High-accuracy location identification is the basis of location awareness and location services. However, because of the influence of GPS signal loss, data drift and repeated access in the individual trajectory data, the efficiency and accuracy of existing algorithms have some deficiencies. Therefore, we propose a two-step clustering approach to extract individuals' locations according to their GPS trajectory data. Firstly, we defined three different types of stop points; secondly, we extracted these points from the trajectory data by using the spatio-temporal clustering algorithm based on time and distance. The experimental results show that the spatio-temporal clustering algorithm outperformed traditional extraction algorithms. It can avoid the problems caused by repeated access and can substantially reduce the effects of GPS signal loss and data drift. Finally, an improved clustering algorithm based on a fast search and identification of density peaks was applied to discover the trajectory locations. Compared to the existing algorithms, our method shows better performance and accuracy.
Population aging has increasingly challenged socio-economic development worldwide, highlighting the significance of relevant research such as accessibility to residential care facilities (RCFs). However, a number of previous studies are carried out only on street (town)-to-district scales, which could cause errors of the accessibility to RCFs for a family. In order to improve the resolution to individual families, we measure and compare the accessibilities to RCFs based on 3494 residential communities and 169 streets of Guangzhou in 2020 through the two-step floating catchment area (2SFCA) method. It was found that the distributions of the elderly and the service-dense blobs of the RCFs show patterns of a three-level spatial distribution, with a characteristic clustering at the center with peripheral dispersion. The resultant accessibility to RCFs in Guangzhou, ranging from 2.5 to 3.45, is generally consistent with the studies focusing on street scales. However, the maximum difference in the accessibility of two residential communities on the same street ranges from less than 0.02 to 0.94 in Guangzhou, indicating large variations. Although the relative errors of the accessibility results based on bi-scale data are relatively low, the cumulative errors can be high, e.g., over 25% in many streets of large cities. Consequently, hundreds of elderly persons per street can be adversely affected by those errors, with six streets over 1000. Therefore, this study focusing on the smaller-scale residential community data may provide more accurate reference to individual households. For the spatial allocation and optimal layout of Guangzhou and similar cities with population aging, we suggest maximizing RCFs in metropolises by taking full advantage of existing residential care facilities with necessary restructuring, improvements, and expansions on service capability. While for less connected cities, we encourage building new RCFs in situ.
Computational fluid dynamics (CFD) numerical simulations play an important role in many research fields, including hydrological basins, rivers, floods, and dam breaks. Currently, much research mainly pursues accuracy, efficiency, scale, dimensions, etc. In most models, the influencing parameters are adjusted manually, and only one constant is designed in a calculation area, resulting in a lack of heterogeneity in the variable space. In this article, using the idea of spatial interpolation by designing some control points along the river, a spatial design of the Manning coefficient is proposed in the CFD numerical simulation, and an adaptive correction model considering the different correction logic is researched to calibrate the model. Finally, the model is proven to be correct and effective, and it is converged with the Malpasset Dam-breaking case. It can help reduce the artificial calibration and improve simulation accuracy by designing the spatial adaptive correction parameter.
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