  Racial residential segregation is a defining and enduring feature of U.S.
society, shaping inter-group relations, racial disparities in income and
health, and access to high-quality public goods and services. The design of
policies aimed at addressing these inequities would be better informed by
descriptive models of segregation that are able to predict neighborhood scale
racial sorting dynamics. While coarse regional population projections are
widely accessible, small area population changes remain challenging to predict
because granular data on migration is limited and mobility behaviors are driven
by complex social and idiosyncratic dynamics. Consequently, to account for such
drivers, it is necessary to develop methods that can extract effective
descriptions of their impacts on population dynamics based solely on
statistical analysis of available data. Here, we develop and validate a
Density-Functional Fluctuation Theory (DFFT) that quantifies segregation using
density-dependent functions extracted from population counts and uses these
functions to accurately forecast how the racial/ethnic compositions of
neighborhoods across the US are likely to change. Importantly, DFFT makes
minimal assumptions about the nature of the underlying causes of segregation
and is designed to quantify segregation for neighborhoods with different total
populations in regions with different compositions. This quantification can be
used to accurately forecast both average changes in neighborhood compositions
and the likelihood of more drastic changes such as those associated with
gentrification and neighborhood tipping. As such, DFFT provides a powerful
framework for researchers and policy makers alike to better quantify and
forecast neighborhood-scale segregation and its associated dynamics.
