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

TL;DR

This paper introduces an extension of Density-Functional Fluctuation Theory (DFFT) to predict neighborhood-scale demographic changes in segregated cities, potentially improving small-area population forecasts by analyzing demographic fluctuations.

Contribution

The paper extends DFFT to multi-component, time-dependent systems for modeling neighborhood demographics, demonstrating its effectiveness through simulations of a segregated city.

Findings

DFFT accurately predicts demographic changes from city to neighborhood scale.

Simulation results show DFFT captures the effects of segregation dynamics.

Potential application to real demographic data for improved forecasts.

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.