The layering process of the historic urban landscape (HUL) has spatial and temporal characteristics. With the help of digital technology, building a database to collect and manage spatial information on HUL is an effective research method. Taking Nanjing since the Ming Dynasty (1368–2024) as an example, our paper proposes a framework for constructing a historical geographic information system (HGIS) for HUL. The results show that (1) ancient Chinese historical archives have good potential for use in the construction of historical landscape databases. The HGIS can visualize the evolution of HUL and realize the storage, expression, querying, and analysis of information in different formats. (2) The Republic of China is a turning point in the evolution of Nanjing’s HUL. In terms of spatial evolution, the main distribution areas of urban form and characteristic places, as well as the NACH (normalized angular choice) and NAIN (normalized angular integration) cores of the road network, all moved from the south to the north of the city. In terms of typological evolution, the urban form changes from a predominantly residential area to one that emphasizes residential, administrative, educational and industrial areas. The main types of characteristic places shifted from house gardens, temples and ancestral halls to scenic buildings and public gardens. (3) Political factors, urban planning, commercial ports, and land prices influence the layering process of the HUL. The constructed database guides the preservation of HUL from the digital heritage perspective and the construction of other historical databases with spatial characteristics.

The construction of refuge spaces in rapidly urbanizing historic cities is a challenging task owing to their complex urban form, unique urban fabric, and historic preservation requirements. Refuge green space (RGS) is a green space that can serve as an emergency shelter in cities, providing a flexible means to increase the emergency shelter capacity for rapidly urbanized historic cities. After major earthquakes, spatial accessibility to GRSs is a critical planning strategy for disaster prevention and emergency response in historic cities. To match the RGS planning with the emergency service demand, we must examine the spatial disparity in access to existing RGSs. In this study, the urban area of Nanjing was selected as the target region to analyze the spatial disparity in access to RGSs using the Gaussian two-step floating catchment area method at four evacuation times (10, 20, 30, and 60 min). The results showed that the spatial accessibility exhibited clustering characteristics, where high-accessibility spaces were mainly distributed in the northern and southern regions of Nanjing. The increase in the evacuation time improved accessibility to RGSs, but the existing RGSs still could not sufficiently satisfy the emergency shelter needs of citizens. Based on the bivariate local Moran’s I analysis of the RGS accessibility and population density, the spatial mismatch regions were dominant in the center of the urban area. These findings not only are expected to assist emergency planners by improving their strategic plans for emergency shelter investment in Nanjing and their ability to respond to catastrophic earthquakes, but also provide a strong reference for the construction of a safe environment in other rapidly developing historic cities that face earthquake threats.

Clustered urban development has caused increasing fragmentation and islanding of regional ecological spaces. Creating a green infrastructure network (GIN) is a practical method of ensuring regional ecological security. This study proposed a method of GIN identification at the regional scale based on the Nanjing Metropolitan Area as an example. In this method, green hubs were identified using morphological spatial pattern analysis and connectivity indexes, green corridors were simulated based on the least-cost path model, and key optimization nodes were identified using circuit theory. The results indicated that green hubs covered an area of 5042.07 km², of which, 15.40% were cross-border, and the potential corridors were distributed in a network, with the key ecological nodes primarily narrowly situated. By comparing the hubs with the statutory green space protection area and the urban ecological control line, the identification results were more than 70% accurate, showing that the results were valid and reliable. This method not only made the identification of regional GIN more practical and replicable but also further identified key areas that need priority protection. This study provides a method for constructing regional GIN and serves as a strong guide for ecological and development planning of other urban clustered areas.

Urban green space systems (UGSS) play a crucial role in enhancing citizens’ well-being and promoting sustainable urban development through their ecosystem service values (ESV). However, understanding the spatiotemporal changes, driving factors, and influencing mechanisms of ESV remains a critical challenge for advancing urban green theories and formulating effective policies. This study focuses on Suzhou, China’s third-largest prefecture-level city by economic volume and ecological core city of the Taihu watershed, to evaluate the ESV of its UGSS from 2010 to 2020, identify key driving factors, and analyze their influencing mechanisms. Using the InVEST model combined with the entropy weight method (EWM), we assessed the ESV changes over the study period. To examine the influencing mechanisms, we employed an innovative XGBoost-GWR approach, where XGBoost was used to screen globally significant factors from 37 potential drivers, and geographically weighted regression (GWR) was applied to model local spatial heterogeneity, providing a research perspective that balances global nonlinear relationships with local spatial heterogeneity. The results revealed three key findings: First, while Suzhou’s UGSS ESV increased by 9.92% from 2010 to 2020, the Global Moran’s I index rose from 0.325 to 0.489, indicating that its spatial distribution became more uneven, highlighting the increased ecological risks. Second, climate, topography, landscape pattern, and vegetation emerged as the most significant driving factors, with topographic factors showing the greatest variation (the negatively impacted area increased by 455.60 km²) and climate having the largest overall impact but least variation. Third, the influencing mechanisms were primarily driven by land use changes resulting from urbanization and industrialization, leading to increased ecological risks such as soil erosion, pollution, landscape fragmentation, and habitat degradation, particularly in the Kunshan, Wujiang, and Zhangjiagang Districts, where agricultural land has been extensively converted to constructed land. This study not only elucidates the mechanisms influencing UGSS’s ESV driving factors but also expands the theoretical understanding of urbanization’s ecological impacts, providing valuable insights for optimizing UGSS layout and informing sustainable urban planning policies.

While newly built urban green spaces aim to address environmental concerns, the resulting green gentrification and social inequality caused by escalating property values have become critical topics of urban socio-spatial research. To prevent green initiatives from becoming unaffordable for their intended beneficiaries in rapidly urbanizing cities, it is essential to examine the spatial and temporal relationships between the construction of new green spaces and rising housing prices. This study employs a difference-in-differences methodology to analyze regional disparities, impact periodicity, and the influence of park size on housing prices, using Nanjing, China as a case study. This result reveals that the introduction of new-built parks in Nanjing significantly impacts housing prices within an 800 m radius. The premium effect of these parks is substantially higher in urban core areas compared to suburban locales, demonstrating spatial differentials. Suburban parks temporally exhibit a prolonged lag and a shorter premium impact duration. Moreover, among various park areas, medium-sized parks demonstrate the most pronounced leverage effect, approximately double that of large parks, while small parks do not significantly affect housing prices. To mitigate the exacerbation of premium effects and enhance social justice in green strategies, we advocate prioritizing the development of small parks, particularly in urban core areas, and leveraging the temporal delay in new-built park impacts for urban policy interventions.