A study on traffic impact by heavy rain using betweenness centrality analysis

2021 ◽  
Vol 32 (1) ◽  
pp. 49-61
Author(s):  
Okyu Kwon ◽  
Byung Sik Kim ◽  
Seung Kwon Jung
Keyword(s):  
Betweenness Centrality ◽  
Heavy Rain ◽  
Traffic Impact ◽  
Centrality Analysis

Centrality Analysis of the United States Network Graph

2018 ◽  
pp. 1746-1756
Author(s):  
Natarajan Meghanathan
Keyword(s):  
United States ◽  
Betweenness Centrality ◽  
The United States ◽  
Power Law Distribution ◽  
Network Graph ◽  
Central State ◽  
Undirected Network ◽  
Centrality Metrics ◽  
The Us ◽  
Centrality Analysis

We model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. We determine a ranking of the states in the US with respect to the four commonly studied centrality metrics: degree, eigenvector, betweenness and closeness. We observe the states of Missouri and Maine to be respectively the most central state and the least central state with respect to all the four centrality metrics. The degree distribution is bi-modal Poisson. The eigenvector and closeness centralities also exhibit Poisson distribution, while the betweenness centrality exhibits power-law distribution. We observe a higher correlation in the ranking of vertices based on the degree centrality and betweenness centrality.

scholarly journals Study on Business Ecosystem Research Trend Using Network Text Analysis

2021 ◽  
Vol 13 (19) ◽  
pp. 10727
Author(s):  
Matthew Minsuk Shin ◽  
Seunghye Jung ◽  
Jin Sung Rha
Keyword(s):  
Text Analysis ◽  
Betweenness Centrality ◽  
Research Trend ◽  
Degree Centrality ◽  
Ecosystem Research ◽  
Business Ecosystem ◽  
Network Text Analysis ◽  
Bibliographic Data ◽  
Centrality Analysis ◽  
New Phase

The management environment is moving into a new phase with the changing global circumstances. The business ecosystem as a management strategy has been studied for the last 30 years since the concept was introduced. The purpose of this study was to analyze the research trend in business ecosystem by using network next analysis and to understand the concept, being one that is still being actively studied. Network text analysis is a commonly used method to analyze research trends by forming networks based on bibliographic data of the articles, namely, keywords. For the analysis, we collected the data and keywords from 340 research papers published in global academic journals related to business ecosystem on the basis of the Scopus database. Through keywords extraction and cleansing, we found that the keywords of “innovation”, “sustainability”, and “platform” were mentioned most frequently, and the research topics were correlated to each other. Moreover, we conducted degree centrality and betweenness centrality analysis along with clustering analysis by transforming the two-mode network into a one-mode network. Degree centrality involves analyzing the degree to which one keyword links to other keywords, and betweenness centrality shows the mediating effects of a keyword to other keywords. In the centrality analysis results, “innovation”, “sustainability”, “platform”, and “business model” showed the highest degree centrality, and “sustainability”, “innovation”, “China”, and “platform” had the highest betweenness centrality. Then, we classified the clusters of subtopics into five groups. The current study examined accumulated research and suggested a comprehensive understanding of the research trend in business ecosystem by incorporating a method enabling research trend analysis to secure objectivity. This research is expected to help researchers to review the research trend in business ecosystem and identify expandable topics for further studies.

Centrality Analysis of the United States Network Graph

2019 ◽  
pp. 312-323
Author(s):  
Natarajan Meghanathan
Keyword(s):  
United States ◽  
Betweenness Centrality ◽  
The United States ◽  
Power Law Distribution ◽  
Network Graph ◽  
Central State ◽  
Undirected Network ◽  
Centrality Metrics ◽  
The Us ◽  
Centrality Analysis

The authors model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. They determine a ranking of the states in the US with respect to the four commonly studied centrality metrics: degree, eigenvector, betweenness, and closeness. They observe the states of Missouri and Maine to be, respectively, the most central state and the least central state with respect to all the four centrality metrics. The degree distribution is bi-modal Poisson. The eigenvector and closeness centralities also exhibit Poisson distribution, while the betweenness centrality exhibits power-law distribution. The authors observe a higher correlation in the ranking of vertices based on the degree centrality and betweenness centrality.

