Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis–Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms.

Roads are closely related to people’s lives, and road network extraction has become one of the most important remote sensing tasks. This study aimed to propose a road extraction network with an embedded attention mechanism to solve the problem of automatic extraction of road networks from a large number of remote sensing images. Channel attention mechanism and spatial attention mechanism were introduced to enhance the use of spectral information and spatial information based on the U-Net framework. Moreover, residual densely connected blocks were introduced to enhance feature reuse and information flow transfer, and a residual dilated convolution module was introduced to extract road network information at different scales. The experimental results showed that the method proposed in this study outperformed the compared algorithms in overall accuracy. This method had fewer false detections, and the extracted roads were closer to ground truth. Ablation experiments showed that the proposed modules could effectively improve road extraction accuracy.

In this study, we investigate the urban landscape patterns in Wuhan, China based on the land use data in the vector format. Using the approach of landscape metric analysis, we calculate forty-four vector-based landscape metrics and then reduce redundant ones through a combination of Spearman correlation analysis and factor analysis, in order to extract a core set of characterizing landscape metrics. We find that the urban landscape can be depicted by six factors including the overall shape and diversity, mean proximity, overall area variation, fragmentation variation, elongation variation, and mean shape complexity. After analyzing typical patterns indicated by the core metrics and the spatial distribution of land use patterns, we compare our findings with other studies and discuss how the core metrics coincide and differ.

The coastal regions of China are abundant in intangible cultural heritage (ICH) resources characterized by distinct maritime features. The comprehensive integration and development of these resources with tourism represent a critical strategy for the scientific protection and sustainable development of coastal ICH. This study examines the spatial distribution of ICH across China's coastal regions. It also establishes an evaluation system to measure tourism response levels toward coastal ICH and employs a GeoDetector model to identify influencing factors. The results indicate that: (1) Different types of ICH resources in coastal areas exhibit distinct spatial distribution patterns, with notable variations in their concentrations. Generally, the spatial distribution of ICH follows a multi-centered structure, exhibiting higher densities in the Yangtze River Delta, Pearl River Delta, and Yellow River Delta. (2) Regional disparities in tourism response levels toward ICH are evident in coastal regions, demonstrating a decreasing gradient from central areas toward the north and south. (3) The tourism response levels of ICH are primarily influenced by factors such as regional GDP, resident population, the tourism environment, and the number of ICH sites, with regional GDP identified as the most influential factor. These findings enhance the understanding of the current developmental status of ICH in China's coastal regions and highlight key distinctions from inland areas.

Social, economic, and environmental statistical data associated with geographic points are currently globally available in large amounts. When conventional thematic maps, such as proportional symbol maps or point diagram maps, are used to represent these data, the maps appear cluttered if the point data volumes are relatively large or cover a relatively dense region. To overcome these limitations, we propose a new type of thematic map for statistical data associated with geographic points: the point grid map. In a point grid map, an input point data set is transformed into a grid in which each point is represented by a square grid cell of equal size while preserving the relative position of each point, which leads to a clear and uncluttered appearance, and the grid cells can be shaded or patterned with symbols or diagrams according to the attributes of the points. We present an algorithm to construct a point grid map and test it with several simulated and real data sets. Furthermore, we present some variants of the point grid map.

The air pollution problem in China continues to become more severe with dramatic economic development. The focus of this study was to create a map representing the spatial–temporal pattern of the air quality in the three major economic zones in China, including the Beijing–Tianjin–Hebei Economic Zone, the Yangtze River Delta Economic Zone, and the Pearl River Delta Economic Zone in 2014. A calendar view was used to visualize the daily condition of air quality and primary pollutant in each city in 2014, and geographic references were added to each visualization according to their spatial relationships. The map provides an efficient way to investigate and understand the current status of air quality and spatial–temporal patterns of air quality.