2025/12/21 by E. Charles-Edwards, James Raymer, P. Wohland · 1 voice
Decision Sciences · Social Sciences · #demographic modeling and climate adaptation #Urban, Neighborhood, and Segregation Studies #Education Systems and Policy
paper · pdf · doi:10.1080/00049182.2025.2599284
openalex publication_date 2025/12/21 · openalex created_date 2025/12/22 · openalex updated_date 2026/06/15
Accurate and timely internal migration data are crucial for understanding population shifts and informing policy during crises. Traditional sources, such as censuses and administrative records, are lagged, hindering the identification of emerging migration patterns. This study explores the potential of digital trace data from Muval, an Australian removalist website, to estimate internal migration in Australia from 2019 to 2023. Using a multiple regression framework, we estimate the relationship between Muval data and internal migration figures from the 2021 Australian Census. This relationship is then leveraged to predict migration at the Statistical Area Level 4 (SA4) for other years. Our predictive models explain over 90% of the variance in Census migration figures, allowing for estimation of migration flows from 2019 to 2023. The utility of these estimates is demonstrated through two case studies: migration to and from Greater Melbourne, and inward and outward migration to high-amenity regions between 2019 and 2023. This study contributes to the field of big data for migration estimation, offering a timelier and more responsive tool for understanding migration dynamics in rapidly changing contexts. Future research should focus on integrating demographic and socioeconomic variables to enhance the precision of migration estimates derived from digital trace data.