, annual movement of people and pendulum duration of migration; type and form of migration. A model of indicators of actual migration (resettlement) that can characterise the overall level of mobility of the population of the territories, the scale, structure, directions and effectiveness of migration flows for a given period is developed and their groups are divided. It is proposed that the results of population migration should be presented in the form of a number of absolute and relative indicators for the purpose of further regression analysis of data, namely, those who arrived for permanent residence from other settlements; those who left for permanent residence to other settlements; migration balance or mechanical growth. Inter-rayon relations are characterised by the strength of migration flows. To obtain the results of migration, we take into account the strength of migration flows, which depend on the population of the territories between which the exchange takes place and on their location. The result of this exchange is expressed in the migration balance or by means of efficiency coefficients of migration ties. The intensity of migration exchange, independent of the population size of both the areas of origin and the places of settlement, is determined by the intensity coefficients of migration ties. The types of migration intensity coefficients are formed depending on the properties, namely the intensity coefficients of arrival (immigration), departure (emigration), reverse migration, and net migration. The intelligent geographic information system implements the lightgbm algorithm for population migration forecasting, which is a decision tree with gradient reinforcement. For data analysis, the migration forecasting system includes regression analysis and neural networks and is capable of predicting international migration or migration between different countries.
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