Community Detection in Very High-Resolution Meteorological Networks

Ceron, Wilson; Santos, Leonardo Bacelar Lima; Dolif, Giovanni; Quiles, Marcos G.; Candido, Onofre A.

doi:10.1109/lgrs.2019.2955508

Cited by 10 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The time series for the diameter is inversely related to the previous ones. With Â etac , structured the network from the largest diameter of Â eta = 1, so reduced the number of connections between TZs, but preserved the most important edges, which already expected it [ 16 , 33 ].…”

Section: Resultsmentioning

confidence: 99%

Topological indexes and community structure for urban mobility networks: Variations in a business day

et al. 2021

Self Cite

View full text Add to dashboard Cite

Topological analysis and community detection in mobility complex networks have an essential role in many contexts, from economics to the environmental agenda. However, in many cases, the dynamic component of mobility data is not considered directly. In this paper, we study how topological indexes and community structure changes in a business day. For the analyzes, we use a mobility database with a high temporal resolution. Our case study is the city of São José dos Campos (Brazil)—the city is divided into 55 traffic zones. More than 20 thousand people were asked about their travels the day before the survey (Origin-Destination Survey). We generated a set of graphs, where each vertex represents a traffic zone, and the edges are weighted by the number of trips between them, restricted to a time window. We calculated topological properties, such as degree, clustering coefficient and diameter, and the network’s community structure. The results show spatially concise community structures related to geographical factors such as highways and the persistence of some communities for different timestamps. These analyses may support the definition and adjustment of public policies to improve urban mobility. For instance, the community structure of the network might be useful for defining inter-zone public transportation.

show abstract

Section: Resultsmentioning

confidence: 99%

Topological indexes and community structure for urban mobility networks: Variations in a business day

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The d corresponds to the higher flow threshold that produces the network with the largest diameter. The motivation behind η d is to get a threshold high enough to not consider the least frequent connections and to not disregard the most frequent ones 6,12 .…”

Section: Methodsmentioning

confidence: 99%

“…Santos et al 14 proposed a graph where the nodes have a known geographical location, and the edges have spatial dependence, the (geo)graph. It provides a simple tool to manage, represent, and analyze geographical complex networks in different domains 6,12 and it is used in this work. The geographical manipulation is performed with the PostgreSQL Database Management System (https:// www.postgresql.org/) and its spatial extension PostGIS.…”

Section: Geographical Visualizationmentioning

confidence: 99%

The correspondence between the structure of the terrestrial mobility network and the spreading of COVID-19 in Brazil

et al. 2020

Self Cite

View full text Add to dashboard Cite

The inter-cities mobility network is of great importance in understanding outbreaks, especially in Brazil, a continental-dimension country. We adopt the data from the Brazilian Ministry of Health and the terrestrial flow of people between cities from the Brazilian Institute of Geography and Statistics database in two scales: cities from Brazil, without the North region, and from the São Paulo State. Grounded on the complex networks approach, and considering that the mobility network serves as a proxy for the SARS-CoV-2 spreading, the nodes and edges represent cities and flows, respectively. Network centrality measures such as strength and degree are ranked and compared to the list of cities, ordered according to the day that they confirmed the first case of COVID-19. The strength measure captures the cities with a higher vulnerability of receiving new cases. Besides, it follows the interiorization process of SARS-CoV-2 in the São Paulo State when the network flows are above specific thresholds. Some countryside cities such as Feira de Santana (Bahia State), Ribeirão Preto (São Paulo State), and Caruaru (Pernambuco State) have strength comparable to states’ capitals. Our analysis offers additional tools for understanding and decision support to inter-cities mobility interventions regarding the SARS-CoV-2 and other epidemics.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Santos et al (2017) (14) proposed a graph where the nodes have a known geographical location, and the edges have spatial dependence, the (geo)graph. It provides a simple tool to manage, represent, and analyze geographical complex networks in different domains (8,13) and it is used in the present work. The geographical manipulation is performed with the PostgreSQL Database Management System and its spatial extension PostGIS.…”

Section: Geographical Visualizationmentioning

confidence: 99%

The correspondence between the structure of the terrestrial mobility network and the emergence of COVID-19 in Brazil

Freitas

Feitosa

Sepetauskas

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

BACKGROUND: the inter-cities mobility network serves as a proxy for the SARS-CoV-2 spreading network in a country. OBJECTIVE: to investigate the correspondences between the structure of the mobility network and the emergence of COVID-19 cases in Brazilian cities. METHODS: we adopt the data from the Brazilian Health Ministry and the terrestrial flow of people between cities from the IBGE database in two scales: Brazilian cities without the North region and cities from the Sao Paulo state. Grounded on the complex networks approach, cities are represented as nodes and the flows as edges. Network centrality measures such as strength and degree are ranked and compared to the list of Brazilian cities, ordered according to the day that they confirmed the first case of COVID-19. FINDINGS: The strength presents the best correspondences and the interiorization process of SARS-CoV-2 is captured in the Sao Paulo state when different thresholds are applied to the network flows. MAIN CONCLUSIONS: the strength captures the cities with a higher vulnerability of receiving new cases of COVID-19. Some countryside cities such as Feira de Santana (Bahia state), Ribeirao Preto (Sao Paulo state), and Caruaru (Pernambuco state) have strength comparable to states' capitals, making them potential super spreaders.

show abstract

Community Detection in Very High-Resolution Meteorological Networks

Cited by 10 publications

References 19 publications

Topological indexes and community structure for urban mobility networks: Variations in a business day

Topological indexes and community structure for urban mobility networks: Variations in a business day

The correspondence between the structure of the terrestrial mobility network and the spreading of COVID-19 in Brazil

The correspondence between the structure of the terrestrial mobility network and the emergence of COVID-19 in Brazil

Contact Info

Product

Resources

About