File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Computational socioeconomics

TitleComputational socioeconomics
Authors
KeywordsComplex networks
Data mining
Economic development
Machine learning
Socio-economic systems
Socioeconomic status
Issue Date2019
Citation
Physics Reports, 2019, v. 817, p. 1-104 How to Cite?
AbstractUncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies.
Persistent Identifierhttp://hdl.handle.net/10722/346707
ISSN
2023 Impact Factor: 23.9
2023 SCImago Journal Rankings: 6.435

 

DC FieldValueLanguage
dc.contributor.authorGao, Jian-
dc.contributor.authorZhang, Yi Cheng-
dc.contributor.authorZhou, Tao-
dc.date.accessioned2024-09-17T04:12:45Z-
dc.date.available2024-09-17T04:12:45Z-
dc.date.issued2019-
dc.identifier.citationPhysics Reports, 2019, v. 817, p. 1-104-
dc.identifier.issn0370-1573-
dc.identifier.urihttp://hdl.handle.net/10722/346707-
dc.description.abstractUncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies.-
dc.languageeng-
dc.relation.ispartofPhysics Reports-
dc.subjectComplex networks-
dc.subjectData mining-
dc.subjectEconomic development-
dc.subjectMachine learning-
dc.subjectSocio-economic systems-
dc.subjectSocioeconomic status-
dc.titleComputational socioeconomics-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.physrep.2019.05.002-
dc.identifier.scopuseid_2-s2.0-85067090365-
dc.identifier.volume817-
dc.identifier.spage1-
dc.identifier.epage104-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats