Tropical Geography ›› 2020, Vol. 40 ›› Issue (2): 323-334.doi: 10.13284/j.cnki.rddl.003210

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Spatial Heterogeneity in Chinese Urban Innovation Capabilities and Its Determinants: Approach Based on the Geographically Weighted Regression Model

Chen Yiman, Li Lixun(), Fu Tianlan   

  1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Received:2019-10-02 Revised:2020-01-21 Online:2020-03-10 Published:2020-05-15
  • Contact: Li Lixun


Innovation is the primary driving force of development and provides strategic support for building a modern economic system. Its role in promoting regional development has been studied and acknowledged by academics. Since the Chinese economic reform, the country’s scientific and technological level and capacity for innovation has significantly improved, and the overall national strength has steadily increased. Presently, innovation plays a critical role in China's economic transformation from a stage of high-speed growth to a stage of high-quality development. Based on urban invention patents, this study analyzes the spatial characteristics of the innovation capabilities of Chinese cities by using the kriging interpolation method and a spatial autocorrelation model. The Geographical Weighted Regression (GWR) model was used to explore the spatial heterogeneity in the factors influencing the innovation ability of 289 cities, and to reveal the spatial differences in the dominant factors. The results indicate that the innovation ability of Chinese cities shows a decreasing trend from the southeast to the west, and the cities with the highest innovation capacity are concentrated in the southeastern coastal areas. The cities in the west, mainly in Tibet, have the lowest innovation capacity. The spatial agglomeration is significant. In terms of the determinants, the significance level of each variable was good. The proportion of significant regions in all cities in order from large to small is financial input, talent element, economic foundation,economy extraversion, financial environment, and information level. In addition to the significant positive correlation between financial input and urban innovation capacity, there are positive and negative correlations among the other determinants. Moreover, the range of the index’s regression coefficient is large and has an obvious spatial differentiation. The talent element is positively correlated with the innovation ability of most cities. The influence of the economic foundation and information level on urban innovation ability is positive for economically developed areas and negative for underdeveloped areas. Economic extraversion is positively correlated with the innovation ability of the eastern coastal areas and the central and western regions, but negatively correlated with that of most cities in northeast and north China. The financial environment, represented by residents' savings, has a positive correlation effect on the innovation output of the northeast and western regions, while it has the opposite effect in the Yangtze river delta and other regions. The input of innovation elements in China is still a leading determinant in improving the country’s innovation ability. For southeastern Chinese cities with a higher level of economic development, more attention should be paid to improving their economic level and promoting the overall improvement of their innovation ability. For cities in the northeast and west, where the economic level is relatively inferior, financial support plays a significant role in the development of their innovation activities. Thus, attention should be paid to the cultivation of the financial market in these areas. The research shows that there are spatial differences in the factors influencing urban innovation capabilities in China, and the characteristics of different cities should be taken into account when formulating innovation policies to make policies more targeted and facilitate the healthy and coordinated development of national urban innovation.

Key words: urban innovation capabilities, spatial differentiation, Geographic Weighted Regression, China

CLC Number: 

  • F299.2