<![CDATA[The COVID-19 pandemic in 2020 triggered a significant urban exodus in major global cities, shifting internal migration patterns in unprecedented ways. This study investigates the determinants of internal net migration across 33 London boroughs to understand the drivers behind this population displacement. Utilizing a spatial econometric framework, the research integrates public health indicators (COVID-19 cases), economic variables (housing prices and GDP), and geographic characteristics (population density and distance to the city center) into an enriched Ordinary Least Squares (OLS) model. The methodology is validated through rigorous diagnostic testing, including Heteroskedasticity-Consistent (HC1) standard errors and spatial autocorrelation checks (Moran's I), confirming that spatial feature engineering sufficiently captures geographic dependencies. The results reveal that the public health shock was the primary driver of the exodus; total COVID-19 cases exhibited a highly significant negative correlation with net migration, indicating that boroughs with higher infection burdens experienced larger population outflows. Population density acted as a secondary push factor, while traditional economic drivers such as housing prices and regional GDP were statistically insignificant. These findings suggest a temporary paradigm shift where immediate health security outweighed economic maximization in residential location decisions during the crisis.]]>
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