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The unequal allocation of healthcare resources raises many fundamental problems, one of which is how to address inequity in population health. This paper focuses on disparities in public transport healthcare accessibility, with a special focus on an expanding subway system. Based on a vulnerability index, including factors that are likely to limit healthcare opportunities, a two-step floating catchment area method was used to assess the distribution of supply and demand for healthcare. Quantity, quality, and walking distance accessibility were aggregated into hexagonal grids. The Theil index was used to measure inequity and understand the influence of subways on spatial disparities in healthcare accessibility. The ongoing construction of the subway has heterogeneous impacts on healthcare accessibility for different parts of the city and exacerbates spatial inequity in many areas. In an environment where people in peri-urban areas are excluded from healthcare access because of low subway coverage, the results suggest that the potential for subways to address inaccessibility is limited. The findings highlight the requirement of efficient public transport services and are relevant to researchers, planners, and policymakers aiming to improve accessibility to healthcare, especially for populations who dwell in winter cities.

The precise leveling method is often used to monitor the uneven deformation of the highway bridge head, which requires manual contact, dangerous and heavy workload. In order to solve these issues, this paper proposes a method to extract deformation features of the highway bridge head based on mobile terrestrial laser scanning (TLS) point clouds. Firstly, an automatic data acquisition system is designed to analyze and determinate the scanning station spacing and scanning resolution. Then acquired road point clouds are denoised based on the plane fitting method, which usually sets the experimental threshold. The road dividing line information is used for point clouds coarse registration, and the weighted average elevation method is used for refined registration. Lastly, the elevation of the deformation monitoring point is the average elevation of all points in the selected grid surface, which will mitigate random errors in the elevation of a single point. Point clouds of three different roads were collected to verify the proposed method. The results show that the accuracy of the elevation repeatability is better than ± 1mm, and the accuracy of the elevation check with the TM50 total station is better than ± 2mm, which meets the requirements of deformation monitoring. In addition, it takes about 3-4 minutes to complete the data collection of a station on the highway bridge head. Therefore, the proposed method based on mobile TLS data can be suitable for highway bridge head deformation measurement.INDEX TERMS Terrestrial laser scanning (TLS), highway bridge head, deformation monitoring, automatic data acquiring system, point cloud processing.

The LiDAR technology is a means of urban 3D modeling in recent years, and the extraction of buildings is a key step in urban 3D modeling. In view of the complexity of most airborne LiDAR building point cloud extraction algorithms that need to combine multiple feature parameters, this study proposes a building point cloud extraction method based on the combination of the Point Cloud Library (PCL) region growth segmentation and the histogram. The filtered LiDAR point cloud is segmented by using the PCL region growth method, and then the local normal vector and direction cosine are calculated for each cluster after segmentation. Finally, the histogram is generated to effectively separate the building point cloud from the non-building.Two sets of airborne LiDAR data in the south and west parts of Tokushima, Japan, are used to test the feasibility of the proposed method. The results are compared with those of the commercial software TerraSolid and the K-means algorithm. Results show that the proposed extraction algorithm has lower type I and II errors and better extraction effect than that of the TerraSolid and the K-means algorithm.