The multi-modal two-step floating catchment area (MM-2SFCA) method is an extension of the two-step floating catchment area (2SFCA) method that incorporates the impact of different transportation modes, thereby facilitating more accurate calculations of the spatial accessibility of public facilities in urban areas. However, the MM-2SFCA method does not account for the impact of distance within the search radius on supply–demand capacities, and it assumes an idealized supply–demand relationship. This paper introduces the gravity model into the MM-2SFCA method, proposing a multi-modal gravity-based 2SFCA (MM-G2SFCA) method to better account for distance decay and supply–demand relationships. Furthermore, a standardized gravity model is proposed based on the traditional gravity model. This model imposes constraints on upper and lower limits for distance decay weights without compromising the fundamental curve characteristics of the gravity model, thereby avoiding extreme weight scenarios. The accessibility of public hospitals in Shenzhen is evaluated through the integration of basic geographic information data, resident travel data, and official statistical data. The findings demonstrate that the standardized gravity model effectively addresses the issue of excessively high local distance weights in the traditional gravity model, making it more suitable as a distance decay function. The MM-G2SFCA method improves the consideration of distance and supply–demand relationships, thereby facilitating a more rational distribution of accessibility on a global scale. This study discovers differences in the spatial allocation of public hospital resources across the Shenzhen’s districts. Accessibility within the metropolitan core is significantly higher than that outside the core. Additionally, there is a notable difference in the level of accessibility among the districts. Accessibility is found to be better in district centers and along the main traffic arteries.

Residential areas and road networks have a strong geographical correlation. The development of a single geographical feature could destroy the geographical correlation. It is necessary to establish collaborative generalization models suitable for multiple features. However, existing road network generalization methods for mapping purposes do not fully consider residential areas. Compared with road networks, residential areas have a higher priority in cartographic generalization. In this regard, this study proposes a road network generalization method constrained by residential areas. First, the roads and settlements obtained from clustering residential areas were classified. Next, the importance of the settlements was evaluated and certain settlements were selected as the control features. Subsequently, a geographical network with the settlements as the nodes was built, and the traffic paths between adjacent settlements were searched. Finally, redundant paths between the settlements were simplified, and the visual continuity and topological connectivity were checked. The data of a 1:50,000 road network and residential areas were used as the experimental data. The experimental results demonstrated that the proposed method preserves the overall structure and relative density characteristics of the road network, as well as the geographical correlation between the road network and residential areas.

Residential areas is one of the basic geographical elements on the map and an important content of the map representation. Multi-scale residential areas matching refers to the process of identifying and associating entities with the same name in different data sources, which can be widely used in map compilation, data fusion, change detection and update. A matching method considering spatial neighborhood features is proposed to solve the complex matching problem of multi-scale residential areas. The method uses Delaunay triangulation to divide complex matching entities in different scales into closed domains through spatial neighborhood clusters, which can obtain many-to-many matching candidate feature sets. At the same time, the geometric features and topological features of the residential areas are fully considered, and the Relief-F algorithm is used to determine the weight values of different similarity features. Then the similarity and spatial neighborhood similarity of the polygon residential areas are calculated, after which the final matching results are obtained. The experimental results show that the accuracy rate, recall rate and F value of the matching method are all above 90%, which has a high matching accuracy. It can identify a variety of matching relationships and overcome the influence of certain positional deviations on matching results. The proposed method can not only take account of the spatial neighborhood characteristics of residential areas, but also identify complex matching relationships in multi-scale residential areas accurately with a good matching effect.

Relief shading is the primary method for effectively representing three-dimensional terrain on a two-dimensional plane. Despite its expressiveness, manual relief shading is difficult and time-consuming. In contrast, although analytical relief shading is fast and efficient, the visual effect is quite different from that of manual relief shading due to the low degree of terrain generalisation, inability to adjust local illumination, and difficulty in exaggerating and selective representation. We introduce deep learning technology to propose a generation method for shaded relief based on conditional generative adversarial nets. This method takes the set of manual relief shading-digital elevation model (DEM) slices as a priori knowledge, optimises network parameters through a continuous game of “generation-discrimination”, and produces a shaded relief map of any region based on the DEM. Test results indicate that the proposed method retains the advantages of manual relief shading and can quickly generate shaded relief with quality and artistic style similar to those of manual shading. Compared with other networks, the shaded relief generated by the proposed method not only depicts the terrain clearly but also achieves a good generalisation effect. Moreover, through the use of an adversarial structure, the network demonstrates stronger cross-scale generation ability.

This study focuses on a skeleton line extraction algorithm of areal hydrographic elements for cartographic generalization. The requirements of skeleton line extraction for areal hydrographic elements were first analysed and a new automatic extraction algorithm that considered spatial and hierarchical relationships was proposed to meet these requirements. A graph with structured areal hydrographic elements was subsequently presented, and the graph pruning operation was conducted based on the spatial relationship and pruning length threshold. The skeleton line connection of the traditional unconstrained edge midpoint algorithm was improved by the guidance of spatial and hierarchical relationships. Finally, the proposed method was used to extract skeleton lines from 1:250,000 hydrographic elements, and compared with other algorithms. The experiments demonstrate that the skeleton lines extracted by the proposed method have certain advantages in natural continuity, hierarchy, and topological consistency; the results are closer to those of manual extraction and avoiding the subsequent optimization process.