Understanding internal migration in the UK before and during the COVID-19 pandemic using twitter data

Area of Science:

• Social Sciences
• Data Science
• Urban Studies

Background:

• The COVID-19 pandemic significantly impacted internal migration patterns, necessitating economical and timely monitoring methods.
• Advancements in geolocation technology offer new avenues for tracking population movements.

Purpose of the Study:

• To monitor internal migration patterns during the COVID-19 pandemic using Twitter data.
• To develop and validate indices for analyzing migration dynamics at various scales.

Main Methods:

• Utilized near real-time, fine-grained Twitter data from January 2019 to December 2021.
• Employed geocoding and home location estimation to derive migration patterns.
• Developed five indices to quantify migration, validated against official UK migration data.

Main Results:

• The pandemic and lockdown policies led to a significant reduction in migration rates.
• Observed a trend of population outflow from major cities to surrounding rural and adjacent urban areas.
• This ruralward migration intensified in 2020, with limited return migration by late 2021, though cities showed quicker recovery.

Conclusions:

• Twitter data provides a valuable, high-granularity tool for analyzing migration trends, despite potential population representation biases.
• The study highlights the complex, spatially and temporally varied social impacts of the pandemic on population mobility.
• Findings offer insights into long-term migration shifts potentially extending beyond the pandemic.