{"doi":"10.1007/s42001-024-00305-3","title":"Small-area population forecasting in a segregated city using density-functional fluctuation theory","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.","journal":"Journal of Computational Social Science","year":2024,"id":468976,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9458,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1302411,"name":"Yunus A. Kinkhabwala","orcid":"0000-0003-2320-1182","position":1,"is_corresponding":false},{"id":1302412,"name":"Boris Barron","orcid":"0000-0002-3261-6585","position":2,"is_corresponding":false},{"id":479764,"name":"Matt Hall","orcid":"0000-0001-7778-5887","position":3,"is_corresponding":false},{"id":1302413,"name":"T. A. Arias","orcid":"0000-0001-5880-0260","position":4,"is_corresponding":false},{"id":339620,"name":"Itai Cohen","orcid":"0000-0001-9218-043X","position":5,"is_corresponding":false},{"id":1302410,"name":"Yuchao Chen","orcid":"0009-0008-2582-7641","position":0,"is_corresponding":true}],"reference_count":67,"raw_metadata":null,"created_at":"2026-07-19T02:05:28.027781Z","pmid":"39524064","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}