Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Abstract

As a fundamental, holistic, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities, given the constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Based on the analysis, there is no “best” migration prediction model, and data are key to forecasting human migration. Social media’s popularity and its increase in data have enabled the application of artificial intelligence in population migration prediction, which has attracted the attention of researchers and government administrators. Future research will aim to incorporate uncertainty into the predictive analysis framework, and explore the characteristics of population migration behaviors and their interactions. The integration of machine-learning and traditional data-driven models will provide a breakthrough for this purpose.