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.

The purpose of this study is to analyze the characteristics of tourism economic links and networks within the tourism sector of China’s land border cities. It seeks to reveal the spatial and temporal evolution of tourism economic links in order to facilitate regional coordination among border cities. The article adopts the modified gravity model to measure the degree of tourism economic linkage of China’s land border cities, and utilizes UCINET 6.0 software, based on social network theory, to analyze the characteristics of the tourism economic linkage network of China’s land border. The findings show that the overall network density of China’s land border tourism economic linkages is relatively low, with uneven development in the “three borders” tourism economic linkages. There is a significant core–periphery structure, with the core area gradually expanding to the northwest and southwest, and geographically neighboring border cities are more likely to form a subgroup. The analysis of the socio-spatial network relationship of China’s land border cities yields suggestions for coordinated regional development, providing a foundation for the sustainable development of land border tourism.

At present, the global environmental problems are intensifying, and land resources are facing multiple dilemmas such as degradation, pollution and shortage. The rise of the concept of sustainable development, the enhancement of public ecological awareness and the promotion of relevant policies and regulations have jointly promoted the generation and development of land ecological security research. This study evaluated the land ecological security of Heilongjiang Province from 2000 to 2022, found out the main factors affecting the land ecological security, provided the basis for scientific formulation of corresponding measures, provided the guarantee for the sustainable development of regional social economy and agriculture, and maintained the long-term coordinated development of the land natural, social and economic complex. Based on the PSR model, this study constructs an index system for evaluating land ecological security. It employs a comprehensive index model to assess and utilizes the entropy-weighted TOPSIS method to determine the weights of various indicators. It calculated the comprehensive index of Heilongjiang Province from 2000 to 2022. Drawing upon relevant literature, this research establishes the land ecological security level of the region. The obstacle factor diagnosis model is used to analyze the main obstacle factors at each level, and corresponding countermeasures are proposed based on the results. The main results are as follows: (1) From 2000 to 2022, the level of land ecological security in Heilongjiang Province has generally shown a fluctuating upward trend, with the safety level changing from dangerous to relatively safe and moving towards a favorable direction. However, it remained mostly in an unsafe state for the past 20 years; (2) The main threats at the criterion layer have changed significantly, with obstacle degrees rising to 59% for pressure systems and 36% for status systems while decreasing to 5% for response systems. Currently, the main threat comes from pressure systems; (3) The main obstacle factors at the indicator layer have evolved from the proportion of tertiary industry output value and effective irrigation area of arable land to pure amount of chemical fertilizer applied in agriculture and proportion of forest area. In 2022, major factors affecting land ecological security in Heilongjiang Province are pure amount of chemical fertilizer applied in agriculture, proportion of forest area, industrial solid waste generation volume, pesticide use volume, and per capita water resources. This study evaluated the land ecological security in Heilongjiang Province, revealed its influencing factors, and put forward specific suggestions for local construction.

The urban physical environment is composed of multiple elements that collectively influence the spatial pattern of crime. Existing research has predominantly focused on the relationship between individual types of facilities and crime, yet there remains a gap in comprehensively examining the integrated effects of the urban physical environment. This study, taking 87 police precincts in the central city of Changchun as units of analysis, innovatively constructs an integrated “Factor Analysis–Negative Binomial Regression” framework. First, factor analysis is applied to reduce the dimensionality of 14 categories of Points of Interest (POI) data, extracting three comprehensive factors that characterize the macro-level functional structure of the city: the “Business and Economic Activities Factor,” the “Residential, Educational, and Transportation Factor,” and the “Leisure and Entertainment Factor.” This approach effectively addresses the issue of multicollinearity among variables and uncovers the underlying macro-level functional factors. Subsequently, a negative binomial regression model is employed to analyze the impact of each factor on crime counts. The results indicate that: (1) The spatial distribution of urban crime is markedly heterogeneous and is systematically driven by the urban functional structure; (2) Both the “Business and Economic Activities Factor” and the “Leisure and Entertainment Factor” exhibit significant positive effects on crime, with each unit increase in their scores associated with an approximately 20% increase in the relative risk of crime; (3) The influence of the “Residential, Educational, and Transportation Factor” is not significant. Collectively, the findings demonstrate that shifting the perspective from “micro-level facilities” to “macro-level functional dimensions” can provide deeper insights into the fundamental formative mechanisms underlying the spatial pattern of crime.