Small-area Population Forecast in a Segregated City using Density-Functional Fluctuation Theory

Abstract

Decisions regarding housing, transportation, and resource allocation would all benefit from accurate small-area population forecasts. While various tried-and-tested forecast methods exist at regional scales, developing an accurate neighborhood-scale forecast remains a challenge partly due to complex drivers of residential choice ranging from housing policies to social preferences and economic status that cumulatively cause drastic neighborhood-scale segregation. Here, we show how to forecast the dynamics of neighborhood-scale demographics by extending a novel statistical physics approach called Density-Functional Fluctuation Theory (DFFT) to multi-component time-dependent systems. In particular, this technique observes the fluctuations in neighborhood-scale demographics to extract effective drivers of segregation. As a demonstration, we simulate a segregated city using a Schelling-type segregation model, and found that DFFT accurately predicts how a city-scale demographic change trickles down to block scales. Should these results extend to actual human populations, DFFT could capitalize on the recent advances in demographic data collection and regional-scale forecasts to improve upon current small-area population forecasts.