Computational socioeconomics

Jian Gao, Yi Cheng Zhang*, Tao Zhou

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

51 Scopus citations

Abstract

Uncovering 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.

Original languageEnglish (US)
Pages (from-to)1-104
Number of pages104
JournalPhysics Reports
Volume817
DOIs
StatePublished - Jul 10 2019

Keywords

  • Complex networks
  • Data mining
  • Economic development
  • Machine learning
  • Socio-economic systems
  • Socioeconomic status

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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