一个大规模的地理上明确的合成人口与美国的社交网络
Na Jiang1, Fuzhen Yin2, Boyu Wang3
1University at Buffalo, Department of Geography, Buffalo, NY, 14261, USA. njiang8@buffalo.edu.
Scientific data
|November 7, 2024
概括
本研究介绍了一种Python方法,使用人口普查数据为美国各州的社会网络生成现实的合成人口. 这有助于地理模拟研究,通过研究人类互动和城市数字双胞胎.
科学领域:
- 地理信息科学 地理信息科学
- 计算社会科学 计算社会科学
- 社会技术系统 社会技术系统
背景情况:
- 基于代理的建模和微型模拟需要合成种群来实现现实的地缘模拟.
- 现有的合成人口生成方法耗时,经常忽略关键的社交网络.
- 社交网络对于理解人与人之间的互动在模拟中至关重要,例如疾病传播或灾害响应.
研究的目的:
- 开发一种高效的基于Python的方法来生成大规模的,地理上明确的合成种群.
- 将风格化的社交网络 (家庭,工作,学校) 纳入合成人口数据中.
- 为地理模拟研究,基于代理的建模和城市数字双胞胎开发提供有价值的资源.
主要方法:
- 利用包括2020年美国人口普查在内的开放数据,创建合成人口.
- 开发了一个基于Python的方法来生成地理显式数据.
- 整合了风格化的社交网络的创建与人口统计数据一起.
主要成果:
- 为美国所有50个州和华盛顿特区生成了一个现实的,大规模的合成人口.
- 成功结合了家庭,工作和学校的社交网络结构.
- 创建了一个适用于各种地理模拟模型的多功能数据集.
结论:
- 开发的方法解决了在地缘模拟中合成人口生成的挑战.
- 社交网络的纳入增强了合成人口的现实性和实用性.
- 这项工作支持人类与环境相互作用和城市数字双胞胎的先进研究.
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