scholarly journals Using complex network analysis to assess the ecological security network for a rapid urbanization region in China

Author(s):  
De Zhou ◽  
Zhulu Lin ◽  
Tingting Yan ◽  
Jialing Qi ◽  
Wenyu Zhong

A sound ecological security network (ESN) promotes the interconnection of ecological sources, improves the pattern of ecological security, and alleviates the degradation of an ecosystem. Rapid urbanization and land use changes may lead to serious fragmentation and islanding of landscape patches and further to deep disturbance of regional ESNs. However, most studies in the recent years focused on the methodological development of ESN identification, reconstruction, and optimization, but lacked the systematic assessment of the network after its construction. The purpose of this study is to use complex network analysis to systematically assess the constructed ESN for the urban agglomeration around Hangzhou (UAHB), a rapid urbanization region in China. By integrating landscape ecology theory, graph theory, and complex network analysis, we abstracted the ESN into a topological network and developed an index system to assess the abstracted network, which was based on the structural elements of the topological network (nodes, edges, and the overall network). Our results show that the connectivity and stability of the UAHB’s ESN have been improved in the last 20 years, although isolated nodes are still existing in the ESN. Our study also shows that the network’s robustness under human disturbance has been affected more than that under non-human disturbance. Finally, we proposed five optimization strategies from the perspective of topological structure and ecological function to maintain a sustainable and well-protected ecological system.

2021 ◽  
Vol 2 (1) ◽  
pp. 113-139
Author(s):  
Dimitrios Tsiotas ◽  
Thomas Krabokoukis ◽  
Serafeim Polyzos

Within the context that tourism-seasonality is a composite phenomenon described by temporal, geographical, and socio-economic aspects, this article develops a multilevel method for studying time patterns of tourism-seasonality in conjunction with its spatial dimension and socio-economic dimension. The study aims to classify the temporal patterns of seasonality into regional groups and to configure distinguishable seasonal profiles facilitating tourism policy and development. The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018. The analysis reveals four groups of seasonality, which are described by distinct seasonal, geographical, and socio-economic profiles. Overall, the analysis supports multidisciplinary and synthetic research in the modeling of tourism research and promotes complex network analysis in the study of socio-economic systems, by providing insights into the physical conceptualization that the community detection based on the modularity optimization algorithm can enjoy to the real-world applications.


2020 ◽  
Vol 67 (6) ◽  
pp. 1134-1138 ◽  
Author(s):  
Zhongke Gao ◽  
Hongtao Wang ◽  
Weidong Dang ◽  
Yongqiang Li ◽  
Xiaolin Hong ◽  
...  

Author(s):  
Emerson Luiz Chiesse da Silva ◽  
Marcelo De Oliveira Rosa ◽  
Keiko Veronica Ono Fonseca ◽  
Ricardo Luders ◽  
Nadia Puchaslki Kozievitch

2018 ◽  
Vol 55 ◽  
pp. 133-142 ◽  
Author(s):  
Wenyu Hou ◽  
Huifang Liu ◽  
Hui Wang ◽  
Fengyang Wu

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