The local spatial autocorrelation and the kernel method for identifying black zones - A comparative approach

dc.creatorFlahaut, B
dc.creatorMouchart, M
dc.creatorSan Martin, E
dc.creatorThomas, I
dc.date.accessioned2024-01-10T13:43:52Z
dc.date.accessioned2024-05-02T20:19:05Z
dc.date.available2024-01-10T13:43:52Z
dc.date.available2024-05-02T20:19:05Z
dc.date.created2024-01-10T13:43:52Z
dc.date.issued2003
dc.identifier10.1016/S0001-4575(02)00107-0
dc.identifier1879-2057
dc.identifier0001-4575
dc.identifierMEDLINE:12971934
dc.identifierhttps://doi.org/10.1016/S0001-4575(02)00107-0
dc.identifierhttps://repositorio.uc.cl/handle/11534/78776
dc.identifierWOS:000185493100020
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9273897
dc.description.abstractThis article aims to determine the location and the length of road sections characterized by a concentration of accidents (black zones). Two methods are compared: one based on a local decomposition of a global autocorrelation index, the other on kernel estimation. After explanation, both methods are applied and compared in terms of operational results, respective advantages and shortcomings, as well as underlying conceptual elements. The operationality of both methods is illustrated by an application to one Belgian road. (C) 2003 Elsevier Science Ltd. All rights reserved.
dc.languageen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.rightsacceso restringido
dc.subjectblack zones
dc.subjectkernel estimators
dc.subjectlocal spatial autocorrelation
dc.subjectroad accidents
dc.subjectASSOCIATION
dc.titleThe local spatial autocorrelation and the kernel method for identifying black zones - A comparative approach
dc.typeartículo


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