In this study, an urban fringe green space classification system was established to explore the spatiotemporal variation of green space landscape and ecosystem service value (ESV) based on multi-source land-use data of Ganjingzi district from 2000 to 2018. (1) Results show that the total green space area declined from 359.57 to 213.46 km² over the study period. Green space large plaque index (LPI) and class area both gradually declined, whereas the number of plaques (NP) and plaque density (PD) gradually increased, indicating green space landscape fragmentation. (2) Additionally, the value of green space ecosystem services reduced from 397.42 to 124.93 million yuan. The dynamic degree of ESV change in green space increased or decreased moderately, always being < 0 and showing a decreasing trend of ESV. From a spatial variation perspective, dynamic degrees of ESV variation in the western and northern regions with relatively intensive green space were higher than those in the east. Regarding ESV of various green space types, forest land had the highest functional values of ecological regulation and support, whereas arable land provided the highest functional values of production supply. (3) The ecological service function value of green space system is negatively correlated with PD, NP, edge density, landscape shape index, and Shannon’s diversity index, and positively correlated with aggregation index, contagion metrics, and LPI. The correlation coefficient between the climate regulation function of forest and the change of number of plaques is -0.874. The correlation coefficient of the recreation and culture of the wetland to the plaque density change is no less than -0.214.

The suitability of human settlements is critical for quality of life and regional development. As comprehensive evaluations and research on the suitability of human settlements are lacking, a comprehensive evaluation of human settlements in the Yangtze River Delta (YRD) was carried out in 2020 by combining natural and human environmental elements based on multi-source data such as digital elevation models, Landsat remote sensing images, meteorological station data, and points of interest, other multi-source data, and constructions of the human settlements’ suitability indexes. The results showed the following: (1) The spatial suitability of the natural environment in the YRD is significantly affected by the topographic conditions and distance from the sea, showing an increasing spatial differentiation from southwest to northeast, with Shanghai and Yancheng having the best natural environment suitability. (2) The suitability of the human environment in urban areas is better than that in non-urban areas and shows a decreasing trend from the south to the north circle. Shanghai, Zhoushan, and Huaibei have the best human environment suitability. (3) The comprehensive suitability of human settlements includes both the spatial differentiation characteristics of the suitability of natural and human environments. Shanghai and Zhoushan have the mosy comprehensive suitability for human settlements, while Huaibei and Xuzhou have the worst. (4) Land with a comprehensive suitability for human settlements of greater than 0.580 accounts for 23.60% of the total and contains 30.08% of the population and 32.31% of the economy, indicating that areas with a high suitability index have been fully utilized, and the populations and economies with human settlements suitability have a high degree of matching.

An important indicator for measuring the resilience and ability of urban networks to recover under external environmental shock, which is essential for the healthy development of the region, is urban network structure resilience. Herein we analyzed the resilience of the urban network structure and explored the influencing factors of resilience in the three provinces of Northeast China. We accomplished this by utilizing the Gephi profiling social network analysis tools based on the Baidu Index, road mileage, statistical data, other multi-source data, construction information, and the transportation, innovation, and economic multiple linkage network. This analysis enabled us to propose relevant suggestions and strategies to optimize urban network structure resilience. Our results indicate that (1) in 2019, the multi-city network structure in the three provinces of Northeast China contains both commonalities and characteristics. Overall, each network demonstrates a spatial distribution pattern of “dense in the north and sparse in the south.” (2) There exist evident hierarchical differences in the resilience characteristics of the multi-city network structure in the three provinces; each provincial capital city and sub-provincial city possesses greater advantages, the innovation network exhibits the most evident hierarchy, the mismatch of the information network is the highest, and the transmission and agglomeration of the economic network are the most prominent. (3) The resilience of the urban network structure of the three provinces is the result of the interaction of several factors. Political and economic factors such as government capacity, economic status, and urban vitality are the main factors affecting the resilience of the network structure.

For megacities, they are in a period of transformation from extensive development to smart growth. Recognizing new characteristics and new changes of the residential space in megacities under the backdrop of new development has great practical significance for realizing the sustainable development of the city. As the only megacity in Northeast China, Shenyang was selected to be the research object, with 1989–2018 as the research period. The research comprehensively used multiple spatial representation methods and statistical methods to study the residential space pattern and driving factors in Shenyang City. The results showed that: (1) Residential space expansion can be divided into four stages: slow development, rapid expansion, speedy expansion, and stable extension. (2) The residential space structure presented a spatial evolution characteristic of overall expansion, forming multiple secondary core density centers. The east-west direction had a larger extension range than the northeast-southwest direction. There was an axisymmetric zonal distribution on both sides of the Hun River. (3) The agglomeration of different residential forms was obvious, and the spatial heterogeneity was increasingly stronger. (4) Urban planning measures and economic strength were the main driving forces of residential space expansion.

The rapid development of the urban network has led to the fact that cities are no longer single individuals, and the network has changed the urban development environment. The interaction between cities has gradually become an important factor for the high-quality development (HQD) of cities. From the perspective of externalities, it is of great significance to explore the impact of agglomeration externalities and network externalities on the HQD of cities to promote the high-quality and sustainable development of the region. Taking the urban agglomeration in the middle reaches of the Yangtze River as an example, this study constructs a theoretical framework to empirically study the influence of agglomeration externalities and network externalities on the HQD of the city. The results show that the integrated network of the urban agglomeration from 2011 to 2020 had a high clustering coefficient and a small average path length with the characteristics of a “small world”. The centrality of urban nodes was hierarchical and had a “pyramid” structure. From 2011 to 2020, the high-quality development level (HQDL) of the urban agglomeration steadily improved and the regional “development gap” gradually narrowed. Wuhan, Changsha, and Nanchang were in a relatively advantageous position in the urban agglomeration. Furthermore, there was a spatial agglomeration effect and a spatial spillover effect in the HQD of urban agglomeration. Network externalities presented difference in different cities, and the influence of agglomeration externalities on HQD presented a u-shaped nonlinear relationship. Network externalities could significantly promote HQD, and the indirect effect of HQD was greater than its direct effect. In addition, factors such as government capacity and level of opening to the outside world also had a significant impact on the HQD of the region.