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.

The COVID-19 pandemic has placed the inequalities in health services in countries around the world under severe pressure. As crucial pillars in the prevention and control of COVID-19, fever clinics and fever sentinels are important sites for the screening, diagnosis, and isolation of patients. This study comprehensively evaluated the spatial-layout characteristics, configuration quantity, and service capacity of 42 fever clinics and 418 fever sentinels in Harbin from the perspective of supply by using GIS spatial-analysis methods such as kernel density analysis. From the perspective of demand, we evaluated the accessibility of fever clinics with the modified two-step floating catchment area (2SFCA) method; the OD cost matrix method and Voronoi diagram method were used to evaluate the accessibility and service pressure of fever sentinels. This study found that a monocentric clustering characterizes the spatial layout of fever clinics, and the design of fever clinics in new urban areas and marginal rural areas is relatively lacking. The spatial layout of fever sentinels includes blank areas, and the service pressure in the central city area is relatively high. Combined with the assessment results, the study discussed optimization strategies and implementation paths for improving the public health and epidemic prevention system for COVID-19 in terms of four aspects: the transformation of governance practice, the spatial-planning response, the digital infrastructure response, and guarantees of policies and regulations.

The cultural and creative industries have garnered widespread attention as an important vehicle for promoting the transformation and upgrading of urban industrial structures. In this investigation, we take Harbin—a city in China with a strong industrial legacy—as a case study. Through kernel density analysis and the DBSCAN clustering algorithm, we identify and analyze the spatial distribution and spatiotemporal evolution patterns of 157 clusters of cultural and creative industries in Harbin. We construct a Geographic Weighted Regression (GWR) model using 20 indicators from three categories (i.e., social, cultural, and economic) to analyze the factors impacting the agglomeration of cultural and creative industries in Harbin. Our findings reveal that the cultural and creative industries exhibit an agglomeration pattern. A large-scale agglomeration area for large enterprises has formed in the city center, while scattered, small-scale agglomeration zones for emerging small enterprises have formed in newly developed areas on the urban periphery. The GWR analysis indicates that economic factors have the most significant impact on the agglomeration of cultural and creative industries; however, night-time economic facilities show a negative correlation. Among social factors, the convenience of public transportation and new energy transportation infrastructure have a significant impact on industrial agglomeration. Regarding cultural factors, cultural and sports facilities have the greatest influence, while public information kiosks and public arts education facilities exhibit spatial variability. These findings provide a scientific basis for policy formulation and contribute to promoting the healthy development of cultural and creative industries.

The optimization of the spatial structure of the city network is conducive to the scientific spatial distribution of industries and the promotion of coordinated regional development. This study selected the top 100 automobile enterprises in the Chinese stock market that belong to China’s pillar industry, a total of 1455 headquarters and branches, to establish an enterprise matrix. Based on the ownership linkage model, the evolution characteristics of city networks in China from 2000 to 2020 are revealed, and the influential factors of city networks are discussed using the negative binomial regression model. The findings are as follows: (1) there are significant differences in the status of automobile cities, forming a “pyramid network” hierarchy. (2) The agglomeration area of automobile cities has formed the development region of “4 + 4 + 1”. (3) The city network with hierarchical connections has formed a spatial structure of a “cross–cobweb” in the middle and “trapezoid–diamond” in the periphery. (4) Urban transportation conditions, the scientific research environment, the enterprise agglomeration economy, GDP per capita, and technological proximity positively impact the formation of a city network, but the total export–import volume has a negative impact. Overall, the government can use this study’s results to formulate policies for the automotive industry and urban development.

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.

Innovation is the core driving force behind the high-quality development and global competitiveness of cities. The spatial distribution characteristics and influencing factors of innovation are of significant value in optimizing the allocation of innovation resources. This study focuses on Harbin, utilizing 46,057 patent application data from 2004 to 2022, and employs spatial analysis methods such as KDE, ESDA, and DBSCAN to analyze the spatiotemporal evolution patterns of innovation space. Through the MGWR model, this study investigates the factors influencing the spatial agglomeration of innovation from five dimensions: innovation subjects, infrastructure support, public service support, population and market, and spatial carrier. The findings are as follows: (1) In terms of spatiotemporal patterns, Harbin’s innovation space distribution shows a “one core, four cluster” spatial structure, with innovation activities clustering primarily around universities as the core and new districts as key support, gradually evolving toward a multi-center agglomeration development model. (2) Regarding influencing factors, the geographic proximity of universities is significantly associated with higher innovation density. Among infrastructure, metro stations show a positive association with innovation agglomeration, potentially reflecting the role of transport accessibility. Public service support displays significant spatial heterogeneity across different regions. (3) The Historical Area requires policy guidance for urban renewal; the New Town Area forms exogenous-driven innovation clusters; the Industrialized Park Area achieves endogenous development through industrial collaboration.