Spatial detection of organization under evacuation situation
Identifieur interne : 000E72 ( Main/Exploration ); précédent : 000E71; suivant : 000E73Spatial detection of organization under evacuation situation
Auteurs : Cyrille Bertelle [France] ; Antoine Dutot [France] ; Michel Nabaa [France] ; Damien Olivier [France] ; Pascal Mallet [France]Source :
English descriptors
Abstract
Recent events have shown that our agglomerations are vulnerable in front of emergency situations. There are a lot of factors to consider and one of them is the structure of towns generated by road networks. One way to study these structures is to modelize the networks by graphs and to use the theory developed on them. Recently, some works have studied the detection of communities in large graphs. A community in a graph is a set of connected nodes strongly connected to each other and less connected to nodes from other communities. The aim of detection is to identify communities according to a predefined criteria but without specifying the number of communities and the size of communites. The known algorithms find communities but without taking into account the time. We propose a dynamic algorithm of communities detection and we explain how it can be applied on Le Havre agglomeration, for example, to estimate the vu
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76821 Mont-Saint-Aignan</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-rouen.fr/</ref></desc></org></tutelle><tutelle active="#struct-301288" type="direct"><org type="department" xml:id="struct-301288" status="VALID"><orgName>Institut National des Sciences Appliquées - Rouen</orgName><orgName type="acronym">INSA Rouen</orgName><desc><address><country key="FR"></country></address></desc><listRelation><relation active="#struct-301232" type="direct"></relation></listRelation></org></tutelle><tutelle active="#struct-301232" type="indirect"><org type="institution" xml:id="struct-301232" status="VALID"><orgName>Institut National des Sciences Appliquées</orgName><orgName type="acronym">INSA</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Le Havre</settlement><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université du Havre</orgName><placeName><settlement type="city">Rouen</settlement><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université de Rouen</orgName></affiliation></author><author><name sortKey="Mallet, Pascal" sort="Mallet, Pascal" uniqKey="Mallet P" first="Pascal" last="Mallet">Pascal Mallet</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-103904" status="INCOMING"><orgName>CODAH</orgName><desc><address><country key="FR"></country></address></desc><listRelation><relation active="#struct-324938" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-324938" type="direct"><org type="institution" xml:id="struct-324938" status="INCOMING"><orgName>CODAH</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>CODAH</term><term>complex systems</term><term>organization detection</term><term>risk management</term><term>vulnerability</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Recent events have shown that our agglomerations are vulnerable in front of emergency situations. There are a lot of factors to consider and one of them is the structure of towns generated by road networks. One way to study these structures is to modelize the networks by graphs and to use the theory developed on them. Recently, some works have studied the detection of communities in large graphs. A community in a graph is a set of connected nodes strongly connected to each other and less connected to nodes from other communities. The aim of detection is to identify communities according to a predefined criteria but without specifying the number of communities and the size of communites. The known algorithms find communities but without taking into account the time. We propose a dynamic algorithm of communities detection and we explain how it can be applied on Le Havre agglomeration, for example, to estimate the vu</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Bertelle, Cyrille" sort="Bertelle, Cyrille" uniqKey="Bertelle C" first="Cyrille" last="Bertelle">Cyrille Bertelle</name></region><name sortKey="Dutot, Antoine" sort="Dutot, Antoine" uniqKey="Dutot A" first="Antoine" last="Dutot">Antoine Dutot</name><name sortKey="Mallet, Pascal" sort="Mallet, Pascal" uniqKey="Mallet P" first="Pascal" last="Mallet">Pascal Mallet</name><name sortKey="Nabaa, Michel" sort="Nabaa, Michel" uniqKey="Nabaa M" first="Michel" last="Nabaa">Michel Nabaa</name><name sortKey="Olivier, Damien" sort="Olivier, Damien" uniqKey="Olivier D" first="Damien" last="Olivier">Damien Olivier</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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