Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples
Ye, Peijun1,2; Hu, Xiaolin3; Yuan, Yong1,2; Wang, Fei-Yue1,2,4
Source PublicationJASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION
2017-10-31
Volume20Issue:4Pages:16
SubtypeArticle
AbstractSynthetic 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.
KeywordPopulation Synthesis Sample-free Iterative Proportional Fitting
WOS HeadingsSocial Sciences
DOI10.18564/jasss.3533
WOS KeywordPROPORTIONAL FITTING PROCEDURE ; CONTINGENCY-TABLES ; GENERATION ; MICROSIMULATION ; CONVERGENCE ; SIMBRITAIN ; MATRICES ; MARGINS
Indexed BySSCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61603381 ; Department of Energy, USA(4000152851) ; 71472174 ; 61533019 ; 71232006)
WOS Research AreaSocial Sciences - Other Topics
WOS SubjectSocial Sciences, Interdisciplinary
WOS IDWOS:000416164100018
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20084
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Affiliation1.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
Recommended Citation
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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