To provide a scientific basis for pollution prevention and control of the urban environment, the current status of heavy-metal pollution in road dust in Harbin was studied. In total, 63 road dust samples in the main urban area of Harbin were collected, and the contents of Cd and Pb, two representative heavy metals, were detected. Using the Moran Index coupled with semi-variable function and geostatistical methods, the spatial correlation, variation structure, and distribution pattern were analyzed, and the estimated probability of the heavy metals exceeding the safety standard was determined. The study showed that: The mean concentrations of Cd and Pb were higher than the background values in Heilongjiang province, and both exhibited moderate variability, while the coefficient of variation of Cd was larger than that of Pb; Cd was weakly correlated in space and randomly distributed, Pb was moderately correlated in space and exhibited good spatial structure, and both were spatially aggregated. The optimal model for fitting the variance function showed that Cd was a spherical model, and Pb was an exponential model. The variation of Cd was mainly influenced by human factors, and the variation of Pb was influenced by both structural and random factors. The optimized interpolation results of the variance function had high accuracy, and the spatial distribution of Cd was elliptical, whereas the distribution of Pb was stripe-shaped, Cd was mainly influenced by traffic factors, such as industrial enterprise distribution and road painting, while Pb was influenced by natural factors such as river sediment or the study area belonging to a geologically high background area, in addition to the above factors. The estimated probabilities indicate a higher potential risk of Cd in the northeastern part of the study area.

China’s express delivery industry has developed rapidly in the past decade, and the spatial distribution of express delivery outlets can reflect variations in regional development to a certain extent. Previous studies lacked point of interest (POI) data as the research object to analyze the limitations of express delivery outlets, and also lack a focus on the patterns that specifically occur in China. This study implemented spatial analysis methods such as nearest neighbor index, kernel density, and exploratory spatial data analysis to explore the spatial distribution patterns and characteristics of China’s express outlets, and used geographic detector factor detection to objectively analyze the factors that affect their distribution. The following are the main conclusions of this study: (1) in China, express delivery outlets are more abundant in the east and sparser in the west; (2) the local agglomeration of express outlets presents a concentric structure, and outlier clusters appeared in the northeast and central regions; (3) the distribution of the express outlets resulted from the combined action of multiple factors (e.g., population factors, etc.). Our study not only explores and analyzes the overall situation in China but also broadens the scope of express outlet research.

The rapid flow of resources and participation of diverse entities in the information age drive the formation of innovation networks, yet how these networks evolve in strategic emerging industries—particularly in transitioning regions like Northeast China—remains underexplored. This study investigates the spatial formation and evolution of cooperative innovation networks in strategic emerging industries, as well as the underlying mechanisms driving their development and transformation. Focusing on China’s three northeastern provinces—a representative old industrial base undergoing transformation—we construct and analyze inter- and intra-city cooperative innovation networks from 2009 to 2023 using invention patent data. Employing social network analysis and spatial econometric models, we reveal that while the external network exhibits limited expansion with Beijing as a dominant hub, the internal network demonstrates increasing cohesion, transitioning to a multi-core structure. Factors such as urban innovation capacity, economic proximity, and institutional proximity play key roles in shaping these networks. These findings offer policymakers empirical evidence to strategically optimize cross-regional innovation ecosystems in resource-dependent regions, supporting urban sustainability and fostering innovation-driven development.

The population of northeast China has decreased by over 10 million from 2010 to 2020, resulting in an ageing population and economic and social development challenges. The decline in population is closely related to fertility desire. Thus, this study establishes a quantitative index system for fertility desire, tailored to the three northeastern provinces of China, using data from 4155 questionnaires. It innovatively examines the ‘three-dimensional differentiation’ characteristics of this system. The study reveals that residents’ fertility desire in the three northeastern provinces exhibits a spatial pattern characterised by ‘one axis and multiple rings’, with high fertility desire values situated east of the Harbin-Changchun-Shenyang-Dalian regional development axis. Residents’ fertility desire is influenced by various factors, including educational background, family pressure, and population migration to the south, indicating a trend towards internalisation. This paper offers a reference for developing differentiated regional population policies.