The accelerated aging of China’s population has made the optimization of elderly care facility locations a critical priority. The field of big data presents innovative approaches for determining the optimal site selection for such facilities. This study uses Xi’an City, in Shaanxi Province, China, as a case study to explore how big data and the ID3 decision tree model can enhance the optimization of elderly care service facility locations. The study begins with a comprehensive analysis of Xi’an’s aging demographics, focusing on the current aging trends and the distribution characteristics of existing elderly care facilities. Utilizing the Baidu Map API, the study collected Point of Interest (POI) data for Xi’an, which were spatially analyzed using ArcGIS 10.8 software to identify the distribution patterns of elderly care facilities and their relationships with other public amenities. The ID3 algorithm was then employed to construct a decision tree model to simulate and predict optimal sites for elderly care facilities in Xi’an. By classifying and filtering POI data and dividing Xi’an into 500 m × 500 m grid units, the model was trained and validated, achieving an accuracy of 85.8%. The findings suggest that suitable sites for elderly care facilities in Xi’an should prioritize proximity to government offices and medical institutions, which would better address the healthcare needs of the elderly population. The application of the ID3 algorithm in planning the locations of elderly care facilities helps mitigate human biases and provides valuable insights for the planning of other public amenities.

Global warming caused by carbon emissions is an environmental issue of great concern to all sectors. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important link to achieve the regional “double carbon” goal. Using 14 cities (prefectures) in Hunan Province as an example, based on the data of carbon emissions generated by land use and human production and life, and on the basis of estimating the carbon emissions in Hunan Province from 2000 to 2020 using the carbon emission coefficient method, this paper uses the Exploratory Spatial–Temporal Data Analysis (ESTDA) framework to analyze the dynamic characteristics of the spatiotemporal pattern of carbon emissions in Hunan Province from 2000 to 2020 through the Local Indicators of Spatial Association (LISA) time path, spatiotemporal transition, and the standard deviation ellipse model. The driving mechanism and spatiotemporal heterogeneity of urban carbon emissions were studied by using the geographically and temporally weighted regression model (GTWR). The results showed that: (1) In the last 20 years, the urban carbon emissions of Hunan Province have had a significant positive spatial correlation, and the spatial convergence shows a trend of first increasing and then decreasing. Therefore, priority should be given to this relevance when formulating carbon emission reduction policies in the future. (2) The center of carbon emission has been distributed between 112°15′57″~112°25′43″ E and 27°43′13″~27°49′21″ N, and the center of gravity has shifted to the southwest. The spatial distribution has changed from the “northwest–southeast” pattern to the “north–south” pattern. Cities in western and southern Hunan are the key areas of carbon emission reduction in the future. (3) Based on LISA analysis results, urban carbon emissions of Hunan from 2000 to 2020 have a strong path dependence in spatial distribution, the local spatial structure has strong stability and integration, and the carbon emissions of each city are affected by the neighborhood space. It is necessary to give full play to the synergistic emission reduction effect among regions and avoid the closure of inter-city emission reduction policies. (4) Economic development level and ecological environment have negative impacts on carbon emissions, and the population, industrial structure, technological progress, per capita energy consumption, and land use have a positive impact on carbon emissions. The regression coefficients are heterogeneous in time and space. The actual situation of each region should be fully considered to formulate differentiated emission reduction policies. The research results can provide reference for the green and low-carbon sustainable development of Hunan Province and the formulation of differentiated emission reduction policies, and provide reference for other similar cities in central China.

In the context of China’s New Urbanization Strategy, a growing number of cities are beginning to experience significant changes in their intra-city structure and size distribution, but systematic research is still lacking. This paper investigated the intra city-size distribution of 214 cities in China’s Yangtze River Delta Region (YRDR) from 1990 to 2020. The study used the Pareto index to portray the equalization differences in intra city-size distribution, and investigated the heterogeneity of its influencing factors using multiscale geographically weighted regression (MGWR). The results showed that most of the cities in the YRDR had a city-size distribution consistent with a Pareto distribution but still tended to be polarized. Among them, the better-developed large cities and regions were more consistent with the Zipf distribution, as were the county-level cities and prefecture-level cities. In terms of spatio-temporal distribution, the intra city-size distribution showed a spatial agglomeration pattern of “polarization in the west and equalization in the east” and the scope and intensity of cold and hot spots reduced. Regarding driving factors, land, industry, and population were essential influences on the intra city-size distribution. Of these, the natural environment and location were important global influencing factors, and economic and social factors only had a more significant influence in 1990 and 2020. The impact of industry restructuring gradually increased, and the population was a significant influence only in the early years. By exploring the intra city-size distribution, this paper provides a scientific basis for the optimization of town structure and policy formulation in small and medium-sized cities, and some new concerns have been proposed.

