In order to meet the people’s growing aspirations for a better life and to improve the efficient supply capacity of the citizens’ diversified needs, our government has made the optimization of the supply of public services in urban communities a key task in urban planning and construction and has taken the community living circle as a starting point to make up for the short boards of urban public services, which is of great practical significance in improving the quality of urban life and optimizing the spatial structure of the city. As an important carrier of urban residents’ public life, urban public open space plays an indispensable role in improving the quality of life of urban residents. Accessibility is an important indicator to characterize the publicity and service level of a public open space. This study focuses on the construction of the community living circle, optimizing the GIS-based, Gaussian, two-part mobile search method to quantitatively calculate and evaluate the accessibility of a public open space within a 500 m walking distance for the residents in Nanshan District, Shenzhen. On this basis, social survey methods, including the questionnaire survey method via a field survey and the interview method, were used to conduct field research on the residents’ accessibility satisfaction. Comparing the differences and correlations between the two results, the deep-seated reasons affecting the residents’ accessibility satisfaction are explored at the levels of the construction system, management services, and structural layout and spatial design of the public open space, and targeted guiding countermeasures are put forward to guide the development and construction of public open space in the future.

Green space in urban areas is one of the most critical infrastructures for the well-being of residents, and its spatial accessibility configuration is a key indicator of urban planning layout and ecological civilization construction. Using the Gaussian two-step floating catchment area (G2SFCA) method, K-means cluster analysis, and Kriging spatial interpolation, this study examines the spatial characteristics of green space accessibility in the Hongkou District, focusing on the relationship between “green space-community” supply. The findings indicate the following: (1) green space accessibility in Hongkou District decreases as the level of green spaces decreases. Higher levels of accessibility are associated with smaller variations in spatial distribution. (2) The green space accessibility in Hongkou District is affected by the surrounding large green space. Compared to other areas, the community green spaces near Lu Xun Park and Peace Park have higher accessibility. (3) The green space accessibility in Hongkou District is impacted by the mismatch between population density and green spaces. An overlay analysis of accessibility and population density reveals that high accessibility areas have average or low-average population density. Based on the results of the green space accessibility analysis, recommendations are proposed to optimize the green space layout in Hongkou District from the perspective of spatial justice. These suggestions are aimed at promoting the equalization of green space services in Hongkou District, improving the overall level of urban green space services, with a view to improving the quality of urban living environment and creating a green and livable urban area.

Three-dimensional (3D) information technology has become an important technical support in digital heritage preservation practice. However, due to the lack of systematic quantitative research, it is difficult to form a comprehensive understanding of the historic and cultural districts, from macro to micro. Our study aimed to establish a systematic 3D spatial diagnostic framework combining 3D scanning and SPSS data descriptive analysis and regression analysis for historic and cultural districts to promote sustainable historic and cultural area preservation. Taking Zhongshan Street in Qi County as an example, data statistical analysis was carried out on morphological feature data from the macro level of the district, the meso level of architecture, and the micro level of elements. The research conclusion shows that at the macro level the street form continues the main features of a traditional alley spatial skyline, height–width ratio, and sectional symbol language. At the meso level, the architecture reveals various periods of style in terms of the facade width and mathematical relationship between traditional architectural facades. At the micro level, architectural detailing explains the main reasons for the recent new construction being inconsistent with the historic and cultural district appearance. This quantitative diagnostic method can accurately analyze the current characteristics of historic and cultural districts and easily provide effective suggestions for follow-up preservation methods.

As an important carrier of urban residents' public life, urban public open space plays an indispensable role in improving the quality of life of urban residents. Accessibility is an important indicator to characterize the publicity and service level of a public open space. This study optimized the GIS-based, Gaussian two-part mobile search method to quantitatively calculate and evaluate the accessibility of public open space within a 5-minute walking distance for residents in Nanshan District, Shenzhen. On this basis, social survey methods, including the questionnaire survey method via a field survey and the interview method, were used to conduct field research on residents' accessibility satisfaction. Comparing the results of the survey on residents' accessibility satisfaction to the GIS quantitative evaluation results of accessibility, the results of the field research show that current residents of Nanshan are more satisfied with the accessibility of public open space. In view of the similarities and differences between the two sets of results, the influencing factors of public open space accessibility were analyzed, corresponding countermeasures were proposed for the construction of public open spaces in the future.

The cultural heritage of the Liao dynasty in Chifeng encompasses significant historical and cultural information that requires systematic digital preservation and management. However, heterogeneous data sources across museums, archives, and research institutions lack semantic interoperability, creating barriers for cross-system integration and knowledge discovery. This study proposes a standardized knowledge graph construction method by integrating the CIDOC Conceptual Reference Model version 7.2 with large language models. A unified ontology framework enables semantic consistency across diverse heritage data, while Generative Pre-trained Transformer-based models automatically extract structured triples from unstructured texts through prompt engineering and entity disambiguation, with the resulting knowledge graph implemented in Neo4j graph database. The constructed knowledge graph integrates 106 immovable cultural heritage records from Chifeng City with approximately 20 types of semantic relationships, forming a comprehensive semantic network covering people, places, events, time, and materials. K-means clustering reveals five cultural value themes, including “Nomadic Imperial Power System” and “Multi-Capital Governance Network”, while geospatial mapping identifies a “dual-core and ring-belt” distribution pattern for heritage protection zoning. This research demonstrates how international semantic standards can be integrated with artificial intelligence technologies to enable interoperable cultural heritage knowledge systems, providing practical implications for cross-institutional heritage management and archaeological survey planning.

Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban residents. By using data field theory and the Geographically Weighted Regression (GWR) modeling method, this study explored the complex relationship between taxi demand and traffic-related factors in urban core areas and revealed the potential factors affecting taxi starting and landing points. This research reveals that during the morning peak hours (7:00–9:00), at locations such as long-distance bus terminals, bus stations, parking areas, train stations, and bike-sharing points, taxi demand significantly increases, particularly in the central and southeastern regions of the urban core. Conversely, demand is lower in high-density intersection areas. Additionally, proximity to train stations is positively correlated with higher taxi demand, likely related to the needs of long-distance travelers. During the evening peak hours (17:00–19:00), the taxi demand pattern resembles that of the morning peak, with long-distance bus terminals, bus stations, and parking and bike sharing areas remaining key areas of demand. Notably, parking areas frequently serve as pick-up points for passengers during this time, possibly associated with evening activities and entertainment. Moreover, taxi demand remains high around train stations. In summary, this study enhances our understanding of the dynamics of urban taxi demand and its relationship with various transportation-related influencing factors within the core urban areas. The proposed grid partitioning and GWR modeling methods provide valuable insights for urban transportation planners, taxi service providers, and policymakers, facilitating service optimization and improved urban mobility.