The rapid urban development associated with China’s reform and opening up has been the source of many urban problems. To understand these issues, it is necessary to have a deep understanding of the distribution of urban spatial structure. Taking the six districts of Dalian as an example, in this study, we integrated the enhanced vegetation index, points of interest, and surface temperature data into night light data. Furthermore, herein, we analyze the kernel density of the points of interest and construct three indices using image geometric mean: a human settlement index (HSI), a HSI-POI (HP) index, and a HSI-POI-LST (HPL) index. Using a support vector machine to identify the land type in Dalian’s built-up area, 1000 sampling points were created for verification. Then, the threshold boundary corresponding to the highest overall accuracy of each index and kappa coefficient was selected. The relevant conclusions are as follows: As compared with the other three types of data, the HPL index constructed in this study exhibited natural and social attributes, and the built-up area extracted using this method had the highest accuracy, a high image spatial resolution, and was able to overcome the omission issues observed when using one or two data sources. In addition, this method produces richer spatial details of the actual built-up area and provides more choices for assessing small-scale urban built-up areas in future research.

This study takes sixteen cities in the Shandong Peninsula Urban Agglomeration as the research object. Based on nighttime lighting data from 2000 to 2020, the EANTLI index is constructed on a five-year time scale using the statistical data comparison method to extract the urban built-up area boundaries, built-up area expansion index, and spatial form characteristics. The following findings have been drawn: (1) The urban expansion rate, intensity, and compactness of the cities in the study area are characterized by a year-on-year decline. Jinan and Qingdao show a double-core pattern of urban expansion. In the first stage, the intensity of built-up area expansion is the strongest, and the compactness also shows a decreasing annually trend from the southwest to the northeast. (2) The highest percentage of sprawl is edge-expansion, with the main urban areas of cities dominated by edge-expansion, while county have a high percentage of outlying. (3) The northern coastal areas of the city's center of gravity and the cities of southern Lunan show varying degrees of divergent trends. The center of gravity of the cities around Jinan is characterized by local concentration, and the mutual attraction of the cities of Weifang and Qingdao is increasing. (4) Based on the analysis of the center of gravity-GTWR, the main drivers of speed expansion are fiscal and economic, population education, transportation, and urban facilities construction. The compactness expansion drivers are financial, medical, population, transportation, education, and urban construction. The drivers are characterized by a more pronounced spatiotemporal heterogeneity.

It is widely recognized that urban resilience is one of the core goals of urban development. As an important part of a city, the resilience level of urban human settlements directly affects the development trend of urban resilience. However, at present, research results on the resilience of urban human settlements are very rare, are mainly concentrated in the central region of China, and rarely take into account the economically backward northeastern region. Therefore, in order to better improve the anti-risk ability of the urban human settlement environment system in three provinces of Northeast China, fully implement the strategic goal of “Comprehensive Revitalization of Northeast China”, and achieve high-quality urban development, this paper focuses on 34 prefecture-level cities in three provinces of Northeast China and proposes an urban human settlement resilience evaluation system with 36 indicators in five dimensions, namely, the natural system, human system, housing system, supporting system, and social system. Using the entropy weight method, the Dagum Gini coefficient, and a geographical probe model, the changes in the resilience level of each city from 2005 to 2020 were measured, and the urban living environment was assessed in terms of the adaptability and resilience of the development level in each subsystem based on the temporal and spatial evolution law and its influencing factors. The following conclusions were drawn: (1) The development level of urban human settlement resilience in the three provinces in Northeast China showed an N-shaped development trend from 2005 to 2020, but the regional differences were significant, and the overall spatial pattern was “high in the south and low in the north”. (2) In terms of the overall difference, the overall difference in urban human settlement resilience in the three northeastern provinces of China was small: the inter-regional difference was the main source of the difference, and the intra-regional difference was the secondary source. The regional differences were in the order of Heilongjiang Province > Liaoning Province > Jilin Province, indicating that Jilin Province had the smallest difference and that the resilience level of urban human settlements does not show a balanced development trend. In terms of the average Gini coefficient between regions, the order of difference was Liaoning Province–Heilongjiang Province > Jilin Province–Liaoning Province > Jilin Province–Heilongjiang Province, indicating that the difference between Liaoning Province and Heilongjiang Province was the most significant. (3) The “natural system”, “human system”, “living system”, “supporting system”, and “social system” had significant spatial and temporal heterogeneity and significantly affected the resilience level of urban human settlements in the three provinces in Northeast China. Among them, the “social system” has always been the main factor affecting the resilience level of urban human settlements.

Enhancing climate resilience is imperative for cities to mitigate the effects of global warming and the rising frequency of extreme weather events. This paper develops an evaluation index system for urban climate resilience in Hebei Province, based on data from 11 cities within the province. It evaluates the levels of climate resilience and identifies their limiting factors using the entropy weight method, an urban climate resilience assessment model, and an obstacle degree model, with a focus on four dimensions: ecological resilience, economic resilience, social resilience, and infrastructure resilience. The results indicate that (1) spatial variations in climate resilience across cities in Hebei Province are minimal, with the majority of cities exhibiting climate resilience levels within the moderate resilience category. (2) The majority of regions display low ecological and infrastructure resilience (0.1–0.3), while economic resilience is distributed across three tiers, with regional variations; social resilience remains moderately resilient (above 0.3). (3) Among the social resilience factors, C₃ and C₈ exhibit the highest obstruction levels, emerging as key barriers. (4) In order to effectively respond to climate change risks and challenges in a scientific manner, differentiated implementation of climate response strategies, the core of which lies in identifying the dominant vulnerability dimensions of different cities and accurately applying policies, such as Shijiazhuang, Baoding, Xingtai, Handan, and other cities with fragile ecological resilience, should comprehensively deepen the construction of sponge cities to alleviate urban flooding and the heat island effect.