A domain-specific measure of centrality for water distribution networks

2019 ◽  
Vol 27 (2) ◽  
pp. 341-355 ◽  
Author(s):  
Seyed Ashkan Zarghami ◽  
Indra Gunawan
Keyword(s):  
Complex Networks ◽  
Betweenness Centrality ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Centrality Measures ◽  
Content Type ◽  
Centrality Measure ◽  
Centrality Analysis ◽  
Practical Implications

Purpose In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features. Design/methodology/approach This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network. Findings Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs. Practical implications The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy. Originality/value The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

Integrated Traffic Impact Analysis Process for Urban Renewal

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yanli Wang ◽  
Hao Sun ◽  
Sicheng Hao ◽  
Bing Wu
Keyword(s):  
Urban Renewal ◽  
Impact Analysis ◽  
Analysis Process ◽  
Traffic Impact

Forecasting Occurrence of Localized Heavy Rain and Tornado

2014 ◽  
Vol 134 (9) ◽  
pp. 604-607
Author(s):  
Shoji KAWASAKI ◽  
Masaaki KOYAMA ◽  
Shunsuke FUKAMI ◽  
Chisa KOBAYASHI
Keyword(s):  
Heavy Rain

DISTRIBUIÇÃO ESPAÇO-TEMPORAL DOS DESASTRES HIDROMETEOROLÓGICOS NA MICRORREGIÃO PLUVIOMÉTRICAMENTE HOMOGÊNEA DO LITORAL PARAIBANO

2019 ◽  
Vol 21 (2) ◽  
pp. 467-476
Author(s):  
Wanessa Janinne Eloy Da Silva ◽  
Maressa Oliveira Lopes Araújo ◽  
Marcelo De Oliveira Moura
Keyword(s):  
Flash Flood ◽  
Temporal Distribution ◽  
Heavy Rain ◽  
Emergency Situations

O presente trabalho tem por objetivo analisar a distribuição espaço-temporal dos reconhecimentos de Situação de Emergência associados à dinâmica hidrometeorológica na microrregião pluviometricamente homogênea do Litoral paraibano, durante o período de 2003 a 2016. Para isso, foram utilizados dados adquiridos no site do Ministério da Integração Nacional, encontrados na página da Secretaria Nacional de Proteção e Defesa Civil, conforme reconhecimentos disponibilizados através de portarias. Como resultados principais, constatou-se um total de 29 reconhecimentos, em que 51,7% corresponde a enchentes; 20,7% a chuvas intensas; 24,2% correspondente a enxurradas e 3,4% a inundações. Considera-se que os resultados obtidos tiveram um cunho mais descritivo, necessitando assim de estudos mais avançados sobre a temática.Palavras chave: Litoral Paraibano, desastres hidrometeorológicos, situação de emergência. ABSTRACTThe present work has for objective analyze the space-temporal distribution of the emergency situations recognizements associated to the hydrometeorological dynamic on the pluviometrically homogenius microregion of the coast of Paraíba, during the period of 2003 to 2016. For that, data were used acquired from the Ministério da Integração Nacional’s site, found on the Secretaria Nacional de Proteção e Defesa Civil’s page, conform available recognizements through ordinances. As main results, a total of 29 recognizements were found, in which 51,7% corresponds to floods; 20,7% to heavy rain; 24,2% corresponding to flash flood and 3,4% to inundations. It’s considered that the obtained results have a descriptive label, needing then advanced studies about the theme.Keywords: Coast of Paraiba, hydrometeorlogical disasters, emergency situations. RESUMENEste documento tiene como objetivo analizar la distribución espacio-temporal de los reconocimientos de situaciones de emergencia com la dinâmica hidrometeorológica em la microrregión de lluvia homogénea de la costa paraibana, de 2003 a 2016. Para este propósito, se utilizaron los datos adquiridos del sitio web del Ministerio de Salud. Integración nacional, que se encuentra en la página de la Secretaría Nacional de Protección y Defensa Civil, como agradecimientos disponibles a través de ordenanzas. Como resultados principales, hubo un total de 29 reconocimientos, de los cuales el 51.7% correspondió a inundaciones; 20.7% a fuertes lluvias; 24.2% correspondientes a enxurradas y 3.4% a inundaciones. Se considera que los resultados obtenidos tuvieron una naturaleza más descriptiva, por lo que requirieron estúdios más avanzados sobre el tema.Palabras clave: Costa de Paraiba, desastres hidrometeorológicos, situación de emergencia.

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