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Spatial Analysis of Big Data Industrial Agglomeration and Development in China

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  • Yanru Lu

    (Department of Geography, National University of Singapore, Singapore 117570, Singapore)

  • Kai Cao

    (Department of Geography, National University of Singapore, Singapore 117570, Singapore)

Abstract

Nowadays, our daily life constantly creates and needs to utilize tremendous amounts of datasets. Fortunately, the technologies of the internet, both in software and hardware, have the capability to transmit, store, and operate big data. With China being the most populous country in the world, developing the big data industry is, therefore, seen as an urgent task. As generating industrial agglomeration is important for forming a mature industry, this study aims to characterize the phenomenon of big data industrial agglomeration in China, and to identify the factors for developing the big data industry using spatial analysis approaches and GIS technology from a geographer’s perspective. The problems and strengths of these representative cities are discussed, from which the solutions and the possible directions for the future are also provided. The findings argued that China is still at the primary stage of the development in the big data industry. Only several cities had the presence of a strong agglomeration, but the intercity space spillover was weak. However, comparing the changes in industry distribution, the trend of agglomeration have appeared, and the benefits of industrial agglomeration have also worked. The principal factors of the big data industry and its agglomeration include the support of government and the outstanding higher education agglomeration. In addition, it was also noted that each city has its own characteristics and potentials to attract more big data enterprises, talent, and investment.

Suggested Citation

  • Yanru Lu & Kai Cao, 2019. "Spatial Analysis of Big Data Industrial Agglomeration and Development in China," Sustainability, MDPI, vol. 11(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1783-:d:216847
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    3. Suhua Zhang & Yasmin Bani & Aslam Izah Selamat & Judhiana Abdul Ghani, 2022. "Impact of Industrial Agglomeration on China’s Residents’ Consumption," Sustainability, MDPI, vol. 14(7), pages 1-18, April.
    4. Devesh Singh, 2022. "Cluster Space Among Labor Productivity, Urbanization, and Agglomeration of Industries in Hungary," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(2), pages 1008-1027, June.
    5. Jun He & Yongxiang Xie, 2024. "Health Bonus of Migrating to Higher Tier Cities: Evidence From China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(1), pages 71-88, October.
    6. Dai Debao & Ma Yinxia & Zhao Min, 2021. "Analysis of big data job requirements based on K-means text clustering in China," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-14, August.
    7. Yihan Chi & Yongheng Fang & Jiamin Liu, 2022. "Spatial–Temporal Evolution Characteristics and Economic Effects of China’s Cultural and Tourism Industries’ Collaborative Agglomeration," Sustainability, MDPI, vol. 14(22), pages 1-23, November.

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