爱尔兰基于主体的建模和微观模拟的开源和空间多样化合成人口数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Seán Caulfield Curley, Karl Mason, Patrick Mannion
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引用次数: 0

摘要

空间微模拟,其中模拟单元代表小区域内的人或家庭,对于在精细尺度上模拟广泛的社会经济情景非常有用。这些模拟种群中的个体特征需要准确地代表目标区域的真实特征,以模拟现实场景。然而,由于隐私问题,包括爱尔兰在内的绝大多数人口都无法获得个人层面的数据。因此,爱尔兰共和国需要一个具有代表性的综合人口。本文给出了在选举司一级产生综合人口的四种方法的数据。现实的个人是通过中央统计局(CSO)劳动力调查的抽样产生的。空间异质性是通过将个人特征的总计数与CSO人口普查小地区人口统计的数据相匹配来实现的。个人被分配了六个特征:年龄组、性别、婚姻状况、房屋大小、主要经济状况和最高教育程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An open-source and spatially diverse synthetic population dataset for agent-based modelling and microsimulation in Ireland
Spatial microsimulations, where simulation units represent people or households in a small area, are extremely useful for modelling a wide range of socio-economic scenarios at a fine scale. The characteristics of individuals in these simulations' populations need to accurately represent the real characteristics of the target area to model realistic scenarios. However, individual-level data is not available for the vast majority of populations, Ireland included, due to privacy concerns. Thus, a representative synthetic population for the Republic of Ireland is needed. The data from four methods of generating synthetic populations at the Electoral Division level are given in this paper. Realistic individuals are created by sampling from the Central Statistics Office (CSO) Labour Force Survey. Spatial heterogeneity is achieved by matching the aggregate counts of individuals' characteristics to those from the CSO Census Small Area Population Statistics. Individuals are assigned six characteristics: age group, sex, marital status, house size, primary economic status, and highest level of education achieved.
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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