With rapid urbanization, the spatial layout and functional characteristics of urban facilities have a strong correlation with the spatial distribution of criminal activities. Using Changsha City as a case study, this research analyzes 2023 urban crime data, Point of Interest (POI) data, and socioeconomic data. The Multi-scale Geographically Weighted Regression (MGWR) model and clustering analysis are applied to examine how different types of urban facilities influence the spatial heterogeneity of crimes and propose tailored urban planning recommendations and crime prevention strategies. The findings reveal the following: (1) The spatial distribution of crimes in Changsha’s central urban area demonstrates significant spatial heterogeneity. Property crimes dominate in frequency and spatial distribution, primarily clustering around commercial hubs and transport nodes, while violent crimes are more common in scenic areas and open spaces with high pedestrian flow. (2) The impact of built facilities on crime exhibits spatial variability. Facilities such as Financial Services Facilities (FSF) and Shopping facilities (SHF) significantly contribute to property crime in core urban areas, while Scientific, educational, and cultural facilities (SEC) suppress crime in university towns. Scenic spots and facilities (SPF) are associated with violent crimes near scenic site entrances and transport hubs. (3) Facility resource allocation and preventive strategies should be optimized based on dominant factors in different areas to enhance security management efficiency through precise and differentiated planning, fostering sustainable urban safety systems. This study provides insights into the spatial patterns of crime distribution and its dominant factors from the perspective of urban facilities, offering a scientific basis for improving urban crime management and facility planning.

Understanding the evolution of ecosystem services (ESs) is crucial for sustainable development in rapidly urbanizing regions. This study quantifies the spatiotemporal evolution of habitat quality (HQ), water yield (WY), and carbon storage (CS) in the Chang-Zhu-Tan City Cluster (CZTCC) of China from 2000 to 2020. Results showed that rapid urbanization—where construction land expanded from 2.78% to 7.40% of the area—was the primary driver of ES degradation. Specifically, low-quality habitat (HQ) areas expanded by 162%, and total carbon storage (CS) declined by 1.83% (11.78 Tg C), while regional WY fluctuated with precipitation. Inter-service relationships also became more conflictual: the trade-off between HQ and WY intensified (Pearson’s R strengthened from −0.696 to −0.735), a persistent trade-off between WY and CS was observed (e.g., R = −0.607 in 2020), while the HQ-CS synergy remained stable and strong (R > 0.88). These findings highlight escalating ecological conflicts and offer a robust scientific basis for optimizing land use to manage ES trade-offs and promote sustainable development.

Urban agglomerations are key to balancing carbon emissions (CEs) and ecosystem services (ESs), yet structural imbalances exist between LUCE and ESs due to the lack of standardized frameworks and clear governance strategies. This study investigates the relationship between LUCE and ESs in the Chang-Zhu-Tan urban agglomeration using multi-source data from 2010 to 2023. The study aims to address three main research questions: (1) How do LUCE and ES networks evolve over time? (2) What factors drive their heterogeneity? (3) How do urbanization and ecological restoration impact LUCE and ES network dynamics? To answer these, we apply centrality metrics and develop heterogeneity indices to evaluate connectivity, accessibility, and driving factors. The findings show that both LUCE and ES networks exhibit corridor-like structures, with asymmetric node distributions. The LUCE-Network’s degree centrality increased from 0.16 to 0.29, while the ES-Network’s rose from 0.16 to 0.23. Heterogeneity was initially positive but turned negative by 2023, indicating a shift from LUCE dominance to an increased emphasis on ES. This transition was influenced by urbanization, land use changes, and ecological restoration efforts. Notably, the proportion of built-up land (X11) grew from 0.0187 in 2010 to 0.1500 in 2023, intensifying the disparity between LUCE and ESs. Similarly, urbanization (X7) surged to 0.1558 in 2023, increasing CEs and the demand for ESs. A collaborative pathway is proposed to address these challenges, involving controlled urban development, restoration of green spaces, and prioritizing multimodal transport and energy efficiency. This framework offers actionable diagnostics for improving low-carbon and ecological governance in urban agglomerations.