Small-area population forecasting in a segregated city using density-functional fluctuation theory
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42001_2024_Article_305.pdf
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Author(s)
Chen, Yuchao
Kinkhabwala, Yunus A.
Barron, Boris
Hall, Matthew
Arias, Tomás A.
Cohen, Itai
Date Issued
August 28, 2024
Journal
Journal of Computational Social Science
Publisher
Springer Nature Singapore
Citation
Chen, Y., Kinkhabwala, Y.A., Barron, B. et al. Small-area population forecasting in a segregated city using density-functional fluctuation theory. J Comput Soc Sc (2024).
Version
Final published version
Abstract
Policy decisions concerning housing, transportation, and resource allocation would all benefit from accurate small-area population forecasts. However, despite the success of regional-scale migration models, developing neighborhood-scale forecasts remains a challenge due to the complex nature of residential choice. Here, we introduce an innovative approach to this challenge by extending density-functional fluctuation theory (DFFT), a proven approach for modeling group spatial behavior in biological systems, to predict small-area population shifts over time. The DFFT method uses observed fluctuations in small-area populations to disentangle and extract effective social and spatial drivers of segregation, and then uses this information to forecast intra-regional migration. To demonstrate the efficacy of our approach in a controlled setting, we consider a simulated city constructed from a Schelling-type model. Our findings indicate that even without direct access to the underlying agent preferences, DFFT accurately predicts how broader demographic changes at the city scale percolate to small-area populations. In particular, our results demonstrate the ability of DFFT to incorporate the impacts of segregation into small-area population forecasting using interactions inferred solely from steady-state population count data.
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1007/s42001-024-00305-3
