In the context of international food price fluctuations, agricultural economic development is usually subject to large shocks, which also leads to clear spatial–temporal differentiation in regional agricultural economic resilience. However, there is a paucity of retrievable studies on the evolutionary process of agricultural economic resilience and its impact mechanisms. Taking the impact of and disturbance caused by international food price fluctuations on the agricultural economy from 2005 to 2021 as the research background, this paper adopts the economic resilience analysis framework and selects gross agricultural output value indicators to measure the agricultural economic resilience of 12 cities in the main grain-producing areas in Northeast China–Sanjiang Plain and Songnen Plain. At the same time, the regression model is used to further analyze the spatial and temporal changes of agricultural economic resilience and the main influencing factors. Based on the results of the research, it can be concluded that: ① international food prices exert different degrees of influence on agricultural economic resilience at different stages. From 2005 to 2013, the agricultural economic resilience of the two plains changed less, and the resilience capacity performed better in both the period of falling and rising international food prices, while from 2014 to 2021, the agricultural economic resilience of the two plains changed more due to the great impact of international food price fluctuations, with a steep decline in resilience capacity in the period of falling international food prices from 2014 to 2017, followed by a rapid rebound between 2018 and 2021. ② There are differences in the resilience capacity of the agricultural economy in the main maize- and rice-producing areas. Although it is a fact that agricultural economic development in the two major plains is faster than the national average in most periods, the comparison reveals that the main corn-producing area (Songnen Plain) is more volatile and its resilience capacity is relatively weak in both periods of falling and rising food prices, while the main rice-producing area (Sanjiang Plain) is less volatile, indicating its relatively strong agricultural economic resilience capacity. ③ Factors affecting agricultural economic resilience behave differently over the three cycles. During the period of rising international food prices, indicators reflecting agricultural production capacity have a greater impact on agricultural economic resilience, while during the period of falling international food prices, indicators reflecting agricultural input intensity and agricultural support capacity have a greater impact on agricultural economic resilience.

Using the Urban Network Analysis Tool, the centrality of a road network (closeness centrality, betweenness centrality, and straightness centrality) was calculated, and the POI data of the commercial facilities were reclassified. KDE estimation was used to estimate the centrality of the traffic network, and the correlation coefficient was calculated to explore the spatial relationship between road network centrality and the types of commercial facilities (catering facilities, shopping facilities, residential life facilities, and financial and insurance facilities). The results indicate the following: (1) Closeness centrality displays a discernible “Core–Periphery” pattern, and the high-value areas of betweenness centrality are mainly concentrated around the main arterial roads of the city. In contrast, straightness centrality unveils a polycentric structure. (2) The spatial distribution of commercial facilities demonstrates a notable correlation with the centrality of the road network. From the perspective of centrality, the distribution of residential life facilities is most strongly influenced by road network centrality, followed by financial and insurance facilities and then catering facilities, with the distribution of shopping facilities being the least affected. (3) The centrality of the road network plays a crucial role in shaping the arrangement of commercial facilities. Closeness centrality significantly influences the distribution of residential life facilities, catering facilities, and shopping facilities. Betweenness centrality has a noteworthy impact on the selection of locations for financial and insurance facilities, as well as residential life facilities. Furthermore, areas characterized by better straightness centrality are preferred for the distribution of residential life facilities, financial and insurance facilities, and catering facilities. (4) The centrality of the road network has a greater influence on the arrangement of various commercial facilities than the population distribution.

