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Mar 3, 2025
| Apr 16, 2025
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Title: An intercity investment network dataset of China based on the enterprise registration records (2000–2020)

Abstract

Intercity investment activities among enterprises reflect the flow of capital between cities, thereby directly illustrating the economic connections between them. However, there is currently no publicly available dataset that captures this important feature. In this study, we introduce an intercity investment network (IIN) dataset for China, covering the period from 2000 to 2020, based on 17,273,411 large-scale enterprise registration records. The dataset represents 367 cities as nodes, with investment frequency between cities serving as edge weights to construct both directed and undirected networks. It captures the spatiotemporal patterns of China’s IIN, highlighting dynamic changes in economic connectivity over time and space. The dataset aligns closely with urban networks formed by China’s population mobility and the economic gravity model, is consistent with official records and existing research findings, and satisfies the distance decay effect, thus validating its scientific reliability. This dataset provides unique opportunities for exploring economic interactions and functional organization between cities, and advancing urban network research in China.

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Online link

Linked dataset

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Online repository

 
Data citation Shu, Tianheng; Yang, Shuo; Yu, Taofang; Cheng, Guangyu; Ren, Yitian; Shi, Fangchen; et al. (2025). An intercity investment network dataset of China based on the enterprise registration records (2000-2020). figshare. Dataset. https://doi.org/10.6084/m9.figshare.28248503.v1

Supporting codes

Part 1
Part 2

Citation

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Shu, T., Yang, S., Yu, T., Cheng, G., Ren, Y., Shi, F., Derudder, B., & Liao, X. (2025). An intercity investment network dataset of China based on the enterprise registration records (2000–2020). Scientific Data, 12(1), 369. https://doi.org/10.1038/s41597-025-04658-w
 
  • urban data
  • data science
  • big data
  • Paper is accepted by Letters in Spatial and Resource SciencesOur paper on FURP has been selected as 2024 Best Paper Award
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