{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/forecasting-the-dynamics-of-segregated","title":"Small-area Population Forecast in a Segregated City using Density-Functional Fluctuation Theory","arxiv_id":"2008.09663","date":"2020-08-21","proceeding":null,"authors":["Yuchao Chen","Yunus A. Kinkhabwala","Boris Barron","Matthew Hall","Tomas A. Arias","Itai Cohen"],"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.","url_abs":"https://arxiv.org/abs/2008.09663v2","url_pdf":"https://arxiv.org/pdf/2008.09663v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"forecasting-the-dynamics-of-segregated","repo_url":"https://github.com/yunuskink/DFFT-Schelling-model","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}