1Faculty of Data Science, City University of Macau, Macau, China
2Department of Big Data and Artificial Intelligence, Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, China
3School of Computer, Beijing Institute of Technology, Zhuhai, China
4School of Finance, Golden Gate University, San Francisco, California, United States
5Faculty of Finance, City University of Macau, Macau, China
In this study, based on Herfindahl Index and special analysis, a deep exploration on competitiveness regional inequality has been performed geographically and statistically. The tool of Geoda is utilized in this paper. Findings indicated that the comprehensive competitiveness of this area exhibits a growing trend with an eastward developing tendency over time. Cities of Hong Kong, Shenzhen, Guangzhou are defined as the first-tier cities of competitiveness, with great advantages in the aspects of science and technology, economic capacity and international competition. Considering its partial advantages and regional influence, this study regards Macau as the second-tier city, Dongguan and Huizhou as the third-tier cities, Zhaoqing as the fourth-tier cities, and Zhuhai, Foshan, Jiangmen and Zhongshan as the fifth-tier cities. First-tier and second-tier cities are in a line dividing the rest cities into two group, the right group of cities show a higher competitiveness level than the left ones. Besides, a low-low local autocorrelation of comprehensive competitiveness is discovered between Guangzhou, Foshan and their adjacent cities. Due to the unevenness of the cities’ development at multiple determining factors, regional inequalities of this area will possibly exist for a long period of time.
| [1] | Rahmat, s., & Sen, J. 2021. A multi-model approach to assess the relative weights and sensitivities of the factors of regional competitiveness. Journal of Urban & Regional Analysis, 13(1).View Article |
| [2] | Li, H., Wei, Y. D., & Swerts, E. 2020. Spatial inequality in the city-regions in the Yangtze River Valley, China. Urban Studies, 57(3), 672-689.View Article |
| [3] | Wei, Y. D., Wu, Y., Liao, F. H., & Zhang, L. 2020. Regional inequality, spatial polarization and place mobility in provincial China: A case study of Jiangsu province. Applied Geography, 124, 102296.View Article |
| [4] | Porter, M. E. 2000. Location, competition, and economic development: Local clusters in a global economy. Economic development quarterly, 14(1), 15-34.View Article |
| [5] | Yuemin, N. & Lizhi, T. 2001. The concept and indicator system of urban competitive capacity. Urban Research, (3), 19-22. |
| [6] | Fan, Z., Yuemin, N. & Xiyang, L. 2019. Competitiveness and regional inequality of China’s mega-city regions. Geographical Research, 38(7), 1664-1677. |
| [7] | Kang, D., & Park, Y. 2019. Analysing diffusion pattern of mobile application services in Korea using the competitive Bass model and Herfindahl index. Applied Economics Letters, 26(3), 222-230.View Article |
| [8] | Anselin, L., Syabri, I., & Kho, Y. 2006. GeoDa: an introduction to spatial data analysis. Geographical analysis, 38(1), 5-22.View Article |
| [9] | Yan-guang, C. H. E. N. 2009. Reconstructing the mathematical process of spatial autocorrelation based on Moran’s statistics. Geographic Study, 28(6), 1449-1463. |