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Computational Socioeconomics

Author

Listed:
  • Jian Gao
  • Yi-Cheng Zhang
  • Tao Zhou

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.

Suggested Citation

  • Jian Gao & Yi-Cheng Zhang & Tao Zhou, 2019. "Computational Socioeconomics," Papers 1905.06166, arXiv.org.
    • Handle: RePEc:arx:papers:1905.06166
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    File URL: http://arxiv.org/pdf/1905.06166
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    Cited by:

    1. Lee, Yan-Li & Dong, Qiang & Zhou, Tao, 2021. "Link prediction via controlling the leading eigenvector," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    2. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    3. Wang, Xi & Pei, Tao & Song, Ci & Chen, Jie & Shu, Hua & Liu, Yaxi & Guo, Sihui & Chen, Xiao, 2023. "How does socioeconomic status influence social relations? A perspective from mobile phone data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    4. Ghosh, Abhik & Mallick, Olivia & Chattopadhay, Souvik & Basu, Banasri, 2022. "Strata-based quantification of distributional uncertainty in socio-economic indicators: A comparative study of Indian states," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    5. Zhao, Na & Li, Jie & Wang, Jian & Li, Tong & Yu, Yong & Zhou, Tao, 2020. "Identifying significant edges via neighborhood information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    6. Wang, Mingyan & Zeng, An & Cui, Xiaohua, 2022. "Collective user switching behavior reveals the influence of TV channels and their hidden community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    7. Min-Xing Wang & Lufei Huang & Zhen-Ming Chen, 2023. "The Impact of Green Financial Policy on the Regional Economic Development Level and AQI—Evidence from Zhejiang Province, China," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    8. Lee, Ji-Hye & Jo, Junghyo & Kim, Jong Won & Lee, Keumsook & Choi, M.Y., 2022. "Spatial distributions of restaurants emerging from pedestrian behavior and online information sharing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    9. Yang, Alex J., 2024. "Unveiling the impact and dual innovation of funded research," Journal of Informetrics, Elsevier, vol. 18(1).

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