Innovation District Space and Element Identification Framework: Empirical Research from Shenzhen, China

Abstract

Science and technology and skills have increasingly become the driving force to lead the development of knowledge economy. With the changes in the demand for knowledge workers and the location of enterprises, a new type of innovation space—innovation districts—has emerged. Countries have begun to identify and nurture innovation districts. Therefore, how to accurately identify innovation districts in cities has become an important research topic. The existing research on identifying innovation districts is mainly based on a qualitative description method at the element level. However, whether there are other potential innovation districts in urban space can be identified by quantitative identification. Therefore, the purpose of this paper is to establish an innovation district identification framework and a case study of the framework. The identification framework includes spatial identification and factor identification. In spatial identification, the identification index system is constructed based on the spatial location, range limit, and the surrounding area of innovative assets. In factor identification, the identification index system is constructed based on innovative assets, physical assets, and network assets, and Kendall Square and Boston innovation districts are used as the reference basis to determine whether the identified districts meet the standards of the constructed innovation districts. In empirical case research, spatial identification identified the Gaoxin South District (GXSD) spatial range. In factor identification, it was found that GXSD does not fully meet the standard of innovation district identification. This paper argues that the framework is essential for urban managers, planners, and urban designers to identify and evaluate high-quality innovation districts.