Concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilité
The objective is to identify places of spatial concentration of road accidents by black zones rather than blackspots. The application of a geographical analysis method based on local measures of spatial autocorrelation permits to localise black zones.The relevance of the method is considered; a sens...
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Unité Mixte de Recherche 8504 Géographie-cités
1999-06-01
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Series: | Cybergeo |
Online Access: | http://journals.openedition.org/cybergeo/5125 |
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doaj-f2877c729dff4be4bc502c931c6f0c682021-10-05T13:20:37ZdeuUnité Mixte de Recherche 8504 Géographie-citésCybergeo1278-33661999-06-0110.4000/cybergeo.5125Concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilitéBenoît FlahautThe objective is to identify places of spatial concentration of road accidents by black zones rather than blackspots. The application of a geographical analysis method based on local measures of spatial autocorrelation permits to localise black zones.The relevance of the method is considered; a sensibility study of the results to several contiguity levels and to several weight functions is proposed. Results are also compared to those obtained with a statistical method of smoothing (based on kernel estimators).The application of the method to some roads shows its appropriation to the problem of black zones identification.http://journals.openedition.org/cybergeo/5125 |
collection |
DOAJ |
language |
deu |
format |
Article |
sources |
DOAJ |
author |
Benoît Flahaut |
spellingShingle |
Benoît Flahaut Concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilité Cybergeo |
author_facet |
Benoît Flahaut |
author_sort |
Benoît Flahaut |
title |
Concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilité |
title_short |
Concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilité |
title_full |
Concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilité |
title_fullStr |
Concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilité |
title_full_unstemmed |
Concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilité |
title_sort |
concentration spatiale des accidents de la route : méthode d'identification des zones noires basée sur l'autocorrélation spatiale application et étude de sensibilité |
publisher |
Unité Mixte de Recherche 8504 Géographie-cités |
series |
Cybergeo |
issn |
1278-3366 |
publishDate |
1999-06-01 |
description |
The objective is to identify places of spatial concentration of road accidents by black zones rather than blackspots. The application of a geographical analysis method based on local measures of spatial autocorrelation permits to localise black zones.The relevance of the method is considered; a sensibility study of the results to several contiguity levels and to several weight functions is proposed. Results are also compared to those obtained with a statistical method of smoothing (based on kernel estimators).The application of the method to some roads shows its appropriation to the problem of black zones identification. |
url |
http://journals.openedition.org/cybergeo/5125 |
work_keys_str_mv |
AT benoitflahaut concentrationspatialedesaccidentsdelaroutemethodedidentificationdeszonesnoiresbaseesurlautocorrelationspatialeapplicationetetudedesensibilite |
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1716841882944798720 |