In the knowledge economy, innovation is playing an increasingly important role in urban sustainable development. Thus, how to strengthen the construction of innovation spaces has become a significant issue. Based on data from high-tech enterprises in the central urban area of Harbin, this study analyzes the distribution, agglomeration characteristics, and influencing factors of urban innovation spaces. The influencing factor index system was constructed based on four aspects: innovation support, service facilities, the social environment, and the natural environment. After analyzing these influencing factors through comparing the results obtained with an ordinary least squares (OLS) model and a geographical weighted regression (GWR) model, the GWR model was determined to have a better fitting effect and was thus used for further analysis. The findings are as follows: (1) Five closely related clusters of innovation spaces are identified. Innovation space has a significant agglomerating distribution pattern. (2) Overall, factors supporting innovation and transportation station factors have a positive effect on the agglomeration of innovation spaces, while natural environment factors have a negative effect on small innovation areas. (3) By region, the aggregation of innovation spaces in downtown areas is slightly negatively affected by service facilities, whereas those in the Harbin New District are mainly supported by policies and those in the Pingfang District are positively affected by the quantity of the labor force. Finally, based on the research results, some suggestions are put forward to promote the development of urban innovation spaces. This paper has certain guiding significance for optimizing and improving the construction and development of urban innovation spaces.

Innovation has become a pivotal factor in driving economic growth for cities and regions. Urban innovation spaces are urban spaces where innovative economic and industrial activities, such as research, teaching, and high-tech manufacturing, are clustered. They have become hot research topics in recent years. Evaluating the performance of urban innovation spaces to promote rational resource allocation and enhance land development potential has become a critical task in urban planning. However, existing studies suffer from insufficient depth of research scales and a lack of quantitative indicators and data analysis. In response to the above gaps, this study constructed a framework for evaluating the performance of urban innovation spaces from 25 indicators of five major types, including core elements of innovation, entrepreneurship support institutions, service facilities, external environments, and diversities, aiming to quantify the performance heterogeneity of innovation spaces at the micro scale. This study took Harbin as an example and employed the entropy, kernel density estimation, and entropy-weighted TOPSIS methods, identifying four high-scoring areas of innovation spaces—the Science and Technology Innovation City area, the High-tech Industrial Development area, the core area of the old city, and the Harbin Veterinary Research Institute area—which were divided into three types: the Entrepreneurial leading area, Environmental Support area, and Balanced Development area. Finally, this study analyzed the interaction between each indicator. It was found that the correlation between the core elements of innovation and the indicators of entrepreneurship support institutions was strong and had a high degree of importance. The correlation of different types of service facility indicators is quite different, and the external environment indicators and diversity indicators are mainly affected by other indicators, especially the core elements of innovation and entrepreneurship support institutions. This paper provides a valuable tool for the performance evaluation of urban innovation spaces for researchers and urban planning decision makers.