Kavli Affiliate: Itai Cohen
| First 5 Authors: Yuchao Chen, Yunus A. Kinkhabwala, Boris Barron, Matthew Hall, Tomas A. Arias
| Summary:
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.
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