Smart COVID-19 GeoStrategies using Spatial Network Voronoï Diagrams

Author(s):  
A. Mabrouk ◽  
A. Boulmakoul
2020 ◽  
Vol 1 (2) ◽  
pp. 159-175
Author(s):  
Yuuhi Okahana ◽  
Yusuke Gotoh

Due to the recent popularization of the Geographic Information System (GIS), spatial network environments that can display the changes of spatial axes on mobile devices are receiving great attention. In spatial network environments, since a query object that seeks location information selects several candidate target objects based on the search conditions, we often use a k-nearest neighbor (kNN) search, which seeks several target objects near the query object. However, since a kNN search needs to find the kNN by calculating the distance from the query to all the objects, the computational complexity might become too large based on the number of objects. To reduce this computation time in a kNN search, many researchers have proposed a search method that divides regions using a Voronoi diagram. However, since conventional methods generate Voronoi diagrams for objects in order, the processing time for generating Voronoi diagrams might become too large when the number of objects is increased. In this paper, we propose a generation method of the Voronoi diagram by parallelizing the generation of Voronoi regions using a contact zone. Our proposed method can reduce the processing time of generating the Voronoi diagram by generating Voronoi regions in parallel based on the number of targets. Our evaluation confirmed that the processing time under the proposed method was reduced about 15.9\% more than conventional methods that are not parallelized.


2007 ◽  
Vol 23 (7) ◽  
pp. 503-511 ◽  
Author(s):  
Shinichi Fukushige ◽  
Hiromasa Suzuki

Algorithmica ◽  
2021 ◽  
Author(s):  
Gill Barequet ◽  
Minati De ◽  
Michael T. Goodrich

2021 ◽  
Vol 96 ◽  
pp. 101746
Author(s):  
Ziyun Huang ◽  
Danny Z. Chen ◽  
Jinhui Xu
Keyword(s):  

Author(s):  
Liping Fu ◽  
Kaibo Xu ◽  
Feng Liu ◽  
Lu Liang ◽  
Zhengmin Wang

Background: The distribution of medical resources in China is seriously imbalanced due to imbalanced economic development in the country; unbalanced distribution of medical resources makes patients try to seek better health services. Against this backdrop, this study aims to analyze the spatial network characteristics and spatial effects of China’s health economy, and then find evidence that affects patient mobility. Methods: Data for this study were drawn from the China Health Statistical Yearbooks and China Statistical Books. The gravitational value of China’s health spatial network was calculated to establish a network of gravitational relationships. The social network analysis method was used for centrality analysis and spillover effect analysis. Results: A gravity correlation matrix was constructed among provinces by calculating the gravitational value, indicating the spatial relationships of different provinces in the health economic network. Economically developed provinces, such as Shanghai and Jiangsu, are at the center of the health economic network (centrality degree = 93.333). These provinces also play a strong intermediary role in the network and have connections with other provinces. In the CONCOR analysis, 31 provinces are divided into four blocks. The spillover effect of the blocks indicates provinces with medical resource centers have beneficial effects, while provinces with insufficient resources have obvious spillover effects. Conclusion: There is a significant gap in the geographical distribution of medical resources, and the health economic spatial network structure needs to be improved. Most medical resources are concentrated in economically developed provinces, and these provinces’ positions in the health economic spatial network are becoming more centralized. By contrast, economically underdeveloped regions are at the edge of the network, causing patients to move to provinces with medical resource centers. There are health risks of the increasing pressure to seek medical treatment in developed provinces with abundant medical resources.


2021 ◽  
Author(s):  
Xintao Liu ◽  
Jianwei Huang ◽  
Jianhui Lai ◽  
Junwei Zhang ◽  
Ahmad M. Senousi ◽  
...  

2019 ◽  
Vol 33 (9) ◽  
pp. 1663-1673 ◽  
Author(s):  
Marie‐Caroline Prima ◽  
Thierry Duchesne ◽  
André Fortin ◽  
Louis‐Paul Rivest ◽  
Pierre Drapeau ◽  
...  

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