Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples
Ye, Peijun1,2; Hu, Xiaolin3; Yuan, Yong1,2; Wang, Fei-Yue1,2,4
2017-10-31
发表期刊JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION
卷号20期号:4页码:16
文章类型Article
摘要Synthetic population is a fundamental input to dynamic micro-simulation in social applications. Based on the review of current major approaches, this paper presents a new sample-free synthesis method by inferring joint distribution of the total target population. Convergence of multivariate Iterative Proportional Fitting used in our method is also proved theoretically. The method, together with other major ones, is applied to generate a nationwide synthetic population database of China by using its overall cross-classification tables as well as a sample from census. Marginal and partial joint distribution consistencies of each database are compared and evaluated quantitatively. Final results manifest sample-based methods have better performances on marginal indicators while the sample-free ones match partial distributions more precisely. Among the five methods, our proposed method can significantly reduce the computational cost for generating synthetic population in large scale. An open source implementation of the population synthesizer based on C# used in this research is available at https://github.com/PeijunYe/PopulationSynthesis.git.
关键词Population Synthesis Sample-free Iterative Proportional Fitting
WOS标题词Social Sciences
DOI10.18564/jasss.3533
关键词[WOS]PROPORTIONAL FITTING PROCEDURE ; CONTINGENCY-TABLES ; GENERATION ; MICROSIMULATION ; CONVERGENCE ; SIMBRITAIN ; MATRICES ; MARGINS
收录类别SSCI
语种英语
项目资助者National Natural Science Foundation of China(61603381 ; Department of Energy, USA(4000152851) ; 71472174 ; 61533019 ; 71232006)
WOS研究方向Social Sciences - Other Topics
WOS类目Social Sciences, Interdisciplinary
WOS记录号WOS:000416164100018
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20084
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
作者单位1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Qingdao Acad Intelligent Ind, Qingdao, Peoples R China
3.Georgia State Univ, Dept Comp Sci, 25 Pk Pl, Atlanta, GA 30084 USA
4.Natl Univ Def & Technol, Mil Computat Expt & Parallel Syst Res Ctr, Changsha, Hunan, Peoples R China
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GB/T 7714
Ye, Peijun,Hu, Xiaolin,Yuan, Yong,et al. Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples[J]. JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION,2017,20(4):16.
APA Ye, Peijun,Hu, Xiaolin,Yuan, Yong,&Wang, Fei-Yue.(2017).Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples.JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION,20(4),16.
MLA Ye, Peijun,et al."Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples".JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION 20.4(2017):16.
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