D20.00007. Predicting residential segregation using statistical physics approaches

Presented by: Yuchao Chen


We introduce a statistical physics based method to predict racial residential segregation in human populations. Such predictions are increasingly important for informing policy decisions as human populations become more diverse and mobile. Here, we demonstrate how to make such predictions by extending a novel statistical physics approach called Density-Functional Fluctuation Theory (DFFT) to multi-component time-dependent systems. This technique uses observations of fluctuations in the local density of neighborhood racial composition to extract functions that separately quantify social and spatial preferences/constraints to predict demographic changes. As a demonstration, we simulate a population distribution using a Schelling-type segregation model, and use DFFT to predict both steady-state probability distributions and migration events after changes in the environment, social interactions, or number of individuals. Should these results extend to actual human populations, DFFT could be applied to demographic data to quantify segregation between different groups of people and predict how such populations will respond to proposed demographic changes.


  • Yuchao Chen
  • Yunus A Kinkhabwala
  • Mallory Gaspard
  • Matthew Hall
  • Tomas Alberto Arias
  • Itai Cohen


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