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The County-Scale Economic Spatial Pattern and Influencing Factors of Seven Urban Agglomerations in the Yellow River Basin—A Study Based on the Integrated Nighttime Light Data

Author

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  • Jingtao Wang

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Haibin Liu

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Di Peng

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Qian Lv

    (School of Information Management, Beijing Information Science and Technology University, Beijing 100192, China)

  • Yu Sun

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Hui Huang

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Hao Liu

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

Abstract

The integrated night light (NTL) datasets were used to represent the economic development level, and visual analysis was carried out on the evolution characteristics of the economic spatial pattern of various urban agglomerations in the Yellow River Basin (YRB), at a county-scale, in 1992, 2005, and 2018. The Global Moran’s I and the local Getis-Ord G methods were used to explore the overall spatial correlation and local cold–hot spot of economic development levels, respectively. The spatial heterogeneity of the influence of relevant factors on the economic development level at the municipal scale was analyzed by using the multi-scale geographically weighted regression (MGWR) model. The results show that the county-level economic spatial pattern of urban agglomeration in the YRB has an obvious “pyramid” characteristic. The hot spots are concentrated in the hinterland of the Guanzhong Plain, the Central Plains, and the Shandong Peninsula urban agglomeration. The cold spots are concentrated in the junction of urban agglomerations, and the characteristics of “cold in the west and hot in the east” are obvious. Labor input and import and exporthave a positive impact on the economic development level for each urban agglomeration, government force has a negative impact, and education shows both positive and negative polarization on economic development.

Suggested Citation

  • Jingtao Wang & Haibin Liu & Di Peng & Qian Lv & Yu Sun & Hui Huang & Hao Liu, 2021. "The County-Scale Economic Spatial Pattern and Influencing Factors of Seven Urban Agglomerations in the Yellow River Basin—A Study Based on the Integrated Nighttime Light Data," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4220-:d:533725
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    1. Xiaoyan Ren & Yuhao Yang & Zongming Wang, 2023. "A Long-Term and Comprehensive Assessment of the Ecological Costs Arising from Urban Agglomeration Expansion in the Middle Reaches of the Yellow River Basin," Land, MDPI, vol. 12(9), pages 1-22, September.
    2. Hang Liu & Xiaohong Chen & Ying Wang & Xiaoqing Xu & Mingxuan Zhang, 2022. "Spatio-Temporal Characteristics and Influencing Factors of Urban Spatial Quality in Northeast China Based on DMSP-OLS and NPP-VIIRS Nighttime Light Data," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    3. Danyu Liu & Ke Zhang, 2022. "Analysis of Spatial Differences and the Influencing Factors in Eco-Efficiency of Urban Agglomerations in China," Sustainability, MDPI, vol. 14(19), pages 1-21, October.
    4. Chaohui Zhang & Xin Dong & Ze Zhang, 2023. "Spatiotemporal Dynamic Distribution, Regional Differences and Spatial Convergence Mechanisms of Carbon Emission Intensity: Evidence from the Urban Agglomerations in the Yellow River Basin," IJERPH, MDPI, vol. 20(4), pages 1-28, February.
    5. Rui Ding & Jun Fu & Yiling Zhang & Ting Zhang & Jian Yin & Yiming Du & Tao Zhou & Linyu Du, 2022. "Research on the Evolution of the Economic Spatial Pattern of Urban Agglomeration and Its Influencing Factors, Evidence from the Chengdu-Chongqing Urban Agglomeration of China," Sustainability, MDPI, vol. 14(17), pages 1-19, September.

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