Small-area population forecasting in a segregated city using density-functional fluctuation theory.

Area of Science:

• Urban planning and computational social science
• Spatial demography and population forecasting

Background:

• Accurate small-area population forecasts are crucial for policy decisions in housing, transportation, and resource allocation.
• Existing regional migration models struggle with neighborhood-scale forecasts due to the complexity of residential choice.

Purpose of the Study:

• To develop an innovative approach for predicting small-area population shifts using density-functional fluctuation theory (DFFT).
• To adapt DFFT, a method successful in biological systems, for forecasting intra-regional migration and population dynamics at the neighborhood level.

Main Methods:

• Extending density-functional fluctuation theory (DFFT) to model small-area population dynamics.
• Utilizing observed population fluctuations to identify social and spatial drivers of segregation.
• Forecasting intra-regional migration based on inferred interaction patterns from population count data.

Main Results:

• DFFT accurately predicts small-area population shifts without needing agent-specific preferences.
• The model effectively forecasts how large-scale demographic changes impact neighborhood populations.
• DFFT successfully incorporates segregation impacts into forecasts using only steady-state population data.

Conclusions:

• Density-functional fluctuation theory offers a powerful new tool for small-area population forecasting.
• This approach can improve urban planning and resource allocation by providing more granular population predictions.
• The method's ability to infer segregation effects from aggregate data enhances its applicability in diverse real-world scenarios.