利用密度函数波动理论预测隔离城市的小区域人口。

IF 2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Journal of Computational Social Science Pub Date : 2024-01-01 Epub Date: 2024-08-28 DOI:10.1007/s42001-024-00305-3
Yuchao Chen, Yunus A Kinkhabwala, Boris Barron, Matthew Hall, Tomás A Arias, Itai Cohen
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引用次数: 0

摘要

有关住房、交通和资源分配的决策都将受益于准确的小区域人口预测。然而,尽管区域尺度的人口迁移模型取得了成功,但由于住宅选择的复杂性,开发邻里尺度的预测仍然是一项挑战。密度函数波动理论(DFFT)是对生物系统中群体空间行为建模的一种行之有效的方法,在这里,我们引入了一种创新的方法来应对这一挑战,即扩展密度函数波动理论来预测小区域人口随时间的变化。密度函数波动理论方法利用观测到的小区域人口波动来分解和提取隔离的有效社会和空间驱动因素,然后利用这些信息来预测区域内人口迁移。为了证明我们的方法在可控环境中的有效性,我们考虑了一个由谢林型模型构建的模拟城市。我们的研究结果表明,即使不能直接获取基本的代理人偏好,DFFT 也能准确预测城市范围内更广泛的人口变化如何渗透到小区域人口中。特别是,我们的研究结果表明,DFFT 能够将种族隔离的影响纳入小区域人口预测中,而这些影响仅仅是通过稳态人口数量数据推断出来的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Small-area population forecasting in a segregated city using density-functional fluctuation theory.

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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来源期刊
Journal of Computational Social Science
Journal of Computational Social Science SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
6.20
自引率
6.20%
发文量
30
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