Innovation is an inevitable way for cities to achieve sustainable development. The occurrence of innovation activities is a complex systemic behavior. Its spatial distribution has some location selection laws, which are the result of interaction and feedback between various spatial influence factors. We explain the impact mechanism from the microscale using a street unit in a city. Hangzhou was selected as a case study. First, we systematically selected factors influencing the spatial distribution of innovation activities as the independent variable based on the demands of innovation subjects. Patents were used as the dependent variable to represent the spatial distribution of innovation activities. Second, ensemble algorithms (Boosting) were used to analyze the influence contribution of independent variables to dependent variables. Then, based on the aspects of innovation driving force, which are innovation resources and innovation environments, relevant factors were divided into the following seven categories: innovation industry concentration, knowledge intensity, innovative talent resources, service facilities, external transportation convenience, public transportation convenience, and ecological environment. We interpreted the impact mechanism and made corresponding suggestions for urban innovation space planning.

Despite various studies regarding polycentric development at metropolis or even larger spatial scales, there is little systematic analysis regarding the rapid urbanization area at the county-level scale. Therefore, this study explored polycentric development in 52 county-level administrative units in Zhejiang Province, China, from a public service perspective. Based on point-of-interest data, our analysis detected the intra-county urban centers and measured their polycentric characteristics. According to the number, scale, and equilibrium value of intra-county polycentricity, the 52 county-level units were classified into three types using a two-step cluster algorithm method. The empirical results suggest that polycentric characteristics vary in the rapid urbanization area, and the spatial distribution of typological units is characterized by agglomeration. Topographical condition, fixed assets investment, public transportation, and residential consumption ability are highly associated with the classification of polycentric urban areas. The conclusion of this study would help local governments initiate better urban development policies and provide potential research directions for further studies about the relationship of inter-county urban centers.