{"help": "https://pro.iepnb.gob.es/catalogo/api/3/action/help_show?name=package_show", "success": true, "result": {"access_rights": "http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations", "alternate_identifier": "ISBN: 978-91-637-7327-3", "author": "Rosell, C., B\u00edl, M., Camps, F., Andrasik, R., Fern\u00e1ndez-Bou, M. y Janoska, Z.", "author_name": "Rosell, C., B\u00edl, M., Camps, F., Andrasik, R., Fern\u00e1ndez-Bou, M. y Janoska, Z.", "classification_variables": {"es": ""}, "conforms_to": ["https://www.boe.es/eli/es/res/2013/02/19/(4)"], "contact_email": "organismo@example.org", "contact_name": "Organismo publicador del Cat\u00e1logo", "contact_role": "http://inspire.ec.europa.eu/metadata-codelist/ResponsiblePartyRole/pointOfContact", "contact_uri": "http://datos.gob.es/recurso/sector-publico/org/Organismo/EA0000000", "contact_url": "https://organismo.example.org/", "created": "2025-05-23", "creator_user_id": "d24a314a-79ce-48c0-abd9-54cb42706da4", "dataset_scope": "non_spatial_dataset", "dcat_type": "http://purl.org/dc/dcmitype/Text", "encoding": "UTF-8", "featured": false, "graphic_overview": "https://i1.rgstatic.net/publication/270957822_Identifying_Roadless_Areas_in_Europe/links/54df95710cf29666378b912d/largepreview.png", "hvd": "non_hvd", "id": "f3cd2a6f-df57-5303-bf46-bad842a3d019", "identifier": "f3cd2a6f-df57-5303-bf46-bad842a3d019", "inspire_id": "", "isopen": true, "language": "http://publications.europa.eu/resource/authority/language/SPA", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "lineage_process_steps": [], "lineage_source": ["2014 IENE International Conference. Programme and abstracts", "pag. 76"], "maintainer": "", "maintainer_name": "", "metadata_created": "2026-06-23T14:47:39.629563", "metadata_modified": "2026-06-25T12:25:44.719166", "metadata_profile": ["https://www.w3.org/TR/vocab-dcat-3/"], "miteco_data_population": {"es": ""}, "miteco_data_territory": {"es": ""}, "miteco_dataset_type": "http://publications.europa.eu/resource/authority/dataset-type/STATISTICAL", "miteco_geo_level": "1", "modified": "2026-06-25", "name": "f3cd2a6f-df57-5303-bf46-bad842a3d019", "notes": "Animal Vehicle Collisions (AVC) often show a pattern of aggregation, with clusters along conflictive road stretches. Road traffic safety is diminished in these hotspots, due to the risk of accidents involving animals. Therefore, the identification of these stretches is essential to apply effective mitigation measures. Data from 2,110 accidents involving ungulates were registered in the 12,124 km of the Catalan (NE Spain) road network during a five-year period (2007-2011). The data were collected by traffic police and completed with data provided by road management teams, the traffic management agency and the wildlife management department. Two different methods were used to determine the location of the clusters of accidents caused by ungulates. First the kernel density estimation (KDE) was applied to the 1-km stretches of road that had previously been identified as the most conflictive by comparing the data with a random situation in which the probability of occurrence along each stretch followed a Poisson distribution. However, this method could not be used to determine the statistical significance of the clusters that were identified. The second method (KDE+) consisted of a procedure based on standard KDE that identify the most hazardous road stretches by testing the statistical significance of the resulting clusters. The clusters were identified where the kernel density function exceeded the significance level corresponding to the 95th percentile, and the application of cluster strength evaluation provided a sorted list of hazardous locations. Traffic accidents caused by ungulates mainly wild boar and deer) have a strong tendency to cluster, with about one third of accidents located at around 1% of the road network length. The results provided by this method allow efforts to apply mitigation measures to be focused on the most hazardous road stretches, where the best benefit-cost ratio can be expected.", "notes_translated": {"en": "Animal Vehicle Collisions (AVC) often show a pattern of aggregation, with clusters along conflictive road stretches. Road traffic safety is diminished in these hotspots, due to the risk of accidents involving animals. Therefore, the identification of these stretches is essential to apply effective mitigation measures. Data from 2,110 accidents involving ungulates were registered in the 12,124 km of the Catalan (NE Spain) road network during a five-year period (2007-2011). The data were collected by traffic police and completed with data provided by road management teams, the traffic management agency and the wildlife management department. Two different methods were used to determine the location of the clusters of accidents caused by ungulates. First the kernel density estimation (KDE) was applied to the 1-km stretches of road that had previously been identified as the most conflictive by comparing the data with a random situation in which the probability of occurrence along each stretch followed a Poisson distribution. However, this method could not be used to determine the statistical significance of the clusters that were identified. The second method (KDE+) consisted of a procedure based on standard KDE that identify the most hazardous road stretches by testing the statistical significance of the resulting clusters. The clusters were identified where the kernel density function exceeded the significance level corresponding to the 95th percentile, and the application of cluster strength evaluation provided a sorted list of hazardous locations. Traffic accidents caused by ungulates  mainly wild boar and deer) have a strong tendency to cluster, with about one third of accidents located at around 1% of the road network length. 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Traffic accidents caused by ungulates mainly wild boar and deer) have a strong tendency to cluster, with about one third of accidents located at around 1% of the road network length. The results provided by this method allow efforts to apply mitigation measures to be focused on the most hazardous road stretches, where the best benefit-cost ratio can be expected."}, "num_resources": 1, "num_tags": 2, "organization": {"id": "bca483f7-26e2-4ed8-99e8-65f5b3c7a26e", "name": "iepnb", "title": "", "type": "organization", "description": "", "image_url": "", "created": "2026-06-23T14:36:08.942021", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "bca483f7-26e2-4ed8-99e8-65f5b3c7a26e", "private": false, "provenance": {"en": "", "es": ""}, "publisher_email": "buzon-bdatos@miteco.es", "publisher_name": "\u00c1rea de Banco de Datos de la Naturaleza. Direcci\u00f3n General Biodiversidad, Bosques y Desertificaci\u00f3n. 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