Green development is not only important for realizing a sustainable development strategy, but also a key approach for constructing an ecological civilization and transforming economic development. On the basis the development concept of a coordinated human–earth relationship and the paradigm of the process–pattern mechanism, this research adopted the drivers, pressures, state, impact, and response (DPSIR) model to build a green development level indicator system. The established indicator system is then applied to explore the spatial-temporal patterns and obstacles in the green development of 34 prefectural cities in Northeast China from 2008 to 2017 by the use of the entropy weight TOPSIS model, the obstacle model and the GIS spatial visualization method. There are three main findings. First, during the research period, the spatial evolution of the green development level of cities in Northeast China has gradually shifted from a small gap at an overall low level to a large gap at an overall high level; the spatial pattern of the green development level in these cities is characterized by a decrease from north to south and obvious spatial agglomeration effects. Second, specific findings in this research fail to indicate that the correlation between the economic development level and green development level of cities in Northeast China is entirely positive. That is, cities with higher economic development levels do not necessarily have higher green development levels, while some cities with lower economic development levels did present higher green development levels, which may be related to each region’s resources and environmental carrying capacity. Third, the mechanisms influencing spatial-temporal variation in the green development level of cities in Northeast China are not identical. Among them, resource endowment conditions, economic development status and government investment scale are playing a vital role in changes in the regional green development level, and they are also behind the diverse evolutionary characteristics presented in the different stages of regional green development. For the cities in Northeast China, in the process of promoting green development and to consolidate their existing green development level, efforts should be made to overcome inefficiencies in socioeconomic growth and to continuously enhance ecological protection and environmental governance. Moreover, it is essential to promote incremental increases in the green development level on the basis of the local conditions through the ingestion, absorption and combination of each city’s own characteristics with lessons from the successful experience of different types of cities. In the future, our research should fully consider the role of urbanization, industrial structure, population density, institutional mechanisms, environmental protection supervision, scientific and technological progress and other factors on the green development level in Northeast China and seek an important entry point to achieve regional human–earth coordination.

The resource-based city is a type of city with pronounced ageing problems. Correctly grasping the socio-economic adaptability to ageing in resource-based cities can help actively guide the direction of urban development and build a new socio-economic order for the elderly. This paper first selects 125 resource-based cities in China from 2000 to 2020 for characteristic analysis, and finds that resource-based cities are characterized by fast growth rate and a large proportion of ageing population, deep ageing, large regional differences and uncoordinated ageing development with regional socio-economic development levels. The research objective of this article is to explore the temporal evolution characteristics, spatial patterns, agglomeration characteristics, and factors hindering the socio-economic adaptability to ageing in resource-based cities in China from 2000 to 2020. Therefore, an indicator system for assessing the socio-economic adaptability to ageing was constructed, and the improved entropy-weighted TOPSIS model was used to measure the socio-economic adaptability to ageing in 113 resource-based cities in China from 2000 to 2020. The spatio-temporal variation characteristics of the socio-economic adaptability to ageing in resource-based cities were analyzed by descriptive analysis and Moran’s index, and the main obstacle dimensions and obstacle indicators were clarified by the obstacle factor model. The findings can be summarized as follows: Temporal Analysis: Over the timeframe assessed, the socio-economic adaptability of ageing in resource cities demonstrates a consistent year-on-year improvement. The spatial clustering pattern exhibits a noteworthy trend of “significant-significant-insignificant”. Spatial Pattern: Examining the spatial arrangement from 2000 to 2020, areas with medium-low and low adaptability are prominently concentrated in Eastern China and Northeastern China, while regions with medium-high and high adaptability are focal in Northern China and Eastern China. Hebei Province and its neighboring provinces consistently display H-H clustering, contrasting with the Southwestern regions that persistently exhibit L-L clustering. Obstacle Degree Analysis: Across the 2000 to 2020 period, dimensions related to economic development, social participation, and social security consistently emerge as the primary obstacles. Notably, the top 12 average annual obstacle indicators are selected, and within economic development dimension all 4 indicators predominate as the key obstacles. Within the social participation dimension, tertiary sector value added and total retail sales of consumer goods per capita feature as primary obstacles. Regarding social security, impediments are primarily associated with the ratio of Medicare coverage, the ratio of pension coverage, alongside the development level of the social security sector. In the domain of service provision, key obstacle indicators include park area per capita, number of books in public libraries per 100 inhabitants, and public trams and buses per 10,000 inhabitants.

Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas.

It is important to understand urban auto markets from a spatial perspective. Specifically, the question of how to simplify and visualize the relatedness of the complicated urban markets arises. Based on the concept of ‘product space’, this research explores the similarity between Chinese cities and identifies the city clusters using data of automobile sales in 2012. A city’s automobile market is shared by different manufacturers and the proximity between two cities is evaluated based on the similarity or relatedness in the structure of the two markets. The spatial structures of the ‘city clusters’ derived from the proximities of automobile markets among cities are mapped, examined, and interpreted. The analysis indicates that cities with higher proximity tend to be similar. According to the intercity proximity index, four geographical city-clusters are identified: the Southeast developed city-cluster, North China city-cluster, Northeast city-cluster, and West city-cluster. Cities in the same cluster tend to share many common characteristics while cities in different clusters exhibit obvious variances, especially in terms of economic status and dominant automakers.